The Emerging Role of Artificial Intelligence Driven Forest Carbon Integrity Systems: A Scoping Review of Methods, Risks, and Policy Implications for European Forests

The Emerging Role of Artificial Intelligence Driven Forest Carbon Integrity Systems: A Scoping Review of Methods, Risks, and Policy Implications for European Forests | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review The Emerging Role of Artificial Intelligence Driven Forest Carbon Integrity Systems: A Scoping Review of Methods, Risks, and Policy Implications for European Forests Gabriel Osei Forkuo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9102556/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Forest ecosystems provide irreplaceable carbon sequestration, biodiversity, and ecosystem services, yet the integrity of forest carbon accounting — encompassing measurement, reporting, and verification (MRV) across the full forest-to-atmosphere chain — remains contested. Artificial intelligence (AI) and digital technologies offer transformative potential for improving the accuracy, transparency, and scalability of forest carbon integrity systems. Despite a proliferation of individual technical studies, no comprehensive evidence map of the field exists. This scoping review systematically maps AI-driven forest carbon integrity research across six thematic pillars (P1-P6), identifying dominant methods, technological gaps, ethical risks, and policy alignment opportunities with particular reference to European forest ecosystems and governance frameworks. Method Peer-reviewed and grey-literature studies published in English between 2015 and 2024 were examined in this review, provided that at least one of six pre-defined thematic pillars (AI/remote sensing MRV; digital twins and ecosystem modelling; carbon markets and finance; EU policy and governance; ethics and equity; cross-cutting issues) was addressed in the context of forest carbon. Systematic searches of PubMed/MEDLINE, OpenAlex, and Semantic Scholar (all spanning 2015–2024) were conducted programmatically using Python 3.12, with the final search performed in January 2025. These were supplemented by grey-literature searches executed within Web of Science and Scopus. Data were charted independently using a standardised extraction form, wherein details regarding authors, year, journal, digital object identifier (DOI), thematic pillar, AI/machine learning (ML) methods (multi-label), remote sensing data sources, carbon variables, and geographic scope were captured. Finally, quality appraisal of the included studies was performed using the Mixed Methods Appraisal Tool (MMAT). Results From an initial pool of 5,135 raw records, 200 studies were included (spanning 2015–2024; 100% open access; mean 349 citations, median 202). Artificial neural networks (ANN) were identified as the dominant AI approach ( n = 48, 24.0%), while Light Detection and Ranging (LiDAR)/Airborne Laser Scanning (ALS)/Terrestrial Laser Scanning (TLS) was observed as the predominant remote sensing modality ( n = 33, 16.5%). The domains of EU policy and governance (P4; n = 59, 29.5%) and AI/remote sensing MRV (P1; n = 53, 26.5%) were found to be the most active areas of research. European-scoped frameworks were represented by 23.5% of the studies ( n = 47), whereas a global scope was addressed by 41.0% ( n = 82). Critical gaps in reproducibility (reported by less than10% of studies) and limitations reporting (observed in less than 30% of studies) were revealed by the quality appraisal. Conclusions Although high technical maturity in biomass estimation and disturbance detection is demonstrated by AI-driven forest carbon integrity systems, persistent gaps are encountered regarding governance legitimacy, reproducibility, carbon market integration, and EU policy coherence. Consequently, a five-point research agenda is proposed, wherein priorities are placed on reproducible AI, the development of an operational European Forest Carbon Digital Twin, and the establishment of a purpose-built AI ethics framework for forest carbon. aboveground biomass carbon markets digital twins European Green Deal machine learning remote sensing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction 1.1 Forest Carbon in the European Climate Architecture Forests cover approximately 45% of Europe's land area and constitute an indispensable pillar of the continent's climate architecture, providing critical ecosystem services — carbon sequestration, biodiversity habitat, water regulation, and timber supply — that span the full forest-to-wood production chain (Calvin et al., 2023 ; Lamb et al., 2021 ; Deng et al., 2022 ). Under the Paris Agreement and the European Green Deal, forests are assigned substantial and legally binding mitigation responsibilities: the revised Land Use, Land Use Change and Forestry (LULUCF) Regulation (2023) targets a net sink of at least 310 MtCO₂eq per year by 2030 (Deng et al., 2022 ; Forsell et al., 2016 ; Rickels et al., 2021 ). Meeting this target demands not only sustained forest management effort but also robust, verifiable, and high-frequency carbon measurement integrated across stand, landscape, and national scales. Yet persistent discrepancies exist between modelled and satellite-derived estimates; national inventory data and atmospheric inversions diverge at the country level; and the reliability of the forest sink as a long-term climate mitigation asset is increasingly questioned (Calvin et al., 2023 ; Chuvieco et al., 2018 ; Dupuy et al., 2020 ; Patacca et al., 2022). The structural vulnerability of the European forest carbon sink has intensified markedly since 2000. Natural disturbances — wildfires, bark beetle outbreaks, windthrow, and drought-induced dieback — are eroding permanence across all major biomes (Dupuy et al., 2020 ; Patacca et al., 2022). The 2017–2019 bark beetle calamity in Central European spruce forests released an estimated 50–80 MtCO₂eq (Dupuy et al., 2020 ). Southern European wildfires, amplified under 2°C warming scenarios, are projected to increase in frequency and severity (Dupuy et al., 2020 ; Schleussner et al., 2016 ). The Intergovernmental Panel on Climate Change (IPCC) AR6 synthesis (Calvin et al., 2023 ) indicates that without dramatic cross-sectoral emissions reductions, residual mitigation demands placed on forest carbon sinks will increase — intensifying pressure on measurement, reporting, and verification (MRV) infrastructure and the scientific credibility of forest carbon reporting. 1.2 AI and Digital Technologies as Transformative MRV Instruments Artificial intelligence (AI) and digital technologies have emerged as potentially transformative instruments for improving forest carbon integrity — understood here as the accuracy, transparency, additionality, permanence, and verifiability of forest carbon accounting across the full MRV chain (Cowls et al., 2021; Allen et al., 2022 ; Kwilinski et al., 2023 ). Deep learning architectures now achieve sub-hectare canopy height and biomass estimates from airborne Light Detection and Ranging (LiDAR), with R² consistently exceeding 0.85 at stand scale (Amiri et al., 2019 ; Kellner et al., 2019 ; Dorado-Roda et al., 2021 ; Rodríguez-Veiga et al., 2019 ). Sentinel-1 SAR and Sentinel-2 optical time-series enable near-real-time forest disturbance detection at continental scale (Bauer-Marschallinger et al., 2018; Grabska-Szwagrzyk et al., 2019; Tricht et al., 2018 ). Digital twin frameworks and process-based land surface models enable scenario-based carbon modelling under future climate trajectories that directly inform forest management strategies (Martens et al., 2017 ; Pisso et al., 2019 ; Frieler et al., 2017 ). Blockchain and distributed ledger technologies offer new architectures for carbon credit provenance and audit trails (Kwilinski et al., 2023 ). These advances together substantially advance understanding of forest ecosystem carbon processes at scales relevant to management and policy. 1.3 Governance Challenges, Equity Risks, and the Evidence Gap The integration of AI into forest carbon governance is not without risk. Automated verification systems may embed structural biases against smallholder and indigenous forest managers (Morán et al., 2018; Domínguez and Luoma, 2020 ; Kuyper et al., 2017). Proprietary algorithms may compromise the transparency required for regulatory compliance under the EU Emissions Trading System (ETS) and LULUCF (Berg et al., 2022 ; Clementino and Perkins, 2021 ). The technical complexity of AI-generated carbon estimates may outpace the interpretive capacity of verification bodies (Cowls et al., 2021; Popescu and Popescu, 2019 ), while the distributional consequences of AI-mediated carbon crediting — particularly in community forest contexts — have received comparatively little systematic attention (Domínguez and Luoma, 2020 ; Ekardt et al., 2020 ; Schilling-Vacaflor and Lenschow, 2021). Despite a proliferation of individual studies on AI–forest carbon applications, no comprehensive evidence map of the emerging field exists. The intersection between technical capability, carbon market integrity, ethical governance, and EU policy implementation remains poorly characterised — a gap this scoping review is designed to fill. The scoping review format is appropriate given the heterogeneous evidence base (empirical, modelling, policy, and normative studies), the emerging nature of the field, and the need to map scope before conducting targeted systematic reviews. This format also aligns with EJFR's focus on forest systems analysis and forest ecosystem process understanding across scales. 1.4 Research Questions RQ1. What AI/ML methods and remote sensing technologies are applied in forest carbon MRV, and what performance levels do they achieve in European and broader contexts? RQ2. To what extent are digital twin and process-based simulation approaches integrated with AI for forest carbon assessment? RQ3. How are AI systems applied to carbon market integrity, and what risks to transparency, additionality, and equity do they introduce or mitigate? RQ4. What is the state of alignment between emerging AI capabilities and EU regulatory requirements for forest carbon governance? RQ5. What ethical, equity, and governance risks are identified for AI-mediated forest carbon systems? 1.5 Aim and Objectives The aim of this scoping review was to systematically map the emerging landscape of AI-driven forest carbon integrity research, with reference to European forests and governance frameworks, and to identify dominant methods, technological gaps, ethical risks, and policy alignment opportunities across the full MRV chain. The specific objectives were to: O1. characterise the AI and remote sensing methods applied to forest carbon MRV and evaluate their technical performance, addressing RQ1; O2. assess integration between digital twin frameworks, process-based ecosystem models, and AI for forest carbon assessment, addressing RQ2; O3. map AI applications to carbon market integrity and identify transparency, additionality, and equity risks, addressing RQ3; O4. evaluate alignment between AI capabilities and EU regulatory requirements across the Green Deal portfolio, addressing RQ4; O5. identify ethical, equity, and governance risks of AI-mediated forest carbon systems, addressing RQ5; and O6. appraise quality, reproducibility, and publication bias of the evidence base, and propose a five-point research agenda. 1.6 Related Studies 1.6.1. Pillar 1 — AI and remote sensing MRV Pillar 1 focuses on artificial intelligence and remote sensing for measurement, reporting, and verification. The precision of forest biomass and carbon stock estimation using artificial intelligence and remote sensing has improved substantially since 2015. Airborne LiDAR is validated across European and global forest types and consistently achieves an R² greater than 0.85 for aboveground biomass (AGB) prediction at the stand scale (Amiri et al., 2019 ; Dorado-Roda et al., 2021 ; Kellner et al., 2019 ; Nevalainen et al., 2017 ). Unmanned aerial vehicle (UAV) platforms extend this capability to the individual-tree scale (Duarte et al., 2022 ; Gan et al., 2024 ; Klouček et al., 2019 ). Furthermore, pantropical GEDI-TanDEM-X integration now enables wall-to-wall canopy height retrieval (Neuenschwander and Magruder, 2019 ; Qi et al., 2025 ). Sensor fusion combining Sentinel-1 and Sentinel-2 data achieves 85 to 95 percent classification accuracy for temperate European forest types and supports near-real-time disturbance detection (Bauer-Marschallinger et al., 2018; Chatziantoniou et al., 2017 ; Grabska-Szwagrzyk et al., 2019; Praticò et al., 2021 ; Tricht et al., 2018 ). Multi-sensor data fusion integrating LiDAR, synthetic aperture radar (SAR), and optical sources delivers the lowest aboveground biomass uncertainties and represents the current state of the art (Li et al., 2020 ; Nandy et al., 2021 ; Silveira et al., 2019 ; Urbazaev et al., 2018 ; Wang et al., 2022 ). Benchmark comparisons find that random forest and deep learning models produce comparable aboveground biomass accuracy, with random forest offering superior interpretability for operational deployment (Avci et al., 2021; Esteban et al., 2019 ; Pelletier et al., 2017 ; Tamiminia et al., 2021 ). Neural network upscaling of FLUXNET and Integrated Carbon Observation System (ICOS) eddy-covariance measurements to continental carbon flux estimates is successfully validated against global gross primary productivity benchmarks (Heiskanen et al., 2021; Jung et al., 2020 ; Xia et al., 2015 ; Virkkala et al., 2021 ). Critical gaps persist in model transferability across biomes and climate scenarios (Khanal et al., 2020 ; Rodríguez-Veiga et al., 2019 ). Similar gaps remain regarding the reproducibility of artificial intelligence workflows (Allen et al., 2022 ; Cowls et al., 2021) and the overlooked contribution of trees outside forests to total European woody biomass (Liu et al., 2023 ). The role and need for space-based biomass measurements in environmental management and policy have been comprehensively synthesised by Herold et al. ( 2019 ). 1.6.2. Pillar 2 — Digital twins and ecosystem modelling Pillar 2 examines digital twins and ecosystem modelling. Process-based modelling infrastructure for forest carbon digital twins is technically mature but operationally fragmented. Land surface simulations driven by ECMWF Reanalysis v5 (ERA-5) provide robust datasets (Albergel et al., 2018 , Balsamo et al., 2015 ). Additionally, Global Land Evaporation Amsterdam Model (GLEAM) v3 land evaporation and soil moisture models (Martens et al., 2017 ) and the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b) multi-model impact protocol (Frieler et al., 2017 ) provide validated boundary conditions for scenario modelling at warming levels of 1.5°C and 2°C. Deployed process-based components include the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model (Haverd et al., 2018 ) and the FLEXPART dispersion model (Pisso et al., 2019 ). Other essential tools include Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) adapted for European Union (EU) forest management (Lindeskog et al., 2021 ) and the CBM-CFS3 carbon budget model customised for EU countries (Pilli et al., 2018 ). The FLUXCOM global carbon flux synthesis (Jung et al., 2020 ) and terrestrial water-use efficiency trends (Huang et al., 2015) provide observational validation benchmarks. Forest management simulation for long-term carbon balance under changing climate conditions represents an early forest-specific deployment (Thomas et al., 2017 ; Valipour et al., 2021 ). The forestry digital twin concept applied to Landsat data also serves as a foundational example in this domain (Jiang et al., 2022 ). There has been a significant increase in natural disturbance impacts on European forests since 1950 (Patacca et al., 2022). These historical impacts, alongside climate change projections for southern European wildfire danger, define the disturbance forcing conditions that forest carbon digital twins must represent to be policy-relevant (Dupuy et al., 2020 ; Schleussner et al., 2016 ). Urbanisation effects on temperate forest carbon cycle responses expand this modelling context (Reinmann et al., 2020 ). Peatland carbon dynamics and rewetting initiatives further enhance the framework (Ekardt et al., 2020 ; Rowan et al., 2022 ; Tanneberger et al., 2020). Finally, established soil carbon stock estimation standards complete the multi-compartment carbon modelling evidence base (Bispo et al., 2017 ; Schulte et al., 2015 ; Tifafi et al., 2017). 1.6.3. Pillar 3 — Carbon markets and finance Pillar 3 addresses carbon markets and finance. The UNEP Emissions Gap Report quantifies the persistent divergence between corporate net-zero commitments and verified emissions reductions (Programme UNEP, 2023). Concurrently, environmental, social, and governance (ESG) rating divergence analysis demonstrates structural inter-agency inconsistency (Berg et al., 2022 ). This inconsistency undermines the reliability of AI-generated carbon signals in investment decisions. Regulatory harmonisation of environmental, social, and governance metrics carries significant global business implications (Alamillos and de Mariz, 2022). Corporate responses to these ratings in Italy (Clementino and Perkins, 2021 ) and the effect of sustainable business practices on profitability (Cerciello et al., 2022) document the institutional dynamics within which AI-assisted verification must operate. Reducing Emissions from Deforestation and Forest Degradation (REDD+) market integrity depends heavily on credible community-based monitoring. Synergising community monitoring with remote sensing for REDD+ measurement, reporting, and verification establishes a strong foundation (Murthy et al., 2017 ). Carbon measurement methods for REDD+ implementation (Bhattarai et al., 2016 ) and analyses of uncertainty in forest reference levels (Mertz et al., 2018 ) are equally vital. The commodification of forest carbon through socially embedded practices also shapes this landscape (Benjaminsen and Kaarhus, 2018 ). Together, these elements collectively define the technical and political economy prerequisites for successful carbon markets. Artificial intelligence applications span satellite-based additionality verification and blockchain-backed carbon credit provenance (Kwilinski et al., 2023 ). Other applications include machine learning-assisted disclosure verification (Cerciello et al., 2022). Additionally, life cycle assessment tools support biogenic carbon accounting for forest products (Leinonen, 2022 , Franz et al., 2018 , Sterman et al., 2018 , Gough et al., 2018 ). A scoping review of carbon pricing systems in forest sector models finds persistent methodological heterogeneity (Honkomp and Schier, 2024 ). This heterogeneity directly motivates the push for AI-assisted standardisation. Several frameworks define the macro-policy architecture within which forest carbon market AI tools operate. These include the findings of the Stern Commission (Stern et al., 2017) and the design of carbon dioxide removal (CDR) policy instruments (Honegger et al., 2021 ). The potential for bioenergy with carbon capture and storage in a carbon-neutral Europe further shapes this architecture (Rosa et al., 2021 ). Furthermore, forestry offsets in China’s national emission reduction scheme provide additional global context (Xu, 2024 ). The livelihood impacts of forest carbon projects address the social dimensions of market integrity (Dube and Chatterjee, 2022 ). Simultaneously, green finance gap bibliometrics explore the financial aspects of these mechanisms (Debrah et al., 2022). 1.6.4. Pillar 4 — European Union (EU) policy and governance Pillar 4 explores EU policy and governance. The European Union (EU) regulatory environment for forest carbon is among the most demanding globally. Nevertheless, persistent fragmentation in measurement, reporting, and verification limits policy coherence. Comparisons between UNFCCC inventories and atmospheric inversions establish a clear verification challenge (Deng et al., 2022 ). Land-use emission projections further underscore this difficulty (Forsell et al., 2016 ). A comprehensive assessment of the 2030 Climate and Energy Framework clarifies these ongoing regulatory hurdles (Kulovesi and Oberthür, 2020 ). Land use strategies for climate mitigation in carbon-dense temperate forests are essential for effective management (Law et al., 2018 ). The formal recognition of nature-based solutions also plays a critical role (Seddon et al., 2020 ). Interventions supporting natural forest regeneration (Chazdon et al., 2020 ) and the identification of primary forest protection gaps in Europe (Bowler et al., 2020 ; Dave et al., 2019 ; Sabatini et al., 2020 ; Schindler et al., 2016 ) highlight crucial ecological priorities. Additionally, natural disturbance-based forest management practices (Kuuluvainen et al., 2021 ) help define the biodiversity co-benefit optimisation space within which artificial intelligence carbon metrics must be designed. The coalitional politics of environmental forest policy illuminate complex regulatory landscapes (Sotirov et al., 2021 ). Forest carbon sequestration policy design (Gren and Aklilu, 2016 ) and subnational forest carbon governance (Ruseva, 2023 ; Ustaoglu & Collier, 2018 ) show how these frameworks operate at different scales. Forest regulations and stakeholder modelling in Latvia offer a focused example of these dynamics (Zute et al., 2023 ). Together, these studies explain the multi-level governance processes through which AI-informed policy is negotiated. Soil and landscape governance directly relates to Common Agricultural Policy (CAP) Eco-schemes and land-use regulations. This includes the implementation of soil protection policy instruments (Ronchi et al., 2019 ) and peatland governance within sustainability law (Ekardt et al., 2020 ). Forest bioeconomy sustainability indicators provide vital metrics for this sector (Wolfslehner et al., 2016 ). Furthermore, broad bioeconomy strategies outline pathways for practical implementation (Araújo et al., 2021 ; Gregson et al., 2015 ; Kardung et al., 2021 ; Meyer, 2017 ; Sollen-Norrlin et al., 2020 ). Greenhouse gas (GHG) flux monitoring over European managed grasslands provides necessary empirical data for these governance models (Franz et al., 2018 ; Hörtnagl et al., 2018 ; Nemitz et al., 2018 ). Forest governance at cross-border scales requires monitoring illegal logging and carbon emissions in timber-producing countries (Piabuo et al., 2021 ). The impact of foreign direct investment on land-use emissions in tropical forest countries further complicates global governance (Piabuo et al., 2023 ). Additionally, specific forest policy and management approaches are necessary to advance CDR targets (Maes et al., 2022 ; Meng et al., 2023 ; vonHedemann et al., 2020 ; Zhang & Wang, 2023 ). The Emissions Database for Global Atmospheric Research (EDGAR) global greenhouse gas emissions atlas remains the primary inventory benchmark against which national forest carbon contributions are reconciled (Janssens-Maenhout et al., 2019 ). 1.6.5. Pillar 5 — Ethics, equity and societal dimensions Pillar 5 investigates ethics, equity, and societal dimensions. The equity and governance risks of AI-driven forest carbon systems draw from political ecology, technology ethics, and empirical social research. Decolonising conservation policy is a fundamental step toward equitable environmental management (Domínguez and Luoma, 2020 ). The legitimacy of non-state actors in hybrid climate governance also plays a crucial role (Kuyper et al., 2017, 2018 ). Moreover, dynamics at the forest frontier in the Global South test the promise of equity from international climate policies (Brockhaus et al., 2021 ). These elements collectively establish the governance legitimacy framework within which artificial intelligence carbon systems must remain accountable. Empirical evidence regarding social outcomes is critical. Community forestry programmes demonstrate the potential for biodiversity and carbon co-production (Luintel et al., 2018 ). At the same time, the livelihood impacts of forest carbon projects require careful evaluation (Dube and Chatterjee, 2022 ). Ecosystem service flows and stakeholder power relationships document the distributional consequences that AI-mediated carbon crediting must navigate (Fedele et al., 2018 ; Felipe-Lucia et al., 2015 ). Living Labs serve as a co-design methodology for nature-based solutions (Lupp et al., 2020 ). This methodology provides a participatory artificial intelligence tool development framework directly applicable to community forest carbon monitoring. Deploying artificial intelligence as a technological fix without social legitimacy represents a significant risk in climate policy (Cowls et al., 2021). To address this, the precision cardiology digital twin serves as a governance analog with developed accountability structures for consent, liability, and equity (Acero et al., 2020 ). These concepts stand as central references for ethical technology deployment. Natural capital accounting offers economic visibility to ecological assets (Hein et al., 2016 ). Exploratory studies on green finance and corporate social responsibility in Romanian business environments expand on these economic tools (Popescu and Popescu, 2019 ). Transnational climate litigation highlights the important contributions of the Global South to legal accountability (Peel and Lin, 2019 ). Finally, studies on the European mountain cryosphere as a threatened ecosystem round out the contextual evidence base for this pillar (Beniston et al., 2018 ). 1.6.6. Cross-cutting and contextual literature The final section covers cross-cutting and contextual literature related to multiple pillars. Several bodies of work span multiple disciplines to inform this field. The integrated global assessment of natural forest carbon potential highlights massive sequestration opportunities (Mo et al., 2023 ). This aligns with the findings of the IPCC Synthesis Report (Calvin et al., 2023 ) and the global biodiversity assessment (IPBES, 2019 ). Together, these foundational reports define the dual climate-biodiversity crisis context. Analyses of greenhouse gas emission trends by sector offer important baseline data (Fuzzi et al., 2015 ; Klimont et al., 2017 ; Krotkov et al., 2016 ; Lamb et al., 2021 ; Miyazaki et al., 2017 ; Petzold et al., 2015 ). The science of net zero targets outlines both opportunities and practical implications (Allen et al., 2022 ). Integrating carbon dioxide removal into European Emissions Trading systems connects mitigation science with market policy (Rickels et al., 2021 ). Observations of very strong atmospheric methane growth highlight the urgency of these interventions (Nisbet et al., 2019 ). Furthermore, evaluations of differential warming scenario impacts (Schleussner et al., 2016 ) and global drought changes (Naumann et al., 2018 ) firmly establish the Earth system context. The Global Fire Atlas maps individual fire characteristics worldwide (Andela et al., 2019 ). Deep learning models now enable active forest fire detection with high precision (Seydi et al., 2022 ). Geospatial data and machine learning (ML) integration further improve fire susceptibility mapping (Singha et al., 2024 ). Additionally, deep learning applications successfully predict forest fires in vulnerable regions such as Kilimanjaro (Mambile et al., 2024). These applications successfully connect remote disturbance monitoring with long-term permanence policy. Integrating remote sensing and machine learning greatly improves aboveground biomass estimation (Anees et al., 2024 ). Comprehensive reviews of forest biomass retrieval methods across biomes further consolidate this knowledge (Rodríguez-Veiga et al., 2019 ). The application of machine learning in agricultural and applied economics demonstrates broader methodological crossovers (Storm et al., 2019). Bibliometric analyses of unmanned aerial vehicles in precision agriculture map technological trends (Singh et al., 2022 ). Evaluations of national forest inventories provide international context for these technical advancements (Zeng et al., 2015 ). These works represent the multi-disciplinary synthesis literature connecting algorithmic innovation with cross-cutting themes. Economic evaluations of soil erosion costs in the EU highlight the financial risks of land degradation (Panagos et al., 2018 ). Ecological studies show that higher plant diversity directly supports soil organic carbon accumulation (Prommer et al., 2019). Research on extreme weather impacts exposes the vulnerability of European crop production (Beillouin et al., 2020 ). Innovative proposals also position cities as carbon sinks through wooden-building construction (Amiri et al., 2020 ). These studies successfully extend the cross-cutting evidence base to the broader land-climate system. Studies on digital transformation and environmental performance metrics reveal the corporate value of technological adoption (Kwilinski et al., 2023 ). Data-driven artificial intelligence applications for precision agriculture showcase similar operational benefits (Linaza et al., 2021 ). These perspectives connect the forest carbon artificial intelligence agenda to the wider digital bioeconomy transformation. Risk management strategies for planted forests and invasive species in Europe present unique ecological challenges (Brundu and Richardson, 2016 ). Forest restoration efforts following surface mining disturbance require robust long-term monitoring (Macdonald et al., 2015 ). Climate change and weather extremes in the eastern Mediterranean further complicate these regional conservation efforts (Zittis et al., 2022 ). These diverse challenges complete the contextual evidence landscape within which European forest carbon artificial intelligence governance must be situated. 2. Materials and Methods This scoping review was conducted and reported following the PRISMA Extension for Scoping Reviews (PRISMA-ScR; Tricco et al., 2018 ; Peters et al., 2020 ), the methodological framework of Arksey and O'Malley (2005), as refined by Levac et al. ( 2010 ) and Joanna Briggs Institute (JBI) guidance (Aromataris & Munn, 2020 ). Publication bias was assessed following Sterne et al. ( 2016 ) and Egger et al. ( 1997 ). Study quality was appraised using the Mixed Methods Appraisal Tool (MMAT; Hong et al., 2018 ), adapted per Munn et al. ( 2018 ). The completed PRISMA-ScR checklist is provided as Supplementary Material . 2.1. Protocol and Registration A review protocol was developed and registered prior to data collection. The registration is available at: https://osf.io/pum9y . The protocol specified the research questions, eligibility criteria, search strategy, data charting form, quality appraisal method, and synthesis approach. However, there were minor deviations from the protocol, specifically the addition of a sixth cross-cutting (CX) pillar to accommodate interdisciplinary studies bridging multiple thematic domains. 2.2. Eligibility Criteria Sources of evidence were eligible if they: (a) reported original empirical research, simulation or modelling studies, methodological contributions substantially advancing understanding of forest ecosystem carbon processes or management strategies, or systematic evidence syntheses; (b) addressed at least one of the six pre-defined thematic pillars (P1: AI and remote sensing MRV; P2: digital twins and ecosystem modelling; P3: carbon markets and finance; P4: EU policy and governance; P5: ethics, equity, and societal dimensions; CX: cross-cutting) in the context of forest carbon; (c) were published in a peer-reviewed journal or identifiable grey-literature outlet between 1 January 2015 and 31 December 2024; and (d) were written in English. The 2015 start date was chosen to capture the period of rapid maturation of Sentinel satellite capabilities, Global Ecosystem Dynamics Investigation (GEDI) LiDAR deployment, and deep learning architectures, all of which fundamentally changed the landscape of AI-forest carbon research. Studies focused exclusively on non-forest or marine ecosystems without direct forest carbon transferability, commentaries without methodological content, and conference abstracts without full-text availability were excluded. Grey literature (institutional reports from IPCC, Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), UNEP, EFI, and EU Commission) was included where it constituted an identifiable source of evidence meeting other criteria. 2.3. Information Sources and Search Strategy Systematic electronic searches were conducted programmatically across three open-access databases using Python 3.12: PubMed/MEDLINE, OpenAlex, and Semantic Scholar. Supplementary grey-literature searches were conducted on the Web of Science (12 structured queries) and Scopus (6 queries). All searches were last executed in January 2025. No contact with authors was made to identify additional unpublished sources, consistent with the scope of this review. Searches were structured around six thematic pillars using Boolean combinations of three concept clusters: (i) forest carbon/biomass/MRV; (ii) artificial intelligence/machine learning/remote sensing; and (iii) pillar-specific governance/market/ethics terms. A total of 74 unique query strings were deployed across OpenAlex (the primary database given its coverage of forest, remote sensing, and environmental science literature), 19 in PubMed/MEDLINE, and 71 in Semantic Scholar (English-language filter applied). The full representative search string for OpenAlex (Pillar 1 — AI and Remote Sensing MRV) is given below. This string is reproducible as entered and can be adapted for other databases by substituting field tags: OpenAlex (P1 — AI & Remote Sensing MRV, 2015–2024) : ("forest carbon" OR "aboveground biomass" OR "forest biomass" OR "forest MRV" OR "carbon stock" OR "canopy height" OR "forest disturbance" OR "tree species classification") AND ("machine learning" OR "deep learning" OR "artificial intelligence" OR "random forest" OR "neural network" OR "support vector machine" OR "convolutional neural network") AND ("LiDAR" OR "SAR" OR "Sentinel" OR "Landsat" OR "UAV" OR "remote sensing" OR "GEDI" OR "ICESat") Limits: publication year = 2015–2024; language = English Representative search strings for all six pillars and all three databases, including full Boolean logic and field tag specifications, are provided in Supplementary File S1 . Example pillar-specific concept clusters are summarised in Table 1 below: Table 1 Concept clusters used per thematic pillar across all databases (representative terms; full strings in Supplementary File S1 ). Pillar Forest/Carbon concept terms AI/Technology concept terms Governance/Context concept terms P1 "forest biomass" OR "carbon stock" OR "aboveground biomass" OR "canopy height" OR "forest disturbance" "machine learning" OR "deep learning" OR "LiDAR" OR "SAR" OR "Sentinel" OR "UAV" OR "GEDI" OR "neural network" "MRV" OR "measurement reporting verification" OR "LULUCF" OR "carbon accounting" P2 "forest carbon cycle" OR "ecosystem carbon" OR "net ecosystem production" OR "soil carbon" "digital twin" OR "process-based model" OR "land surface model" OR "simulation" OR "CABLE" OR "FLEXPART" OR "ERA-5" "climate scenario" OR "carbon permanence" OR "disturbance modelling" OR "ISIMIP" P3 "forest carbon credit" OR "REDD+" OR "carbon offset" OR "forest carbon market" OR "additionality" "blockchain" OR "AI verification" OR "satellite monitoring" OR "ESG" OR "machine learning" "voluntary carbon market" OR "EU ETS" OR "carbon integrity" OR "greenwashing" P4 "forest policy" OR "LULUCF" OR "forest governance" OR "EU Forest Strategy" OR "forest management" "remote sensing" OR "AI" OR "digital" OR "satellite" "European Green Deal" OR "Fit-for-55" OR "Nature Restoration Law" OR "CSRD" OR "EU ETS" P5 "forest carbon" OR "REDD+" OR "community forestry" OR "indigenous forest" "algorithmic bias" OR "AI ethics" OR "data sovereignty" OR "automated decision" "equity" OR "justice" OR "consent" OR "decolonisation" OR "smallholder" CX "forest carbon stock" OR "aboveground biomass" OR "forest disturbance" "multi-source" OR "data fusion" OR "ICOS" OR "integrated" OR "cross-cutting" "carbon market" OR "policy" OR "governance" OR "biodiversity co-benefit" 2.4. Selection of Sources of Evidence Records retrieved from all databases were processed through a structured PRISMA-ScR pipeline by a single reviewer, with a 10% random sample independently screened at each step by a second reviewer. Discrepancies were resolved by consensus. The pipeline was implemented in Python 3.12 and is fully reproducible (see source code in Supplementary File S2 ). The addition of the CX pillar, comprising 22 studies, represents a minor protocol deviation. Cross-cutting studies explicitly integrating two or more pillars were assigned to this category to avoid double-counting. The complete PRISMA-ScR flow diagram is presented in Fig. 1 . Additionally, all screening decisions are available in the Supplementary Workbook within the Selection_Summary sheet. 2.5. Data Charting Process A standardised data charting form was developed a priori and piloted on a random sample of 20 records before full application. The form was designed to capture all variables specified in the protocol. Charting was conducted by the author from abstract and title content, following the scoping review convention of not requiring full-text access for all records (Levac et al., 2010 ; Peters et al., 2020 ). A 15% random sample was independently re-charted by a second reviewer. Discrepancies were resolved by consensus discussion. No contact with study authors was made to obtain or confirm data, as all required variables were extractable from publicly available abstracts and metadata. The completed charting form for all 200 studies is provided in the Supplementary Workbook (Included_200 sheet). 2.6. Data Items The following variables were sought for each included source of evidence. Definitions and coding assumptions are specified in parentheses: Bibliographic metadata: first author surname, publication year, journal/venue, digital object identifier (DOI), open-access status Primary thematic pillar (P1–P5 or CX; single assignment by dominant content; ambiguous cases assigned CX) AI/ML methods employed (multi-label; categories: ANN, Random Forest, SVM, Deep Learning Convolutional Neural Network (CNN)/Transformer, Regression/Statistical ML, Gradient Boosting, Simulation/Digital Twin, Object-Based Image Analysis (OBIA), Blockchain/ Distributed Ledger Technology (DLT), Not Specified) Remote sensing data source(s) (multi-label; categories: Light Detection and Ranging (LiDAR)/Airborne Laser Scanning (ALS)/Terrestrial Laser Scanning (TLS), Optical Satellite, SAR, UAV/Drone, Hyperspectral, Field Inventory, Not Specified) Carbon variable(s) addressed (multi-label; categories: AGB, soil carbon, carbon flux/NEP, fire carbon, peat carbon, forest carbon market/credit) Geographic scope (single label; categories: Europe, Global, North America, Asia, Other/Not Specified) Citation count at date of search (source: OpenAlex API; used for stratified selection and citation distribution analysis) Several key simplifying assumptions were established for this review. First, studies with no identifiable artificial intelligence or machine learning method in the abstract were coded as "Not Specified" rather than being excluded. Second, the geographic scope of each paper was assigned based on the study area description rather than the institutional affiliations of the authors. Third, all applicable computational and sensor categories were recorded for multi-method studies using a multi-label approach. However, only a single primary thematic pillar was assigned to each of these studies. These assumptions are consistent with the scoping review convention of maximising inclusivity at the charting stage. 2.7. Critical Appraisal of Individual Sources of Evidence Critical appraisal was conducted to characterise the methodological quality of the evidence base. This process also helped identify patterns of risk that would inform the research agenda rather than serving to exclude studies. This approach remains completely consistent with the primary purpose of a scoping review. The rationale for conducting this appraisal alongside the scoping review stems from the potential regulatory deployment of these technologies in high-stakes EU carbon governance contexts. Therefore, understanding the methodological maturity of this field provides directly actionable insights for policy development. A ten-criterion framework was applied during this process. This framework was adapted from the Mixed Methods Appraisal Tool developed by Hong et al. ( 2018 ), following the recommendations of Munn et al. ( 2018 ). The first criterion evaluated whether the research objective was clearly stated. The second and third criteria assessed if the methodology and study design were adequately described. The fourth and fifth criteria checked for the reporting of the sampling strategy and the clear description of the data source. The sixth and seventh criteria determined whether performance metrics were provided and if statistical rigour was properly addressed. The eighth and ninth criteria evaluated the discussion of study limitations and the clear reporting of results. Finally, the tenth criterion assessed whether reproducibility was addressed through code or data availability. Each criterion was assessed as either ‘ met’ or ‘ not met’ based on the abstract and metadata content of each study. Publication bias was also assessed using Egger-style funnel plots as described by Sterne et al. ( 2016 ). These plots illustrate the log-citation count against the age of the study. 2.8. Synthesis of Results Evidence was synthesised narratively according to each thematic pillar and sub-theme. This synthesis included a descriptive quantitative analysis of method frequencies, geographic distributions, and citation patterns. The co-occurrence of artificial intelligence (AI) methods across different pillars was assessed using heatmaps. Temporal trends were subsequently mapped using year-by-pillar heatmaps. Furthermore, pillar capability profiles across eight distinct analytical dimensions were visualised using radar charts. Citation distributions were then examined using violin plots featuring symmetrical logarithmic scaling. The overall synthesis was framed around the five core research questions. All quantitative analyses were implemented using Python version 3.12, along with the pandas 2.2, NumPy 1.26, and matplotlib 3.8 libraries. The complete source code for these analyses is readily available in Supplementary File S2. 2.9. Study Characteristics Overview Table 2 summarises the characteristics of the 200 included sources of evidence. Full bibliographic details and charted variables for all studies are provided in the Supplementary Workbook (Included_200 sheet). Table 2 Summary characteristics of included sources of evidence ( n = 200) Characteristic Value ( n = 200) Publication years 2015–2024 Emerging phase (2015–2019) n = 101 (50.5%) Acceleration phase (2020–2024) n = 99 (49.5%) Open-access sources n = 200 (100%) Total raw records retrieved n = 5,135 (PubMed 695; OpenAlex 3,981; Semantic Scholar 459) Mean citations ± SD 349 ± 412 Median citations (IQR) 202 (87–533) Maximum citations 2,645 (Calvin et al., 2023 – IPCC AR6 Synthesis Report) European-scope studies n = 47 (23.5%) Global-scope studies n = 82 (41.0%) Top journal Remote Sensing ( n = 22, 11.0%) Dominant AI/ML method Artificial Neural Network ( n = 48, 24.0%) Dominant remote sensing modality LiDAR/ALS/TLS ( n = 33, 16.5%) Studies reporting reproducible code/data n < 20 (< 10%) Studies reporting limitations n < 60 (< 30%) P1: AI & Remote Sensing MRV n = 53 (26.5%) P2: Digital Twins & Ecosystem Modelling n = 30 (15.0%) P3: Carbon Markets & Finance n = 22 (11.0%) P4: EU Policy & Governance n = 59 (29.5%) P5: Ethics, Equity & Societal Dimensions n = 14 (7.0%) CX: Cross-cutting themes n = 22 (11.0%) 3. Results 3.1. Selection of Sources of Evidence and Evidence Base Overview From 5,135 raw records retrieved, the PRISMA-ScR pipeline yielded 200 included sources. The corpus spans 2015–2024, with near-symmetrical temporal distribution (Figs. 2 a and 2 b): the emerging phase (2015–2019) yielded 101 studies (50.5%), and the acceleration phase (2020–2024) contributes 99 studies (49.5%). Annual output peaked in 2021–2022 before consolidating. All 200 studies are open access (Fig. 2 c). As shown in Fig. 2 d, Remote Sensing is the leading venue ( n = 22, 11.0%), followed by Environmental Research Letters ( n = 5), Geoscientific Model Development, Atmospheric Chemistry and Physics, PLoS ONE, Earth System Science Data, Proceedings of the National Academy of Sciences, Global Change Biology, Land, and Sustainability (all n = 4). Mean citation count (Fig. 2 e) is 349 (median 202; max 2,645), with the highest-cited works being landmark climate and biodiversity assessments (Calvin et al., 2023 ; IPBES, 2019 ; Lamb et al., 2021 ). Geographically, 47 studies (23.5%) are European in scope; 82 (41.0%) are global; 67 (33.5%) are not geographically specified or are regionally mixed (Fig. 2 f). The pillar distribution (Fig. 2 g ) is P4 ( n = 59, 29.5%), P1 ( n = 53, 26.5%), P2 ( n = 30, 15.0%), P3 and CX (each n = 22, 11.0%), and P5 ( n = 14, 7.0%). The dominance of P4 reflects the density of EU policy literature directly bearing on forest carbon governance; the near-equivalent P1 share reflects the technical maturity of remote sensing and ML biomass estimation. 3.2. Pillar 1 — AI and Remote Sensing MRV: Results Addressing RQ1 The AI/remote sensing MRV pillar ( n = 53, 26.5%) encompasses forest biomass estimation, canopy height mapping, tree species classification, disturbance detection, and carbon flux upscaling across LiDAR, SAR, optical, and UAV data sources (Fig. 3 ). Several studies address the full forest ecosystem monitoring chain from individual tree to continental scale (Adesipo et al., 2020 ; Alexakis et al., 2017 ; Allen et al., 2022 ; Amiri et al., 2019 ; Avci et al., 2021; Bauer-Marschallinger et al., 2018; Berg et al., 2022 ; Cao et al., 2018 ; Chatziantoniou et al., 2017 ; Cheng et al., 2024 ; Chuvieco et al., 2018 ; Cowls et al., 2021; DeLancey et al., 2019 ; Dorado-Roda et al., 2021 ; Duarte et al., 2022 ; Esteban et al., 2019 ; Farmonov et al., 2023 ; Fawzy et al., 2020 ; Grabska-Szwagrzyk et al., 2019; Haya et al., 2023 ; He et al., 2022 ; Immitzer et al., 2019 ; Jung et al., 2020 ; Kellner et al., 2019 ; Khanal et al., 2020 ; Klouček et al., 2019 ; Lambers et al., 2019 ; Li et al., 2015 ; Linaza et al., 2021 ; Liu et al., 2023 ; Liu et al., 2024 ; Näsi et al., 2015 ; Nevalainen et al., 2017 ; Patacca et al., 2022; Pelletier et al., 2017 ; Praticò et al., 2021 ; Radočaj et al., 2022 ; Rodríguez-Veiga et al., 2019 ; Rowan et al., 2022 ; Segarra et al., 2020 ; Storm et al., 2019; Tifafi et al., 2017; Tricht et al., 2018 ; Venter et al., 2022 ; Virkkala et al., 2021 ; Vreugdenhil et al., 2016 ; Werff and Meer, 2016 ; Xie et al., 2020 ; Yu et al., 2015 ; Zittis et al., 2022 ). 3.2.1. LiDAR-based biomass and carbon estimation LiDAR, ALS and TLS represent the highest-precision modality for above-ground biomass (AGB) and canopy structure estimation. Machine learning models applied to LiDAR-derived structural metrics consistently achieve R² greater than 0.85 for AGB prediction at stand and landscape scales (Amiri et al., 2019 ; Dorado-Roda et al., 2021 ; Kellner et al., 2019 ; Lambers et al., 2019 ; Nevalainen et al., 2017 ; Yu et al., 2015 ). UAV-LiDAR and photogrammetric platforms extend sub-metre structural assessment to operational plot-level monitoring (Duarte et al., 2022 ; Kellner et al., 2019 ; Klouček et al., 2019 ; Näsi et al., 2015 ; Nevalainen et al., 2017 ). GEDI spaceborne LiDAR provides AGB estimation at 25 m footprint across tropical and temperate forests (Dorado-Roda et al., 2021 ). Multi-source ALS fusion with Sentinel-2 and aerial photogrammetry substantially improves species-level classification and carbon mapping (Amiri et al., 2019 ; Immitzer et al., 2019 ; Lambers et al., 2019 ; Yu et al., 2015 ). Recent studies integrating UAV-LiDAR, multi-sensor data fusion, and national forest inventory data confirm high-precision AGB retrieval for temperate and subtropical forests (Gan et al., 2024 ; Li et al., 2020 ; Liu et al., 2018 ; Tamiminia et al., 2021 ; Urbazaev et al., 2018 ; Vafaei et al., 2018 ; Zhai et al., 2025 ; Zhang et al., 2019 ). 3.2.2. SAR and optical satellite integration Sentinel-1 SAR is applied extensively for soil moisture monitoring, vegetation backscatter analysis, and forest disturbance detection across European forest biomes (Alexakis et al., 2017 ; Bauer-Marschallinger et al., 2018; Chatziantoniou et al., 2017 ; Tricht et al., 2018 ; Vreugdenhil et al., 2016 ; Werff and Meer, 2016 ). Synergistic use of Sentinel-1 and Sentinel-2 substantially outperforms single-sensor approaches for land-use/land-cover and forest species mapping, achieving 85–95% overall accuracy for temperate European forest types (Chatziantoniou et al., 2017 ; Grabska-Szwagrzyk et al., 2019; Pelletier et al., 2017 ; Praticò et al., 2021 ; Radočaj et al., 2022 ; Tricht et al., 2018 ). Landsat time-series support decadal forest cover change detection (Chuvieco et al., 2018 ; Venter et al., 2022 ; Werff and Meer, 2016 ). Hyperspectral sensors improve tree species discrimination and biotic stress detection relevant to permanence monitoring (Duarte et al., 2022 ; Farmonov et al., 2023 ; Liu et al., 2018 ; Näsi et al., 2015 ). 3.2.3. Carbon flux upscaling Figure 4 shows the carbon variable focus distribution of this study. Neural network-based upscaling of eddy-covariance measurements to continental carbon flux estimates is well-established at ICOS sites, with FLUXCOM achieving R² greater than 0.80 against global gross primary productivity benchmarks (Heiskanen et al., 2021; Jung et al., 2020 ; Virkkala et al., 2021 ; Xia et al., 2015 ). Machine learning integration of phenology and remote sensing data improves partitioning of net ecosystem production (NEP) across forest types (Jung et al., 2020 ; Xia et al., 2015 ). Global burned area products and wildfire atlases provide disturbance forcing for carbon flux modelling (Andela et al., 2019 ; Chuvieco et al., 2018 ). Soil carbon stock models combining machine learning with EU pedological databases support LULUCF accounting (Bispo et al., 2017 ; Schulte et al., 2015 ; Tifafi et al., 2017). Deep learning on multi-temporal Landsat and GEDI data enables simulation of long-term forest carbon stock trajectories (Reinmann et al., 2020 ; Thomas et al., 2017 ; Valipour et al., 2021 ; Zhang et al., 2023 ). 3.2.4. UAV applications and forest health UAV-borne photogrammetry, hyperspectral imaging, and LiDAR enable high-resolution forest health monitoring, bark beetle infestation mapping, and plot-level biomass estimation that directly informs management decisions (Duarte et al., 2022 ; Klouček et al., 2019 ; Näsi et al., 2015 ; Nevalainen et al., 2017 ). ML classifiers applied to UAV hyperspectral imagery distinguish healthy from infested trees with > 90% accuracy (Duarte et al., 2022 ; Klouček et al., 2019 ; Näsi et al., 2015 ), providing early-warning capacity for disturbance events that threaten carbon permanence and which trigger LULUCF reporting obligations. UAV-LiDAR fusion studies confirm that individual-tree structural metrics from drone platforms achieve biomass accuracy competitive with conventional ALS at substantially lower cost-per-hectare (Gan et al., 2024 ; Hämmerle et al., 2017 ). 3.2.5. AI architectures for MRV: performance and transferability Figure 5 presents AI/ML method frequency across all 200 included sources. The dominant AI approaches in P1 are ANN ( n = 30, 57% of pillar), random forest ( n = 11), support vector machines ( n = 7), regression/statistical ML ( n = 9), deep learning CNN ( n = 5), and OBIA (n = 2). Comparative studies demonstrate that random forest and deep learning produce comparable AGB estimation accuracy (within ± 5–10% RMSE), with random forest offering superior interpretability and computational efficiency for operational deployment (Avci et al., 2021; DeLancey et al., 2019 ; Esteban et al., 2019 ; Haya et al., 2023 ; Pelletier et al., 2017 ; Tamiminia et al., 2021 ). Model transferability across forest biomes and climate scenarios — essential for EU-wide deployment — is rarely tested systematically, representing a critical gap for regulatory reliability (Amiri et al., 2019 ; Pelletier et al., 2017 ; Rodríguez-Veiga et al., 2019 ). 3.3. Pillar 2 — Digital Twins and Ecosystem Modelling: Results Addressing RQ2 The digital twin and ecosystem modelling pillar ( n = 30, 15.0%) encompasses process-based land surface models, atmospheric inversions, and data-driven simulation frameworks for forest carbon assessment. The IPCC AR6 Synthesis Report (Calvin et al., 2023 ) establishes the definitive climate context — 1.5°C and 2°C scenario envelopes — that forest carbon digital twins must reproduce and project to inform management strategy. GLEAM v3 satellite-based land evaporation and root-zone soil moisture (Martens et al., 2017 ) provides essential inputs for water-carbon coupling. ERA-Interim/Land (Balsamo et al., 2015 ) and ERA-5 (Albergel et al., 2018 ) supply boundary conditions for land surface simulations; CABLE (Haverd et al., 2018 ), ISBA (Albergel et al., 2018 ), FLEXPART (Pisso et al., 2019 ), and LPJ-GUESS (Lindeskog et al., 2021 ) represent deployed process-based components validated against European flux networks. The ISIMIP2b multi-model impact modelling protocol (Frieler et al., 2017 ) standardises climate impact projections applicable to forest carbon permanence assessment. More recent studies explore forestry-specific digital twin approaches integrating ML with multi-temporal Landsat data to estimate forest carbon stocks (Jiang et al., 2022 ), forest management simulation models for long-term carbon balance under climate scenarios (Lindeskog et al., 2021 ; Thomas et al., 2017 ; Valipour et al., 2021 ), and object-based random forest modelling of AGB in heterogeneous environments (Silveira et al., 2019 ). The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) adapted for European Union conditions (Pilli et al., 2018 ) provides a deployable carbon accounting framework. Urbanisation effects on temperate forest carbon cycles (Reinmann et al., 2020 ) extend the evidence base to forest management in peri-urban contexts. The digital twin concept as applied in precision cardiology (Acero et al., 2020 ) illustrates governance and validation challenges — consent, liability, interpretability — directly applicable to deploying such systems in consequential forest carbon governance contexts. 3.4. Pillar 3 — Carbon Markets and Finance: Results Addressing RQ3 The carbon markets and finance pillar ( n = 22, 11.0%) addresses the integrity, transparency, and efficiency of voluntary and compliance forest carbon markets. Voluntary carbon markets face well-documented structural failures: the UN Emissions Gap Report (Programme UNEP, 2023) documents persistent gaps between corporate net-zero commitments and verified emissions reductions; ESG rating divergence analysis (Berg et al., 2022 ) demonstrates that inter-agency ratings are inconsistent, raising fundamental questions about algorithmic transparency in carbon crediting; and community-based forest monitoring for REDD + MRV (Murthy et al., 2017 ) and carbon measurement overviews for REDD+ implementation (Bhattarai et al., 2016 ) document the technical and social prerequisites for credible market integration. AI applications to carbon market integrity include satellite-based permanence monitoring and additionality verification; blockchain/DLT carbon credit provenance tracking (Kwilinski et al., 2023 ; Rowan et al., 2022 ); ML-assisted ESG disclosure verification (Alamillos and de Mariz, 2022; Cerciello et al., 2022; Clementino and Perkins, 2021 ); LCA-informed carbon accounting for bioenergy and forest products (Franz et al., 2018 ; Leinonen et al., 2022; Nemitz et al., 2018 ); and automated deforestation risk assessment for supply chains (Domínguez and Luoma, 2020 ; Schilling-Vacaflor and Lenschow, 2021). The commodification of forest carbon and socially-embedded REDD+ practices (Benjaminsen and Kaarhus, 2018 ) and uncertainty in forest reference levels (Mertz et al., 2018 ) highlight fundamental challenges for AI-based verification in voluntary markets. The risk of "precision greenwashing" — technically sophisticated but opaque AI estimates satisfying formal requirements while misrepresenting ecological reality — is a cross-cutting concern (Berg et al., 2022 ; Clementino and Perkins, 2021 ; Domínguez and Luoma, 2020 ; Honkomp and Schier, 2024 ; Schilling-Vacaflor and Lenschow, 2021). 3.5. Pillar 4 — EU Policy and Governance: Results Addressing RQ4 Pillar 4 is the largest research domain ( n = 59, 29.5%), reflecting the centrality of EU regulatory frameworks as both drivers and recipients of AI forest carbon innovations. Table 3 and Fig. 6 present AI method frequencies by pillar, while Table 4 maps eleven EU policy instruments to AI integrity implications, with supporting evidence. Key P4 findings by instrument: The EU Forest Strategy 2030 demands joint optimisation of carbon sequestration, biodiversity, and socioeconomic functions — a multi-criteria problem well-suited to AI-assisted multi-objective analysis (Kuuluvainen et al., 2021 ; Law et al., 2018 ; Roberts et al., 2017 ; Sabatini et al., 2020 ; Seddon et al., 2020 ). The LULUCF Regulation creates direct regulatory demand for AI-enhanced biomass and carbon stock estimation (Bispo et al., 2017 ; Deng et al., 2022 ; Forsell et al., 2016 ; Law et al., 2018 ; Pilli et al., 2018 ). EU ETS and CDR policy create market incentives for AI-backed forest carbon verification (Farghali et al., 2023 ; Gough et al., 2018 ; Honegger et al., 2021 ; Kabeyi & Olanrewaju, 2022 ; Rickels et al., 2021 ; Rosa et al., 2021 ; Sovacool et al., 2022 ; Sterman et al., 2018 ; Tutak et al., 2021 ). CAP Eco-schemes require scalable AI-verifiable MRV for soil and forest carbon (Batáry et al., 2015 ; Boix-Fayos & De Vente, 2023 ; Pe'er et al., 2016; Ray et al., 2019 ; Ronchi et al., 2019 ; Schröder et al., 2017; Telo Da Gama, 2023 ). ICOS infrastructure provides the European eddy-covariance validation network against which AI upscaling must be benchmarked (Franz et al., 2018 ; Heiskanen et al., 2021; Virkkala et al., 2021 ). Forest carbon sequestration governance (Gren and Aklilu, 2016 ), subnational forest carbon governance (Ruseva, 2023 ), forest regulations and stakeholder needs modelling (Zute et al., 2023 ), and foreign direct investment and LULUCF emissions (Piabuo et al., 2023 ) extend the P4 policy coherence analysis. Deep learning-based fire detection (Mambile et al., 2024; Seydi et al., 2022 ), life cycle assessment of biogenic carbon (Leinonen et al., 2022), and forestry offsets under carbon markets (Xu, 2024 ) further advance the policy implications frontier. Table 3 AI/ML method frequency by thematic pillar ( n = 200; multi-label extraction) AI/ML Method P1 P2 P3 P4 P5 CX Total Artificial Neural Network 30 5 3 5 1 4 48 Regression / Statistical ML 9 3 1 2 0 1 16 Random Forest 11 2 0 0 0 2 15 Support Vector Machine (SVM) 7 0 0 1 0 1 9 Deep Learning (CNN/Transformer) 5 0 0 0 0 0 5 OBIA 2 0 0 1 0 1 4 Simulation / Digital Twin 1 2 0 0 0 0 3 Gradient Boosting 1 0 0 0 0 1 2 Blockchain / DLT 1 0 0 0 0 0 1 Not Specified 22 9 14 37 14 8 104 Table 4 Mapping of EU policy instruments to AI-driven forest carbon integrity implications EU Policy Instrument Key Objective AI/Digital Integrity Implication Key Studies EU Forest Strategy 2030 Biodiversity/carbon co-benefits; afforestation AI-assisted co-benefit mapping; multi-criteria optimisation; disturbance-resilience monitoring Chazdon et al. ( 2020 ); Kuuluvainen et al. ( 2021 ); Law et al. ( 2018 ); Roberts et al. ( 2017 ); Sabatini et al. ( 2020 ); Seddon et al. ( 2020 ) LULUCF Regulation (2023) 310 MtCO₂eq net sink target 2030 AI-enhanced biomass/carbon stock estimation; national MRV compliance; cross-country comparability Bispo et al. ( 2017 ); Deng et al. ( 2022 ); Forsell et al. ( 2016 ); Kern et al. ( 2017 ); Law et al. ( 2018 ); Pilli et al. ( 2018 ) EU Taxonomy Regulation Sustainable finance do-no-significant-harm screening AI-driven ESG/carbon integrity verification; greenwashing detection; disclosure audit Alamillos and de Mariz (2022); Berg et al. ( 2022 ); Cerciello et al. (2022); Clementino and Perkins ( 2021 ) EU ETS (Revision 2023) Carbon pricing; net-zero pathway AI-enabled additionality/permanence verification; leakage detection; fraud prevention Honegger et al. ( 2021 ); Honkomp and Schier ( 2024 ); Rickels et al. ( 2021 ); Sovacool et al. ( 2022 ); Stern et al. (2017) Nature Restoration Law (2024) Ecosystem restoration targets; peatland rewetting Remote sensing monitoring of restoration trajectory; AI carbon stock verification Navarro and Pereira ( 2015 ); Sabatini et al. ( 2020 ); Seddon et al. ( 2020 ); Tanneberger et al. (2020) Green Deal / Fit-for-55 55% GHG reduction by 2030 AI-integrated national carbon budgets; sectoral attribution; inventory reconciliation Allen et al. ( 2022 ); Chen et al. ( 2023 ); Kulovesi and Oberthür ( 2020 ); Kyriakopoulos and Sebos ( 2023 ); Lamb et al. ( 2021 ) CSRD / ESRS Corporate double-materiality reporting Blockchain/DLT audit trails; AI disclosure verification; climate risk quantification Alamillos and de Mariz (2022); Cerciello et al. (2022); Clementino and Perkins ( 2021 ); Kwilinski et al. ( 2023 ) ICOS Research Infrastructure European flux measurement network AI upscaling eddy-covariance flux; gap-filling; continental carbon balance Franz et al. ( 2018 ); Heiskanen et al. (2021); Jung et al. ( 2020 ); Virkkala et al. ( 2021 ) EU RED III (Bioenergy) Renewable energy sustainability criteria AI feedstock traceability; carbon permanence assurance; avoided deforestation verification Gough et al. ( 2018 ); Leinonen et al. (2022); Meyer ( 2017 ); Sterman et al. ( 2018 ) CAP Eco-schemes (2023–27) Carbon payment to farmers/foresters AI-driven soil/biomass MRV at farm scale; payment verification; additionality assessment Batáry et al. ( 2015 ); Bispo et al. ( 2017 ); Ronchi et al. ( 2019 ); Schröder et al. (2017) CSDDD Mandatory supply chain due diligence AI deforestation detection; satellite forest alerts; supply chain traceability Domínguez and Luoma ( 2020 ); Schilling-Vacaflor and Lenschow (2021) 3.6. Pillar 5 — Ethics, Equity and Societal Dimensions: Results Addressing RQ5 The ethics and societal dimensions pillar (n = 14, 7.0%) is the smallest but addresses the most consequential governance deficits. The artificial intelligence gambit in climate policy demonstrates the dangers of deploying these tools as a technological fix without adequate social legitimacy (Cowls et al., 2021). Research documents how algorithmic systems may concentrate benefits among well-resourced actors while marginalising smallholders and indigenous peoples (Bernes et al., 2018 , Domínguez and Luoma, 2020 , Kuyper et al., 2017, Morán et al., 2018). The legitimacy of non-state actors in climate governance provides a foundational framework for anticipating equity risks in artificial intelligence deployment (Kuyper et al., 2017, Peel and Lin, 2019 ). Similarly, community acceptance and the co-design of environmental interventions help mitigate these risks (Lupp et al., 2020 ). Observations of landscape-scale land-use change effects on ecosystem services further inform this ethical framework (Fedele et al., 2018 , Statuto et al., 2017 ). Dynamics at the forest frontier in the Global South illustrate how international climate change policies promise development and equity while simultaneously reproducing inequalities through technology-mediated resource governance (Brockhaus et al., 2021 ). Community forestry programme evidence underscores that local governance capacity independently predicts carbon and biodiversity outcomes (Luintel et al., 2018 ). This means that artificial intelligence-based measurement, reporting, and verification cannot substitute for true institutional legitimacy. Digital health governance provides instructive precedents for this challenge. For example, artificial intelligence clinical decision support in precision cardiology offers highly developed accountability structures (Acero et al., 2020 ). These structures effectively address algorithmic transparency, consent, liability, and equity across socioeconomic strata. Forest carbon artificial intelligence governance should systematically adapt these robust healthcare frameworks. 3.7. Cross-Cutting Issues Twenty-two studies successfully bridge multiple thematic pillars (Andela et al., 2019 , Anees et al., 2024 , Beillouin et al., 2020 , De Luca et al., 2019 , Heiskanen et al., 2021, Kwilinski et al., 2023 , Panagos et al., 2018 , Singh et al., 2022 , Zeng et al., 2015 ). The ICOS integrated carbon observation infrastructure exemplifies this cross-pillar integration (Heiskanen et al., 2021). This initiative embeds a technical monitoring system within a broader governance architecture alongside strict ethical data-sharing guidelines. Multi-sensor above-ground biomass estimation studies collectively advance the cross-pillar technical and governance interface. These encompass studies integrating ICESat-2, Sentinel-1, and Sentinel-2 data (Nandy et al., 2021 ). Other research combines airborne LiDAR, synthetic aperture radar, and optical satellite data (Urbazaev et al., 2018 ). Further examples involve multi-source remote sensing in northeast China (Wang et al., 2022 ) and optical remote sensing merged with laser point cloud fusion (Zheng et al., 2024 ). Additionally, researchers have integrated GF-1 images for forest carbon storage dynamics (Liu et al., 2024 ) and conducted pantropical canopy height mapping using GEDI and TanDEM-X sensors (Qi et al., 2025 ). A scoping review of carbon pricing in forest sector models connects economic policies with governance frameworks (Honkomp and Schier, 2024 ). The integrated global assessment of natural forest carbon potential also bridges these domains (Mo et al., 2023 ). Deep learning models for forest fire prediction at Kilimanjaro link technical disturbance detection with long-term permanence policy (Mambile et al., 2024). Fire susceptibility mapping in India serves a similar integrative function (Singha et al., 2024 ). Furthermore, national forest inventory methodology connects technical field measurement with broad governance reporting (Zeng et al., 2015 ). Global land-use and land-cover dataset comparisons also bridge these essential categories (Venter et al., 2022 ). Critical appraisal results are summarised in Fig. 7 . Fewer than 10% of studies across all pillars report reproducible code or data. Limitations disclosure remains below 30% in all pillars. Research objective clarity and results reporting are successfully met in over 70% of studies across all domains. Performance metrics are provided in most technical studies but appear in fewer than 40% of policy-focused studies. These specific deficits directly inform the future research agenda priorities outlined in Section 5 . 3.8. Citation Distribution and Publication Bias The citation impact across the different thematic pillars was examined using violin plots with symmetrical logarithmic scaling (Fig. 8 ). The analysis reveals that the most technically established domains, particularly Pillar 2 (Digital Twins) and Pillar 1 (AI/Remote Sensing MRV), exhibit high mean and median citation counts, though significant variance exists within all pillars. To evaluate the risk of publication bias within the synthesized literature, scatter plots and Egger-style inverted funnel plots were generated (Fig. 9 ) following established methodological frameworks (Egger et al., 1997 , Sterne et al., 2016 ). The distribution of log-transformed citation counts against study age demonstrates broad symmetry across most thematic pillars, indicating a generally low overall risk of publication bias in the evidence base. A minor right-sided asymmetry observed in Pillar 4 (EU Policy and Governance) likely reflects the targeted inclusion of highly cited institutional and policy reports alongside standard academic outputs. 4. Discussion This scoping review systematically mapped the emerging landscape of AI-driven forest carbon integrity research—spanning remote sensing advancements, digital twin ecosystem models, carbon market applications, EU policy alignment, and ethical frameworks—to identify dominant methodologies and persistent technological gaps across the measurement, reporting, and verification (MRV) chain. By critically appraising the literature and confirming a generally low risk of publication bias across the evidence base (Fig. 10 ), this synthesis establishes a reliable foundation for identifying critical risks and proposing a targeted, policy-relevant research agenda for European forests. 4.1. Technical Readiness and Gaps (RQ1) Addressing the first research question, the 200-study corpus confirms substantial and accelerating technical capability. LiDAR-based biomass estimation with machine learning is sufficiently accurate for stand-level LULUCF compliance reporting, consistently achieving an R² greater than 0.85 (Amiri et al., 2019 , Dorado-Roda et al., 2021 , Kellner et al., 2019 , Rodríguez-Veiga et al., 2019 ). Sensor fusion combining Sentinel-1 and Sentinel-2 data supports near-real-time disturbance detection at a 10-to-20-metre resolution with 85 to 95 percent overall accuracy (Bauer-Marschallinger et al., 2018, Grabska-Szwagrzyk et al., 2019, Tricht et al., 2018 ). Furthermore, neural network flux upscaling is successfully validated against ICOS sites with acceptable uncertainty for continental-scale carbon balance computations (Heiskanen et al., 2021, Jung et al., 2020 , Virkkala et al., 2021 , Xia et al., 2015 ). The expanding corpus of multi-sensor fusion studies confirms that above-ground biomass estimation accuracy continues to improve through data integration. Pantropical canopy height mapping from GEDI and TanDEM-X currently represents the ultimate technical frontier in this field (Qi et al., 2025 ). However, reproducibility remains critically deficient. Fewer than 10% of studies provide code or reproducible workflows, as detailed in Fig. 16. This lack of transparency directly undermines the auditability required for European Union regulatory deployment. Performance metrics are inconsistently reported across the literature. Independent validation is also far from universal. Model transferability across European forest biomes and climate scenarios is rarely tested systematically (Amiri et al., 2019 , Pelletier et al., 2017 , Rodríguez-Veiga et al., 2019 ). This major gap directly undermines the European Union-wide regulatory reliability required by LULUCF and the European Union Emissions Trading System (Deng et al., 2022 , Kulovesi and Oberthür, 2020 , Rickels et al., 2021 ). 4.2. The Digital Twin Gap (RQ2) Addressing the second research question, digital twin technology for forest carbon is progressing rapidly in earth system modelling (Haverd et al., 2018 , Jiang et al., 2022 , Lindeskog et al., 2021 , Martens et al., 2017 , Pilli et al., 2018 , Pisso et al., 2019 ). Nevertheless, this technology remains entirely disconnected from operational carbon governance. The necessary data infrastructures already exist through platforms like ICOS, ERA-5, Sentinel archives, and EDGAR. Despite this, no integrated operational European Forest Carbon Digital Twin has yet been demonstrated. The precision cardiology digital twin precedent illustrates that closing this gap requires regulatory sandbox frameworks, liability allocation, and clear interpretability standards alongside technical integration (Acero et al., 2020 ). Process-based model limitations struggle to represent compound disturbance dynamics, such as simultaneous bark beetle outbreaks and drought observed in Central Europe between 2017 and 2019 (Dupuy et al., 2020 , Haverd et al., 2018 , Patacca et al., 2022). These limitations further complicate reliable carbon permanence projections. Closing this gap is a crucial priority for advancing the understanding of European forest ecosystem processes under accelerating climate change. 4.3. Carbon Market Integrity (RQ3) Addressing the third research question, artificial intelligence verification offers a potential solution to chronic voluntary carbon market integrity failures (Berg et al., 2022 , Programme UNEP, 2023, Stern et al., 2017). Automated permanence monitoring, additionality verification, and leakage detection could readily replace expensive and inconsistently applied field audits. However, proprietary algorithms embedded within commercially interested verification bodies create significant new opacity risks (Berg et al., 2022 , Clementino and Perkins, 2021 ). Corporate sustainability reporting requirements, supply chain provisions, and the European Union Taxonomy collectively create immense demand for artificial intelligence-generated forest carbon data. This data must be simultaneously verified, transparent, and completely interoperable. Most current prototypes simply do not meet these stringent standards. The developing-country REDD+ context adds further complexity to this issue. Uncertainty in reference levels introduces baseline measurement challenges (Mertz et al., 2018 ). Commodification critiques raise valid socioeconomic concerns (Benjaminsen and Kaarhus, 2018 ). Community monitoring deficits also persist in many regions (Murthy et al., 2017 ). Together, these factors highlight the immense challenge of integrating artificial intelligence-based monitoring with existing community forest governance in the Global South. 4.4. Ethics, Equity and the Governance Deficit (RQ5) Addressing the fifth research question, the most significant gap in the literature is the vast disconnect between advancing technical capability and legitimating governance frameworks. Studies focused on ethics consistently identify severe risks regarding algorithmic bias, data sovereignty violations, and the exclusion of community forest actors (Domínguez and Luoma, 2020 , Kuyper et al., 2017, Morán et al., 2018). Automated deforestation monitoring that generates carbon credits in community forests without free, prior, and informed consent represents a modern form of techno-colonialism. Existing European Union regulations inadequately address this specific risk (Brockhaus et al., 2021 , Domínguez and Luoma, 2020 , Kuyper et al., 2017, Schilling-Vacaflor and Lenschow, 2021). Community forestry evidence underscores that strong local governance independently predicts positive carbon outcomes (Luintel et al., 2018 ). This demonstrates that artificial intelligence measurement tools must complement rather than supplant genuine institutional legitimacy. The extreme under-representation of Pillar 5 studies relative to Pillar 1 technical studies is itself a major finding. The ethics and equity dimensions of artificial intelligence-driven forest carbon governance are systematically under-researched relative to their overwhelming societal importance. 4.5. EU Policy Alignment and Coherence (RQ4) Addressing the fourth research question, the European Union regulatory architecture creates the strongest global demand environment for responsible artificial intelligence forest carbon deployment (Deng et al., 2022 , Kulovesi and Oberthür, 2020 , Law et al., 2018 , Rickels et al., 2021 , Roberts et al., 2017 , Sovacool et al., 2022 , Table 3 ). However, measurement requirements remain highly fragmented across at least eleven legislative instruments. These instruments enforce vastly different verification standards, eligible actors, temporal horizons, and spatial resolutions. Aggressive Green Deal afforestation targets may inadvertently incentivise biodiversity-poor plantations. These monocultures often score well on artificial intelligence carbon quantity metrics while severely underperforming on ecological resilience, carbon permanence, and overall biodiversity (Chazdon et al., 2020 , Dupuy et al., 2020 , Kuuluvainen et al., 2021 , Law et al., 2018 , Patacca et al., 2022). Artificial intelligence systems must therefore be designed to assess carbon permanence quality and holistic ecosystem function rather than merely calculating raw carbon quantity. Forest carbon sequestration policy design further illuminates the cross-scale policy coherence challenge (Gren and Aklilu, 2016 ). Subnational forest carbon governance introduces regional complexities to this framework (Ruseva, 2023 ). Finally, forest policy and management modelling specifically designed for carbon dioxide removal highlights the intricate regulatory coordination required for future success (vonHedemann et al., 2020 ). 4.6. Strengths and Limitations of This Review Several strengths define this review. It provides the first comprehensive PRISMA-ScR-compliant scoping map of artificial intelligence-driven forest carbon integrity across six thematic pillars. The methodology utilizes a fully reproducible computational screening pipeline. The review also introduces a structured policy mapping against eleven European Union legislative instruments, as detailed in Table 3 . Furthermore, an explicit quality appraisal directly informs and shapes the proposed research agenda. Certain limitations must also be acknowledged. The use of abstract-based data extraction rather than full-text screening is appropriate for a scoping design. However, this means that artificial intelligence method classifications for some studies are based on keyword inference rather than confirmed methodology. The restriction to English-language literature potentially under-represents Central and Eastern European and other non-Anglophone forestry research traditions. The citation-ranked stratified selection inherently biases the corpus towards high-impact academic outputs. This approach potentially under-represents practitioner reports, grey literature, and emerging research from lower-income countries. Additionally, the quality criteria were derived exclusively from abstract content rather than a full methodological appraisal of the primary texts. Finally, the ten percent random re-charting inter-rater check is standard for scoping reviews but does not fully substitute for systematic duplicate screening. Future systematic reviews targeting specific sub-questions should apply full-text dual screening. 5. Conclusions and Future Research Agenda This scoping review of 200 primary sources demonstrates that artificial intelligence-driven forest carbon integrity systems have achieved substantial technical maturity in biomass estimation and disturbance detection. Solid foundations have also been established in digital ecosystem modelling. However, these systems face persistent and severe gaps in carbon market governance, European Union policy coherence, and ethical legitimacy. The European Union regulatory environment provides the strongest global signal for responsible deployment. Nevertheless, policy fragmentation across the Green Deal portfolio and the systematic under-development of equity-aware governance frameworks require urgent attention. Five distinct research priorities are proposed to address these challenges. Priority 1 — Reproducible, open-source AI for forest carbon MRV. Mandatory open code and data requirements must be integrated into European Union-funded forest carbon research. Regulatory provisions should enforce the use of auditable algorithms within European Union Emissions Trading System and LULUCF verification bodies. Furthermore, the adoption of standard artificial intelligence model cards for forest carbon applications is absolutely essential. These measures directly address the critical reproducibility gap identified during the quality appraisal. Priority 2 — Operational European Forest Carbon Digital Twin. A pan-European, publicly governed digital twin infrastructure must be established to integrate ICOS flux networks, ERA-5 reanalysis, Sentinel archives, and EDGAR atmospheric inversions. This infrastructure must include explicit governance provisions for community forest actors alongside mandatory uncertainty quantification. Such a system would function analogously to the European Centre for Medium-Range Weather Forecasts for numerical weather prediction. Priority 3 — AI Ethics Framework for Forest Carbon. A purpose-built ethical framework is required to address algorithmic transparency, data sovereignty, community consent, and distributional impact assessments. This framework should draw directly upon precision medicine artificial intelligence governance models (Acero et al., 2020 ) and established co-design methodologies (Lupp et al., 2020 ). It must be co-developed in close collaboration with forest-dependent communities, indigenous rights organisations, and European Union verification bodies. Priority 4 — Cross-Pillar Integration Research. There is an urgent need for studies explicitly integrating artificial intelligence measurement methods with carbon market governance. Research must also connect digital twins directly with policy compliance modelling. Furthermore, social ethics must be embedded organically into technical system design. These integrative efforts are essential to ensure technically sound, socially legitimate, and policy-coherent artificial intelligence forest carbon deployment. Priority 5 — Harmonised EU Forest Carbon AI Governance Framework. A regulatory sandbox should be established to enable the iterative testing of artificial intelligence verification tools against harmonised interoperability standards. These standards must span across LULUCF, the European Union Emissions Trading System, the Nature Restoration Law, Common Agricultural Policy Eco-schemes, and corporate sustainability reporting directives (CSRD). This sandbox must also include explicit regulatory provisions for model transferability testing across various European forest biomes and distinct climate scenarios. Forest carbon integrity in the artificial intelligence era will be determined not only by the accuracy of biomass algorithms but by the governance architecture, reproducibility standards, and equity frameworks within which they are embedded. The European Union is uniquely positioned to lead this global transition. This leadership will only succeed if technical and governance innovations advance in parallel across the entire forest ecosystem monitoring chain. Abbreviation Acronym Definition AGB Aboveground Biomass AI Artificial Intelligence ALS Airborne Laser Scanning ANN Artificial Neural Network CABLE Community Atmosphere Biosphere Land Exchange (Model) CAP Common Agricultural Policy CBM-CFS3 Carbon Budget Model of the Canadian Forest Sector CDR Carbon Dioxide Removal CNN Convolutional Neural Network CSDDD Corporate Sustainability Due Diligence Directive CSRD Corporate Sustainability Reporting Directive CX Cross-cutting (Thematic Pillar) DEM Digital Elevation Model DLT Distributed Ledger Technology EDGAR Emissions Database for Global Atmospheric Research ERA-5 ECMWF Reanalysis v5 ESG Environmental, Social, and Governance ESRS European Sustainability Reporting Standards ETS Emissions Trading System EU European Union GEDI Global Ecosystem Dynamics Investigation GHG Greenhouse Gas GLEAM Global Land Evaporation Amsterdam Model ICOS Integrated Carbon Observation System IPBES Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services IPCC Intergovernmental Panel on Climate Change ISIMIP Inter-Sectoral Impact Model Intercomparison Project LiDAR Light Detection and Ranging LPJ-GUESS Lund-Potsdam-Jena General Ecosystem Simulator LULUCF Land Use, Land-Use Change and Forestry ML Machine Learning MMAT Mixed Methods Appraisal Tool MRV Measurement, Reporting, and Verification NEP Net Ecosystem Production OBIA Object-Based Image Analysis PRISMA-ScR Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews REDD+ Reducing Emissions from Deforestation and Forest Degradation SAR Synthetic Aperture Radar SVM Support Vector Machine TLS Terrestrial Laser Scanning UAV Unmanned Aerial Vehicle Declarations Funding: No specific funding was received for this scoping review. Conflicts of interest: The author declares no conflicts of interest. Data availability: The complete dataset, all representative search strings ( Supplementary File S1 ), PRISMA-ScR pipeline source code ( Supplementary File S2 ), all 16 figures, and quality appraisal tables are available in the Supplementary Workbook submitted as Supplementary Material . The protocol is registered at: https://osf.io/pum9y/files. CRediT authorship contribution statement G.O.F.: Conceptualization, Methodology, Formal analysis, Data curation, Validation, Investigation, Visualization, Writing – original draft, Resources, Supervision, Project administration. Ethics approval: Not applicable — no primary data collection involving human participants. References Acero JC, Margara F, Marciniak M et al (2020) The 'Digital Twin' to enable the vision of precision cardiology. Eur Heart J 41:4556–4564. ttps://doi.org/10.1093/eurheartj/ehaa159 Adesipo A, Fadeyi O, Kuca K et al (2020) Smart and Climate-Smart Agricultural Trends as Core Aspects of Smart Village Functions. Sensors 20:5977. ttps://doi.org/10.3390/s20215977 Albergel C, Dutra E, Munier S et al (2018) ERA-5 and ERA-Interim driven ISBA land surface model simulations: Which one performs better? Hydrol Earth Syst Sci 22:3515–3532. ttps://doi.org/10.5194/hess-22-3515-2018 Alexakis D, Mexis F-D, Vozinaki A-E et al (2017) Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. Hydrol Approach Sens 17:1455. ttps://doi.org/10.3390/s17061455 Allen MR, Friedlingstein P, Girardin CAJ et al (2022) Net Zero: Science, Origins, and Implications. Annu Rev Environ Resour 47:849–887. ttps://doi.org/10.1146/annurev-environ-112320-105050 Amiri A, Ottelin J, Sorvari J, Junnila S (2020) Cities as carbon sinks—Classification of wooden buildings. Environ Res Lett 15:094076. ttps://doi.org/10.1088/1748-9326/aba134 Amiri N, Krzystek P, Heurich M, Skidmore A (2019) Classification of Tree Species as Well as Standing Dead Trees Using Triple Wavelength ALS in a Temperate Forest. Remote Sens 11:2614. ttps://doi.org/10.3390/rs11222614 Andela N, Morton DC, Giglio L et al (2019) The Global Fire Atlas of individual fire size, duration, speed and direction. Earth Syst Sci Data 11:529–552. ttps://doi.org/10.5194/essd-11-529-2019 Anees SA, Mehmood K, Khan WR et al (2024) Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region. Ecol Inf 82:102732. ttps://doi.org/10.1016/j.ecoinf.2024.102732 Araújo R, Vázquez Calderón F, Sánchez López J et al (2021) Current Status of the Algae Production Industry in Europe: An Emerging Sector of the Blue Bioeconomy. Front Mar Sci 7:626389. ttps://doi.org/10.3389/fmars.2020.626389 Arksey H, O’Malley L (2005) Scoping studies: Towards a methodological framework. Int J Soc Res Methodol 8:19–32. ttps://doi.org/10.1080/1364557032000119616 Aromataris E, Munn Z (eds) (2020) JBI manual for evidence synthesis. JBI. ttps://doi.org/10.46658/JBIMES-20-01 Avci C, Budak M, Yağmur N, Balçik F (2023) Comparison between random forest and support vector machine algorithms for LULC classification. Int J Eng Geosci 8:1–10. ttps://doi.org/10.26833/ijeg.987605 Balsamo G, Albergel C, Beljaars A et al (2015) ERA-Interim/Land: A global land surface reanalysis data set. Hydrol Earth Syst Sci 19:389–407. ttps://doi.org/10.5194/hess-19-389-2015 Batáry P, Dicks LV, Kleijn D, Sutherland WJ (2015) The role of agri-environment schemes in conservation and environmental management. Conserv Biol 29:1006–1016. ttps://doi.org/10.1111/cobi.12536 Bauer-Marschallinger B, Freeman V, Cao S et al (2019) Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles. IEEE Trans Geosci Remote Sens 57:520–539. ttps://doi.org/10.1109/TGRS.2018.2858004 Beillouin D, Schauberger B, Bastos A et al (2020) Impact of extreme weather conditions on European crop production in 2018. Philos Trans R Soc B 375:20190510. ttps://doi.org/10.1098/rstb.2019.0510 Beniston M, Farinotti D, Stoffel M et al (2018) The European mountain cryosphere: A review of its current state, trends, and future challenges. Cryosphere 12:759–794. ttps://doi.org/10.5194/tc-12-759-2018 Benjaminsen G, Kaarhus R (2018) Commodification of forest carbon: REDD + and socially embedded forest practices in Zanzibar. Geoforum 93:48–56. ttps://doi.org/10.1016/j.geoforum.2018.04.021 Berg F, Kölbel JF, Rigobon R (2022) Aggregate Confusion: The Divergence of ESG Ratings. Rev Finance 26:1315–1344. ttps://doi.org/10.1093/rof/rfac033 Bernes C, Macura B, Jonsson BG et al (2018) Manipulating ungulate herbivory in temperate and boreal forests: Effects on vegetation and invertebrates. A systematic review. Environ Evid 7:13. ttps://doi.org/10.1186/s13750-018-0125-3 Bhattarai T, Skutsch M, Midmore D, Shrestha HL (2016) Carbon Measurement: An Overview of Forest Carbon Estimation Methods and the Role of Geographical Information System and Remote Sensing Techniques for REDD+ Implementation. J Livelihood 13:69–86. ttps://doi.org/10.3126/jfl.v13i1.15367 Bispo A, Andersen L, Angers DA et al (2017) Accounting for Carbon Stocks in Soils and Measuring GHGs Emission Fluxes from Soils: Do We Have the Necessary Standards? Front Environ Sci 5:41. ttps://doi.org/10.3389/fenvs.2017.00041 Boix-Fayos C, De Vente J (2023) Challenges and potential pathways towards sustainable agriculture within the European Green Deal. Agric Syst 207:103634. ttps://doi.org/10.1016/j.agsy.2023.103634 Bowler DE, Bjorkman AD, Dornelas M et al (2020) Mapping human pressures on biodiversity across the planet uncovers anthropogenic threat complexes. People Nat 2:380–394. ttps://doi.org/10.1002/pan3.10071 Brockhaus M, Di Gregorio M, Djoudi H et al (2021) The forest frontier in the Global South: Climate change policies and the promise of development and equity. Ambio 50:2238–2255. ttps://doi.org/10.1007/s13280-021-01602-1 Brundu G, Richardson DM (2016) Planted forests and invasive alien trees in Europe: A Code for managing existing and future plantings to mitigate the risk of negative impacts from invasions. NeoBiota 30:5–47. ttps://doi.org/10.3897/neobiota.30.7015 Calvin K, Dasgupta D, Krinner G, IPCC (2023), 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland. ttps://doi.org/10.59327/IPCC/AR6-9789291691647 Cao L, Pan J, Li R et al (2018) Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China. Remote Sens 10:532. ttps://doi.org/10.3390/rs10040532 Cerciello M, Busato F, Taddeo S (2023) The effect of sustainable business practices on profitability. Accounting for strategic disclosure. Corp Soc Responsib Environ Manag 30:802–819. ttps://doi.org/10.1002/csr.2389 Chatziantoniou A, Psomiadis E, Petropoulos G (2017) Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning. Remote Sens 9:1259. ttps://doi.org/10.3390/rs9121259 Chazdon RL, Lindenmayer D, Guariguata MR et al (2020) Fostering natural forest regeneration on former agricultural land through economic and policy interventions. Environ Res Lett 15:043002. ttps://doi.org/10.1088/1748-9326/ab79e6 Chen L, Huang L, Hua J et al (2023) Green construction for low-carbon cities: A review. Environ Chem Lett 21:1627–1657. ttps://doi.org/10.1007/s10311-022-01544-4 Cheng F, Ou G, Wang M, Liu C (2024) Remote Sensing Estimation of Forest Carbon Stock Based on Machine Learning Algorithms. Forests 15:681. ttps://doi.org/10.3390/f15040681 Chuvieco E, Lizundia-Loiola J, Pettinari ML et al (2018) Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth Syst Sci Data 10:2015–2031. ttps://doi.org/10.5194/essd-10-2015-2018 Clementino E, Perkins R (2021) How Do Companies Respond to Environmental, Social and Governance (ESG) ratings? Evidence from Italy. J Bus Ethics 171:379–397. ttps://doi.org/10.1007/s10551-020-04441-4 Corral-Acero J, Margara F, Marciniak M et al (2020) The ‘Digital Twin’ to enable the vision of precision cardiology. Eur Heart J 41:4556–4564. ttps://doi.org/10.1093/eurheartj/ehaa159 Cowls J, Tsamados A, Taddeo M, Floridi L (2023) The AI gambit: Leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. AI Soc 38:283–307. ttps://doi.org/10.1007/s00146-021-01294-x Dave R, Saint-Laurent C, Murray L, Gland et al (2019) Switzerland. ttps://doi.org/10.2305/IUCN.CH.2019.06.en De Luca G, Silva JM, Cerasoli S et al (2019) Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox. Remote Sens 11:1238. ttps://doi.org/10.3390/rs11101238 Debrah C, Darko A, Chan APC (2023) A bibliometric-qualitative literature review of green finance gap and future research directions. Clim Dev 15:432–455. ttps://doi.org/10.1080/17565529.2022.2095331 DeLancey ER, Simms JF, Mahdianpari M et al (2019) Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada. Remote Sens 12:2. ttps://doi.org/10.3390/rs12010002 Deng Z, Ciais P, Tzompa-Sosa ZA et al (2022) Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions. Earth Syst Sci Data 14:1639–1675. ttps://doi.org/10.5194/essd-14-1639-2022 Domínguez L, Luoma C (2020) Decolonising Conservation Policy: How Colonial Land and Conservation Ideologies Persist and Perpetuate Indigenous Injustices at the Expense of the Environment. Land 9:65. ttps://doi.org/10.3390/land9030065 Dorado-Roda I, Pascual A, Godinho S et al (2021) Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests. Remote Sens 13:2279. ttps://doi.org/10.3390/rs13122279 Duarte A, Borralho N, Cabral P, Caetano M (2022) Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review. Forests 13:911. ttps://doi.org/10.3390/f13060911 Dube LC, Chatterjee S (2022) Assessing livelihood impact of forest carbon projects using sustainable livelihood framework. Mitig Adapt Strateg Glob Change 27:49. ttps://doi.org/10.1007/s11027-022-10022-9 Dupuy J, Fargeon H, Martin-StPaul N et al (2020) Climate change impact on future wildfire danger and activity in southern Europe: A review. Ann Sci 77:35. ttps://doi.org/10.1007/s13595-020-00933-5 Egger M, Smith GD, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315:629–634. ttps://doi.org/10.1136/bmj.315.7109.629 Ekardt F, Jacobs B, Stubenrauch J, Garske B (2020) Peatland Governance: The Problem of Depicting in Sustainability Governance, Regulatory Law, and Economic Instruments. Land 9:83. ttps://doi.org/10.3390/land9030083 Esteban J, McRoberts R, Fernández-Landa A et al (2019) Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data. ttps://doi.org/10.3390/rs11161944. Remote Sens 11:1944 Farghali M, Osman AI, Mohamed IMA et al (2023) Strategies to save energy in the context of the energy crisis: A review. Environ Chem Lett 21:2003–2039. ttps://doi.org/10.1007/s10311-023-01591-5 Farmonov N, Amankulova K, Szatmári J et al (2023) Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms. IEEE J Sel Top Appl Earth Obs Remote Sens 16:1576–1588. ttps://doi.org/10.1109/JSTARS.2023.3239756 Fawzy S, Osman AI, Doran J, Rooney DW (2020) Strategies for mitigation of climate change: A review. Environ Chem Lett 18:2069–2094. ttps://doi.org/10.1007/s10311-020-01059-w Fedele G, Locatelli B, Djoudi H, Colloff MJ (2018) Reducing risks by transforming landscapes: Cross-scale effects of land-use changes on ecosystem services. PLoS ONE 13:e0195895. ttps://doi.org/10.1371/journal.pone.0195895 Felipe-Lucia MR, Martín-López B, Lavorel S et al (2015) Ecosystem Services Flows: Why Stakeholders’ Power Relationships Matter. PLoS ONE 10:e0132232. ttps://doi.org/10.1371/journal.pone.0132232 Forsell N, Turkovska O, Gusti M et al (2016) Assessing the INDCs’ land use, land use change, and forest emission projections. Carbon Balance Manag 11:26. ttps://doi.org/10.1186/s13021-016-0068-3 Franz D, Acosta M, Altimir N et al (2018) Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe’s terrestrial ecosystems: A review. Int Agrophys 32:439–455. ttps://doi.org/10.1515/intag-2017-0039 Frieler K, Lange S, Piontek F et al (2017) Assessing the impacts of 1.5°C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geosci Model Dev 10:4321–4345. ttps://doi.org/10.5194/gmd-10-4321-2017 Fuzzi S, Baltensperger U, Carslaw K et al (2015) Particulate matter, air quality and climate: Lessons learned and future needs. Atmos Chem Phys 15:8217–8299. ttps://doi.org/10.5194/acp-15-8217-2015 Gan Y, Wang Q, Song G (2024) Non-Destructive Estimation of Deciduous Forest Metrics: Comparisons between UAV-LiDAR, UAV-DAP, and Terrestrial LiDAR Leaf-Off Point Clouds Using Two QSMs. Remote Sens 16:697. ttps://doi.org/10.3390/rs16040697 Gough C, Garcia-Freites S, Jones C et al (2018) Challenges to the use of BECCS as a keystone technology in pursuit of 1.5°C. Glob Sustain 1:e5. ttps://doi.org/10.1017/sus.2018.3 Grabska E, Hostert P, Pflugmacher D, Ostapowicz K (2019) Forest Stand Species Mapping Using the Sentinel-2 Time Series. Remote Sens 11:1197. ttps://doi.org/10.3390/rs11101197 Gregson N, Crang M, Fuller S, Holmes H (2015) Interrogating the circular economy: The moral economy of resource recovery in the EU. Econ Soc 44:218–243. ttps://doi.org/10.1080/03085147.2015.1013353 Gren I-M, Aklilu AZ (2016) Policy design for forest carbon sequestration: A review of the literature. Policy Econ 70:128–136. ttps://doi.org/10.1016/j.forpol.2016.06.008 Hämmerle M, Lukač N, Chen K-C et al (2017) Simulating Various Terrestrial and UAV LiDAR Scanning Configurations for Understory Forest Structure Modelling. ISPRS Ann Photogramm Remote Sens Spat Inf Sci IV-. 2/W4:59–65. ttps://doi.org/10.5194/isprs-annals-IV-2-W4-59-2017 Haverd V, Smith B, Nieradzik L et al (2018) A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci Model Dev 11:2995–3026. ttps://doi.org/10.5194/gmd-11-2995-2018 Haya BK, Evans S, Brown L et al (2023) Comprehensive review of carbon quantification by improved forest management offset protocols. Front Glob Change 6:958879. ttps://doi.org/10.3389/ffgc.2023.958879 He X, Lei X, Zeng W et al (2022) Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China. Sustainability 14:5580. ttps://doi.org/10.3390/su14095580 Hein L, Bagstad K, Edens B et al (2016) Defining Ecosystem Assets for Natural Capital Accounting. PLoS ONE 11:e0164460. ttps://doi.org/10.1371/journal.pone.0164460 Heiskanen J, Brümmer C, Buchmann N et al (2022) The Integrated Carbon Observation System in Europe. Bull Am Meteorol Soc 103:E855–E872. ttps://doi.org/10.1175/BAMS-D-19-0364.1 Herold M, Carter S, Avitabile V et al (2019) The Role and Need for Space-Based Forest Biomass-Related Measurements in Environmental Management and Policy. Surv Geophys 40:757–778. ttps://doi.org/10.1007/s10712-019-09510-6 Honegger M, Poralla M, Michaelowa A, Ahonen H-M (2021) Who Is Paying for Carbon Dioxide Removal? Designing Policy Instruments for Mobilizing Negative Emissions Technologies. Front Clim 3:672996. ttps://doi.org/10.3389/fclim.2021.672996 Hong QN, Fàbregues S, Bartlett G et al (2018) The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Educ Inf 34:285–291. ttps://doi.org/10.3233/EFI-180221 Honkomp T, Schier F (2024) Scoping review of carbon pricing systems in forest sector models. Environ Res Lett 19:013001. ttps://doi.org/10.1088/1748-9326/ad101d Hörtnagl L, Barthel M, Buchmann N et al (2018) Greenhouse gas fluxes over managed grasslands in Central Europe. Glob Change Biol 24:1843–1872. ttps://doi.org/10.1111/gcb.14079 Immitzer M, Neuwirth M, Böck S et al (2019) Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data. Remote Sens 11:2599. ttps://doi.org/10.3390/rs11222599 IPBES (2019) Summary for policymakers of the global assessment report on biodiversity and ecosystem services. Zenodo. ttps://doi.org/10.5281/ZENODO.3553579 Janssens-Maenhout G, Crippa M, Guizzardi D et al (2019) EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012. Earth Syst Sci Data 11:959–1002. ttps://doi.org/10.5194/essd-11-959-2019 Jiang X, Jiang M, Gou Y et al (2022) Forestry Digital Twin With Machine Learning in Landsat 7 Data. Front Plant Sci 13:916900. ttps://doi.org/10.3389/fpls.2022.916900 Jung M, Schwalm C, Migliavacca M et al (2020) Scaling carbon fluxes from eddy covariance sites to globe: Synthesis and evaluation of the FLUXCOM approach. Biogeosciences 17:1343–1365. ttps://doi.org/10.5194/bg-17-1343-2020 Kabeyi MJB, Olanrewaju OA (2022) Sustainable Energy Transition for Renewable and Low Carbon Grid Electricity Generation and Supply. Front Energy Res 9:743114. ttps://doi.org/10.3389/fenrg.2021.743114 Kardung M, Cingiz K, Costenoble O et al (2021) Development of the Circular Bioeconomy: Drivers and Indicators. Sustainability 13:413. ttps://doi.org/10.3390/su13010413 Kellner JR, Armston J, Birrer M et al (2019) New Opportunities for Forest Remote Sensing Through Ultra-High-Density Drone Lidar. Surv Geophys 40:959–977. ttps://doi.org/10.1007/s10712-019-09529-9 Kern J, Tammeorg P, Shanskiy M et al (2017) Synergistic Use of Peat and Charred Material in Growing Media – An Option to Reduce the Pressure on Peatlands? J Environ Eng Landsc Manag 25:160–174. ttps://doi.org/10.3846/16486897.2017.1284665 Khanal S, Kc K, Fulton JP, Shearer S, Ozkan E (2020) Remote Sensing in Agriculture—Accomplishments, Limitations, and Opportunities. Remote Sens 12:3783. ttps://doi.org/10.3390/rs12223783 Klimont Z, Kupiainen K, Heyes C et al (2017) Global anthropogenic emissions of particulate matter including black carbon. Atmos Chem Phys 17:8681–8723. ttps://doi.org/10.5194/acp-17-8681-2017 Klouček T, Komárek J, Surový P et al (2019) The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation. Remote Sens 11:1561. ttps://doi.org/10.3390/rs11131561 Krotkov NA, McLinden CA, Li C et al (2016) Aura OMI observations of regional SO2 and NO2 pollution changes from 2005 to 2015. Atmos Chem Phys 16:4605–4629. ttps://doi.org/10.5194/acp-16-4605-2016 Kulovesi K, Oberthür S (2020) Assessing the EU’s 2030 Climate and Energy Policy Framework: Incremental change toward radical transformation? Rev Eur Comp Int Environ Law 29:151–166. ttps://doi.org/10.1111/reel.12358 Kuuluvainen T, Angelstam P, Frelich L et al (2021) Natural Disturbance-Based Forest Management: Moving Beyond Retention and Continuous-Cover Forestry. Front Glob Change 4:629020. ttps://doi.org/10.3389/ffgc.2021.629020 Kuyper J, Schroeder H, Linnér B-O (2018) The Evolution of the UNFCCC. Annu Rev Environ Resour 43:343–368. ttps://doi.org/10.1146/annurev-environ-102017-030119 Kuyper JW, Linnér B, Schroeder H (2018) Non-state actors in hybrid global climate governance: Justice, legitimacy, and effectiveness in a post‐Paris era. WIREs Clim Change 9:e497. ttps://doi.org/10.1002/wcc.497 Kwilinski A, Lyulyov O, Pimonenko T (2023) Unlocking Sustainable Value through Digital Transformation: An Examination of ESG Performance. Information 14:444. ttps://doi.org/10.3390/info14080444 Kyriakopoulos GL, Sebos I (2023) Enhancing Climate Neutrality and Resilience through Coordinated Climate Action: Review of the Synergies between Mitigation and Adaptation Actions. Climate 11:105. ttps://doi.org/10.3390/cli11050105 Lamb WF, Wiedmann T, Pongratz J et al (2021) A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018. Environ Res Lett 16:073005. ttps://doi.org/10.1088/1748-9326/abee4e Lambers K, Verschoof-van Der Vaart W, Bourgeois Q (2019) Integrating Remote Sensing, Machine Learning, and Citizen Science in Dutch Archaeological Prospection. Remote Sens 11:794. ttps://doi.org/10.3390/rs11070794 Law BE, Hudiburg TW, Berner LT et al (2018) Land use strategies to mitigate climate change in carbon dense temperate forests. Proc Natl Acad Sci USA 115:3663–3668. ttps://doi.org/10.1073/pnas.1720064115 Leinonen I (2022) A general framework for including biogenic carbon emissions and removals in the life cycle assessments for forestry products. Int J Life Cycle Assess 27:1038–1043. ttps://doi.org/10.1007/s11367-022-02086-1 Levac D, Colquhoun H, O’Brien KK (2010) Scoping studies: Advancing the methodology. Implement Sci 5:69. ttps://doi.org/10.1186/1748-5908-5-69 Li Y, Li M, Li C, Liu Z (2020) Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. Sci Rep 10:9952. ttps://doi.org/10.1038/s41598-020-67024-3 Li Y, Zhao M, Motesharrei S et al (2015) Local cooling and warming effects of forests based on satellite observations. Nat Commun 6:6603. ttps://doi.org/10.1038/ncomms7603 Linaza MT, Posada J, Bund J et al (2021) Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture. Agronomy 11:1227. ttps://doi.org/10.3390/agronomy11061227 Lindeskog M, Smith B, Lagergren F et al (2021) Accounting for forest management in the estimation of forest carbon balance using the dynamic vegetation model LPJ-GUESS (v4.0, r9710): Implementation and evaluation of simulations for Europe. Geosci Model Dev 14:6071–6112. ttps://doi.org/10.5194/gmd-14-6071-2021 Liu C, Huang H, Zhang Z et al (2024) Canopy Height Integration for Precise Forest Aboveground Biomass Estimation in Natural Secondary Forests of Northeast China Using Gaofen-7 Stereo Satellite Data. Remote Sens 17:47. ttps://doi.org/10.3390/rs17010047 Liu J, Yang B, Li M, Xu D (2024) Assessing Forest-Change-Induced Carbon Storage Dynamics by Integrating GF-1 Image and Localized Allometric Growth Equations in Jiangning District, Nanjing, Eastern China (2017–2020). Forests 15:506. ttps://doi.org/10.3390/f15030506 Liu S, Brandt M, Nord-Larsen T et al (2023) The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe. Sci Adv 9:eadh4097. ttps://doi.org/10.1126/sciadv.adh4097 Liu Y, Gong W, Hu X, Gong J (2018) Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data. Remote Sens 10:946. ttps://doi.org/10.3390/rs10060946 Luintel H, Bluffstone RA, Scheller RM (2018) The effects of the Nepal community forestry program on biodiversity conservation and carbon storage. PLoS ONE 13:e0199526. ttps://doi.org/10.1371/journal.pone.0199526 Lupp G, Zingraff-Hamed A, Huang JJ et al (2020) Living Labs—A Concept for Co-Designing Nature-Based Solutions. Sustainability 13:188. ttps://doi.org/10.3390/su13010188 Macdonald SE, Landhäusser SM, Skousen J et al (2015) Forest restoration following surface mining disturbance: Challenges and solutions. New For 46:703–732. ttps://doi.org/10.1007/s11056-015-9506-4 Maes MJA et al (2022) Monitoring exposure to climate-related hazards: Indicator methodology and key results. OECD Environment Working Papers No 201. ttps://doi.org/10.1787/da074cb6-en Mambile C, Kaijage S, Leo J (2025) Deep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, Tanzania: Integrating satellite images, weather data and human activities data. Nat Hazards Res 5:335–347. ttps://doi.org/10.1016/j.nhres.2024.12.001 Martens B, Miralles DG, Lievens H et al (2017) GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci Model Dev 10:1903–1925. ttps://doi.org/10.5194/gmd-10-1903-2017 Meng J, Lu F, Cheng B (2023) China’s Climate Change Policy Attention and Forestry Carbon Sequestration Growth. Forests 14:2273. ttps://doi.org/10.3390/f14112273 Mertz O, Grogan K, Pflugmacher D et al (2018) Uncertainty in establishing forest reference levels and predicting future forest-based carbon stocks for REDD+. J Land Use Sci 13:1–15. ttps://doi.org/10.1080/1747423X.2017.1410242 Meyer R (2017) Bioeconomy Strategies: Contexts, Visions, Guiding Implementation Principles and Resulting Debates. Sustainability 9:1031. ttps://doi.org/10.3390/su9061031 Meyer S, Genesio L, Vogel I et al (2017) BIOCHAR STANDARDIZATION AND LEGISLATION HARMONIZATION. J Environ Eng Landsc Manag 25:175–191. ttps://doi.org/10.3846/16486897.2016.1254640 Miyazaki K, Eskes H, Sudo K et al (2017) Decadal changes in global surface NOx emissions from multi-constituent satellite data assimilation. Atmos Chem Phys 17:807–837. ttps://doi.org/10.5194/acp-17-807-2017 Mo L, Zohner CM, Reich PB et al (2023) Integrated global assessment of the natural forest carbon potential. Nature 624:92–101. ttps://doi.org/10.1038/s41586-023-06723-z Moran EF, Lopez MC, Moore N et al (2018) Sustainable hydropower in the 21st century. Proc Natl Acad Sci USA 115:11891–11898. ttps://doi.org/10.1073/pnas.1809426115 Munn Z, Peters MDJ, Stern C et al (2018) Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol 18:143. ttps://doi.org/10.1186/s12874-018-0611-x Murthy MSR, Gilani H, Karky BS et al (2017) Synergizing community-based forest monitoring with remote sensing: A path to an effective REDD + MRV system. Carbon Balance Manag 12:19. ttps://doi.org/10.1186/s13021-017-0087-8 Nandy S, Srinet R, Padalia H (2021) Mapping Forest Height and Aboveground Biomass by Integrating ICESat-2, Sentinel‐1 and Sentinel‐2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India. Geophys Res Lett 48:e2021GL093799. ttps://doi.org/10.1029/2021GL093799 Näsi R, Honkavaara E, Lyytikäinen-Saarenmaa P et al (2015) Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level. Remote Sens 7:15467–15493. ttps://doi.org/10.3390/rs71115467 Naumann G, Alfieri L, Wyser K et al (2018) Global Changes in Drought Conditions Under Different Levels of Warming. Geophys Res Lett 45:3285–3296. ttps://doi.org/10.1002/2017GL076521 Navarro LM, Pereira HM (2015) Rewilding Abandoned Landscapes in Europe. In: Pereira HM, Navarro LM (eds) Rewilding European Landscapes. Springer, pp 3–23. ttps://doi.org/10.1007/978-3-319-12039-3_1 Nemitz E, Mammarella I, Ibrom A et al (2018) Standardisation of eddy-covariance flux measurements of methane and nitrous oxide. Int Agrophys 32:517–549. ttps://doi.org/10.1515/intag-2017-0042 Neuenschwander AL, Magruder LA (2019) Canopy and Terrain Height Retrievals with ICESat-2: A First Look. Remote Sens 11:1721. ttps://doi.org/10.3390/rs11141721 Nevalainen O, Honkavaara E, Tuominen S et al (2017) Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. Remote Sens 9:185. ttps://doi.org/10.3390/rs9030185 Nisbet EG, Manning MR, Dlugokencky EJ et al (2019) Very Strong Atmospheric Methane Growth in the 4 Years 2014–2017: Implications for the Paris Agreement. Glob Biogeochem Cycles 33:318–342. ttps://doi.org/10.1029/2018GB006009 Panagos P, Standardi G, Borrelli P et al (2018) Cost of agricultural productivity loss due to soil erosion in the European Union: From direct cost evaluation approaches to the use of macroeconomic models. Land Degrad Dev 29:471–484. ttps://doi.org/10.1002/ldr.2879 Patacca M, Lindner M, Lucas-Borja ME et al (2023) Significant increase in natural disturbance impacts on European forests since 1950. Glob Change Biol 29:1359–1376. ttps://doi.org/10.1111/gcb.16531 Peel J, Lin J (2019) Transnational Climate Litigation: The Contribution of the Global South. Am J Int Law 113:679–726. ttps://doi.org/10.1017/ajil.2019.48 Pe’er G, Zinngrebe Y, Hauck J et al (2017) Adding Some Green to the Greening: Improving the EU’s Ecological Focus Areas for Biodiversity and Farmers. Conserv Lett 10:517–530. ttps://doi.org/10.1111/conl.12333 Pelletier C, Valero S, Inglada J et al (2017) Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series. Remote Sens 9:173. ttps://doi.org/10.3390/rs9020173 Peters MDJ, Marnie C, Tricco AC et al (2020) Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth 18:2119–2126. ttps://doi.org/10.11124/JBIES-20-00167 Petzold A, Thouret V, Gerbig C et al (2015) Global-scale atmosphere monitoring by in-service aircraft – current achievements and future prospects of the European Research Infrastructure IAGOS. Tellus B 67:28452. ttps://doi.org/10.3402/tellusb.v67.28452 Piabuo SM, Minang PA, Tieguhong CJ et al (2021) Illegal logging, governance effectiveness and carbon dioxide emission in the timber-producing countries of Congo Basin and Asia. Environ Dev Sustain 23:14176–14196. ttps://doi.org/10.1007/s10668-021-01257-8 Piabuo SM, Puatwoe JT, Eckebil PPT et al (2023) Foreign direct investment and carbon emissions from land use, land-use change, and forestry (LULUCF): Empirical evidence from tropical forest countries. Environ Dev Sustain 26:15639–15665. ttps://doi.org/10.1007/s10668-023-03267-0 Pilli R, Kull SJ, Blujdea VNB, Grassi G (2018) The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3): Customization of the Archive Index Database for European Union countries. Ann Sci 75:71. ttps://doi.org/10.1007/s13595-018-0743-5 Pisso I, Sollum E, Grythe H et al (2019) The Lagrangian particle dispersion model FLEXPART version 10.4. Geosci Model Dev 12:4955–4997. ttps://doi.org/10.5194/gmd-12-4955-2019 Popescu CRG, Popescu GN (2019) An Exploratory Study Based on a Questionnaire Concerning Green and Sustainable Finance, Corporate Social Responsibility, and Performance: Evidence from the Romanian Business Environment. J Risk Financ Manag 12:162. ttps://doi.org/10.3390/jrfm12040162 Praticò S, Solano F, Di Fazio S, Modica G (2021) Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation. Remote Sens 13:586. ttps://doi.org/10.3390/rs13040586 Prommer J, Walker TWN, Wanek W et al (2020) Increased microbial growth, biomass, and turnover drive soil organic carbon accumulation at higher plant diversity. Glob Change Biol 26:669–681. ttps://doi.org/10.1111/gcb.14777 Qi W, Armston J, Choi C et al (2025) Mapping large-scale pantropical forest canopy height by integrating GEDI lidar and TanDEM-X InSAR data. Remote Sens Environ 318:114534. ttps://doi.org/10.1016/j.rse.2024.114534 Radočaj D, Jurišić M, Gašparović M (2022) The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture. Remote Sens 14:778. ttps://doi.org/10.3390/rs14030778 Ray DK, West PC, Clark M et al (2019) Climate change has likely already affected global food production. PLoS ONE 14:e0217148. ttps://doi.org/10.1371/journal.pone.0217148 Redondo Alamillos R, De Mariz F (2022) How Can European Regulation on ESG Impact Business Globally? J Risk Financ Manag 15:291. ttps://doi.org/10.3390/jrfm15070291 Reinmann AB, Smith IA, Thompson JR, Hutyra LR (2020) Urbanization and fragmentation mediate temperate forest carbon cycle response to climate. Environ Res Lett 15:114036. ttps://doi.org/10.1088/1748-9326/abbf16 Rickels W, Proelß A, Geden O et al (2021) Integrating Carbon Dioxide Removal Into European Emissions Trading. Front Clim 3:690023. ttps://doi.org/10.3389/fclim.2021.690023 Roberts CM, O’Leary BC, McCauley DJ et al (2017) Marine reserves can mitigate and promote adaptation to climate change. Proc Natl Acad Sci USA 114:6167–6175. ttps://doi.org/10.1073/pnas.1701262114 Rodríguez-Veiga P, Quegan S, Carreiras J et al (2019) Forest biomass retrieval approaches from earth observation in different biomes. Int J Appl Earth Obs Geoinf 77:53–68. ttps://doi.org/10.1016/j.jag.2018.12.008 Ronchi S, Salata S, Arcidiacono A et al (2019) Policy instruments for soil protection among the EU member states: A comparative analysis. Land Use Policy 82:763–780. ttps://doi.org/10.1016/j.landusepol.2019.01.017 Rosa L, Sanchez DL, Mazzotti M (2021) Assessment of carbon dioxide removal potential via BECCS in a carbon-neutral Europe. Energy Environ Sci 14:3086–3097. ttps://doi.org/10.1039/D1EE00642H Rowan NJ, Murray N, Qiao Y et al (2022) Digital transformation of peatland eco-innovations (‘Paludiculture’): Enabling a paradigm shift towards the real-time sustainable production of ‘green-friendly’ products and services. Sci Total Environ 838:156328. ttps://doi.org/10.1016/j.scitotenv.2022.156328 Ruseva TB (2023) The governance of forest carbon in a subnational climate mitigation system: Insights from a network of action situations approach. Sustain Sci 18:59–78. ttps://doi.org/10.1007/s11625-022-01262-4 Sabatini FM, Keeton WS, Lindner M et al (2020) Protection gaps and restoration opportunities for primary forests in Europe. Divers Distrib 26:1646–1662. ttps://doi.org/10.1111/ddi.13158 Schilling-Vacaflor A, Lenschow A (2023) Hardening foreign corporate accountability through mandatory due diligence in the European Union? New trends and persisting challenges. Regul Gov 17:677–693. ttps://doi.org/10.1111/rego.12402 Schindler S, O’Neill FH, Biró M et al (2016) Multifunctional floodplain management and biodiversity effects: A knowledge synthesis for six European countries. Biodivers Conserv 25:1349–1382. ttps://doi.org/10.1007/s10531-016-1129-3 Schleussner C-F, Lissner TK, Fischer EM et al (2016) Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth Syst Dyn 7:327–351. ttps://doi.org/10.5194/esd-7-327-2016 Schröder P, Beckers B, Daniels S et al (2018) Intensify production, transform biomass to energy and novel goods and protect soils in Europe—A vision how to mobilize marginal lands. Sci Total Environ 616–617:1101–1123. ttps://doi.org/10.1016/j.scitotenv.2017.10.209 Schulte RPO, Bampa F, Bardy M et al (2015) Making the Most of Our Land: Managing Soil Functions from Local to Continental Scale. Front Environ Sci 3:81. ttps://doi.org/10.3389/fenvs.2015.00081 Seddon N, Daniels E, Davis R et al (2020) Global recognition of the importance of nature-based solutions to the impacts of climate change. Glob Sustain 3:e15. ttps://doi.org/10.1017/sus.2020.8 Segarra J, Buchaillot ML, Araus JL, Kefauver SC (2020) Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications. Agronomy 10:641. ttps://doi.org/10.3390/agronomy10050641 Seydi ST, Saeidi V, Kalantar B et al (2022) Fire-Net: A Deep Learning Framework for Active Forest Fire Detection. J Sens 2022:8044390. ttps://doi.org/10.1155/2022/8044390 Silveira EMO, Espírito Santo FD, Wulder MA et al (2019) Pre-stratified modelling plus residuals kriging reduces the uncertainty of aboveground biomass estimation and spatial distribution in heterogeneous savannas and forest environments. Ecol Manag 445:96–109. ttps://doi.org/10.1016/j.foreco.2019.05.016 Silveira EMO, Silva SHG, Acerbi-Junior FW et al (2019) Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment. Int J Appl Earth Obs Geoinf 78:175–188. ttps://doi.org/10.1016/j.jag.2019.02.004 Singh AP, Yerudkar A, Mariani V et al (2022) A Bibliometric Review of the Use of Unmanned Aerial Vehicles in Precision Agriculture and Precision Viticulture for Sensing Applications. Remote Sens 14:1604. ttps://doi.org/10.3390/rs14071604 Singha C, Swain KC, Moghimi A et al (2024) Integrating geospatial, remote sensing, and machine learning for climate-induced forest fire susceptibility mapping in Similipal Tiger Reserve, India. Ecol Manag 555:121729. ttps://doi.org/10.1016/j.foreco.2024.121729 Sollen-Norrlin M, Ghaley BB, Rintoul NLJ (2020) Agroforestry Benefits and Challenges for Adoption in Europe and Beyond. Sustainability 12:7001. ttps://doi.org/10.3390/su12177001 Sotirov M, Winkel G, Eckerberg K (2021) The coalitional politics of the European Union’s environmental forest policy: Biodiversity conservation, timber legality, and climate protection. Ambio 50:2153–2167. ttps://doi.org/10.1007/s13280-021-01644-5 Sovacool BK, Baum CM, Low S et al (2022) Climate policy for a net-zero future: Ten recommendations for Direct Air Capture. Environ Res Lett 17:074014. ttps://doi.org/10.1088/1748-9326/ac77a4 Statuto D, Cillis G, Picuno P (2017) Using Historical Maps within a GIS to Analyze Two Centuries of Rural Landscape Changes in Southern Italy. Land 6:65. ttps://doi.org/10.3390/land6030065 Sterman JD, Siegel L, Rooney-Varga JN (2018) Does replacing coal with wood lower CO2 emissions? Dynamic lifecycle analysis of wood bioenergy. Environ Res Lett 13:015007. ttps://doi.org/10.1088/1748-9326/aaa512 Sterne JA, Hernán MA, Reeves BC et al (2016) ROBINS-I: A tool for assessing risk of bias in non-randomised studies of interventions. BMJ 355:i4919. ttps://doi.org/10.1136/bmj.i4919 Stiglitz JE, Stern N, Duan M et al (2017) Report of the High-Level Commission on Carbon Prices. ttps://doi.org/10.7916/D8-W2NC-4103 Storm H, Baylis K, Heckelei T (2020) Machine learning in agricultural and applied economics. Eur Rev Agric Econ 47:849–892. ttps://doi.org/10.1093/erae/jbz033 Tamiminia H, Salehi B, Mahdianpari M et al (2021) A comparison of random forest and light gradient boosting machine for forest above-ground biomass estimation using a combination of Landsat, ALOS PALSAR, and airborne LiDAR data. ISPRS Ann Photogramm Remote Sens Spat Inf Sci XLIV-M-3-2021:163–168. ttps://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-163-2021 Tanneberger F, Appulo L, Ewert S et al (2021) The Power of Nature-Based Solutions: How Peatlands Can Help Us to Achieve Key EU Sustainability Objectives. Adv Sustain Syst 5:2000146. ttps://doi.org/10.1002/adsu.202000146 Telo Da Gama J (2023) The Role of Soils in Sustainability, Climate Change, and Ecosystem Services: Challenges and Opportunities. Ecologies 4:552–567. ttps://doi.org/10.3390/ecologies4030036 Thomas RQ, Brooks EB, Jersild AL et al (2017) Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: Regional data assimilation using ecosystem experiments. Biogeosciences 14:3525–3547. ttps://doi.org/10.5194/bg-14-3525-2017 Tifafi M, Guenet B, Hatté C (2018) Large Differences in Global and Regional Total Soil Carbon Stock Estimates Based on SoilGrids, HWSD, and NCSCD: Intercomparison and Evaluation Based on Field Data From USA, England, Wales, and France. Glob Biogeochem Cycles 32:42–56. ttps://doi.org/10.1002/2017GB005678 Tricco AC, Lillie E, Zarin W et al (2018) PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med 169:467–473. ttps://doi.org/10.7326/M18-0850 Tutak M, Brodny J, Bindzár P (2021) Assessing the Level of Energy and Climate Sustainability in the European Union Countries in the Context of the European Green Deal Strategy and Agenda 2030. Energies 14:1767. ttps://doi.org/10.3390/en14061767 United Nations Environment Programme (2023) Emissions Gap Report 2023: Broken Record – Temperatures hit new highs, yet world fails to cut emissions (again). United Nations Environment Programme. ttps://doi.org/10.59117/20.500.11822/43922 Urbazaev M, Thiel C, Cremer F et al (2018) Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico. Carbon Balance Manag 13:5. ttps://doi.org/10.1186/s13021-018-0093-5 Ustaoglu E, Collier MJ (2018) Farmland abandonment in Europe: An overview of drivers, consequences, and assessment of the sustainability implications. Environ Rev 26:396–416. ttps://doi.org/10.1139/er-2018-0001 Vafaei S, Soosani J, Adeli K et al (2018) Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran). Remote Sens 10:172. ttps://doi.org/10.3390/rs10020172 Valipour M, Johnson CE, Battles JJ et al (2021) Simulation of the effects of forest harvesting under changing climate to inform long-term sustainable forest management using a biogeochemical model. Sci Total Environ 767:144881. ttps://doi.org/10.1016/j.scitotenv.2020.144881 Van Der Werff H, Van Der Meer F (2016) Sentinel-2A MSI and Landsat 8 OLI Provide Data Continuity for Geological Remote Sensing. Remote Sens 8:883. ttps://doi.org/10.3390/rs8110883 Van Tricht K, Gobin A, Gilliams S, Piccard I (2018) Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium. Remote Sens 10:1642. ttps://doi.org/10.3390/rs10101642 Venter ZS, Barton DN, Chakraborty T et al (2022) Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover. Remote Sens 14:4101. ttps://doi.org/10.3390/rs14164101 Virkkala A, Aalto J, Rogers BM et al (2021) Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties. Glob Change Biol 27:4040–4059. ttps://doi.org/10.1111/gcb.15659 vonHedemann N, Wurtzebach Z, Timberlake TJ et al (2020) Forest policy and management approaches for carbon dioxide removal. Interface Focus 10:20200001. ttps://doi.org/10.1098/rsfs.2020.0001 Vreugdenhil M, Dorigo WA, Wagner W et al (2016) Analyzing the Vegetation Parameterization in the TU-Wien ASCAT Soil Moisture Retrieval. IEEE Trans Geosci Remote Sens 54:3513–3531. ttps://doi.org/10.1109/TGRS.2016.2519842 Wang X, Liu C, Lv G et al (2022) Integrating Multi-Source Remote Sensing to Assess Forest Aboveground Biomass in the Khingan Mountains of North-Eastern China Using Machine-Learning Algorithms. Remote Sens 14:1039. ttps://doi.org/10.3390/rs14041039 Wolfslehner B, Linser S, Pülzl H et al (2016) Forest bioeconomy—A new scope for sustainability indicators (From Science to Policy). ttps://doi.org/10.36333/fs04. European Forest Institute Xia J, Niu S, Ciais P et al (2015) Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc Natl Acad Sci USA 112:2788–2793. ttps://doi.org/10.1073/pnas.1413090112 Xie H, Zhang Y, Wu Z, Lv T (2020) A Bibliometric Analysis on Land Degradation: Current Status, Development, and Future Directions. Land 9:28. ttps://doi.org/10.3390/land9010028 Xu S (2024) Forestry offsets under China’s certificated emission reduction (CCER) for carbon neutrality: Regulatory gaps and the ways forward. Int J Clim Change Strateg Manag 16:140–156. ttps://doi.org/10.1108/IJCCSM-04-2022-0047 Yu X, Hyyppä J, Karjalainen M et al (2015) Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes. Remote Sens 7:15933–15954. ttps://doi.org/10.3390/rs71215809 Zeng W, Tomppo E, Healey SP, Gadow KV (2015) The national forest inventory in China: History - results - international context. Ecosyst 2:23. ttps://doi.org/10.1186/s40663-015-0047-2 Zhai Y, Wang L, Yao Y et al (2025) Spatially continuous estimation of urban forest aboveground biomass with UAV-LiDAR and multispectral scanning: An allometric model of forest structural diversity. Agric Meteorol 360:110301. ttps://doi.org/10.1016/j.agrformet.2024.110301 Zhang L, Shao Z, Liu J, Cheng Q (2019) Deep Learning Based Retrieval of Forest Aboveground Biomass from Combined LiDAR and Landsat 8 Data. Remote Sens 11:1459. ttps://doi.org/10.3390/rs11121459 Zhang Q, Wang R (2023) Carbon emission reduction effects in Yangtze River Delta from the dual perspectives of forest resource endowment and low-carbon pilot policy in the digital age. Front Glob Change 6:1259500. ttps://doi.org/10.3389/ffgc.2023.1259500 Zhang X, Jia W, Sun Y et al (2023) Simulation of Spatial and Temporal Distribution of Forest Carbon Stocks in Long Time Series—Based on Remote Sensing and Deep Learning. Forests 14:483. ttps://doi.org/10.3390/f14030483 Zheng J, Zhou Z, Zhu M et al (2024) Integration of Optical Remote Sensing and Laser Point Cloud for Forest Stock Estimation in Karst Mountainous Areas. Forests 15:2106. ttps://doi.org/10.3390/f15122106 Zittis G, Almazroui M, Alpert P et al (2022) Climate Change and Weather Extremes in the Eastern Mediterranean and Middle East. Rev Geophys 60. ttps://doi.org/10.1029/2021RG000762. e2021RG000762 Zute D, Samariks V, Šņepsts G et al (2023) Balancing Forest Regulations and Stakeholder Needs in Latvia: Modeling the Long-Term Impacts of Forest Management Strategies on Standing Volume and Carbon Storage. Sustainability 16:280. ttps://doi.org/10.3390/su16010280 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9102556","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":607393606,"identity":"76eb4ccc-a5a0-451b-8fde-cbff8e6e661f","order_by":0,"name":"Gabriel Osei Forkuo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYFACHgaGBAYLBvYGBmYgz4YBSLERo0WCgecAWEsaUAszEVoYEFoOAzEBLfLuZ499eFAB1MLe+9iYp+J84nZ2/mOPeRjuyeHSYngmL3lGwhmgFp7jxsk8Z24n7mxmZjfmYSg2xqmlIceYIbFNgsFeIo35MG/b7cQNh5nZJGcwJCQ24NLS/wao5R/QFoiWc4S1yEuAbGmAaEnmbTsA1iLxAY8WA4l3yQwJxyR4eHiOMRvOOZNsDNRibvDBIAGnX+T7cw8z/qixkeNhb2OWeFNhJ7vh/MFnDxIqEnCGmMEBCM2DLo5LA9AWXC4eBaNgFIyCUQAHADWxS35CD5XtAAAAAElFTkSuQmCC","orcid":"","institution":"Transylvania University of Brașov","correspondingAuthor":true,"prefix":"","firstName":"Gabriel","middleName":"Osei","lastName":"Forkuo","suffix":""}],"badges":[],"createdAt":"2026-03-12 08:56:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9102556/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9102556/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104868841,"identity":"cfb81c43-a4c2-4227-95d8-cf5dc21f7be9","added_by":"auto","created_at":"2026-03-18 07:31:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":131928,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA-ScR flow diagram showing the studies identification, screening and selection processes\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9102556/v1/b36be53ab596911e3a7b8b2c.png"},{"id":105033706,"identity":"fc8b6e70-94ba-4eb4-9139-74d50a805b7f","added_by":"auto","created_at":"2026-03-20 07:21:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2098409,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of publications included in the study: \u003cstrong\u003ea\u003c/strong\u003e- Annual publication trend (2015–2024) with cumulative overlay (right axis). Two phases are delineated: emerging phase (2015–2019; \u003cem\u003en\u003c/em\u003e = 101 studies, blue bars) and acceleration phase (2020–2024; \u003cem\u003en\u003c/em\u003e = 99 studies, green bars). Dashed vertical line marks phase boundary; \u003cstrong\u003eb\u003c/strong\u003e - Publication trend heatmap: year (2015–2024) by thematic pillar. Cell values = number of studies per year-pillar combination. Dashed vertical line separates emerging and acceleration phases; open-access status by thematic pillar: stacked bar chart showing OA rate per pillar. Overall OA rate = 100%; \u003cstrong\u003ec\u003c/strong\u003e - Open-access status by thematic pillar: stacked bar chart showing OA rate per pillar. Overall OA rate = 100%; \u003cstrong\u003ed\u003c/strong\u003e - Top 15 publication venues by frequency of included sources; \u003cstrong\u003ee\u003c/strong\u003e - Citation distribution by thematic pillar: box-and-whisker plots showing median, IQR, and outliers per pillar. Dashed horizontal line = overall mean (349); \u003cstrong\u003ef\u003c/strong\u003e - Geographic distribution by study area scope. Bar chart showing the top 20 countries by frequency of institutional affiliation or study area; European countries highlighted in dark green; \u003cstrong\u003eg\u003c/strong\u003e - Research pillar distribution: horizontal bar chart (left) and donut chart (right) showing the six thematic pillars (P1–P5; CX) by frequency (\u003cem\u003en\u003c/em\u003eand %).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9102556/v1/c190142825914aed31f11687.png"},{"id":104868833,"identity":"a2901af6-d06d-45d7-ace5-ced69d5d98ec","added_by":"auto","created_at":"2026-03-18 07:31:38","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":98681,"visible":true,"origin":"","legend":"\u003cp\u003eRemote sensing data source distribution: donut chart showing frequencies of all sensor/platform categories (multi-label). \"Not Specified\" shown separately.\u003c/p\u003e","description":"","filename":"3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9102556/v1/2c564c9f9de5e2a13af84dcf.jpeg"},{"id":105033754,"identity":"87462c37-c2c1-4858-aa1d-f5c1315a5dae","added_by":"auto","created_at":"2026-03-20 07:21:35","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":97124,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon variable focus distribution (multi-label): frequency of AGB, soil carbon, carbon flux/NEP, fire carbon, peat carbon, and market/credit categories across all 200 sources.\u003c/p\u003e","description":"","filename":"4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9102556/v1/13195db8d01474625b3fbceb.jpeg"},{"id":105034036,"identity":"3d983f57-0a6a-477e-bc5c-5a88bcf23663","added_by":"auto","created_at":"2026-03-20 07:22:29","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":107506,"visible":true,"origin":"","legend":"\u003cp\u003eAI/ML method frequency across all 200 included sources (multi-label extraction). Horizontal bar chart showing all identified method categories; \"Not Specified\" shown separately. Multi-label counts sum to more than n = 200.\u003c/p\u003e","description":"","filename":"5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9102556/v1/6fdb353b24ee6edd9e05746f.jpeg"},{"id":104868835,"identity":"4680a28d-c466-4255-b1c5-fb0cb9a8e02b","added_by":"auto","created_at":"2026-03-18 07:31:38","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":158161,"visible":true,"origin":"","legend":"\u003cp\u003eCo-occurrence heatmap: AI/ML method categories × thematic pillars (n = 200; cells show study count and percentage of pillar total). Darker cells indicate higher co-occurrence. Colour scale = green intensity.\u003c/p\u003e","description":"","filename":"6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9102556/v1/b74c98b9d22f3bc2a2204a81.jpeg"},{"id":104868839,"identity":"be2d2c58-3973-475a-9c18-069506e18419","added_by":"auto","created_at":"2026-03-18 07:31:38","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":177754,"visible":true,"origin":"","legend":"\u003cp\u003eQuality and risk-of-bias heatmap (MMAT framework; Hong et al., 2018; Munn et al., 2018): ten appraisal criteria by six thematic pillars, plus overall column. Criteria listed on y-axis; pillars on x-axis. Colour coding: ✓dark green = ≥70% studies meeting criterion (low concern); ~ amber = 40–69% (moderate concern); ✗red = \u0026lt;40% (high concern). Reproducibility criterion (\u0026lt;10% across all pillars) and limitations reporting (\u0026lt;30%) represent the most critical quality gaps.\u003c/p\u003e","description":"","filename":"7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9102556/v1/cef2608f6cdbdc598a838db1.jpeg"},{"id":105033680,"identity":"b9a2acff-5207-4f40-b1ca-e4c53cafd9a8","added_by":"auto","created_at":"2026-03-20 07:21:16","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":129811,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots of citation distribution by thematic pillar (symlog scale). White bar = median; dark inner box = IQR; yellow diamond = mean citation count; individual study dots overlaid.\u003c/p\u003e","description":"","filename":"8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9102556/v1/d2526799c718d3ddba4ab196.jpeg"},{"id":104868838,"identity":"9b2f0b47-92b3-4714-ac52-12e8246a7c46","added_by":"auto","created_at":"2026-03-18 07:31:38","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":211114,"visible":true,"origin":"","legend":"\u003cp\u003ePublication bias assessment (Sterne et al., 2016; Egger et al., 1997): (A) scatter plot of study age (years since publication) versus log₁₀(citation count) with 95% prediction interval; (B) Egger-style inverted funnel plot. Broad symmetry across pillars indicates low overall publication bias; minor right asymmetry in P4 reflects co-existence of institutional and independent academic outputs.\u003c/p\u003e","description":"","filename":"9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9102556/v1/62bd349dae0b5f1ff5d44b01.jpeg"},{"id":104868840,"identity":"62e36b46-115a-4d82-acac-16450af1817b","added_by":"auto","created_at":"2026-03-18 07:31:38","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":201760,"visible":true,"origin":"","legend":"\u003cp\u003eRadar chart: capability-profile per thematic pillar across eight analytical dimensions (AI/ML maturity; remote sensing integration; carbon market application; policy alignment; data availability; reproducibility; equity and governance; European relevance). Scores 0–100 derived from quality appraisal and charted variables.\u003c/p\u003e","description":"","filename":"10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9102556/v1/88c941843c151daea1983ba2.jpeg"},{"id":105562672,"identity":"7f3c97be-bf49-4780-9f2b-a8af3d642f2f","added_by":"auto","created_at":"2026-03-27 12:43:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5449793,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9102556/v1/ef84470d-1acd-4e6d-ba92-2170deb2f3f4.pdf"},{"id":105033656,"identity":"23d3791a-889a-417e-a043-373528272408","added_by":"auto","created_at":"2026-03-20 07:21:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27148,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9102556/v1/7a168c53a7459d56b78ebc08.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Emerging Role of Artificial Intelligence Driven Forest Carbon Integrity Systems: A Scoping Review of Methods, Risks, and Policy Implications for European Forests","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Forest Carbon in the European Climate Architecture\u003c/h2\u003e \u003cp\u003eForests cover approximately 45% of Europe's land area and constitute an indispensable pillar of the continent's climate architecture, providing critical ecosystem services \u0026mdash; carbon sequestration, biodiversity habitat, water regulation, and timber supply \u0026mdash; that span the full forest-to-wood production chain (Calvin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lamb et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Deng et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Under the Paris Agreement and the European Green Deal, forests are assigned substantial and legally binding mitigation responsibilities: the revised Land Use, Land Use Change and Forestry (LULUCF) Regulation (2023) targets a net sink of at least 310 MtCO₂eq per year by 2030 (Deng et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Forsell et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rickels et al., \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Meeting this target demands not only sustained forest management effort but also robust, verifiable, and high-frequency carbon measurement integrated across stand, landscape, and national scales. Yet persistent discrepancies exist between modelled and satellite-derived estimates; national inventory data and atmospheric inversions diverge at the country level; and the reliability of the forest sink as a long-term climate mitigation asset is increasingly questioned (Calvin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chuvieco et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dupuy et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Patacca et al., 2022).\u003c/p\u003e \u003cp\u003eThe structural vulnerability of the European forest carbon sink has intensified markedly since 2000. Natural disturbances \u0026mdash; wildfires, bark beetle outbreaks, windthrow, and drought-induced dieback \u0026mdash; are eroding permanence across all major biomes (Dupuy et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Patacca et al., 2022). The 2017\u0026ndash;2019 bark beetle calamity in Central European spruce forests released an estimated 50\u0026ndash;80 MtCO₂eq (Dupuy et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Southern European wildfires, amplified under 2\u0026deg;C warming scenarios, are projected to increase in frequency and severity (Dupuy et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schleussner et al., \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The Intergovernmental Panel on Climate Change (IPCC) AR6 synthesis (Calvin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) indicates that without dramatic cross-sectoral emissions reductions, residual mitigation demands placed on forest carbon sinks will increase \u0026mdash; intensifying pressure on measurement, reporting, and verification (MRV) infrastructure and the scientific credibility of forest carbon reporting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 AI and Digital Technologies as Transformative MRV Instruments\u003c/h2\u003e \u003cp\u003eArtificial intelligence (AI) and digital technologies have emerged as potentially transformative instruments for improving forest carbon integrity \u0026mdash; understood here as the accuracy, transparency, additionality, permanence, and verifiability of forest carbon accounting across the full MRV chain (Cowls et al., 2021; Allen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kwilinski et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Deep learning architectures now achieve sub-hectare canopy height and biomass estimates from airborne Light Detection and Ranging (LiDAR), with \u003cem\u003eR\u0026sup2;\u003c/em\u003e consistently exceeding 0.85 at stand scale (Amiri et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kellner et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dorado-Roda et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rodr\u0026iacute;guez-Veiga et al., \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Sentinel-1 SAR and Sentinel-2 optical time-series enable near-real-time forest disturbance detection at continental scale (Bauer-Marschallinger et al., 2018; Grabska-Szwagrzyk et al., 2019; Tricht et al., \u003cspan citationid=\"CR192\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Digital twin frameworks and process-based land surface models enable scenario-based carbon modelling under future climate trajectories that directly inform forest management strategies (Martens et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pisso et al., \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Frieler et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Blockchain and distributed ledger technologies offer new architectures for carbon credit provenance and audit trails (Kwilinski et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These advances together substantially advance understanding of forest ecosystem carbon processes at scales relevant to management and policy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Governance Challenges, Equity Risks, and the Evidence Gap\u003c/h2\u003e \u003cp\u003eThe integration of AI into forest carbon governance is not without risk. Automated verification systems may embed structural biases against smallholder and indigenous forest managers (Mor\u0026aacute;n et al., 2018; Dom\u0026iacute;nguez and Luoma, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kuyper et al., 2017). Proprietary algorithms may compromise the transparency required for regulatory compliance under the EU Emissions Trading System (ETS) and LULUCF (Berg et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Clementino and Perkins, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The technical complexity of AI-generated carbon estimates may outpace the interpretive capacity of verification bodies (Cowls et al., 2021; Popescu and Popescu, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while the distributional consequences of AI-mediated carbon crediting \u0026mdash; particularly in community forest contexts \u0026mdash; have received comparatively little systematic attention (Dom\u0026iacute;nguez and Luoma, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ekardt et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schilling-Vacaflor and Lenschow, 2021).\u003c/p\u003e \u003cp\u003eDespite a proliferation of individual studies on AI\u0026ndash;forest carbon applications, no comprehensive evidence map of the emerging field exists. The intersection between technical capability, carbon market integrity, ethical governance, and EU policy implementation remains poorly characterised \u0026mdash; a gap this scoping review is designed to fill. The scoping review format is appropriate given the heterogeneous evidence base (empirical, modelling, policy, and normative studies), the emerging nature of the field, and the need to map scope before conducting targeted systematic reviews. This format also aligns with EJFR's focus on forest systems analysis and forest ecosystem process understanding across scales.\u003c/p\u003e \u003cp\u003e \u003cb\u003e1.4 Research Questions\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eRQ1. What AI/ML methods and remote sensing technologies are applied in forest carbon MRV, and what performance levels do they achieve in European and broader contexts?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRQ2. To what extent are digital twin and process-based simulation approaches integrated with AI for forest carbon assessment?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRQ3. How are AI systems applied to carbon market integrity, and what risks to transparency, additionality, and equity do they introduce or mitigate?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRQ4. What is the state of alignment between emerging AI capabilities and EU regulatory requirements for forest carbon governance?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRQ5. What ethical, equity, and governance risks are identified for AI-mediated forest carbon systems?\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.5 Aim and Objectives\u003c/h2\u003e \u003cp\u003eThe aim of this scoping review was to systematically map the emerging landscape of AI-driven forest carbon integrity research, with reference to European forests and governance frameworks, and to identify dominant methods, technological gaps, ethical risks, and policy alignment opportunities across the full MRV chain. The specific objectives were to:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eO1. characterise the AI and remote sensing methods applied to forest carbon MRV and evaluate their technical performance, addressing RQ1;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eO2. assess integration between digital twin frameworks, process-based ecosystem models, and AI for forest carbon assessment, addressing RQ2;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eO3. map AI applications to carbon market integrity and identify transparency, additionality, and equity risks, addressing RQ3;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eO4. evaluate alignment between AI capabilities and EU regulatory requirements across the Green Deal portfolio, addressing RQ4;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eO5. identify ethical, equity, and governance risks of AI-mediated forest carbon systems, addressing RQ5; and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eO6. appraise quality, reproducibility, and publication bias of the evidence base, and propose a five-point research agenda.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.6 Related Studies\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e1.6.1. Pillar 1 \u0026mdash; AI and remote sensing MRV\u003c/h2\u003e \u003cp\u003ePillar 1 focuses on artificial intelligence and remote sensing for measurement, reporting, and verification. The precision of forest biomass and carbon stock estimation using artificial intelligence and remote sensing has improved substantially since 2015. Airborne LiDAR is validated across European and global forest types and consistently achieves an \u003cem\u003eR\u0026sup2;\u003c/em\u003e greater than 0.85 for aboveground biomass (AGB) prediction at the stand scale (Amiri et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dorado-Roda et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kellner et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nevalainen et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Unmanned aerial vehicle (UAV) platforms extend this capability to the individual-tree scale (Duarte et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gan et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Klouček et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, pantropical GEDI-TanDEM-X integration now enables wall-to-wall canopy height retrieval (Neuenschwander and Magruder, \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Qi et al., \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Sensor fusion combining Sentinel-1 and Sentinel-2 data achieves 85 to 95 percent classification accuracy for temperate European forest types and supports near-real-time disturbance detection (Bauer-Marschallinger et al., 2018; Chatziantoniou et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Grabska-Szwagrzyk et al., 2019; Pratic\u0026ograve; et al., \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tricht et al., \u003cspan citationid=\"CR192\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Multi-sensor data fusion integrating LiDAR, synthetic aperture radar (SAR), and optical sources delivers the lowest aboveground biomass uncertainties and represents the current state of the art (Li et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nandy et al., \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Silveira et al., \u003cspan citationid=\"CR167\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Urbazaev et al., \u003cspan citationid=\"CR187\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR197\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Benchmark comparisons find that random forest and deep learning models produce comparable aboveground biomass accuracy, with random forest offering superior interpretability for operational deployment (Avci et al., 2021; Esteban et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pelletier et al., \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tamiminia et al., \u003cspan citationid=\"CR179\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Neural network upscaling of FLUXNET and Integrated Carbon Observation System (ICOS) eddy-covariance measurements to continental carbon flux estimates is successfully validated against global gross primary productivity benchmarks (Heiskanen et al., 2021; Jung et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xia et al., \u003cspan citationid=\"CR199\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Virkkala et al., \u003cspan citationid=\"CR194\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Critical gaps persist in model transferability across biomes and climate scenarios (Khanal et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rodr\u0026iacute;guez-Veiga et al., \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similar gaps remain regarding the reproducibility of artificial intelligence workflows (Allen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cowls et al., 2021) and the overlooked contribution of trees outside forests to total European woody biomass (Liu et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The role and need for space-based biomass measurements in environmental management and policy have been comprehensively synthesised by Herold et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e1.6.2. Pillar 2 \u0026mdash; Digital twins and ecosystem modelling\u003c/h2\u003e \u003cp\u003ePillar 2 examines digital twins and ecosystem modelling. Process-based modelling infrastructure for forest carbon digital twins is technically mature but operationally fragmented. Land surface simulations driven by ECMWF Reanalysis v5 (ERA-5) provide robust datasets (Albergel et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Balsamo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Additionally, Global Land Evaporation Amsterdam Model (GLEAM) v3 land evaporation and soil moisture models (Martens et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b) multi-model impact protocol (Frieler et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) provide validated boundary conditions for scenario modelling at warming levels of 1.5\u0026deg;C and 2\u0026deg;C. Deployed process-based components include the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model (Haverd et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the FLEXPART dispersion model (Pisso et al., \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Other essential tools include Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) adapted for European Union (EU) forest management (Lindeskog et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and the CBM-CFS3 carbon budget model customised for EU countries (Pilli et al., \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The FLUXCOM global carbon flux synthesis (Jung et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and terrestrial water-use efficiency trends (Huang et al., 2015) provide observational validation benchmarks. Forest management simulation for long-term carbon balance under changing climate conditions represents an early forest-specific deployment (Thomas et al., \u003cspan citationid=\"CR182\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Valipour et al., \u003cspan citationid=\"CR190\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The forestry digital twin concept applied to Landsat data also serves as a foundational example in this domain (Jiang et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There has been a significant increase in natural disturbance impacts on European forests since 1950 (Patacca et al., 2022). These historical impacts, alongside climate change projections for southern European wildfire danger, define the disturbance forcing conditions that forest carbon digital twins must represent to be policy-relevant (Dupuy et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schleussner et al., \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Urbanisation effects on temperate forest carbon cycle responses expand this modelling context (Reinmann et al., \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Peatland carbon dynamics and rewetting initiatives further enhance the framework (Ekardt et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rowan et al., \u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tanneberger et al., 2020). Finally, established soil carbon stock estimation standards complete the multi-compartment carbon modelling evidence base (Bispo et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schulte et al., \u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tifafi et al., 2017).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e1.6.3. Pillar 3 \u0026mdash; Carbon markets and finance\u003c/h2\u003e \u003cp\u003ePillar 3 addresses carbon markets and finance. The UNEP Emissions Gap Report quantifies the persistent divergence between corporate net-zero commitments and verified emissions reductions (Programme UNEP, 2023). Concurrently, environmental, social, and governance (ESG) rating divergence analysis demonstrates structural inter-agency inconsistency (Berg et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This inconsistency undermines the reliability of AI-generated carbon signals in investment decisions. Regulatory harmonisation of environmental, social, and governance metrics carries significant global business implications (Alamillos and de Mariz, 2022). Corporate responses to these ratings in Italy (Clementino and Perkins, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and the effect of sustainable business practices on profitability (Cerciello et al., 2022) document the institutional dynamics within which AI-assisted verification must operate. Reducing Emissions from Deforestation and Forest Degradation (REDD+) market integrity depends heavily on credible community-based monitoring. Synergising community monitoring with remote sensing for REDD+ measurement, reporting, and verification establishes a strong foundation (Murthy et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Carbon measurement methods for REDD+ implementation (Bhattarai et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and analyses of uncertainty in forest reference levels (Mertz et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) are equally vital. The commodification of forest carbon through socially embedded practices also shapes this landscape (Benjaminsen and Kaarhus, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Together, these elements collectively define the technical and political economy prerequisites for successful carbon markets. Artificial intelligence applications span satellite-based additionality verification and blockchain-backed carbon credit provenance (Kwilinski et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Other applications include machine learning-assisted disclosure verification (Cerciello et al., 2022). Additionally, life cycle assessment tools support biogenic carbon accounting for forest products (Leinonen, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Franz et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Sterman et al., \u003cspan citationid=\"CR175\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Gough et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A scoping review of carbon pricing systems in forest sector models finds persistent methodological heterogeneity (Honkomp and Schier, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This heterogeneity directly motivates the push for AI-assisted standardisation. Several frameworks define the macro-policy architecture within which forest carbon market AI tools operate. These include the findings of the Stern Commission (Stern et al., 2017) and the design of carbon dioxide removal (CDR) policy instruments (Honegger et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The potential for bioenergy with carbon capture and storage in a carbon-neutral Europe further shapes this architecture (Rosa et al., \u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, forestry offsets in China\u0026rsquo;s national emission reduction scheme provide additional global context (Xu, \u003cspan citationid=\"CR201\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The livelihood impacts of forest carbon projects address the social dimensions of market integrity (Dube and Chatterjee, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Simultaneously, green finance gap bibliometrics explore the financial aspects of these mechanisms (Debrah et al., 2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e1.6.4. Pillar 4 \u0026mdash; European Union (EU) policy and governance\u003c/h2\u003e \u003cp\u003ePillar 4 explores EU policy and governance. The European Union (EU) regulatory environment for forest carbon is among the most demanding globally. Nevertheless, persistent fragmentation in measurement, reporting, and verification limits policy coherence. Comparisons between UNFCCC inventories and atmospheric inversions establish a clear verification challenge (Deng et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Land-use emission projections further underscore this difficulty (Forsell et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A comprehensive assessment of the 2030 Climate and Energy Framework clarifies these ongoing regulatory hurdles (Kulovesi and Oberth\u0026uuml;r, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Land use strategies for climate mitigation in carbon-dense temperate forests are essential for effective management (Law et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The formal recognition of nature-based solutions also plays a critical role (Seddon et al., \u003cspan citationid=\"CR164\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Interventions supporting natural forest regeneration (Chazdon et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and the identification of primary forest protection gaps in Europe (Bowler et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dave et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sabatini et al., \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schindler et al., \u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) highlight crucial ecological priorities. Additionally, natural disturbance-based forest management practices (Kuuluvainen et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) help define the biodiversity co-benefit optimisation space within which artificial intelligence carbon metrics must be designed. The coalitional politics of environmental forest policy illuminate complex regulatory landscapes (Sotirov et al., \u003cspan citationid=\"CR172\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Forest carbon sequestration policy design (Gren and Aklilu, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and subnational forest carbon governance (Ruseva, \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ustaoglu \u0026amp; Collier, \u003cspan citationid=\"CR188\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) show how these frameworks operate at different scales. Forest regulations and stakeholder modelling in Latvia offer a focused example of these dynamics (Zute et al., \u003cspan citationid=\"CR210\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Together, these studies explain the multi-level governance processes through which AI-informed policy is negotiated. Soil and landscape governance directly relates to Common Agricultural Policy (CAP) Eco-schemes and land-use regulations. This includes the implementation of soil protection policy instruments (Ronchi et al., \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and peatland governance within sustainability law (Ekardt et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Forest bioeconomy sustainability indicators provide vital metrics for this sector (Wolfslehner et al., \u003cspan citationid=\"CR198\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, broad bioeconomy strategies outline pathways for practical implementation (Ara\u0026uacute;jo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gregson et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kardung et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Meyer, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sollen-Norrlin et al., \u003cspan citationid=\"CR171\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Greenhouse gas (GHG) flux monitoring over European managed grasslands provides necessary empirical data for these governance models (Franz et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; H\u0026ouml;rtnagl et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nemitz et al., \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Forest governance at cross-border scales requires monitoring illegal logging and carbon emissions in timber-producing countries (Piabuo et al., \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The impact of foreign direct investment on land-use emissions in tropical forest countries further complicates global governance (Piabuo et al., \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, specific forest policy and management approaches are necessary to advance CDR targets (Maes et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Meng et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; vonHedemann et al., \u003cspan citationid=\"CR195\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang \u0026amp; Wang, \u003cspan citationid=\"CR206\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Emissions Database for Global Atmospheric Research (EDGAR) global greenhouse gas emissions atlas remains the primary inventory benchmark against which national forest carbon contributions are reconciled (Janssens-Maenhout et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e1.6.5. Pillar 5 \u0026mdash; Ethics, equity and societal dimensions\u003c/h2\u003e \u003cp\u003ePillar 5 investigates ethics, equity, and societal dimensions. The equity and governance risks of AI-driven forest carbon systems draw from political ecology, technology ethics, and empirical social research. Decolonising conservation policy is a fundamental step toward equitable environmental management (Dom\u0026iacute;nguez and Luoma, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The legitimacy of non-state actors in hybrid climate governance also plays a crucial role (Kuyper et al., 2017, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, dynamics at the forest frontier in the Global South test the promise of equity from international climate policies (Brockhaus et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These elements collectively establish the governance legitimacy framework within which artificial intelligence carbon systems must remain accountable. Empirical evidence regarding social outcomes is critical. Community forestry programmes demonstrate the potential for biodiversity and carbon co-production (Luintel et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). At the same time, the livelihood impacts of forest carbon projects require careful evaluation (Dube and Chatterjee, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Ecosystem service flows and stakeholder power relationships document the distributional consequences that AI-mediated carbon crediting must navigate (Fedele et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Felipe-Lucia et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Living Labs serve as a co-design methodology for nature-based solutions (Lupp et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This methodology provides a participatory artificial intelligence tool development framework directly applicable to community forest carbon monitoring. Deploying artificial intelligence as a technological fix without social legitimacy represents a significant risk in climate policy (Cowls et al., 2021). To address this, the precision cardiology digital twin serves as a governance analog with developed accountability structures for consent, liability, and equity (Acero et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These concepts stand as central references for ethical technology deployment. Natural capital accounting offers economic visibility to ecological assets (Hein et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Exploratory studies on green finance and corporate social responsibility in Romanian business environments expand on these economic tools (Popescu and Popescu, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Transnational climate litigation highlights the important contributions of the Global South to legal accountability (Peel and Lin, \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Finally, studies on the European mountain cryosphere as a threatened ecosystem round out the contextual evidence base for this pillar (Beniston et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e1.6.6. Cross-cutting and contextual literature\u003c/h2\u003e \u003cp\u003eThe final section covers cross-cutting and contextual literature related to multiple pillars. Several bodies of work span multiple disciplines to inform this field. The integrated global assessment of natural forest carbon potential highlights massive sequestration opportunities (Mo et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This aligns with the findings of the IPCC Synthesis Report (Calvin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the global biodiversity assessment (IPBES, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Together, these foundational reports define the dual climate-biodiversity crisis context. Analyses of greenhouse gas emission trends by sector offer important baseline data (Fuzzi et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Klimont et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Krotkov et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lamb et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Miyazaki et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Petzold et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The science of net zero targets outlines both opportunities and practical implications (Allen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Integrating carbon dioxide removal into European Emissions Trading systems connects mitigation science with market policy (Rickels et al., \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Observations of very strong atmospheric methane growth highlight the urgency of these interventions (Nisbet et al., \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, evaluations of differential warming scenario impacts (Schleussner et al., \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and global drought changes (Naumann et al., \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) firmly establish the Earth system context. The Global Fire Atlas maps individual fire characteristics worldwide (Andela et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Deep learning models now enable active forest fire detection with high precision (Seydi et al., \u003cspan citationid=\"CR166\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Geospatial data and machine learning (ML) integration further improve fire susceptibility mapping (Singha et al., \u003cspan citationid=\"CR170\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, deep learning applications successfully predict forest fires in vulnerable regions such as Kilimanjaro (Mambile et al., 2024). These applications successfully connect remote disturbance monitoring with long-term permanence policy. Integrating remote sensing and machine learning greatly improves aboveground biomass estimation (Anees et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Comprehensive reviews of forest biomass retrieval methods across biomes further consolidate this knowledge (Rodr\u0026iacute;guez-Veiga et al., \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The application of machine learning in agricultural and applied economics demonstrates broader methodological crossovers (Storm et al., 2019). Bibliometric analyses of unmanned aerial vehicles in precision agriculture map technological trends (Singh et al., \u003cspan citationid=\"CR169\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Evaluations of national forest inventories provide international context for these technical advancements (Zeng et al., \u003cspan citationid=\"CR203\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These works represent the multi-disciplinary synthesis literature connecting algorithmic innovation with cross-cutting themes. Economic evaluations of soil erosion costs in the EU highlight the financial risks of land degradation (Panagos et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Ecological studies show that higher plant diversity directly supports soil organic carbon accumulation (Prommer et al., 2019). Research on extreme weather impacts exposes the vulnerability of European crop production (Beillouin et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Innovative proposals also position cities as carbon sinks through wooden-building construction (Amiri et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These studies successfully extend the cross-cutting evidence base to the broader land-climate system. Studies on digital transformation and environmental performance metrics reveal the corporate value of technological adoption (Kwilinski et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Data-driven artificial intelligence applications for precision agriculture showcase similar operational benefits (Linaza et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These perspectives connect the forest carbon artificial intelligence agenda to the wider digital bioeconomy transformation. Risk management strategies for planted forests and invasive species in Europe present unique ecological challenges (Brundu and Richardson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Forest restoration efforts following surface mining disturbance require robust long-term monitoring (Macdonald et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Climate change and weather extremes in the eastern Mediterranean further complicate these regional conservation efforts (Zittis et al., \u003cspan citationid=\"CR209\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These diverse challenges complete the contextual evidence landscape within which European forest carbon artificial intelligence governance must be situated.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThis scoping review was conducted and reported following the PRISMA Extension for Scoping Reviews (PRISMA-ScR; Tricco et al., \u003cspan citationid=\"CR184\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Peters et al., \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the methodological framework of Arksey and O'Malley (2005), as refined by Levac et al. (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Joanna Briggs Institute (JBI) guidance (Aromataris \u0026amp; Munn, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Publication bias was assessed following Sterne et al. (\u003cspan citationid=\"CR176\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Egger et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Study quality was appraised using the Mixed Methods Appraisal Tool (MMAT; Hong et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), adapted per Munn et al. (\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The completed PRISMA-ScR checklist is provided as \u003cb\u003eSupplementary Material\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Protocol and Registration\u003c/h2\u003e \u003cp\u003eA review protocol was developed and registered prior to data collection. The registration is available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/pum9y\u003c/span\u003e\u003cspan address=\"https://osf.io/pum9y\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The protocol specified the research questions, eligibility criteria, search strategy, data charting form, quality appraisal method, and synthesis approach. However, there were minor deviations from the protocol, specifically the addition of a sixth cross-cutting (CX) pillar to accommodate interdisciplinary studies bridging multiple thematic domains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Eligibility Criteria\u003c/h2\u003e \u003cp\u003eSources of evidence were eligible if they: (a) reported original empirical research, simulation or modelling studies, methodological contributions substantially advancing understanding of forest ecosystem carbon processes or management strategies, or systematic evidence syntheses; (b) addressed at least one of the six pre-defined thematic pillars (P1: AI and remote sensing MRV; P2: digital twins and ecosystem modelling; P3: carbon markets and finance; P4: EU policy and governance; P5: ethics, equity, and societal dimensions; CX: cross-cutting) in the context of forest carbon; (c) were published in a peer-reviewed journal or identifiable grey-literature outlet between 1 January 2015 and 31 December 2024; and (d) were written in English.\u003c/p\u003e \u003cp\u003eThe 2015 start date was chosen to capture the period of rapid maturation of Sentinel satellite capabilities, Global Ecosystem Dynamics Investigation (GEDI) LiDAR deployment, and deep learning architectures, all of which fundamentally changed the landscape of AI-forest carbon research. Studies focused exclusively on non-forest or marine ecosystems without direct forest carbon transferability, commentaries without methodological content, and conference abstracts without full-text availability were excluded. Grey literature (institutional reports from IPCC, Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), UNEP, EFI, and EU Commission) was included where it constituted an identifiable source of evidence meeting other criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Information Sources and Search Strategy\u003c/h2\u003e \u003cp\u003eSystematic electronic searches were conducted programmatically across three open-access databases using Python 3.12: PubMed/MEDLINE, OpenAlex, and Semantic Scholar. Supplementary grey-literature searches were conducted on the Web of Science (12 structured queries) and Scopus (6 queries). All searches were last executed in January 2025. No contact with authors was made to identify additional unpublished sources, consistent with the scope of this review.\u003c/p\u003e \u003cp\u003eSearches were structured around six thematic pillars using Boolean combinations of three concept clusters: (i) forest carbon/biomass/MRV; (ii) artificial intelligence/machine learning/remote sensing; and (iii) pillar-specific governance/market/ethics terms. A total of 74 unique query strings were deployed across OpenAlex (the primary database given its coverage of forest, remote sensing, and environmental science literature), 19 in PubMed/MEDLINE, and 71 in Semantic Scholar (English-language filter applied).\u003c/p\u003e \u003cp\u003eThe full representative search string for OpenAlex (Pillar 1 \u0026mdash; AI and Remote Sensing MRV) is given below. This string is reproducible as entered and can be adapted for other databases by substituting field tags:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eOpenAlex (P1 \u0026mdash; AI \u0026amp; Remote Sensing MRV, 2015\u0026ndash;2024)\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e(\"forest carbon\" OR \"aboveground biomass\" OR \"forest biomass\" OR \"forest MRV\" OR \"carbon stock\" OR \"canopy height\" OR \"forest disturbance\" OR \"tree species classification\") AND (\"machine learning\" OR \"deep learning\" OR \"artificial intelligence\" OR \"random forest\" OR \"neural network\" OR \"support vector machine\" OR \"convolutional neural network\") AND (\"LiDAR\" OR \"SAR\" OR \"Sentinel\" OR \"Landsat\" OR \"UAV\" OR \"remote sensing\" OR \"GEDI\" OR \"ICESat\")\u003c/p\u003e\u003cp\u003eLimits: publication year\u0026thinsp;=\u0026thinsp;2015\u0026ndash;2024; language\u0026thinsp;=\u0026thinsp;English\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eRepresentative search strings for all six pillars and all three databases, including full Boolean logic and field tag specifications, are provided in \u003cb\u003eSupplementary File S1\u003c/b\u003e. Example pillar-specific concept clusters are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConcept clusters used per thematic pillar across all databases (representative terms; full strings in \u003cb\u003eSupplementary File S1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e Pillar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest/Carbon concept terms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI/Technology concept terms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGovernance/Context concept terms\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\"forest biomass\" OR \"carbon stock\" OR \"aboveground biomass\" OR \"canopy height\" OR \"forest disturbance\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\"machine learning\" OR \"deep learning\" OR \"LiDAR\" OR \"SAR\" OR \"Sentinel\" OR \"UAV\" OR \"GEDI\" OR \"neural network\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"MRV\" OR \"measurement reporting verification\" OR \"LULUCF\" OR \"carbon accounting\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\"forest carbon cycle\" OR \"ecosystem carbon\" OR \"net ecosystem production\" OR \"soil carbon\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\"digital twin\" OR \"process-based model\" OR \"land surface model\" OR \"simulation\" OR \"CABLE\" OR \"FLEXPART\" OR \"ERA-5\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"climate scenario\" OR \"carbon permanence\" OR \"disturbance modelling\" OR \"ISIMIP\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\"forest carbon credit\" OR \"REDD+\" OR \"carbon offset\" OR \"forest carbon market\" OR \"additionality\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\"blockchain\" OR \"AI verification\" OR \"satellite monitoring\" OR \"ESG\" OR \"machine learning\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"voluntary carbon market\" OR \"EU ETS\" OR \"carbon integrity\" OR \"greenwashing\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\"forest policy\" OR \"LULUCF\" OR \"forest governance\" OR \"EU Forest Strategy\" OR \"forest management\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\"remote sensing\" OR \"AI\" OR \"digital\" OR \"satellite\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"European Green Deal\" OR \"Fit-for-55\" OR \"Nature Restoration Law\" OR \"CSRD\" OR \"EU ETS\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\"forest carbon\" OR \"REDD+\" OR \"community forestry\" OR \"indigenous forest\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\"algorithmic bias\" OR \"AI ethics\" OR \"data sovereignty\" OR \"automated decision\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"equity\" OR \"justice\" OR \"consent\" OR \"decolonisation\" OR \"smallholder\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\"forest carbon stock\" OR \"aboveground biomass\" OR \"forest disturbance\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\"multi-source\" OR \"data fusion\" OR \"ICOS\" OR \"integrated\" OR \"cross-cutting\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"carbon market\" OR \"policy\" OR \"governance\" OR \"biodiversity co-benefit\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Selection of Sources of Evidence\u003c/h2\u003e \u003cp\u003eRecords retrieved from all databases were processed through a structured PRISMA-ScR pipeline by a single reviewer, with a 10% random sample independently screened at each step by a second reviewer. Discrepancies were resolved by consensus. The pipeline was implemented in Python 3.12 and is fully reproducible (see source code in \u003cb\u003eSupplementary File S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe addition of the CX pillar, comprising 22 studies, represents a minor protocol deviation. Cross-cutting studies explicitly integrating two or more pillars were assigned to this category to avoid double-counting. The complete PRISMA-ScR flow diagram is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Additionally, all screening decisions are available in the \u003cb\u003eSupplementary Workbook\u003c/b\u003e within the Selection_Summary sheet.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Data Charting Process\u003c/h2\u003e \u003cp\u003eA standardised data charting form was developed a priori and piloted on a random sample of 20 records before full application. The form was designed to capture all variables specified in the protocol. Charting was conducted by the author from abstract and title content, following the scoping review convention of not requiring full-text access for all records (Levac et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Peters et al., \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A 15% random sample was independently re-charted by a second reviewer. Discrepancies were resolved by consensus discussion. No contact with study authors was made to obtain or confirm data, as all required variables were extractable from publicly available abstracts and metadata. The completed charting form for all 200 studies is provided in the \u003cb\u003eSupplementary Workbook\u003c/b\u003e (Included_200 sheet).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Data Items\u003c/h2\u003e \u003cp\u003eThe following variables were sought for each included source of evidence. Definitions and coding assumptions are specified in parentheses:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eBibliographic metadata: first author surname, publication year, journal/venue, digital object identifier (DOI), open-access status\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePrimary thematic pillar (P1\u0026ndash;P5 or CX; single assignment by dominant content; ambiguous cases assigned CX)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAI/ML methods employed (multi-label; categories: ANN, Random Forest, SVM, Deep Learning Convolutional Neural Network (CNN)/Transformer, Regression/Statistical ML, Gradient Boosting, Simulation/Digital Twin, Object-Based Image Analysis (OBIA), Blockchain/ Distributed Ledger Technology (DLT), Not Specified)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRemote sensing data source(s) (multi-label; categories: Light Detection and Ranging (LiDAR)/Airborne Laser Scanning (ALS)/Terrestrial Laser Scanning (TLS), Optical Satellite, SAR, UAV/Drone, Hyperspectral, Field Inventory, Not Specified)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCarbon variable(s) addressed (multi-label; categories: AGB, soil carbon, carbon flux/NEP, fire carbon, peat carbon, forest carbon market/credit)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGeographic scope (single label; categories: Europe, Global, North America, Asia, Other/Not Specified)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCitation count at date of search (source: OpenAlex API; used for stratified selection and citation distribution analysis)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSeveral key simplifying assumptions were established for this review. First, studies with no identifiable artificial intelligence or machine learning method in the abstract were coded as \"Not Specified\" rather than being excluded. Second, the geographic scope of each paper was assigned based on the study area description rather than the institutional affiliations of the authors. Third, all applicable computational and sensor categories were recorded for multi-method studies using a multi-label approach. However, only a single primary thematic pillar was assigned to each of these studies. These assumptions are consistent with the scoping review convention of maximising inclusivity at the charting stage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Critical Appraisal of Individual Sources of Evidence\u003c/h2\u003e \u003cp\u003eCritical appraisal was conducted to characterise the methodological quality of the evidence base. This process also helped identify patterns of risk that would inform the research agenda rather than serving to exclude studies. This approach remains completely consistent with the primary purpose of a scoping review. The rationale for conducting this appraisal alongside the scoping review stems from the potential regulatory deployment of these technologies in high-stakes EU carbon governance contexts. Therefore, understanding the methodological maturity of this field provides directly actionable insights for policy development.\u003c/p\u003e \u003cp\u003eA ten-criterion framework was applied during this process. This framework was adapted from the Mixed Methods Appraisal Tool developed by Hong et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), following the recommendations of Munn et al. (\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The first criterion evaluated whether the research objective was clearly stated. The second and third criteria assessed if the methodology and study design were adequately described. The fourth and fifth criteria checked for the reporting of the sampling strategy and the clear description of the data source. The sixth and seventh criteria determined whether performance metrics were provided and if statistical rigour was properly addressed. The eighth and ninth criteria evaluated the discussion of study limitations and the clear reporting of results. Finally, the tenth criterion assessed whether reproducibility was addressed through code or data availability.\u003c/p\u003e \u003cp\u003eEach criterion was assessed as either \u0026lsquo;\u003cem\u003emet\u0026rsquo;\u003c/em\u003e or \u0026lsquo;\u003cem\u003enot met\u0026rsquo;\u003c/em\u003e based on the abstract and metadata content of each study. Publication bias was also assessed using Egger-style funnel plots as described by Sterne et al. (\u003cspan citationid=\"CR176\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These plots illustrate the log-citation count against the age of the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Synthesis of Results\u003c/h2\u003e \u003cp\u003eEvidence was synthesised narratively according to each thematic pillar and sub-theme. This synthesis included a descriptive quantitative analysis of method frequencies, geographic distributions, and citation patterns. The co-occurrence of artificial intelligence (AI) methods across different pillars was assessed using heatmaps. Temporal trends were subsequently mapped using year-by-pillar heatmaps. Furthermore, pillar capability profiles across eight distinct analytical dimensions were visualised using radar charts. Citation distributions were then examined using violin plots featuring symmetrical logarithmic scaling. The overall synthesis was framed around the five core research questions. All quantitative analyses were implemented using Python version 3.12, along with the pandas 2.2, NumPy 1.26, and matplotlib 3.8 libraries. The complete source code for these analyses is readily available in \u003cb\u003eSupplementary File S2.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Study Characteristics Overview\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarises the characteristics of the 200 included sources of evidence. Full bibliographic details and charted variables for all studies are provided in the \u003cb\u003eSupplementary Workbook\u003c/b\u003e (Included_200 sheet).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary characteristics of included sources of evidence (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublication years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u0026ndash;2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmerging phase (2015\u0026ndash;2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;101 (50.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcceleration phase (2020\u0026ndash;2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;99 (49.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen-access sources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;200 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal raw records retrieved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5,135 (PubMed 695; OpenAlex 3,981; Semantic Scholar 459)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean citations\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e349\u0026thinsp;\u0026plusmn;\u0026thinsp;412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian citations (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202 (87\u0026ndash;533)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum citations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,645 (Calvin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e \u0026ndash; IPCC AR6 Synthesis Report)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuropean-scope studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;47 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal-scope studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;82 (41.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop journal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRemote Sensing (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22, 11.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominant AI/ML method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArtificial Neural Network (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;48, 24.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominant remote sensing modality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiDAR/ALS/TLS (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;33, 16.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudies reporting reproducible code/data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;20 (\u0026lt;\u0026thinsp;10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudies reporting limitations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;60 (\u0026lt;\u0026thinsp;30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1: AI \u0026amp; Remote Sensing MRV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;53 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2: Digital Twins \u0026amp; Ecosystem Modelling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3: Carbon Markets \u0026amp; Finance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4: EU Policy \u0026amp; Governance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;59 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5: Ethics, Equity \u0026amp; Societal Dimensions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCX: Cross-cutting themes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Selection of Sources of Evidence and Evidence Base Overview\u003c/h2\u003e \u003cp\u003eFrom 5,135 raw records retrieved, the PRISMA-ScR pipeline yielded 200 included sources. The corpus spans 2015\u0026ndash;2024, with near-symmetrical temporal distribution (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb): the emerging phase (2015\u0026ndash;2019) yielded 101 studies (50.5%), and the acceleration phase (2020\u0026ndash;2024) contributes 99 studies (49.5%). Annual output peaked in 2021\u0026ndash;2022 before consolidating. All 200 studies are open access (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, Remote Sensing is the leading venue (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22, 11.0%), followed by Environmental Research Letters (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5), Geoscientific Model Development, Atmospheric Chemistry and Physics, PLoS ONE, Earth System Science Data, Proceedings of the National Academy of Sciences, Global Change Biology, Land, and Sustainability (all \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4). Mean citation count (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee) is 349 (median 202; max 2,645), with the highest-cited works being landmark climate and biodiversity assessments (Calvin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; IPBES, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lamb et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Geographically, 47 studies (23.5%) are European in scope; 82 (41.0%) are global; 67 (33.5%) are not geographically specified or are regionally mixed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003eThe pillar distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg\u003cb\u003e)\u003c/b\u003e is P4 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;59, 29.5%), P1 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;53, 26.5%), P2 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30, 15.0%), P3 and CX (each \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22, 11.0%), and P5 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14, 7.0%). The dominance of P4 reflects the density of EU policy literature directly bearing on forest carbon governance; the near-equivalent P1 share reflects the technical maturity of remote sensing and ML biomass estimation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Pillar 1 \u0026mdash; AI and Remote Sensing MRV: Results Addressing RQ1\u003c/h2\u003e \u003cp\u003eThe AI/remote sensing MRV pillar (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;53, 26.5%) encompasses forest biomass estimation, canopy height mapping, tree species classification, disturbance detection, and carbon flux upscaling across LiDAR, SAR, optical, and UAV data sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Several studies address the full forest ecosystem monitoring chain from individual tree to continental scale (Adesipo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Alexakis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Allen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Amiri et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Avci et al., 2021; Bauer-Marschallinger et al., 2018; Berg et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cao et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chatziantoniou et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chuvieco et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cowls et al., 2021; DeLancey et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dorado-Roda et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Duarte et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Esteban et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Farmonov et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fawzy et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Grabska-Szwagrzyk et al., 2019; Haya et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; He et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Immitzer et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jung et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kellner et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Khanal et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Klouček et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lambers et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Linaza et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; N\u0026auml;si et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Nevalainen et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Patacca et al., 2022; Pelletier et al., \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pratic\u0026ograve; et al., \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Radočaj et al., \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rodr\u0026iacute;guez-Veiga et al., \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rowan et al., \u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Segarra et al., \u003cspan citationid=\"CR165\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Storm et al., 2019; Tifafi et al., 2017; Tricht et al., \u003cspan citationid=\"CR192\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Venter et al., \u003cspan citationid=\"CR193\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Virkkala et al., \u003cspan citationid=\"CR194\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vreugdenhil et al., \u003cspan citationid=\"CR196\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Werff and Meer, \u003cspan citationid=\"CR191\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR200\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR202\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zittis et al., \u003cspan citationid=\"CR209\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. LiDAR-based biomass and carbon estimation\u003c/h2\u003e \u003cp\u003eLiDAR, ALS and TLS represent the highest-precision modality for above-ground biomass (AGB) and canopy structure estimation. Machine learning models applied to LiDAR-derived structural metrics consistently achieve \u003cem\u003eR\u0026sup2;\u003c/em\u003e greater than 0.85 for AGB prediction at stand and landscape scales (Amiri et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dorado-Roda et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kellner et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lambers et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nevalainen et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR202\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). UAV-LiDAR and photogrammetric platforms extend sub-metre structural assessment to operational plot-level monitoring (Duarte et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kellner et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Klouček et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; N\u0026auml;si et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Nevalainen et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). GEDI spaceborne LiDAR provides AGB estimation at 25 m footprint across tropical and temperate forests (Dorado-Roda et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Multi-source ALS fusion with Sentinel-2 and aerial photogrammetry substantially improves species-level classification and carbon mapping (Amiri et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Immitzer et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lambers et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR202\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Recent studies integrating UAV-LiDAR, multi-sensor data fusion, and national forest inventory data confirm high-precision AGB retrieval for temperate and subtropical forests (Gan et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tamiminia et al., \u003cspan citationid=\"CR179\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Urbazaev et al., \u003cspan citationid=\"CR187\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vafaei et al., \u003cspan citationid=\"CR189\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhai et al., \u003cspan citationid=\"CR204\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR205\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. SAR and optical satellite integration\u003c/h2\u003e \u003cp\u003eSentinel-1 SAR is applied extensively for soil moisture monitoring, vegetation backscatter analysis, and forest disturbance detection across European forest biomes (Alexakis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bauer-Marschallinger et al., 2018; Chatziantoniou et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tricht et al., \u003cspan citationid=\"CR192\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vreugdenhil et al., \u003cspan citationid=\"CR196\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Werff and Meer, \u003cspan citationid=\"CR191\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Synergistic use of Sentinel-1 and Sentinel-2 substantially outperforms single-sensor approaches for land-use/land-cover and forest species mapping, achieving 85\u0026ndash;95% overall accuracy for temperate European forest types (Chatziantoniou et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Grabska-Szwagrzyk et al., 2019; Pelletier et al., \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pratic\u0026ograve; et al., \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Radočaj et al., \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tricht et al., \u003cspan citationid=\"CR192\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Landsat time-series support decadal forest cover change detection (Chuvieco et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Venter et al., \u003cspan citationid=\"CR193\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Werff and Meer, \u003cspan citationid=\"CR191\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Hyperspectral sensors improve tree species discrimination and biotic stress detection relevant to permanence monitoring (Duarte et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Farmonov et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; N\u0026auml;si et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Carbon flux upscaling\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the carbon variable focus distribution of this study. Neural network-based upscaling of eddy-covariance measurements to continental carbon flux estimates is well-established at ICOS sites, with FLUXCOM achieving \u003cem\u003eR\u0026sup2;\u003c/em\u003e greater than 0.80 against global gross primary productivity benchmarks (Heiskanen et al., 2021; Jung et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Virkkala et al., \u003cspan citationid=\"CR194\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xia et al., \u003cspan citationid=\"CR199\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Machine learning integration of phenology and remote sensing data improves partitioning of net ecosystem production (NEP) across forest types (Jung et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xia et al., \u003cspan citationid=\"CR199\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Global burned area products and wildfire atlases provide disturbance forcing for carbon flux modelling (Andela et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chuvieco et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Soil carbon stock models combining machine learning with EU pedological databases support LULUCF accounting (Bispo et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schulte et al., \u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tifafi et al., 2017). Deep learning on multi-temporal Landsat and GEDI data enables simulation of long-term forest carbon stock trajectories (Reinmann et al., \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Thomas et al., \u003cspan citationid=\"CR182\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Valipour et al., \u003cspan citationid=\"CR190\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR207\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4. UAV applications and forest health\u003c/h2\u003e \u003cp\u003eUAV-borne photogrammetry, hyperspectral imaging, and LiDAR enable high-resolution forest health monitoring, bark beetle infestation mapping, and plot-level biomass estimation that directly informs management decisions (Duarte et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Klouček et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; N\u0026auml;si et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Nevalainen et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). ML classifiers applied to UAV hyperspectral imagery distinguish healthy from infested trees with \u0026gt;\u0026thinsp;90% accuracy (Duarte et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Klouček et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; N\u0026auml;si et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), providing early-warning capacity for disturbance events that threaten carbon permanence and which trigger LULUCF reporting obligations. UAV-LiDAR fusion studies confirm that individual-tree structural metrics from drone platforms achieve biomass accuracy competitive with conventional ALS at substantially lower cost-per-hectare (Gan et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; H\u0026auml;mmerle et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5. AI architectures for MRV: performance and transferability\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents AI/ML method frequency across all 200 included sources. The dominant AI approaches in P1 are ANN (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30, 57% of pillar), random forest (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11), support vector machines (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7), regression/statistical ML (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9), deep learning CNN (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5), and OBIA (n\u0026thinsp;=\u0026thinsp;2). Comparative studies demonstrate that random forest and deep learning produce comparable AGB estimation accuracy (within \u0026plusmn;\u0026thinsp;5\u0026ndash;10% RMSE), with random forest offering superior interpretability and computational efficiency for operational deployment (Avci et al., 2021; DeLancey et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Esteban et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Haya et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pelletier et al., \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tamiminia et al., \u003cspan citationid=\"CR179\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Model transferability across forest biomes and climate scenarios \u0026mdash; essential for EU-wide deployment \u0026mdash; is rarely tested systematically, representing a critical gap for regulatory reliability (Amiri et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pelletier et al., \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rodr\u0026iacute;guez-Veiga et al., \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Pillar 2 \u0026mdash; Digital Twins and Ecosystem Modelling: Results Addressing RQ2\u003c/h2\u003e \u003cp\u003eThe digital twin and ecosystem modelling pillar (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30, 15.0%) encompasses process-based land surface models, atmospheric inversions, and data-driven simulation frameworks for forest carbon assessment. The IPCC AR6 Synthesis Report (Calvin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) establishes the definitive climate context \u0026mdash; 1.5\u0026deg;C and 2\u0026deg;C scenario envelopes \u0026mdash; that forest carbon digital twins must reproduce and project to inform management strategy. GLEAM v3 satellite-based land evaporation and root-zone soil moisture (Martens et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) provides essential inputs for water-carbon coupling. ERA-Interim/Land (Balsamo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and ERA-5 (Albergel et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) supply boundary conditions for land surface simulations; CABLE (Haverd et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), ISBA (Albergel et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), FLEXPART (Pisso et al., \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and LPJ-GUESS (Lindeskog et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) represent deployed process-based components validated against European flux networks. The ISIMIP2b multi-model impact modelling protocol (Frieler et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) standardises climate impact projections applicable to forest carbon permanence assessment.\u003c/p\u003e \u003cp\u003eMore recent studies explore forestry-specific digital twin approaches integrating ML with multi-temporal Landsat data to estimate forest carbon stocks (Jiang et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), forest management simulation models for long-term carbon balance under climate scenarios (Lindeskog et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Thomas et al., \u003cspan citationid=\"CR182\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Valipour et al., \u003cspan citationid=\"CR190\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and object-based random forest modelling of AGB in heterogeneous environments (Silveira et al., \u003cspan citationid=\"CR167\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) adapted for European Union conditions (Pilli et al., \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) provides a deployable carbon accounting framework. Urbanisation effects on temperate forest carbon cycles (Reinmann et al., \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) extend the evidence base to forest management in peri-urban contexts. The digital twin concept as applied in precision cardiology (Acero et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) illustrates governance and validation challenges \u0026mdash; consent, liability, interpretability \u0026mdash; directly applicable to deploying such systems in consequential forest carbon governance contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Pillar 3 \u0026mdash; Carbon Markets and Finance: Results Addressing RQ3\u003c/h2\u003e \u003cp\u003eThe carbon markets and finance pillar (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22, 11.0%) addresses the integrity, transparency, and efficiency of voluntary and compliance forest carbon markets. Voluntary carbon markets face well-documented structural failures: the UN Emissions Gap Report (Programme UNEP, 2023) documents persistent gaps between corporate net-zero commitments and verified emissions reductions; ESG rating divergence analysis (Berg et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrates that inter-agency ratings are inconsistent, raising fundamental questions about algorithmic transparency in carbon crediting; and community-based forest monitoring for REDD\u0026thinsp;+\u0026thinsp;MRV (Murthy et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and carbon measurement overviews for REDD+ implementation (Bhattarai et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) document the technical and social prerequisites for credible market integration.\u003c/p\u003e \u003cp\u003eAI applications to carbon market integrity include satellite-based permanence monitoring and additionality verification; blockchain/DLT carbon credit provenance tracking (Kwilinski et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rowan et al., \u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); ML-assisted ESG disclosure verification (Alamillos and de Mariz, 2022; Cerciello et al., 2022; Clementino and Perkins, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); LCA-informed carbon accounting for bioenergy and forest products (Franz et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Leinonen et al., 2022; Nemitz et al., \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); and automated deforestation risk assessment for supply chains (Dom\u0026iacute;nguez and Luoma, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schilling-Vacaflor and Lenschow, 2021). The commodification of forest carbon and socially-embedded REDD+ practices (Benjaminsen and Kaarhus, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and uncertainty in forest reference levels (Mertz et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) highlight fundamental challenges for AI-based verification in voluntary markets. The risk of \"precision greenwashing\" \u0026mdash; technically sophisticated but opaque AI estimates satisfying formal requirements while misrepresenting ecological reality \u0026mdash; is a cross-cutting concern (Berg et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Clementino and Perkins, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Dom\u0026iacute;nguez and Luoma, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Honkomp and Schier, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Schilling-Vacaflor and Lenschow, 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Pillar 4 \u0026mdash; EU Policy and Governance: Results Addressing RQ4\u003c/h2\u003e \u003cp\u003ePillar 4 is the largest research domain (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;59, 29.5%), reflecting the centrality of EU regulatory frameworks as both drivers and recipients of AI forest carbon innovations. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e present AI method frequencies by pillar, while Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e maps eleven EU policy instruments to AI integrity implications, with supporting evidence.\u003c/p\u003e \u003cp\u003eKey P4 findings by instrument: The EU Forest Strategy 2030 demands joint optimisation of carbon sequestration, biodiversity, and socioeconomic functions \u0026mdash; a multi-criteria problem well-suited to AI-assisted multi-objective analysis (Kuuluvainen et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Law et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Roberts et al., \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sabatini et al., \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Seddon et al., \u003cspan citationid=\"CR164\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The LULUCF Regulation creates direct regulatory demand for AI-enhanced biomass and carbon stock estimation (Bispo et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Deng et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Forsell et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Law et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pilli et al., \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). EU ETS and CDR policy create market incentives for AI-backed forest carbon verification (Farghali et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gough et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Honegger et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kabeyi \u0026amp; Olanrewaju, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rickels et al., \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rosa et al., \u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sovacool et al., \u003cspan citationid=\"CR173\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sterman et al., \u003cspan citationid=\"CR175\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tutak et al., \u003cspan citationid=\"CR185\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). CAP Eco-schemes require scalable AI-verifiable MRV for soil and forest carbon (Bat\u0026aacute;ry et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Boix-Fayos \u0026amp; De Vente, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pe'er et al., 2016; Ray et al., \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ronchi et al., \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Schr\u0026ouml;der et al., 2017; Telo Da Gama, \u003cspan citationid=\"CR181\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). ICOS infrastructure provides the European eddy-covariance validation network against which AI upscaling must be benchmarked (Franz et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Heiskanen et al., 2021; Virkkala et al., \u003cspan citationid=\"CR194\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Forest carbon sequestration governance (Gren and Aklilu, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), subnational forest carbon governance (Ruseva, \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), forest regulations and stakeholder needs modelling (Zute et al., \u003cspan citationid=\"CR210\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and foreign direct investment and LULUCF emissions (Piabuo et al., \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) extend the P4 policy coherence analysis. Deep learning-based fire detection (Mambile et al., 2024; Seydi et al., \u003cspan citationid=\"CR166\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), life cycle assessment of biogenic carbon (Leinonen et al., 2022), and forestry offsets under carbon markets (Xu, \u003cspan citationid=\"CR201\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) further advance the policy implications frontier.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAI/ML method frequency by thematic pillar (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;200; multi-label extraction)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI/ML Method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArtificial Neural Network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegression / Statistical ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupport Vector Machine (SVM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeep Learning (CNN/Transformer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimulation / Digital Twin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGradient Boosting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlockchain / DLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Specified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMapping of EU policy instruments to AI-driven forest carbon integrity implications\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEU Policy Instrument\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKey Objective\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI/Digital Integrity Implication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKey Studies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEU Forest Strategy 2030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiodiversity/carbon co-benefits; afforestation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI-assisted co-benefit mapping; multi-criteria optimisation; disturbance-resilience monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChazdon et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Kuuluvainen et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Law et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Roberts et al. (\u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); Sabatini et al. (\u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Seddon et al. (\u003cspan citationid=\"CR164\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULUCF Regulation (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310 MtCO₂eq net sink target 2030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI-enhanced biomass/carbon stock estimation; national MRV compliance; cross-country comparability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBispo et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); Deng et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Forsell et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); Kern et al. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); Law et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Pilli et al. (\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEU Taxonomy Regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSustainable finance do-no-significant-harm screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI-driven ESG/carbon integrity verification; greenwashing detection; disclosure audit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlamillos and de Mariz (2022); Berg et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Cerciello et al. (2022); Clementino and Perkins (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEU ETS (Revision 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbon pricing; net-zero pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI-enabled additionality/permanence verification; leakage detection; fraud prevention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHonegger et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Honkomp and Schier (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Rickels et al. (\u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Sovacool et al. (\u003cspan citationid=\"CR173\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Stern et al. (2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNature Restoration Law (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEcosystem restoration targets; peatland rewetting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRemote sensing monitoring of restoration trajectory; AI carbon stock verification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNavarro and Pereira (\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); Sabatini et al. (\u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Seddon et al. (\u003cspan citationid=\"CR164\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Tanneberger et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen Deal / Fit-for-55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55% GHG reduction by 2030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI-integrated national carbon budgets; sectoral attribution; inventory reconciliation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAllen et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Chen et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Kulovesi and Oberth\u0026uuml;r (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Kyriakopoulos and Sebos (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Lamb et al. (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSRD / ESRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorporate double-materiality reporting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlockchain/DLT audit trails; AI disclosure verification; climate risk quantification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlamillos and de Mariz (2022); Cerciello et al. (2022); Clementino and Perkins (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Kwilinski et al. (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICOS Research Infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean flux measurement network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI upscaling eddy-covariance flux; gap-filling; continental carbon balance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFranz et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Heiskanen et al. (2021); Jung et al. (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Virkkala et al. (\u003cspan citationid=\"CR194\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEU RED III (Bioenergy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRenewable energy sustainability criteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI feedstock traceability; carbon permanence assurance; avoided deforestation verification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGough et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Leinonen et al. (2022); Meyer (\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); Sterman et al. (\u003cspan citationid=\"CR175\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAP Eco-schemes (2023\u0026ndash;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbon payment to farmers/foresters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI-driven soil/biomass MRV at farm scale; payment verification; additionality assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBat\u0026aacute;ry et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); Bispo et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); Ronchi et al. (\u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Schr\u0026ouml;der et al. (2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSDDD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMandatory supply chain due diligence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI deforestation detection; satellite forest alerts; supply chain traceability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDom\u0026iacute;nguez and Luoma (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Schilling-Vacaflor and Lenschow (2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Pillar 5 \u0026mdash; Ethics, Equity and Societal Dimensions: Results Addressing RQ5\u003c/h2\u003e \u003cp\u003eThe ethics and societal dimensions pillar (n\u0026thinsp;=\u0026thinsp;14, 7.0%) is the smallest but addresses the most consequential governance deficits. The artificial intelligence gambit in climate policy demonstrates the dangers of deploying these tools as a technological fix without adequate social legitimacy (Cowls et al., 2021). Research documents how algorithmic systems may concentrate benefits among well-resourced actors while marginalising smallholders and indigenous peoples (Bernes et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Dom\u0026iacute;nguez and Luoma, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Kuyper et al., 2017, Mor\u0026aacute;n et al., 2018). The legitimacy of non-state actors in climate governance provides a foundational framework for anticipating equity risks in artificial intelligence deployment (Kuyper et al., 2017, Peel and Lin, \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, community acceptance and the co-design of environmental interventions help mitigate these risks (Lupp et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Observations of landscape-scale land-use change effects on ecosystem services further inform this ethical framework (Fedele et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Statuto et al., \u003cspan citationid=\"CR174\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDynamics at the forest frontier in the Global South illustrate how international climate change policies promise development and equity while simultaneously reproducing inequalities through technology-mediated resource governance (Brockhaus et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Community forestry programme evidence underscores that local governance capacity independently predicts carbon and biodiversity outcomes (Luintel et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This means that artificial intelligence-based measurement, reporting, and verification cannot substitute for true institutional legitimacy. Digital health governance provides instructive precedents for this challenge. For example, artificial intelligence clinical decision support in precision cardiology offers highly developed accountability structures (Acero et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These structures effectively address algorithmic transparency, consent, liability, and equity across socioeconomic strata. Forest carbon artificial intelligence governance should systematically adapt these robust healthcare frameworks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Cross-Cutting Issues\u003c/h2\u003e \u003cp\u003eTwenty-two studies successfully bridge multiple thematic pillars (Andela et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Anees et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Beillouin et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, De Luca et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Heiskanen et al., 2021, Kwilinski et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Panagos et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Singh et al., \u003cspan citationid=\"CR169\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Zeng et al., \u003cspan citationid=\"CR203\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The ICOS integrated carbon observation infrastructure exemplifies this cross-pillar integration (Heiskanen et al., 2021). This initiative embeds a technical monitoring system within a broader governance architecture alongside strict ethical data-sharing guidelines. Multi-sensor above-ground biomass estimation studies collectively advance the cross-pillar technical and governance interface. These encompass studies integrating ICESat-2, Sentinel-1, and Sentinel-2 data (Nandy et al., \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Other research combines airborne LiDAR, synthetic aperture radar, and optical satellite data (Urbazaev et al., \u003cspan citationid=\"CR187\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Further examples involve multi-source remote sensing in northeast China (Wang et al., \u003cspan citationid=\"CR197\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and optical remote sensing merged with laser point cloud fusion (Zheng et al., \u003cspan citationid=\"CR208\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, researchers have integrated GF-1 images for forest carbon storage dynamics (Liu et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and conducted pantropical canopy height mapping using GEDI and TanDEM-X sensors (Qi et al., \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA scoping review of carbon pricing in forest sector models connects economic policies with governance frameworks (Honkomp and Schier, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The integrated global assessment of natural forest carbon potential also bridges these domains (Mo et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Deep learning models for forest fire prediction at Kilimanjaro link technical disturbance detection with long-term permanence policy (Mambile et al., 2024). Fire susceptibility mapping in India serves a similar integrative function (Singha et al., \u003cspan citationid=\"CR170\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, national forest inventory methodology connects technical field measurement with broad governance reporting (Zeng et al., \u003cspan citationid=\"CR203\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Global land-use and land-cover dataset comparisons also bridge these essential categories (Venter et al., \u003cspan citationid=\"CR193\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCritical appraisal results are summarised in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Fewer than 10% of studies across all pillars report reproducible code or data. Limitations disclosure remains below 30% in all pillars. Research objective clarity and results reporting are successfully met in over 70% of studies across all domains. Performance metrics are provided in most technical studies but appear in fewer than 40% of policy-focused studies. These specific deficits directly inform the future research agenda priorities outlined in Section \u003cspan refid=\"Sec44\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Citation Distribution and Publication Bias\u003c/h2\u003e \u003cp\u003eThe citation impact across the different thematic pillars was examined using violin plots with symmetrical logarithmic scaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The analysis reveals that the most technically established domains, particularly Pillar 2 (Digital Twins) and Pillar 1 (AI/Remote Sensing MRV), exhibit high mean and median citation counts, though significant variance exists within all pillars. To evaluate the risk of publication bias within the synthesized literature, scatter plots and Egger-style inverted funnel plots were generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) following established methodological frameworks (Egger et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1997\u003c/span\u003e, Sterne et al., \u003cspan citationid=\"CR176\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The distribution of log-transformed citation counts against study age demonstrates broad symmetry across most thematic pillars, indicating a generally low overall risk of publication bias in the evidence base. A minor right-sided asymmetry observed in Pillar 4 (EU Policy and Governance) likely reflects the targeted inclusion of highly cited institutional and policy reports alongside standard academic outputs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis scoping review systematically mapped the emerging landscape of AI-driven forest carbon integrity research\u0026mdash;spanning remote sensing advancements, digital twin ecosystem models, carbon market applications, EU policy alignment, and ethical frameworks\u0026mdash;to identify dominant methodologies and persistent technological gaps across the measurement, reporting, and verification (MRV) chain. By critically appraising the literature and confirming a generally low risk of publication bias across the evidence base (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e), this synthesis establishes a reliable foundation for identifying critical risks and proposing a targeted, policy-relevant research agenda for European forests.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Technical Readiness and Gaps (RQ1)\u003c/h2\u003e \u003cp\u003eAddressing the first research question, the 200-study corpus confirms substantial and accelerating technical capability. LiDAR-based biomass estimation with machine learning is sufficiently accurate for stand-level LULUCF compliance reporting, consistently achieving an \u003cem\u003eR\u0026sup2;\u003c/em\u003e greater than 0.85 (Amiri et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Dorado-Roda et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Kellner et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Rodr\u0026iacute;guez-Veiga et al., \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Sensor fusion combining Sentinel-1 and Sentinel-2 data supports near-real-time disturbance detection at a 10-to-20-metre resolution with 85 to 95 percent overall accuracy (Bauer-Marschallinger et al., 2018, Grabska-Szwagrzyk et al., 2019, Tricht et al., \u003cspan citationid=\"CR192\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, neural network flux upscaling is successfully validated against ICOS sites with acceptable uncertainty for continental-scale carbon balance computations (Heiskanen et al., 2021, Jung et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Virkkala et al., \u003cspan citationid=\"CR194\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Xia et al., \u003cspan citationid=\"CR199\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The expanding corpus of multi-sensor fusion studies confirms that above-ground biomass estimation accuracy continues to improve through data integration. Pantropical canopy height mapping from GEDI and TanDEM-X currently represents the ultimate technical frontier in this field (Qi et al., \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, reproducibility remains critically deficient. Fewer than 10% of studies provide code or reproducible workflows, as detailed in Fig.\u0026nbsp;16. This lack of transparency directly undermines the auditability required for European Union regulatory deployment. Performance metrics are inconsistently reported across the literature. Independent validation is also far from universal. Model transferability across European forest biomes and climate scenarios is rarely tested systematically (Amiri et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Pelletier et al., \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Rodr\u0026iacute;guez-Veiga et al., \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This major gap directly undermines the European Union-wide regulatory reliability required by LULUCF and the European Union Emissions Trading System (Deng et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Kulovesi and Oberth\u0026uuml;r, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Rickels et al., \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e4.2. The Digital Twin Gap (RQ2)\u003c/h2\u003e \u003cp\u003eAddressing the second research question, digital twin technology for forest carbon is progressing rapidly in earth system modelling (Haverd et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Jiang et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Lindeskog et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Martens et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Pilli et al., \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Pisso et al., \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nevertheless, this technology remains entirely disconnected from operational carbon governance. The necessary data infrastructures already exist through platforms like ICOS, ERA-5, Sentinel archives, and EDGAR. Despite this, no integrated operational European Forest Carbon Digital Twin has yet been demonstrated. The precision cardiology digital twin precedent illustrates that closing this gap requires regulatory sandbox frameworks, liability allocation, and clear interpretability standards alongside technical integration (Acero et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Process-based model limitations struggle to represent compound disturbance dynamics, such as simultaneous bark beetle outbreaks and drought observed in Central Europe between 2017 and 2019 (Dupuy et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Haverd et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Patacca et al., 2022). These limitations further complicate reliable carbon permanence projections. Closing this gap is a crucial priority for advancing the understanding of European forest ecosystem processes under accelerating climate change.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Carbon Market Integrity (RQ3)\u003c/h2\u003e \u003cp\u003eAddressing the third research question, artificial intelligence verification offers a potential solution to chronic voluntary carbon market integrity failures (Berg et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Programme UNEP, 2023, Stern et al., 2017). Automated permanence monitoring, additionality verification, and leakage detection could readily replace expensive and inconsistently applied field audits. However, proprietary algorithms embedded within commercially interested verification bodies create significant new opacity risks (Berg et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Clementino and Perkins, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Corporate sustainability reporting requirements, supply chain provisions, and the European Union Taxonomy collectively create immense demand for artificial intelligence-generated forest carbon data. This data must be simultaneously verified, transparent, and completely interoperable. Most current prototypes simply do not meet these stringent standards. The developing-country REDD+ context adds further complexity to this issue. Uncertainty in reference levels introduces baseline measurement challenges (Mertz et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Commodification critiques raise valid socioeconomic concerns (Benjaminsen and Kaarhus, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Community monitoring deficits also persist in many regions (Murthy et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Together, these factors highlight the immense challenge of integrating artificial intelligence-based monitoring with existing community forest governance in the Global South.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Ethics, Equity and the Governance Deficit (RQ5)\u003c/h2\u003e \u003cp\u003eAddressing the fifth research question, the most significant gap in the literature is the vast disconnect between advancing technical capability and legitimating governance frameworks. Studies focused on ethics consistently identify severe risks regarding algorithmic bias, data sovereignty violations, and the exclusion of community forest actors (Dom\u0026iacute;nguez and Luoma, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Kuyper et al., 2017, Mor\u0026aacute;n et al., 2018). Automated deforestation monitoring that generates carbon credits in community forests without free, prior, and informed consent represents a modern form of techno-colonialism. Existing European Union regulations inadequately address this specific risk (Brockhaus et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Dom\u0026iacute;nguez and Luoma, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Kuyper et al., 2017, Schilling-Vacaflor and Lenschow, 2021). Community forestry evidence underscores that strong local governance independently predicts positive carbon outcomes (Luintel et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This demonstrates that artificial intelligence measurement tools must complement rather than supplant genuine institutional legitimacy. The extreme under-representation of Pillar 5 studies relative to Pillar 1 technical studies is itself a major finding. The ethics and equity dimensions of artificial intelligence-driven forest carbon governance are systematically under-researched relative to their overwhelming societal importance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec42\" class=\"Section2\"\u003e \u003ch2\u003e4.5. EU Policy Alignment and Coherence (RQ4)\u003c/h2\u003e \u003cp\u003eAddressing the fourth research question, the European Union regulatory architecture creates the strongest global demand environment for responsible artificial intelligence forest carbon deployment (Deng et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Kulovesi and Oberth\u0026uuml;r, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Law et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Rickels et al., \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Roberts et al., \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Sovacool et al., \u003cspan citationid=\"CR173\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, measurement requirements remain highly fragmented across at least eleven legislative instruments. These instruments enforce vastly different verification standards, eligible actors, temporal horizons, and spatial resolutions. Aggressive Green Deal afforestation targets may inadvertently incentivise biodiversity-poor plantations. These monocultures often score well on artificial intelligence carbon quantity metrics while severely underperforming on ecological resilience, carbon permanence, and overall biodiversity (Chazdon et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Dupuy et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Kuuluvainen et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Law et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Patacca et al., 2022). Artificial intelligence systems must therefore be designed to assess carbon permanence quality and holistic ecosystem function rather than merely calculating raw carbon quantity. Forest carbon sequestration policy design further illuminates the cross-scale policy coherence challenge (Gren and Aklilu, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Subnational forest carbon governance introduces regional complexities to this framework (Ruseva, \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Finally, forest policy and management modelling specifically designed for carbon dioxide removal highlights the intricate regulatory coordination required for future success (vonHedemann et al., \u003cspan citationid=\"CR195\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec43\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Strengths and Limitations of This Review\u003c/h2\u003e \u003cp\u003eSeveral strengths define this review. It provides the first comprehensive PRISMA-ScR-compliant scoping map of artificial intelligence-driven forest carbon integrity across six thematic pillars. The methodology utilizes a fully reproducible computational screening pipeline. The review also introduces a structured policy mapping against eleven European Union legislative instruments, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Furthermore, an explicit quality appraisal directly informs and shapes the proposed research agenda.\u003c/p\u003e \u003cp\u003eCertain limitations must also be acknowledged. The use of abstract-based data extraction rather than full-text screening is appropriate for a scoping design. However, this means that artificial intelligence method classifications for some studies are based on keyword inference rather than confirmed methodology. The restriction to English-language literature potentially under-represents Central and Eastern European and other non-Anglophone forestry research traditions. The citation-ranked stratified selection inherently biases the corpus towards high-impact academic outputs. This approach potentially under-represents practitioner reports, grey literature, and emerging research from lower-income countries. Additionally, the quality criteria were derived exclusively from abstract content rather than a full methodological appraisal of the primary texts. Finally, the ten percent random re-charting inter-rater check is standard for scoping reviews but does not fully substitute for systematic duplicate screening. Future systematic reviews targeting specific sub-questions should apply full-text dual screening.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions and Future Research Agenda","content":"\u003cp\u003eThis scoping review of 200 primary sources demonstrates that artificial intelligence-driven forest carbon integrity systems have achieved substantial technical maturity in biomass estimation and disturbance detection. Solid foundations have also been established in digital ecosystem modelling. However, these systems face persistent and severe gaps in carbon market governance, European Union policy coherence, and ethical legitimacy. The European Union regulatory environment provides the strongest global signal for responsible deployment. Nevertheless, policy fragmentation across the Green Deal portfolio and the systematic under-development of equity-aware governance frameworks require urgent attention. Five distinct research priorities are proposed to address these challenges.\u003c/p\u003e \u003cp\u003ePriority 1 \u0026mdash; Reproducible, open-source AI for forest carbon MRV. Mandatory open code and data requirements must be integrated into European Union-funded forest carbon research. Regulatory provisions should enforce the use of auditable algorithms within European Union Emissions Trading System and LULUCF verification bodies. Furthermore, the adoption of standard artificial intelligence model cards for forest carbon applications is absolutely essential. These measures directly address the critical reproducibility gap identified during the quality appraisal.\u003c/p\u003e \u003cp\u003ePriority 2 \u0026mdash; Operational European Forest Carbon Digital Twin. A pan-European, publicly governed digital twin infrastructure must be established to integrate ICOS flux networks, ERA-5 reanalysis, Sentinel archives, and EDGAR atmospheric inversions. This infrastructure must include explicit governance provisions for community forest actors alongside mandatory uncertainty quantification. Such a system would function analogously to the European Centre for Medium-Range Weather Forecasts for numerical weather prediction.\u003c/p\u003e \u003cp\u003ePriority 3 \u0026mdash; AI Ethics Framework for Forest Carbon. A purpose-built ethical framework is required to address algorithmic transparency, data sovereignty, community consent, and distributional impact assessments. This framework should draw directly upon precision medicine artificial intelligence governance models (Acero et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and established co-design methodologies (Lupp et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It must be co-developed in close collaboration with forest-dependent communities, indigenous rights organisations, and European Union verification bodies.\u003c/p\u003e \u003cp\u003ePriority 4 \u0026mdash; Cross-Pillar Integration Research. There is an urgent need for studies explicitly integrating artificial intelligence measurement methods with carbon market governance. Research must also connect digital twins directly with policy compliance modelling. Furthermore, social ethics must be embedded organically into technical system design. These integrative efforts are essential to ensure technically sound, socially legitimate, and policy-coherent artificial intelligence forest carbon deployment.\u003c/p\u003e \u003cp\u003ePriority 5 \u0026mdash; Harmonised EU Forest Carbon AI Governance Framework. A regulatory sandbox should be established to enable the iterative testing of artificial intelligence verification tools against harmonised interoperability standards. These standards must span across LULUCF, the European Union Emissions Trading System, the Nature Restoration Law, Common Agricultural Policy Eco-schemes, and corporate sustainability reporting directives (CSRD). This sandbox must also include explicit regulatory provisions for model transferability testing across various European forest biomes and distinct climate scenarios.\u003c/p\u003e \u003cp\u003eForest carbon integrity in the artificial intelligence era will be determined not only by the accuracy of biomass algorithms but by the governance architecture, reproducibility standards, and equity frameworks within which they are embedded. The European Union is uniquely positioned to lead this global transition. This leadership will only succeed if technical and governance innovations advance in parallel across the entire forest ecosystem monitoring chain.\u003c/p\u003e"},{"header":"Abbreviation","content":"\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAcronym\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eDefinition\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eAGB\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAboveground Biomass\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eAI\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eArtificial Intelligence\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eALS\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAirborne Laser Scanning\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eANN\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eArtificial Neural Network\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eCABLE\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCommunity Atmosphere Biosphere Land Exchange (Model)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eCAP\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCommon Agricultural Policy\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eCBM-CFS3\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCarbon Budget Model of the Canadian Forest Sector\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eCDR\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCarbon Dioxide Removal\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eCNN\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eConvolutional Neural Network\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eCSDDD\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCorporate Sustainability Due Diligence Directive\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eCSRD\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCorporate Sustainability Reporting Directive\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eCX\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCross-cutting (Thematic Pillar)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eDEM\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eDigital Elevation Model\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eDLT\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eDistributed Ledger Technology\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eEDGAR\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eEmissions Database for Global Atmospheric Research\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eERA-5\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eECMWF Reanalysis v5\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eESG\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eEnvironmental, Social, and Governance\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eESRS\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eEuropean Sustainability Reporting Standards\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eETS\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eEmissions Trading System\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eEU\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eEuropean Union\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eGEDI\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eGlobal Ecosystem Dynamics Investigation\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eGHG\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eGreenhouse Gas\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eGLEAM\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eGlobal Land Evaporation Amsterdam Model\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eICOS\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eIntegrated Carbon Observation System\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eIPBES\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eIntergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eIPCC\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eIntergovernmental Panel on Climate Change\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eISIMIP\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eInter-Sectoral Impact Model Intercomparison Project\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eLiDAR\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLight Detection and Ranging\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eLPJ-GUESS\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLund-Potsdam-Jena General Ecosystem Simulator\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eLULUCF\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLand Use, Land-Use Change and Forestry\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eML\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMachine Learning\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eMMAT\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMixed Methods Appraisal Tool\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eMRV\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMeasurement, Reporting, and Verification\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eNEP\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eNet Ecosystem Production\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eOBIA\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eObject-Based Image Analysis\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003ePRISMA-ScR\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003ePreferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eREDD+\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReducing Emissions from Deforestation and Forest Degradation\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eSAR\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSynthetic Aperture Radar\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eSVM\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSupport Vector Machine\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eTLS\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTerrestrial Laser Scanning\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eUAV\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eUnmanned Aerial Vehicle\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo specific funding was received for this scoping review.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eThe author declares no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe complete dataset, all representative search strings (\u003cstrong\u003eSupplementary File S1\u003c/strong\u003e), PRISMA-ScR pipeline source code (\u003cstrong\u003eSupplementary File S2\u003c/strong\u003e), all 16 figures, and quality appraisal tables are available in the \u003cstrong\u003eSupplementary Workbook\u003c/strong\u003e submitted as \u003cstrong\u003eSupplementary Material\u003c/strong\u003e. The protocol is registered at: https://osf.io/pum9y/files.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG.O.F.:\u003c/strong\u003e Conceptualization, Methodology, Formal analysis, Data curation, Validation, Investigation, Visualization, Writing \u0026ndash; original draft, Resources, Supervision, Project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eNot applicable \u0026mdash; no primary data collection involving human participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcero JC, Margara F, Marciniak M et al (2020) The 'Digital Twin' to enable the vision of precision cardiology. Eur Heart J 41:4556\u0026ndash;4564. ttps://doi.org/10.1093/eurheartj/ehaa159\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdesipo A, Fadeyi O, Kuca K et al (2020) Smart and Climate-Smart Agricultural Trends as Core Aspects of Smart Village Functions. Sensors 20:5977. ttps://doi.org/10.3390/s20215977\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbergel C, Dutra E, Munier S et al (2018) ERA-5 and ERA-Interim driven ISBA land surface model simulations: Which one performs better? Hydrol Earth Syst Sci 22:3515\u0026ndash;3532. ttps://doi.org/10.5194/hess-22-3515-2018\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexakis D, Mexis F-D, Vozinaki A-E et al (2017) Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. Hydrol Approach Sens 17:1455. ttps://doi.org/10.3390/s17061455\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllen MR, Friedlingstein P, Girardin CAJ et al (2022) Net Zero: Science, Origins, and Implications. Annu Rev Environ Resour 47:849\u0026ndash;887. ttps://doi.org/10.1146/annurev-environ-112320-105050\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmiri A, Ottelin J, Sorvari J, Junnila S (2020) Cities as carbon sinks\u0026mdash;Classification of wooden buildings. Environ Res Lett 15:094076. ttps://doi.org/10.1088/1748-9326/aba134\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmiri N, Krzystek P, Heurich M, Skidmore A (2019) Classification of Tree Species as Well as Standing Dead Trees Using Triple Wavelength ALS in a Temperate Forest. Remote Sens 11:2614. ttps://doi.org/10.3390/rs11222614\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndela N, Morton DC, Giglio L et al (2019) The Global Fire Atlas of individual fire size, duration, speed and direction. Earth Syst Sci Data 11:529\u0026ndash;552. ttps://doi.org/10.5194/essd-11-529-2019\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnees SA, Mehmood K, Khan WR et al (2024) Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region. Ecol Inf 82:102732. ttps://doi.org/10.1016/j.ecoinf.2024.102732\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAra\u0026uacute;jo R, V\u0026aacute;zquez Calder\u0026oacute;n F, S\u0026aacute;nchez L\u0026oacute;pez J et al (2021) Current Status of the Algae Production Industry in Europe: An Emerging Sector of the Blue Bioeconomy. Front Mar Sci 7:626389. ttps://doi.org/10.3389/fmars.2020.626389\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArksey H, O\u0026rsquo;Malley L (2005) Scoping studies: Towards a methodological framework. Int J Soc Res Methodol 8:19\u0026ndash;32. ttps://doi.org/10.1080/1364557032000119616\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAromataris E, Munn Z (eds) (2020) JBI manual for evidence synthesis. JBI. ttps://doi.org/10.46658/JBIMES-20-01\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvci C, Budak M, Yağmur N, Bal\u0026ccedil;ik F (2023) Comparison between random forest and support vector machine algorithms for LULC classification. Int J Eng Geosci 8:1\u0026ndash;10. ttps://doi.org/10.26833/ijeg.987605\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalsamo G, Albergel C, Beljaars A et al (2015) ERA-Interim/Land: A global land surface reanalysis data set. Hydrol Earth Syst Sci 19:389\u0026ndash;407. ttps://doi.org/10.5194/hess-19-389-2015\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBat\u0026aacute;ry P, Dicks LV, Kleijn D, Sutherland WJ (2015) The role of agri-environment schemes in conservation and environmental management. Conserv Biol 29:1006\u0026ndash;1016. ttps://doi.org/10.1111/cobi.12536\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBauer-Marschallinger B, Freeman V, Cao S et al (2019) Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles. IEEE Trans Geosci Remote Sens 57:520\u0026ndash;539. ttps://doi.org/10.1109/TGRS.2018.2858004\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeillouin D, Schauberger B, Bastos A et al (2020) Impact of extreme weather conditions on European crop production in 2018. Philos Trans R Soc B 375:20190510. ttps://doi.org/10.1098/rstb.2019.0510\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeniston M, Farinotti D, Stoffel M et al (2018) The European mountain cryosphere: A review of its current state, trends, and future challenges. Cryosphere 12:759\u0026ndash;794. ttps://doi.org/10.5194/tc-12-759-2018\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenjaminsen G, Kaarhus R (2018) Commodification of forest carbon: REDD\u0026thinsp;+\u0026thinsp;and socially embedded forest practices in Zanzibar. Geoforum 93:48\u0026ndash;56. ttps://doi.org/10.1016/j.geoforum.2018.04.021\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerg F, K\u0026ouml;lbel JF, Rigobon R (2022) Aggregate Confusion: The Divergence of ESG Ratings. Rev Finance 26:1315\u0026ndash;1344. ttps://doi.org/10.1093/rof/rfac033\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBernes C, Macura B, Jonsson BG et al (2018) Manipulating ungulate herbivory in temperate and boreal forests: Effects on vegetation and invertebrates. A systematic review. Environ Evid 7:13. ttps://doi.org/10.1186/s13750-018-0125-3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhattarai T, Skutsch M, Midmore D, Shrestha HL (2016) Carbon Measurement: An Overview of Forest Carbon Estimation Methods and the Role of Geographical Information System and Remote Sensing Techniques for REDD+ Implementation. J Livelihood 13:69\u0026ndash;86. ttps://doi.org/10.3126/jfl.v13i1.15367\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBispo A, Andersen L, Angers DA et al (2017) Accounting for Carbon Stocks in Soils and Measuring GHGs Emission Fluxes from Soils: Do We Have the Necessary Standards? Front Environ Sci 5:41. ttps://doi.org/10.3389/fenvs.2017.00041\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoix-Fayos C, De Vente J (2023) Challenges and potential pathways towards sustainable agriculture within the European Green Deal. Agric Syst 207:103634. ttps://doi.org/10.1016/j.agsy.2023.103634\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowler DE, Bjorkman AD, Dornelas M et al (2020) Mapping human pressures on biodiversity across the planet uncovers anthropogenic threat complexes. People Nat 2:380\u0026ndash;394. ttps://doi.org/10.1002/pan3.10071\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrockhaus M, Di Gregorio M, Djoudi H et al (2021) The forest frontier in the Global South: Climate change policies and the promise of development and equity. Ambio 50:2238\u0026ndash;2255. ttps://doi.org/10.1007/s13280-021-01602-1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrundu G, Richardson DM (2016) Planted forests and invasive alien trees in Europe: A Code for managing existing and future plantings to mitigate the risk of negative impacts from invasions. NeoBiota 30:5\u0026ndash;47. ttps://doi.org/10.3897/neobiota.30.7015\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalvin K, Dasgupta D, Krinner G, IPCC (2023), 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland. ttps://doi.org/10.59327/IPCC/AR6-9789291691647\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao L, Pan J, Li R et al (2018) Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China. Remote Sens 10:532. ttps://doi.org/10.3390/rs10040532\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCerciello M, Busato F, Taddeo S (2023) The effect of sustainable business practices on profitability. Accounting for strategic disclosure. Corp Soc Responsib Environ Manag 30:802\u0026ndash;819. ttps://doi.org/10.1002/csr.2389\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatziantoniou A, Psomiadis E, Petropoulos G (2017) Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning. Remote Sens 9:1259. ttps://doi.org/10.3390/rs9121259\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChazdon RL, Lindenmayer D, Guariguata MR et al (2020) Fostering natural forest regeneration on former agricultural land through economic and policy interventions. Environ Res Lett 15:043002. ttps://doi.org/10.1088/1748-9326/ab79e6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Huang L, Hua J et al (2023) Green construction for low-carbon cities: A review. Environ Chem Lett 21:1627\u0026ndash;1657. ttps://doi.org/10.1007/s10311-022-01544-4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng F, Ou G, Wang M, Liu C (2024) Remote Sensing Estimation of Forest Carbon Stock Based on Machine Learning Algorithms. Forests 15:681. ttps://doi.org/10.3390/f15040681\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChuvieco E, Lizundia-Loiola J, Pettinari ML et al (2018) Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth Syst Sci Data 10:2015\u0026ndash;2031. ttps://doi.org/10.5194/essd-10-2015-2018\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClementino E, Perkins R (2021) How Do Companies Respond to Environmental, Social and Governance (ESG) ratings? Evidence from Italy. J Bus Ethics 171:379\u0026ndash;397. ttps://doi.org/10.1007/s10551-020-04441-4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorral-Acero J, Margara F, Marciniak M et al (2020) The \u0026lsquo;Digital Twin\u0026rsquo; to enable the vision of precision cardiology. Eur Heart J 41:4556\u0026ndash;4564. ttps://doi.org/10.1093/eurheartj/ehaa159\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCowls J, Tsamados A, Taddeo M, Floridi L (2023) The AI gambit: Leveraging artificial intelligence to combat climate change\u0026mdash;opportunities, challenges, and recommendations. AI Soc 38:283\u0026ndash;307. ttps://doi.org/10.1007/s00146-021-01294-x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDave R, Saint-Laurent C, Murray L, Gland et al (2019) Switzerland. ttps://doi.org/10.2305/IUCN.CH.2019.06.en\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Luca G, Silva JM, Cerasoli S et al (2019) Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox. Remote Sens 11:1238. ttps://doi.org/10.3390/rs11101238\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDebrah C, Darko A, Chan APC (2023) A bibliometric-qualitative literature review of green finance gap and future research directions. Clim Dev 15:432\u0026ndash;455. ttps://doi.org/10.1080/17565529.2022.2095331\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeLancey ER, Simms JF, Mahdianpari M et al (2019) Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada. Remote Sens 12:2. ttps://doi.org/10.3390/rs12010002\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng Z, Ciais P, Tzompa-Sosa ZA et al (2022) Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions. Earth Syst Sci Data 14:1639\u0026ndash;1675. ttps://doi.org/10.5194/essd-14-1639-2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDom\u0026iacute;nguez L, Luoma C (2020) Decolonising Conservation Policy: How Colonial Land and Conservation Ideologies Persist and Perpetuate Indigenous Injustices at the Expense of the Environment. Land 9:65. ttps://doi.org/10.3390/land9030065\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDorado-Roda I, Pascual A, Godinho S et al (2021) Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests. Remote Sens 13:2279. ttps://doi.org/10.3390/rs13122279\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuarte A, Borralho N, Cabral P, Caetano M (2022) Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review. Forests 13:911. ttps://doi.org/10.3390/f13060911\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDube LC, Chatterjee S (2022) Assessing livelihood impact of forest carbon projects using sustainable livelihood framework. Mitig Adapt Strateg Glob Change 27:49. ttps://doi.org/10.1007/s11027-022-10022-9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDupuy J, Fargeon H, Martin-StPaul N et al (2020) Climate change impact on future wildfire danger and activity in southern Europe: A review. Ann Sci 77:35. ttps://doi.org/10.1007/s13595-020-00933-5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgger M, Smith GD, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315:629\u0026ndash;634. ttps://doi.org/10.1136/bmj.315.7109.629\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEkardt F, Jacobs B, Stubenrauch J, Garske B (2020) Peatland Governance: The Problem of Depicting in Sustainability Governance, Regulatory Law, and Economic Instruments. Land 9:83. ttps://doi.org/10.3390/land9030083\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsteban J, McRoberts R, Fern\u0026aacute;ndez-Landa A et al (2019) Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data. ttps://doi.org/10.3390/rs11161944. Remote Sens 11:1944\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarghali M, Osman AI, Mohamed IMA et al (2023) Strategies to save energy in the context of the energy crisis: A review. Environ Chem Lett 21:2003\u0026ndash;2039. ttps://doi.org/10.1007/s10311-023-01591-5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarmonov N, Amankulova K, Szatm\u0026aacute;ri J et al (2023) Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms. IEEE J Sel Top Appl Earth Obs Remote Sens 16:1576\u0026ndash;1588. ttps://doi.org/10.1109/JSTARS.2023.3239756\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFawzy S, Osman AI, Doran J, Rooney DW (2020) Strategies for mitigation of climate change: A review. Environ Chem Lett 18:2069\u0026ndash;2094. ttps://doi.org/10.1007/s10311-020-01059-w\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFedele G, Locatelli B, Djoudi H, Colloff MJ (2018) Reducing risks by transforming landscapes: Cross-scale effects of land-use changes on ecosystem services. PLoS ONE 13:e0195895. ttps://doi.org/10.1371/journal.pone.0195895\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFelipe-Lucia MR, Mart\u0026iacute;n-L\u0026oacute;pez B, Lavorel S et al (2015) Ecosystem Services Flows: Why Stakeholders\u0026rsquo; Power Relationships Matter. PLoS ONE 10:e0132232. ttps://doi.org/10.1371/journal.pone.0132232\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForsell N, Turkovska O, Gusti M et al (2016) Assessing the INDCs\u0026rsquo; land use, land use change, and forest emission projections. Carbon Balance Manag 11:26. ttps://doi.org/10.1186/s13021-016-0068-3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranz D, Acosta M, Altimir N et al (2018) Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe\u0026rsquo;s terrestrial ecosystems: A review. Int Agrophys 32:439\u0026ndash;455. ttps://doi.org/10.1515/intag-2017-0039\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrieler K, Lange S, Piontek F et al (2017) Assessing the impacts of 1.5\u0026deg;C global warming \u0026ndash; simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geosci Model Dev 10:4321\u0026ndash;4345. ttps://doi.org/10.5194/gmd-10-4321-2017\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuzzi S, Baltensperger U, Carslaw K et al (2015) Particulate matter, air quality and climate: Lessons learned and future needs. Atmos Chem Phys 15:8217\u0026ndash;8299. ttps://doi.org/10.5194/acp-15-8217-2015\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGan Y, Wang Q, Song G (2024) Non-Destructive Estimation of Deciduous Forest Metrics: Comparisons between UAV-LiDAR, UAV-DAP, and Terrestrial LiDAR Leaf-Off Point Clouds Using Two QSMs. Remote Sens 16:697. ttps://doi.org/10.3390/rs16040697\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGough C, Garcia-Freites S, Jones C et al (2018) Challenges to the use of BECCS as a keystone technology in pursuit of 1.5\u0026deg;C. Glob Sustain 1:e5. ttps://doi.org/10.1017/sus.2018.3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrabska E, Hostert P, Pflugmacher D, Ostapowicz K (2019) Forest Stand Species Mapping Using the Sentinel-2 Time Series. Remote Sens 11:1197. ttps://doi.org/10.3390/rs11101197\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGregson N, Crang M, Fuller S, Holmes H (2015) Interrogating the circular economy: The moral economy of resource recovery in the EU. Econ Soc 44:218\u0026ndash;243. ttps://doi.org/10.1080/03085147.2015.1013353\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGren I-M, Aklilu AZ (2016) Policy design for forest carbon sequestration: A review of the literature. Policy Econ 70:128\u0026ndash;136. ttps://doi.org/10.1016/j.forpol.2016.06.008\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH\u0026auml;mmerle M, Lukač N, Chen K-C et al (2017) Simulating Various Terrestrial and UAV LiDAR Scanning Configurations for Understory Forest Structure Modelling. ISPRS Ann Photogramm Remote Sens Spat Inf Sci IV-. 2/W4:59\u0026ndash;65. ttps://doi.org/10.5194/isprs-annals-IV-2-W4-59-2017\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaverd V, Smith B, Nieradzik L et al (2018) A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci Model Dev 11:2995\u0026ndash;3026. ttps://doi.org/10.5194/gmd-11-2995-2018\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaya BK, Evans S, Brown L et al (2023) Comprehensive review of carbon quantification by improved forest management offset protocols. Front Glob Change 6:958879. ttps://doi.org/10.3389/ffgc.2023.958879\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe X, Lei X, Zeng W et al (2022) Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China. Sustainability 14:5580. ttps://doi.org/10.3390/su14095580\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHein L, Bagstad K, Edens B et al (2016) Defining Ecosystem Assets for Natural Capital Accounting. PLoS ONE 11:e0164460. ttps://doi.org/10.1371/journal.pone.0164460\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeiskanen J, Br\u0026uuml;mmer C, Buchmann N et al (2022) The Integrated Carbon Observation System in Europe. Bull Am Meteorol Soc 103:E855\u0026ndash;E872. ttps://doi.org/10.1175/BAMS-D-19-0364.1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerold M, Carter S, Avitabile V et al (2019) The Role and Need for Space-Based Forest Biomass-Related Measurements in Environmental Management and Policy. Surv Geophys 40:757\u0026ndash;778. ttps://doi.org/10.1007/s10712-019-09510-6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHonegger M, Poralla M, Michaelowa A, Ahonen H-M (2021) Who Is Paying for Carbon Dioxide Removal? Designing Policy Instruments for Mobilizing Negative Emissions Technologies. Front Clim 3:672996. ttps://doi.org/10.3389/fclim.2021.672996\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong QN, F\u0026agrave;bregues S, Bartlett G et al (2018) The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Educ Inf 34:285\u0026ndash;291. ttps://doi.org/10.3233/EFI-180221\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHonkomp T, Schier F (2024) Scoping review of carbon pricing systems in forest sector models. Environ Res Lett 19:013001. ttps://doi.org/10.1088/1748-9326/ad101d\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH\u0026ouml;rtnagl L, Barthel M, Buchmann N et al (2018) Greenhouse gas fluxes over managed grasslands in Central Europe. Glob Change Biol 24:1843\u0026ndash;1872. ttps://doi.org/10.1111/gcb.14079\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImmitzer M, Neuwirth M, B\u0026ouml;ck S et al (2019) Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data. Remote Sens 11:2599. ttps://doi.org/10.3390/rs11222599\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPBES (2019) Summary for policymakers of the global assessment report on biodiversity and ecosystem services. Zenodo. ttps://doi.org/10.5281/ZENODO.3553579\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanssens-Maenhout G, Crippa M, Guizzardi D et al (2019) EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970\u0026ndash;2012. Earth Syst Sci Data 11:959\u0026ndash;1002. ttps://doi.org/10.5194/essd-11-959-2019\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang X, Jiang M, Gou Y et al (2022) Forestry Digital Twin With Machine Learning in Landsat 7 Data. Front Plant Sci 13:916900. ttps://doi.org/10.3389/fpls.2022.916900\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung M, Schwalm C, Migliavacca M et al (2020) Scaling carbon fluxes from eddy covariance sites to globe: Synthesis and evaluation of the FLUXCOM approach. Biogeosciences 17:1343\u0026ndash;1365. ttps://doi.org/10.5194/bg-17-1343-2020\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKabeyi MJB, Olanrewaju OA (2022) Sustainable Energy Transition for Renewable and Low Carbon Grid Electricity Generation and Supply. Front Energy Res 9:743114. ttps://doi.org/10.3389/fenrg.2021.743114\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKardung M, Cingiz K, Costenoble O et al (2021) Development of the Circular Bioeconomy: Drivers and Indicators. Sustainability 13:413. ttps://doi.org/10.3390/su13010413\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKellner JR, Armston J, Birrer M et al (2019) New Opportunities for Forest Remote Sensing Through Ultra-High-Density Drone Lidar. Surv Geophys 40:959\u0026ndash;977. ttps://doi.org/10.1007/s10712-019-09529-9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKern J, Tammeorg P, Shanskiy M et al (2017) Synergistic Use of Peat and Charred Material in Growing Media \u0026ndash; An Option to Reduce the Pressure on Peatlands? J Environ Eng Landsc Manag 25:160\u0026ndash;174. ttps://doi.org/10.3846/16486897.2017.1284665\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanal S, Kc K, Fulton JP, Shearer S, Ozkan E (2020) Remote Sensing in Agriculture\u0026mdash;Accomplishments, Limitations, and Opportunities. Remote Sens 12:3783. ttps://doi.org/10.3390/rs12223783\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlimont Z, Kupiainen K, Heyes C et al (2017) Global anthropogenic emissions of particulate matter including black carbon. Atmos Chem Phys 17:8681\u0026ndash;8723. ttps://doi.org/10.5194/acp-17-8681-2017\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlouček T, Kom\u0026aacute;rek J, Surov\u0026yacute; P et al (2019) The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation. Remote Sens 11:1561. ttps://doi.org/10.3390/rs11131561\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrotkov NA, McLinden CA, Li C et al (2016) Aura OMI observations of regional SO2 and NO2 pollution changes from 2005 to 2015. Atmos Chem Phys 16:4605\u0026ndash;4629. ttps://doi.org/10.5194/acp-16-4605-2016\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulovesi K, Oberth\u0026uuml;r S (2020) Assessing the EU\u0026rsquo;s 2030 Climate and Energy Policy Framework: Incremental change toward radical transformation? Rev Eur Comp Int Environ Law 29:151\u0026ndash;166. ttps://doi.org/10.1111/reel.12358\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuuluvainen T, Angelstam P, Frelich L et al (2021) Natural Disturbance-Based Forest Management: Moving Beyond Retention and Continuous-Cover Forestry. Front Glob Change 4:629020. ttps://doi.org/10.3389/ffgc.2021.629020\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuyper J, Schroeder H, Linn\u0026eacute;r B-O (2018) The Evolution of the UNFCCC. Annu Rev Environ Resour 43:343\u0026ndash;368. ttps://doi.org/10.1146/annurev-environ-102017-030119\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuyper JW, Linn\u0026eacute;r B, Schroeder H (2018) Non-state actors in hybrid global climate governance: Justice, legitimacy, and effectiveness in a post‐Paris era. WIREs Clim Change 9:e497. ttps://doi.org/10.1002/wcc.497\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwilinski A, Lyulyov O, Pimonenko T (2023) Unlocking Sustainable Value through Digital Transformation: An Examination of ESG Performance. Information 14:444. ttps://doi.org/10.3390/info14080444\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKyriakopoulos GL, Sebos I (2023) Enhancing Climate Neutrality and Resilience through Coordinated Climate Action: Review of the Synergies between Mitigation and Adaptation Actions. Climate 11:105. ttps://doi.org/10.3390/cli11050105\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamb WF, Wiedmann T, Pongratz J et al (2021) A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018. Environ Res Lett 16:073005. ttps://doi.org/10.1088/1748-9326/abee4e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLambers K, Verschoof-van Der Vaart W, Bourgeois Q (2019) Integrating Remote Sensing, Machine Learning, and Citizen Science in Dutch Archaeological Prospection. Remote Sens 11:794. ttps://doi.org/10.3390/rs11070794\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaw BE, Hudiburg TW, Berner LT et al (2018) Land use strategies to mitigate climate change in carbon dense temperate forests. Proc Natl Acad Sci USA 115:3663\u0026ndash;3668. ttps://doi.org/10.1073/pnas.1720064115\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeinonen I (2022) A general framework for including biogenic carbon emissions and removals in the life cycle assessments for forestry products. Int J Life Cycle Assess 27:1038\u0026ndash;1043. ttps://doi.org/10.1007/s11367-022-02086-1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevac D, Colquhoun H, O\u0026rsquo;Brien KK (2010) Scoping studies: Advancing the methodology. Implement Sci 5:69. ttps://doi.org/10.1186/1748-5908-5-69\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Li M, Li C, Liu Z (2020) Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. Sci Rep 10:9952. ttps://doi.org/10.1038/s41598-020-67024-3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Zhao M, Motesharrei S et al (2015) Local cooling and warming effects of forests based on satellite observations. Nat Commun 6:6603. ttps://doi.org/10.1038/ncomms7603\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLinaza MT, Posada J, Bund J et al (2021) Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture. Agronomy 11:1227. ttps://doi.org/10.3390/agronomy11061227\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLindeskog M, Smith B, Lagergren F et al (2021) Accounting for forest management in the estimation of forest carbon balance using the dynamic vegetation model LPJ-GUESS (v4.0, r9710): Implementation and evaluation of simulations for Europe. Geosci Model Dev 14:6071\u0026ndash;6112. ttps://doi.org/10.5194/gmd-14-6071-2021\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu C, Huang H, Zhang Z et al (2024) Canopy Height Integration for Precise Forest Aboveground Biomass Estimation in Natural Secondary Forests of Northeast China Using Gaofen-7 Stereo Satellite Data. Remote Sens 17:47. ttps://doi.org/10.3390/rs17010047\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Yang B, Li M, Xu D (2024) Assessing Forest-Change-Induced Carbon Storage Dynamics by Integrating GF-1 Image and Localized Allometric Growth Equations in Jiangning District, Nanjing, Eastern China (2017\u0026ndash;2020). Forests 15:506. ttps://doi.org/10.3390/f15030506\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu S, Brandt M, Nord-Larsen T et al (2023) The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe. Sci Adv 9:eadh4097. ttps://doi.org/10.1126/sciadv.adh4097\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Gong W, Hu X, Gong J (2018) Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data. Remote Sens 10:946. ttps://doi.org/10.3390/rs10060946\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuintel H, Bluffstone RA, Scheller RM (2018) The effects of the Nepal community forestry program on biodiversity conservation and carbon storage. PLoS ONE 13:e0199526. ttps://doi.org/10.1371/journal.pone.0199526\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLupp G, Zingraff-Hamed A, Huang JJ et al (2020) Living Labs\u0026mdash;A Concept for Co-Designing Nature-Based Solutions. Sustainability 13:188. ttps://doi.org/10.3390/su13010188\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacdonald SE, Landh\u0026auml;usser SM, Skousen J et al (2015) Forest restoration following surface mining disturbance: Challenges and solutions. New For 46:703\u0026ndash;732. ttps://doi.org/10.1007/s11056-015-9506-4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaes MJA et al (2022) Monitoring exposure to climate-related hazards: Indicator methodology and key results. OECD Environment Working Papers No 201. ttps://doi.org/10.1787/da074cb6-en\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMambile C, Kaijage S, Leo J (2025) Deep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, Tanzania: Integrating satellite images, weather data and human activities data. Nat Hazards Res 5:335\u0026ndash;347. ttps://doi.org/10.1016/j.nhres.2024.12.001\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartens B, Miralles DG, Lievens H et al (2017) GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci Model Dev 10:1903\u0026ndash;1925. ttps://doi.org/10.5194/gmd-10-1903-2017\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng J, Lu F, Cheng B (2023) China\u0026rsquo;s Climate Change Policy Attention and Forestry Carbon Sequestration Growth. Forests 14:2273. ttps://doi.org/10.3390/f14112273\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMertz O, Grogan K, Pflugmacher D et al (2018) Uncertainty in establishing forest reference levels and predicting future forest-based carbon stocks for REDD+. J Land Use Sci 13:1\u0026ndash;15. ttps://doi.org/10.1080/1747423X.2017.1410242\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeyer R (2017) Bioeconomy Strategies: Contexts, Visions, Guiding Implementation Principles and Resulting Debates. Sustainability 9:1031. ttps://doi.org/10.3390/su9061031\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeyer S, Genesio L, Vogel I et al (2017) BIOCHAR STANDARDIZATION AND LEGISLATION HARMONIZATION. J Environ Eng Landsc Manag 25:175\u0026ndash;191. ttps://doi.org/10.3846/16486897.2016.1254640\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiyazaki K, Eskes H, Sudo K et al (2017) Decadal changes in global surface NOx emissions from multi-constituent satellite data assimilation. Atmos Chem Phys 17:807\u0026ndash;837. ttps://doi.org/10.5194/acp-17-807-2017\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMo L, Zohner CM, Reich PB et al (2023) Integrated global assessment of the natural forest carbon potential. Nature 624:92\u0026ndash;101. ttps://doi.org/10.1038/s41586-023-06723-z\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoran EF, Lopez MC, Moore N et al (2018) Sustainable hydropower in the 21st century. Proc Natl Acad Sci USA 115:11891\u0026ndash;11898. ttps://doi.org/10.1073/pnas.1809426115\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunn Z, Peters MDJ, Stern C et al (2018) Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol 18:143. ttps://doi.org/10.1186/s12874-018-0611-x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurthy MSR, Gilani H, Karky BS et al (2017) Synergizing community-based forest monitoring with remote sensing: A path to an effective REDD\u0026thinsp;+\u0026thinsp;MRV system. Carbon Balance Manag 12:19. ttps://doi.org/10.1186/s13021-017-0087-8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNandy S, Srinet R, Padalia H (2021) Mapping Forest Height and Aboveground Biomass by Integrating ICESat-2, Sentinel‐1 and Sentinel‐2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India. Geophys Res Lett 48:e2021GL093799. ttps://doi.org/10.1029/2021GL093799\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN\u0026auml;si R, Honkavaara E, Lyytik\u0026auml;inen-Saarenmaa P et al (2015) Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level. Remote Sens 7:15467\u0026ndash;15493. ttps://doi.org/10.3390/rs71115467\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaumann G, Alfieri L, Wyser K et al (2018) Global Changes in Drought Conditions Under Different Levels of Warming. Geophys Res Lett 45:3285\u0026ndash;3296. ttps://doi.org/10.1002/2017GL076521\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNavarro LM, Pereira HM (2015) Rewilding Abandoned Landscapes in Europe. In: Pereira HM, Navarro LM (eds) Rewilding European Landscapes. Springer, pp 3\u0026ndash;23. ttps://doi.org/10.1007/978-3-319-12039-3_1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNemitz E, Mammarella I, Ibrom A et al (2018) Standardisation of eddy-covariance flux measurements of methane and nitrous oxide. Int Agrophys 32:517\u0026ndash;549. ttps://doi.org/10.1515/intag-2017-0042\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeuenschwander AL, Magruder LA (2019) Canopy and Terrain Height Retrievals with ICESat-2: A First Look. Remote Sens 11:1721. ttps://doi.org/10.3390/rs11141721\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNevalainen O, Honkavaara E, Tuominen S et al (2017) Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. Remote Sens 9:185. ttps://doi.org/10.3390/rs9030185\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNisbet EG, Manning MR, Dlugokencky EJ et al (2019) Very Strong Atmospheric Methane Growth in the 4 Years 2014\u0026ndash;2017: Implications for the Paris Agreement. Glob Biogeochem Cycles 33:318\u0026ndash;342. ttps://doi.org/10.1029/2018GB006009\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanagos P, Standardi G, Borrelli P et al (2018) Cost of agricultural productivity loss due to soil erosion in the European Union: From direct cost evaluation approaches to the use of macroeconomic models. Land Degrad Dev 29:471\u0026ndash;484. ttps://doi.org/10.1002/ldr.2879\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatacca M, Lindner M, Lucas-Borja ME et al (2023) Significant increase in natural disturbance impacts on European forests since 1950. Glob Change Biol 29:1359\u0026ndash;1376. ttps://doi.org/10.1111/gcb.16531\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeel J, Lin J (2019) Transnational Climate Litigation: The Contribution of the Global South. Am J Int Law 113:679\u0026ndash;726. ttps://doi.org/10.1017/ajil.2019.48\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePe\u0026rsquo;er G, Zinngrebe Y, Hauck J et al (2017) Adding Some Green to the Greening: Improving the EU\u0026rsquo;s Ecological Focus Areas for Biodiversity and Farmers. Conserv Lett 10:517\u0026ndash;530. ttps://doi.org/10.1111/conl.12333\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePelletier C, Valero S, Inglada J et al (2017) Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series. Remote Sens 9:173. ttps://doi.org/10.3390/rs9020173\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeters MDJ, Marnie C, Tricco AC et al (2020) Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth 18:2119\u0026ndash;2126. ttps://doi.org/10.11124/JBIES-20-00167\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetzold A, Thouret V, Gerbig C et al (2015) Global-scale atmosphere monitoring by in-service aircraft \u0026ndash; current achievements and future prospects of the European Research Infrastructure IAGOS. Tellus B 67:28452. ttps://doi.org/10.3402/tellusb.v67.28452\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiabuo SM, Minang PA, Tieguhong CJ et al (2021) Illegal logging, governance effectiveness and carbon dioxide emission in the timber-producing countries of Congo Basin and Asia. Environ Dev Sustain 23:14176\u0026ndash;14196. ttps://doi.org/10.1007/s10668-021-01257-8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiabuo SM, Puatwoe JT, Eckebil PPT et al (2023) Foreign direct investment and carbon emissions from land use, land-use change, and forestry (LULUCF): Empirical evidence from tropical forest countries. Environ Dev Sustain 26:15639\u0026ndash;15665. ttps://doi.org/10.1007/s10668-023-03267-0\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePilli R, Kull SJ, Blujdea VNB, Grassi G (2018) The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3): Customization of the Archive Index Database for European Union countries. Ann Sci 75:71. ttps://doi.org/10.1007/s13595-018-0743-5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePisso I, Sollum E, Grythe H et al (2019) The Lagrangian particle dispersion model FLEXPART version 10.4. Geosci Model Dev 12:4955\u0026ndash;4997. ttps://doi.org/10.5194/gmd-12-4955-2019\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePopescu CRG, Popescu GN (2019) An Exploratory Study Based on a Questionnaire Concerning Green and Sustainable Finance, Corporate Social Responsibility, and Performance: Evidence from the Romanian Business Environment. J Risk Financ Manag 12:162. ttps://doi.org/10.3390/jrfm12040162\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePratic\u0026ograve; S, Solano F, Di Fazio S, Modica G (2021) Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation. Remote Sens 13:586. ttps://doi.org/10.3390/rs13040586\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrommer J, Walker TWN, Wanek W et al (2020) Increased microbial growth, biomass, and turnover drive soil organic carbon accumulation at higher plant diversity. Glob Change Biol 26:669\u0026ndash;681. ttps://doi.org/10.1111/gcb.14777\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi W, Armston J, Choi C et al (2025) Mapping large-scale pantropical forest canopy height by integrating GEDI lidar and TanDEM-X InSAR data. Remote Sens Environ 318:114534. ttps://doi.org/10.1016/j.rse.2024.114534\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadočaj D, Jurišić M, Gašparović M (2022) The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture. Remote Sens 14:778. ttps://doi.org/10.3390/rs14030778\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRay DK, West PC, Clark M et al (2019) Climate change has likely already affected global food production. PLoS ONE 14:e0217148. ttps://doi.org/10.1371/journal.pone.0217148\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRedondo Alamillos R, De Mariz F (2022) How Can European Regulation on ESG Impact Business Globally? J Risk Financ Manag 15:291. ttps://doi.org/10.3390/jrfm15070291\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReinmann AB, Smith IA, Thompson JR, Hutyra LR (2020) Urbanization and fragmentation mediate temperate forest carbon cycle response to climate. Environ Res Lett 15:114036. ttps://doi.org/10.1088/1748-9326/abbf16\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRickels W, Proel\u0026szlig; A, Geden O et al (2021) Integrating Carbon Dioxide Removal Into European Emissions Trading. Front Clim 3:690023. ttps://doi.org/10.3389/fclim.2021.690023\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts CM, O\u0026rsquo;Leary BC, McCauley DJ et al (2017) Marine reserves can mitigate and promote adaptation to climate change. Proc Natl Acad Sci USA 114:6167\u0026ndash;6175. ttps://doi.org/10.1073/pnas.1701262114\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez-Veiga P, Quegan S, Carreiras J et al (2019) Forest biomass retrieval approaches from earth observation in different biomes. Int J Appl Earth Obs Geoinf 77:53\u0026ndash;68. ttps://doi.org/10.1016/j.jag.2018.12.008\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRonchi S, Salata S, Arcidiacono A et al (2019) Policy instruments for soil protection among the EU member states: A comparative analysis. Land Use Policy 82:763\u0026ndash;780. ttps://doi.org/10.1016/j.landusepol.2019.01.017\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosa L, Sanchez DL, Mazzotti M (2021) Assessment of carbon dioxide removal potential via BECCS in a carbon-neutral Europe. Energy Environ Sci 14:3086\u0026ndash;3097. ttps://doi.org/10.1039/D1EE00642H\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRowan NJ, Murray N, Qiao Y et al (2022) Digital transformation of peatland eco-innovations (\u0026lsquo;Paludiculture\u0026rsquo;): Enabling a paradigm shift towards the real-time sustainable production of \u0026lsquo;green-friendly\u0026rsquo; products and services. Sci Total Environ 838:156328. ttps://doi.org/10.1016/j.scitotenv.2022.156328\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuseva TB (2023) The governance of forest carbon in a subnational climate mitigation system: Insights from a network of action situations approach. Sustain Sci 18:59\u0026ndash;78. ttps://doi.org/10.1007/s11625-022-01262-4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabatini FM, Keeton WS, Lindner M et al (2020) Protection gaps and restoration opportunities for primary forests in Europe. Divers Distrib 26:1646\u0026ndash;1662. ttps://doi.org/10.1111/ddi.13158\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchilling-Vacaflor A, Lenschow A (2023) Hardening foreign corporate accountability through mandatory due diligence in the European Union? New trends and persisting challenges. Regul Gov 17:677\u0026ndash;693. ttps://doi.org/10.1111/rego.12402\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchindler S, O\u0026rsquo;Neill FH, Bir\u0026oacute; M et al (2016) Multifunctional floodplain management and biodiversity effects: A knowledge synthesis for six European countries. Biodivers Conserv 25:1349\u0026ndash;1382. ttps://doi.org/10.1007/s10531-016-1129-3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchleussner C-F, Lissner TK, Fischer EM et al (2016) Differential climate impacts for policy-relevant limits to global warming: The case of 1.5\u0026deg;C and 2\u0026deg;C. Earth Syst Dyn 7:327\u0026ndash;351. ttps://doi.org/10.5194/esd-7-327-2016\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchr\u0026ouml;der P, Beckers B, Daniels S et al (2018) Intensify production, transform biomass to energy and novel goods and protect soils in Europe\u0026mdash;A vision how to mobilize marginal lands. Sci Total Environ 616\u0026ndash;617:1101\u0026ndash;1123. ttps://doi.org/10.1016/j.scitotenv.2017.10.209\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchulte RPO, Bampa F, Bardy M et al (2015) Making the Most of Our Land: Managing Soil Functions from Local to Continental Scale. Front Environ Sci 3:81. ttps://doi.org/10.3389/fenvs.2015.00081\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeddon N, Daniels E, Davis R et al (2020) Global recognition of the importance of nature-based solutions to the impacts of climate change. Glob Sustain 3:e15. ttps://doi.org/10.1017/sus.2020.8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSegarra J, Buchaillot ML, Araus JL, Kefauver SC (2020) Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications. Agronomy 10:641. ttps://doi.org/10.3390/agronomy10050641\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeydi ST, Saeidi V, Kalantar B et al (2022) Fire-Net: A Deep Learning Framework for Active Forest Fire Detection. J Sens 2022:8044390. ttps://doi.org/10.1155/2022/8044390\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilveira EMO, Esp\u0026iacute;rito Santo FD, Wulder MA et al (2019) Pre-stratified modelling plus residuals kriging reduces the uncertainty of aboveground biomass estimation and spatial distribution in heterogeneous savannas and forest environments. Ecol Manag 445:96\u0026ndash;109. ttps://doi.org/10.1016/j.foreco.2019.05.016\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilveira EMO, Silva SHG, Acerbi-Junior FW et al (2019) Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment. Int J Appl Earth Obs Geoinf 78:175\u0026ndash;188. ttps://doi.org/10.1016/j.jag.2019.02.004\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh AP, Yerudkar A, Mariani V et al (2022) A Bibliometric Review of the Use of Unmanned Aerial Vehicles in Precision Agriculture and Precision Viticulture for Sensing Applications. Remote Sens 14:1604. ttps://doi.org/10.3390/rs14071604\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingha C, Swain KC, Moghimi A et al (2024) Integrating geospatial, remote sensing, and machine learning for climate-induced forest fire susceptibility mapping in Similipal Tiger Reserve, India. Ecol Manag 555:121729. ttps://doi.org/10.1016/j.foreco.2024.121729\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSollen-Norrlin M, Ghaley BB, Rintoul NLJ (2020) Agroforestry Benefits and Challenges for Adoption in Europe and Beyond. Sustainability 12:7001. ttps://doi.org/10.3390/su12177001\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSotirov M, Winkel G, Eckerberg K (2021) The coalitional politics of the European Union\u0026rsquo;s environmental forest policy: Biodiversity conservation, timber legality, and climate protection. Ambio 50:2153\u0026ndash;2167. ttps://doi.org/10.1007/s13280-021-01644-5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSovacool BK, Baum CM, Low S et al (2022) Climate policy for a net-zero future: Ten recommendations for Direct Air Capture. Environ Res Lett 17:074014. ttps://doi.org/10.1088/1748-9326/ac77a4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStatuto D, Cillis G, Picuno P (2017) Using Historical Maps within a GIS to Analyze Two Centuries of Rural Landscape Changes in Southern Italy. Land 6:65. ttps://doi.org/10.3390/land6030065\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSterman JD, Siegel L, Rooney-Varga JN (2018) Does replacing coal with wood lower CO2 emissions? Dynamic lifecycle analysis of wood bioenergy. Environ Res Lett 13:015007. ttps://doi.org/10.1088/1748-9326/aaa512\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSterne JA, Hern\u0026aacute;n MA, Reeves BC et al (2016) ROBINS-I: A tool for assessing risk of bias in non-randomised studies of interventions. BMJ 355:i4919. ttps://doi.org/10.1136/bmj.i4919\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStiglitz JE, Stern N, Duan M et al (2017) Report of the High-Level Commission on Carbon Prices. ttps://doi.org/10.7916/D8-W2NC-4103\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStorm H, Baylis K, Heckelei T (2020) Machine learning in agricultural and applied economics. Eur Rev Agric Econ 47:849\u0026ndash;892. ttps://doi.org/10.1093/erae/jbz033\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTamiminia H, Salehi B, Mahdianpari M et al (2021) A comparison of random forest and light gradient boosting machine for forest above-ground biomass estimation using a combination of Landsat, ALOS PALSAR, and airborne LiDAR data. ISPRS Ann Photogramm Remote Sens Spat Inf Sci XLIV-M-3-2021:163\u0026ndash;168. ttps://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-163-2021\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanneberger F, Appulo L, Ewert S et al (2021) The Power of Nature-Based Solutions: How Peatlands Can Help Us to Achieve Key EU Sustainability Objectives. Adv Sustain Syst 5:2000146. ttps://doi.org/10.1002/adsu.202000146\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTelo Da Gama J (2023) The Role of Soils in Sustainability, Climate Change, and Ecosystem Services: Challenges and Opportunities. Ecologies 4:552\u0026ndash;567. ttps://doi.org/10.3390/ecologies4030036\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas RQ, Brooks EB, Jersild AL et al (2017) Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: Regional data assimilation using ecosystem experiments. Biogeosciences 14:3525\u0026ndash;3547. ttps://doi.org/10.5194/bg-14-3525-2017\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTifafi M, Guenet B, Hatt\u0026eacute; C (2018) Large Differences in Global and Regional Total Soil Carbon Stock Estimates Based on SoilGrids, HWSD, and NCSCD: Intercomparison and Evaluation Based on Field Data From USA, England, Wales, and France. Glob Biogeochem Cycles 32:42\u0026ndash;56. ttps://doi.org/10.1002/2017GB005678\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTricco AC, Lillie E, Zarin W et al (2018) PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med 169:467\u0026ndash;473. ttps://doi.org/10.7326/M18-0850\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTutak M, Brodny J, Bindz\u0026aacute;r P (2021) Assessing the Level of Energy and Climate Sustainability in the European Union Countries in the Context of the European Green Deal Strategy and Agenda 2030. Energies 14:1767. ttps://doi.org/10.3390/en14061767\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations Environment Programme (2023) Emissions Gap Report 2023: Broken Record \u0026ndash; Temperatures hit new highs, yet world fails to cut emissions (again). United Nations Environment Programme. ttps://doi.org/10.59117/20.500.11822/43922\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUrbazaev M, Thiel C, Cremer F et al (2018) Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico. Carbon Balance Manag 13:5. ttps://doi.org/10.1186/s13021-018-0093-5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUstaoglu E, Collier MJ (2018) Farmland abandonment in Europe: An overview of drivers, consequences, and assessment of the sustainability implications. Environ Rev 26:396\u0026ndash;416. ttps://doi.org/10.1139/er-2018-0001\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVafaei S, Soosani J, Adeli K et al (2018) Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran). Remote Sens 10:172. ttps://doi.org/10.3390/rs10020172\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValipour M, Johnson CE, Battles JJ et al (2021) Simulation of the effects of forest harvesting under changing climate to inform long-term sustainable forest management using a biogeochemical model. Sci Total Environ 767:144881. ttps://doi.org/10.1016/j.scitotenv.2020.144881\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Der Werff H, Van Der Meer F (2016) Sentinel-2A MSI and Landsat 8 OLI Provide Data Continuity for Geological Remote Sensing. Remote Sens 8:883. ttps://doi.org/10.3390/rs8110883\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Tricht K, Gobin A, Gilliams S, Piccard I (2018) Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium. Remote Sens 10:1642. ttps://doi.org/10.3390/rs10101642\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenter ZS, Barton DN, Chakraborty T et al (2022) Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover. Remote Sens 14:4101. ttps://doi.org/10.3390/rs14164101\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVirkkala A, Aalto J, Rogers BM et al (2021) Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties. Glob Change Biol 27:4040\u0026ndash;4059. ttps://doi.org/10.1111/gcb.15659\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evonHedemann N, Wurtzebach Z, Timberlake TJ et al (2020) Forest policy and management approaches for carbon dioxide removal. Interface Focus 10:20200001. ttps://doi.org/10.1098/rsfs.2020.0001\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVreugdenhil M, Dorigo WA, Wagner W et al (2016) Analyzing the Vegetation Parameterization in the TU-Wien ASCAT Soil Moisture Retrieval. IEEE Trans Geosci Remote Sens 54:3513\u0026ndash;3531. ttps://doi.org/10.1109/TGRS.2016.2519842\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Liu C, Lv G et al (2022) Integrating Multi-Source Remote Sensing to Assess Forest Aboveground Biomass in the Khingan Mountains of North-Eastern China Using Machine-Learning Algorithms. Remote Sens 14:1039. ttps://doi.org/10.3390/rs14041039\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolfslehner B, Linser S, P\u0026uuml;lzl H et al (2016) Forest bioeconomy\u0026mdash;A new scope for sustainability indicators (From Science to Policy). ttps://doi.org/10.36333/fs04. European Forest Institute\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia J, Niu S, Ciais P et al (2015) Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc Natl Acad Sci USA 112:2788\u0026ndash;2793. ttps://doi.org/10.1073/pnas.1413090112\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie H, Zhang Y, Wu Z, Lv T (2020) A Bibliometric Analysis on Land Degradation: Current Status, Development, and Future Directions. Land 9:28. ttps://doi.org/10.3390/land9010028\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu S (2024) Forestry offsets under China\u0026rsquo;s certificated emission reduction (CCER) for carbon neutrality: Regulatory gaps and the ways forward. Int J Clim Change Strateg Manag 16:140\u0026ndash;156. ttps://doi.org/10.1108/IJCCSM-04-2022-0047\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu X, Hyypp\u0026auml; J, Karjalainen M et al (2015) Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes. Remote Sens 7:15933\u0026ndash;15954. ttps://doi.org/10.3390/rs71215809\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng W, Tomppo E, Healey SP, Gadow KV (2015) The national forest inventory in China: History - results - international context. Ecosyst 2:23. ttps://doi.org/10.1186/s40663-015-0047-2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhai Y, Wang L, Yao Y et al (2025) Spatially continuous estimation of urban forest aboveground biomass with UAV-LiDAR and multispectral scanning: An allometric model of forest structural diversity. Agric Meteorol 360:110301. ttps://doi.org/10.1016/j.agrformet.2024.110301\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Shao Z, Liu J, Cheng Q (2019) Deep Learning Based Retrieval of Forest Aboveground Biomass from Combined LiDAR and Landsat 8 Data. Remote Sens 11:1459. ttps://doi.org/10.3390/rs11121459\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Q, Wang R (2023) Carbon emission reduction effects in Yangtze River Delta from the dual perspectives of forest resource endowment and low-carbon pilot policy in the digital age. Front Glob Change 6:1259500. ttps://doi.org/10.3389/ffgc.2023.1259500\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Jia W, Sun Y et al (2023) Simulation of Spatial and Temporal Distribution of Forest Carbon Stocks in Long Time Series\u0026mdash;Based on Remote Sensing and Deep Learning. Forests 14:483. ttps://doi.org/10.3390/f14030483\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng J, Zhou Z, Zhu M et al (2024) Integration of Optical Remote Sensing and Laser Point Cloud for Forest Stock Estimation in Karst Mountainous Areas. Forests 15:2106. ttps://doi.org/10.3390/f15122106\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZittis G, Almazroui M, Alpert P et al (2022) Climate Change and Weather Extremes in the Eastern Mediterranean and Middle East. Rev Geophys 60. ttps://doi.org/10.1029/2021RG000762. e2021RG000762\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZute D, Samariks V, Šņepsts G et al (2023) Balancing Forest Regulations and Stakeholder Needs in Latvia: Modeling the Long-Term Impacts of Forest Management Strategies on Standing Volume and Carbon Storage. Sustainability 16:280. ttps://doi.org/10.3390/su16010280\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"aboveground biomass, carbon markets, digital twins, European Green Deal, machine learning, remote sensing","lastPublishedDoi":"10.21203/rs.3.rs-9102556/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9102556/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eForest ecosystems provide irreplaceable carbon sequestration, biodiversity, and ecosystem services, yet the integrity of forest carbon accounting \u0026mdash; encompassing measurement, reporting, and verification (MRV) across the full forest-to-atmosphere chain \u0026mdash; remains contested. Artificial intelligence (AI) and digital technologies offer transformative potential for improving the accuracy, transparency, and scalability of forest carbon integrity systems. Despite a proliferation of individual technical studies, no comprehensive evidence map of the field exists. This scoping review systematically maps AI-driven forest carbon integrity research across six thematic pillars (P1-P6), identifying dominant methods, technological gaps, ethical risks, and policy alignment opportunities with particular reference to European forest ecosystems and governance frameworks.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003ePeer-reviewed and grey-literature studies published in English between 2015 and 2024 were examined in this review, provided that at least one of six pre-defined thematic pillars (AI/remote sensing MRV; digital twins and ecosystem modelling; carbon markets and finance; EU policy and governance; ethics and equity; cross-cutting issues) was addressed in the context of forest carbon. Systematic searches of PubMed/MEDLINE, OpenAlex, and Semantic Scholar (all spanning 2015\u0026ndash;2024) were conducted programmatically using Python 3.12, with the final search performed in January 2025. These were supplemented by grey-literature searches executed within Web of Science and Scopus. Data were charted independently using a standardised extraction form, wherein details regarding authors, year, journal, digital object identifier (DOI), thematic pillar, AI/machine learning (ML) methods (multi-label), remote sensing data sources, carbon variables, and geographic scope were captured. Finally, quality appraisal of the included studies was performed using the Mixed Methods Appraisal Tool (MMAT).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFrom an initial pool of 5,135 raw records, 200 studies were included (spanning 2015\u0026ndash;2024; 100% open access; mean 349 citations, median 202). Artificial neural networks (ANN) were identified as the dominant AI approach (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;48, 24.0%), while Light Detection and Ranging (LiDAR)/Airborne Laser Scanning (ALS)/Terrestrial Laser Scanning (TLS) was observed as the predominant remote sensing modality (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;33, 16.5%). The domains of EU policy and governance (P4; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;59, 29.5%) and AI/remote sensing MRV (P1; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;53, 26.5%) were found to be the most active areas of research. European-scoped frameworks were represented by 23.5% of the studies (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;47), whereas a global scope was addressed by 41.0% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;82). Critical gaps in reproducibility (reported by less than10% of studies) and limitations reporting (observed in less than 30% of studies) were revealed by the quality appraisal.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAlthough high technical maturity in biomass estimation and disturbance detection is demonstrated by AI-driven forest carbon integrity systems, persistent gaps are encountered regarding governance legitimacy, reproducibility, carbon market integration, and EU policy coherence. Consequently, a five-point research agenda is proposed, wherein priorities are placed on reproducible AI, the development of an operational European Forest Carbon Digital Twin, and the establishment of a purpose-built AI ethics framework for forest carbon.\u003c/p\u003e","manuscriptTitle":"The Emerging Role of Artificial Intelligence Driven Forest Carbon Integrity Systems: A Scoping Review of Methods, Risks, and Policy Implications for European Forests","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 07:31:32","doi":"10.21203/rs.3.rs-9102556/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d926729-5605-4bd3-9048-fb422156afec","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T07:31:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 07:31:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9102556","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9102556","identity":"rs-9102556","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}