Prospective Macro-Level Life Cycle Assessment: A Systematic Review

Prospective Macro-Level Life Cycle Assessment: A Systematic Review | 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 Prospective Macro-Level Life Cycle Assessment: A Systematic Review Aaron Paris, Jeroen Guinée, Nils Thonemann This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7468270/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 Purpose : Transitions to sustainable societies require assessments of future environmental impacts at the macro-level. We examined how prospective process-based Life Cycle Assessment (LCA) is used to model environmental impacts at national to global scales. Our research objectives were to (i) provide an overview of modelling approaches in prospective macro-level LCA; (ii) identify common pitfalls and best practices; and (iii) highlight key challenges and suggest priorities for future research. Method : We conducted a systematic literature review. An initial search in Web of Science, complemented by studies reviewed by Bisinella et al. (2021), yielded 925 studies. After screening based on predefined inclusion criteria and adding 34 additional articles through citation tracking, a final set of 87 peer-reviewed articles was analysed. We reviewed these studies with a primary focus on how system scaling, temporal evolution, and temporal distribution were addressed in the inventory analysis phase. In addition, we assessed elements from the other three LCA phases, including research objectives, temporal scope, system boundaries, and the treatment of sensitivity and uncertainty. We also examined terminology use and transparency. Results and Discussion : We classified the reviewed approaches by how system scaling is treated in the foreground system: coupling with Dynamic Stock Models, which captures stock dynamics but overlooks socioeconomic aspects; coupling with Energy System Models, which provides detailed energy insights but is sector-limited; coupling with Integrated Assessment Models, which offers broader socioeconomic coverage but operates at coarse resolution and typically requires collaboration with model developers; and uncoupled approaches, which allow flexibility but risk oversimplification. We identify twelve key pitfalls, including simplified treatments of system scaling, temporal dynamics, and distribution; a narrow climate focus; limited scenario diversity; and weak internal consistency. We also highlight several best practices. Conclusions and Recommendations : Our review reveals a diverse field with inconsistent terminology, assumptions, and modelling practices. To strengthen the field, we recommend improving transparency and adopting consistent terminology; improving the representation of the complexity of sustainability transitions; strengthening policy relevance; and developing methodological guidance. Addressing these priorities will improve the robustness of prospective macro-level LCA and advance understanding of sustainability transitions. Life Cycle Assessment Macro-level LCA Large-scale Environmental Impact Assessment Prospective LCA Dynamic LCA Systematic Literature Review Figures Figure 1 Figure 2 1 Introduction Transitions to sustainable societies require informed decisions about which technologies and policies to support today, especially in sectors with long-lived assets, where current investments can lock-in environmental impacts for decades (Geels et al. 2017). Prospective environmental assessments at national, continental, and global scales—hereafter referred to as macro-level—provide a basis for these decisions by anticipating how aggregate environmental impacts evolve. Process-based Life Cycle Assessment (LCA) is an established method for such assessments, with three core strengths: (i) technological resolution, enabling explicit modelling of technological advancements, technology-specific interventions, and market share changes at the process-level (Hertwich et al. 2015; Wiedenhofer et al. 2024); (ii) a system perspective, covering the full life cycle (Sacchi and Hahn-Menacho 2024); and (iii) the capacity to assess multiple environmental impact categories (Sacchi and Hahn-Menacho 2024). The latter two strengths are important for avoiding problem-shifting (Finnveden et al. 2009). Conventional LCA, as standardised in ISO14040:2006 (ISO 2006), has three main limitations for prospective macro-level environmental analyses. First, it lacks a time dimension (Guinée et al. 2017), meaning that it disregards the temporal distribution of processes, emissions, and effects (Guinée 2002), implicitly assuming that all processes occur simultaneously. This simplification can be problematic, especially for long-lived systems like infrastructure. Second, it neglects temporal evolution (Müller et al., unpublished manuscript under review)—that is, changes over time such as technological advancements or structural shifts in supply chains and the broader economy (Arvesen et al. 2011; Luderer et al. 2019; Hung et al. 2022). Third, it assumes a linear relationship between the functional unit and the scale of the processes providing it (Heijungs 2020; Pizzol et al. 2021). This linear system scaling does not capture important aspects, including economies of scale, demand constraints (e.g., market saturation for co-products), and supply limitations (e.g., manufacturing capacities, resource availability, and technology deployment constraints) (Yang and Heijungs 2018, 2019; Pizzol et al. 2021). These constraints become especially important in dynamic transitions where bottlenecks, such as green hydrogen availability, may arise (Hung et al. 2022). Recent advances in prospective LCA—defined here as "LCA that models the product system at a future point in time relative to the time at which the study is conducted" (Arvidsson et al. 2024)—have improved the modelling of temporal evolution. Particularly, the coupling with Integrated Assessment Models (IAM) (Mendoza Beltran et al. 2018; Sacchi et al. 2022) has led to the increased consideration of temporal evolution in LCA studies. While many macro-level studies incorporate prospective elements, the field of prospective LCA has mainly focused on emerging technologies at the micro-level (Thonemann et al. 2020). Meanwhile, calls to broaden LCA’s scope (Jeswani et al. 2010; Guinée et al. 2011) have led to an increasing number of studies at the macro-level (Bisinella et al. 2021; Hellweg et al. 2023). Particularly, the energy transition has been the focus of seminal work (e.g., Hertwich et al. 2015). We propose the following as a first attempt at defining macro-level LCA: assessment of environmental impacts associated with the life cycle of a system fulfilling a specific function for the demand at the national, continental, or global scale . We suggest distinguishing two key dimensions: the type of the functional unit (ranging from single products to sectors or the entire economy) and the scale of the functional unit (national, continental, or global), with only the latter determining macro-level status. Current prospective macro-level LCAs vary widely in scope, scenario design, modelling of foreground (FG) and background (BG) systems, and terminology. Approaches range from simple parameter adjustments (e.g., future demand) to advanced couplings with dynamic Material Flow Analysis or IAMs. As Hellweg et al. (2023) point out, standardisation and methodological guidance are needed. However, such guidance must be grounded in a systematic overview of the state-of-the-art, including best practices and methodological challenges. While several reviews address prospective LCA, none focus specifically on macro-level approaches. Some address temporal dynamics in LCA without emphasising the macro-level (Beloin-Saint-Pierre et al. 2020; Lueddeckens et al. 2020; Sohn et al. 2020); others focus on emerging technologies (Buyle et al. 2019; Bergerson et al. 2020; Thonemann et al. 2020; Tsoy et al. 2020; van der Giesen et al. 2020; Erakca et al. 2024); or specific sectors such as metals (Harpprecht et al. 2024). Reviews on the combination of LCA with other types of models focus on the model combination, regardless of the assessment scale and temporal scope (Beaussier et al. 2019; Palazzo et al. 2020; Barkhausen et al. 2023). Other reviews discuss aspects relevant to macro-level prospective LCA, but do not analyse existing approaches (Bisinella et al. 2021; Hellweg et al. 2023; Caiardi et al. 2024; Riondet et al. 2024; Wiedenhofer et al. 2024). To the best of our knowledge, no review has systematically examined process-based LCAs that are both prospective and macro-level in scope. As a result, the current state of the art remains unclear, including which modelling approaches are available, what key challenges and pitfalls exist, and where future research should focus. Accordingly, our research objectives were to (i) provide an overview of modelling approaches in prospective macro-level LCA, focusing on modelling of system scaling (micro- to macro-level), temporal evolution, and temporal distribution; (ii) identify common pitfalls and best practices; and (iii) highlight key challenges and suggest priorities for future research. To fulfil the research objectives, we conducted a systematic literature review of prospective macro-level LCAs covering all LCA phases, with particular emphasis on the inventory phase. Temporal evolution modelling and system scaling guided our literature selection, but we also analysed how temporal distribution is addressed within the included studies. Providing detailed methodological guidance for prospective macro-level LCA is beyond the scope of this paper. Instead, we aim to lay the groundwork for future developments in that direction. We acknowledge the significant progress already made in prospective macro-level LCA. Our intention is not to dismiss these efforts, but to reflect on current practices and encourage dialogue on how the field can advance. 2 Methods Our review was informed by the methodological guidance of Siddaway et al. (2019) and Zumsteg et al. (2012). A flowchart outlining the literature selection process is shown in Figure 1. 2.1 Search The initial literature search was conducted in the Web of Science Core Collection using a query developed from 17 benchmark studies identified during an exploratory review (see Supplementary Information). The search string was: ‘ TS=("life cycle" OR "life-cycle" OR "lifecycle" OR "LCA") AND TS=("prospectiv*") AND TS=("*sector*" OR "*fleet*" OR "nation*" OR "transition*" OR "macro*" OR “at scale” OR “large-scale” OR “large scale” OR "stock*") and English (Languages)’ . Here, TS denotes a Topic Search , retrieving records where these terms appear in the title, abstract, or author keywords. The search was last updated on July 15, 2025; studies published after this date were excluded. Due to the lack of standard terminology for prospective macro-level LCA, covering all potential keywords would have resulted in an unmanageable number of studies. Therefore, while we included a broad range of terms related to scale, we adopted a restrictive strategy for the prospective dimension, requiring the root term "prospectiv*" . We acknowledge this may have excluded some relevant studies. To mitigate this risk, we additionally: (i) added the 514 studies reviewed by Bisinella et al. (2021), who focused on LCA combined with future scenarios; and (ii) conducted forward and backward citation tracking on included studies using the Web of Science. All identified studies were subject to the selection criteria outlined below. 2.2 Screening Screening was conducted in two stages, starting with a review of titles and abstracts, followed by a full-text review, as shown in Figure 1. Studies were included only if they met all of the following criteria: (1) Bibliographic requirements: Peer-reviewed journal articles in English. Letters, books, reports, and conference papers were excluded. No restrictions were placed on the publication year. (2) Case study : Pure method papers without applications (e.g., Ventura 2023) were excluded, but referenced when relevant (e.g., Gibon et al. 2015; Arvesen et al. 2018). (3) Life cycle perspective: Studies had to cover at least two life cycle stages. (4) Process-based modelling in FG and BG systems : We define the FG system as the set of processes that constitute the object of analysis , i.e., the focus of the study. The BG system includes all other processes that provide inputs to the FG system . Studies based solely on Input-Output Life Cycle Assessment (IO-LCA) for the FG or BG system were excluded. We did not require studies to perform their own inventory modelling. (5) Environmental impact assessment: Studies had to include a characterisation of environmental flows for at least one impact category. (6) Macro-level scope : Studies had to fulfil our above definition of macro-level LCA. However, since functional units were rarely stated explicitly, we based inclusion or exclusion on the scale of the reported results. (7) Consideration of temporal evolution : Studies had to report results for at least one explicit future year, reflecting changes over time in both the FG and the BG system. Changing demand alone was insufficient unless accompanied by changes in how that demand is met over time. For example, studies that changed the material composition of buildings or the composition of the building stock were included, even if the underlying unit processes remained unchanged. Studies with insufficient documentation (including Supplementary Information) to assess these criteria were excluded. While some relevant studies may have been unintentionally excluded, we believe the range and diversity of works covered provide a comprehensive overview of current approaches in prospective macro-level process-based LCA research. As shown in Figure 1, of the 925 articles initially identified, 702 were excluded after title and abstract screening, and another 170 after full-text screening. 33 additional articles were found through citation tracking. Ultimately, 86 publications were reviewed in detail. A complete list of retrieved articles, including reasons for exclusion, is provided in the Supplementary Information. 2.3 Review We reviewed the included studies with a primary focus on how system scaling, temporal evolution, and temporal distribution were modelled in the inventory analysis phase. In addition, we assessed elements from the other three LCA phases, including research objectives, temporal scope, system boundaries, and the treatment of sensitivity and uncertainty. We also examined terminology use and transparency. We reviewed both the main article and the Supplementary Information, with the exception of model code. An overview of all review variables and their evaluation across the included studies is provided in our Supplementary Information. 3 Overview of modelling approaches To address the limitations of process-based LCA for prospective macro-level analyses, particularly the challenge of scaling systems from the typical micro-level scope of LCA to the macro-level, studies often rely on coupling LCA with other types of models. We classify the reviewed approaches based on how the FG system was scaled to the macro-level, beginning with those that use specific coupled models, followed by approaches that do not involve explicit model coupling. The main groups are Dynamic Stock Models (DSM), Energy System Models (ESM), and IAMs. These groups are not mutually exclusive, and their boundaries may overlap. For example, many IAMs incorporate ESMs and DSMs (Krey et al. 2019 ), but we treat these as separate approaches. Some studies do not fit neatly into a single category. For example, Hertwich et al. ( 2015 ) use pre-defined International Energy Agency (IEA) scenarios as stock trajectory input to a DSM. Since these scenarios are based on the IEA’s own ESM, we classify the study as coupling with ESM (ESM–LCA), although classifying it as coupling with DSM (DSM–LCA) would also be possible. Generally, studies scale results from micro-level LCAs—which we refer to from hereon as micro-level characterisation results —rather than scaling within the framework of LCA itself, i.e., the Life Cycle Inventory (LCI). Figure 2 provides a stylised overview of the typical information flows in each modelling approach. Note that by activity level , we mean the quantitative extent of a process or system’s operation; that is, how much a process is used. Table 1 presents a more detailed overview of these approaches and their key characteristics, including how they typically model temporal evolution and distribution. By temporal distribution , we refer specifically to the timing of LCA processes—that is, whether they are modelled as occurring instantaneously or distributed over time—rather than to the presence of a temporal dimension in the coupled model. Table 1 Overview of main modelling approaches in prospective macro-level Life Cycle Assessment (LCA), grouped by how the foreground system was scaled to macro-level: coupling process-based LCA with a Dynamic Stock Model (DSM-LCA), an Energy System Model (ESM-LCA), an Integrated Assessment Model (IAM-LCA), or without a coupled model. Less common approaches are discussed separately in subsection 3.5. DSM-LCA ESM-LCA IAM-LCA No explicit model coupling System scaling Scaling with inflows, outflows, and stock levels from stock-driven DSMs Scaling with ESM outputs (capacity additions and energy generation volume) OR incorporated directly into the ESM Scaling with IAM outputs (activity levels, capacity additions) OR incorporated directly into the IAM Scaling with demand trajectories, based on extrapolated trends, fixed growth rates, external scenarios, or present-day levels Temporal evolution Stock composition changes; selected parameters (e.g., electricity mix) Typically based on the parameter development of the ESM Typically based on the parameter development of the IAM Selected parameters (e.g., electricity mix) Temporal distribution Differentiation of life cycle stages and temporal distribution based on inflows, stocks, and outflows Differentiation of life cycle stages and temporal distribution based on capacity additions and energy generation volumes Differentiation of life cycle stages and temporal distribution based on capacity additions and activity levels over time Not consistently considered Strengths • Endogenous stock dynamics • Stock–flow consistency and mass balance • Low access barriers • Flexibility in scope and scale • Detailed representation of energy system dynamics • Captures system constraints • Strong policy link • Endogenous Representation of technological change and macro-level feedbacks • Established scenario framework • Global coverage • Strong policy link • Low access barriers • Flexible Weaknesses • Lacks macroeconomic/ systemic feedbacks • No standardised scenario framework • High data requirements (lifetimes, stocks) • Weak policy link • Application restricted to the energy system • Mismatches of geographical and technological resolution • High expertise and access barriers • Application restricted to IAM core systems • Mismatches of geographical and technological resolution • High expertise and access barriers • No endogenous dynamics or feedbacks • Low comparability Common applications Buildings, transport, electricity systems, and materials Electricity, fuels, hydrogen, batteries, sector coupling IAM core sectors, like energy or transport Materials, chemicals, and emerging technologies Example research questions How stock dynamics of a system influence its life cycle impacts over time How the life cycle impacts of future energy systems evolve How global systems meet climate targets and associated life cycle impacts How future life cycle impacts change under various assumptions Number of studies 35 27 6 15 Our focus is on the strengths and weaknesses of these approaches rather than on the specific models themselves, as reviewing each model is beyond the scope of this paper. These strengths and weaknesses are based on our observations, the limitations stated by the authors of the reviewed papers, and relevant literature. The following subsections examine each approach in detail. Note that Table 1 and Fig. 1 do not include all identified approaches—those that are rarely applied are discussed separately in subsection 3.5. 3.1 Dynamic Stock Models The first approach combines LCA with DSMs. DSMs, also referred to as Vintage Stock Models, represent stocks of products used longer than one year (Wiedenhofer et al. 2024 ) and their inflows (additions) and outflows (retirements) over time. DSMs can be stock-driven, with predefined stock trajectories and inflows/outflows derived via lifetime distributions; or inflow-driven, where stocks and outflows follow inflow time series (Deng et al. 2023 ). Studies in this group correspond to what Barkhausen et al. ( 2023 ) term “environmentally extended Material Flow Analysis”. They first use a DSM to project future inflows, stocks, and outflows, which are then combined with micro-level LCA characterisation results for upstream, use, and downstream stages, integrated over time for each stage. For example, Milovanoff et al. ( 2019 ), use a dynamic stock model to project future light-duty vehicle production, vehicle stock, and disposal volumes for the U.S. fleet, and then multiply each by micro-level characterisation results for vehicle production, use, and End-of-Life (EoL) disposal. For a given year, the total impact is then calculated by summing all impacts occurring in that year: use-phase impacts from all active cohorts, production impacts from the new cohort, and EoL impacts from stocks retiring that year. However, not all DSM-LCA studies account for temporal distribution: two aggregate micro-level results across the considered life cycle stages into a single time point before applying them to inflows or stocks, while two focus exclusively on upstream impacts. Temporal evolution is typically modelled via cohort-specific parameters (e.g., energy efficiency, market shares), BG system parameters (e.g., electricity mix), or global DSM parameters (e.g., lifetimes, recycling rates), often in scenario form. Five studies use premise , a popular approach for modelling of prospective BG systems (Sacchi et al. 2022 ) that links IAM variables to LCA processes to generate prospective BG system inventories “by adjusting technologies’ penetration share, efficiency and emission factors for a specific scenario and year” (Šimaitis et al. 2025 ). Strengths of the DSM-LCA approach include the inherent ability to model temporal distribution, the granular annual resolution, and the ability to capture system delays from technology diffusion and lock-ins. For example, Milovanoff et al. ( 2022 ) demonstrate how climate change mitigation is restrained by vehicle stock turnover despite even under assumptions of aggressive electrification in new vehicle market shares. The DSM-LCA approach is flexible in scale (Ventura 2022 ) and system of analysis, supports scenario customisation, and benefits from low access barriers through open-source tools like ODYM (Pauliuk and Heeren 2020 ). Weaknesses include high data demands, particularly lifetimes and stock levels, missing treatment of macro-level feedback, non-linear system behaviour, and relying on exogenous assumptions for socioeconomic drivers (Wiedenhofer et al. 2024 ). Few studies model technology diffusion endogenously and representation of policies is often lacking. Applications focus on transport (18 studies), buildings (8), the electricity system (4), material cycles (4) and consumer goods (1). Research objectives include methodological advances (e.g., Hung et al. 2022 ) and evaluating strategies like lightweighting (e.g., Milovanoff et al. 2019 ), EV adoption (e.g., Raugei et al. 2021 ), and circularity (e.g., Pauliuk et al. 2024b ). Many studies (12 of 35) do not cover the full life cycle. Temporal resolution is typically annual (31 of 35 studies). Noteworthy methodological advances include coupling DSMs with discrete choice models (Brand et al. 2013 ; Mastrucci et al. 2024 ), which simulate how individual agents make decisions among alternative technologies based on internal preferences and external factors (Brand et al. 2013 ); improving technological granularity (Alaux et al. 2024 ); adopting nonlinear technology diffusion models (Sigüenza et al. 2021 ); and including optimisation (Hung et al. 2022 ). Future work should focus on better representing behaviour, policies, and macro-level feedbacks; exploring approaches to endogenise socioeconomic drivers and dynamics; improving spatial and technological resolution; addressing distributional aspects (Pauliuk 2024 ); and differentiating drivers of stock levels beyond averages (e.g., urban vs. rural buildings, Zhang et al. 2024 ). 3.2 Energy System Models The second approach combines LCA with ESMs. As mentioned above, we include studies using pre-defined IEA scenarios, since they are based on the IEA’s internal ESM. ESMs model how resource inputs—defined by potentials and cost curves—are used to meet end-use demands through primary (e.g., electricity) or secondary (e.g., heat pumps) conversion technologies, under policy or economic constraints (Blanco et al. 2020 ). Depending on their goal, they can be used to identify least-cost or least-impact pathways to a desired future state (Weidner et al. 2022 ). ESMs typically rely on partial equilibrium modelling (Blanco et al. 2020 ). Inputs include energy resource availability, existing capacities and capacity limitations, (future) demand and demand profiles, technology data, and scenario assumptions like policy targets (Weidner et al. 2022 ). Two main combination approaches with LCA exist (Blanco et al. 2020 ): (i) ex-post assessments (20 studies), where LCA is applied after the ESM runs independently (often optimising for cost or direct emissions), and (ii) endogenous inclusion of micro-level characterisation results (7 studies), where they are directly embedded in the ESM. These are depicted in Fig. 2 as ‘a’ and ‘b’, respectively. For ex-post assessments, model structures are often similar to DSM-LCA approaches: ESM outputs, i.e., new capacity installations and electricity generation activity levels by technology, are linked to upstream and use-phase micro-level characterisation results, respectively. Two studies combine DSMs with IEA scenario values by using future installed capacity trajectories from the IEA scenarios to drive a vintage stock model, which allocates life cycle impacts of power plants—construction, operation, and decommissioning—to the years they occur (Hertwich et al. 2015 ; Gibon et al. 2017 ). EoL is rarely explicitly treated. Some studies allocate EoL impacts to the construction of new capacities as well (Junne et al. 2020 ), creating temporal mismatches. Temporal evolution is typically inherently modelled by the ESM through improving conversion efficiencies or the emergence of new technologies, which are then harmonised with the LCA data. Six of the studies use prospective BG system data generated by premise (Sacchi et al. 2022 ). Most studies project to 2050, with temporal resolution ranging from yearly to 20-year steps. Strengths of the approach include detailed energy system representation and non-linear dynamics (e.g., considering merit-order effects); the representation of constraints, such as limited capacities for specific technologies or investment cost; and the inherent inclusion of temporal distribution, at least for new capacity additions and use phase. Furthermore, they have a better representation of policies than DSM-LCA typically have. Weaknesses include the energy-sector focus, limiting application beyond it; uncertain mapping of ESM and LCA data due to differences in technological parameters and resolution (Vandepaer and Gibon 2018 ; Hahn Menacho et al. 2025b ); and that many ESMs produce only one optimised future per set of assumptions, limiting their ability to capture disruptive changes or unexpected transitions (Reinert et al. 2021 ). Furthermore, ESM modelling also requires significant expertise, and model documentation is often lacking (Vandepaer et al. 2020 ), reducing accessibility and transparency. A key challenge for ESM-LCA is double-counting. ESMs calculate total energy demand, including supply chains. If the micro-level characterisation results include embedded energy, impacts may be overestimated (Volkart et al. 2017 ; Blanco et al. 2020 ). National models double-count domestic supply chains; global models all chains unless inventories are adjusted (Volkart et al. 2017 ). Double-counting also occurs when the micro-level characterisation results are not only applied at the end-use level, for example, accounting for electricity used in both heat pump operation and the generation of that electricity (Blanco et al. 2020 ). Many studies have developed methods to mitigate double-counting (e.g., Volkart et al. 2018 ; Vandepaer et al. 2020 ). Studies focus on the energy systems (18), or specific intermediate goods like hydrogen, ammonia, and batteries. The most common objective is assessing the environmental impacts of future energy systems, often under climate mitigation targets and a focus on the role of specific technologies. The scope of the studies varies, with 6 being cradle-to-gate, 4 cradle-to-use, 16 cradle-to-grave and one focussing on the EoL. Future work should align technological assumptions between ESMs and LCA, enhance transparency, and develop standardised coupling methods. 3.3 Integrated Assessment Models The third approach couples LCA with IAMs (IAM-LCA), which integrate models of climate, economy, and society to explore policy questions (Hellweg et al. 2023 ). We focus on process-based IAMs, which—unlike cost-benefit IAMs—are technology-rich and scenario-driven (Pauliuk et al. 2017 ). While many IAMs have ESMs at their core, they differ by explicitly modelling the carbon cycle, land-use change, and the global economy (Blanco et al. 2020 ). IAMs produce long-term projections that align with radiative forcing targets set by Representative Concentration Pathways and are situated within broader socio-economic storylines, the Shared Socioeconomic Pathways (SSP) (Sacchi et al. 2022 ). They can take the form of either optimisation models, based on myopic or perfect foresight (Pauliuk et al. 2017 ), or simulation models and are typically based on general or partial equilibrium frameworks (Keppo et al. 2021 ). For a concise discussion of how IAMs work and their implications for LCA, see Evans and Hausfather ( 2018 ) and Šimaitis et al. ( 2025 ). Five of the six studies reviewed apply ex-post methods (labelled ‘a’ in Fig. 2 ), using IAM scenario outputs such as cement production volumes (Müller et al. 2024 ), to scale micro-level LCA inventories or characterisation results. Only Pehl et al. ( 2017 ) incorporate embodied energy coefficients based on micro-level LCIs into the IAM REMIND using its endogenous emission factors and carbon price assumptions (labelled ‘b’ in Fig. 2 ). This approach can, similar to ESM-LCAs, lead to double-counting if not correcting the LCA-based coefficients (Arvesen et al. 2018 ). It is important to note that while ex-post and endogenous inclusion assessments of ESMs often share similar system boundaries—typically focused on the energy system—endogenous inclusion in IAM-LCA encompasses all sectors represented within the IAM, whereas ex-post assessments in IAM-LCA usually target a single system only (e.g., cement; Müller et al. 2024 ). Temporal evolution is captured via IAM market shares and technological parameters (e.g., Dirnaichner et al. 2022 ; Müller et al. 2024 ) or based on literature and simple assumptions (e.g., 100% adoption of a specific technology in a specific year, Cabrera-Jiménez et al. 2025 ). Two of the studies rely on premise (Müller et al. 2024 ; Cabrera-Jiménez et al. 2025 ) and two (Pehl et al. 2017 ; Luderer et al. 2019 ) on THEMIS (Gibon et al. 2015 ) to model temporal evolution. IAMs typically operate in time steps of 5–10 years. Three of the six studies include temporal distribution through differentiating construction and operation and mapping them to electricity generation capacity and volume (Pehl et al. 2017 ; Luderer et al. 2019 ) or through simply assuming fixed lifetimes afterwards based on the IAM output (Knobloch et al. 2020 ). The other three studies do not consider temporal distribution. Strengths of the approach include the global scope; endogenous data on energy and technology development from the IAM, which are based on a large variety of projection strategies and assumptions (Krey et al. 2019 ); and the inclusion of macroeconomic feedbacks and policy links. The alignment with the SSPs is another advantage (Steubing and Koning 2021 ). Some IAMs also embed regional constraints like land and resource availability (Pehl et al. 2017 ). Weaknesses stem from coarse geographical and technological resolution, requiring mapping to LCA data, introducing uncertainty (Sacchi et al. 2022 ; Šimaitis et al. 2025 ). The complexity of IAMs and their high entry barriers pose challenges to interpretation and often necessitate collaboration with model developers, particularly when seeking to move beyond predefined model assumptions (Keppo et al. 2021 ; Wilson et al. 2021 ). These difficulties are compounded by often incomplete and scattered documentation, which can further hinder transparency and reproducibility (Pauliuk et al. 2017 ). There are more than 30 IAMs, representing a wide variety of approaches (Pauliuk et al. 2017 ), which makes generalisations difficult (de Bortoli et al. 2025 ). Nevertheless, we want to highlight a few important general criticisms, which include overreliance on negative emission technologies (van Vuuren et al. 2017 ; Creutzig et al. 2021 ); underestimation of other technologies like renewables (Creutzig et al. 2017 ; Way et al. 2022 ); a weak representation of material cycles (Pauliuk et al. 2017 ); neglecting actor heterogeneity (Keppo et al. 2021 ); underrepresenting distributive justice (de Bortoli et al. 2025 ); and reliance on economic equilibrium frameworks (Keppo et al. 2021 ), which themselves are subject to a broad set of critiques (see subsection 3.5). Relatedly, IAMs have been criticised for a lack of alternative economic paradigms, potentially excluding viable policy options (Hickel et al. 2021 ; Proctor 2023 ; de Bortoli et al. 2025 ). For a more comprehensive overview of criticisms and corresponding responses from the IAM community, see Keppo et al. ( 2021 ). Some of these issues are being actively addressed; for example, through the development of scenarios that avoid the use of negative emissions technologies (Grubler et al. 2018 ). Because IAMs represent in detail only those sectors most relevant to climate change (Steubing et al. 2023 ), IAM-LCA studies are likewise limited to these sectors: electricity (Pehl et al. 2017 ; Luderer et al. 2019 ), transport (Knobloch et al. 2020 ; Dirnaichner et al. 2022 ; Cabrera-Jiménez et al. 2025 ), space heating (Knobloch et al. 2020 ), and cement (Müller et al. 2024 ). The typical research objective is to evaluate decarbonisation strategies and their trade-offs with other impact categories, which are not represented in IAMs. 3.4 No explicit model coupling Fifteen studies did not rely on a coupled model for system scaling. Instead, scaling is based on assumed demand trajectories (e.g., Weidner et al. 2023 ), constant present-day levels (e.g., Adrianto et al. 2023 ), extrapolation of historical trends (e.g., Kuipers et al. 2018 ), assumed annual growth rates (e.g., Bohnes et al. 2022 ), or scenarios from government-adjacent organisations (e.g., Douziech et al. 2024 ), industry groups (e.g., Pedneault et al. 2021 ), or other literature (e.g., Shirmohammadi et al. 2025 ). Temporal evolution is generally informed by market shares drawn from the same literature sources or simple scenario assumptions, such as a linear transition to 100% market share (e.g., Zheng and Suh 2019 ) or regression of historical data (e.g., Kuipers et al. 2018 ). Technological change is introduced separately via annual improvement rates or adoption curves, often based on expert judgment, roadmaps, or external scenarios. Parameters addressed include the electricity supply mix, the deployment speed of technologies (e.g., Pedneault et al. 2021 ), energy efficiency, and specific factors like ore grades (e.g., van der Voet et al. 2019 ). Seven studies use premise (Sacchi et al. 2022 ), with four applying it to both the FG and the BG system (e.g., Weidner et al. 2023 ). None of the studies in this group accounts for the temporal distribution of processes. Uncoupled approaches offer flexibility and relative simplicity, but they have notable limitations. Without coupled models, they cannot systematically capture dynamic stock changes, non-linear system behaviours, or temporal distributions. Purely assumption-based scaling can undermine comparability and robustness, and reliance on fixed demand trajectories limits the exploration of diverse futures. While accessible, these approaches risk oversimplification. Half the studies focus on intermediate goods, like metals (Kuipers et al. 2018 ; van der Voet et al. 2019 ; Pedneault et al. 2021 ; Adrianto et al. 2023 ), hydrogen (Weidner et al. 2023 ) or plastics (Zheng and Suh 2019 ). Consequently, most use a cradle-to-gate scope (7). Temporal resolution varies widely. Research objectives include methodological contributions (e.g., Douziech et al. 2024 ), assessments of specific technologies (e.g., Adrianto et al. 2023 ), evaluation of government strategies (e.g., Bohnes et al. 2022 ), or general impact assessment of the system under analysis (e.g., Horup et al. 2025 ). 3.5 Less explored modelling approaches Two modelling approaches appeared rarely and are therefore briefly discussed here. General Equilibrium Models (GEM) determine “price and quantity jointly in all sectors and regions in the world economy using a solvable system of equations” (Palazzo et al. 2020 ). Only two studies used GEMs, both in the waste sector (Ljunggren Söderman et al. 2016 ; Arushanyan et al. 2017 ). However, GEMs underpin many IAMs (Keppo et al. 2021 ; Proctor 2023 ), while Partial Equilibrium Models (PEMs)—which “represent the market for a particular good (or small set of goods) in isolation from the rest of the economy” (Palazzo et al. 2020 )—are common in both IAMs and ESMs (Blanco et al. 2020 ; Keppo et al. 2021 ). Thus, equilibrium approaches are more prevalent than they initially appear. Their strengths include modelling prices, substitution, and feedback effects, with strong links to policy instruments (Plevin 2017 ; Beaussier et al. 2019 ). Many critiques apply to both, notably their reliance on neoclassical assumptions such as consumer utility maximisation, producer profit maximisation, perfect information, and market clearing (Suh and Yang 2014 ; Yang and Heijungs 2018 ; Palazzo et al. 2020 ; Wiedenhofer et al. 2024 ). Some criticisms apply more to GEMs than PEMs, particularly their unrealistic assumption of full capacity utilisation (Wiedenhofer et al. 2024 ). Where stylised production functions are used—more common in GEMs than in PEMs, which often draw on engineering detail—these typically violate biophysical and thermodynamic consistency (Wiedenhofer et al. 2024 ). The limited uptake of GEM in particular may stem from data demands, complexity, and poor fit with LCA’s granularity (Earles and Halog 2011 ; Yang and Heijungs 2018 ; Beaussier et al. 2019 ). Given the strong assumptions underpinning equilibrium models, there is a need to diversify the macroeconomic approaches, for instance by incorporating post-Keynesian or stock–flow consistent macroeconomics (Wiebe et al. 2023 ). System Dynamics (SD) models simulate system behaviour through stocks, flows, and feedbacks (Moon 2017 ). They enable the modelling of complex and non-linear system behaviour, are inherently able to account for time and support easy parametrisation (Beaussier et al. 2019 ; Palazzo et al. 2020 ; Yi et al. 2023 ). Despite existing combinations of LCA and SD (McAvoy et al. 2021 ), we found only one study (Alaux et al. 2025a ) using causal loop diagrams to derive scenario parameters and one study (Ginster et al. 2024 ) building a full SD model, that is very close to a DSM-LCA. Complexity, lack of standardisation, as well as high computational and data requirements may hinder uptake (Beaussier et al. 2019 ; Palazzo et al. 2020 ). 3.6 Models only used in supporting roles We expected to find several modelling methods used more frequently, but they mostly appeared only in supporting roles. Below, we briefly outline these approaches, highlight their key features, and discuss possible reasons for their limited use. Linear Programming Models (LPM) use linear optimisation to represent technology choice under constraints. While many IAMs and ESMs also employ optimisation methods, we focus here specifically on LPMs applied directly to the mathematical structure of LCA. Five studies use optimisation, two of which combine it with a DSM (Hung et al. 2022 ; Rossi et al. 2023 ) and are therefore classified as DSM-LCA. The other three (Zibunas et al. 2022 ; Lechtenberg et al. 2024 ; Zibunas et al. 2024 ) rely on simple system scaling (fixed or linearly growing demand) and are classified as without explicit model coupling. Technology diffusion is modelled endogenously in the optimisation: the models determine which technologies are adopted based on environmental performance and constraint satisfaction. The main advantage of this approach is the ability to incorporate constraints directly, while its main drawback lies in the high access barriers. The newly developed tool optimex (Diepers and Tautorus 2025 ) may encourage broader uptake. Agent-Based Models (ABM) simulate interactions between agents and their environment, driving system evolution (Moon 2017 ). While couplings of LCA and ABM exist, macro-level applications are rare (Beaussier et al. 2019 ; Wiedenhofer et al. 2024 ). In our sample, ABM appeared only in supporting roles. ABMs enable more realistic modelling of (non-linear) behaviour, actor heterogeneity and technology diffusion than traditional economic models (Beaussier et al. 2019 ; Palazzo et al. 2020 ; Wiedenhofer et al. 2024 ), but share SD’s drawbacks: complexity, a lack of standardisation, and high computational and data demands (Beaussier et al. 2019 ). Input-Output LCA (IO-LCA) uses economic input-output tables to trace sectoral transactions within and across regions, enabling quantification of environmental impacts across global supply chains when combined with environmental data (Hagenaars et al. 2025 ). IO-LCA is relatively easy to implement and is often considered more comprehensive because it captures the whole economy and is not limited by cut-offs in contrast to LCA (Beaussier et al. 2019 ; Hagenaars et al. 2025 ). However, fixed prices and static structure limit its reliability to short-term analysis (Beaussier et al. 2019 ; Le Luu et al. 2024 ). Furthermore, IO-LCA has a low technological resolution and lacks a representation of use and EoL stages (Hagenaars et al. 2025 ). In our sample, IO-LCA was only used to extend BG system coverage (e.g., Arvesen et al. 2011 ), not to scale FG systems. The reason remains unclear to us, considering the increased use of combinations of IO-LCA and process-based LCA in recent years (Hagenaars et al. 2025 ). Econometric models identify statistically significant relationships between variables using historical data, often serving as inputs for other models (Wiedenhofer et al. 2024 ). Beyond their role in IAMs (e.g., Knobloch et al. 2020 ) and their occasional use to project future stock in stock-driven DSM-LCAs (e.g., Brand et al. 2012 ), none of the studies in our review applied econometrics as a primary system-scaling framework. Their main strength lies in avoiding the restrictive assumptions characteristic of GEM approaches. However, they are generally constrained to the short term and exhibit strong path dependency (Beaussier et al. 2019 ; Wiedenhofer et al. 2024 ). 4 Pitfalls, challenges, and best practices We identified 12 pitfalls and challenges in the reviewed studies. These pitfalls and challenges are not ranked by priority. Instead, we start with the three main LCA limitations for prospective macro-level analyses introduced earlier, then cover the rest roughly following the four phases of LCA. 4.1 System scaling In all reviewed cases, micro-level characterisation results are calculated first, with system scaling conducted outside the LCA framework. Whether the functional unit maintains a linear relationship with the scale of the processes that provide it (Heijungs 2020 ; Pizzol et al. 2021 ) depends on the selected modelling approach. ESM and IAM frameworks introduce non-linearities in certain aspects; for example, ESMs may model the utilisation of specific energy technologies using merit-order curves. We found only one study explicitly adjusting unit processes based on scale: van der Meide et al. ( 2022 ), who include changing ore grades based on production volume. Apart from that, such effects can arise indirectly when parameters from ESMs or IAMs—which often endogenously represent technological learning based on deployment or investment (Keppo et al. 2021 )—are incorporated into the modelling of unit processes for micro-level LCA. Other approaches generally preserve linearity and model technological change as a function of time rather than scale. For example, Tang et al. ( 2023 ) assume identical efficiency improvements for battery electric vehicles in scenarios with both no uptake and 100% sales share by 2030. The importance of linking performance to the scale of deployment (or investment) depends on the maturity of the technology (Pizzol et al. 2021 ). It is important to keep in mind that ecoinvent includes production volumes for certain technologies to construct market processes (Wernet et al. 2016 ), implying that unit process data are valid for a specific production quantity; though whether they hold at other capacities is uncertain. 4.2 Temporal evolution modelling Temporal evolution in prospective LCA affects both FG and BG systems, through parameters like technological change (e.g., efficiency, lifetime), system composition (e.g., market shares), and broader configurations (e.g., recycling rates, ore grade degradation) (Vandepaer and Gibon 2018 ). Outside IAM- or ESM-based studies, technological change of FG processes is typically represented through selective, exogenous updates of a few parameters—typically electricity mixes or efficiencies—based on literature, policy documents, or assumptions. This fragmented approach can create temporal mismatches across subsystems (Arvidsson et al. 2018 ; Thonemann et al. 2020 ). Prospective LCI databases like premise (Sacchi et al. 2022 ) help reduce this, but not entirely. While premise is the most comprehensive approach, it has so far only integrated IAM variables related to electricity production, steel production, cement production, fuel production and transport (Sacchi 2025 ), meaning that some temporal mismatches remain. For a recent critique of the use of IAMs for prospective LCI databases, see de Bortoli et al. ( 2025 ). Among the 20 studies that use micro-level characterisation results from existing studies or databases—without their own LCI modelling—19 fail to apply temporal evolution consistently. Often, changes in BG processes are not propagated consistently. For example, Brand et al. ( 2012 ) assume a future reduction of the emission intensity of electricity supply, but apply it only to vehicle use, not to upstream or downstream processes. Apart from not being linked to system scale (see previous subsection), technological change is also rarely modelled with the needed complexity. That it is actor-driven and can be environmentally regressive (van Nielen et al. 2025 ) was not explicitly considered in any study. Few studies distinguish between emerging (Technology Readiness Level (TRL) < 9) and mature (TRL ≥ 9) technologies, despite fundamentally different dynamics: emerging technologies may still undergo major shifts during scale-up, while mature ones evolve incrementally (Buyle et al. 2019 ). Technological development is seldom modelled explicitly; linear interpolation remains common (e.g., Knobloch et al. 2020 ). For TRL ≥ 9, van Nielen et al. ( 2025 ) propose a structured approach to modelling learning, and guidance on scaling-up emerging technologies is also available (e.g., Thonemann et al. 2020 ; Tsoy et al. 2020 ; Erakca et al. 2024 ). Technological detail varies widely. Some studies include only a few options—like one drivetrain per vehicle (e.g., Tang et al. 2023 )—while others cover a broader range, such as six battery types (Tarabay et al. 2023 ). Studies that incorporate a wide spectrum of technological variants offer a more comprehensive view of the potential future. Market shares are usually treated as static or scenario-based; few studies model them endogenously, e.g., via discrete choice models (Brand et al. 2012 ; Mastrucci et al. 2024 ). Many studies update only market shares (especially electricity) without considering technology development at the process-level. Overall, systematic modelling of FG systems' evolution remains rare. A notable exception is Alaux et al. ( 2025a ), who use the SIMPL framework (Langkau et al. 2023 ) to develop scenarios for the greenhouse gas emissions of the Austrian building stock. 4.3 Temporal distribution modelling About half of the studies (41) neglect the temporal distribution of processes. The remaining 45 studies differentiate life cycle stages—typically upstream, use, and/or downstream—and link micro-level characterisation results for each to temporally distributed outputs of the respective coupled model, as described in section 3. It is worth noting that many studies omitting temporal distribution focus exclusively on either production or EoL (21 studies), often because their scope is limited to material-related impacts. More studies addressed temporal distribution than expected, but none added a temporal dimension at the process-level. While life cycle stage-level differentiation offers a partial solution, resolving the temporal distribution of processes within up- and downstream stages remains an important area of future research (Müller et al., unpublished manuscript under review). The tool bw_timex (Diepers et al. 2024 ) shows promise in advancing this approach. Note that considering temporal distributions for all processes in a life cycle—including those in the BG system—would require historical data, as some inflows originate from capital goods built long ago. 4.4 Inconsistent terminologies Terminological inconsistencies have long been a challenge in LCA, leading to what has been described as an "alphabet soup" of approaches (Guinée et al. 2018 ). In macro-level assessments, terms like large-scale , economy-wide , or system-wide are often used ambiguously or interchangeably, without formal definitions. For instance, Hellweg et al. ( 2023 ) mention “large-scale LCA analyses (in contrast to product-level LCAs)”, but do not clarify the meaning of large-scale . To our knowledge, no formal definition exists. To bring clarity, we define macro-level LCA as assessments of environmental impacts associated with the life cycle of a system fulfilling a specific function for the demand at the national, continental, or global scale , as shown in the introduction. We employ the term macro-level LCA , first used by Dandres et al. ( 2012 ) and inspired by economic terminology (micro, meso, macro), mainly to avoid confusion with upscaling , which in prospective LCA typically refers to technological maturity (Tsoy et al. 2020 ). For temporal aspects, we recommend following the terminology of Müller et al. (unpublished manuscript under review): dynamic LCA for modelling temporal distribution, prospective LCA for modelling temporal evolution, and time-explicit LCA when both are addressed. Regarding the micro-level LCA outputs scaled to the macro-level, most studies use the term emission factor , impact factor , or LCA coefficient . We find all three terms potentially confusing. Impact factor can easily be mistaken for characterisation factor , a distinct concept in LCA. Emission factor does not indicate that the values have already been characterised; emissions are typically associated with the LCI. LCA coefficient , meanwhile, is vague; a coefficient derived from LCA could refer to almost anything. We use and recommend the term characterisation result , which aligns with standard LCA terminology (Guinée 2002 ). 4.5 Functional units, system boundaries and multifunctionality Although the functional unit is central to LCA, only 43% of studies define it, and just 21% specify it at the macro-level. Examples of macro-level functional units include meeting the European Union’s energy demand by 2050 (Blanco et al. 2020 ), operating the United Kingdom’s light-duty vehicle fleet for a year (Raugei et al. 2021 ), and managing Sweden’s annual non-hazardous waste (Arushanyan et al. 2017 ). These demonstrate that meaningful functional units can be formulated at the macro-level. In prospective studies, it is important to account for changing functional units over time. This can be done, for example, by defining different functional units for each scenario and time frame (Moni et al. 2020 ). Generally, when comparing across time, it is important to account for changes in what the system provides, for example different levels of demand. With regard to system boundaries, many studies omit parts of the life cycle, especially the EoL. While acceptable depending on objectives, this risks incomplete results and suboptimal decisions. Particularly, the EoL stage for systems with long lifetimes, as many studies evaluate here, can be very important because its EoL lies far in the future, and the respective processes might look very different then (Cucurachi et al. 2023 ). Evolving technologies and societal needs can lead to shifts in process and system functions, with important implications in four main respects. First, allocation factors may need to be adapted to future conditions. Second, co-products may become scarce as fossil fuel production decreases, with consequences for resource availability (Månberger 2021 ; Hahn Menacho et al. 2025a ). Third, especially at the macro-level, the handling of co-products becomes increasingly important, for instance due to demand constraints. This aspect has been addressed by two studies (Adrianto et al. 2023 ; Nabera et al. 2024 ). Finally, system expansion can be particularly challenging, as additional functions are not necessarily consistent across scenarios. For example, in one scenario agri-photovoltaic may be included, adding an additional function to the system, while in another they may not, leading to a comparison between a system that provides both agricultural products and electricity and one that provides only the former. This issue is not specific to the macro-level but applies to any prospective LCA. 4.6 Lack of thematic and scenario diversity The reviewed studies exhibit a narrow thematic focus: 93% cover just four sectors—energy systems (25 studies); transport (26), mainly the transition to electric vehicles; intermediate goods (19), such as aluminium and hydrogen; and buildings (11). This leaves important areas like food, water, sanitation, consumer goods, and waste largely absent, despite their relevance for sustainability transitions. Broadening the thematic scope is important. Scenario choices also show limited diversity. Many studies rely (33) on IEA scenarios for system scaling or modelling temporal evolution, typically electricity mix projections. While this improves comparability, it risks reinforcing shared assumptions and biases (Schulze et al. 2024 ), such as underestimating the growth of renewable energy (Creutzig et al. 2017 ; Way et al. 2022 ; Lopez et al. 2025 ) . Regarding the SSP framework (O’Neill et al. 2017 ), often used via IAMs for system scaling or temporal evolution, as well as sometimes applied directly (e.g., Mastrucci et al. 2024 ), there is a strong focus on SSP2, the middle-of-the-road scenario. Despite the importance of scenario diversity (Bruhn et al. 2023 ), the intention of the SSP framework to explore diverse futures (Riahi et al. 2017 ) and premise supporting SSP1 and SSP5 as well (Sacchi 2025 ), we find only five studies (Pedneault et al. 2021 ; Kalt et al. 2022 ; Arvesen et al. 2024 ; Alaux et al. 2025a ; Horup et al. 2025 ) using other SSPs. For Representative Concentration Pathways (van Vuuren et al. 2011 ), we find greater diversity. Generally, IAMs often assume convergence towards Western lifestyles with increasing wealth, making modelling of alternative, more imaginative futures difficult (Hickel et al. 2021 ). This tendency is already embedded in the SSPs, all of which presuppose continued global economic growth (de Bortoli et al. 2025 ) Temporal scope also lacks variation: 71% of studies end in 2050, while only six extend beyond 2060. As Schulze et al. ( 2024 ) note, this can be problematic for sectors with long-lived assets, where shorter horizons may miss long-term implications due to system inertia or delayed material availability. To improve future analyses, we recommend focusing on a small set of contrasting, informative scenarios covering a wide range of possible futures. The SIMPL approach (Langkau et al. 2023 ), and its use in Alaux et al. ( 2025a ), demonstrate good practice. Furthermore, with 2050 only 25 years away, longer timeframes should be considered; despite greater uncertainty for longer time horizons (Steubing et al. 2023 ), especially with regard to disruptive technologies (Sacchi et al. 2022 ). 4.7 Internal consistency Beyond the inconsistencies in modelling temporal evolution discussed in subsection 4.2, we identify several additional sources of inconsistency. First, inconsistencies can arise between the models in the FG system, which are typically coupled one-way: external models inform LCA but not vice versa. Beaussier et al. ( 2019 ) highlight the need for high-level coupling—defined as “models that are linked and run together involving variables (…) in closed loops”—when (i) environmental impacts affect system processes or (ii) agents respond to environmental policies. Some ESM-based studies address this by optimising for least environmental impacts based on LCA results (e.g., Vandepaer et al. 2020 ). Generally, researchers need to be aware that model coupling inherits assumptions and modelling choices from the other model (de Bortoli et al. 2025 ), posing a potential challenge to consistency. Second, scenario misalignments are common; e.g., ambitious FG system policies paired with less ambitious BG system scenarios. Aligning the FG and BG system narratives is important for greater internal consistency. When using premise (Sacchi et al. 2022 ), it is important to recognise that it reflects the assumptions of the underlying IAM, which may not always align with FG system scenarios developed using other modelling frameworks, such as ESMs (Weidner et al. 2022 ). Third, one more issue we want to highlight is mass balance, a core principle in DSM methods but often violated in other models, such as IAMs (Pauliuk et al. 2017 ). A systematic assessment was not possible due to limited transparency in many studies. Here, we simply aim to raise awareness that mass balance cannot be assumed—especially when coupling models or representing closed-loop systems—and must be explicitly checked. Finally, for macro-level LCAs, the assumption that the BG system is independent of the FG system no longer holds. Given the scale of the FG system, it must be considered that BG processes may also source from the FG system (Charalambous et al. 2024 ). A few studies address this (e.g., Hertwich et al. 2015 ). Charalambous et al. ( 2024 ) demonstrate how FG system changes can be propagated through the BG system in premise by adjusting BG markets. However, inconsistencies with the original IAM scenario remain, as it does not account for the modelled FG system developments. The significance of FG–BG system inconsistency also depends on the study’s scope. ESMs, for example, cover entire electricity or energy systems in the FG system, leaving a small BG. Therefore, a limited error can be assumed. Nevertheless, building on Gibon et al. ( 2015 ) and Charalambous et al. ( 2024 ) to better integrate FG and BG systems is a key next step. 4.8 Representing the complexity of sustainability transitions Sustainability transitions are inherently complex, and no single model captures all relevant dimensions. Understanding each model’s scope and limitations is therefore essential. For example, as Wiedenhofer et al. ( 2024 ) note, there is often a trade-off between capturing socio-economic complexity and adhering to thermodynamic principles: models like LCA provide detailed representations of technologies and their interlinkages through energy and mass flows but typically omit inter-sectoral relationships and broader economic feedbacks. Rather than proposing a comprehensive framework for modelling transitions, we want to highlight aspects addressed or overlooked in prospective macro-level LCA literature. As discussed above, technological learning is typically included but often oversimplified (van Nielen et al. 2025 ) and cross-sector effects are only captured by a few studies (e.g., Rüdisüli et al. 2022 ). Whether IAM-LCA studies capture these depends on the coupling: approaches including micro-level characterisation results in the IAM (Pehl et al. 2017 ) represent all aspects also included in the IAM, whereas ex-post applications (e.g., Müller et al. 2024 ) capture only those that affect the scaling variables, e.g. demand. The same applies to ESM-LCA studies. Aspects that we have not found represented explicitly include rebound effects, threshold effects, incumbent power, institutional roles, behaviour, the role of institutions (Hafner et al. 2020 ), and tipping points (Binder et al. 2025 ). Actor heterogeneity is only captured in some IAM–LCA studies indirectly through the IAM, and then only to a limited extent, mostly focusing on income inequality (Keppo et al. 2021 ; de Bortoli et al. 2025 ). Generally, models focus on aggregate outcomes, obscuring differences in access, needs, and responsibility. Prospective macro-level LCA could better represent sustainability transitions by leveraging social science perspectives, such as heterodox economics (Hafner et al. 2020 ), underused methods like SD or ABM, and new model combinations. Promising research directions include provisioning systems and sufficiency corridors (Pauliuk 2024 ; Pichler et al. 2025 ). Regarding mitigation strategies, Pichler et al. ( 2025 ) propose a 2×2 framework—supply vs. demand and individual vs. systemic—yielding four perspectives: techno-innovation, industrial transformation, individual decision-making, and embedded lifestyles. They highlight that focusing exclusively on supply or demand leaves critical gaps and clarify that sufficiency measures can also apply to the supply side and efficiency measures to the demand side. Applying the framework to our sample is challenging, as the drivers behind mitigation measures are rarely explicit. For example, assumed renewable energy uptake could reflect incentives or regulation, and fleet reductions could result from demand or supply changes. We do not apply the framework here but view it as valuable for guiding future modeling and stress the importance of clarifying underlying drivers in future studies. While enhancing the representation of sustainability transitions in current modelling practice is important, it is equally important to keep in mind that modelling complexity has trade-offs, like risking interpretability, and must be purpose-driven (Saltelli et al. 2020 ; Kaufman and Bataille 2025 ). Models should be as simple as possible but as complex as necessary and relying on multiple different approaches will always be needed. 4.9 Modelling of constraints Distinguishing constraints from scenario assumptions, parameters, or model structures is often difficult. A detailed discussion and conceptualisation are beyond the scope of this study, and since reviewing each coupled model in detail is not feasible here; we instead highlight only a few important points. Constraints help assess whether scenarios are not only environmentally beneficial but also feasible. Many studies include constraints, often through coupled models. In particular, ESMs and IAMs introduce constraints related to climate targets, capacities, infrastructure availability, technology-specific deployment, budgets, and many other aspects (e.g., Louis et al. 2018 ; Luderer et al. 2019 ; Reinert et al. 2021 ). DSMs often constrain secondary material availability (e.g., Milovanoff et al. 2019 ). Optimisation approaches are also able to include constraints well, such as manufacturing capacity (Hung et al. 2022 ; Lechtenberg et al. 2024 ). By contrast, studies without model coupling often omit explicit constraints, instead discussing limitations retrospectively, such as comparisons to current production (e.g., Ginster et al. 2024 ) or resource reserves (e.g., Zhang et al. 2024 ). While useful for context, this does not improve the feasibility of the modelled scenarios. Some types of constraints are not explicitly mentioned by any study, such as labour availability or incumbent power (Geels et al. 2017 ). Only two studies (Adrianto et al. 2023 ; Nabera et al. 2024 ) considered constrained co-product market absorption. Another aspect not found explicitly in the literature is competition for production capacities and resources between different systems of analysis (Hung et al. 2022 ); here, more granular and explicit modelling is needed. To guide future research, we propose, inspired by Wiedenhofer et al. ( 2024 ), four non-exclusive constraint types: (i) physical/ecological (e.g., raw material availability), (ii) social/political (e.g., labour shortages or incumbent interests), (iii) technological (e.g., technology availability or manufacturing capacities), and (iv) economic (e.g., insufficient investment). Importantly, the temporal dynamics of constraints deserve greater attention (Hung et al. 2022 ). A resource may be available in the long term, but short-term bottlenecks—such as mine development (Schulze et al. 2024 )—can limit the feasibility of scenarios (Hung et al. 2022 ). Additionally, geopolitical aspects may gain importance (Post and Le Billon 2025 ). Explicitly incorporating some of these constraints—both in model formulations and in scenario narratives—can improve realism in prospective macro-level LCA. Life cycle optimisation tools (e.g., Lechtenberg et al. 2024 ) offer promising means to implement such constraints. 4.10 Narrow focus on climate change impacts About half of the studies (44) assess climate change only. While crucial, this narrow focus neglects LCA’s strength in revealing trade-offs across impact categories. Broadening the scope is essential for future macro-LCAs (Hellweg et al. 2023 ), as existing research has shown that decarbonisation can lead to large environmental co-benefits and trade-offs (Luderer et al. 2019 ). Although a detailed discussion of Life Cycle Impact Assessment (LCIA) in prospective and macro-level LCA is beyond this study, we want to highlight a few important points. First, LCIA typically treats emissions as instantaneous pulses, ignoring temporal dynamics that may be relevant (Cardellini et al. 2018 ). Second, inconsistencies exist between inventory and impact phases regarding time horizons (Levasseur et al. 2010 ; Beloin-Saint-Pierre et al. 2020 ). Third, many models rely on static baseline concentrations, though these may change (Vandepaer and Gibon 2018 ; Lueddeckens et al. 2020 ). Using IAMs to project future background concentrations has been proposed, but not implemented (Mendoza Beltran et al. 2018 ). Fourth, emerging technologies may introduce novel emissions not yet covered by LCIA methods, which can result in overlooked impacts and a false sense of certainty (Thonemann et al. 2020 ; Cucurachi et al. 2023 ). Existing dynamic LCIA methods are so far focused on climate change (Levasseur et al. 2010 ; Milovanoff et al. 2022 ; Ventura 2023 ; Diepers and Müller 2025 ). Finally, no study addressed scale-dependent changes to LCIA methods—an aspect that deserves further attention. 4.11 Uncertainty and sensitivity Uncertainty and sensitivity are widely discussed in LCA but inconsistently defined and applied (Heijungs 2024 ), including in prospective LCA. For instance, Spielmann et al. ( 2005 ) view epistemic uncertainty—arising from limited knowledge of future systems—as central and best addressed through scenarios. In contrast, Langkau et al. ( 2023 ) associate it with methodological choices not captured by future scenarios. Here, we focus on how uncertainty and sensitivity are treated in the reviewed studies. Most studies use multiple scenarios, though only some explicitly frame them as addressing uncertainty (e.g., Horup et al. 2025 ). Dedicated uncertainty analyses using distributions for specific parameters are rare; only five studies apply Monte Carlo simulation (Dirnaichner et al. 2022 ; Kalt et al. 2022 ; Tang et al. 2023 ; Hahn Menacho et al. 2025a ; Shirmohammadi et al. 2025 ). LCIA uncertainty is not considered, except in Sacchi et al. ( 2023 ). Challenges include data limitations, especially projecting future uncertainty ranges, and applying uncertainty across coupled models. Given the absence of a ‘true’ future, we see structured, transparent cornerstone scenarios as the most practical way to address uncertainty (Langkau et al. 2023 ). About half of the studies (39) include sensitivity analyses, predominantly using local, one-at-a-time approaches (following the classification of Heijungs ( 2024 )). Changing coupled models (Boyce et al. 2024 ), LCIA methods (Reinert et al. 2021 ), accounting scope (Pehl et al. 2017 ), allocation approach (Dong et al. 2022 ), foresight horizon (Zibunas et al. 2024 ), or optimisation targets (Louis et al. 2018 ) can also be seen as local, one-at-a-time sensitivity analysis. Some studies build scenarios by systematically varying inputs, resembling sensitivity analysis (Ginster et al. 2024 ; Pauliuk et al. 2024b ). Only two studies apply global sensitivity analysis (Alaux et al. 2025b ; Hahn Menacho et al. 2025a ). The potential advantages of applying global sensitivity analysis more widely warrant closer examination. 4.12 Transparency and reproducibility Transparent documentation of methods, open-access publication, open-source models, and open data are essential for scientific progress; not only to ensure reproducibility but also to enhance effectiveness by fostering collaboration, enabling critical peer review, improving public trust in research, and upholding ethical standards (Pfenninger et al. 2017 ; Pauliuk 2020 ). In the reviewed studies, transparency is often limited: unclear objectives, functional units, system boundaries, and modelling choices (e.g., form of interpolation) are common. For instance, 32 studies using ecoinvent fail to specify the system model applied (Wernet et al. 2016 ). Model coupling is rarely well-documented. Clear illustrations of model interactions (e.g., Junne et al. 2020 ) and the FG system, for example through flowcharts, are needed. Scenario variables should be made explicit, ideally summarized in a concise overview as in van der Voet et al. ( 2019 ). A clear distinction between model inputs and outputs is equally essential. In addition, data sources and software should be disclosed, including version information where applicable (Steubing et al. 2023 ). While more challenging for multi-model studies, we recommend aligning with the ISO reporting requirements (ISO 2006 ), particularly for temporal scope, functional unit, and boundaries. Most studies (65) share input data, but we did not assess whether shared data is sufficient for reproducibility. Only 23 studies share their models. Müller et al. ( 2024 ) provide a good example for sharing data, and Alaux et al. ( 2024 ) for a reusable model. We recommend using repositories that support rich metadata, persistent identifiers, clear licensing, version control, and long-term preservation (e.g., Zenodo). For further guidance, see Pauliuk et al. ( 2024a ). 5 Conclusions and recommendations We reviewed 86 prospective macro-level LCA studies, categorising them into four main (DSM-LCA, ESM-LCA, IAM-LCA, no coupled model) and two less common (coupling GEM and SD) approaches. The approaches vary in focus and complexity. Each brings specific strengths and trade-offs: DSM-LCAs capture stock dynamics but lack coverage of socioeconomic aspects; ESM-LCAs provide detailed insights into energy systems but are also limited to it; IAM-LCAs provide better socioeconomic coverage but operate at coarse resolution and typically require collaboration with IAM developers; and approaches without a coupled model offer flexibility but risk oversimplification. We also identified common pitfalls, methodological challenges, and best practices. Our review reveals a diverse and growing but fragmented field with inconsistent terminology, assumptions, and modelling practices that limit comparability. Based on our findings, we suggest four priorities for advancing the field: · Improve the modelling of sustainability transitions . Better representation of temporal dynamics—especially for long-lived assets—and the implementation of feedbacks between FG and BG systems are important. Capturing the complexity of sustainability transitions requires advancing existing approaches: drawing on perspectives from the social sciences, such as heterodox economics (Hafner et al. 2020), exploring underused methods like SD and ABM, and experimenting with new model combinations. However, increased complexity must be purpose-driven; models should remain as simple as possible, but as complex as necessary (Saltelli et al. 2020; Kaufman and Bataille 2025). No single model can address all research questions. · Strengthen policy relevance. Stronger links between modelling and policy are needed, particularly in DSM-LCA and uncoupled approaches. Representing distributional aspects, demand-side measures, and behavioural responses will help support more just and effective policy design. · Ensure transparency, reproducibility and consistent terminology. Clearly reporting functional units, system boundaries, and data flows between coupled models is crucial. We also urge the field to converge on shared terminology, for which we propose macro-level LCA . For the time dimension, we recommend using prospective LCA for modelling temporal evolution and dynamic LCA for temporal distribution (Müller et al., unpublished manuscript under review). Models and data should be, as far as confidentiality considerations allow, openly available. · Develop methodological guidance . As the scope of LCA expands, so does its complexity, underscoring the need for guidance that maintains modelling freedom while at the same time facilitating transparency, quality, common language, and comparability (Bisinella et al. 2021). By addressing the aspects mentioned above, the field of prospective macro-level LCA can improve our modelling and understanding of pathways to sustainable societies. Abbreviations ABM Agent-Based Modelling BG Background DSM Dynamic Stock Model DSM-LCA Coupling of process-based Life Cycle Assessment with a Dynamic Stock Model EoL End-of-Life ESM Energy System Model ESM-LCA Coupling of process-based Life Cycle Assessment with an Energy System Model FG Foreground GEM General Equilibrium Model IAM Integrated Assessment Model IAM-LCA Coupling of process-based Life Cycle Assessment with an Integrated Assessment Model IEA International Energy Agency IO-LCA Input-Output Life Cycle Assessment LCA Process-based Life Cycle Assessment LCI Life Cycle Inventory LCIA Life Cycle Impact Assessment PEM Partial Equilibrium Model SD System Dynamics SSP Shared Socioeconomic Pathway TRL Technology Readiness Level Declarations Author contributions: This article is part of AP’s doctoral research. The concept for the article was developed by AP and NT, under the supervision of JG. AP conducted the literature search, carried out the analysis, and drafted the manuscript. All authors contributed to methodological discussions and provided critical revisions of the manuscript. Funding: This research at Leiden University has received funding from the Ministry of Education, Culture and Science of The Netherlands under the Starter Grant programme. Open access funding was provided by Leiden University. Data availability: All data are made available in the Supplementary Information. Competing interest: NT served as Guest Editor for the Special Issue to which this paper was submitted but was not involved in the editorial handling of this manuscript. JG is a member of the Editorial Board of the journal. Acknowledgements: We thank Thomas Arblaster for insightful discussions about the definition of macro-level LCA. References Adrianto LR, Ciacci L, Pfister S, Hellweg S (2023) Toward sustainable reprocessing and valorization of sulfidic copper tailings: Scenarios and prospective LCA. Sci Total Environ 871:162038. https://doi.org/10.1016/j.scitotenv.2023.162038 Alaux N, Schwark B, Hörmann M, Ruschi Mendes Saade M, Passer A (2024) Assessing the prospective environmental impacts and circularity potentials of building stocks: An open‐source model from Austria (PULSE‐AT). J of Industrial Ecology. https://doi.org/10.1111/jiec.13558 Alaux N, Steubing B, Habert G, Ruschi Mendes Saade M, Passer A (2025a) Future Life-Cycle Greenhouse Gas Emission Scenarios for the Austrian Building Stock: A Systematic Approach. Environ Sci Technol. https://doi.org/10.1021/acs.est.4c12138 Alaux N, Schwark B, Scherz M, Ruschi Mendes Saade M, Passer A (2025b) Parameters influencing the prospective life cycle emissions of the Austrian building stock: a global sensitivity analysis. Sustainable Production and Consumption 56:618–631. https://doi.org/10.1016/j.spc.2025.04.024 Arushanyan Y, Björklund A, Eriksson O, Finnveden G, Ljunggren Söderman M, Sundqvist J-O, Stenmarck Å (2017) Environmental Assessment of Possible Future Waste Management Scenarios. Energies 10:247. https://doi.org/10.3390/en10020247 Arvesen A, Bright RM, Hertwich EG (2011) Considering only first-order effects? How simplifications lead to unrealistic technology optimism in climate change mitigation. Energy Policy 39:7448–7454. https://doi.org/10.1016/j.enpol.2011.09.013 Arvesen A, Luderer G, Pehl M, Bodirsky BL, Hertwich EG (2018) Deriving life cycle assessment coefficients for application in integrated assessment modelling. Environmental Modelling & Software 99:111–125. https://doi.org/10.1016/j.envsoft.2017.09.010 Arvesen A, Humpenöder F, Navarrete Gutierrez T, Gibon T, Baustert P, Dietrich JP, Stadler K, Iordan C-M, Luderer G, Popp A, Cherubini F (2024) Advancing life cycle assessment of bioenergy crops with global land use models. Environ. Res. Commun. 6:125004. https://doi.org/10.1088/2515-7620/ad97ac Arvidsson R, Tillman A-M, Sandén BA, Janssen M, Nordelöf A, Kushnir D, Molander S (2018) Environmental Assessment of Emerging Technologies: Recommendations for Prospective LCA. J of Industrial Ecology 22:1286–1294. https://doi.org/10.1111/jiec.12690 Arvidsson R, Svanström M, Sandén BA, Thonemann N, Steubing B, Cucurachi S (2024) Terminology for future-oriented life cycle assessment: review and recommendations. Int J Life Cycle Assess 29:607–613. https://doi.org/10.1007/s11367-023-02265-8 Barkhausen R, Rostek L, Miao ZC, Zeller V (2023) Combinations of material flow analysis and life cycle assessment and their applicability to assess circular economy requirements in EU product regulations. A systematic literature review. Journal of Cleaner Production 407:137017. https://doi.org/10.1016/j.jclepro.2023.137017 Beaussier T, Caurla S, Bellon-Maurel V, Loiseau E (2019) Coupling economic models and environmental assessment methods to support regional policies: A critical review. Journal of Cleaner Production 216:408–421. https://doi.org/10.1016/j.jclepro.2019.01.020 Beloin-Saint-Pierre D, Albers A, Hélias A, Tiruta-Barna L, Fantke P, Levasseur A, Benetto E, Benoist A, Collet P (2020) Addressing temporal considerations in life cycle assessment. Sci Total Environ 743:140700. https://doi.org/10.1016/j.scitotenv.2020.140700 Bergerson JA, Brandt A, Cresko J, Carbajales‐Dale M, MacLean HL, Matthews HS, McCoy S, McManus M, Miller SA, Morrow WR, Posen ID, Seager T, Skone T, Sleep S (2020) Life cycle assessment of emerging technologies: Evaluation techniques at different stages of market and technical maturity. J of Industrial Ecology 24:11–25. https://doi.org/10.1111/jiec.12954 Binder CR, Athanassiadis A, Bristow D, Haberl H, Kennedy C (2025) Tipping points toward sustainability: The role of industrial ecology. J of Industrial Ecology. https://doi.org/10.1111/jiec.70000 Bisinella V, Christensen TH, Astrup TF (2021) Future scenarios and life cycle assessment: systematic review and recommendations. Int J Life Cycle Assess 26:2143–2170. https://doi.org/10.1007/s11367-021-01954-6 Blanco H, Codina V, Laurent A, Nijs W, Maréchal F, Faaij A (2020) Life cycle assessment integration into energy system models: An application for Power-to-Methane in the EU. Applied Energy 259:114160. https://doi.org/10.1016/j.apenergy.2019.114160 Bohnes FA, Hauschild MZ, Schlundt J, Nielsen M, Laurent A (2022) Environmental sustainability of future aquaculture production: Analysis of Singaporean and Norwegian policies. Aquaculture 549:737717. https://doi.org/10.1016/j.aquaculture.2021.737717 Boyce J, Sacchi R, Goetheer E, Steubing B (2024) A prospective life cycle assessment of global ammonia decarbonisation scenarios. Heliyon 10:e27547. https://doi.org/10.1016/j.heliyon.2024.e27547 Brand C, Tran M, Anable J (2012) The UK transport carbon model: An integrated life cycle approach to explore low carbon futures. Energy Policy 41:107–124. https://doi.org/10.1016/j.enpol.2010.08.019 Brand C, Anable J, Tran M (2013) Accelerating the transformation to a low carbon passenger transport system: The role of car purchase taxes, feebates, road taxes and scrappage incentives in the UK. Transportation Research Part A: Policy and Practice 49:132–148. https://doi.org/10.1016/j.tra.2013.01.010 Bruhn S, Sacchi R, Cimpan C, Birkved M (2023) Ten questions concerning prospective LCA for decision support for the built environment. Building and Environment 242:110535. https://doi.org/10.1016/j.buildenv.2023.110535 Buyle M, Audenaert A, Billen P, Boonen K, van Passel S (2019) The Future of Ex-Ante LCA? Lessons Learned and Practical Recommendations. Sustainability 11:5456. https://doi.org/10.3390/su11195456 Cabrera-Jiménez R, Mateo JM, Jiménez L, Pozo C (2025) Prospective life-cycle assessment of sustainable alternatives for road freight transport. Renewable and Sustainable Energy Reviews 211:115243. https://doi.org/10.1016/j.rser.2024.115243 Caiardi F, Azzaro-Pantel C, Le-Boulch D (2024) Exploring carbon neutrality scenarios through the life cycle assessment lens: a review of literature and methodological challenges. Environ Dev Sustain. https://doi.org/10.1007/s10668-024-05598-y Cardellini G, Mutel CL, Vial E, Muys B (2018) Temporalis, a generic method and tool for dynamic Life Cycle Assessment. Sci Total Environ 645:585–595. https://doi.org/10.1016/j.scitotenv.2018.07.044 Charalambous MA, Sacchi R, Tulus V, Guillén-Gosálbez G (2024) Integrating emerging technologies deployed at scale within prospective life cycle assessments. Sustainable Production and Consumption 50:499–510. https://doi.org/10.1016/j.spc.2024.08.016 Creutzig F, Agoston P, Goldschmidt JC, Luderer G, Nemet G, Pietzcker RC (2017) The underestimated potential of solar energy to mitigate climate change. Nat Energy 2. https://doi.org/10.1038/nenergy.2017.140 Creutzig F, Erb K-H, Haberl H, Hof C, Hunsberger C, Roe S (2021) Considering sustainability thresholds for BECCS in IPCC and biodiversity assessments. GCB Bioenergy 13:510–515. https://doi.org/10.1111/gcbb.12798 Cucurachi S, Blanco CF, Florin MV, Rusu AG (2023) Practical solutions for ex-ante LCA illustrated by emerging PV technologies. In: Florin M-V (ed) Ensuring the environmental sustainability of emerging technologies -2. Edited volume. Lausanne, EPFL, Lausanne, Switzerland Dandres T, Gaudreault C, Tirado-Seco P, Samson R (2012) Macroanalysis of the economic and environmental impacts of a 2005–2025 European Union bioenergy policy using the GTAP model and life cycle assessment. Renewable and Sustainable Energy Reviews 16:1180–1192. https://doi.org/10.1016/j.rser.2011.11.003 de Bortoli A, Chanel A, Chabas C, Greffe T, Louineau E (2025) More rationality and inclusivity are imperative in reference transition scenarios based on IAMs and shared socioeconomic pathways - recommendations for prospective LCA. Renewable and Sustainable Energy Reviews 222:115924. https://doi.org/10.1016/j.rser.2025.115924 Deng T, Zhang Y, Fu C (2023) Modelling dynamic interactions between material flow and stock: A review of dynamic material flow analysis. Ecological Indicators 156:111098. https://doi.org/10.1016/j.ecolind.2023.111098 Diepers T, Müller A (2025) dynamic_characterization documentation. https://dynamic-characterization.readthedocs.io/en/latest/. Accessed 12 June 2025 Diepers T, Müller A, Jakobs A (2024) bw_timex documentation: Time-explicit LCA with bw_timex. https://docs.brightway.dev/projects/bw-timex/en/latest/. Accessed 5 June 2025 Diepers T, Tautorus J (2025) bw_optimex documentation: Time-explicit Transition Pathway Optimization with. https://optimex.readthedocs.io/en/latest/. Accessed 2 July 2025 Dirnaichner A, Rottoli M, Sacchi R, Rauner S, Cox B, Mutel C, Bauer C, Luderer G (2022) Life-cycle impacts from different decarbonization pathways for the European car fleet. Environ. Res. Lett. 17:44009. https://doi.org/10.1088/1748-9326/ac4fdb Dong D, Tukker A, Steubing B, van Oers L, Rechberger H, Alonso Aguilar-Hernandez G, Li H, van der Voet E (2022) Assessing China’s potential for reducing primary copper demand and associated environmental impacts in the context of energy transition and “Zero waste” policies. Waste Manag 144:454–467. https://doi.org/10.1016/j.wasman.2022.04.006 Douziech M, Besseau R, Jolivet R, Shoai‐Tehrani B, Bourmaud J-Y, Busato G, Gresset‐Bourgeois M, Pérez‐López P (2024) Life cycle assessment of prospective trajectories: A parametric approach for tailor‐made inventories and its computational implementation. J of Industrial Ecology 28:25–40. https://doi.org/10.1111/jiec.13432 Earles JM, Halog A (2011) Consequential life cycle assessment: a review. Int J Life Cycle Assess 16:445–453. https://doi.org/10.1007/s11367-011-0275-9 Erakca M, Baumann M, Helbig C, Weil M (2024) Systematic review of scale-up methods for prospective life cycle assessment of emerging technologies. Journal of Cleaner Production 451:142161. https://doi.org/10.1016/j.jclepro.2024.142161 Evans S, Hausfather Z (2018) Q&A: How ‘integrated assessment models’ are used to study climate change. https://www.carbonbrief.org/qa-how-integrated-assessment-models-are-used-to-study-climate-change/. Accessed 20 May 2025 Geels FW, Sovacool BK, Schwanen T, Sorrell S (2017) Sociotechnical transitions for deep decarbonization. Science 357:1242–1244. https://doi.org/10.1126/science.aao3760 Gibon T, Wood R, Arvesen A, Bergesen JD, Suh S, Hertwich EG (2015) A Methodology for Integrated, Multiregional Life Cycle Assessment Scenarios under Large-Scale Technological Change. Environ Sci Technol 49:11218–11226. https://doi.org/10.1021/acs.est.5b01558 Gibon T, Hertwich EG, Arvesen A, Singh B, Verones F (2017) Health benefits, ecological threats of low-carbon electricity. Environ. Res. Lett. 12:34023. https://doi.org/10.1088/1748-9326/aa6047 Ginster R, Blömeke S, Popien J-L, Scheller C, Cerdas F, Herrmann C, Spengler TS (2024) Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts. J of Industrial Ecology 28:1165–1182. https://doi.org/10.1111/jiec.13527 Grubler A, Wilson C, Bento N, Boza-Kiss B, Krey V, McCollum DL, Rao ND, Riahi K, Rogelj J, Stercke S de, Cullen J, Frank S, Fricko O, Guo F, Gidden M, Havlík P, Huppmann D, Kiesewetter G, Rafaj P, Schoepp W, Valin H (2018) A low energy demand scenario for meeting the 1.5 °C target and sustainable development goals without negative emission technologies. Nat Energy 3:515–527. https://doi.org/10.1038/s41560-018-0172-6 Guinée JB (2002) Handbook on Life Cycle Assessment: Operational Guide to the ISO Standards. Eco-Efficiency in Industry and Science Ser, v.7. Springer Netherlands, Dordrecht Guinée JB, Heijungs R, Huppes G, Zamagni A, Masoni P, Buonamici R, Ekvall T, Rydberg T (2011) Life cycle assessment: past, present, and future. Environ Sci Technol 45:90–96. https://doi.org/10.1021/es101316v Guinée JB, Heijungs R, Vijver MG, Peijnenburg WJGM (2017) Setting the stage for debating the roles of risk assessment and life-cycle assessment of engineered nanomaterials. Nat Nanotechnol 12:727–733. https://doi.org/10.1038/nnano.2017.135 Guinée JB, Cucurachi S, Henriksson PJG, Heijungs R (2018) Digesting the alphabet soup of LCA. Int J Life Cycle Assess 23:1507–1511. https://doi.org/10.1007/s11367-018-1478-0 Hafner S, Anger-Kraavi A, Monasterolo I, Jones A (2020) Emergence of New Economics Energy Transition Models: A Review. Ecological Economics 177:106779. https://doi.org/10.1016/j.ecolecon.2020.106779 Hagenaars RH, Heijungs R, Tukker A, Wang R (2025) Hybrid LCA for sustainable transitions: principles, applications, and prospects. Renewable and Sustainable Energy Reviews 212:115443. https://doi.org/10.1016/j.rser.2025.115443 Hahn Menacho AJ, Sacchi R, Bauer C, Panos E, McKenna R, Burgherr P (2025a) The material-energy nexus in net-zero transition scenarios: exploring environmental trade-offs and uncertainties. Resources, Conservation and Recycling 218:108251. https://doi.org/10.1016/j.resconrec.2025.108251 Hahn Menacho AJ, Sacchi R, Bauer C, Moretti C, Panos E, McKenna R, Burgherr P (2025b) The global environmental footprint of Switzerland’s net-zero energy system uncovers impacts abroad. Commun Earth Environ 6. https://doi.org/10.1038/s43247-025-02220-5 Harpprecht C, Miranda Xicotencatl B, van Nielen S, van der Meide M, Li C, Li Z, Tukker A, Steubing B (2024) Future environmental impacts of metals: A systematic review of impact trends, modelling approaches, and challenges. Resources, Conservation and Recycling 205:107572. https://doi.org/10.1016/j.resconrec.2024.107572 Heijungs R (2020) Is mainstream LCA linear? Int J Life Cycle Assess 25:1872–1882. https://doi.org/10.1007/s11367-020-01810-z Heijungs R (2024) Probability, Statistics and Life Cycle Assessment. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-031-49317-1 Hellweg S, Benetto E, Huijbregts MAJ, Verones F, Wood R (2023) Life-cycle assessment to guide solutions for the triple planetary crisis. Nat Rev Earth Environ 4:471–486. https://doi.org/10.1038/s43017-023-00449-2 Hertwich EG, Gibon T, Bouman EA, Arvesen A, Suh S, Heath GA, Bergesen JD, Ramirez A, Vega MI, Shi L (2015) Integrated life-cycle assessment of electricity-supply scenarios confirms global environmental benefit of low-carbon technologies. Proc Natl Acad Sci U S A 112:6277–6282. https://doi.org/10.1073/pnas.1312753111 Hickel J, Brockway P, Kallis G, Keyßer L, Lenzen M, Slameršak A, Steinberger J, Ürge-Vorsatz D (2021) Urgent need for post-growth climate mitigation scenarios. Nat Energy 6:766–768. https://doi.org/10.1038/s41560-021-00884-9 Horup L, Bruhn S, Hoxha E, Birgisdottir H, Secher AQ, Ohms P, Hauschild M (2025) Absolute sustainability assessment of the Danish building sector through prospective LCA. Sci Total Environ 966:178780. https://doi.org/10.1016/j.scitotenv.2025.178780 Hung CR, Kishimoto P, Krey V, Strømman AH, Majeau‐Bettez G (2022) ECOPT 2 : An adaptable life cycle assessment model for the environmentally constrained optimization of prospective technology transitions. J of Industrial Ecology 26:1616–1630. https://doi.org/10.1111/jiec.13331 ISO (2006) ISO 14040: Environmental management - Life cycle assessment - Principles and framework, International Organization for Standardization, Geneva Jeswani HK, Azapagic A, Schepelmann P, Ritthoff M (2010) Options for broadening and deepening the LCA approaches. Journal of Cleaner Production 18:120–127. https://doi.org/10.1016/j.jclepro.2009.09.023 Junne T, Simon S, Buchgeister J, Saiger M, Baumann M, Haase M, Wulf C, Naegler T (2020) Environmental Sustainability Assessment of Multi-Sectoral Energy Transformation Pathways: Methodological Approach and Case Study for Germany. Sustainability 12:8225. https://doi.org/10.3390/su12198225 Kalt G, Thunshirn P, Krausmann F, Haberl H (2022) Material requirements of global electricity sector pathways to 2050 and associated greenhouse gas emissions. Journal of Cleaner Production 358:132014. https://doi.org/10.1016/j.jclepro.2022.132014 Kaufman N, Bataille C (2025) Avoiding misuses of energy-economic modelling in climate policymaking. Nat. Clim. Chang. 15:463–465. https://doi.org/10.1038/s41558-025-02280-7 Keppo I, Butnar I, Bauer N, Caspani M, Edelenbosch O, Emmerling J, Fragkos P, Guivarch C, Harmsen M, Lefèvre J, Le Gallic T, Leimbach M, McDowall W, Mercure J-F, Schaeffer R, Trutnevyte E, Wagner F (2021) Exploring the possibility space: taking stock of the diverse capabilities and gaps in integrated assessment models. Environ. Res. Lett. 16:53006. https://doi.org/10.1088/1748-9326/abe5d8 Knobloch F, Hanssen S, Lam A, Pollitt H, Salas P, Chewpreecha U, Huijbregts MAJ, Mercure J-F (2020) Net emission reductions from electric cars and heat pumps in 59 world regions over time. Nat Sustain 3:437–447. https://doi.org/10.1038/s41893-020-0488-7 Krey V, Guo F, Kolp P, Zhou W, Schaeffer R, Awasthy A, Bertram C, Boer H-S de, Fragkos P, Fujimori S, He C, Iyer G, Keramidas K, Köberle AC, Oshiro K, Reis LA, Shoai-Tehrani B, Vishwanathan S, Capros P, Drouet L, Edmonds JE, Garg A, Gernaat DE, Jiang K, Kannavou M, Kitous A, Kriegler E, Luderer G, Mathur R, Muratori M, Sano F, van Vuuren DP (2019) Looking under the hood: A comparison of techno-economic assumptions across national and global integrated assessment models. Energy 172:1254–1267. https://doi.org/10.1016/j.energy.2018.12.131 Kuipers KJ, van Oers LF, Verboon M, van der Voet E (2018) Assessing environmental implications associated with global copper demand and supply scenarios from 2010 to 2050. Global Environmental Change 49:106–115. https://doi.org/10.1016/j.gloenvcha.2018.02.008 Langkau S, Steubing B, Mutel C, Ajie MP, Erdmann L, Voglhuber-Slavinsky A, Janssen M (2023) A stepwise approach for Scenario-based Inventory Modelling for Prospective LCA (SIMPL). Int J Life Cycle Assess 28:1169–1193. https://doi.org/10.1007/s11367-023-02175-9 Le Luu Q, Gibon T, Cellura M, Sanseverino ER, Longo S (2024) Integrated hybrid multi-regional input-output for assessing life cycle air emissions of the Italian power system. Energy 290:130109. https://doi.org/10.1016/j.energy.2023.130109 Lechtenberg F, Istrate R, Tulus V, Espuña A, Graells M, Guillén-Gosálbez G (2024) PULPO: A framework for efficient integration of life cycle inventory models into life cycle product optimization. Journal of Industrial Ecology 28:1449–1463. https://doi.org/10.1111/jiec.13561 Levasseur A, Lesage P, Margni M, Deschênes L, Samson R (2010) Considering time in LCA: dynamic LCA and its application to global warming impact assessments. Environ Sci Technol 44:3169–3174. https://doi.org/10.1021/es9030003 Ljunggren Söderman M, Eriksson O, Björklund A, Östblom G, Ekvall T, Finnveden G, Arushanyan Y, Sundqvist J-O (2016) Integrated Economic and Environmental Assessment of Waste Policy Instruments. Sustainability 8:411. https://doi.org/10.3390/su8050411 Lopez G, Pourjamal Y, Breyer C (2025) Paving the way towards a sustainable future or lagging behind? An ex-post analysis of the International Energy Agency’s World Energy Outlook. Renewable and Sustainable Energy Reviews 212:115371. https://doi.org/10.1016/j.rser.2025.115371 Louis J-N, Allard S, Debusschere V, Mima S, Tran-Quoc T, Hadjsaid N (2018) Environmental impact indicators for the electricity mix and network development planning towards 2050 – A POLES and EUTGRID model. Energy 163:618–628. https://doi.org/10.1016/j.energy.2018.08.093 Luderer G, Pehl M, Arvesen A, Gibon T, Bodirsky BL, Boer HS de, Fricko O, Hejazi M, Humpenöder F, Iyer G, Mima S, Mouratiadou I, Pietzcker RC, Popp A, van den Berg M, van Vuuren D, Hertwich EG (2019) Environmental co-benefits and adverse side-effects of alternative power sector decarbonization strategies. Nat Commun 10:5229. https://doi.org/10.1038/s41467-019-13067-8 Lueddeckens S, Saling P, Guenther E (2020) Temporal issues in life cycle assessment—a systematic review. Int J Life Cycle Assess 25:1385–1401. https://doi.org/10.1007/s11367-020-01757-1 Månberger A (2021) Reduced Use of Fossil Fuels can Reduce Supply of Critical Resources. Biophys Econ Sust 6. https://doi.org/10.1007/s41247-021-00088-5 Mastrucci A, Guo F, Zhong X, Maczek F, van Ruijven B (2024) Circular strategies for building sector decarbonization in China: A scenario analysis. J of Industrial Ecology 28:1089–1102. https://doi.org/10.1111/jiec.13523 McAvoy S, Grant T, Smith C, Bontinck P (2021) Combining Life Cycle Assessment and System Dynamics to improve impact assessment: A systematic review. Journal of Cleaner Production 315:128060. https://doi.org/10.1016/j.jclepro.2021.128060 Mendoza Beltran A, Cox B, Mutel C, Vuuren DP, Font Vivanco D, Deetman S, Edelenbosch OY, Guinée J, Tukker A (2018) When the Background Matters: Using Scenarios from Integrated Assessment Models in Prospective Life Cycle Assessment. Journal of Industrial Ecology 24:64–79. https://doi.org/10.1111/jiec.12825 Milovanoff A, Kim HC, Kleine R de, Wallington TJ, Posen ID, MacLean HL (2019) A Dynamic Fleet Model of U.S Light-Duty Vehicle Lightweighting and Associated Greenhouse Gas Emissions from 2016 to 2050. Environ Sci Technol 53:2199–2208. https://doi.org/10.1021/acs.est.8b04249 Milovanoff A, MacLean HL, Abdul-Manan AFN, Posen ID (2022) Does the metric matter? Climate change impacts of light-duty vehicle electrification in the US. Environ. Res.: Infrastruct. Sustain. 2:35007. https://doi.org/10.1088/2634-4505/ac8071 Moni SM, Mahmud R, High K, Carbajales‐Dale M (2020) Life cycle assessment of emerging technologies: A review. J of Industrial Ecology 24:52–63. https://doi.org/10.1111/jiec.12965 Moon YB (2017) Simulation modelling for sustainability: a review of the literature. International Journal of Sustainable Engineering 10:2–19. https://doi.org/10.1080/19397038.2016.1220990 Müller A, Harpprecht C, Sacchi R, Maes B, van Sluisveld M, Daioglou V, Šavija B, Steubing B (2024) Decarbonizing the cement industry: Findings from coupling prospective life cycle assessment of clinker with integrated assessment model scenarios. Journal of Cleaner Production 450:141884. https://doi.org/10.1016/j.jclepro.2024.141884 Nabera A, José Martín A, Istrate R, Pérez-Ramírez J, Guillén-Gosálbez G (2024) Integrating climate policies in the sustainability analysis of green chemicals. Green Chem 26:6461–6469. https://doi.org/10.1039/d4gc00392f O’Neill BC, Kriegler E, Ebi KL, Kemp-Benedict E, Riahi K, Rothman DS, van Ruijven BJ, van Vuuren DP, Birkmann J, Kok K, Levy M, Solecki W (2017) The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21 st century. Global Environmental Change 42:169–180. https://doi.org/10.1016/j.gloenvcha.2015.01.004 Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. https://doi.org/10.1136/bmj.n71 Palazzo J, Geyer R, Suh S (2020) A review of methods for characterizing the environmental consequences of actions in life cycle assessment. J of Industrial Ecology 24:815–829. https://doi.org/10.1111/jiec.12983 Pauliuk S (2020) Making sustainability science a cumulative effort. Nat Sustain 3:2–4. https://doi.org/10.1038/s41893-019-0443-7 Pauliuk S (2024) Decent living standards, prosperity, and excessive consumption in the Lorenz curve. Ecological Economics 220:108161. https://doi.org/10.1016/j.ecolecon.2024.108161 Pauliuk S, Heeren N (2020) ODYM—An open software framework for studying dynamic material systems: Principles, implementation, and data structures. J of Industrial Ecology 24:446–458. https://doi.org/10.1111/jiec.12952 Pauliuk S, Arvesen A, Stadler K, Hertwich EG (2017) Industrial ecology in integrated assessment models. Nature Clim Change 7:13–20. https://doi.org/10.1038/nclimate3148 Pauliuk S, Helbig C, Lupton R, Pichler PP, Schulte S, Stadler K, Wang P, Wiedenhofer D (2024a) Guidelines and good practice examples for complete traceability of workflows and reproducibility of results in industrial ecology research. https://doi.org/10.6094/UNIFR/255618 Pauliuk S, Carrer F, Heeren N, Hertwich EG (2024b) Scenario analysis of supply‐ and demand‐side solutions for circular economy and climate change mitigation in the global building sector. J of Industrial Ecology. https://doi.org/10.1111/jiec.13557 Pedneault J, Majeau-Bettez G, Krey V, Margni M (2021) What future for primary aluminium production in a decarbonizing economy? Global Environmental Change 69:102316. https://doi.org/10.1016/j.gloenvcha.2021.102316 Pehl M, Arvesen A, Humpenöder F, Popp A, Hertwich EG, Luderer G (2017) Understanding future emissions from low-carbon power systems by integration of life-cycle assessment and integrated energy modelling. Nat Energy 2:939–945. https://doi.org/10.1038/s41560-017-0032-9 Pfenninger S, DeCarolis J, Hirth L, Quoilin S, Staffell I (2017) The importance of open data and software: Is energy research lagging behind? Energy Policy 101:211–215. https://doi.org/10.1016/j.enpol.2016.11.046 Pichler M, Bärnthaler R, Wiedenhofer D, Roux N, Gingrich S (2025) Conceptualizing supply- and demand-side climate change mitigation: A typology and new research directions. Energy Research & Social Science 127:104225. https://doi.org/10.1016/j.erss.2025.104225 Pizzol M, Sacchi R, Köhler S, Anderson Erjavec A (2021) Non-linearity in the Life Cycle Assessment of Scalable and Emerging Technologies. Front. Sustain. 1. https://doi.org/10.3389/frsus.2020.611593 Plevin RJ (2017) Assessing the Climate Effects of Biofuels Using Integrated Assessment Models, Part I: Methodological Considerations. J of Industrial Ecology 21:1478–1487. https://doi.org/10.1111/jiec.12507 Post E, Le Billon P (2025) The ‘Green War’: Geopolitical Metabolism and Green Extractivisms. Geopolitics 30:760–800. https://doi.org/10.1080/14650045.2024.2385411 Proctor JC (2023) Expanding the possible: exploring the role for heterodox economics in integrated climate-economy modeling. Rev Evol Polit Econ:1–21. https://doi.org/10.1007/s43253-023-00098-7 Raugei M, Kamran M, Hutchinson A (2021) Environmental implications of the ongoing electrification of the UK light duty vehicle fleet. Resources, Conservation and Recycling 174:105818. https://doi.org/10.1016/j.resconrec.2021.105818 Reinert C, Deutz S, Minten H, Dörpinghaus L, Pfingsten S von, Baumgärtner N, Bardow A (2021) Environmental impacts of the future German energy system from integrated energy systems optimization and dynamic life cycle assessment. Computers & Chemical Engineering 153:107406. https://doi.org/10.1016/j.compchemeng.2021.107406 Riahi K, van Vuuren DP, Kriegler E, Edmonds J, O’Neill BC, Fujimori S, Bauer N, Calvin K, Dellink R, Fricko O, Lutz W, Popp A, Cuaresma JC, KC S, Leimbach M, Jiang L, Kram T, Rao S, Emmerling J, Ebi K, Hasegawa T, Havlik P, Humpenöder F, Da Silva LA, Smith S, Stehfest E, Bosetti V, Eom J, Gernaat D, Masui T, Rogelj J, Strefler J, Drouet L, Krey V, Luderer G, Harmsen M, Takahashi K, Baumstark L, Doelman JC, Kainuma M, Klimont Z, Marangoni G, Lotze-Campen H, Obersteiner M, Tabeau A, Tavoni M (2017) The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change 42:153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009 Riondet L, Rio M, Perrot-Bernardet V, Zwolinski P (2024) Emerging technologies upscaling: A framework for matching LCA practices with upscaling archetypes. Sustainable Production and Consumption 50:347–363. https://doi.org/10.1016/j.spc.2024.07.032 Rossi F, Tosti L, Basosi R, Cusenza MA, Parisi ML, Sinicropi A (2023) Environmental optimization model for the European batteries industry based on prospective life cycle assessment and material flow analysis. Renewable and Sustainable Energy Reviews 183:113485. https://doi.org/10.1016/j.rser.2023.113485 Rüdisüli M, Bach C, Bauer C, Beloin-Saint-Pierre D, Elber U, Georges G, Limpach R, Pareschi G, Kannan R, Teske SL (2022) Prospective life-cycle assessment of greenhouse gas emissions of electricity-based mobility options. Applied Energy 306:118065. https://doi.org/10.1016/j.apenergy.2021.118065 Sacchi R, Hahn-Menacho AJ (2024) pathways: life cycle assessment of energy transition scenarios. JOSS 9:7309. https://doi.org/10.21105/joss.07309 Sacchi R, Becattini V, Gabrielli P, Cox B, Dirnaichner A, Bauer C, Mazzotti M (2023) How to make climate-neutral aviation fly. Nat Commun 14:3989. https://doi.org/10.1038/s41467-023-39749-y Sacchi R (2025) User guide — premise documentation 2025. 6 June 2025 Sacchi R, Terlouw T, Siala K, Dirnaichner A, Bauer C, Cox B, Mutel C, Daioglou V, Luderer G (2022) PRospective EnvironMental Impact asSEment (premise): A streamlined approach to producing databases for prospective life cycle assessment using integrated assessment models. Renewable and Sustainable Energy Reviews 160:112311. https://doi.org/10.1016/j.rser.2022.112311 Saltelli A, Bammer G, Bruno I, Charters E, Di Fiore M, Didier E, Nelson Espeland W, Kay J, Lo Piano S, Mayo D, Pielke R, Portaluri T, Porter TM, Puy A, Rafols I, Ravetz JR, Reinert E, Sarewitz D, Stark PB, Stirling A, van der Sluijs J, Vineis P (2020) Five ways to ensure that models serve society: a manifesto. Nature 582:482–484. https://doi.org/10.1038/d41586-020-01812-9 Schulze K, Kullmann F, Weinand JM, Stolten D (2024) Overcoming the challenges of assessing the global raw material demand of future energy systems. Joule 8:1936–1957. https://doi.org/10.1016/j.joule.2024.05.016 Shirmohammadi R, Vázquez D, Calvo-Serrano R, Llovell F, Gonzalez-Olmos R (2025) Absolute Environmental Sustainability Assessment of Emerging Working Fluids in Organic Rankine Cycles. ACS Sustainable Chem. Eng. https://doi.org/10.1021/acssuschemeng.5c01290 Siddaway AP, Wood AM, Hedges LV (2019) How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-Analyses, and Meta-Syntheses. Annual Review of Psychology 70:747–770. https://doi.org/10.1146/annurev-psych-010418-102803 Sigüenza CP, Cucurachi S, Tukker A (2021) Circular business models of washing machines in the Netherlands: Material and climate change implications toward 2050. Sustainable Production and Consumption 26:1084–1098. https://doi.org/10.1016/j.spc.2021.01.011 Šimaitis J, Butnar I, Sacchi R, Lupton R, Vagg C, Allen S (2025) Expanding scenario diversity in prospective LCA: Coupling the TIAM-UCL integrated assessment model with Premise and ecoinvent. Renewable and Sustainable Energy Reviews 211:115298. https://doi.org/10.1016/j.rser.2024.115298 Sohn J, Kalbar P, Goldstein B, Birkved M (2020) Defining Temporally Dynamic Life Cycle Assessment: A Review. Integr Environ Assess Manag 16:314–323. https://doi.org/10.1002/ieam.4235 Spielmann M, Scholz R, Tietje O, Haan P de (2005) Scenario Modelling in Prospective LCA of Transport Systems. Application of Formative Scenario Analysis (11 pp). Int J Life Cycle Assessment 10:325–335. https://doi.org/10.1065/lca2004.10.188 Steubing B, Koning D de (2021) Making the use of scenarios in LCA easier: the superstructure approach. Int J Life Cycle Assess 26:2248–2262. https://doi.org/10.1007/s11367-021-01974-2 Steubing B, Mendoza Beltran A, Sacchi R (2023) Conditions for the broad application of prospective life cycle inventory databases. Int J Life Cycle Assess 28:1092–1103. https://doi.org/10.1007/s11367-023-02192-8 Suh S, Yang Y (2014) On the uncanny capabilities of consequential LCA. Int J Life Cycle Assess 19:1179–1184. https://doi.org/10.1007/s11367-014-0739-9 Tang C, Tukker A, Sprecher B, Mogollón JM (2023) Assessing the European Electric-Mobility Transition: Emissions from Electric Vehicle Manufacturing and Use in Relation to the EU Greenhouse Gas Emission Targets. Environ Sci Technol 57:44–52. https://doi.org/10.1021/acs.est.2c06304 Tarabay B, Milovanoff A, Abdul-Manan AF, McKechnie J, MacLean HL, Posen ID (2023) New cathodes now, recycling later: Dynamic scenarios to reduce battery material use and greenhouse gas emissions from U.S. light-duty electric vehicle fleet. Resources, Conservation and Recycling 196:107028. https://doi.org/10.1016/j.resconrec.2023.107028 Thonemann N, Schulte A, Maga D (2020) How to Conduct Prospective Life Cycle Assessment for Emerging Technologies? A Systematic Review and Methodological Guidance. Sustainability 12:1192. https://doi.org/10.3390/su12031192 Tsoy N, Steubing B, van der Giesen C, Guinée J (2020) Upscaling methods used in ex ante life cycle assessment of emerging technologies: a review. Int J Life Cycle Assess 25:1680–1692. https://doi.org/10.1007/s11367-020-01796-8 van der Giesen C, Cucurachi S, Guinée J, Kramer GJ, Tukker A (2020) A critical view on the current application of LCA for new technologies and recommendations for improved practice. Journal of Cleaner Production 259:120904. https://doi.org/10.1016/j.jclepro.2020.120904 van der Meide M, Harpprecht C, Northey S, Yang Y, Steubing B (2022) Effects of the energy transition on environmental impacts of cobalt supply: A prospective life cycle assessment study on future supply of cobalt. J of Industrial Ecology 26:1631–1645. https://doi.org/10.1111/jiec.13258 van der Voet E, van Oers L, Verboon M, Kuipers K (2019) Environmental Implications of Future Demand Scenarios for Metals: Methodology and Application to the Case of Seven Major Metals. J of Industrial Ecology 23:141–155. https://doi.org/10.1111/jiec.12722 van Nielen SS, Kleijn R, Tukker A (2025) Accounting for learning in prospective LCA: Theory and practical guidance. J of Industrial Ecology. https://doi.org/10.1111/jiec.70002 van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque J-F, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK (2011) The representative concentration pathways: an overview. Climatic Change 109:5–31. https://doi.org/10.1007/s10584-011-0148-z van Vuuren DP, Hof AF, van Sluisveld MAE, Riahi K (2017) Open discussion of negative emissions is urgently needed. Nat Energy 2:902–904. https://doi.org/10.1038/s41560-017-0055-2 Vandepaer L, Gibon T (2018) The integration of energy scenarios into LCA: LCM2017 Conference Workshop, Luxembourg, September 5, 2017. Int J Life Cycle Assess 23:970–977. https://doi.org/10.1007/s11367-017-1435-3 Vandepaer L, Panos E, Bauer C, Amor B (2020) Energy System Pathways with Low Environmental Impacts and Limited Costs: Minimizing Climate Change Impacts Produces Environmental Cobenefits and Challenges in Toxicity and Metal Depletion Categories. Environ Sci Technol 54:5081–5092. https://doi.org/10.1021/acs.est.9b06484 Ventura A (2022) Transition life cycle assessment: A new method to face ecological transition. Front. Sustain. 3. https://doi.org/10.3389/frsus.2022.801668 Ventura A (2023) Conceptual issue of the dynamic GWP indicator and solution. Int J Life Cycle Assess 28:788–799. https://doi.org/10.1007/s11367-022-02028-x Volkart K, Weidmann N, Bauer C, Hirschberg S (2017) Multi-criteria decision analysis of energy system transformation pathways: A case study for Switzerland. Energy Policy 106:155–168. https://doi.org/10.1016/j.enpol.2017.03.026 Volkart K, Mutel CL, Panos E (2018) Integrating life cycle assessment and energy system modelling: Methodology and application to the world energy scenarios. Sustainable Production and Consumption 16:121–133. https://doi.org/10.1016/j.spc.2018.07.001 Way R, Ives MC, Mealy P, Farmer JD (2022) Empirically grounded technology forecasts and the energy transition. Joule 6:2057–2082. https://doi.org/10.1016/j.joule.2022.08.009 Weidner T, Galán-Martín Á, Ryberg MW, Guillén-Gosálbez G (2022) Energy systems modeling and optimization for absolute environmental sustainability: current landscape and opportunities. Computers & Chemical Engineering 164:107883. https://doi.org/10.1016/j.compchemeng.2022.107883 Weidner T, Tulus V, Guillén-Gosálbez G (2023) Environmental sustainability assessment of large-scale hydrogen production using prospective life cycle analysis. International Journal of Hydrogen Energy 48:8310–8327. https://doi.org/10.1016/j.ijhydene.2022.11.044 Wernet G, Bauer C, Steubing B, Reinhard J, Moreno-Ruiz E, Weidema B (2016) The ecoinvent database version 3 (part I): overview and methodology. Int J Life Cycle Assess 21:1218–1230. https://doi.org/10.1007/s11367-016-1087-8 Wiebe KS, Aponte FR, Kaufmann R, Lampropoulos D (2023) A Review of macroeconomic approaches to modelling Wellbeing, Inclusion, and Sustainability: Final Version of WISE Horizons Deliverable 1.2. https://doi.org/10.5281/zenodo.8370002. Accessed 28 September 2023. Accessed 28 September 2023 Wiedenhofer D, Streeck J, Wiese F, Verdolini E, Mastrucci A, Ju Y, Boza-Kiss B, Min J, Norman J, Wieland H, Bento N, León MFG, Magalar L, Mayer A, Gingrich S, Hayashi A, Jupesta J, Ünlü G, Niamir L, Cao T, Zanon-Zotin M, Plank B, Vélez-Henao J, Masanet E, Krey V, Akimoto K, Grubler A, van Ruijven B, Pauliuk S (2024) Industry Transformations for High Service Provisioning with Lower Energy and Material Demand: A Review of Models and Scenarios. Annu. Rev. Environ. Resour. https://doi.org/10.1146/annurev-environ-110822-044428 Wilson C, Guivarch C, Kriegler E, van Ruijven B, van Vuuren DP, Krey V, Schwanitz VJ, Thompson EL (2021) Evaluating process-based integrated assessment models of climate change mitigation. Climatic Change 166. https://doi.org/10.1007/s10584-021-03099-9 Yang Y, Heijungs R (2018) On the use of different models for consequential life cycle assessment. Int J Life Cycle Assess 23:751–758. https://doi.org/10.1007/s11367-017-1337-4 Yang Y, Heijungs R (2019) Moving from completing system boundaries to more realistic modeling of the economy in life cycle assessment. Int J Life Cycle Assess 24:211–218. https://doi.org/10.1007/s11367-018-1532-y Yi Y, Wu J, Zuliani F, Lavagnolo MC, Manzardo A (2023) Integration of life cycle assessment and system dynamics modeling for environmental scenario analysis: A systematic review. Sci Total Environ 903:166545. https://doi.org/10.1016/j.scitotenv.2023.166545 Zhang X, Heeren N, Bauer C, Burgherr P, McKenna R, Habert G (2024) The impacts of future sectoral change on the greenhouse gas emissions of construction materials for Swiss residential buildings. Energy and Buildings 303:113824. https://doi.org/10.1016/j.enbuild.2023.113824 Zheng J, Suh S (2019) Strategies to reduce the global carbon footprint of plastics. Nat. Clim. Chang. 9:374–378. https://doi.org/10.1038/s41558-019-0459-z Zibunas C, Meys R, Kätelhön A, Bardow A (2022) Cost-optimal pathways towards net-zero chemicals and plastics based on a circular carbon economy. Computers & Chemical Engineering 162:107798. https://doi.org/10.1016/j.compchemeng.2022.107798 Zibunas C, Meys R, Kätelhön A, Bardow A (2024) The cost and climate impact of myopic investment decisions in the chemical industry. Computers & Chemical Engineering 187:108721. https://doi.org/10.1016/j.compchemeng.2024.108721 Zumsteg JM, Cooper JS, Noon MS (2012) Systematic Review Checklist: A Standardized Technique for Assessing and Reporting Reviews of Life Cycle Assessment Data. Journal of Industrial Ecology 16:S12-S21. https://doi.org/10.1111/j.1530-9290.2012.00476.x Additional Declarations The authors declare potential competing interests as follows: NT serves as Guest Editor for the Special Issue to which this paper was submitted but is not involved in the editorial handling of this manuscript. JG is a member of the Editorial Board of the journal. Supplementary Files SupplementaryInformation.xlsx Supplementary Information 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. 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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-7468270","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":506184967,"identity":"dc45fb1d-9d50-45c8-a9cc-a6c422e9d89e","order_by":0,"name":"Aaron Paris","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYNACAwY5BgbmBoYHDBKMDSCBBCDmI6DFmIEBqDgBWQsbAXsSGyBaGCBaGPBo4W/gPfi5oqAuvX/aQaCWCgvZfukeswcP2xjycGmROMCXLHnG4HDujNtAixLOSBjPnHPG3CCxjaEYp8MO8BhINhgcyN0gDdSS2CaRuOFGjplEwhkgG4cO+QM8xj8bDOrSDcBa/kkk7iekxeAAjxnQFuYEiJYGoC0SIC0VuLUYHuZLs2wwOGwI8suBhGMSxjNupJUBtUjg1CJ3vPfwzYY/dfL8s5MPPvhQUyfbPyN5m+QPA5vEflzeZ+ZBCgokcQlcGoCAB4/cKBgFo2AUjAIQAAD5l1dKfHJ2LQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-0432-3275","institution":"Institute of Environmental Sciences (CML), Leiden University","correspondingAuthor":true,"prefix":"","firstName":"Aaron","middleName":"","lastName":"Paris","suffix":""},{"id":506184968,"identity":"9f383868-769f-4b9c-8060-b490508818a8","order_by":1,"name":"Jeroen Guinée","email":"","orcid":"https://orcid.org/0000-0003-2558-6493","institution":"Institute of Environmental Sciences (CML), Leiden University","correspondingAuthor":false,"prefix":"","firstName":"Jeroen","middleName":"","lastName":"Guinée","suffix":""},{"id":506184969,"identity":"1fe91d7b-f80a-4085-847f-437ed60e3ed0","order_by":2,"name":"Nils Thonemann","email":"","orcid":"https://orcid.org/0000-0001-5966-2656","institution":"Institute of Environmental Sciences (CML), Leiden University","correspondingAuthor":false,"prefix":"","firstName":"Nils","middleName":"","lastName":"Thonemann","suffix":""}],"badges":[],"createdAt":"2025-08-27 06:19:33","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7468270/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7468270/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90073102,"identity":"db015e14-6cda-4cb7-8e09-538727cac233","added_by":"auto","created_at":"2025-08-28 07:24:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":106927,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the literature selection process, adapted from Page et al. (2021) and based on the recommendations of Siddaway et al. (2019). A complete list of retrieved articles, including reasons for exclusion, is provided in the Supplementary Information.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7468270/v1/74d8ce413d5b5307942923ce.png"},{"id":90073086,"identity":"eb42b794-5399-4c9a-ade8-7bb0f2359287","added_by":"auto","created_at":"2025-08-28 07:24:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":184980,"visible":true,"origin":"","legend":"\u003cp\u003eStylised Overview of main modelling approaches in prospective macro-level Life Cycle Assessment (LCA), grouped by how the foreground (FG) system was scaled to macro-level: coupling process-based LCA with a Dynamic Stock Model (DSM-LCA), an Energy System Model (ESM-LCA), an Integrated Assessment Model (IAM-LCA), or without a coupled model. Labels ‘a’ and ‘b’ indicate whether the combination is applied ex post (after running the ESM or IAM) or endogenously (with micro-level LCA characterisation results directly embedded). Coupling is illustrated in the FG system; background (BG) system couplings (e.g., via \u003cem\u003epremise\u003c/em\u003e (Sacchi et al. 2022)) are not shown. Note that the characterisation results can be either integrated over time for the whole life cycle or separated by life cycle stage. For ESM- and IAM-LCA approaches, we distinguish two modelling variants; see below for explanation. The diagrams reflect generalised structures; individual studies may differ.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7468270/v1/e5f3a44da49cb9e5b3362a74.png"},{"id":90073928,"identity":"e18e54bb-6e53-4e08-bb23-e4b00d2ea618","added_by":"auto","created_at":"2025-08-28 07:32:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1649262,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7468270/v1/4b3ae313-8fda-490c-9794-8ced53b5f5c8.pdf"},{"id":90073092,"identity":"70c83520-300d-483d-b5e6-c225532171b4","added_by":"auto","created_at":"2025-08-28 07:24:21","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":343990,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Information\u003c/p\u003e","description":"","filename":"SupplementaryInformation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7468270/v1/7e4995b1981c65b740afa993.xlsx"}],"financialInterests":"The authors declare potential competing interests as follows: NT serves as Guest Editor for the Special Issue to which this paper was submitted but is not involved in the editorial handling of this manuscript. JG is a member of the Editorial Board of the journal.\n","formattedTitle":"\u003cp\u003eProspective Macro-Level Life Cycle Assessment: A Systematic Review\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eTransitions to sustainable societies require informed decisions about which technologies and policies to support today, especially in sectors with long-lived assets, where current investments can lock-in environmental impacts for decades (Geels et al. 2017). Prospective environmental assessments at national, continental, and global scales—hereafter referred to as macro-level—provide a basis for these decisions by anticipating how aggregate environmental impacts evolve.\u003c/p\u003e\n\u003cp\u003eProcess-based Life Cycle Assessment (LCA) is an established method for such assessments, with three core strengths: (i) technological resolution, enabling explicit modelling of technological advancements, technology-specific interventions, and market share changes at the process-level (Hertwich et al. 2015; Wiedenhofer et al. 2024); (ii) a system perspective, covering the full life cycle (Sacchi and Hahn-Menacho 2024); and (iii) the capacity to assess multiple environmental impact categories (Sacchi and Hahn-Menacho 2024). The latter two strengths are important for avoiding problem-shifting (Finnveden et al. 2009).\u003c/p\u003e\n\u003cp\u003eConventional LCA, as standardised in ISO14040:2006 (ISO 2006), has three main limitations for prospective macro-level environmental analyses. First, it lacks a time dimension (Guinée et al. 2017), meaning that it disregards the \u003cem\u003etemporal distribution\u003c/em\u003e of processes, emissions, and effects (Guinée 2002), implicitly assuming that all processes occur simultaneously. This simplification can be problematic, especially for long-lived systems like infrastructure. Second, it neglects \u003cem\u003etemporal evolution\u003c/em\u003e (Müller et al., unpublished manuscript under review)—that is, changes over time such as technological advancements or structural shifts in supply chains and the broader economy (Arvesen et al. 2011; Luderer et al. 2019; Hung et al. 2022). Third, it assumes a linear relationship between the functional unit and the scale of the processes providing it (Heijungs 2020; Pizzol et al. 2021). This linear \u003cem\u003esystem scaling\u003c/em\u003e does not capture important aspects, including economies of scale, demand constraints (e.g., market saturation for co-products), and supply limitations (e.g., manufacturing capacities, resource availability, and technology deployment constraints) (Yang and Heijungs 2018, 2019; Pizzol et al. 2021). These constraints become especially important in dynamic transitions where bottlenecks, such as green hydrogen availability, may arise (Hung et al. 2022).\u003c/p\u003e\n\u003cp\u003eRecent advances in prospective LCA—defined here as \"LCA that models the product system at a future point in time relative to the time at which the study is conducted\" (Arvidsson et al. 2024)—have improved the modelling of temporal evolution. Particularly, the coupling with Integrated Assessment Models (IAM) (Mendoza Beltran et al. 2018; Sacchi et al. 2022) has led to the increased consideration of temporal evolution in LCA studies. While many macro-level studies incorporate prospective elements, the field of prospective LCA has mainly focused on emerging technologies at the micro-level (Thonemann et al. 2020). Meanwhile, calls to broaden LCA’s scope (Jeswani et al. 2010; Guinée et al. 2011) have led to an increasing number of studies at the macro-level (Bisinella et al. 2021; Hellweg et al. 2023). Particularly, the energy transition has been the focus of seminal work (e.g., Hertwich et al. 2015).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe propose the following as a first attempt at defining macro-level LCA: \u003cem\u003eassessment of environmental impacts associated with the life cycle of a system fulfilling a specific function for the demand at the national, continental, or global scale\u003c/em\u003e. We suggest distinguishing two key dimensions: the \u003cem\u003etype of the functional unit\u003c/em\u003e (ranging from single products to sectors or the entire economy) and \u003cem\u003ethe scale of the functional unit\u003c/em\u003e (national, continental, or global), with only the latter determining macro-level status.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCurrent prospective macro-level LCAs vary widely in scope, scenario design, modelling of foreground (FG) and background (BG) systems, and terminology. Approaches range from simple parameter adjustments (e.g., future demand) to advanced couplings with dynamic Material Flow Analysis or IAMs. As Hellweg et al. (2023) point out, standardisation and methodological guidance are needed. However, such guidance must be grounded in a systematic overview of the state-of-the-art, including best practices and methodological challenges.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile several reviews address prospective LCA, none focus specifically on macro-level approaches. Some address temporal dynamics in LCA without emphasising the macro-level (Beloin-Saint-Pierre et al. 2020; Lueddeckens et al. 2020; Sohn et al. 2020); others focus on emerging technologies (Buyle et al. 2019; Bergerson et al. 2020; Thonemann et al. 2020; Tsoy et al. 2020; van der Giesen et al. 2020; Erakca et al. 2024); or specific sectors such as metals (Harpprecht et al. 2024). Reviews on the combination of LCA with other types of models focus on the model combination, regardless of the assessment scale and temporal scope (Beaussier et al. 2019; Palazzo et al. 2020; Barkhausen et al. 2023). Other reviews discuss aspects relevant to macro-level prospective LCA, but do not analyse existing approaches (Bisinella et al. 2021; Hellweg et al. 2023; Caiardi et al. 2024; Riondet et al. 2024; Wiedenhofer et al. 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo the best of our knowledge, no review has systematically examined process-based LCAs that are both prospective and macro-level in scope. As a result, the current state of the art remains unclear, including which modelling approaches are available, what key challenges and pitfalls exist, and where future research should focus. Accordingly, our research objectives were to\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(i)\u0026nbsp; \u0026nbsp;provide an overview of modelling approaches in prospective macro-level LCA, focusing on modelling of system scaling (micro- to macro-level), temporal evolution, and temporal distribution;\u003c/p\u003e\n\u003cp\u003e(ii)\u0026nbsp;\u0026nbsp;identify common pitfalls and best practices; and\u003c/p\u003e\n\u003cp\u003e(iii)\u0026nbsp;highlight key challenges and suggest priorities for future research.\u003c/p\u003e\n\u003cp\u003eTo fulfil the research objectives, we conducted a systematic literature review of prospective macro-level LCAs covering all LCA phases, with particular emphasis on the inventory phase. \u003cem\u003eTemporal evolution\u0026nbsp;\u003c/em\u003emodelling and \u003cem\u003esystem scaling\u003c/em\u003e guided our literature selection, but we also analysed how \u003cem\u003etemporal distribution\u003c/em\u003e is addressed within the included studies. Providing detailed methodological guidance for prospective macro-level LCA is beyond the scope of this paper. Instead, we aim to lay the groundwork for future developments in that direction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe acknowledge the significant progress already made in prospective macro-level LCA. Our intention is not to dismiss these efforts, but to reflect on current practices and encourage dialogue on how the field can advance.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003eOur review was informed by the methodological guidance of Siddaway et al. (2019) and Zumsteg et al. (2012). A flowchart outlining the literature selection process is shown in Figure 1.\u003c/p\u003e\n\u003ch2\u003e2.1\u0026nbsp; \u0026nbsp;\u0026nbsp;Search\u003c/h2\u003e\n\u003cp\u003eThe initial literature search was conducted in the \u003cem\u003eWeb of Science Core Collection\u003c/em\u003e using a query developed from 17 benchmark studies identified during an exploratory review (see Supplementary Information). The search string was: \u0026lsquo;\u003cem\u003eTS=(\u0026quot;life cycle\u0026quot; OR \u0026quot;life-cycle\u0026quot; OR \u0026quot;lifecycle\u0026quot; OR \u0026quot;LCA\u0026quot;) AND TS=(\u0026quot;prospectiv*\u0026quot;) AND TS=(\u0026quot;*sector*\u0026quot; OR \u0026quot;*fleet*\u0026quot; OR \u0026quot;nation*\u0026quot; OR \u0026quot;transition*\u0026quot; OR \u0026quot;macro*\u0026quot; OR \u0026ldquo;at scale\u0026rdquo; OR \u0026ldquo;large-scale\u0026rdquo; OR \u0026ldquo;large scale\u0026rdquo; OR \u0026quot;stock*\u0026quot;) and English (Languages)\u0026rsquo;\u003c/em\u003e. Here, \u003cem\u003eTS\u003c/em\u003e denotes a \u003cem\u003eTopic Search\u003c/em\u003e, retrieving records where these terms appear in the title, abstract, or author keywords. The search was last updated on July 15, 2025; studies published after this date were excluded.\u003c/p\u003e\n\u003cp\u003eDue to the lack of standard terminology for prospective macro-level LCA, covering all potential keywords would have resulted in an unmanageable number of studies. Therefore, while we included a broad range of terms related to scale, we adopted a restrictive strategy for the prospective dimension, requiring the root term \u003cem\u003e\u0026quot;prospectiv*\u0026quot;\u003c/em\u003e. We acknowledge this may have excluded some relevant studies. To mitigate this risk, we additionally: (i) added the 514 studies reviewed by Bisinella et al. (2021), who focused on LCA combined with future scenarios; and (ii) conducted forward and backward citation tracking on included studies using the Web of Science. All identified studies were subject to the selection criteria outlined below.\u003c/p\u003e\n\u003ch2 id=\"_Toc196292479\"\u003e2.2\u0026nbsp; \u0026nbsp;\u0026nbsp;Screening\u003c/h2\u003e\n\u003cp\u003eScreening was conducted in two stages, starting with a review of titles and abstracts, followed by a full-text review, as shown in Figure 1. Studies were included only if they met all of the following criteria:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(1) \u003cstrong\u003eBibliographic requirements:\u003c/strong\u003e Peer-reviewed journal articles in English. Letters, books, reports, and conference papers were excluded. No restrictions were placed on the publication year.\u003c/p\u003e\n\u003cp\u003e(2) \u003cstrong\u003eCase study\u003c/strong\u003e: Pure method papers without applications (e.g., Ventura 2023) were excluded, but referenced when relevant (e.g., Gibon et al. 2015; Arvesen et al. 2018).\u003c/p\u003e\n\u003cp\u003e(3) \u003cstrong\u003eLife cycle perspective:\u0026nbsp;\u003c/strong\u003eStudies had to cover at least two life cycle stages.\u003c/p\u003e\n\u003cp\u003e(4) \u003cstrong\u003eProcess-based modelling in FG and BG systems\u003c/strong\u003e: We define the FG system as \u003cem\u003ethe set of processes that constitute the object of analysis\u003c/em\u003e, i.e., the focus of the study. The BG system includes \u003cem\u003eall other processes that provide inputs to the FG system\u003c/em\u003e. Studies based solely on Input-Output Life Cycle Assessment (IO-LCA)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003efor the FG or BG system were excluded. We did not require studies to perform their own inventory modelling.\u003c/p\u003e\n\u003cp\u003e(5) \u003cstrong\u003eEnvironmental impact assessment:\u0026nbsp;\u003c/strong\u003eStudies had to include a characterisation of environmental flows for at least one impact category.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(6) \u003cstrong\u003eMacro-level scope\u003c/strong\u003e: Studies had to fulfil our above definition of macro-level LCA. However, since functional units were rarely stated explicitly, we based inclusion or exclusion on the scale of the reported results.\u003c/p\u003e\n\u003cp\u003e(7) \u003cstrong\u003eConsideration of temporal evolution\u003c/strong\u003e: Studies had to report results for at least one explicit future year, reflecting changes over time in both the FG and the BG system. Changing demand alone was insufficient unless accompanied by changes in how that demand is met over time. For example, studies that changed the material composition of buildings or the composition of the building stock were included, even if the underlying unit processes remained unchanged.\u003c/p\u003e\n\u003cp\u003eStudies with insufficient documentation (including Supplementary Information) to assess these criteria were excluded. While some relevant studies may have been unintentionally excluded, we believe the range and diversity of works covered provide a comprehensive overview of current approaches in prospective macro-level process-based LCA research.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 1, of the 925 articles initially identified, 702 were excluded after title and abstract screening, and another 170 after full-text screening. 33 additional articles were found through citation tracking. Ultimately, 86 publications were reviewed in detail. A complete list of retrieved articles, including reasons for exclusion, is provided in the Supplementary Information.\u003c/p\u003e\n\u003ch2 id=\"_Toc196292480\"\u003e2.3\u0026nbsp; \u0026nbsp;\u0026nbsp;Review\u003c/h2\u003e\n\u003cp\u003eWe reviewed the included studies with a primary focus on how system scaling, temporal evolution, and temporal distribution were modelled in the inventory analysis phase. In addition, we assessed elements from the other three LCA phases, including research objectives, temporal scope, system boundaries, and the treatment of sensitivity and uncertainty. We also examined terminology use and transparency. We reviewed both the main article and the Supplementary Information, with the exception of model code. An overview of all review variables and their evaluation across the included studies is provided in our Supplementary Information.\u003c/p\u003e"},{"header":"3 Overview of modelling approaches","content":"\u003cp\u003eTo address the limitations of process-based LCA for prospective macro-level analyses, particularly the challenge of scaling systems from the typical micro-level scope of LCA to the macro-level, studies often rely on coupling LCA with other types of models.\u003c/p\u003e\u003cp\u003eWe classify the reviewed approaches based on how the FG system was scaled to the macro-level, beginning with those that use specific coupled models, followed by approaches that do not involve explicit model coupling. The main groups are Dynamic Stock Models (DSM), Energy System Models (ESM), and IAMs. These groups are not mutually exclusive, and their boundaries may overlap. For example, many IAMs incorporate ESMs and DSMs (Krey et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), but we treat these as separate approaches. Some studies do not fit neatly into a single category. For example, Hertwich et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) use pre-defined International Energy Agency (IEA) scenarios as stock trajectory input to a DSM. Since these scenarios are based on the IEA\u0026rsquo;s own ESM, we classify the study as coupling with ESM (ESM\u0026ndash;LCA), although classifying it as coupling with DSM (DSM\u0026ndash;LCA) would also be possible.\u003c/p\u003e\u003cp\u003eGenerally, studies scale results from micro-level LCAs\u0026mdash;which we refer to from hereon as \u003cem\u003emicro-level characterisation results\u003c/em\u003e\u0026mdash;rather than scaling within the framework of LCA itself, i.e., the Life Cycle Inventory (LCI). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a stylised overview of the typical information flows in each modelling approach. Note that by \u003cem\u003eactivity level\u003c/em\u003e, we mean the quantitative extent of a process or system\u0026rsquo;s operation; that is, how much a process is used.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a more detailed overview of these approaches and their key characteristics, including how they typically model temporal evolution and distribution. By \u003cem\u003etemporal distribution\u003c/em\u003e, we refer specifically to the timing of LCA processes\u0026mdash;that is, whether they are modelled as occurring instantaneously or distributed over time\u0026mdash;rather than to the presence of a temporal dimension in the coupled model.\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\u003eOverview of main modelling approaches in prospective macro-level Life Cycle Assessment (LCA), grouped by how the foreground system was scaled to macro-level: coupling process-based LCA with a Dynamic Stock Model (DSM-LCA), an Energy System Model (ESM-LCA), an Integrated Assessment Model (IAM-LCA), or without a coupled model. Less common approaches are discussed separately in subsection 3.5.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDSM-LCA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eESM-LCA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIAM-LCA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo explicit model coupling\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystem scaling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScaling with inflows, outflows, and stock levels from stock-driven DSMs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScaling with ESM outputs (capacity additions and energy generation volume)\u003c/p\u003e\u003cp\u003eOR incorporated directly into the ESM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eScaling with IAM outputs (activity levels, capacity additions) OR incorporated directly into the IAM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eScaling with demand trajectories, based on extrapolated trends, fixed growth rates, external scenarios, or present-day levels\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemporal evolution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStock composition changes; selected parameters (e.g., electricity mix)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTypically based on the parameter development of the ESM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTypically based on the parameter development of the IAM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSelected parameters (e.g., electricity mix)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemporal distribution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDifferentiation of life cycle stages and temporal distribution based on inflows, stocks, and outflows\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifferentiation of life cycle stages and temporal distribution based on capacity additions and energy generation volumes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDifferentiation of life cycle stages and temporal distribution based on capacity additions and activity levels over time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot consistently considered\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStrengths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026bull; Endogenous stock dynamics\u003c/p\u003e\u003cp\u003e\u0026bull; Stock\u0026ndash;flow consistency and mass balance\u003c/p\u003e\u003cp\u003e\u0026bull; Low access barriers\u003c/p\u003e\u003cp\u003e\u0026bull; Flexibility in scope and scale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Detailed representation of energy system dynamics\u003c/p\u003e\u003cp\u003e\u0026bull; Captures system constraints\u003c/p\u003e\u003cp\u003e\u0026bull; Strong policy link\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026bull; Endogenous Representation of technological change and macro-level feedbacks\u003c/p\u003e\u003cp\u003e\u0026bull; Established scenario framework\u003c/p\u003e\u003cp\u003e\u0026bull; Global coverage\u003c/p\u003e\u003cp\u003e\u0026bull; Strong policy link\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026bull; Low access barriers\u003c/p\u003e\u003cp\u003e\u0026bull; Flexible\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeaknesses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026bull; Lacks macroeconomic/ systemic feedbacks\u003c/p\u003e\u003cp\u003e\u0026bull; No standardised scenario framework\u003c/p\u003e\u003cp\u003e\u0026bull; High data requirements (lifetimes, stocks)\u003c/p\u003e\u003cp\u003e\u0026bull; Weak policy link\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Application restricted to the energy system\u003c/p\u003e\u003cp\u003e\u0026bull; Mismatches of geographical and technological resolution\u003c/p\u003e\u003cp\u003e\u0026bull; High expertise and access barriers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026bull; Application restricted to IAM core systems\u003c/p\u003e\u003cp\u003e\u0026bull; Mismatches of geographical and technological resolution\u003c/p\u003e\u003cp\u003e\u0026bull; High expertise and access barriers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026bull; No endogenous dynamics or feedbacks\u003c/p\u003e\u003cp\u003e\u0026bull; Low comparability\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommon applications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBuildings, transport, electricity systems, and materials\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eElectricity, fuels, hydrogen, batteries, sector coupling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIAM core sectors, like energy or transport\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMaterials, chemicals, and emerging technologies\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExample research questions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHow stock dynamics of a system influence its life cycle impacts over time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHow the life cycle impacts of future energy systems evolve\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHow global systems meet climate targets and associated life cycle impacts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHow future life cycle impacts change under various assumptions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of studies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15\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\u003eOur focus is on the strengths and weaknesses of these approaches rather than on the specific models themselves, as reviewing each model is beyond the scope of this paper. These strengths and weaknesses are based on our observations, the limitations stated by the authors of the reviewed papers, and relevant literature. The following subsections examine each approach in detail. Note that Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e do not include all identified approaches\u0026mdash;those that are rarely applied are discussed separately in subsection 3.5.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Dynamic Stock Models\u003c/h2\u003e\u003cp\u003eThe first approach combines LCA with DSMs. DSMs, also referred to as Vintage Stock Models, represent stocks of products used longer than one year (Wiedenhofer et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and their inflows (additions) and outflows (retirements) over time. DSMs can be stock-driven, with predefined stock trajectories and inflows/outflows derived via lifetime distributions; or inflow-driven, where stocks and outflows follow inflow time series (Deng et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudies in this group correspond to what Barkhausen et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) term \u0026ldquo;environmentally extended Material Flow Analysis\u0026rdquo;. They first use a DSM to project future inflows, stocks, and outflows, which are then combined with micro-level LCA characterisation results for upstream, use, and downstream stages, integrated over time for each stage. For example, Milovanoff et al. (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), use a dynamic stock model to project future light-duty vehicle production, vehicle stock, and disposal volumes for the U.S. fleet, and then multiply each by micro-level characterisation results for vehicle production, use, and End-of-Life (EoL) disposal. For a given year, the total impact is then calculated by summing all impacts occurring in that year: use-phase impacts from all active cohorts, production impacts from the new cohort, and EoL impacts from stocks retiring that year. However, not all DSM-LCA studies account for temporal distribution: two aggregate micro-level results across the considered life cycle stages into a single time point before applying them to inflows or stocks, while two focus exclusively on upstream impacts.\u003c/p\u003e\u003cp\u003eTemporal evolution is typically modelled via cohort-specific parameters (e.g., energy efficiency, market shares), BG system parameters (e.g., electricity mix), or global DSM parameters (e.g., lifetimes, recycling rates), often in scenario form. Five studies use \u003cem\u003epremise\u003c/em\u003e, a popular approach for modelling of prospective BG systems (Sacchi et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that links IAM variables to LCA processes to generate prospective BG system inventories \u0026ldquo;by adjusting technologies\u0026rsquo; penetration share, efficiency and emission factors for a specific scenario and year\u0026rdquo; (Šimaitis et al. \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStrengths of the DSM-LCA approach include the inherent ability to model temporal distribution, the granular annual resolution, and the ability to capture system delays from technology diffusion and lock-ins. For example, Milovanoff et al. (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrate how climate change mitigation is restrained by vehicle stock turnover despite even under assumptions of aggressive electrification in new vehicle market shares. The DSM-LCA approach is flexible in scale (Ventura \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and system of analysis, supports scenario customisation, and benefits from low access barriers through open-source tools like \u003cem\u003eODYM\u003c/em\u003e (Pauliuk and Heeren \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWeaknesses include high data demands, particularly lifetimes and stock levels, missing treatment of macro-level feedback, non-linear system behaviour, and relying on exogenous assumptions for socioeconomic drivers (Wiedenhofer et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Few studies model technology diffusion endogenously and representation of policies is often lacking.\u003c/p\u003e\u003cp\u003eApplications focus on transport (18 studies), buildings (8), the electricity system (4), material cycles (4) and consumer goods (1). Research objectives include methodological advances (e.g., Hung et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and evaluating strategies like lightweighting (e.g., Milovanoff et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), EV adoption (e.g., Raugei et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and circularity (e.g., Pauliuk et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Many studies (12 of 35) do not cover the full life cycle. Temporal resolution is typically annual (31 of 35 studies).\u003c/p\u003e\u003cp\u003eNoteworthy methodological advances include coupling DSMs with discrete choice models (Brand et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mastrucci et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which simulate how individual agents make decisions among alternative technologies based on internal preferences and external factors (Brand et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); improving technological granularity (Alaux et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); adopting nonlinear technology diffusion models (Sig\u0026uuml;enza et al. \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); and including optimisation (Hung et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFuture work should focus on better representing behaviour, policies, and macro-level feedbacks; exploring approaches to endogenise socioeconomic drivers and dynamics; improving spatial and technological resolution; addressing distributional aspects (Pauliuk \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); and differentiating drivers of stock levels beyond averages (e.g., urban vs. rural buildings, Zhang et al. \u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Energy System Models\u003c/h2\u003e\u003cp\u003eThe second approach combines LCA with ESMs. As mentioned above, we include studies using pre-defined IEA scenarios, since they are based on the IEA\u0026rsquo;s internal ESM. ESMs model how resource inputs\u0026mdash;defined by potentials and cost curves\u0026mdash;are used to meet end-use demands through primary (e.g., electricity) or secondary (e.g., heat pumps) conversion technologies, under policy or economic constraints (Blanco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Depending on their goal, they can be used to identify least-cost or least-impact pathways to a desired future state (Weidner et al. \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). ESMs typically rely on partial equilibrium modelling (Blanco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Inputs include energy resource availability, existing capacities and capacity limitations, (future) demand and demand profiles, technology data, and scenario assumptions like policy targets (Weidner et al. \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTwo main combination approaches with LCA exist (Blanco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e): (i) ex-post assessments (20 studies), where LCA is applied after the ESM runs independently (often optimising for cost or direct emissions), and (ii) endogenous inclusion of micro-level characterisation results (7 studies), where they are directly embedded in the ESM. These are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e as \u0026lsquo;a\u0026rsquo; and \u0026lsquo;b\u0026rsquo;, respectively.\u003c/p\u003e\u003cp\u003eFor ex-post assessments, model structures are often similar to DSM-LCA approaches: ESM outputs, i.e., new capacity installations and electricity generation activity levels by technology, are linked to upstream and use-phase micro-level characterisation results, respectively. Two studies combine DSMs with IEA scenario values by using future installed capacity trajectories from the IEA scenarios to drive a vintage stock model, which allocates life cycle impacts of power plants\u0026mdash;construction, operation, and decommissioning\u0026mdash;to the years they occur (Hertwich et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gibon et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). EoL is rarely explicitly treated. Some studies allocate EoL impacts to the construction of new capacities as well (Junne et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), creating temporal mismatches.\u003c/p\u003e\u003cp\u003eTemporal evolution is typically inherently modelled by the ESM through improving conversion efficiencies or the emergence of new technologies, which are then harmonised with the LCA data. Six of the studies use prospective BG system data generated by \u003cem\u003epremise\u003c/em\u003e (Sacchi et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Most studies project to 2050, with temporal resolution ranging from yearly to 20-year steps.\u003c/p\u003e\u003cp\u003eStrengths of the approach include detailed energy system representation and non-linear dynamics (e.g., considering merit-order effects); the representation of constraints, such as limited capacities for specific technologies or investment cost; and the inherent inclusion of temporal distribution, at least for new capacity additions and use phase. Furthermore, they have a better representation of policies than DSM-LCA typically have.\u003c/p\u003e\u003cp\u003eWeaknesses include the energy-sector focus, limiting application beyond it; uncertain mapping of ESM and LCA data due to differences in technological parameters and resolution (Vandepaer and Gibon \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hahn Menacho et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e); and that many ESMs produce only one optimised future per set of assumptions, limiting their ability to capture disruptive changes or unexpected transitions (Reinert et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, ESM modelling also requires significant expertise, and model documentation is often lacking (Vandepaer et al. \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), reducing accessibility and transparency.\u003c/p\u003e\u003cp\u003eA key challenge for ESM-LCA is double-counting. ESMs calculate total energy demand, including supply chains. If the micro-level characterisation results include embedded energy, impacts may be overestimated (Volkart et al. \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Blanco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). National models double-count domestic supply chains; global models all chains unless inventories are adjusted (Volkart et al. \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Double-counting also occurs when the micro-level characterisation results are not only applied at the end-use level, for example, accounting for electricity used in both heat pump operation and the generation of that electricity (Blanco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Many studies have developed methods to mitigate double-counting (e.g., Volkart et al. \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vandepaer et al. \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudies focus on the energy systems (18), or specific intermediate goods like hydrogen, ammonia, and batteries. The most common objective is assessing the environmental impacts of future energy systems, often under climate mitigation targets and a focus on the role of specific technologies. The scope of the studies varies, with 6 being cradle-to-gate, 4 cradle-to-use, 16 cradle-to-grave and one focussing on the EoL. Future work should align technological assumptions between ESMs and LCA, enhance transparency, and develop standardised coupling methods.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Integrated Assessment Models\u003c/h2\u003e\u003cp\u003eThe third approach couples LCA with IAMs (IAM-LCA), which integrate models of climate, economy, and society to explore policy questions (Hellweg et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We focus on process-based IAMs, which\u0026mdash;unlike cost-benefit IAMs\u0026mdash;are technology-rich and scenario-driven (Pauliuk et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While many IAMs have ESMs at their core, they differ by explicitly modelling the carbon cycle, land-use change, and the global economy (Blanco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). IAMs produce long-term projections that align with radiative forcing targets set by Representative Concentration Pathways and are situated within broader socio-economic storylines, the Shared Socioeconomic Pathways (SSP) (Sacchi et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). They can take the form of either optimisation models, based on myopic or perfect foresight (Pauliuk et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), or simulation models and are typically based on general or partial equilibrium frameworks (Keppo et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For a concise discussion of how IAMs work and their implications for LCA, see Evans and Hausfather (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Šimaitis et al. (\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFive of the six studies reviewed apply ex-post methods (labelled \u0026lsquo;a\u0026rsquo; in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), using IAM scenario outputs such as cement production volumes (M\u0026uuml;ller et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), to scale micro-level LCA inventories or characterisation results. Only Pehl et al. (\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) incorporate embodied energy coefficients based on micro-level LCIs into the IAM REMIND using its endogenous emission factors and carbon price assumptions (labelled \u0026lsquo;b\u0026rsquo; in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This approach can, similar to ESM-LCAs, lead to double-counting if not correcting the LCA-based coefficients (Arvesen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is important to note that while ex-post and endogenous inclusion assessments of ESMs often share similar system boundaries\u0026mdash;typically focused on the energy system\u0026mdash;endogenous inclusion in IAM-LCA encompasses all sectors represented within the IAM, whereas ex-post assessments in IAM-LCA usually target a single system only (e.g., cement; M\u0026uuml;ller et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTemporal evolution is captured via IAM market shares and technological parameters (e.g., Dirnaichner et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; M\u0026uuml;ller et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) or based on literature and simple assumptions (e.g., 100% adoption of a specific technology in a specific year, Cabrera-Jim\u0026eacute;nez et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Two of the studies rely on \u003cem\u003epremise\u003c/em\u003e (M\u0026uuml;ller et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cabrera-Jim\u0026eacute;nez et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and two (Pehl et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Luderer et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) on \u003cem\u003eTHEMIS\u003c/em\u003e (Gibon et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) to model temporal evolution.\u003c/p\u003e\u003cp\u003eIAMs typically operate in time steps of 5\u0026ndash;10 years. Three of the six studies include temporal distribution through differentiating construction and operation and mapping them to electricity generation capacity and volume (Pehl et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Luderer et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) or through simply assuming fixed lifetimes afterwards based on the IAM output (Knobloch et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The other three studies do not consider temporal distribution.\u003c/p\u003e\u003cp\u003eStrengths of the approach include the global scope; endogenous data on energy and technology development from the IAM, which are based on a large variety of projection strategies and assumptions (Krey et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); and the inclusion of macroeconomic feedbacks and policy links. The alignment with the SSPs is another advantage (Steubing and Koning \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Some IAMs also embed regional constraints like land and resource availability (Pehl et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWeaknesses stem from coarse geographical and technological resolution, requiring mapping to LCA data, introducing uncertainty (Sacchi et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Šimaitis et al. \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The complexity of IAMs and their high entry barriers pose challenges to interpretation and often necessitate collaboration with model developers, particularly when seeking to move beyond predefined model assumptions (Keppo et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wilson et al. \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These difficulties are compounded by often incomplete and scattered documentation, which can further hinder transparency and reproducibility (Pauliuk et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThere are more than 30 IAMs, representing a wide variety of approaches (Pauliuk et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which makes generalisations difficult (de Bortoli et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Nevertheless, we want to highlight a few important general criticisms, which include overreliance on negative emission technologies (van Vuuren et al. \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Creutzig et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); underestimation of other technologies like renewables (Creutzig et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Way et al. \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); a weak representation of material cycles (Pauliuk et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); neglecting actor heterogeneity (Keppo et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); underrepresenting distributive justice (de Bortoli et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); and reliance on economic equilibrium frameworks (Keppo et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which themselves are subject to a broad set of critiques (see subsection 3.5). Relatedly, IAMs have been criticised for a lack of alternative economic paradigms, potentially excluding viable policy options (Hickel et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Proctor \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; de Bortoli et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For a more comprehensive overview of criticisms and corresponding responses from the IAM community, see Keppo et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Some of these issues are being actively addressed; for example, through the development of scenarios that avoid the use of negative emissions technologies (Grubler et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBecause IAMs represent in detail only those sectors most relevant to climate change (Steubing et al. \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), IAM-LCA studies are likewise limited to these sectors: electricity (Pehl et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Luderer et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), transport (Knobloch et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dirnaichner et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cabrera-Jim\u0026eacute;nez et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), space heating (Knobloch et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and cement (M\u0026uuml;ller et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The typical research objective is to evaluate decarbonisation strategies and their trade-offs with other impact categories, which are not represented in IAMs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4 No explicit model coupling\u003c/h2\u003e\u003cp\u003eFifteen studies did not rely on a coupled model for system scaling. Instead, scaling is based on assumed demand trajectories (e.g., Weidner et al. \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), constant present-day levels (e.g., Adrianto et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), extrapolation of historical trends (e.g., Kuipers et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), assumed annual growth rates (e.g., Bohnes et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), or scenarios from government-adjacent organisations (e.g., Douziech et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), industry groups (e.g., Pedneault et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), or other literature (e.g., Shirmohammadi et al. \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTemporal evolution is generally informed by market shares drawn from the same literature sources or simple scenario assumptions, such as a linear transition to 100% market share (e.g., Zheng and Suh \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) or regression of historical data (e.g., Kuipers et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Technological change is introduced separately via annual improvement rates or adoption curves, often based on expert judgment, roadmaps, or external scenarios. Parameters addressed include the electricity supply mix, the deployment speed of technologies (e.g., Pedneault et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), energy efficiency, and specific factors like ore grades (e.g., van der Voet et al. \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Seven studies use \u003cem\u003epremise\u003c/em\u003e (Sacchi et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with four applying it to both the FG and the BG system (e.g., Weidner et al. \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). None of the studies in this group accounts for the temporal distribution of processes.\u003c/p\u003e\u003cp\u003eUncoupled approaches offer flexibility and relative simplicity, but they have notable limitations. Without coupled models, they cannot systematically capture dynamic stock changes, non-linear system behaviours, or temporal distributions. Purely assumption-based scaling can undermine comparability and robustness, and reliance on fixed demand trajectories limits the exploration of diverse futures. While accessible, these approaches risk oversimplification.\u003c/p\u003e\u003cp\u003eHalf the studies focus on intermediate goods, like metals (Kuipers et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; van der Voet et al. \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pedneault et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Adrianto et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), hydrogen (Weidner et al. \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) or plastics (Zheng and Suh \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, most use a cradle-to-gate scope (7). Temporal resolution varies widely.\u003c/p\u003e\u003cp\u003eResearch objectives include methodological contributions (e.g., Douziech et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), assessments of specific technologies (e.g., Adrianto et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), evaluation of government strategies (e.g., Bohnes et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), or general impact assessment of the system under analysis (e.g., Horup et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Less explored modelling approaches\u003c/h2\u003e\u003cp\u003eTwo modelling approaches appeared rarely and are therefore briefly discussed here.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGeneral Equilibrium Models (GEM)\u003c/b\u003e determine \u0026ldquo;price and quantity jointly in all sectors and regions in the world economy using a solvable system of equations\u0026rdquo; (Palazzo et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Only two studies used GEMs, both in the waste sector (Ljunggren S\u0026ouml;derman et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Arushanyan et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, GEMs underpin many IAMs (Keppo et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Proctor \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while Partial Equilibrium Models (PEMs)\u0026mdash;which \u0026ldquo;represent the market for a particular good (or small set of goods) in isolation from the rest of the economy\u0026rdquo; (Palazzo et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u0026mdash;are common in both IAMs and ESMs (Blanco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Keppo et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, equilibrium approaches are more prevalent than they initially appear.\u003c/p\u003e\u003cp\u003eTheir strengths include modelling prices, substitution, and feedback effects, with strong links to policy instruments (Plevin \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Beaussier et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Many critiques apply to both, notably their reliance on neoclassical assumptions such as consumer utility maximisation, producer profit maximisation, perfect information, and market clearing (Suh and Yang \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yang and Heijungs \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Palazzo et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wiedenhofer et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Some criticisms apply more to GEMs than PEMs, particularly their unrealistic assumption of full capacity utilisation (Wiedenhofer et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Where stylised production functions are used\u0026mdash;more common in GEMs than in PEMs, which often draw on engineering detail\u0026mdash;these typically violate biophysical and thermodynamic consistency (Wiedenhofer et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe limited uptake of GEM in particular may stem from data demands, complexity, and poor fit with LCA\u0026rsquo;s granularity (Earles and Halog \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Yang and Heijungs \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Beaussier et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Given the strong assumptions underpinning equilibrium models, there is a need to diversify the macroeconomic approaches, for instance by incorporating post-Keynesian or stock\u0026ndash;flow consistent macroeconomics (Wiebe et al. \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSystem Dynamics (SD)\u003c/b\u003e models simulate system behaviour through stocks, flows, and feedbacks (Moon \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). They enable the modelling of complex and non-linear system behaviour, are inherently able to account for time and support easy parametrisation (Beaussier et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Palazzo et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yi et al. \u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite existing combinations of LCA and SD (McAvoy et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we found only one study (Alaux et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e) using causal loop diagrams to derive scenario parameters and one study (Ginster et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) building a full SD model, that is very close to a DSM-LCA. Complexity, lack of standardisation, as well as high computational and data requirements may hinder uptake (Beaussier et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Palazzo et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Models only used in supporting roles\u003c/h2\u003e\u003cp\u003eWe expected to find several modelling methods used more frequently, but they mostly appeared only in supporting roles. Below, we briefly outline these approaches, highlight their key features, and discuss possible reasons for their limited use.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLinear Programming Models (LPM)\u003c/b\u003e use linear optimisation to represent technology choice under constraints. While many IAMs and ESMs also employ optimisation methods, we focus here specifically on LPMs applied directly to the mathematical structure of LCA. Five studies use optimisation, two of which combine it with a DSM (Hung et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rossi et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and are therefore classified as DSM-LCA. The other three (Zibunas et al. \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lechtenberg et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zibunas et al. \u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) rely on simple system scaling (fixed or linearly growing demand) and are classified as without explicit model coupling. Technology diffusion is modelled endogenously in the optimisation: the models determine which technologies are adopted based on environmental performance and constraint satisfaction. The main advantage of this approach is the ability to incorporate constraints directly, while its main drawback lies in the high access barriers. The newly developed tool \u003cem\u003eoptimex\u003c/em\u003e (Diepers and Tautorus \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) may encourage broader uptake.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAgent-Based Models (ABM)\u003c/b\u003e simulate interactions between agents and their environment, driving system evolution (Moon \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While couplings of LCA and ABM exist, macro-level applications are rare (Beaussier et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wiedenhofer et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In our sample, ABM appeared only in supporting roles. ABMs enable more realistic modelling of (non-linear) behaviour, actor heterogeneity and technology diffusion than traditional economic models (Beaussier et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Palazzo et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wiedenhofer et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), but share SD\u0026rsquo;s drawbacks: complexity, a lack of standardisation, and high computational and data demands (Beaussier et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput-Output LCA (IO-LCA)\u003c/b\u003e uses economic input-output tables to trace sectoral transactions within and across regions, enabling quantification of environmental impacts across global supply chains when combined with environmental data (Hagenaars et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). IO-LCA is relatively easy to implement and is often considered more comprehensive because it captures the whole economy and is not limited by cut-offs in contrast to LCA (Beaussier et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hagenaars et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, fixed prices and static structure limit its reliability to short-term analysis (Beaussier et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Le Luu et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, IO-LCA has a low technological resolution and lacks a representation of use and EoL stages (Hagenaars et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In our sample, IO-LCA was only used to extend BG system coverage (e.g., Arvesen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), not to scale FG systems. The reason remains unclear to us, considering the increased use of combinations of IO-LCA and process-based LCA in recent years (Hagenaars et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEconometric models\u003c/b\u003e identify statistically significant relationships between variables using historical data, often serving as inputs for other models (Wiedenhofer et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Beyond their role in IAMs (e.g., Knobloch et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and their occasional use to project future stock in stock-driven DSM-LCAs (e.g., Brand et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), none of the studies in our review applied econometrics as a primary system-scaling framework. Their main strength lies in avoiding the restrictive assumptions characteristic of GEM approaches. However, they are generally constrained to the short term and exhibit strong path dependency (Beaussier et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wiedenhofer et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Pitfalls, challenges, and best practices","content":"\u003cp\u003eWe identified 12 pitfalls and challenges in the reviewed studies. These pitfalls and challenges are not ranked by priority. Instead, we start with the three main LCA limitations for prospective macro-level analyses introduced earlier, then cover the rest roughly following the four phases of LCA.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1 System scaling\u003c/h2\u003e\u003cp\u003eIn all reviewed cases, micro-level characterisation results are calculated first, with system scaling conducted outside the LCA framework. Whether the functional unit maintains a linear relationship with the scale of the processes that provide it (Heijungs \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pizzol et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) depends on the selected modelling approach.\u003c/p\u003e\u003cp\u003eESM and IAM frameworks introduce non-linearities in certain aspects; for example, ESMs may model the utilisation of specific energy technologies using merit-order curves. We found only one study explicitly adjusting unit processes based on scale: van der Meide et al. (\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who include changing ore grades based on production volume. Apart from that, such effects can arise indirectly when parameters from ESMs or IAMs\u0026mdash;which often endogenously represent technological learning based on deployment or investment (Keppo et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u0026mdash;are incorporated into the modelling of unit processes for micro-level LCA. Other approaches generally preserve linearity and model technological change as a function of time rather than scale. For example, Tang et al. (\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) assume identical efficiency improvements for battery electric vehicles in scenarios with both no uptake and 100% sales share by 2030.\u003c/p\u003e\u003cp\u003eThe importance of linking performance to the scale of deployment (or investment) depends on the maturity of the technology (Pizzol et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is important to keep in mind that ecoinvent includes production volumes for certain technologies to construct market processes (Wernet et al. \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), implying that unit process data are valid for a specific production quantity; though whether they hold at other capacities is uncertain.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Temporal evolution modelling\u003c/h2\u003e\u003cp\u003eTemporal evolution in prospective LCA affects both FG and BG systems, through parameters like technological change (e.g., efficiency, lifetime), system composition (e.g., market shares), and broader configurations (e.g., recycling rates, ore grade degradation) (Vandepaer and Gibon \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Outside IAM- or ESM-based studies, technological change of FG processes is typically represented through selective, exogenous updates of a few parameters\u0026mdash;typically electricity mixes or efficiencies\u0026mdash;based on literature, policy documents, or assumptions. This fragmented approach can create temporal mismatches across subsystems (Arvidsson et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Thonemann et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Prospective LCI databases like \u003cem\u003epremise\u003c/em\u003e (Sacchi et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) help reduce this, but not entirely. While \u003cem\u003epremise\u003c/em\u003e is the most comprehensive approach, it has so far only integrated IAM variables related to electricity production, steel production, cement production, fuel production and transport (Sacchi \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), meaning that some temporal mismatches remain. For a recent critique of the use of IAMs for prospective LCI databases, see de Bortoli et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmong the 20 studies that use micro-level characterisation results from existing studies or databases\u0026mdash;without their own LCI modelling\u0026mdash;19 fail to apply temporal evolution consistently. Often, changes in BG processes are not propagated consistently. For example, Brand et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) assume a future reduction of the emission intensity of electricity supply, but apply it only to vehicle use, not to upstream or downstream processes.\u003c/p\u003e\u003cp\u003eApart from not being linked to system scale (see previous subsection), technological change is also rarely modelled with the needed complexity. That it is actor-driven and can be environmentally regressive (van Nielen et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) was not explicitly considered in any study. Few studies distinguish between emerging (Technology Readiness Level (TRL)\u0026thinsp;\u0026lt;\u0026thinsp;9) and mature (TRL\u0026thinsp;\u0026ge;\u0026thinsp;9) technologies, despite fundamentally different dynamics: emerging technologies may still undergo major shifts during scale-up, while mature ones evolve incrementally (Buyle et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Technological development is seldom modelled explicitly; linear interpolation remains common (e.g., Knobloch et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For TRL\u0026thinsp;\u0026ge;\u0026thinsp;9, van Nielen et al. (\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) propose a structured approach to modelling learning, and guidance on scaling-up emerging technologies is also available (e.g., Thonemann et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tsoy et al. \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Erakca et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTechnological detail varies widely. Some studies include only a few options\u0026mdash;like one drivetrain per vehicle (e.g., Tang et al. \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u0026mdash;while others cover a broader range, such as six battery types (Tarabay et al. \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Studies that incorporate a wide spectrum of technological variants offer a more comprehensive view of the potential future. Market shares are usually treated as static or scenario-based; few studies model them endogenously, e.g., via discrete choice models (Brand et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mastrucci et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Many studies update only market shares (especially electricity) without considering technology development at the process-level.\u003c/p\u003e\u003cp\u003eOverall, systematic modelling of FG systems' evolution remains rare. A notable exception is Alaux et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e), who use the \u003cem\u003eSIMPL\u003c/em\u003e framework (Langkau et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) to develop scenarios for the greenhouse gas emissions of the Austrian building stock.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Temporal distribution modelling\u003c/h2\u003e\u003cp\u003eAbout half of the studies (41) neglect the temporal distribution of processes. The remaining 45 studies differentiate life cycle stages\u0026mdash;typically upstream, use, and/or downstream\u0026mdash;and link micro-level characterisation results for each to temporally distributed outputs of the respective coupled model, as described in section 3. It is worth noting that many studies omitting temporal distribution focus exclusively on either production or EoL (21 studies), often because their scope is limited to material-related impacts.\u003c/p\u003e\u003cp\u003eMore studies addressed temporal distribution than expected, but none added a temporal dimension at the process-level. While life cycle stage-level differentiation offers a partial solution, resolving the temporal distribution of processes within up- and downstream stages remains an important area of future research (M\u0026uuml;ller et al., unpublished manuscript under review). The tool \u003cem\u003ebw_timex\u003c/em\u003e (Diepers et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) shows promise in advancing this approach. Note that considering temporal distributions for all processes in a life cycle\u0026mdash;including those in the BG system\u0026mdash;would require historical data, as some inflows originate from capital goods built long ago.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Inconsistent terminologies\u003c/h2\u003e\u003cp\u003eTerminological inconsistencies have long been a challenge in LCA, leading to what has been described as an \"alphabet soup\" of approaches (Guin\u0026eacute;e et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In macro-level assessments, terms like \u003cem\u003elarge-scale\u003c/em\u003e, \u003cem\u003eeconomy-wide\u003c/em\u003e, or \u003cem\u003esystem-wide\u003c/em\u003e are often used ambiguously or interchangeably, without formal definitions. For instance, Hellweg et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) mention \u0026ldquo;large-scale LCA analyses (in contrast to product-level LCAs)\u0026rdquo;, but do not clarify the meaning of \u003cem\u003elarge-scale\u003c/em\u003e. To our knowledge, no formal definition exists. To bring clarity, we define macro-level LCA as \u003cem\u003eassessments of environmental impacts associated with the life cycle of a system fulfilling a specific function for the demand at the national, continental, or global scale\u003c/em\u003e, as shown in the introduction. We employ the term \u003cem\u003emacro-level LCA\u003c/em\u003e, first used by Dandres et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and inspired by economic terminology (micro, meso, macro), mainly to avoid confusion with \u003cem\u003eupscaling\u003c/em\u003e, which in prospective LCA typically refers to technological maturity (Tsoy et al. \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor temporal aspects, we recommend following the terminology of M\u0026uuml;ller et al. (unpublished manuscript under review): \u003cem\u003edynamic LCA\u003c/em\u003e for modelling temporal distribution, \u003cem\u003eprospective LCA\u003c/em\u003e for modelling temporal evolution, and \u003cem\u003etime-explicit LCA\u003c/em\u003e when both are addressed.\u003c/p\u003e\u003cp\u003eRegarding the micro-level LCA outputs scaled to the macro-level, most studies use the term \u003cem\u003eemission factor\u003c/em\u003e, \u003cem\u003eimpact factor\u003c/em\u003e, or \u003cem\u003eLCA coefficient\u003c/em\u003e. We find all three terms potentially confusing. \u003cem\u003eImpact factor\u003c/em\u003e can easily be mistaken for \u003cem\u003echaracterisation factor\u003c/em\u003e, a distinct concept in LCA. \u003cem\u003eEmission factor\u003c/em\u003e does not indicate that the values have already been characterised; emissions are typically associated with the LCI. \u003cem\u003eLCA coefficient\u003c/em\u003e, meanwhile, is vague; a coefficient derived from LCA could refer to almost anything. We use and recommend the term \u003cem\u003echaracterisation result\u003c/em\u003e, which aligns with standard LCA terminology (Guin\u0026eacute;e \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Functional units, system boundaries and multifunctionality\u003c/h2\u003e\u003cp\u003eAlthough the functional unit is central to LCA, only 43% of studies define it, and just 21% specify it at the macro-level. Examples of macro-level functional units include meeting the European Union\u0026rsquo;s energy demand by 2050 (Blanco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), operating the United Kingdom\u0026rsquo;s light-duty vehicle fleet for a year (Raugei et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and managing Sweden\u0026rsquo;s annual non-hazardous waste (Arushanyan et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These demonstrate that meaningful functional units can be formulated at the macro-level.\u003c/p\u003e\u003cp\u003eIn prospective studies, it is important to account for changing functional units over time. This can be done, for example, by defining different functional units for each scenario and time frame (Moni et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Generally, when comparing across time, it is important to account for changes in what the system provides, for example different levels of demand.\u003c/p\u003e\u003cp\u003eWith regard to system boundaries, many studies omit parts of the life cycle, especially the EoL. While acceptable depending on objectives, this risks incomplete results and suboptimal decisions. Particularly, the EoL stage for systems with long lifetimes, as many studies evaluate here, can be very important because its EoL lies far in the future, and the respective processes might look very different then (Cucurachi et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEvolving technologies and societal needs can lead to shifts in process and system functions, with important implications in four main respects. First, allocation factors may need to be adapted to future conditions. Second, co-products may become scarce as fossil fuel production decreases, with consequences for resource availability (M\u0026aring;nberger \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hahn Menacho et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). Third, especially at the macro-level, the handling of co-products becomes increasingly important, for instance due to demand constraints. This aspect has been addressed by two studies (Adrianto et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nabera et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Finally, system expansion can be particularly challenging, as additional functions are not necessarily consistent across scenarios. For example, in one scenario agri-photovoltaic may be included, adding an additional function to the system, while in another they may not, leading to a comparison between a system that provides both agricultural products and electricity and one that provides only the former. This issue is not specific to the macro-level but applies to any prospective LCA.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Lack of thematic and scenario diversity\u003c/h2\u003e\u003cp\u003eThe reviewed studies exhibit a narrow thematic focus: 93% cover just four sectors\u0026mdash;energy systems (25 studies); transport (26), mainly the transition to electric vehicles; intermediate goods (19), such as aluminium and hydrogen; and buildings (11). This leaves important areas like food, water, sanitation, consumer goods, and waste largely absent, despite their relevance for sustainability transitions. Broadening the thematic scope is important.\u003c/p\u003e\u003cp\u003eScenario choices also show limited diversity. Many studies rely (33) on IEA scenarios for system scaling or modelling temporal evolution, typically electricity mix projections. While this improves comparability, it risks reinforcing shared assumptions and biases (Schulze et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), such as underestimating the growth of renewable energy (Creutzig et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Way et al. \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lopez et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) .\u003c/p\u003e\u003cp\u003eRegarding the SSP framework (O\u0026rsquo;Neill et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), often used via IAMs for system scaling or temporal evolution, as well as sometimes applied directly (e.g., Mastrucci et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), there is a strong focus on SSP2, the middle-of-the-road scenario. Despite the importance of scenario diversity (Bruhn et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the intention of the SSP framework to explore diverse futures (Riahi et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and \u003cem\u003epremise\u003c/em\u003e supporting SSP1 and SSP5 as well (Sacchi \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), we find only five studies (Pedneault et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kalt et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Arvesen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Alaux et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e; Horup et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) using other SSPs. For Representative Concentration Pathways (van Vuuren et al. \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), we find greater diversity. Generally, IAMs often assume convergence towards Western lifestyles with increasing wealth, making modelling of alternative, more imaginative futures difficult (Hickel et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This tendency is already embedded in the SSPs, all of which presuppose continued global economic growth (de Bortoli et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eTemporal scope also lacks variation: 71% of studies end in 2050, while only six extend beyond 2060. As Schulze et al. (\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) note, this can be problematic for sectors with long-lived assets, where shorter horizons may miss long-term implications due to system inertia or delayed material availability.\u003c/p\u003e\u003cp\u003eTo improve future analyses, we recommend focusing on a small set of contrasting, informative scenarios covering a wide range of possible futures. The \u003cem\u003eSIMPL\u003c/em\u003e approach (Langkau et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and its use in Alaux et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e), demonstrate good practice. Furthermore, with 2050 only 25 years away, longer timeframes should be considered; despite greater uncertainty for longer time horizons (Steubing et al. \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), especially with regard to disruptive technologies (Sacchi et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.7 Internal consistency\u003c/h2\u003e\u003cp\u003eBeyond the inconsistencies in modelling temporal evolution discussed in subsection 4.2, we identify several additional sources of inconsistency. First, inconsistencies can arise between the models in the FG system, which are typically coupled one-way: external models inform LCA but not vice versa. Beaussier et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) highlight the need for high-level coupling\u0026mdash;defined as \u0026ldquo;models that are linked and run together involving variables (\u0026hellip;) in closed loops\u0026rdquo;\u0026mdash;when (i) environmental impacts affect system processes or (ii) agents respond to environmental policies. Some ESM-based studies address this by optimising for least environmental impacts based on LCA results (e.g., Vandepaer et al. \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Generally, researchers need to be aware that model coupling inherits assumptions and modelling choices from the other model (de Bortoli et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), posing a potential challenge to consistency.\u003c/p\u003e\u003cp\u003eSecond, scenario misalignments are common; e.g., ambitious FG system policies paired with less ambitious BG system scenarios. Aligning the FG and BG system narratives is important for greater internal consistency. When using \u003cem\u003epremise\u003c/em\u003e (Sacchi et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), it is important to recognise that it reflects the assumptions of the underlying IAM, which may not always align with FG system scenarios developed using other modelling frameworks, such as ESMs (Weidner et al. \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThird, one more issue we want to highlight is mass balance, a core principle in DSM methods but often violated in other models, such as IAMs (Pauliuk et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A systematic assessment was not possible due to limited transparency in many studies. Here, we simply aim to raise awareness that mass balance cannot be assumed\u0026mdash;especially when coupling models or representing closed-loop systems\u0026mdash;and must be explicitly checked.\u003c/p\u003e\u003cp\u003eFinally, for macro-level LCAs, the assumption that the BG system is independent of the FG system no longer holds. Given the scale of the FG system, it must be considered that BG processes may also source from the FG system (Charalambous et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A few studies address this (e.g., Hertwich et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Charalambous et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrate how FG system changes can be propagated through the BG system in \u003cem\u003epremise\u003c/em\u003e by adjusting BG markets. However, inconsistencies with the original IAM scenario remain, as it does not account for the modelled FG system developments. The significance of FG\u0026ndash;BG system inconsistency also depends on the study\u0026rsquo;s scope. ESMs, for example, cover entire electricity or energy systems in the FG system, leaving a small BG. Therefore, a limited error can be assumed. Nevertheless, building on Gibon et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Charalambous et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) to better integrate FG and BG systems is a key next step.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.8 Representing the complexity of sustainability transitions\u003c/h2\u003e\u003cp\u003eSustainability transitions are inherently complex, and no single model captures all relevant dimensions. Understanding each model\u0026rsquo;s scope and limitations is therefore essential. For example, as Wiedenhofer et al. (\u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) note, there is often a trade-off between capturing socio-economic complexity and adhering to thermodynamic principles: models like LCA provide detailed representations of technologies and their interlinkages through energy and mass flows but typically omit inter-sectoral relationships and broader economic feedbacks.\u003c/p\u003e\u003cp\u003eRather than proposing a comprehensive framework for modelling transitions, we want to highlight aspects addressed or overlooked in prospective macro-level LCA literature. As discussed above, technological learning is typically included but often oversimplified (van Nielen et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and cross-sector effects are only captured by a few studies (e.g., R\u0026uuml;dis\u0026uuml;li et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Whether IAM-LCA studies capture these depends on the coupling: approaches including micro-level characterisation results in the IAM (Pehl et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) represent all aspects also included in the IAM, whereas ex-post applications (e.g., M\u0026uuml;ller et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) capture only those that affect the scaling variables, e.g. demand. The same applies to ESM-LCA studies.\u003c/p\u003e\u003cp\u003eAspects that we have not found represented explicitly include rebound effects, threshold effects, incumbent power, institutional roles, behaviour, the role of institutions (Hafner et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and tipping points (Binder et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Actor heterogeneity is only captured in some IAM\u0026ndash;LCA studies indirectly through the IAM, and then only to a limited extent, mostly focusing on income inequality (Keppo et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; de Bortoli et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Generally, models focus on aggregate outcomes, obscuring differences in access, needs, and responsibility.\u003c/p\u003e\u003cp\u003eProspective macro-level LCA could better represent sustainability transitions by leveraging social science perspectives, such as heterodox economics (Hafner et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), underused methods like SD or ABM, and new model combinations. Promising research directions include provisioning systems and sufficiency corridors (Pauliuk \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pichler et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRegarding mitigation strategies, Pichler et al. (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) propose a 2\u0026times;2 framework\u0026mdash;supply vs. demand and individual vs. systemic\u0026mdash;yielding four perspectives: techno-innovation, industrial transformation, individual decision-making, and embedded lifestyles. They highlight that focusing exclusively on supply or demand leaves critical gaps and clarify that sufficiency measures can also apply to the supply side and efficiency measures to the demand side. Applying the framework to our sample is challenging, as the drivers behind mitigation measures are rarely explicit. For example, assumed renewable energy uptake could reflect incentives or regulation, and fleet reductions could result from demand or supply changes. We do not apply the framework here but view it as valuable for guiding future modeling and stress the importance of clarifying underlying drivers in future studies.\u003c/p\u003e\u003cp\u003eWhile enhancing the representation of sustainability transitions in current modelling practice is important, it is equally important to keep in mind that modelling complexity has trade-offs, like risking interpretability, and must be purpose-driven (Saltelli et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kaufman and Bataille \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Models should be as simple as possible but as complex as necessary and relying on multiple different approaches will always be needed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.9 Modelling of constraints\u003c/h2\u003e\u003cp\u003eDistinguishing constraints from scenario assumptions, parameters, or model structures is often difficult. A detailed discussion and conceptualisation are beyond the scope of this study, and since reviewing each coupled model in detail is not feasible here; we instead highlight only a few important points.\u003c/p\u003e\u003cp\u003eConstraints help assess whether scenarios are not only environmentally beneficial but also feasible. Many studies include constraints, often through coupled models. In particular, ESMs and IAMs introduce constraints related to climate targets, capacities, infrastructure availability, technology-specific deployment, budgets, and many other aspects (e.g., Louis et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Luderer et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Reinert et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). DSMs often constrain secondary material availability (e.g., Milovanoff et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Optimisation approaches are also able to include constraints well, such as manufacturing capacity (Hung et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lechtenberg et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By contrast, studies without model coupling often omit explicit constraints, instead discussing limitations retrospectively, such as comparisons to current production (e.g., Ginster et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) or resource reserves (e.g., Zhang et al. \u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While useful for context, this does not improve the feasibility of the modelled scenarios.\u003c/p\u003e\u003cp\u003eSome types of constraints are not explicitly mentioned by any study, such as labour availability or incumbent power (Geels et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Only two studies (Adrianto et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nabera et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) considered constrained co-product market absorption. Another aspect not found explicitly in the literature is competition for production capacities and resources between different systems of analysis (Hung et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); here, more granular and explicit modelling is needed.\u003c/p\u003e\u003cp\u003eTo guide future research, we propose, inspired by Wiedenhofer et al. (\u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), four non-exclusive constraint types: (i) physical/ecological (e.g., raw material availability), (ii) social/political (e.g., labour shortages or incumbent interests), (iii) technological (e.g., technology availability or manufacturing capacities), and (iv) economic (e.g., insufficient investment). Importantly, the temporal dynamics of constraints deserve greater attention (Hung et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A resource may be available in the long term, but short-term bottlenecks\u0026mdash;such as mine development (Schulze et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u0026mdash;can limit the feasibility of scenarios (Hung et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, geopolitical aspects may gain importance (Post and Le Billon \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Explicitly incorporating some of these constraints\u0026mdash;both in model formulations and in scenario narratives\u0026mdash;can improve realism in prospective macro-level LCA. Life cycle optimisation tools (e.g., Lechtenberg et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) offer promising means to implement such constraints.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.10 Narrow focus on climate change impacts\u003c/h2\u003e\u003cp\u003eAbout half of the studies (44) assess climate change only. While crucial, this narrow focus neglects LCA\u0026rsquo;s strength in revealing trade-offs across impact categories. Broadening the scope is essential for future macro-LCAs (Hellweg et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), as existing research has shown that decarbonisation can lead to large environmental co-benefits and trade-offs (Luderer et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough a detailed discussion of Life Cycle Impact Assessment (LCIA) in prospective and macro-level LCA is beyond this study, we want to highlight a few important points. First, LCIA typically treats emissions as instantaneous pulses, ignoring temporal dynamics that may be relevant (Cardellini et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Second, inconsistencies exist between inventory and impact phases regarding time horizons (Levasseur et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Beloin-Saint-Pierre et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Third, many models rely on static baseline concentrations, though these may change (Vandepaer and Gibon \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lueddeckens et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Using IAMs to project future background concentrations has been proposed, but not implemented (Mendoza Beltran et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Fourth, emerging technologies may introduce novel emissions not yet covered by LCIA methods, which can result in overlooked impacts and a false sense of certainty (Thonemann et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cucurachi et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Existing dynamic LCIA methods are so far focused on climate change (Levasseur et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Milovanoff et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ventura \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Diepers and M\u0026uuml;ller \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Finally, no study addressed scale-dependent changes to LCIA methods\u0026mdash;an aspect that deserves further attention.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.11 Uncertainty and sensitivity\u003c/h2\u003e\u003cp\u003eUncertainty and sensitivity are widely discussed in LCA but inconsistently defined and applied (Heijungs \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), including in prospective LCA. For instance, Spielmann et al. (\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) view epistemic uncertainty\u0026mdash;arising from limited knowledge of future systems\u0026mdash;as central and best addressed through scenarios. In contrast, Langkau et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) associate it with methodological choices not captured by \u003cem\u003efuture\u003c/em\u003e scenarios. Here, we focus on how uncertainty and sensitivity are treated in the reviewed studies.\u003c/p\u003e\u003cp\u003eMost studies use multiple scenarios, though only some explicitly frame them as addressing uncertainty (e.g., Horup et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Dedicated uncertainty analyses using distributions for specific parameters are rare; only five studies apply Monte Carlo simulation (Dirnaichner et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kalt et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tang et al. \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hahn Menacho et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e; Shirmohammadi et al. \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). LCIA uncertainty is not considered, except in Sacchi et al. (\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Challenges include data limitations, especially projecting future uncertainty ranges, and applying uncertainty across coupled models. Given the absence of a \u0026lsquo;true\u0026rsquo; future, we see structured, transparent cornerstone scenarios as the most practical way to address uncertainty (Langkau et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAbout half of the studies (39) include sensitivity analyses, predominantly using local, one-at-a-time approaches (following the classification of Heijungs (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)). Changing coupled models (Boyce et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), LCIA methods (Reinert et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), accounting scope (Pehl et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), allocation approach (Dong et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), foresight horizon (Zibunas et al. \u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), or optimisation targets (Louis et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) can also be seen as local, one-at-a-time sensitivity analysis. Some studies build scenarios by systematically varying inputs, resembling sensitivity analysis (Ginster et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pauliuk et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Only two studies apply global sensitivity analysis (Alaux et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e; Hahn Menacho et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). The potential advantages of applying global sensitivity analysis more widely warrant closer examination.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.12 Transparency and reproducibility\u003c/h2\u003e\u003cp\u003eTransparent documentation of methods, open-access publication, open-source models, and open data are essential for scientific progress; not only to ensure reproducibility but also to enhance effectiveness by fostering collaboration, enabling critical peer review, improving public trust in research, and upholding ethical standards (Pfenninger et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pauliuk \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the reviewed studies, transparency is often limited: unclear objectives, functional units, system boundaries, and modelling choices (e.g., form of interpolation) are common. For instance, 32 studies using \u003cem\u003eecoinvent\u003c/em\u003e fail to specify the system model applied (Wernet et al. \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Model coupling is rarely well-documented. Clear illustrations of model interactions (e.g., Junne et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and the FG system, for example through flowcharts, are needed. Scenario variables should be made explicit, ideally summarized in a concise overview as in van der Voet et al. (\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A clear distinction between model inputs and outputs is equally essential. In addition, data sources and software should be disclosed, including version information where applicable (Steubing et al. \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While more challenging for multi-model studies, we recommend aligning with the ISO reporting requirements (ISO \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), particularly for temporal scope, functional unit, and boundaries.\u003c/p\u003e\u003cp\u003eMost studies (65) share input data, but we did not assess whether shared data is sufficient for reproducibility. Only 23 studies share their models. M\u0026uuml;ller et al. (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) provide a good example for sharing data, and Alaux et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for a reusable model. We recommend using repositories that support rich metadata, persistent identifiers, clear licensing, version control, and long-term preservation (e.g., Zenodo). For further guidance, see Pauliuk et al. (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusions and recommendations","content":"\u003cp\u003eWe reviewed 86 prospective macro-level LCA studies, categorising them into four main (DSM-LCA, ESM-LCA, IAM-LCA, no coupled model) and two less common (coupling GEM and SD) approaches. The approaches vary in focus and complexity. Each brings specific strengths and trade-offs: DSM-LCAs capture stock dynamics but lack coverage of socioeconomic aspects; ESM-LCAs provide detailed insights into energy systems but are also limited to it; IAM-LCAs provide better socioeconomic coverage but operate at coarse resolution and typically require collaboration with IAM developers; and approaches without a coupled model offer flexibility but risk oversimplification. We also identified common pitfalls, methodological challenges, and best practices. Our review reveals a diverse and growing but fragmented field with inconsistent terminology, assumptions, and modelling practices that limit comparability.\u003c/p\u003e\n\u003cp\u003eBased on our findings, we suggest four priorities for advancing the field:\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eImprove the modelling of sustainability transitions\u003c/strong\u003e. Better representation of temporal dynamics\u0026mdash;especially for long-lived assets\u0026mdash;and the implementation of feedbacks between FG and BG systems are important. Capturing the complexity of sustainability transitions requires advancing existing approaches: drawing on perspectives from the social sciences, such as heterodox economics (Hafner et al. 2020), exploring underused methods like SD and ABM, and experimenting with new model combinations. However, increased complexity must be purpose-driven; models should remain as simple as possible, but as complex as necessary (Saltelli et al. 2020; Kaufman and Bataille 2025). No single model can address all research questions.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eStrengthen policy relevance.\u003c/strong\u003e Stronger links between modelling and policy are needed, particularly in DSM-LCA and uncoupled approaches. Representing distributional aspects, demand-side measures, and behavioural responses will help support more just and effective policy design.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eEnsure transparency, reproducibility and consistent terminology.\u003c/strong\u003e Clearly reporting functional units, system boundaries, and data flows between coupled models is crucial. We also urge the field to converge on shared terminology, for which we propose \u003cem\u003emacro-level LCA\u003c/em\u003e. For the time dimension, we recommend using \u003cem\u003eprospective LCA\u003c/em\u003e for modelling temporal evolution and \u003cem\u003edynamic LCA\u003c/em\u003e for temporal distribution (M\u0026uuml;ller et al., unpublished manuscript under review). Models and data should be, as far as confidentiality considerations allow, openly available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eDevelop methodological guidance\u003c/strong\u003e. As the scope of LCA expands, so does its complexity, underscoring the need for guidance that maintains modelling freedom while at the same time facilitating transparency, quality, common language, and comparability (Bisinella et al. 2021).\u003c/p\u003e\n\u003cp\u003eBy addressing the aspects mentioned above, the field of prospective macro-level LCA can improve our modelling and understanding of pathways to sustainable societies.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eABM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Agent-Based Modelling\u003c/p\u003e\n\u003cp\u003eBG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Background\u003c/p\u003e\n\u003cp\u003eDSM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Dynamic Stock Model\u003c/p\u003e\n\u003cp\u003eDSM-LCA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Coupling of process-based Life Cycle Assessment with a Dynamic Stock Model\u003c/p\u003e\n\u003cp\u003eEoL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;End-of-Life\u003c/p\u003e\n\u003cp\u003eESM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Energy System Model\u003c/p\u003e\n\u003cp\u003eESM-LCA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Coupling of process-based Life Cycle Assessment with an Energy System Model\u003c/p\u003e\n\u003cp\u003eFG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Foreground\u003c/p\u003e\n\u003cp\u003eGEM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;General Equilibrium Model\u003c/p\u003e\n\u003cp\u003eIAM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Integrated Assessment Model\u003c/p\u003e\n\u003cp\u003eIAM-LCA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Coupling of process-based Life Cycle Assessment with an Integrated Assessment Model\u003c/p\u003e\n\u003cp\u003eIEA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;International Energy Agency\u003c/p\u003e\n\u003cp\u003eIO-LCA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Input-Output Life Cycle Assessment\u003c/p\u003e\n\u003cp\u003eLCA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Process-based Life Cycle Assessment\u003c/p\u003e\n\u003cp\u003eLCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Life Cycle Inventory\u003c/p\u003e\n\u003cp\u003eLCIA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Life Cycle Impact Assessment\u003c/p\u003e\n\u003cp\u003ePEM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Partial Equilibrium Model\u003c/p\u003e\n\u003cp\u003eSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;System Dynamics\u003c/p\u003e\n\u003cp\u003eSSP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Shared Socioeconomic Pathway\u003c/p\u003e\n\u003cp\u003eTRL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Technology Readiness Level\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e This article is part of AP’s doctoral research. The concept for the article was developed by AP and NT, under the supervision of JG. AP conducted the literature search, carried out the analysis, and drafted the manuscript. All authors contributed to methodological discussions and provided critical revisions of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research at Leiden University has received funding from the Ministry of Education, Culture and Science of The Netherlands under the Starter Grant programme. Open access funding was provided by Leiden University.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e All data are made available in the Supplementary Information.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest:\u003c/strong\u003e NT served as Guest Editor for the Special Issue to which this paper was submitted but was not involved in the editorial handling of this manuscript. JG is a member of the Editorial Board of the journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe thank Thomas Arblaster for insightful discussions about the definition of macro-level LCA.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdrianto LR, Ciacci L, Pfister S, Hellweg S (2023) Toward sustainable reprocessing and valorization of sulfidic copper tailings: Scenarios and prospective LCA. Sci Total Environ 871:162038. https://doi.org/10.1016/j.scitotenv.2023.162038\u003c/li\u003e\n \u003cli\u003eAlaux N, Schwark B, H\u0026ouml;rmann M, Ruschi Mendes Saade M, Passer A (2024) Assessing the prospective environmental impacts and circularity potentials of building stocks: An open‐source model from Austria (PULSE‐AT). J of Industrial Ecology. https://doi.org/10.1111/jiec.13558\u003c/li\u003e\n \u003cli\u003eAlaux N, Steubing B, Habert G, Ruschi Mendes Saade M, Passer A (2025a) Future Life-Cycle Greenhouse Gas Emission Scenarios for the Austrian Building Stock: A Systematic Approach. Environ Sci Technol. https://doi.org/10.1021/acs.est.4c12138\u003c/li\u003e\n \u003cli\u003eAlaux N, Schwark B, Scherz M, Ruschi Mendes Saade M, Passer A (2025b) Parameters influencing the prospective life cycle emissions of the Austrian building stock: a global sensitivity analysis. Sustainable Production and Consumption 56:618\u0026ndash;631. https://doi.org/10.1016/j.spc.2025.04.024\u003c/li\u003e\n \u003cli\u003eArushanyan Y, Bj\u0026ouml;rklund A, Eriksson O, Finnveden G, Ljunggren S\u0026ouml;derman M, Sundqvist J-O, Stenmarck \u0026Aring; (2017) Environmental Assessment of Possible Future Waste Management Scenarios. Energies 10:247. https://doi.org/10.3390/en10020247\u003c/li\u003e\n \u003cli\u003eArvesen A, Bright RM, Hertwich EG (2011) Considering only first-order effects? How simplifications lead to unrealistic technology optimism in climate change mitigation. Energy Policy 39:7448\u0026ndash;7454. https://doi.org/10.1016/j.enpol.2011.09.013\u003c/li\u003e\n \u003cli\u003eArvesen A, Luderer G, Pehl M, Bodirsky BL, Hertwich EG (2018) Deriving life cycle assessment coefficients for application in integrated assessment modelling. Environmental Modelling \u0026amp; Software 99:111\u0026ndash;125. https://doi.org/10.1016/j.envsoft.2017.09.010\u003c/li\u003e\n \u003cli\u003eArvesen A, Humpen\u0026ouml;der F, Navarrete Gutierrez T, Gibon T, Baustert P, Dietrich JP, Stadler K, Iordan C-M, Luderer G, Popp A, Cherubini F (2024) Advancing life cycle assessment of bioenergy crops with global land use models. Environ. Res. Commun. 6:125004. https://doi.org/10.1088/2515-7620/ad97ac\u003c/li\u003e\n \u003cli\u003eArvidsson R, Tillman A-M, Sand\u0026eacute;n BA, Janssen M, Nordel\u0026ouml;f A, Kushnir D, Molander S (2018) Environmental Assessment of Emerging Technologies: Recommendations for Prospective LCA. J of Industrial Ecology 22:1286\u0026ndash;1294. https://doi.org/10.1111/jiec.12690\u003c/li\u003e\n \u003cli\u003eArvidsson R, Svanstr\u0026ouml;m M, Sand\u0026eacute;n BA, Thonemann N, Steubing B, Cucurachi S (2024) Terminology for future-oriented life cycle assessment: review and recommendations. Int J Life Cycle Assess 29:607\u0026ndash;613. https://doi.org/10.1007/s11367-023-02265-8\u003c/li\u003e\n \u003cli\u003eBarkhausen R, Rostek L, Miao ZC, Zeller V (2023) Combinations of material flow analysis and life cycle assessment and their applicability to assess circular economy requirements in EU product regulations. A systematic literature review. Journal of Cleaner Production 407:137017. https://doi.org/10.1016/j.jclepro.2023.137017\u003c/li\u003e\n \u003cli\u003eBeaussier T, Caurla S, Bellon-Maurel V, Loiseau E (2019) Coupling economic models and environmental assessment methods to support regional policies: A critical review. Journal of Cleaner Production 216:408\u0026ndash;421. https://doi.org/10.1016/j.jclepro.2019.01.020\u003c/li\u003e\n \u003cli\u003eBeloin-Saint-Pierre D, Albers A, H\u0026eacute;lias A, Tiruta-Barna L, Fantke P, Levasseur A, Benetto E, Benoist A, Collet P (2020) Addressing temporal considerations in life cycle assessment. Sci Total Environ 743:140700. https://doi.org/10.1016/j.scitotenv.2020.140700\u003c/li\u003e\n \u003cli\u003eBergerson JA, Brandt A, Cresko J, Carbajales‐Dale M, MacLean HL, Matthews HS, McCoy S, McManus M, Miller SA, Morrow WR, Posen ID, Seager T, Skone T, Sleep S (2020) Life cycle assessment of emerging technologies: Evaluation techniques at different stages of market and technical maturity. J of Industrial Ecology 24:11\u0026ndash;25. https://doi.org/10.1111/jiec.12954\u003c/li\u003e\n \u003cli\u003eBinder CR, Athanassiadis A, Bristow D, Haberl H, Kennedy C (2025) Tipping points toward sustainability: The role of industrial ecology. J of Industrial Ecology. https://doi.org/10.1111/jiec.70000\u003c/li\u003e\n \u003cli\u003eBisinella V, Christensen TH, Astrup TF (2021) Future scenarios and life cycle assessment: systematic review and recommendations. Int J Life Cycle Assess 26:2143\u0026ndash;2170. https://doi.org/10.1007/s11367-021-01954-6\u003c/li\u003e\n \u003cli\u003eBlanco H, Codina V, Laurent A, Nijs W, Mar\u0026eacute;chal F, Faaij A (2020) Life cycle assessment integration into energy system models: An application for Power-to-Methane in the EU. Applied Energy 259:114160. https://doi.org/10.1016/j.apenergy.2019.114160\u003c/li\u003e\n \u003cli\u003eBohnes FA, Hauschild MZ, Schlundt J, Nielsen M, Laurent A (2022) Environmental sustainability of future aquaculture production: Analysis of Singaporean and Norwegian policies. Aquaculture 549:737717. https://doi.org/10.1016/j.aquaculture.2021.737717\u003c/li\u003e\n \u003cli\u003eBoyce J, Sacchi R, Goetheer E, Steubing B (2024) A prospective life cycle assessment of global ammonia decarbonisation scenarios. Heliyon 10:e27547. https://doi.org/10.1016/j.heliyon.2024.e27547\u003c/li\u003e\n \u003cli\u003eBrand C, Tran M, Anable J (2012) The UK transport carbon model: An integrated life cycle approach to explore low carbon futures. Energy Policy 41:107\u0026ndash;124. https://doi.org/10.1016/j.enpol.2010.08.019\u003c/li\u003e\n \u003cli\u003eBrand C, Anable J, Tran M (2013) Accelerating the transformation to a low carbon passenger transport system: The role of car purchase taxes, feebates, road taxes and scrappage incentives in the UK. Transportation Research Part A: Policy and Practice 49:132\u0026ndash;148. https://doi.org/10.1016/j.tra.2013.01.010\u003c/li\u003e\n \u003cli\u003eBruhn S, Sacchi R, Cimpan C, Birkved M (2023) Ten questions concerning prospective LCA for decision support for the built environment. Building and Environment 242:110535. https://doi.org/10.1016/j.buildenv.2023.110535\u003c/li\u003e\n \u003cli\u003eBuyle M, Audenaert A, Billen P, Boonen K, van Passel S (2019) The Future of Ex-Ante LCA? Lessons Learned and Practical Recommendations. Sustainability 11:5456. https://doi.org/10.3390/su11195456\u003c/li\u003e\n \u003cli\u003eCabrera-Jim\u0026eacute;nez R, Mateo JM, Jim\u0026eacute;nez L, Pozo C (2025) Prospective life-cycle assessment of sustainable alternatives for road freight transport. Renewable and Sustainable Energy Reviews 211:115243. https://doi.org/10.1016/j.rser.2024.115243\u003c/li\u003e\n \u003cli\u003eCaiardi F, Azzaro-Pantel C, Le-Boulch D (2024) Exploring carbon neutrality scenarios through the life cycle assessment lens: a review of literature and methodological challenges. Environ Dev Sustain. https://doi.org/10.1007/s10668-024-05598-y\u003c/li\u003e\n \u003cli\u003eCardellini G, Mutel CL, Vial E, Muys B (2018) Temporalis, a generic method and tool for dynamic Life Cycle Assessment. Sci Total Environ 645:585\u0026ndash;595. https://doi.org/10.1016/j.scitotenv.2018.07.044\u003c/li\u003e\n \u003cli\u003eCharalambous MA, Sacchi R, Tulus V, Guill\u0026eacute;n-Gos\u0026aacute;lbez G (2024) Integrating emerging technologies deployed at scale within prospective life cycle assessments. Sustainable Production and Consumption 50:499\u0026ndash;510. https://doi.org/10.1016/j.spc.2024.08.016\u003c/li\u003e\n \u003cli\u003eCreutzig F, Agoston P, Goldschmidt JC, Luderer G, Nemet G, Pietzcker RC (2017) The underestimated potential of solar energy to mitigate climate change. Nat Energy 2. https://doi.org/10.1038/nenergy.2017.140\u003c/li\u003e\n \u003cli\u003eCreutzig F, Erb K-H, Haberl H, Hof C, Hunsberger C, Roe S (2021) Considering sustainability thresholds for BECCS in IPCC and biodiversity assessments. GCB Bioenergy 13:510\u0026ndash;515. https://doi.org/10.1111/gcbb.12798\u003c/li\u003e\n \u003cli\u003eCucurachi S, Blanco CF, Florin MV, Rusu AG (2023) Practical solutions for ex-ante LCA illustrated by emerging PV technologies. In: Florin M-V (ed) Ensuring the environmental sustainability of emerging technologies -2. Edited volume. Lausanne, EPFL, Lausanne, Switzerland\u003c/li\u003e\n \u003cli\u003eDandres T, Gaudreault C, Tirado-Seco P, Samson R (2012) Macroanalysis of the economic and environmental impacts of a 2005\u0026ndash;2025 European Union bioenergy policy using the GTAP model and life cycle assessment. Renewable and Sustainable Energy Reviews 16:1180\u0026ndash;1192. https://doi.org/10.1016/j.rser.2011.11.003\u003c/li\u003e\n \u003cli\u003ede Bortoli A, Chanel A, Chabas C, Greffe T, Louineau E (2025) More rationality and inclusivity are imperative in reference transition scenarios based on IAMs and shared socioeconomic pathways - recommendations for prospective LCA. Renewable and Sustainable Energy Reviews 222:115924. https://doi.org/10.1016/j.rser.2025.115924\u003c/li\u003e\n \u003cli\u003eDeng T, Zhang Y, Fu C (2023) Modelling dynamic interactions between material flow and stock: A review of dynamic material flow analysis. Ecological Indicators 156:111098. https://doi.org/10.1016/j.ecolind.2023.111098\u003c/li\u003e\n \u003cli\u003eDiepers T, M\u0026uuml;ller A (2025) dynamic_characterization documentation. https://dynamic-characterization.readthedocs.io/en/latest/. Accessed 12 June 2025\u003c/li\u003e\n \u003cli\u003eDiepers T, M\u0026uuml;ller A, Jakobs A (2024) bw_timex documentation: Time-explicit LCA with bw_timex. https://docs.brightway.dev/projects/bw-timex/en/latest/. Accessed 5 June 2025\u003c/li\u003e\n \u003cli\u003eDiepers T, Tautorus J (2025) bw_optimex documentation: Time-explicit Transition Pathway Optimization with. https://optimex.readthedocs.io/en/latest/. Accessed 2 July 2025\u003c/li\u003e\n \u003cli\u003eDirnaichner A, Rottoli M, Sacchi R, Rauner S, Cox B, Mutel C, Bauer C, Luderer G (2022) Life-cycle impacts from different decarbonization pathways for the European car fleet. Environ. Res. Lett. 17:44009. https://doi.org/10.1088/1748-9326/ac4fdb\u003c/li\u003e\n \u003cli\u003eDong D, Tukker A, Steubing B, van Oers L, Rechberger H, Alonso Aguilar-Hernandez G, Li H, van der Voet E (2022) Assessing China\u0026rsquo;s potential for reducing primary copper demand and associated environmental impacts in the context of energy transition and \u0026ldquo;Zero waste\u0026rdquo; policies. Waste Manag 144:454\u0026ndash;467. https://doi.org/10.1016/j.wasman.2022.04.006\u003c/li\u003e\n \u003cli\u003eDouziech M, Besseau R, Jolivet R, Shoai‐Tehrani B, Bourmaud J-Y, Busato G, Gresset‐Bourgeois M, P\u0026eacute;rez‐L\u0026oacute;pez P (2024) Life cycle assessment of prospective trajectories: A parametric approach for tailor‐made inventories and its computational implementation. J of Industrial Ecology 28:25\u0026ndash;40. https://doi.org/10.1111/jiec.13432\u003c/li\u003e\n \u003cli\u003eEarles JM, Halog A (2011) Consequential life cycle assessment: a review. Int J Life Cycle Assess 16:445\u0026ndash;453. https://doi.org/10.1007/s11367-011-0275-9\u003c/li\u003e\n \u003cli\u003eErakca M, Baumann M, Helbig C, Weil M (2024) Systematic review of scale-up methods for prospective life cycle assessment of emerging technologies. Journal of Cleaner Production 451:142161. https://doi.org/10.1016/j.jclepro.2024.142161\u003c/li\u003e\n \u003cli\u003eEvans S, Hausfather Z (2018) Q\u0026amp;A: How \u0026lsquo;integrated assessment models\u0026rsquo; are used to study climate change. https://www.carbonbrief.org/qa-how-integrated-assessment-models-are-used-to-study-climate-change/. Accessed 20 May 2025\u003c/li\u003e\n \u003cli\u003eGeels FW, Sovacool BK, Schwanen T, Sorrell S (2017) Sociotechnical transitions for deep decarbonization. Science 357:1242\u0026ndash;1244. https://doi.org/10.1126/science.aao3760\u003c/li\u003e\n \u003cli\u003eGibon T, Wood R, Arvesen A, Bergesen JD, Suh S, Hertwich EG (2015) A Methodology for Integrated, Multiregional Life Cycle Assessment Scenarios under Large-Scale Technological Change. Environ Sci Technol 49:11218\u0026ndash;11226. https://doi.org/10.1021/acs.est.5b01558\u003c/li\u003e\n \u003cli\u003eGibon T, Hertwich EG, Arvesen A, Singh B, Verones F (2017) Health benefits, ecological threats of low-carbon electricity. Environ. Res. Lett. 12:34023. https://doi.org/10.1088/1748-9326/aa6047\u003c/li\u003e\n \u003cli\u003eGinster R, Bl\u0026ouml;meke S, Popien J-L, Scheller C, Cerdas F, Herrmann C, Spengler TS (2024) Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts. J of Industrial Ecology 28:1165\u0026ndash;1182. https://doi.org/10.1111/jiec.13527\u003c/li\u003e\n \u003cli\u003eGrubler A, Wilson C, Bento N, Boza-Kiss B, Krey V, McCollum DL, Rao ND, Riahi K, Rogelj J, Stercke S de, Cullen J, Frank S, Fricko O, Guo F, Gidden M, Havl\u0026iacute;k P, Huppmann D, Kiesewetter G, Rafaj P, Schoepp W, Valin H (2018) A low energy demand scenario for meeting the 1.5 \u0026deg;C target and sustainable development goals without negative emission technologies. Nat Energy 3:515\u0026ndash;527. https://doi.org/10.1038/s41560-018-0172-6\u003c/li\u003e\n \u003cli\u003eGuin\u0026eacute;e JB (2002) Handbook on Life Cycle Assessment: Operational Guide to the ISO Standards. Eco-Efficiency in Industry and Science Ser, v.7. Springer Netherlands, Dordrecht\u003c/li\u003e\n \u003cli\u003eGuin\u0026eacute;e JB, Heijungs R, Huppes G, Zamagni A, Masoni P, Buonamici R, Ekvall T, Rydberg T (2011) Life cycle assessment: past, present, and future. Environ Sci Technol 45:90\u0026ndash;96. https://doi.org/10.1021/es101316v\u003c/li\u003e\n \u003cli\u003eGuin\u0026eacute;e JB, Heijungs R, Vijver MG, Peijnenburg WJGM (2017) Setting the stage for debating the roles of risk assessment and life-cycle assessment of engineered nanomaterials. Nat Nanotechnol 12:727\u0026ndash;733. https://doi.org/10.1038/nnano.2017.135\u003c/li\u003e\n \u003cli\u003eGuin\u0026eacute;e JB, Cucurachi S, Henriksson PJG, Heijungs R (2018) Digesting the alphabet soup of LCA. Int J Life Cycle Assess 23:1507\u0026ndash;1511. https://doi.org/10.1007/s11367-018-1478-0\u003c/li\u003e\n \u003cli\u003eHafner S, Anger-Kraavi A, Monasterolo I, Jones A (2020) Emergence of New Economics Energy Transition Models: A Review. Ecological Economics 177:106779. https://doi.org/10.1016/j.ecolecon.2020.106779\u003c/li\u003e\n \u003cli\u003eHagenaars RH, Heijungs R, Tukker A, Wang R (2025) Hybrid LCA for sustainable transitions: principles, applications, and prospects. Renewable and Sustainable Energy Reviews 212:115443. https://doi.org/10.1016/j.rser.2025.115443\u003c/li\u003e\n \u003cli\u003eHahn Menacho AJ, Sacchi R, Bauer C, Panos E, McKenna R, Burgherr P (2025a) The material-energy nexus in net-zero transition scenarios: exploring environmental trade-offs and uncertainties. Resources, Conservation and Recycling 218:108251. https://doi.org/10.1016/j.resconrec.2025.108251\u003c/li\u003e\n \u003cli\u003eHahn Menacho AJ, Sacchi R, Bauer C, Moretti C, Panos E, McKenna R, Burgherr P (2025b) The global environmental footprint of Switzerland\u0026rsquo;s net-zero energy system uncovers impacts abroad. Commun Earth Environ 6. https://doi.org/10.1038/s43247-025-02220-5\u003c/li\u003e\n \u003cli\u003eHarpprecht C, Miranda Xicotencatl B, van Nielen S, van der Meide M, Li C, Li Z, Tukker A, Steubing B (2024) Future environmental impacts of metals: A systematic review of impact trends, modelling approaches, and challenges. Resources, Conservation and Recycling 205:107572. https://doi.org/10.1016/j.resconrec.2024.107572\u003c/li\u003e\n \u003cli\u003eHeijungs R (2020) Is mainstream LCA linear? Int J Life Cycle Assess 25:1872\u0026ndash;1882. https://doi.org/10.1007/s11367-020-01810-z\u003c/li\u003e\n \u003cli\u003eHeijungs R (2024) Probability, Statistics and Life Cycle Assessment. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-031-49317-1\u003c/li\u003e\n \u003cli\u003eHellweg S, Benetto E, Huijbregts MAJ, Verones F, Wood R (2023) Life-cycle assessment to guide solutions for the triple planetary crisis. Nat Rev Earth Environ 4:471\u0026ndash;486. https://doi.org/10.1038/s43017-023-00449-2\u003c/li\u003e\n \u003cli\u003eHertwich EG, Gibon T, Bouman EA, Arvesen A, Suh S, Heath GA, Bergesen JD, Ramirez A, Vega MI, Shi L (2015) Integrated life-cycle assessment of electricity-supply scenarios confirms global environmental benefit of low-carbon technologies. Proc Natl Acad Sci U S A 112:6277\u0026ndash;6282. https://doi.org/10.1073/pnas.1312753111\u003c/li\u003e\n \u003cli\u003eHickel J, Brockway P, Kallis G, Key\u0026szlig;er L, Lenzen M, Slamer\u0026scaron;ak A, Steinberger J, \u0026Uuml;rge-Vorsatz D (2021) Urgent need for post-growth climate mitigation scenarios. Nat Energy 6:766\u0026ndash;768. https://doi.org/10.1038/s41560-021-00884-9\u003c/li\u003e\n \u003cli\u003eHorup L, Bruhn S, Hoxha E, Birgisdottir H, Secher AQ, Ohms P, Hauschild M (2025) Absolute sustainability assessment of the Danish building sector through prospective LCA. Sci Total Environ 966:178780. https://doi.org/10.1016/j.scitotenv.2025.178780\u003c/li\u003e\n \u003cli\u003eHung CR, Kishimoto P, Krey V, Str\u0026oslash;mman AH, Majeau‐Bettez G (2022) ECOPT 2 : An adaptable life cycle assessment model for the environmentally constrained optimization of prospective technology transitions. J of Industrial Ecology 26:1616\u0026ndash;1630. https://doi.org/10.1111/jiec.13331\u003c/li\u003e\n \u003cli\u003eISO (2006) ISO 14040: Environmental management - Life cycle assessment - Principles and framework, International Organization for Standardization, Geneva\u003c/li\u003e\n \u003cli\u003eJeswani HK, Azapagic A, Schepelmann P, Ritthoff M (2010) Options for broadening and deepening the LCA approaches. Journal of Cleaner Production 18:120\u0026ndash;127. https://doi.org/10.1016/j.jclepro.2009.09.023\u003c/li\u003e\n \u003cli\u003eJunne T, Simon S, Buchgeister J, Saiger M, Baumann M, Haase M, Wulf C, Naegler T (2020) Environmental Sustainability Assessment of Multi-Sectoral Energy Transformation Pathways: Methodological Approach and Case Study for Germany. Sustainability 12:8225. https://doi.org/10.3390/su12198225\u003c/li\u003e\n \u003cli\u003eKalt G, Thunshirn P, Krausmann F, Haberl H (2022) Material requirements of global electricity sector pathways to 2050 and associated greenhouse gas emissions. Journal of Cleaner Production 358:132014. https://doi.org/10.1016/j.jclepro.2022.132014\u003c/li\u003e\n \u003cli\u003eKaufman N, Bataille C (2025) Avoiding misuses of energy-economic modelling in climate policymaking. Nat. Clim. Chang. 15:463\u0026ndash;465. https://doi.org/10.1038/s41558-025-02280-7\u003c/li\u003e\n \u003cli\u003eKeppo I, Butnar I, Bauer N, Caspani M, Edelenbosch O, Emmerling J, Fragkos P, Guivarch C, Harmsen M, Lef\u0026egrave;vre J, Le Gallic T, Leimbach M, McDowall W, Mercure J-F, Schaeffer R, Trutnevyte E, Wagner F (2021) Exploring the possibility space: taking stock of the diverse capabilities and gaps in integrated assessment models. Environ. Res. Lett. 16:53006. https://doi.org/10.1088/1748-9326/abe5d8\u003c/li\u003e\n \u003cli\u003eKnobloch F, Hanssen S, Lam A, Pollitt H, Salas P, Chewpreecha U, Huijbregts MAJ, Mercure J-F (2020) Net emission reductions from electric cars and heat pumps in 59 world regions over time. Nat Sustain 3:437\u0026ndash;447. https://doi.org/10.1038/s41893-020-0488-7\u003c/li\u003e\n \u003cli\u003eKrey V, Guo F, Kolp P, Zhou W, Schaeffer R, Awasthy A, Bertram C, Boer H-S de, Fragkos P, Fujimori S, He C, Iyer G, Keramidas K, K\u0026ouml;berle AC, Oshiro K, Reis LA, Shoai-Tehrani B, Vishwanathan S, Capros P, Drouet L, Edmonds JE, Garg A, Gernaat DE, Jiang K, Kannavou M, Kitous A, Kriegler E, Luderer G, Mathur R, Muratori M, Sano F, van Vuuren DP (2019) Looking under the hood: A comparison of techno-economic assumptions across national and global integrated assessment models. Energy 172:1254\u0026ndash;1267. https://doi.org/10.1016/j.energy.2018.12.131\u003c/li\u003e\n \u003cli\u003eKuipers KJ, van Oers LF, Verboon M, van der Voet E (2018) Assessing environmental implications associated with global copper demand and supply scenarios from 2010 to 2050. Global Environmental Change 49:106\u0026ndash;115. https://doi.org/10.1016/j.gloenvcha.2018.02.008\u003c/li\u003e\n \u003cli\u003eLangkau S, Steubing B, Mutel C, Ajie MP, Erdmann L, Voglhuber-Slavinsky A, Janssen M (2023) A stepwise approach for Scenario-based Inventory Modelling for Prospective LCA (SIMPL). Int J Life Cycle Assess 28:1169\u0026ndash;1193. https://doi.org/10.1007/s11367-023-02175-9\u003c/li\u003e\n \u003cli\u003eLe Luu Q, Gibon T, Cellura M, Sanseverino ER, Longo S (2024) Integrated hybrid multi-regional input-output for assessing life cycle air emissions of the Italian power system. Energy 290:130109. https://doi.org/10.1016/j.energy.2023.130109\u003c/li\u003e\n \u003cli\u003eLechtenberg F, Istrate R, Tulus V, Espu\u0026ntilde;a A, Graells M, Guill\u0026eacute;n-Gos\u0026aacute;lbez G (2024) PULPO: A framework for efficient integration of life cycle inventory models into life cycle product optimization. Journal of Industrial Ecology 28:1449\u0026ndash;1463. https://doi.org/10.1111/jiec.13561\u003c/li\u003e\n \u003cli\u003eLevasseur A, Lesage P, Margni M, Desch\u0026ecirc;nes L, Samson R (2010) Considering time in LCA: dynamic LCA and its application to global warming impact assessments. Environ Sci Technol 44:3169\u0026ndash;3174. https://doi.org/10.1021/es9030003\u003c/li\u003e\n \u003cli\u003eLjunggren S\u0026ouml;derman M, Eriksson O, Bj\u0026ouml;rklund A, \u0026Ouml;stblom G, Ekvall T, Finnveden G, Arushanyan Y, Sundqvist J-O (2016) Integrated Economic and Environmental Assessment of Waste Policy Instruments. Sustainability 8:411. https://doi.org/10.3390/su8050411\u003c/li\u003e\n \u003cli\u003eLopez G, Pourjamal Y, Breyer C (2025) Paving the way towards a sustainable future or lagging behind? An ex-post analysis of the International Energy Agency\u0026rsquo;s World Energy Outlook. Renewable and Sustainable Energy Reviews 212:115371. https://doi.org/10.1016/j.rser.2025.115371\u003c/li\u003e\n \u003cli\u003eLouis J-N, Allard S, Debusschere V, Mima S, Tran-Quoc T, Hadjsaid N (2018) Environmental impact indicators for the electricity mix and network development planning towards 2050 \u0026ndash; A POLES and EUTGRID model. Energy 163:618\u0026ndash;628. https://doi.org/10.1016/j.energy.2018.08.093\u003c/li\u003e\n \u003cli\u003eLuderer G, Pehl M, Arvesen A, Gibon T, Bodirsky BL, Boer HS de, Fricko O, Hejazi M, Humpen\u0026ouml;der F, Iyer G, Mima S, Mouratiadou I, Pietzcker RC, Popp A, van den Berg M, van Vuuren D, Hertwich EG (2019) Environmental co-benefits and adverse side-effects of alternative power sector decarbonization strategies. Nat Commun 10:5229. https://doi.org/10.1038/s41467-019-13067-8\u003c/li\u003e\n \u003cli\u003eLueddeckens S, Saling P, Guenther E (2020) Temporal issues in life cycle assessment\u0026mdash;a systematic review. Int J Life Cycle Assess 25:1385\u0026ndash;1401. https://doi.org/10.1007/s11367-020-01757-1\u003c/li\u003e\n \u003cli\u003eM\u0026aring;nberger A (2021) Reduced Use of Fossil Fuels can Reduce Supply of Critical Resources. Biophys Econ Sust 6. https://doi.org/10.1007/s41247-021-00088-5\u003c/li\u003e\n \u003cli\u003eMastrucci A, Guo F, Zhong X, Maczek F, van Ruijven B (2024) Circular strategies for building sector decarbonization in China: A scenario analysis. J of Industrial Ecology 28:1089\u0026ndash;1102. https://doi.org/10.1111/jiec.13523\u003c/li\u003e\n \u003cli\u003eMcAvoy S, Grant T, Smith C, Bontinck P (2021) Combining Life Cycle Assessment and System Dynamics to improve impact assessment: A systematic review. Journal of Cleaner Production 315:128060. https://doi.org/10.1016/j.jclepro.2021.128060\u003c/li\u003e\n \u003cli\u003eMendoza Beltran A, Cox B, Mutel C, Vuuren DP, Font Vivanco D, Deetman S, Edelenbosch OY, Guin\u0026eacute;e J, Tukker A (2018) When the Background Matters: Using Scenarios from Integrated Assessment Models in Prospective Life Cycle Assessment. Journal of Industrial Ecology 24:64\u0026ndash;79. https://doi.org/10.1111/jiec.12825\u003c/li\u003e\n \u003cli\u003eMilovanoff A, Kim HC, Kleine R de, Wallington TJ, Posen ID, MacLean HL (2019) A Dynamic Fleet Model of U.S Light-Duty Vehicle Lightweighting and Associated Greenhouse Gas Emissions from 2016 to 2050. Environ Sci Technol 53:2199\u0026ndash;2208. https://doi.org/10.1021/acs.est.8b04249\u003c/li\u003e\n \u003cli\u003eMilovanoff A, MacLean HL, Abdul-Manan AFN, Posen ID (2022) Does the metric matter? Climate change impacts of light-duty vehicle electrification in the US. Environ. Res.: Infrastruct. Sustain. 2:35007. https://doi.org/10.1088/2634-4505/ac8071\u003c/li\u003e\n \u003cli\u003eMoni SM, Mahmud R, High K, Carbajales‐Dale M (2020) Life cycle assessment of emerging technologies: A review. J of Industrial Ecology 24:52\u0026ndash;63. https://doi.org/10.1111/jiec.12965\u003c/li\u003e\n \u003cli\u003eMoon YB (2017) Simulation modelling for sustainability: a review of the literature. International Journal of Sustainable Engineering 10:2\u0026ndash;19. https://doi.org/10.1080/19397038.2016.1220990\u003c/li\u003e\n \u003cli\u003eM\u0026uuml;ller A, Harpprecht C, Sacchi R, Maes B, van Sluisveld M, Daioglou V, \u0026Scaron;avija B, Steubing B (2024) Decarbonizing the cement industry: Findings from coupling prospective life cycle assessment of clinker with integrated assessment model scenarios. Journal of Cleaner Production 450:141884. https://doi.org/10.1016/j.jclepro.2024.141884\u003c/li\u003e\n \u003cli\u003eNabera A, Jos\u0026eacute; Mart\u0026iacute;n A, Istrate R, P\u0026eacute;rez-Ram\u0026iacute;rez J, Guill\u0026eacute;n-Gos\u0026aacute;lbez G (2024) Integrating climate policies in the sustainability analysis of green chemicals. Green Chem 26:6461\u0026ndash;6469. https://doi.org/10.1039/d4gc00392f\u003c/li\u003e\n \u003cli\u003eO\u0026rsquo;Neill BC, Kriegler E, Ebi KL, Kemp-Benedict E, Riahi K, Rothman DS, van Ruijven BJ, van Vuuren DP, Birkmann J, Kok K, Levy M, Solecki W (2017) The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21\u003csup\u003est\u003c/sup\u003e century. Global Environmental Change 42:169\u0026ndash;180. https://doi.org/10.1016/j.gloenvcha.2015.01.004\u003c/li\u003e\n \u003cli\u003ePage MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hr\u0026oacute;bjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. https://doi.org/10.1136/bmj.n71\u003c/li\u003e\n \u003cli\u003ePalazzo J, Geyer R, Suh S (2020) A review of methods for characterizing the environmental consequences of actions in life cycle assessment. J of Industrial Ecology 24:815\u0026ndash;829. https://doi.org/10.1111/jiec.12983\u003c/li\u003e\n \u003cli\u003ePauliuk S (2020) Making sustainability science a cumulative effort. Nat Sustain 3:2\u0026ndash;4. https://doi.org/10.1038/s41893-019-0443-7\u003c/li\u003e\n \u003cli\u003ePauliuk S (2024) Decent living standards, prosperity, and excessive consumption in the Lorenz curve. Ecological Economics 220:108161. https://doi.org/10.1016/j.ecolecon.2024.108161\u003c/li\u003e\n \u003cli\u003ePauliuk S, Heeren N (2020) ODYM\u0026mdash;An open software framework for studying dynamic material systems: Principles, implementation, and data structures. J of Industrial Ecology 24:446\u0026ndash;458. https://doi.org/10.1111/jiec.12952\u003c/li\u003e\n \u003cli\u003ePauliuk S, Arvesen A, Stadler K, Hertwich EG (2017) Industrial ecology in integrated assessment models. Nature Clim Change 7:13\u0026ndash;20. https://doi.org/10.1038/nclimate3148\u003c/li\u003e\n \u003cli\u003ePauliuk S, Helbig C, Lupton R, Pichler PP, Schulte S, Stadler K, Wang P, Wiedenhofer D (2024a) Guidelines and good practice examples for complete traceability of workflows and reproducibility of results in industrial ecology research. https://doi.org/10.6094/UNIFR/255618\u003c/li\u003e\n \u003cli\u003ePauliuk S, Carrer F, Heeren N, Hertwich EG (2024b) Scenario analysis of supply‐ and demand‐side solutions for circular economy and climate change mitigation in the global building sector. J of Industrial Ecology. https://doi.org/10.1111/jiec.13557\u003c/li\u003e\n \u003cli\u003ePedneault J, Majeau-Bettez G, Krey V, Margni M (2021) What future for primary aluminium production in a decarbonizing economy? Global Environmental Change 69:102316. https://doi.org/10.1016/j.gloenvcha.2021.102316\u003c/li\u003e\n \u003cli\u003ePehl M, Arvesen A, Humpen\u0026ouml;der F, Popp A, Hertwich EG, Luderer G (2017) Understanding future emissions from low-carbon power systems by integration of life-cycle assessment and integrated energy modelling. Nat Energy 2:939\u0026ndash;945. https://doi.org/10.1038/s41560-017-0032-9\u003c/li\u003e\n \u003cli\u003ePfenninger S, DeCarolis J, Hirth L, Quoilin S, Staffell I (2017) The importance of open data and software: Is energy research lagging behind? Energy Policy 101:211\u0026ndash;215. https://doi.org/10.1016/j.enpol.2016.11.046\u003c/li\u003e\n \u003cli\u003ePichler M, B\u0026auml;rnthaler R, Wiedenhofer D, Roux N, Gingrich S (2025) Conceptualizing supply- and demand-side climate change mitigation: A typology and new research directions. Energy Research \u0026amp; Social Science 127:104225. https://doi.org/10.1016/j.erss.2025.104225\u003c/li\u003e\n \u003cli\u003ePizzol M, Sacchi R, K\u0026ouml;hler S, Anderson Erjavec A (2021) Non-linearity in the Life Cycle Assessment of Scalable and Emerging Technologies. Front. Sustain. 1. https://doi.org/10.3389/frsus.2020.611593\u003c/li\u003e\n \u003cli\u003ePlevin RJ (2017) Assessing the Climate Effects of Biofuels Using Integrated Assessment Models, Part I: Methodological Considerations. J of Industrial Ecology 21:1478\u0026ndash;1487. https://doi.org/10.1111/jiec.12507\u003c/li\u003e\n \u003cli\u003ePost E, Le Billon P (2025) The \u0026lsquo;Green War\u0026rsquo;: Geopolitical Metabolism and Green Extractivisms. Geopolitics 30:760\u0026ndash;800. https://doi.org/10.1080/14650045.2024.2385411\u003c/li\u003e\n \u003cli\u003eProctor JC (2023) Expanding the possible: exploring the role for heterodox economics in integrated climate-economy modeling. Rev Evol Polit Econ:1\u0026ndash;21. https://doi.org/10.1007/s43253-023-00098-7\u003c/li\u003e\n \u003cli\u003eRaugei M, Kamran M, Hutchinson A (2021) Environmental implications of the ongoing electrification of the UK light duty vehicle fleet. Resources, Conservation and Recycling 174:105818. https://doi.org/10.1016/j.resconrec.2021.105818\u003c/li\u003e\n \u003cli\u003eReinert C, Deutz S, Minten H, D\u0026ouml;rpinghaus L, Pfingsten S von, Baumg\u0026auml;rtner N, Bardow A (2021) Environmental impacts of the future German energy system from integrated energy systems optimization and dynamic life cycle assessment. Computers \u0026amp; Chemical Engineering 153:107406. https://doi.org/10.1016/j.compchemeng.2021.107406\u003c/li\u003e\n \u003cli\u003eRiahi K, van Vuuren DP, Kriegler E, Edmonds J, O\u0026rsquo;Neill BC, Fujimori S, Bauer N, Calvin K, Dellink R, Fricko O, Lutz W, Popp A, Cuaresma JC, KC S, Leimbach M, Jiang L, Kram T, Rao S, Emmerling J, Ebi K, Hasegawa T, Havlik P, Humpen\u0026ouml;der F, Da Silva LA, Smith S, Stehfest E, Bosetti V, Eom J, Gernaat D, Masui T, Rogelj J, Strefler J, Drouet L, Krey V, Luderer G, Harmsen M, Takahashi K, Baumstark L, Doelman JC, Kainuma M, Klimont Z, Marangoni G, Lotze-Campen H, Obersteiner M, Tabeau A, Tavoni M (2017) The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change 42:153\u0026ndash;168. https://doi.org/10.1016/j.gloenvcha.2016.05.009\u003c/li\u003e\n \u003cli\u003eRiondet L, Rio M, Perrot-Bernardet V, Zwolinski P (2024) Emerging technologies upscaling: A framework for matching LCA practices with upscaling archetypes. Sustainable Production and Consumption 50:347\u0026ndash;363. https://doi.org/10.1016/j.spc.2024.07.032\u003c/li\u003e\n \u003cli\u003eRossi F, Tosti L, Basosi R, Cusenza MA, Parisi ML, Sinicropi A (2023) Environmental optimization model for the European batteries industry based on prospective life cycle assessment and material flow analysis. Renewable and Sustainable Energy Reviews 183:113485. https://doi.org/10.1016/j.rser.2023.113485\u003c/li\u003e\n \u003cli\u003eR\u0026uuml;dis\u0026uuml;li M, Bach C, Bauer C, Beloin-Saint-Pierre D, Elber U, Georges G, Limpach R, Pareschi G, Kannan R, Teske SL (2022) Prospective life-cycle assessment of greenhouse gas emissions of electricity-based mobility options. Applied Energy 306:118065. https://doi.org/10.1016/j.apenergy.2021.118065\u003c/li\u003e\n \u003cli\u003eSacchi R, Hahn-Menacho AJ (2024) pathways: life cycle assessment of energy transition scenarios. JOSS 9:7309. https://doi.org/10.21105/joss.07309\u003c/li\u003e\n \u003cli\u003eSacchi R, Becattini V, Gabrielli P, Cox B, Dirnaichner A, Bauer C, Mazzotti M (2023) How to make climate-neutral aviation fly. Nat Commun 14:3989. https://doi.org/10.1038/s41467-023-39749-y\u003c/li\u003e\n \u003cli\u003eSacchi R (2025) User guide \u0026mdash; premise documentation 2025. 6 June 2025\u003c/li\u003e\n \u003cli\u003eSacchi R, Terlouw T, Siala K, Dirnaichner A, Bauer C, Cox B, Mutel C, Daioglou V, Luderer G (2022) PRospective EnvironMental Impact asSEment (premise): A streamlined approach to producing databases for prospective life cycle assessment using integrated assessment models. Renewable and Sustainable Energy Reviews 160:112311. https://doi.org/10.1016/j.rser.2022.112311\u003c/li\u003e\n \u003cli\u003eSaltelli A, Bammer G, Bruno I, Charters E, Di Fiore M, Didier E, Nelson Espeland W, Kay J, Lo Piano S, Mayo D, Pielke R, Portaluri T, Porter TM, Puy A, Rafols I, Ravetz JR, Reinert E, Sarewitz D, Stark PB, Stirling A, van der Sluijs J, Vineis P (2020) Five ways to ensure that models serve society: a manifesto. Nature 582:482\u0026ndash;484. https://doi.org/10.1038/d41586-020-01812-9\u003c/li\u003e\n \u003cli\u003eSchulze K, Kullmann F, Weinand JM, Stolten D (2024) Overcoming the challenges of assessing the global raw material demand of future energy systems. Joule 8:1936\u0026ndash;1957. https://doi.org/10.1016/j.joule.2024.05.016\u003c/li\u003e\n \u003cli\u003eShirmohammadi R, V\u0026aacute;zquez D, Calvo-Serrano R, Llovell F, Gonzalez-Olmos R (2025) Absolute Environmental Sustainability Assessment of Emerging Working Fluids in Organic Rankine Cycles. ACS Sustainable Chem. Eng. https://doi.org/10.1021/acssuschemeng.5c01290\u003c/li\u003e\n \u003cli\u003eSiddaway AP, Wood AM, Hedges LV (2019) How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-Analyses, and Meta-Syntheses. Annual Review of Psychology 70:747\u0026ndash;770. https://doi.org/10.1146/annurev-psych-010418-102803\u003c/li\u003e\n \u003cli\u003eSig\u0026uuml;enza CP, Cucurachi S, Tukker A (2021) Circular business models of washing machines in the Netherlands: Material and climate change implications toward 2050. Sustainable Production and Consumption 26:1084\u0026ndash;1098. https://doi.org/10.1016/j.spc.2021.01.011\u003c/li\u003e\n \u003cli\u003e\u0026Scaron;imaitis J, Butnar I, Sacchi R, Lupton R, Vagg C, Allen S (2025) Expanding scenario diversity in prospective LCA: Coupling the TIAM-UCL integrated assessment model with Premise and ecoinvent. Renewable and Sustainable Energy Reviews 211:115298. https://doi.org/10.1016/j.rser.2024.115298\u003c/li\u003e\n \u003cli\u003eSohn J, Kalbar P, Goldstein B, Birkved M (2020) Defining Temporally Dynamic Life Cycle Assessment: A Review. Integr Environ Assess Manag 16:314\u0026ndash;323. https://doi.org/10.1002/ieam.4235\u003c/li\u003e\n \u003cli\u003eSpielmann M, Scholz R, Tietje O, Haan P de (2005) Scenario Modelling in Prospective LCA of Transport Systems. Application of Formative Scenario Analysis (11 pp). Int J Life Cycle Assessment 10:325\u0026ndash;335. https://doi.org/10.1065/lca2004.10.188\u003c/li\u003e\n \u003cli\u003eSteubing B, Koning D de (2021) Making the use of scenarios in LCA easier: the superstructure approach. Int J Life Cycle Assess 26:2248\u0026ndash;2262. https://doi.org/10.1007/s11367-021-01974-2\u003c/li\u003e\n \u003cli\u003eSteubing B, Mendoza Beltran A, Sacchi R (2023) Conditions for the broad application of prospective life cycle inventory databases. Int J Life Cycle Assess 28:1092\u0026ndash;1103. https://doi.org/10.1007/s11367-023-02192-8\u003c/li\u003e\n \u003cli\u003eSuh S, Yang Y (2014) On the uncanny capabilities of consequential LCA. Int J Life Cycle Assess 19:1179\u0026ndash;1184. https://doi.org/10.1007/s11367-014-0739-9\u003c/li\u003e\n \u003cli\u003eTang C, Tukker A, Sprecher B, Mogoll\u0026oacute;n JM (2023) Assessing the European Electric-Mobility Transition: Emissions from Electric Vehicle Manufacturing and Use in Relation to the EU Greenhouse Gas Emission Targets. Environ Sci Technol 57:44\u0026ndash;52. https://doi.org/10.1021/acs.est.2c06304\u003c/li\u003e\n \u003cli\u003eTarabay B, Milovanoff A, Abdul-Manan AF, McKechnie J, MacLean HL, Posen ID (2023) New cathodes now, recycling later: Dynamic scenarios to reduce battery material use and greenhouse gas emissions from U.S. light-duty electric vehicle fleet. Resources, Conservation and Recycling 196:107028. https://doi.org/10.1016/j.resconrec.2023.107028\u003c/li\u003e\n \u003cli\u003eThonemann N, Schulte A, Maga D (2020) How to Conduct Prospective Life Cycle Assessment for Emerging Technologies? A Systematic Review and Methodological Guidance. Sustainability 12:1192. https://doi.org/10.3390/su12031192\u003c/li\u003e\n \u003cli\u003eTsoy N, Steubing B, van der Giesen C, Guin\u0026eacute;e J (2020) Upscaling methods used in ex ante life cycle assessment of emerging technologies: a review. Int J Life Cycle Assess 25:1680\u0026ndash;1692. https://doi.org/10.1007/s11367-020-01796-8\u003c/li\u003e\n \u003cli\u003evan der Giesen C, Cucurachi S, Guin\u0026eacute;e J, Kramer GJ, Tukker A (2020) A critical view on the current application of LCA for new technologies and recommendations for improved practice. Journal of Cleaner Production 259:120904. https://doi.org/10.1016/j.jclepro.2020.120904\u003c/li\u003e\n \u003cli\u003evan der Meide M, Harpprecht C, Northey S, Yang Y, Steubing B (2022) Effects of the energy transition on environmental impacts of cobalt supply: A prospective life cycle assessment study on future supply of cobalt. J of Industrial Ecology 26:1631\u0026ndash;1645. https://doi.org/10.1111/jiec.13258\u003c/li\u003e\n \u003cli\u003evan der Voet E, van Oers L, Verboon M, Kuipers K (2019) Environmental Implications of Future Demand Scenarios for Metals: Methodology and Application to the Case of Seven Major Metals. J of Industrial Ecology 23:141\u0026ndash;155. https://doi.org/10.1111/jiec.12722\u003c/li\u003e\n \u003cli\u003evan Nielen SS, Kleijn R, Tukker A (2025) Accounting for learning in prospective LCA: Theory and practical guidance. J of Industrial Ecology. https://doi.org/10.1111/jiec.70002\u003c/li\u003e\n \u003cli\u003evan Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque J-F, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK (2011) The representative concentration pathways: an overview. Climatic Change 109:5\u0026ndash;31. https://doi.org/10.1007/s10584-011-0148-z\u003c/li\u003e\n \u003cli\u003evan Vuuren DP, Hof AF, van Sluisveld MAE, Riahi K (2017) Open discussion of negative emissions is urgently needed. Nat Energy 2:902\u0026ndash;904. https://doi.org/10.1038/s41560-017-0055-2\u003c/li\u003e\n \u003cli\u003eVandepaer L, Gibon T (2018) The integration of energy scenarios into LCA: LCM2017 Conference Workshop, Luxembourg, September 5, 2017. Int J Life Cycle Assess 23:970\u0026ndash;977. https://doi.org/10.1007/s11367-017-1435-3\u003c/li\u003e\n \u003cli\u003eVandepaer L, Panos E, Bauer C, Amor B (2020) Energy System Pathways with Low Environmental Impacts and Limited Costs: Minimizing Climate Change Impacts Produces Environmental Cobenefits and Challenges in Toxicity and Metal Depletion Categories. Environ Sci Technol 54:5081\u0026ndash;5092. https://doi.org/10.1021/acs.est.9b06484\u003c/li\u003e\n \u003cli\u003eVentura A (2022) Transition life cycle assessment: A new method to face ecological transition. Front. Sustain. 3. https://doi.org/10.3389/frsus.2022.801668\u003c/li\u003e\n \u003cli\u003eVentura A (2023) Conceptual issue of the dynamic GWP indicator and solution. Int J Life Cycle Assess 28:788\u0026ndash;799. https://doi.org/10.1007/s11367-022-02028-x\u003c/li\u003e\n \u003cli\u003eVolkart K, Weidmann N, Bauer C, Hirschberg S (2017) Multi-criteria decision analysis of energy system transformation pathways: A case study for Switzerland. Energy Policy 106:155\u0026ndash;168. https://doi.org/10.1016/j.enpol.2017.03.026\u003c/li\u003e\n \u003cli\u003eVolkart K, Mutel CL, Panos E (2018) Integrating life cycle assessment and energy system modelling: Methodology and application to the world energy scenarios. Sustainable Production and Consumption 16:121\u0026ndash;133. https://doi.org/10.1016/j.spc.2018.07.001\u003c/li\u003e\n \u003cli\u003eWay R, Ives MC, Mealy P, Farmer JD (2022) Empirically grounded technology forecasts and the energy transition. Joule 6:2057\u0026ndash;2082. https://doi.org/10.1016/j.joule.2022.08.009\u003c/li\u003e\n \u003cli\u003eWeidner T, Gal\u0026aacute;n-Mart\u0026iacute;n \u0026Aacute;, Ryberg MW, Guill\u0026eacute;n-Gos\u0026aacute;lbez G (2022) Energy systems modeling and optimization for absolute environmental sustainability: current landscape and opportunities. Computers \u0026amp; Chemical Engineering 164:107883. https://doi.org/10.1016/j.compchemeng.2022.107883\u003c/li\u003e\n \u003cli\u003eWeidner T, Tulus V, Guill\u0026eacute;n-Gos\u0026aacute;lbez G (2023) Environmental sustainability assessment of large-scale hydrogen production using prospective life cycle analysis. International Journal of Hydrogen Energy 48:8310\u0026ndash;8327. https://doi.org/10.1016/j.ijhydene.2022.11.044\u003c/li\u003e\n \u003cli\u003eWernet G, Bauer C, Steubing B, Reinhard J, Moreno-Ruiz E, Weidema B (2016) The ecoinvent database version 3 (part I): overview and methodology. Int J Life Cycle Assess 21:1218\u0026ndash;1230. https://doi.org/10.1007/s11367-016-1087-8\u003c/li\u003e\n \u003cli\u003eWiebe KS, Aponte FR, Kaufmann R, Lampropoulos D (2023) A Review of macroeconomic approaches to modelling Wellbeing, Inclusion, and Sustainability: Final Version of WISE Horizons Deliverable 1.2. https://doi.org/10.5281/zenodo.8370002. Accessed 28 September 2023. Accessed 28 September 2023\u003c/li\u003e\n \u003cli\u003eWiedenhofer D, Streeck J, Wiese F, Verdolini E, Mastrucci A, Ju Y, Boza-Kiss B, Min J, Norman J, Wieland H, Bento N, Le\u0026oacute;n MFG, Magalar L, Mayer A, Gingrich S, Hayashi A, Jupesta J, \u0026Uuml;nl\u0026uuml; G, Niamir L, Cao T, Zanon-Zotin M, Plank B, V\u0026eacute;lez-Henao J, Masanet E, Krey V, Akimoto K, Grubler A, van Ruijven B, Pauliuk S (2024) Industry Transformations for High Service Provisioning with Lower Energy and Material Demand: A Review of Models and Scenarios. Annu. Rev. Environ. Resour. https://doi.org/10.1146/annurev-environ-110822-044428\u003c/li\u003e\n \u003cli\u003eWilson C, Guivarch C, Kriegler E, van Ruijven B, van Vuuren DP, Krey V, Schwanitz VJ, Thompson EL (2021) Evaluating process-based integrated assessment models of climate change mitigation. Climatic Change 166. https://doi.org/10.1007/s10584-021-03099-9\u003c/li\u003e\n \u003cli\u003eYang Y, Heijungs R (2018) On the use of different models for consequential life cycle assessment. Int J Life Cycle Assess 23:751\u0026ndash;758. https://doi.org/10.1007/s11367-017-1337-4\u003c/li\u003e\n \u003cli\u003eYang Y, Heijungs R (2019) Moving from completing system boundaries to more realistic modeling of the economy in life cycle assessment. Int J Life Cycle Assess 24:211\u0026ndash;218. https://doi.org/10.1007/s11367-018-1532-y\u003c/li\u003e\n \u003cli\u003eYi Y, Wu J, Zuliani F, Lavagnolo MC, Manzardo A (2023) Integration of life cycle assessment and system dynamics modeling for environmental scenario analysis: A systematic review. Sci Total Environ 903:166545. https://doi.org/10.1016/j.scitotenv.2023.166545\u003c/li\u003e\n \u003cli\u003eZhang X, Heeren N, Bauer C, Burgherr P, McKenna R, Habert G (2024) The impacts of future sectoral change on the greenhouse gas emissions of construction materials for Swiss residential buildings. Energy and Buildings 303:113824. https://doi.org/10.1016/j.enbuild.2023.113824\u003c/li\u003e\n \u003cli\u003eZheng J, Suh S (2019) Strategies to reduce the global carbon footprint of plastics. Nat. Clim. Chang. 9:374\u0026ndash;378. https://doi.org/10.1038/s41558-019-0459-z\u003c/li\u003e\n \u003cli\u003eZibunas C, Meys R, K\u0026auml;telh\u0026ouml;n A, Bardow A (2022) Cost-optimal pathways towards net-zero chemicals and plastics based on a circular carbon economy. Computers \u0026amp; Chemical Engineering 162:107798. https://doi.org/10.1016/j.compchemeng.2022.107798\u003c/li\u003e\n \u003cli\u003eZibunas C, Meys R, K\u0026auml;telh\u0026ouml;n A, Bardow A (2024) The cost and climate impact of myopic investment decisions in the chemical industry. Computers \u0026amp; Chemical Engineering 187:108721. https://doi.org/10.1016/j.compchemeng.2024.108721\u003c/li\u003e\n \u003cli\u003eZumsteg JM, Cooper JS, Noon MS (2012) Systematic Review Checklist: A Standardized Technique for Assessing and Reporting Reviews of Life Cycle Assessment Data. Journal of Industrial Ecology 16:S12-S21. https://doi.org/10.1111/j.1530-9290.2012.00476.x\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"88f0b884-7cb8-45c0-a89f-632da9bdaeb7","identifier":"10.13039/501100003245","name":"Ministerie van Onderwijs, Cultuur en Wetenschap","awardNumber":"not applicable","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Leiden University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Life Cycle Assessment, Macro-level LCA, Large-scale Environmental Impact Assessment, Prospective LCA, Dynamic LCA, Systematic Literature Review","lastPublishedDoi":"10.21203/rs.3.rs-7468270/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7468270/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: Transitions to sustainable societies require assessments of future environmental impacts at the macro-level. We examined how prospective process-based Life Cycle Assessment (LCA) is used to model environmental impacts at national to global scales. Our research objectives were to (i) provide an overview of modelling approaches in prospective macro-level LCA; (ii) identify common pitfalls and best practices; and (iii) highlight key challenges and suggest priorities for future research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e: We conducted a systematic literature review. An initial search in Web of Science, complemented by studies reviewed by Bisinella et al. (2021), yielded 925 studies. After screening based on predefined inclusion criteria and adding 34 additional articles through citation tracking, a final set of 87 peer-reviewed articles was analysed. We reviewed these studies with a primary focus on how system scaling, temporal evolution, and temporal distribution were addressed in the inventory analysis phase. In addition, we assessed elements from the other three LCA phases, including research objectives, temporal scope, system boundaries, and the treatment of sensitivity and uncertainty. We also examined terminology use and transparency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults and Discussion\u003c/strong\u003e: We classified the reviewed approaches by how system scaling is treated in the foreground system: coupling with Dynamic Stock Models, which captures stock dynamics but overlooks socioeconomic aspects; coupling with Energy System Models, which provides detailed energy insights but is sector-limited; coupling with Integrated Assessment Models, which offers broader socioeconomic coverage but operates at coarse resolution and typically requires collaboration with model developers; and uncoupled approaches, which allow flexibility but risk oversimplification. We identify twelve key pitfalls, including simplified treatments of system scaling, temporal dynamics, and distribution; a narrow climate focus; limited scenario diversity; and weak internal consistency. We also highlight several best practices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions and Recommendations\u003c/strong\u003e: Our review reveals a diverse field with inconsistent terminology, assumptions, and modelling practices. To strengthen the field, we recommend improving transparency and adopting consistent terminology; improving the representation of the complexity of sustainability transitions; strengthening policy relevance; and developing methodological guidance. Addressing these priorities will improve the robustness of prospective macro-level LCA and advance understanding of sustainability transitions.\u003c/p\u003e","manuscriptTitle":"Prospective Macro-Level Life Cycle Assessment: A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-28 07:23:33","doi":"10.21203/rs.3.rs-7468270/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":"e66d30bb-61dd-46c6-87a0-c842fc2c5ab1","owner":[],"postedDate":"August 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-28T07:23:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-28 07:23:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7468270","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7468270","identity":"rs-7468270","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}