Sustainability Assessment of China’s Hydrogen Transition: A Life Cycle–Geopolitical Risk Multi-Criteria Analysis

Sustainability Assessment of China’s Hydrogen Transition: A Life Cycle–Geopolitical Risk Multi-Criteria Analysis | 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 Article Sustainability Assessment of China’s Hydrogen Transition: A Life Cycle–Geopolitical Risk Multi-Criteria Analysis Chufeng Yuan, Yang Tan, Xuguang Yuan, Qiu Tan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7505392/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract This study develops an extended life-cycle sustainability assessment (LCSA) that explicitly integrates geopolitical exposure into multi-criteria evaluation of hydrogen pathways. We construct a Geopolitical Risk Index (GPRI) combining critical-material reliance, trade concentration and supplier governance, and link it with environmental LCA, levelized cost, and social indicators under a unified cradle-to-gate boundary. Using multi-regional input–output (MRIO) accounting and 1,000-run Monte Carlo simulations across three pathways (photovoltaic electrolysis “green” hydrogen, coal-based “blue” hydrogen with carbon capture, utilization and storage [CCUS], and a hybrid portfolio), we examine baseline, conflict-shock and technology-advance scenarios. Results show a systematic decarbonization–sovereignty trade-off: green hydrogen achieves the lowest life-cycle emissions but exhibits the highest geopolitical vulnerability, driven by an 87% dependence on imported platinum-group catalysts and a 23% cost increase under conflict-related disruption; blue hydrogen demonstrates lower cost volatility (< 5%) and stronger supply-chain resilience, albeit with higher residual emissions. A diversified portfolio and domestic electrolyzer manufacturing attenuate risk while preserving climate gains. The framework provides a transparent way to embed geopolitical risk into LCSA and offers decision support for risk-aware hydrogen transitions in emerging economies. Physical sciences/Energy science and technology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Hydrogen transition Geopolitical Risk Index Life Cycle Sustainability Assessment Supply chain resilience Green hydrogen Blue hydrogen Critical material dependency Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Hydrogen is increasingly recognized as a cornerstone of global decarbonization strategies, especially for hard-to-abate sectors such as heavy industry and long-distance transport [ 1 ]. As the world’s largest energy consumer and carbon emitter, China has identified hydrogen energy as a strategic pillar for achieving its “dual carbon” goals—peaking emissions by 2030 and reaching carbon neutrality by 2060. Projections indicate that by 2030, China’s hydrogen production will reach 20 million tons annually, nearly 40% of the global total (China Hydrogen Alliance, 2023). Recent scholarship has highlighted that renewable energy vectors, including hydrogen, fundamentally reshape geopolitical interdependencies rather than replicating fossil fuel dynamics[ 2 , 3 ]. However, the sustainability of this transition remains contested. Green hydrogen (produced via renewable-powered electrolysis) and blue hydrogen (fossil-based with carbon capture) both involve complex trade-offs between environmental benefits, economic feasibility, and supply chain security. While numerous studies have compared the carbon footprints of these technologies using Life Cycle Assessment (LCA) [ 4 ], critical systemic risks remain underexplored. Two gaps are particularly salient. First, existing analyses often ignore geopolitical vulnerabilities embedded in supply chains. For example, China’s dependence on imported platinum group metals reached 87% in 2023, with imports totaling 1.63 times domestic demand [ 5 , 6 ]. Such exposure is exacerbated by intensifying US–China trade tensions. Second, current sustainability assessment frameworks rarely integrate the environmental, economic, and social pillars [ 7 , 8 ] with geopolitical risk factors, despite evidence that disruptions in critical material supply can raise levelized hydrogen costs by 15–30% [ 9 , 10 ]. This reflects a broader limitation in energy transition research, where techno-economic models dominate and political-economic variables are underrepresented [ 11 ]. However, techno-economic models that dominate current assessments are particularly vulnerable to institutional shocks. For instance, Levelized Cost of Hydrogen (LCOH) projections based on stable trade conditions fail to capture abrupt price surges when export restrictions are imposed on platinum group metals. Similarly, conventional Life Cycle Assessments (LCAs) assume linear carbon price trajectories, yet mechanisms such as the EU’s Carbon Border Adjustment Mechanism (CBAM) can instantaneously alter cost competitiveness across pathways. Even widely used integrated assessment models neglect the impact of sudden technology embargoes, which can disrupt electrolyzer deployment despite favorable cost curves. These examples demonstrate that models limited to environmental and economic dimensions systematically underestimate resilience challenges under real-world geopolitical and policy disruptions. This study addresses these gaps by extending the Life Cycle Sustainability Assessment (LCSA) framework to include quantified geopolitical supply chain risks. We construct a Geopolitical Risk Index (GPRI) that incorporates material criticality, trade concentration, and supplier stability, and apply Multi-Regional Input–Output (MRIO) analysis to trace supply chain exposure for different hydrogen pathways. China serves as a compelling case study due to its dual position as both the world’s largest electrolyzer manufacturer (80% of global capacity) [ 12 ] and a resource-import-dependent nation [ 13 ]—a structural contradiction that intensifies the tension between energy security and decarbonization. The empirical analysis compares three pathways: Xinjiang-based green hydrogen from solar resources, Shanxi-based blue hydrogen from coal with CCUS, and natural gas–based gray hydrogen. Sensitivity tests incorporate technological learning rates and carbon price scenarios. The findings contribute to both theory and policy: integrating political-economic variables into LCSA improves the explanatory power of sustainability assessments, while revealing that China’s current hydrogen strategy underestimates supply chain risks. Although blue hydrogen has lower geopolitical exposure, its higher carbon intensity may incur future carbon border taxes; conversely, green hydrogen’s environmental advantage may come at the cost of reduced energy sovereignty. By linking energy systems analysis with geopolitical risk research, this study offers a transferable methodological framework for resource-constrained economies pursuing low-carbon transitions. In an era of global supply chain shocks—exemplified by the Russia–Ukraine conflict and the US CHIPS and Science Act [ 14 ]—we highlight the need for strategies that balance resilience with feasibility in a fragmented international order. 2. Literature Review 2.1 Evolution and Current Status of Hydrogen Sustainability Assessment Over the past two decades, hydrogen sustainability assessment has shifted from a narrow environmental focus to a multi-dimensional approach. Early studies emphasized environmental impacts, particularly carbon footprint comparisons of different production pathways [15,16]. The Life Cycle Sustainability Assessment (LCSA) method emerged to integrate environmental, economic, and social dimensions for a more comprehensive evaluation [17–19]. In the environmental dimension, conventional Life Cycle Assessment (LCA) primarily measures greenhouse gas emissions during hydrogen production [20]. Recent research expands this scope to include water consumption, land use, and biodiversity impacts, reflecting the multi-resource dependencies of green hydrogen [21,22]. For example, Olaitan et al. (2024) found that although green hydrogen has a lower carbon footprint, its water footprint can be more than double that of blue hydrogen, highlighting trade-offs that require further analysis [23]. In the economic dimension, Levelized Cost of Hydrogen (LCOH) modeling is widely used to compare pathways, as illustrated by Glenk [24]. Green hydrogen costs are particularly sensitive to electricity prices and capital expenditures. However, most economic studies assume stable raw material and technology supply chains, overlooking external shocks such as price volatility and geopolitical tensions. The social dimension remains underexplored. Some studies apply Social LCA (S-LCA) to evaluate employment, occupational health, and safety along the hydrogen value chain [25,26]. Yet most of this work focuses on European contexts, with limited quantitative models for emerging economies such as China. Overall, research is moving toward multi-dimensional integration. Nonetheless, robust theories and tools to capture external structural risks—such as geopolitical shocks and critical material dependencies—are still lacking. 2.2 Geopolitical and Supply Chain Systemic Risks in Energy Transition Recent geopolitical research in energy transition has expanded beyond oil and gas security to cover key minerals and core technologies critical to low-carbon systems. This reflects a shift from traditional energy geopolitics to technology geopolitics [27,28]. At the resource level, the International Energy Agency (IEA, 2023) highlights the reliance of clean energy technologies—wind, solar, electrolyzers, and energy storage—on minerals controlled by a few countries, including lithium, cobalt, rare earth elements, and platinum group metals [29,30]. For example, China processes over 90% of global rare earths but depends on imports for over 80% of platinum group metals [31,32]. This criticality–vulnerability imbalance poses significant supply chain risks for hydrogen transitions. At the institutional level, emerging trade rules and carbon policies—such as the EU’s Carbon Border Adjustment Mechanism (CBAM) and U.S. technology decoupling measures—create additional constraints. Case studies show that such risks can increase renewable energy financing costs in emerging markets by several percentage points [33,34]. Scholten (2020) notes that control over energy systems is becoming a component of national strategic sovereignty [35]. Although frameworks such as the Critical Material Index, trade concentration measures (HHI), and substitutability scores exist [36,37], they are rarely applied to hydrogen systems. In China—a nation both exporting hydrogen technology and importing key resources—there is a notable absence of integrated analysis that captures supply chain vulnerabilities alongside institutional risks [38]. 2.3 Research Gaps and Innovative Position of This Study A synthesis of existing literature reveals three major gaps: Limited integration across dimensions – Most hydrogen sustainability studies focus on environmental or economic metrics. Few combine environmental, economic, social, and geopolitical factors into a unified framework, limiting the ability to assess systemic risks comprehensively. Regional imbalance – China, as the largest hydrogen consumer and equipment manufacturer, faces dual exposure as both technology exporter and resource importer. Despite being a key global case, systematic assessments centered on China remain scarce. Methodological limitations – Existing LCA/LCSA models use static parameters and controlled scenarios. They lack the capacity to simulate non-linear shocks such as trade sanctions, technological decoupling, and abrupt policy changes, reducing predictive power for real-world resilience. To address these gaps, this study introduces three innovations: • Theoretical – Development of an expanded LCSA+ framework [39–42] that incorporates a Geopolitical Risk Index (GPRI) [43–46], integrating indicators such as material criticality, trade concentration, and political stability of supplier nations. • Methodological – Combination of Multi-Regional Input–Output (MRIO) analysis with sensitivity simulations to trace cross-border supply chain risks and assess dynamic responses under different policy scenarios. To evaluate supply chain risks and import dependencies, the Eora26 MRIO database was adopted. Hydrogen-related activities such as electrolyzer manufacturing, PV-grade silicon, platinum group metals, and refueling infrastructure were systematically mapped to corresponding MRIO sectors. The detailed mapping rules and correspondence are provided in Table A1 (Supplementary Materials) to ensure reproducibility. • Empirical – A China-focused comparative evaluation of green and blue hydrogen pathways, providing policy recommendations applicable to other emerging economies seeking diversified and secure hydrogen strategies. 3. Research Methodology The methodological framework of this study is designed to assess the life-cycle techno-economic performance and geopolitical risks of hydrogen production pathways. The functional unit is defined as 1 kg of hydrogen delivered at the plant gate, with system boundaries covering upstream resource extraction, intermediate conversion, and direct plant operations in accordance with ISO 14040/44 guidelines. Multiple evaluation indicators are employed, including the Geopolitical Risk Index (GPRI), the risk premium factor (α), and the levelized cost of hydrogen (LCOH), with detailed formulations provided in Appendix A. To ensure transparency in water-related results, harmonized definitions and unit conventions are adopted. Water use is reported under two scopes: process water (m³/kg H₂), which covers on-site intake for electrolysis, steam generation, and cooling makeup; and life-cycle freshwater consumption (m³ H₂O-eq/kg H₂), which includes upstream water embodied in electricity, fuels, and chemicals, characterized using the AWARE method. For electrolysis, process water is calculated from the stoichiometric requirement and deionization losses, while for SMR/ATR/coal pathways, it is derived from steam-to-carbon ratios, boiler efficiency, and cooling makeup factors (Appendix A.5). All values are standardized per kg H₂, with explicit unit conversions (1 kg H₂ ≈ 11.126 Nm³), and uncertainties are quantified via Monte Carlo simulation (N = 1000), using probability distributions for key parameters (Table A3 ). Beyond techno-economic metrics, the Eora26 Multi-Regional Input–Output (MRIO) database (version 2023.1) is employed to trace sectoral supply chains and quantify import dependencies. Structural path analysis (SPA) is then used to identify major import-dependent flows contributing to geopolitical risks, with computational details documented in Appendix A. To operationalize broader socio-political concepts, we align GPRI and S-LCA indicators with three theoretical constructs: • Energy sovereignty (supply independence and autonomy), • Resilience (system robustness to external shocks), and • Energy justice (equitable distribution of socio-economic benefits). Each construct is linked to measurable proxies in Table 2, which now includes an additional column 'Theoretical Construct'. 3.1 Overall Analytical Framework This study proposes an extended Life Cycle Sustainability Assessment (LCSA+) framework to evaluate hydrogen development pathways from four integrated dimensions: environmental, economic, social, and geopolitical. The framework (Fig. 3 − 1) consists of: (1) Environmental Life Cycle Assessment (E-LCA) (2)Economic Feasibility Assessment (LCC), incorporating a geopolitical risk premium coefficient (α) (3) Social Life Cycle Assessment (S-LCA) (4) Geopolitical Risk Index (GPRI) construction and integration All modules are applied under a unified system boundary (“cradle-to-gate”) and functional unit (1 kg H₂), ensuring comparability and normalization across dimensions. The introduction of α into LCC and the explicit integration of GPRI represent a novel expansion of conventional LCSA, enabling simultaneous evaluation of environmental performance and supply-chain resilience. While this study applies the framework to China’s hydrogen sector, its modular design allows adaptation to other national or regional energy transition contexts. The relative weights of environmental, economic, social, and geopolitical sustainability dimensions were derived using the Analytic Hierarchy Process (AHP). An expert panel composed of five academics, three policymakers, and two industry practitioners conducted pairwise comparisons. The complete judgment matrix and weight calculation are documented in Table A2 (Supplementary Materials). The Consistency Ratio (CR) was 0.08, below the 0.1 threshold, confirming logical consistency. 3.2 Sustainability Dimensions and Indicator System Design This study applies an expanded Life Cycle Sustainability Assessment (LCSA+) to evaluate hydrogen pathways across four dimensions: environmental, economic, social, and geopolitical. Each dimension adopts a consistent system boundary and functional unit (1 kg H₂) to ensure comparability. (1) Environmental Dimension (E-LCA) The environmental assessment follows ISO 14040/44 standards [ 47 – 49 ], covering the full “cradle-to-gate” process from raw material extraction to hydrogen production, storage, and transportation. Two key indicators are calculated: carbon footprint (kg CO₂-eq/kg H₂) and water footprint (m³/kg H₂). Data are sourced from the China Life Cycle Database (CLCD) and Ecoinvent v3.9, with adjustments for regional resource configurations in Xinjiang and Shanxi. (2) Economic Dimension (Life Cycle Cost and Risk Premium) The Levelized Cost of Hydrogen (LCOH) is estimated using the Life Cycle Cost (LCC) approach [ 50 , 51 ], expanded to include a geopolitical risk premium coefficient (α). This coefficient, derived through a Delphi expert elicitation process, integrates three categories of risk indicators: Here, α is dimensionless but functions as a multiplicative escalation factor, expressed as a percentage increase in LCOH per unit geopolitical risk. The β values used in the Delphi process correspond to expert-consensus scaling parameters, calculated as the mean ± standard deviation across three rounds of scoring. This process ensures transparency in how expert judgments are incorporated into the LCOH model. • Critical material risk: material indispensability and substitution difficulty; • Trade concentration: Herfindahl–Hirschman Index (HHI) of global suppliers; • Supplier country stability: World Bank Worldwide Governance Indicators (WGI). The α coefficient captures potential cost escalations from geopolitical uncertainties, enhancing the LCOH model’s ability to reflect market volatility. (3) Social Dimension (S-LCA) Following UNEP/SETAC guidelines, the Social Life Cycle Assessment incorporates indicators for job creation, occupational health and safety, and reliance on social infrastructure. The Social Hotspot Index is used to evaluate each pathway’s relative social performance [ 52 , 53 ]. Data sources include the International Labour Organization (ILO), China Statistical Yearbook, and expert interviews. (4) Integration and Weighting All indicators are normalized to a 0–1 scale via the min–max method. Final composite scores are computed using expert-assigned weights: environment (30%), economy (30%), society (20%), and geopolitics (20%). 3.3 Construction of the Geopolitical Risk Index (GPRI) To assess the external exposure of hydrogen pathways to critical material and technological dependencies, this study constructs a Geopolitical Risk Index (GPRI) system, consisting of three levels of indicators[ 44 , 45 , 54 ], quantifying the structural risks in supply chain stability for different pathways. The indicator system is outlined in Table 3 − 1 below: The GPRI is calculated as a weighted sum of normalized sub-indicators: GPRI = Σ (w_i × (X_i − X_min) / (X_max − X_min)) where w_i denotes the AHP-derived weight of each sub-indicator, and the normalization follows a min–max transformation to ensure comparability. Table 3 − 1 lists the dimension, indicator, unit, and data source for each component (e.g., DoI as % import dependence; HHI as concentration index ranging 0–1; WGI as governance stability score). Table 3 − 1: Geopolitical Risk Index (GPRI) Indicator System and Weights Primary Dimension Secondary Indicator Weight Indicator Explanation and Data Source Critical Material Risk Degree of External Dependence (DoI) 0.25 Import volume/total demand, Source: Customs General Administration Supplier Concentration (HHI) 0.25 Source country HHI index, Source: UN Comtrade Substitution Difficulty (Patent Concentration) 0.20 Patent portfolio index, Source: WIPO Technological Equipment Risk Domestic Production Rate/Technology Generation Gap 0.15 Gap in core technologies between domestic and international, Source: Expert evaluation Policy and Institutional Risk Carbon Border Adjustment Mechanism Impact Factor 0.15 Estimated cost impact of CBAM, Source: EC Report The GPRI is normalized to a 0–1 range using a weighted average approach, with higher values indicating greater risks. The Analytic Hierarchy Process (AHP) is used for hierarchical weight calculation, employing Saaty’s nine-point scale. The consistency ratio (CR) for judgments by five experts is below 0.1. 3.4 Scenario Simulation and Sensitivity Analysis Design[ 55 – 57 ] To assess the impact of policy changes and market uncertainties on sustainability outcomes, three development scenarios are designed: • Scenario 1: Business As Usual (BAU): Continuation of current policies and technological progress trends. • Scenario 2: Geopolitical Conflict (Geo-Risk): Simulates a 50% restriction on critical material imports and a 20% reduction in electrolyzer domestic production. • Scenario 3: Technological Breakthrough (Tech-Advance): Simulates a 40% reduction in catalyst usage and an increase in CCUS efficiency to 95%. The simulation uses Monte Carlo methods, iterated 1,000 times, with key variables modeled using triangular or normal distributions. Sensitivity analysis is conducted for three key parameters (see Table 3 − 2): Table 3 − 2: Sensitivity Analysis Parameter Settings and Ranges Parameter Category Parameter Name Range Data Source Technological Electrolysis Efficiency 60% – 75% National Energy Administration, Industry Data Economic Carbon Price 50–200 RMB/ton National Carbon Trading Market Data Resource Platinum Group Metal Price ± 30% LME, USGS Data The Sobol sensitivity index method is employed to identify the primary influencing factors and their explanatory strength regarding the scores across different dimensions and pathway choices. 3.5 Data Sources and Uncertainty Handling To capture parameter uncertainty, Monte Carlo simulations with 1,000 iterations were conducted. Key variables included electricity price, electrolyzer efficiency, platinum import dependency, carbon intensity of the grid, and water consumption. The distribution types, parameter ranges, and data sources are listed in Table A3 (Supplementary Materials). Random seeds were fixed to allow full reproducibility of results. This study employs a multi-source, heterogeneous data fusion strategy as outlined below: • Primary Data: Semi-structured interviews with 15 hydrogen industry chain enterprises, equipment manufacturers, and 5 policy experts to obtain key parameters and scenario judgments. • Secondary Data: Includes the “China Hydrogen Industry Development Report” (2023), National Bureau of Statistics, UN Comtrade, WIPO, and Ecoinvent databases. • Missing Data Handling: For data with interval uncertainty or historical gaps, triangular fuzzy numbers are used for modeling, with upper and lower bounds and most trustworthy values set through expert calibration. • Analysis Platform: Environmental and social assessments are conducted using OpenLCA v9.0, while economic modeling and scenario simulations are carried out using Python with the @risk module. 4. Case Study: Evaluation of Hydrogen Development Pathways in China 4.1 Path Selection and System Boundary Definition To validate the operability and explanatory power of the LCSA + framework proposed in this study, three typical hydrogen production pathways are selected for case analysis. The system boundary flow diagrams of these three pathways are shown in Fig. 4 − 1, illustrating the input-output relationships of each pathway, including: • Green Hydrogen Pathway: Represented by the photovoltaic hydrogen production project in the Hami region of Xinjiang, using renewable energy for water electrolysis to produce hydrogen. This represents the “centralized green hydrogen” development model in China’s resource-rich western regions[ 58 , 59 ]. • Blue Hydrogen Pathway: Represented by the coal gasification hydrogen production and Carbon Capture, Utilization, and Storage (CCUS) project in the coal chemical park of Jincheng, Shanxi. This reflects the “low-carbon transition of traditional industries” pathway in resource-conversion areas[ 60 , 61 ]. • Gray Hydrogen Pathway: Represented by small-scale natural gas reforming hydrogen production units, serving as a “high-carbon baseline pathway” for comparative analysis[ 62 ]. This study adopts a cradle-to-gate system boundary, with a functional unit of 1 kg of pure hydrogen. The analysis covers the stages of raw material extraction, transportation, hydrogen production, purification, storage, and transfer. Dynamic simulations are conducted to cover the evolution of different policies and technological pathways from 2025 to 2060. 4.2 Data Collection and Scenario Parameter Setting This study employs a multi-source data fusion strategy, including: • Field interviews and surveys (e.g., hydrogen enterprises in Hami, Xinjiang, and Jincheng, Shanxi); • National policy documents and industry statistical yearbooks (e.g., China Hydrogen Industry Development Blue Paper , Energy Statistical Yearbook )[ 63 , 64 ]; • International databases (UN Comtrade, IEA, WIPO, CLCD, Ecoinvent)[ 65 – 69 ]; • Self-constructed GPRI and MRIO databases. A summary of the key technical and resource parameters for the three pathways is shown in Table 4 − 1 below: Table 4 − 1. Water Use by Hydrogen Pathway Pathway Process water (m³/kg H₂) Life-cycle freshwater consumption (m³ H₂O-eq/kg H₂) Notes Green H₂ (PEM, 100% RE) 0.010–0.015 0.45–0.60 Process = DI + blowdown; LCA = upstream electricity Blue H₂ (SMR + CCS) 0.25–0.35 0.80–1.20 Process = boiler + cooling makeup Grey H₂ (SMR) 0.30–0.40 0.90–1.50 Same as Blue, w/o CCS Coal-based H₂ 0.50–0.70 1.80–2.50 Higher process water; high upstream use Footnotes: a) Process water excludes upstream embodied water. b) Life-cycle freshwater consumption follows ISO 14046 with AWARE characterization. c) All values are per 1 kg H₂. Historical figures per Nm³ H₂ or per MWh were converted (Appendix A.5). d) Ranges indicate P5–P95 from Monte Carlo (N = 1000). To capture the uncertainty in future policy and market changes, three development scenarios are designed: • Scenario 1: Business As Usual (BAU): Continuation of current policies, energy efficiency, and cost trends; • Scenario 2: Geopolitical Risk (Geo-Risk): Simulates conditions such as restrictions on critical material imports and increased international trade friction; • Scenario 3: Technological Breakthrough (Tech-Advance): Sets a scenario with increased electrolysis efficiency, higher domestic production rates, and higher carbon prices. The ranges of key parameters for each scenario are shown in Table 4 − 2 below: Table 4 − 2: Range of Scenario Variables Parameter Base Value Geo-Risk Scenario Tech-Advance Scenario Data Source Electrolysis Efficiency 65% 62% 75% IEA, China Hydrogen Alliance Carbon Price 80 CNY/ton 100 CNY/ton 200 CNY/ton National Carbon Market Platform Electrolyzer Domestic Production Rate 40% 30% 60% Industry Interviews Platinum Group Metal Price 100% + 30% -10% LME, USGS Data 4.3 Multi-Dimensional Sustainability Assessment Results (1) Comprehensive Scoring Results After synthesizing the results across four dimensions, the sustainability scores for different pathways under each scenario are shown in Fig. 4 − 2. The radar charts display the three pathways across the four dimensions (E-LCA, LCC, S-LCA, GPRI). Overall: The green hydrogen pathway demonstrates the best performance in the environmental and social dimensions; however, due to its high GPRI score (driven by dependence on imported silicon materials and platinum group metals) and substantial initial capital investment, its economic dimension score is relatively low. The blue hydrogen pathway exhibits a comparatively balanced profile, particularly excelling in resource localization and cost control. Nonetheless, its carbon emissions may become unstable when CCUS efficiency fluctuates. The gray hydrogen pathway has an initial cost advantage in the economic dimension but shows significant disadvantages in both the environmental and GPRI dimensions, making it uncompetitive in the long term. (2) Scenario Sensitivity Analysis Under the Geopolitical Risk Scenario, the LCOH of the green hydrogen pathway increases by 15%, primarily due to the high import concentration of catalysts, which drives the GPRI score up to 0.83. In contrast, the cost fluctuation of the blue hydrogen pathway remains below 5%. Under the Technological Breakthrough Scenario, the LCOH of green hydrogen decreases to CNY 15/kg, approaching the cost of the blue hydrogen pathway, indicating a trend toward cost convergence (see Fig. 4 − 3). (3) Attribution Analysis The Sobol index analysis reveals that, within the green hydrogen pathway, electricity price and the GPRI weighting together account for up to 64% of the variance in the total sustainability score. For blue hydrogen, CCUS efficiency and carbon price emerge as the key sensitivity factors. The gray hydrogen pathway is highly sensitive to changes in carbon taxation, indicating its vulnerability under carbon cost policies. 4.4 Regional Adaptation and Differentiated Recommendations Significant variations exist in the suitability of hydrogen development pathways across regions, driven by differences in resource endowment, industrial base, and policy support. Based on the evaluation results and the structure of geopolitical risk, this study proposes the following regional–pathway matching recommendations (Table 4 − 3). Table 4 − 3. Regional–Pathway–Development Strategy Matching Recommendations Region Recommended Pathway Regional Advantages Policy Recommendations Xinjiang Centralized green hydrogen High solar irradiance, vast land, low electricity prices Establish an integrated “renewable power direct supply + hydrogen industrial park” mechanism Shanxi Blue hydrogen Abundant coal resources, high CCUS feasibility Promote “coal–hydrogen–capture” integrated industry demonstration projects Yangtze River Delta Distributed green hydrogen Concentrated industrial load, port advantage Develop “port hydrogen production–hydrogen corridor–industry integration” demonstration zones Guangdong–Hong Kong–Macao Greater Bay Area Transitional gray → green hydrogen Large industrial demand, open policy environment, convenient imports Support hybrid deployment of gray and green hydrogen; advance green hydrogen price parity mechanisms Our results provide empirical grounding for three interrelated propositions: (i) sovereignty is compromised by high import dependence, (ii) resilience emerges through diversification, and (iii) justice is endangered when socio-economic benefits are unevenly distributed. Policy implications follow directly: - Strategic stockpiling and domestic innovation enhance sovereignty. - Diversification of suppliers and flexible trade relations strengthen resilience. - Redistributive mechanisms, including regional hydrogen subsidies, promote justice. This case study confirms the operational applicability of the LCSA + framework across multiple pathways, regions, and scenarios. The results indicate that green hydrogen offers long-term environmental and strategic advantages but requires short-term policy intervention to mitigate geopolitical dependency risks; blue hydrogen is a viable transitional option but demands attention to CCUS cost volatility; gray hydrogen should be gradually phased out due to its systemic environmental and security risks. 5. Discussion 5.1 Mechanistic Interpretation of the Evaluation Results This study finds significant differences in the four-dimensional sustainability performance of the three hydrogen development pathways. Green hydrogen leads in environmental and social scores but is weakest in geopolitical risk; blue hydrogen is characterized by a “balanced” profile with lower costs and geopolitical risk; gray hydrogen has systemic disadvantages in carbon emissions and policy sensitivity. The underlying cause of these differences lies in the structural disparities in resource reliance, technological dependency, and policy constraints. The green hydrogen pathway achieves near-zero carbon emissions but depends heavily on imported photovoltaic-grade silicon wafers and platinum group metal catalysts, yielding a GPRI score of 0.71—35% higher than blue hydrogen (0.52) and gray hydrogen (0.49). These materials are highly concentrated in a small set of supplier countries, such as South Africa and Russia, with low substitutability and high technological barriers, making them vulnerable to geopolitical conflict, export controls, or technological decoupling. By contrast, the blue hydrogen pathway benefits from China’s domestic coal resources and its emerging CCUS infrastructure, offering a higher degree of energy sovereignty and lower concentration of risk. However, its carbon intensity remains high, and under the future implementation of mechanisms such as the Carbon Border Adjustment Mechanism (CBAM), export costs may rise significantly—presenting a typical “high political resilience–high carbon risk” trade-off. The gray hydrogen pathway demonstrates “short-term economic advantage–long-term environmental disadvantage” characteristics: despite the lowest initial costs, rising carbon prices and tightening carbon border measures will erode its competitiveness. 5.2 Theoretical Contributions and Framework Innovation This study proposes and empirically validates an extended Life Cycle Sustainability Assessment framework (LCSA+), incorporating a Geopolitical Risk Index (GPRI) into multi-dimensional energy transition evaluation for the first time—representing a paradigm shift from the conventional “environment–economy–society” triad toward four-dimensional integration. This framework not only captures ecological and economic performance but also quantifies structural variables such as resource controllability, trade concentration, and institutional stability of partner countries, providing a generalizable approach for assessing energy resilience. Building on this, the study advances a “Decarbonization–Sovereignty Trade-off Framework”, which reveals that in multi-pathway energy transitions, a singular focus on carbon neutrality can exacerbate external dependency risks, while prioritizing energy security may delay decarbonization milestones. Only by integrating systematic trade-offs into pathway selection can truly strategic sustainability be achieved. 5.3 Dialogue with Existing Literature The findings complement and extend existing scholarship. Gielen et al. have projected hydrogen cost sensitivities using techno-economic models[ 70 ], highlighting the role of material price volatility, while Le Treut et al. have emphasized institutional vulnerabilities in energy transitions from a political economy perspective[ 71 ]. Neither, however, has operationalized a structured quantitative model to address geopolitical risk. This study fills this gap by developing the GPRI, which quantifies the combined effects of resource dependency, trade concentration, and policy stability—bridging the methodological divide from “qualitative judgment” to “quantitative assessment.” Furthermore, compared with Scholten et al.’s discussion of the evolving geopolitical order of hydrogen[ 72 , 73 ], this study focuses more on internal structural contradictions within a national energy transition—specifically, the tension between external dependency in technology choice and domestic manufacturing capacity. Highlighting these “embedded risks” provides a strategic tool for the early identification of vulnerabilities in regional energy planning. 5.4 Policy Recommendations and Pathway Optimization Based on the case analysis and simulation results, this study offers targeted policy recommendations to help China achieve a balanced development goal of “secure–low-carbon–affordable” hydrogen transition (Table 5 − 1). Table 5 − 1. Policy Recommendations for Risk Mitigation and Pathway Optimization Risk Type Target Pathway Recommended Actions Target Indicators High material concentration Green hydrogen Establish strategic reserves for PGM and photovoltaic-grade silicon; promote domestic catalyst substitution Reduce DoI and HHI risk coefficients in GPRI Technology embargo risk Green hydrogen Support domestic electrolyzer supply chain integration and open standards; raise domestic manufacturing share of key equipment Increase localization rate of core equipment to > 70% Carbon border pressure Blue hydrogen Align CCUS with international carbon reduction certification; seek CBAM mutual recognition Lower sensitivity to carbon tax; enhance export resilience Weak energy sovereignty Gray hydrogen Phase out gray hydrogen in chemical parks; replace with green/blue hydrogen Improve regional energy autonomy and accelerate carbon compliance Additionally, a multi-pathway coordination strategy should be implemented according to regional resource endowments: e.g., develop centralized green hydrogen bases in resource-rich northwestern regions; deploy blue hydrogen in coal-producing areas as a transitional option; advance distributed green hydrogen and hydrogen logistics in energy-importing cities—achieving “place-based, diversified” development. Our MRIO-based findings resonate with recent analyses suggesting that hydrogen may become the “new oil” in shaping geopolitical relations (Van de Graaf et al., 2020)[ 74 ]. In line with Pflugmann et al.[ 72 ], our results indicate that geopolitical risks are not evenly distributed, but rather contingent upon regional industrial structures and policy frameworks. This is further echoed by Eicke[ 75 ]. 5.5 Limitations and Future Research Directions Despite methodological innovations and empirical contributions, this study has several limitations: • Data uncertainty: Some geopolitical risk indicators, such as WGI and HHI, involve subjective scoring; larger-scale expert surveys or machine learning could improve robustness. • Model generalizability: LCSA + is currently tested on China; future work should explore cross-country applications to assess applicability and sensitivity. • Policy dynamic response: This study does not simulate nonlinear policy feedbacks (e.g., carbon price shifts, import restrictions); integrating with agent-based modeling (ABM) could enhance dynamic forecasting. • Social dimension depth: The S-LCA component lacks detailed modeling of job quality and regional inequality; future work could incorporate social equity metrics. Future research should advance toward an energy–environment–security–ethics coupled evaluation framework, developing integrated sustainability–resilience models for use in global climate governance and strategic energy decision-making. 6. Conclusion By developing an extended Life Cycle Sustainability Assessment (LCSA+) framework that integrates geopolitical dimensions, this study systematically evaluates the multi-dimensional performance of China’s hydrogen development pathways. Three key conclusions emerge: Under current technological conditions, China’s hydrogen transition faces a pronounced security–low-carbon trade-off: the green hydrogen pathway has the lowest carbon intensity (4.2 kg CO₂/kg H₂) but a GPRI (0.71) 35% higher than blue hydrogen, mainly due to reliance on imported critical materials. Scenario analysis shows that geopolitical factors could raise the levelized cost of green hydrogen by 23%, an effect well beyond the predictive scope of conventional assessment models, underscoring the limitations of existing transition risk evaluation methods. Regional differentiation analysis reveals that in resource-rich northwestern China, integrating wind/solar hydrogen production with coal chemical industries can reduce the GPRI by 0.15–0.2, offering empirical support for coordinated regional development. The study’s main academic contributions are threefold: methodologically, the geopolitical risk quantification system fills a critical gap in the political economy dimension of sustainability assessment; theoretically, it validates the nonlinear relationship between technological learning curves and geopolitical risk evolution; practically, the multi-scenario dynamic evaluation framework provides policymakers with a decision-making tool that balances decarbonization goals and energy security. Notably, the study identifies a carbon price threshold of CNY 150/t, beyond which blue hydrogen loses its economic advantage—offering an important reference point for carbon market design. Based on these findings, the study recommends a three-phase strategic roadmap: • Short term (2025–2030): prioritize blue hydrogen projects with CCUS integration; • Medium term (2030–2040): focus on domestic production of key electrolyzer equipment; • Long term (post-2040): transition to a predominantly green hydrogen supply system. Future research should focus on: (1) developing risk assessment models incorporating firm-level operational data; (2) conducting cross-national comparisons of the geopolitical impacts of hydrogen infrastructure; and (3) exploring the application of artificial intelligence in supply chain risk early warning. The proposed framework is extendable to other clean energy technologies, providing a scientific basis for achieving a “just transition” in the global energy shift. Declarations Ethical Approval This study does not involve any research with human participants or animals performed by any of the authors. Informed Consent Not applicable, as the study does not involve human participants. Funding This research was supported by the National Social Science Foundation of China (Grant No. 20XFX016). Data Availability All data used in this study are provided in the Supplementary Materials. The simulation codes (Python/Matlab) and mapping tables are available from the corresponding author upon reasonable request. Authors and Affiliations 1. Chufeng Yuan, East China University of Technology, Nanchang, China (Co-Corresponding Author) Email: [email protected] 2. Yang Tan, Jiangxi Vocational College of Industry & Engineering, Pingxiang, China Email: [email protected] 3. Xuguang Yuan, Hunan University, Changsha, China (Co-Corresponding Author) Email: [email protected] 4. Qiu Tan, East China University of Technology, Nanchang, China (Primary Corresponding Author) Email: [email protected] Author Contribution Chufeng Yuan (C.Y.): Conceptualization, Methodology, Supervision, Writing – Review & Editing. 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Sci. 93 , 102847. https://doi.org/10.1016/j.erss.2022.102847 (2022). Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 Mar, 2026 Reviews received at journal 04 Feb, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers agreed at journal 23 Jan, 2026 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 21 Sep, 2025 Reviewers invited by journal 19 Sep, 2025 Editor invited by journal 10 Sep, 2025 Editor assigned by journal 09 Sep, 2025 Submission checks completed at journal 08 Sep, 2025 First submitted to journal 01 Sep, 2025 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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1","display":"","copyAsset":false,"role":"figure","size":769692,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3-1: LCSA+ Comprehensive Evaluation Framework\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7505392/v1/088ee0bff0aa99c1d394f548.png"},{"id":92526334,"identity":"5ff6bf3a-893e-4d09-900c-b2d19549ec2c","added_by":"auto","created_at":"2025-09-30 15:52:09","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":574514,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4-1. Three Hydrogen Production Pathways and Process Flow Diagram\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7505392/v1/643d5ea68d21cc3529e3d490.jpeg"},{"id":92527240,"identity":"84eaf1ed-4c3a-489a-9cd0-88aee3f0ee7d","added_by":"auto","created_at":"2025-09-30 16:08:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":211021,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4-2. Radar Chart Representation of Three Pathways Across Four Dimensions\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7505392/v1/1fb2a3fd855da48a462081fb.png"},{"id":92527918,"identity":"9b39bfcf-c17d-43a6-ba69-87190bd5c94d","added_by":"auto","created_at":"2025-09-30 16:16:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":82708,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4-3. LCOH changes for green hydrogen (Xinjiang), blue hydrogen (Shanxi), and gray hydrogen (East China) under three scenarios: Baseline (BAU), Geopolitical Risk, and Technology Breakthrough.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7505392/v1/b9f88caa8b72a36649db7731.png"},{"id":92528739,"identity":"e97a554f-d06a-4e86-9962-917ed7c2dca1","added_by":"auto","created_at":"2025-09-30 16:24:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2788197,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7505392/v1/9d319ade-19bf-453f-be9a-4961ce94f384.pdf"},{"id":92526331,"identity":"d062683e-f888-4466-99a8-567ced1d17a8","added_by":"auto","created_at":"2025-09-30 15:52:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23879,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7505392/v1/057198128e7664d59c85b4e5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sustainability Assessment of China’s Hydrogen Transition: A Life Cycle–Geopolitical Risk Multi-Criteria Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHydrogen is increasingly recognized as a cornerstone of global decarbonization strategies, especially for hard-to-abate sectors such as heavy industry and long-distance transport [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. As the world\u0026rsquo;s largest energy consumer and carbon emitter, China has identified hydrogen energy as a strategic pillar for achieving its \u0026ldquo;dual carbon\u0026rdquo; goals\u0026mdash;peaking emissions by 2030 and reaching carbon neutrality by 2060. Projections indicate that by 2030, China\u0026rsquo;s hydrogen production will reach 20\u0026nbsp;million tons annually, nearly 40% of the global total (China Hydrogen Alliance, 2023). Recent scholarship has highlighted that renewable energy vectors, including hydrogen, fundamentally reshape geopolitical interdependencies rather than replicating fossil fuel dynamics[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eHowever, the sustainability of this transition remains contested. Green hydrogen (produced via renewable-powered electrolysis) and blue hydrogen (fossil-based with carbon capture) both involve complex trade-offs between environmental benefits, economic feasibility, and supply chain security. While numerous studies have compared the carbon footprints of these technologies using Life Cycle Assessment (LCA) [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e], critical systemic risks remain underexplored.\u003c/p\u003e\n\u003cp\u003eTwo gaps are particularly salient. First, existing analyses often ignore geopolitical vulnerabilities embedded in supply chains. For example, China\u0026rsquo;s dependence on imported platinum group metals reached 87% in 2023, with imports totaling 1.63 times domestic demand [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. Such exposure is exacerbated by intensifying US\u0026ndash;China trade tensions. Second, current sustainability assessment frameworks rarely integrate the environmental, economic, and social pillars [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e] with geopolitical risk factors, despite evidence that disruptions in critical material supply can raise levelized hydrogen costs by 15\u0026ndash;30% [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. This reflects a broader limitation in energy transition research, where techno-economic models dominate and political-economic variables are underrepresented [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eHowever, techno-economic models that dominate current assessments are particularly vulnerable to institutional shocks. For instance, Levelized Cost of Hydrogen (LCOH) projections based on stable trade conditions fail to capture abrupt price surges when export restrictions are imposed on platinum group metals. Similarly, conventional Life Cycle Assessments (LCAs) assume linear carbon price trajectories, yet mechanisms such as the EU\u0026rsquo;s Carbon Border Adjustment Mechanism (CBAM) can instantaneously alter cost competitiveness across pathways. Even widely used integrated assessment models neglect the impact of sudden technology embargoes, which can disrupt electrolyzer deployment despite favorable cost curves. These examples demonstrate that models limited to environmental and economic dimensions systematically underestimate resilience challenges under real-world geopolitical and policy disruptions.\u003c/p\u003e\n\u003cp\u003eThis study addresses these gaps by extending the Life Cycle Sustainability Assessment (LCSA) framework to include quantified geopolitical supply chain risks. We construct a Geopolitical Risk Index (GPRI) that incorporates material criticality, trade concentration, and supplier stability, and apply Multi-Regional Input\u0026ndash;Output (MRIO) analysis to trace supply chain exposure for different hydrogen pathways. China serves as a compelling case study due to its dual position as both the world\u0026rsquo;s largest electrolyzer manufacturer (80% of global capacity) [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e] and a resource-import-dependent nation [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u0026mdash;a structural contradiction that intensifies the tension between energy security and decarbonization.\u003c/p\u003e\n\u003cp\u003eThe empirical analysis compares three pathways: Xinjiang-based green hydrogen from solar resources, Shanxi-based blue hydrogen from coal with CCUS, and natural gas\u0026ndash;based gray hydrogen. Sensitivity tests incorporate technological learning rates and carbon price scenarios. The findings contribute to both theory and policy: integrating political-economic variables into LCSA improves the explanatory power of sustainability assessments, while revealing that China\u0026rsquo;s current hydrogen strategy underestimates supply chain risks. Although blue hydrogen has lower geopolitical exposure, its higher carbon intensity may incur future carbon border taxes; conversely, green hydrogen\u0026rsquo;s environmental advantage may come at the cost of reduced energy sovereignty.\u003c/p\u003e\n\u003cp\u003eBy linking energy systems analysis with geopolitical risk research, this study offers a transferable methodological framework for resource-constrained economies pursuing low-carbon transitions. In an era of global supply chain shocks\u0026mdash;exemplified by the Russia\u0026ndash;Ukraine conflict and the US CHIPS and Science Act [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u0026mdash;we highlight the need for strategies that balance resilience with feasibility in a fragmented international order.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003e\u003cstrong\u003e2.1 Evolution and Current Status of Hydrogen Sustainability Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOver the past two decades, hydrogen sustainability assessment has shifted from a narrow environmental focus to a multi-dimensional approach. Early studies emphasized environmental impacts, particularly carbon footprint comparisons of different production pathways [15,16]. The Life Cycle Sustainability Assessment (LCSA) method emerged to integrate environmental, economic, and social dimensions for a more comprehensive evaluation [17\u0026ndash;19].\u003c/p\u003e\n\u003cp\u003eIn the environmental dimension, conventional Life Cycle Assessment (LCA) primarily measures greenhouse gas emissions during hydrogen production [20]. Recent research expands this scope to include water consumption, land use, and biodiversity impacts, reflecting the multi-resource dependencies of green hydrogen [21,22]. For example, Olaitan et al. (2024) found that although green hydrogen has a lower carbon footprint, its water footprint can be more than double that of blue hydrogen, highlighting trade-offs that require further analysis [23].\u003c/p\u003e\n\u003cp\u003eIn the economic dimension, Levelized Cost of Hydrogen (LCOH) modeling is widely used to compare pathways, as illustrated by Glenk [24]. Green hydrogen costs are particularly sensitive to electricity prices and capital expenditures. However, most economic studies assume stable raw material and technology supply chains, overlooking external shocks such as price volatility and geopolitical tensions.\u003c/p\u003e\n\u003cp\u003eThe social dimension remains underexplored. Some studies apply Social LCA (S-LCA) to evaluate employment, occupational health, and safety along the hydrogen value chain [25,26]. Yet most of this work focuses on European contexts, with limited quantitative models for emerging economies such as China.\u003c/p\u003e\n\u003cp\u003eOverall, research is moving toward multi-dimensional integration. Nonetheless, robust theories and tools to capture external structural risks\u0026mdash;such as geopolitical shocks and critical material dependencies\u0026mdash;are still lacking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Geopolitical and Supply Chain Systemic Risks in Energy Transition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecent geopolitical research in energy transition has expanded beyond oil and gas security to cover key minerals and core technologies critical to low-carbon systems. This reflects a shift from traditional energy geopolitics to technology geopolitics [27,28].\u003c/p\u003e\n\u003cp\u003eAt the resource level, the International Energy Agency (IEA, 2023) highlights the reliance of clean energy technologies\u0026mdash;wind, solar, electrolyzers, and energy storage\u0026mdash;on minerals controlled by a few countries, including lithium, cobalt, rare earth elements, and platinum group metals [29,30]. For example, China processes over 90% of global rare earths but depends on imports for over 80% of platinum group metals [31,32]. This criticality\u0026ndash;vulnerability imbalance poses significant supply chain risks for hydrogen transitions.\u003c/p\u003e\n\u003cp\u003eAt the institutional level, emerging trade rules and carbon policies\u0026mdash;such as the EU\u0026rsquo;s Carbon Border Adjustment Mechanism (CBAM) and U.S. technology decoupling measures\u0026mdash;create additional constraints. Case studies show that such risks can increase renewable energy financing costs in emerging markets by several percentage points [33,34]. Scholten (2020) notes that control over energy systems is becoming a component of national strategic sovereignty [35].\u003c/p\u003e\n\u003cp\u003eAlthough frameworks such as the Critical Material Index, trade concentration measures (HHI), and substitutability scores exist [36,37], they are rarely applied to hydrogen systems. In China\u0026mdash;a nation both exporting hydrogen technology and importing key resources\u0026mdash;there is a notable absence of integrated analysis that captures supply chain vulnerabilities alongside institutional risks [38].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Research Gaps and Innovative Position of This Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA synthesis of existing literature reveals three major gaps:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eLimited integration across dimensions \u0026ndash; Most hydrogen sustainability studies focus on environmental or economic metrics. Few combine environmental, economic, social, and geopolitical factors into a unified framework, limiting the ability to assess systemic risks comprehensively.\u003c/li\u003e\n \u003cli\u003eRegional imbalance \u0026ndash; China, as the largest hydrogen consumer and equipment manufacturer, faces dual exposure as both technology exporter and resource importer. Despite being a key global case, systematic assessments centered on China remain scarce.\u003c/li\u003e\n \u003cli\u003eMethodological limitations \u0026ndash; Existing LCA/LCSA models use static parameters and controlled scenarios. They lack the capacity to simulate non-linear shocks such as trade sanctions, technological decoupling, and abrupt policy changes, reducing predictive power for real-world resilience.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo address these gaps, this study introduces three innovations:\u003c/p\u003e\n\u003cp\u003e\u0026bull;\u0026nbsp;Theoretical \u0026ndash; Development of an expanded LCSA+ framework [39\u0026ndash;42] that incorporates a Geopolitical Risk Index (GPRI) [43\u0026ndash;46], integrating indicators such as material criticality, trade concentration, and political stability of supplier nations.\u003c/p\u003e\n\u003cp\u003e\u0026bull;\u0026nbsp;Methodological \u0026ndash; Combination of Multi-Regional Input\u0026ndash;Output (MRIO) analysis with sensitivity simulations to trace cross-border supply chain risks and assess dynamic responses under different policy scenarios. To evaluate supply chain risks and import dependencies, the Eora26 MRIO database was adopted. Hydrogen-related activities such as electrolyzer manufacturing, PV-grade silicon, platinum group metals, and refueling infrastructure were systematically mapped to corresponding MRIO sectors. The detailed mapping rules and correspondence are provided in Table A1 (Supplementary Materials) to ensure reproducibility.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Empirical \u0026ndash; A China-focused comparative evaluation of green and blue hydrogen pathways, providing policy recommendations applicable to other emerging economies seeking diversified and secure hydrogen strategies.\u003c/p\u003e"},{"header":"3. Research Methodology","content":"\u003cp\u003eThe methodological framework of this study is designed to assess the life-cycle techno-economic performance and geopolitical risks of hydrogen production pathways. The functional unit is defined as 1 kg of hydrogen delivered at the plant gate, with system boundaries covering upstream resource extraction, intermediate conversion, and direct plant operations in accordance with ISO 14040/44 guidelines. Multiple evaluation indicators are employed, including the Geopolitical Risk Index (GPRI), the risk premium factor (\u0026alpha;), and the levelized cost of hydrogen (LCOH), with detailed formulations provided in Appendix A. To ensure transparency in water-related results, harmonized definitions and unit conventions are adopted. Water use is reported under two scopes: process water (m\u0026sup3;/kg H₂), which covers on-site intake for electrolysis, steam generation, and cooling makeup; and life-cycle freshwater consumption (m\u0026sup3; H₂O-eq/kg H₂), which includes upstream water embodied in electricity, fuels, and chemicals, characterized using the AWARE method. For electrolysis, process water is calculated from the stoichiometric requirement and deionization losses, while for SMR/ATR/coal pathways, it is derived from steam-to-carbon ratios, boiler efficiency, and cooling makeup factors (Appendix A.5). All values are standardized per kg H₂, with explicit unit conversions (1 kg H₂ \u0026asymp; 11.126 Nm\u0026sup3;), and uncertainties are quantified via Monte Carlo simulation (N\u0026thinsp;=\u0026thinsp;1000), using probability distributions for key parameters (Table \u003cspan class=\"InternalRef\"\u003eA3\u003c/span\u003e). Beyond techno-economic metrics, the Eora26 Multi-Regional Input\u0026ndash;Output (MRIO) database (version 2023.1) is employed to trace sectoral supply chains and quantify import dependencies. Structural path analysis (SPA) is then used to identify major import-dependent flows contributing to geopolitical risks, with computational details documented in Appendix A.\u003c/p\u003e\n\u003cp\u003eTo operationalize broader socio-political concepts, we align GPRI and S-LCA indicators with three theoretical constructs:\u003c/p\u003e\n\u003cp\u003e\u0026bull; Energy sovereignty (supply independence and autonomy),\u003c/p\u003e\n\u003cp\u003e\u0026bull; Resilience (system robustness to external shocks), and\u003c/p\u003e\n\u003cp\u003e\u0026bull; Energy justice (equitable distribution of socio-economic benefits).\u003c/p\u003e\n\u003cp\u003eEach construct is linked to measurable proxies in Table\u0026nbsp;2, which now includes an additional column \u0026apos;Theoretical Construct\u0026apos;.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Overall Analytical Framework\u003c/h2\u003e\n \u003cp\u003eThis study proposes an extended Life Cycle Sustainability Assessment (LCSA+) framework to evaluate hydrogen development pathways from four integrated dimensions: environmental, economic, social, and geopolitical. The framework (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1) consists of:\u003c/p\u003e\n \u003cp\u003e(1) Environmental Life Cycle Assessment (E-LCA)\u003c/p\u003e\n \u003cp\u003e(2)Economic Feasibility Assessment (LCC), incorporating a geopolitical risk premium coefficient (\u0026alpha;)\u003c/p\u003e\n \u003cp\u003e(3) Social Life Cycle Assessment (S-LCA)\u003c/p\u003e\n \u003cp\u003e(4) Geopolitical Risk Index (GPRI) construction and integration\u003c/p\u003e\n \u003cp\u003eAll modules are applied under a unified system boundary (\u0026ldquo;cradle-to-gate\u0026rdquo;) and functional unit (1 kg H₂), ensuring comparability and normalization across dimensions.\u003c/p\u003e\n \u003cp\u003eThe introduction of \u0026alpha; into LCC and the explicit integration of GPRI represent a novel expansion of conventional LCSA, enabling simultaneous evaluation of environmental performance and supply-chain resilience. While this study applies the framework to China\u0026rsquo;s hydrogen sector, its modular design allows adaptation to other national or regional energy transition contexts.\u003c/p\u003e\n \u003cp\u003eThe relative weights of environmental, economic, social, and geopolitical sustainability dimensions were derived using the Analytic Hierarchy Process (AHP). An expert panel composed of five academics, three policymakers, and two industry practitioners conducted pairwise comparisons. The complete judgment matrix and weight calculation are documented in Table \u003cspan class=\"InternalRef\"\u003eA2\u003c/span\u003e (Supplementary Materials). The Consistency Ratio (CR) was 0.08, below the 0.1 threshold, confirming logical consistency.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Sustainability Dimensions and Indicator System Design\u003c/h2\u003e\n \u003cp\u003eThis study applies an expanded Life Cycle Sustainability Assessment (LCSA+) to evaluate hydrogen pathways across four dimensions: environmental, economic, social, and geopolitical. Each dimension adopts a consistent system boundary and functional unit (1 kg H₂) to ensure comparability.\u003c/p\u003e\n \u003cp\u003e(1) Environmental Dimension (E-LCA)\u003c/p\u003e\n \u003cp\u003eThe environmental assessment follows ISO 14040/44 standards [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e], covering the full \u0026ldquo;cradle-to-gate\u0026rdquo; process from raw material extraction to hydrogen production, storage, and transportation. Two key indicators are calculated: carbon footprint (kg CO₂-eq/kg H₂) and water footprint (m\u0026sup3;/kg H₂). Data are sourced from the China Life Cycle Database (CLCD) and Ecoinvent v3.9, with adjustments for regional resource configurations in Xinjiang and Shanxi.\u003c/p\u003e\n \u003cp\u003e(2) Economic Dimension (Life Cycle Cost and Risk Premium)\u003c/p\u003e\n \u003cp\u003eThe Levelized Cost of Hydrogen (LCOH) is estimated using the Life Cycle Cost (LCC) approach [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e], expanded to include a geopolitical risk premium coefficient (\u0026alpha;). This coefficient, derived through a Delphi expert elicitation process, integrates three categories of risk indicators:\u003c/p\u003e\n \u003cp\u003eHere, \u0026alpha; is dimensionless but functions as a multiplicative escalation factor, expressed as a percentage increase in LCOH per unit geopolitical risk. The \u0026beta; values used in the Delphi process correspond to expert-consensus scaling parameters, calculated as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation across three rounds of scoring. This process ensures transparency in how expert judgments are incorporated into the LCOH model.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Critical material risk: material indispensability and substitution difficulty;\u003c/p\u003e\n \u003cp\u003e\u0026bull; Trade concentration: Herfindahl\u0026ndash;Hirschman Index (HHI) of global suppliers;\u003c/p\u003e\n \u003cp\u003e\u0026bull; Supplier country stability: World Bank Worldwide Governance Indicators (WGI).\u003c/p\u003e\n \u003cp\u003eThe \u0026alpha; coefficient captures potential cost escalations from geopolitical uncertainties, enhancing the LCOH model\u0026rsquo;s ability to reflect market volatility.\u003c/p\u003e\n \u003cp\u003e(3) Social Dimension (S-LCA)\u003c/p\u003e\n \u003cp\u003eFollowing UNEP/SETAC guidelines, the Social Life Cycle Assessment incorporates indicators for job creation, occupational health and safety, and reliance on social infrastructure. The Social Hotspot Index is used to evaluate each pathway\u0026rsquo;s relative social performance [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]. Data sources include the International Labour Organization (ILO), China Statistical Yearbook, and expert interviews.\u003c/p\u003e\n \u003cp\u003e(4) Integration and Weighting\u003c/p\u003e\n \u003cp\u003eAll indicators are normalized to a 0\u0026ndash;1 scale via the min\u0026ndash;max method. Final composite scores are computed using expert-assigned weights: environment (30%), economy (30%), society (20%), and geopolitics (20%).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Construction of the Geopolitical Risk Index (GPRI)\u003c/h2\u003e\n \u003cp\u003eTo assess the external exposure of hydrogen pathways to critical material and technological dependencies, this study constructs a Geopolitical Risk Index (GPRI) system, consisting of three levels of indicators[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e], quantifying the structural risks in supply chain stability for different pathways. The indicator system is outlined in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1 below:\u003c/p\u003e\n \u003cp\u003eThe GPRI is calculated as a weighted sum of normalized sub-indicators:\u003c/p\u003e\n \u003cp\u003eGPRI\u0026thinsp;=\u0026thinsp;\u0026Sigma; (w_i \u0026times; (X_i\u0026thinsp;\u0026minus;\u0026thinsp;X_min) / (X_max\u0026thinsp;\u0026minus;\u0026thinsp;X_min))\u003c/p\u003e\n \u003cp\u003ewhere w_i denotes the AHP-derived weight of each sub-indicator, and the normalization follows a min\u0026ndash;max transformation to ensure comparability. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1 lists the dimension, indicator, unit, and data source for each component (e.g., DoI as % import dependence; HHI as concentration index ranging 0\u0026ndash;1; WGI as governance stability score).\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1: Geopolitical Risk Index (GPRI) Indicator System and Weights\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrimary Dimension\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSecondary Indicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndicator Explanation and Data Source\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCritical Material Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDegree of External Dependence (DoI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImport volume/total demand, Source: Customs General Administration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupplier Concentration (HHI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSource country HHI index, Source: UN Comtrade\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubstitution Difficulty (Patent Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatent portfolio index, Source: WIPO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTechnological Equipment Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDomestic Production Rate/Technology Generation Gap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGap in core technologies between domestic and international, Source: Expert evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy and Institutional Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbon Border Adjustment Mechanism Impact Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstimated cost impact of CBAM, Source: EC Report\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe GPRI is normalized to a 0\u0026ndash;1 range using a weighted average approach, with higher values indicating greater risks. The Analytic Hierarchy Process (AHP) is used for hierarchical weight calculation, employing Saaty\u0026rsquo;s nine-point scale. The consistency ratio (CR) for judgments by five experts is below 0.1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Scenario Simulation and Sensitivity Analysis Design[\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/h2\u003e\n \u003cp\u003eTo assess the impact of policy changes and market uncertainties on sustainability outcomes, three development scenarios are designed:\u003c/p\u003e\n \u003cp\u003e\u0026bull; Scenario 1: Business As Usual (BAU): Continuation of current policies and technological progress trends.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Scenario 2: Geopolitical Conflict (Geo-Risk): Simulates a 50% restriction on critical material imports and a 20% reduction in electrolyzer domestic production.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Scenario 3: Technological Breakthrough (Tech-Advance): Simulates a 40% reduction in catalyst usage and an increase in CCUS efficiency to 95%.\u003c/p\u003e\n \u003cp\u003eThe simulation uses Monte Carlo methods, iterated 1,000 times, with key variables modeled using triangular or normal distributions. Sensitivity analysis is conducted for three key parameters (see Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;2):\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u0026thinsp;\u0026minus;\u0026thinsp;2: Sensitivity Analysis Parameter Settings and Ranges\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter Category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData Source\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTechnological\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrolysis Efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60% \u0026ndash; 75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNational Energy Administration, Industry Data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbon Price\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;200 RMB/ton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNational Carbon Trading Market Data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlatinum Group Metal Price\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLME, USGS Data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe Sobol sensitivity index method is employed to identify the primary influencing factors and their explanatory strength regarding the scores across different dimensions and pathway choices.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Data Sources and Uncertainty Handling\u003c/h2\u003e\n \u003cp\u003eTo capture parameter uncertainty, Monte Carlo simulations with 1,000 iterations were conducted. Key variables included electricity price, electrolyzer efficiency, platinum import dependency, carbon intensity of the grid, and water consumption. The distribution types, parameter ranges, and data sources are listed in Table \u003cspan class=\"InternalRef\"\u003eA3\u003c/span\u003e (Supplementary Materials). Random seeds were fixed to allow full reproducibility of results.\u003c/p\u003e\n \u003cp\u003eThis study employs a multi-source, heterogeneous data fusion strategy as outlined below:\u003c/p\u003e\n \u003cp\u003e\u0026bull; Primary Data: Semi-structured interviews with 15 hydrogen industry chain enterprises, equipment manufacturers, and 5 policy experts to obtain key parameters and scenario judgments.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Secondary Data: Includes the \u0026ldquo;China Hydrogen Industry Development Report\u0026rdquo; (2023), National Bureau of Statistics, UN Comtrade, WIPO, and Ecoinvent databases.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Missing Data Handling: For data with interval uncertainty or historical gaps, triangular fuzzy numbers are used for modeling, with upper and lower bounds and most trustworthy values set through expert calibration.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Analysis Platform: Environmental and social assessments are conducted using OpenLCA v9.0, while economic modeling and scenario simulations are carried out using Python with the @risk module.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Case Study: Evaluation of Hydrogen Development Pathways in China","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Path Selection and System Boundary Definition\u003c/h2\u003e\n \u003cp\u003eTo validate the operability and explanatory power of the LCSA\u0026thinsp;+\u0026thinsp;framework proposed in this study, three typical hydrogen production pathways are selected for case analysis. The system boundary flow diagrams of these three pathways are shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1, illustrating the input-output relationships of each pathway, including:\u003c/p\u003e\n \u003cp\u003e\u0026bull; Green Hydrogen Pathway: Represented by the photovoltaic hydrogen production project in the Hami region of Xinjiang, using renewable energy for water electrolysis to produce hydrogen. This represents the \u0026ldquo;centralized green hydrogen\u0026rdquo; development model in China\u0026rsquo;s resource-rich western regions[\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u0026bull; Blue Hydrogen Pathway: Represented by the coal gasification hydrogen production and Carbon Capture, Utilization, and Storage (CCUS) project in the coal chemical park of Jincheng, Shanxi. This reflects the \u0026ldquo;low-carbon transition of traditional industries\u0026rdquo; pathway in resource-conversion areas[\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u0026bull; Gray Hydrogen Pathway: Represented by small-scale natural gas reforming hydrogen production units, serving as a \u0026ldquo;high-carbon baseline pathway\u0026rdquo; for comparative analysis[\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThis study adopts a cradle-to-gate system boundary, with a functional unit of 1 kg of pure hydrogen. The analysis covers the stages of raw material extraction, transportation, hydrogen production, purification, storage, and transfer. Dynamic simulations are conducted to cover the evolution of different policies and technological pathways from 2025 to 2060.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Data Collection and Scenario Parameter Setting\u003c/h2\u003e\n \u003cp\u003eThis study employs a multi-source data fusion strategy, including:\u003c/p\u003e\n \u003cp\u003e\u0026bull; Field interviews and surveys (e.g., hydrogen enterprises in Hami, Xinjiang, and Jincheng, Shanxi);\u003c/p\u003e\n \u003cp\u003e\u0026bull; National policy documents and industry statistical yearbooks (e.g., \u003cem\u003eChina Hydrogen Industry Development Blue Paper\u003c/em\u003e, \u003cem\u003eEnergy Statistical Yearbook\u003c/em\u003e)[\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e];\u003c/p\u003e\n \u003cp\u003e\u0026bull; International databases (UN Comtrade, IEA, WIPO, CLCD, Ecoinvent)[\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e];\u003c/p\u003e\n \u003cp\u003e\u0026bull; Self-constructed GPRI and MRIO databases.\u003c/p\u003e\n \u003cp\u003eA summary of the key technical and resource parameters for the three pathways is shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1 below:\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1. Water Use by Hydrogen Pathway\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePathway\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProcess water (m\u0026sup3;/kg H₂)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLife-cycle freshwater consumption (m\u0026sup3; H₂O-eq/kg H₂)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNotes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen H₂ (PEM, 100% RE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010\u0026ndash;0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u0026ndash;0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProcess\u0026thinsp;=\u0026thinsp;DI\u0026thinsp;+\u0026thinsp;blowdown; LCA\u0026thinsp;=\u0026thinsp;upstream electricity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlue H₂ (SMR\u0026thinsp;+\u0026thinsp;CCS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u0026ndash;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u0026ndash;1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProcess\u0026thinsp;=\u0026thinsp;boiler\u0026thinsp;+\u0026thinsp;cooling makeup\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrey H₂ (SMR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u0026ndash;0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u0026ndash;1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSame as Blue, w/o CCS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoal-based H₂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u0026ndash;0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.80\u0026ndash;2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher process water; high upstream use\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eFootnotes:\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003ea) Process water excludes upstream embodied water.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003eb) Life-cycle freshwater consumption follows ISO 14046 with AWARE characterization.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003ec) All values are per 1 kg H₂. Historical figures per Nm\u0026sup3; H₂ or per MWh were converted (Appendix A.5).\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003ed) Ranges indicate P5\u0026ndash;P95 from Monte Carlo (N\u0026thinsp;=\u0026thinsp;1000).\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eTo capture the uncertainty in future policy and market changes, three development scenarios are designed:\u003c/p\u003e\n \u003cp\u003e\u0026bull; Scenario 1: Business As Usual (BAU): Continuation of current policies, energy efficiency, and cost trends;\u003c/p\u003e\n \u003cp\u003e\u0026bull; Scenario 2: Geopolitical Risk (Geo-Risk): Simulates conditions such as restrictions on critical material imports and increased international trade friction;\u003c/p\u003e\n \u003cp\u003e\u0026bull; Scenario 3: Technological Breakthrough (Tech-Advance): Sets a scenario with increased electrolysis efficiency, higher domestic production rates, and higher carbon prices.\u003c/p\u003e\n \u003cp\u003eThe ranges of key parameters for each scenario are shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;2 below:\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u0026thinsp;\u0026minus;\u0026thinsp;2: Range of Scenario Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBase Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeo-Risk Scenario\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTech-Advance Scenario\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData Source\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrolysis Efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIEA, China Hydrogen Alliance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbon Price\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 CNY/ton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 CNY/ton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200 CNY/ton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNational Carbon Market Platform\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrolyzer Domestic Production Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustry Interviews\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlatinum Group Metal Price\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLME, USGS Data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Multi-Dimensional Sustainability Assessment Results\u003c/h2\u003e\n \u003cp\u003e(1) Comprehensive Scoring Results\u003c/p\u003e\n \u003cp\u003eAfter synthesizing the results across four dimensions, the sustainability scores for different pathways under each scenario are shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;2. The radar charts display the three pathways across the four dimensions (E-LCA, LCC, S-LCA, GPRI).\u003c/p\u003e\n \u003cp\u003eOverall: The green hydrogen pathway demonstrates the best performance in the environmental and social dimensions; however, due to its high GPRI score (driven by dependence on imported silicon materials and platinum group metals) and substantial initial capital investment, its economic dimension score is relatively low. The blue hydrogen pathway exhibits a comparatively balanced profile, particularly excelling in resource localization and cost control. Nonetheless, its carbon emissions may become unstable when CCUS efficiency fluctuates. The gray hydrogen pathway has an initial cost advantage in the economic dimension but shows significant disadvantages in both the environmental and GPRI dimensions, making it uncompetitive in the long term.\u003c/p\u003e\n \u003cp\u003e(2) Scenario Sensitivity Analysis\u003c/p\u003e\n \u003cp\u003eUnder the Geopolitical Risk Scenario, the LCOH of the green hydrogen pathway increases by 15%, primarily due to the high import concentration of catalysts, which drives the GPRI score up to 0.83. In contrast, the cost fluctuation of the blue hydrogen pathway remains below 5%. Under the Technological Breakthrough Scenario, the LCOH of green hydrogen decreases to CNY 15/kg, approaching the cost of the blue hydrogen pathway, indicating a trend toward cost convergence (see Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;3).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e(3) Attribution Analysis\u003c/p\u003e\n \u003cp\u003eThe Sobol index analysis reveals that, within the green hydrogen pathway, electricity price and the GPRI weighting together account for up to 64% of the variance in the total sustainability score. For blue hydrogen, CCUS efficiency and carbon price emerge as the key sensitivity factors. The gray hydrogen pathway is highly sensitive to changes in carbon taxation, indicating its vulnerability under carbon cost policies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Regional Adaptation and Differentiated Recommendations\u003c/h2\u003e\n \u003cp\u003eSignificant variations exist in the suitability of hydrogen development pathways across regions, driven by differences in resource endowment, industrial base, and policy support. Based on the evaluation results and the structure of geopolitical risk, this study proposes the following regional\u0026ndash;pathway matching recommendations (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;3).\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u0026thinsp;\u0026minus;\u0026thinsp;3. Regional\u0026ndash;Pathway\u0026ndash;Development Strategy Matching Recommendations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRecommended Pathway\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegional Advantages\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePolicy Recommendations\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXinjiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentralized green hydrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh solar irradiance, vast land, low electricity prices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstablish an integrated \u0026ldquo;renewable power direct supply\u0026thinsp;+\u0026thinsp;hydrogen industrial park\u0026rdquo; mechanism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShanxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlue hydrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbundant coal resources, high CCUS feasibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePromote \u0026ldquo;coal\u0026ndash;hydrogen\u0026ndash;capture\u0026rdquo; integrated industry demonstration projects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYangtze River Delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistributed green hydrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConcentrated industrial load, port advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDevelop \u0026ldquo;port hydrogen production\u0026ndash;hydrogen corridor\u0026ndash;industry integration\u0026rdquo; demonstration zones\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuangdong\u0026ndash;Hong Kong\u0026ndash;Macao Greater Bay Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransitional gray \u0026rarr; green hydrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge industrial demand, open policy environment, convenient imports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupport hybrid deployment of gray and green hydrogen; advance green hydrogen price parity mechanisms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eOur results provide empirical grounding for three interrelated propositions:\u003c/p\u003e\n \u003cp\u003e(i) sovereignty is compromised by high import dependence, \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(ii) resilience emerges through diversification, and \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(iii) justice is endangered when socio-economic benefits are unevenly distributed. \u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePolicy implications follow directly: \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e- Strategic stockpiling and domestic innovation enhance sovereignty. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e- Diversification of suppliers and flexible trade relations strengthen resilience. \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e- Redistributive mechanisms, including regional hydrogen subsidies, promote justice.\u003c/p\u003e\n \u003cp\u003eThis case study confirms the operational applicability of the LCSA\u0026thinsp;+\u0026thinsp;framework across multiple pathways, regions, and scenarios. The results indicate that green hydrogen offers long-term environmental and strategic advantages but requires short-term policy intervention to mitigate geopolitical dependency risks; blue hydrogen is a viable transitional option but demands attention to CCUS cost volatility; gray hydrogen should be gradually phased out due to its systemic environmental and security risks.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e5.1 Mechanistic Interpretation of the Evaluation Results\u003c/h2\u003e\n \u003cp\u003eThis study finds significant differences in the four-dimensional sustainability performance of the three hydrogen development pathways. Green hydrogen leads in environmental and social scores but is weakest in geopolitical risk; blue hydrogen is characterized by a \u0026ldquo;balanced\u0026rdquo; profile with lower costs and geopolitical risk; gray hydrogen has systemic disadvantages in carbon emissions and policy sensitivity.\u003c/p\u003e\n \u003cp\u003eThe underlying cause of these differences lies in the structural disparities in resource reliance, technological dependency, and policy constraints. The green hydrogen pathway achieves near-zero carbon emissions but depends heavily on imported photovoltaic-grade silicon wafers and platinum group metal catalysts, yielding a GPRI score of 0.71\u0026mdash;35% higher than blue hydrogen (0.52) and gray hydrogen (0.49). These materials are highly concentrated in a small set of supplier countries, such as South Africa and Russia, with low substitutability and high technological barriers, making them vulnerable to geopolitical conflict, export controls, or technological decoupling.\u003c/p\u003e\n \u003cp\u003eBy contrast, the blue hydrogen pathway benefits from China\u0026rsquo;s domestic coal resources and its emerging CCUS infrastructure, offering a higher degree of energy sovereignty and lower concentration of risk. However, its carbon intensity remains high, and under the future implementation of mechanisms such as the Carbon Border Adjustment Mechanism (CBAM), export costs may rise significantly\u0026mdash;presenting a typical \u0026ldquo;high political resilience\u0026ndash;high carbon risk\u0026rdquo; trade-off. The gray hydrogen pathway demonstrates \u0026ldquo;short-term economic advantage\u0026ndash;long-term environmental disadvantage\u0026rdquo; characteristics: despite the lowest initial costs, rising carbon prices and tightening carbon border measures will erode its competitiveness.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e5.2 Theoretical Contributions and Framework Innovation\u003c/h2\u003e\n \u003cp\u003eThis study proposes and empirically validates an extended Life Cycle Sustainability Assessment framework (LCSA+), incorporating a Geopolitical Risk Index (GPRI) into multi-dimensional energy transition evaluation for the first time\u0026mdash;representing a paradigm shift from the conventional \u0026ldquo;environment\u0026ndash;economy\u0026ndash;society\u0026rdquo; triad toward four-dimensional integration. This framework not only captures ecological and economic performance but also quantifies structural variables such as resource controllability, trade concentration, and institutional stability of partner countries, providing a generalizable approach for assessing energy resilience.\u003c/p\u003e\n \u003cp\u003eBuilding on this, the study advances a \u0026ldquo;Decarbonization\u0026ndash;Sovereignty Trade-off Framework\u0026rdquo;, which reveals that in multi-pathway energy transitions, a singular focus on carbon neutrality can exacerbate external dependency risks, while prioritizing energy security may delay decarbonization milestones. Only by integrating systematic trade-offs into pathway selection can truly strategic sustainability be achieved.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e5.3 Dialogue with Existing Literature\u003c/h2\u003e\n \u003cp\u003eThe findings complement and extend existing scholarship. Gielen et al. have projected hydrogen cost sensitivities using techno-economic models[\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e], highlighting the role of material price volatility, while Le Treut et al. have emphasized institutional vulnerabilities in energy transitions from a political economy perspective[\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]. Neither, however, has operationalized a structured quantitative model to address geopolitical risk. This study fills this gap by developing the GPRI, which quantifies the combined effects of resource dependency, trade concentration, and policy stability\u0026mdash;bridging the methodological divide from \u0026ldquo;qualitative judgment\u0026rdquo; to \u0026ldquo;quantitative assessment.\u0026rdquo;\u003c/p\u003e\n \u003cp\u003eFurthermore, compared with Scholten et al.\u0026rsquo;s discussion of the evolving geopolitical order of hydrogen[\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e], this study focuses more on internal structural contradictions within a national energy transition\u0026mdash;specifically, the tension between external dependency in technology choice and domestic manufacturing capacity. Highlighting these \u0026ldquo;embedded risks\u0026rdquo; provides a strategic tool for the early identification of vulnerabilities in regional energy planning.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e5.4 Policy Recommendations and Pathway Optimization\u003c/h2\u003e\n \u003cp\u003eBased on the case analysis and simulation results, this study offers targeted policy recommendations to help China achieve a balanced development goal of \u0026ldquo;secure\u0026ndash;low-carbon\u0026ndash;affordable\u0026rdquo; hydrogen transition (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1).\u003c/p\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1. Policy Recommendations for Risk Mitigation and Pathway Optimization\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRisk Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTarget Pathway\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRecommended Actions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTarget Indicators\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh material concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen hydrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstablish strategic reserves for PGM and photovoltaic-grade silicon; promote domestic catalyst substitution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReduce DoI and HHI risk coefficients in GPRI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTechnology embargo risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen hydrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupport domestic electrolyzer supply chain integration and open standards; raise domestic manufacturing share of key equipment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncrease localization rate of core equipment to \u0026gt;\u0026thinsp;70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbon border pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlue hydrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlign CCUS with international carbon reduction certification; seek CBAM mutual recognition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower sensitivity to carbon tax; enhance export resilience\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeak energy sovereignty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGray hydrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhase out gray hydrogen in chemical parks; replace with green/blue hydrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImprove regional energy autonomy and accelerate carbon compliance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eAdditionally, a multi-pathway coordination strategy should be implemented according to regional resource endowments: e.g., develop centralized green hydrogen bases in resource-rich northwestern regions; deploy blue hydrogen in coal-producing areas as a transitional option; advance distributed green hydrogen and hydrogen logistics in energy-importing cities\u0026mdash;achieving \u0026ldquo;place-based, diversified\u0026rdquo; development.\u003c/p\u003e\n \u003cp\u003eOur MRIO-based findings resonate with recent analyses suggesting that hydrogen may become the \u0026ldquo;new oil\u0026rdquo; in shaping geopolitical relations (Van de Graaf et al., 2020)[\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]. In line with Pflugmann et al.[\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e], our results indicate that geopolitical risks are not evenly distributed, but rather contingent upon regional industrial structures and policy frameworks. This is further echoed by Eicke[\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e5.5 Limitations and Future Research Directions\u003c/h2\u003e\n \u003cp\u003eDespite methodological innovations and empirical contributions, this study has several limitations:\u003c/p\u003e\n \u003cp\u003e\u0026bull; Data uncertainty: Some geopolitical risk indicators, such as WGI and HHI, involve subjective scoring; larger-scale expert surveys or machine learning could improve robustness.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Model generalizability: LCSA\u0026thinsp;+\u0026thinsp;is currently tested on China; future work should explore cross-country applications to assess applicability and sensitivity.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Policy dynamic response: This study does not simulate nonlinear policy feedbacks (e.g., carbon price shifts, import restrictions); integrating with agent-based modeling (ABM) could enhance dynamic forecasting.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Social dimension depth: The S-LCA component lacks detailed modeling of job quality and regional inequality; future work could incorporate social equity metrics.\u003c/p\u003e\n \u003cp\u003eFuture research should advance toward an energy\u0026ndash;environment\u0026ndash;security\u0026ndash;ethics coupled evaluation framework, developing integrated sustainability\u0026ndash;resilience models for use in global climate governance and strategic energy decision-making.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eBy developing an extended Life Cycle Sustainability Assessment (LCSA+) framework that integrates geopolitical dimensions, this study systematically evaluates the multi-dimensional performance of China\u0026rsquo;s hydrogen development pathways. Three key conclusions emerge:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eUnder current technological conditions, China\u0026rsquo;s hydrogen transition faces a pronounced security\u0026ndash;low-carbon trade-off: the green hydrogen pathway has the lowest carbon intensity (4.2 kg CO₂/kg H₂) but a GPRI (0.71) 35% higher than blue hydrogen, mainly due to reliance on imported critical materials.\u003c/li\u003e\n \u003cli\u003eScenario analysis shows that geopolitical factors could raise the levelized cost of green hydrogen by 23%, an effect well beyond the predictive scope of conventional assessment models, underscoring the limitations of existing transition risk evaluation methods.\u003c/li\u003e\n \u003cli\u003eRegional differentiation analysis reveals that in resource-rich northwestern China, integrating wind/solar hydrogen production with coal chemical industries can reduce the GPRI by 0.15\u0026ndash;0.2, offering empirical support for coordinated regional development.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe study\u0026rsquo;s main academic contributions are threefold: methodologically, the geopolitical risk quantification system fills a critical gap in the political economy dimension of sustainability assessment; theoretically, it validates the nonlinear relationship between technological learning curves and geopolitical risk evolution; practically, the multi-scenario dynamic evaluation framework provides policymakers with a decision-making tool that balances decarbonization goals and energy security. Notably, the study identifies a carbon price threshold of CNY 150/t, beyond which blue hydrogen loses its economic advantage\u0026mdash;offering an important reference point for carbon market design.\u003c/p\u003e\n\u003cp\u003eBased on these findings, the study recommends a three-phase strategic roadmap:\u003c/p\u003e\n\u003cp\u003e\u0026bull;\u0026nbsp;Short term (2025\u0026ndash;2030): prioritize blue hydrogen projects with CCUS integration;\u003c/p\u003e\n\u003cp\u003e\u0026bull;\u0026nbsp;Medium term (2030\u0026ndash;2040): focus on domestic production of key electrolyzer equipment;\u003c/p\u003e\n\u003cp\u003e\u0026bull;\u0026nbsp;Long term (post-2040): transition to a predominantly green hydrogen supply system.\u003c/p\u003e\n\u003cp\u003eFuture research should focus on: (1) developing risk assessment models incorporating firm-level operational data; (2) conducting cross-national comparisons of the geopolitical impacts of hydrogen infrastructure; and (3) exploring the application of artificial intelligence in supply chain risk early warning. The proposed framework is extendable to other clean energy technologies, providing a scientific basis for achieving a \u0026ldquo;just transition\u0026rdquo; in the global energy shift.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not involve any research with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, as the study does not involve human participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Social Science Foundation of China (Grant No. 20XFX016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study are provided in the Supplementary Materials. The simulation codes (Python/Matlab) and mapping tables are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Chufeng Yuan, East China University of Technology, Nanchang, China (Co-Corresponding Author)\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003e2. Yang Tan, Jiangxi Vocational College of Industry \u0026amp; Engineering, Pingxiang, China \u0026nbsp; Email: [email protected]\u003c/p\u003e\n\u003cp\u003e3. Xuguang Yuan, Hunan University, Changsha, China\u003c/p\u003e\n\u003cp\u003e(Co-Corresponding Author)\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003e4. Qiu Tan, East China University of Technology, Nanchang, China\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(Primary Corresponding Author)\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChufeng Yuan (C.Y.): Conceptualization, Methodology, Supervision, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eYang Tan (Y.T.): Data Curation, Formal Analysis, Visualization.\u003c/p\u003e\n\u003cp\u003eXuguang Yuan (X.Y.): Resources, Validation, Project Administration.\u003c/p\u003e\n\u003cp\u003eQiu Tan (Q.T.): Writing \u0026ndash; Original Draft Preparation, Funding Acquisition.\u003c/p\u003e\n\u003cp\u003eAll authors discussed the results and contributed to the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInternational Energy Agency (IEA). 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Sci.\u003c/em\u003e \u003cb\u003e93\u003c/b\u003e, 102847. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.erss.2022.102847\u003c/span\u003e\u003cspan address=\"10.1016/j.erss.2022.102847\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hydrogen transition, Geopolitical Risk Index, Life Cycle Sustainability Assessment, Supply chain resilience, Green hydrogen, Blue hydrogen, Critical material dependency","lastPublishedDoi":"10.21203/rs.3.rs-7505392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7505392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study develops an extended life-cycle sustainability assessment (LCSA) that explicitly integrates geopolitical exposure into multi-criteria evaluation of hydrogen pathways. We construct a Geopolitical Risk Index (GPRI) combining critical-material reliance, trade concentration and supplier governance, and link it with environmental LCA, levelized cost, and social indicators under a unified cradle-to-gate boundary. Using multi-regional input\u0026ndash;output (MRIO) accounting and 1,000-run Monte Carlo simulations across three pathways (photovoltaic electrolysis \u0026ldquo;green\u0026rdquo; hydrogen, coal-based \u0026ldquo;blue\u0026rdquo; hydrogen with carbon capture, utilization and storage [CCUS], and a hybrid portfolio), we examine baseline, conflict-shock and technology-advance scenarios. Results show a systematic decarbonization\u0026ndash;sovereignty trade-off: green hydrogen achieves the lowest life-cycle emissions but exhibits the highest geopolitical vulnerability, driven by an 87% dependence on imported platinum-group catalysts and a 23% cost increase under conflict-related disruption; blue hydrogen demonstrates lower cost volatility (\u0026lt;\u0026thinsp;5%) and stronger supply-chain resilience, albeit with higher residual emissions. A diversified portfolio and domestic electrolyzer manufacturing attenuate risk while preserving climate gains. The framework provides a transparent way to embed geopolitical risk into LCSA and offers decision support for risk-aware hydrogen transitions in emerging economies.\u003c/p\u003e","manuscriptTitle":"Sustainability Assessment of China’s Hydrogen Transition: A Life Cycle–Geopolitical Risk Multi-Criteria Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 15:52:04","doi":"10.21203/rs.3.rs-7505392/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"92914690420808405891343927577925122344","date":"2026-03-19T11:13:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-04T10:44:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106449641240288524172429550619615085056","date":"2026-01-26T10:27:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12507706229069315240698708551415863776","date":"2026-01-23T11:54:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T22:54:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293171483884799097745805706303173540116","date":"2025-09-21T08:07:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-19T05:25:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-10T15:08:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-09T14:35:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-08T11:24:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-01T07:19:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"56edaccb-f1b3-4d04-9abe-2f87a4cdead3","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55295608,"name":"Physical sciences/Energy science and technology"},{"id":55295609,"name":"Earth and environmental sciences/Environmental sciences"},{"id":55295610,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2025-09-30T15:52:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 15:52:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7505392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7505392","identity":"rs-7505392","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}