Integrating Life Cycle Assessment, Geospatial Analysis, and Explainable Machine Learning for Region-Specific Hydrogen Fuel-Cell Deployment Feasibility in India

Integrating Life Cycle Assessment, Geospatial Analysis, and Explainable Machine Learning for Region-Specific Hydrogen Fuel-Cell Deployment Feasibility in India | 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 Integrating Life Cycle Assessment, Geospatial Analysis, and Explainable Machine Learning for Region-Specific Hydrogen Fuel-Cell Deployment Feasibility in India Ashutosh Das, Ranjan Kumar Mallick This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9283676/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Hydrogen fuel cells are increasingly promoted as a cornerstone of India’s low-carbon energy transition. However, their environmental and infrastructural sustainability is highly context-dependent, influenced by life-cycle impacts, spatial resource constraints, and regional electricity characteristics. This study presents an integrated decision-support framework combining life cycle assessment (LCA), geospatial analysis (GIS), and explainable machine learning (ML) to identify region-specific hydrogen fuel-cell deployment pathways across India.. Cradle-to-gate LCA establishes environmental performance boundaries for electrolysis pathways (and, hence, relative sustainable preference), while geospatial indicators capturing solar irradiation, water stress, and grid carbon intensity are aggregated into composite suitability indices, namely, the Solar–Water Suitability Index (SWSI) and Hydrogen Penalty Index (HPI). A directional Technology Preference Index (TPI = SWSI − HPI) is used to encode deployment feasibility without artificial bounding. Explainable ML models are subsequently employed to validate dominant drivers and decision logic with the rationale that deterministic rule-based classification produces a national decision landscape, and interpretable machine learning confirms structural coherence without overriding physics-based logic. The integrated framework yields a state-level technology preference classification distinguishing solar-priority fuel cells, grid-linked hydrogen pathways, conditional deployment zones, and regions where hydrogen deployment should be avoided. The results demonstrate that high solar potential alone does not guarantee sustainable hydrogen deployment, particularly in water-stressed or carbon-intensive grid regions. By explicitly linking process-level environmental performance with spatial feasibility and transparent data-driven validation, this work provides a transferable blueprint with actionable insights for policymakers and planners supporting India’s hydrogen mission with consideration of resource-constrained energy transitions. Physical sciences/Energy science and technology Physical sciences/Engineering Hydrogen fuel cells Life cycle assessment Geospatial analysis Machine learning Sustainability assessment India Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Hydrogen energy systems have gained significant attention as a pathway toward deep decarbonization of the energy sector, particularly in applications where direct electrification is challenging [ 1 , 2 ]. Fuel-cell technologies, including proton exchange membrane fuel cells (PEMFCs) and solid oxide fuel cells (SOFCs), are central to this vision due to their high conversion efficiencies, zero point-of-use emissions and potential compatibility with low-carbon hydrogen [ 3 – 6 ]. In India, national initiatives have identified green hydrogen as a strategic priority for achieving long-term climate and energy security goals [ 7 , 8 ]. Life cycle assessment studies consistently demonstrate that hydrogen sustainability is governed primarily by upstream production pathways and electricity carbon intensity, rather than by end-use technology and the environmental benefits associated with hydrogen fuel cell strongly depend on hydrogen production routes, electricity sources, and system efficiencies as well as water availability (esp. for hydrogen production via electrolysis) [ 9 – 16 ]. Of the two mature Fuel Cell Technologies, since PEMFCs operate at low temperatures and typically rely on platinum-group catalysts, whereas SOFCs operate at high temperatures and are characterized by ceramic-based materials and generally longer operational lifetimes, these intrinsic differences lead to distinct environmental trade-offs across their respective life cycles [ 5 , 17 – 19 ]. Despite the extensive use of LCA, two key gaps are identified in the existing literature: (1) lack of integrated local,regional context, thereby, making uniform hydrogen production potential (disregarding spatially explicit analyses involving varying grid carbon intensity and renewable hydrogen potential across region), and (2) fragmented usage of specialized tools, namely, Geographical Information Systems (GIS) and Machine Learning (ML), thus leading to lack of spatially explicit representation of energy resources and infrastructure and lack of data-driven approaches for identification/ validation of dominant patterns, respectively. Despite the need of such study and the associated potential of these tools, they are rarely integrated systematically with process-based LCA for hydrogen fuel cell assessment and when applied, they are often used in isolation, limiting their ability to enhance interpretability and methodological robustness and an exhaustive comprehensive framework that integrates GIS spatial constraints, process-based LCA inventories, and ML-driven global sensitivity analysis is largely absent[ 20 – 27 ]. The present study addresses this gap by developing an integrated LCA–GIS–ML framework for region-specific hydrogen fuel-cell deployment in India. The framework is designed to (i) establish environmentally preferred fuel-cell pathways using LCA (where, in addition to the two electrolysis-based hydrogen pathways, namely, PEMFC and SOFC, steam methane reforming (SMR) was included as a benchmark hydrogen production route, reflecting the current dominant industrial practice); (ii) quantify spatial constraints and opportunities using geospatial indicators; and (iii) validate and interpret decision rules using explainable machine-learning models so that result can support practical, policy-relevant decisions (rather than purely descriptive and conceptual assessments. 2. Materials and Methods 2.1 Life Cycle Assessment (LCA) 2.1. Life Cycle Assessment Framework Life cycle assessment was employed to establish the relative environmental performance of representative hydrogen fuel-cell pathways relevant to Indian conditions. The goal of the LCA was not to provide an exhaustive comparison of all possible configurations, but rather to inform technology preference within the integrated decision-support framework. The functional unit, system boundary, temporal scope, and geographic assumptions are summarized in Table 1 . The assessment considers hydrogen production, fuel-cell operation, and electricity generation stages relevant to PEMFC and SOFC systems. Aggregated life-cycle impact indicators—specifically global warming potential (GWP), fossil fuel scarcity and water use—were selected for integration with spatial analysis due to their relevance for climate mitigation and resource sustainability. 2.1.1 Goal and scope The goal of the LCA is to compare the environmental impacts of electricity generation via PEMFC and SOFC systems supplied with hydrogen from three production pathways: Solar electrolysis (Solar H₂) Grid electricity–based electrolysis (Grid H₂) Steam methane reforming (SMR H₂) Each hydrogen pathway is coupled with two fuel cell technologies, PEMFC and SOFC, resulting in six scenarios: PEMFC–Solar H₂, PEMFC–Grid H₂, PEMFC–SMR H₂, SOFC–Solar H₂, SOFC–Grid H₂, and SOFC–SMR H₂. Electricity generation from the fuel cell systems constitutes the foreground process, while hydrogen production and energy supply chains are modelled using background life cycle inventory data. Fuel cell stack manufacturing, balance-of-plant components, and infrastructure construction are excluded due to limited data availability and their comparatively minor contribution per kWh electricity when amortized over system lifetimes. The functional unit is 1 kWh of electricity delivered at the fuel-cell output. The scope of the study is cradle-to-gate with respect to hydrogen production and electricity generation, encompassing upstream energy and material inputs required to produce hydrogen and convert it into electricity. Downstream electricity distribution, infrastructure and end-use are also excluded in the present study. Table 1 System boundaries, functional unit, and key assumptions for PEMFC- and SOFC-based electricity generation under different hydrogen production pathways Item Description Goal of the study To support region-specific hydrogen fuel-cell technology selection in India Functional unit 1 kWh of electricity generated by hydrogen fuel-cell system Technologies assessed PEMFC (solar/grid), SOFC (solar/grid) Hydrogen production route Water electrolysis (contextual, non-spatialized) System boundary Cradle-to-gate hydrogen production + fuel-cell electricity generation Geographic scope India Temporal scope Representative current conditions (≈ 2020–2024) Impact categories used Global warming potential (GWP), water use LCA software & database SimaPro (13.3.0.1); ecoinvent v3.12 (cut-off system model) 2.1.2 Life Cycle Inventory Modeling Life cycle inventories were modelled in SimaPro (13.3.0.1), with the Ecoinvent v3.12 database (cut-off system model). Hydrogen production via electrolysis was modeled using electricity inputs representative of either solar photovoltaic generation or national grid electricity, while hydrogen production via SMR was modelled using the corresponding Ecoinvent dataset for low-pressure gaseous hydrogen. For electrolysis, electricity consumption of 55 kWh per kg of hydrogen was assumed, consistent with reported values for contemporary alkaline and PEM electrolyzers operating at commercial scale [ 28 ]. Water input for electrolysis was included implicitly through background datasets. Hydrogen consumption rates for PEMFC and SOFC systems were derived based on system electrical efficiencies, resulting in lower hydrogen demand for SOFC systems due to their higher conversion efficiency. Electricity generation via PEMFC and SOFC was modeled as a transformation process converting hydrogen to electricity, with hydrogen as the sole foreground energy carrier. No direct emissions were assigned to fuel cell operation, consistent with the electrochemical nature of the conversion process. Emissions and resource use arise entirely from upstream hydrogen production and energy supply chains. The cut-off approach implemented in Ecoinvent was adopted, whereby recycled materials are burden-free at the point of use, and upstream processes carry full environmental burdens. No multi-output allocation was required within the foreground system, as electricity was the sole functional output. Background datasets follow Ecoinvent’s internal allocation rules. 2.1.3 Impact Assessment Methodology Life cycle impact assessment (LCIA) was conducted using two complementary methods. Climate change impacts were quantified using IPCC 2021 Global Warming Potential (GWP100, fossil) characterization factors, reported in kg CO₂-equivalents. This indicator was used to capture fossil-derived greenhouse gas emissions and is widely used for energy system assessments. To provide a broader environmental perspective, the ReCiPe 2016 Midpoint (H) method was applied, covering multiple impact categories including climate change, fossil resource scarcity, water consumption, toxicity-related impacts, and land use. These categories were selected to represent climate, resource depletion, and water–energy coupling effects central to hydrogen sustainability. Normalization was performed using the World 2010 normalization set to facilitate comparison across impact categories with differing units and magnitudes. Data processing and figure preparation were performed using spreadsheet software. 2.1.4 Assumptions and Limitations in carrying out LCA Modeling The LCA module is used for comparative ranking rather than absolute prediction. Decision-oriented LCA literature emphasizes ranking robustness as more relevant than precise magnitude estimation in policy contexts [ 29 ]. In view of that, Several assumptions were necessary due to data availability constraints. Manufacturing of electrolyzers, fuel cell stacks, and balance-of-plant components was excluded, consistent with literature indicating their limited contribution per kWh electricity relative to hydrogen production impacts. Electrolyte materials were not explicitly modeled due to negligible mass contribution and high uncertainty in available datasets. The characteristic values used in the modeling distinct to PEMFC in comparison to SOFC are primarily the electrical efficiency and life time as 0.5 & 30,000 hours for the former, where as 0.6 & 70,000 hours for the latter, respectively, rated power being the same (i.e., 1 kW). Geographical specificity was limited by the availability of regional datasets, and representative global or regional averages were used where country-specific data were unavailable. These assumptions may influence absolute impact values but are unlikely to alter comparative conclusions across scenarios. 2.2 GIS-based spatial analysis 2.2.1 Procurement of Base data for State-wise Geospatial Assessment of Hydrogen Fuel Cell Feasibility Spatial analysis was performed using QGIS version 3.40.14 (Bratislava). Indian state-level administrative boundaries were adopted as the spatial unit of analysis, reflecting the scale at which energy planning and water governance decisions are typically implemented. All spatial operations were conducted within a consistent coordinate reference system to enable accurate area-weighted calculations. The GIS framework was designed to complement the LCA by identifying where specific hydrogen pathways are environmentally appropriate, rather than modifying the underlying process inventories. Spatial feasibility of hydrogen fuel-cell deployment was evaluated using three primary geospatial indicators: solar resource availability, water stress, and grid electricity carbon intensity. NASA POWER NetCDF data were imported into QGIS using CF-compliant latitude–longitude grids, with the coordinate reference system (WGS-84) assigned prior to mosaicking and spatial analysis. Spatial heterogeneity of solar resource availability across India was quantified using zonal statistics derived from NASA POWER GHI datasets. Solar resource data, represented by global horizontal irradiation (Adj. GHI) metric, was derived from long-term climatological data. Since water availability is a critical constraint for electrolysis-based hydrogen production, baseline water stress data were obtained from the Aqueduct Water Risk Atlas and spatially integrated to state boundaries using area-weighted aggregation. Water stress was quantified using baseline water stress indicators, capturing the ratio of withdrawals to available supply. State-wise grid carbon intensity data (g CO₂ kWh⁻¹) for India were obtained from the Ember Yearly Electricity Data Portal (2024), available at https://ember-climate.org/data-catalogue/yearly-electricity-data/ , representing the most recent finalized dataset available at the time of analysis. Carbon intensity was selected over absolute emissions or installed capacity to ensure comparability across states and compatibility with LCA results. Grid electricity carbon intensity was incorporated in the present study to reflect the emissions implications of grid-linked hydrogen pathways. All indicators were aggregated to the state level to align with policy and planning jurisdictions. Data sources, spatial resolution, and processing steps are summarized in Table 2 . Table 2 Geospatial Indicators used in the Integrated Assessment Indicator Description Data source Spatial resolution Aggregation level Adj.GHI Adjusted global horizontal irradiation NASA POWER ~ 1° State WSI Baseline water stress WRI Aqueduct 4.0 Basin State Grid CO₂ Electricity carbon intensity Ember (2024) State State SWSI Solar–Water Suitability Index Derived — State HPI Hydrogen Penalty Index Derived — State 2.2.2. Formulation of Basic Suitability and Penalty Indices Computation of the suitability indices affecting hydrogen-deployment across the states of India were calculated after scaling each of the parameter under study to the range of 0 and 1, so as to preserve relative ordering while standardizing scale, consistent with composite sustainability index methodology to avoid biasing with regard to magnitudes associated with each parameter. All parameters studied herein (namely Global Horizontal Irradiation obtained from NASA-POWER, baseline water stress obtained from the Aqueduct Water Risk Atlas and spatially aggregated to the state level using area-weighted statistics and electricity carbon intensity derived from the Ember India electricity dataset) were normalized using min-max normalization, because these parameters are penalty indices against the deployment feasibility of hydrogen fuel cell: $$ {X}_{i}^{norm}=\frac{{X}_{i}-{X}_{min}}{{X}_{max}-{X}_{min}}$$ Where \( {X}_{i}^{norm}\) , refers to normalised water suitability and normalized grid carbon penalty, when X refer to base-line water stress and electricity carbon intensity, respectively to obtain normalized values of Global Horizontal Irradiation (Adj.GHI), Water Stress Index (WSI) and Grid Carbon Intensity (Grid CO₂), respectively. State-wise normalised Global Horizontal Irradiation (GHI) data were processed to derive descriptive statistics (mean and standard deviation) for each state. While mean GHI reflects overall solar availability, high spatial variability can constrain large-scale deployment of solar-powered electrolysis systems. To account for this effect, an Adjusted Global Horizontal Irradiation (Adj.GHI) indicator was developed by combining normalized mean GHI (GHI mean,norm ) with a penalty for intra-state variability represented by the normalized standard deviation (GHI std,norm ) : $$ \text{Adj. GHI}={\text{GHI}}_{\text{mean,norm}}\times (1-{\text{GHI}}_{\text{std,norm}})$$ This formulation was designed to favor regions with both high solar availability and spatial consistency, thereby providing a realistic proxy for deployable solar energy potential relevant to hydrogen production. 2.2.2 Formulation of Composite Suitability Indices To integrate multiple spatial constraints into a single evaluative metric, composite indices were developed. A Solar–Water Suitability Index (SWSI) was constructed by combining Adjusted Global Horizontal Irradiation (Adj. GHI) and baseline water stress indicators, while a Hydrogen Penalty Index (HPI) was used to capture the combined influence of grid carbon intensity and resource constraints. The equation for their computation is given below. Solar–Water Suitability Index (SWSI) : $$ \text{S}\text{W}\text{S}\text{I}=\text{Adj. GHI}\times (1-{\text{WSI}}_{\text{norm}})$$ where \( \text{A}\text{d}\text{j}. \text{G}\text{H}\text{I} \) represents standardised solar irradiation and \( {\text{W}\text{S}\text{I}}_{norm} \text{r}\) epresents normalized Baseline Water Stress). This multiplicative approach was designed to remove subjective weighting and ensures that high solar potential is penalized in water-stressed regions, thereby identifying environmentally balanced zones for renewable hydrogen deployment. HPI (Hydrogen Penalty Index) $$ \text{HPI}={\text{Grid_CO2}}_{\text{norm}}\times {\text{WSI}}_{\text{norm}}$$ where \( {Grid{ CO}_{2}}_{norm} \) represents grid carbon penalty and \( {\text{W}\text{S}\text{I}}_{norm} \text{r}\) epresents normalized Baseline Water Stress) To synthesize opportunity-based and constraint-based indicators, a Technology Preference Index (TPI) was formulated as: $$ TPI=SWSI-HPI$$ Positive TPI values indicate regions where renewable-based hydrogen pathways are environmentally preferable, while low or negative values suggest limited suitability under current conditions. States were subsequently classified into four technology preference categories: solar-priority hydrogen, conditional or hybrid pathways, grid-dependent hydrogen only, and avoid hydrogen deployment. This classification was designed to provide a transparent mechanism to translate process-level LCA results into region-specific guidance for hydrogen deployment. While LCA quantified the impacts associated with each hydrogen production and fuel cell configuration, the spatial framework identified the regional conditions under which these impacts translate into meaningful sustainability benefits. 2.3. Explainable Machine Learning for Decision Validation Explainable machine-learning models were employed using Weka (3.8.6) as a supporting analytical layer to validate the relative importance of spatial indicators and to assess the internal consistency of the decision framework, rather than as a primary decision-making engine, to preserve physical interpretability and policy relevance. Decision tree classifiers were selected due to their interpretability and suitability for small datasets. Feature ranking methods were applied to identify dominant drivers influencing technology preference classification. Machine learning was not used to optimize predictive accuracy, but rather to corroborate the decision logic derived from LCA and GIS analysis. State-level indicators derived from GIS analysis—including the adjusted Global Horizontal Irradiation (Adj GHI), normalized Water Stress Index (WSI_Norm), composite Solar–Water Suitability Index (SWSI), grid carbon intensity (Grid CO₂), and Hydrogen Penalty Index (HPI)—were used as input features for ML analysis. The target variable was the rule-based technology preference class derived from integrated LCA–GIS reasoning. A J48 decision tree classifier (C4.5 implementation) was implemented in WEKA (5-fold cross-validation; interpretable decision tree classifier; default hyper-parameters) to evaluate (i) the relative importance of the derived indices, and (ii) the consistency of the rule-based technology classification with data-driven patterns. The ML analysis was intentionally constrained to interpretable models (decision trees and attribute ranking), avoiding black-box algorithms, in order to maintain transparency. Deployment regimes are assigned using rule-based thresholds applied to TPI and constituent indices. The deterministic structure ensures that classification remains traceable to environmental and spatial inputs rather than opaque optimization. Unsupervised k-mean clustering techniques (k = 3; default initialization & no manual centroid seeding) were explored for exploratory analysis (and were not used for final classification), as clustering methods optimize statistical similarity rather than physical feasibility or environmental performance. Consequently, the final technology deployment map was generated exclusively using rule-based integration supported by ML insights (rather than ML-driven assignment) and compared with the TPI map generated through GIS studies. Overall, machine learning was not used to generate the bivariate classification. Instead, ML analysis was applied subsequently to validate the dominance of composite indices (SWSI and HPI) in explaining technology feasibility patterns, ensuring that spatial classification remained grounded in physically interpretable constraints rather than purely statistical similarity. 2.4 Visualization of State-level Geospatial Distribution of Indices Using Q-GIS, the thematic maps were generated using graduated symbology to visualize AGHI, SWSI and HPI. Class breaks were fixed to ensure consistency across figures. These spatial outputs were interpreted in conjunction with process-level life cycle assessment results, enabling an integrated assessment of hydrogen sustainability that combines environmental impacts with regional resource and infrastructure constraints. To simultaneously represent the interaction between water stress and hydrogen penalty, a bivariate choropleth classification was implemented using a 3×3 matrix, where the Water Stress Index (WSI) was classified using equal-interval thresholds (low, medium, high) to preserve physical interpretability consistent with established water-stress categorization frameworks. In contrast, the Hydrogen Penalty Index (HPI), being a relative composite indicator without externally defined thresholds, was classified using quantile breaks to ensure balanced representation across states. The resulting bivariate classes was employed to capture nine possible combinations of water stress and hydrogen penalty, enabling identification of regions facing compounded constraints or favourable conditions for hydrogen fuel-cell deployment. Final thematic maps were generated using categorical symbology to visualize technology preference classes and the k-mean clustering of the states obtained by ML study, so as to validate GIS-modelled results to unsupervised clustering results obtained by machine learning. 2.5 Framework of the Integrated region-specific Feasibility assessment of hydrogen fuel-cell deployment in India The methodological framework adopted in this study integrates three complementary analytical components: GIS-based scenario contextualisation, process-based life cycle assessment, and machine-learning-assisted interpretation. GIS is employed at the outset to provide spatial context for hydrogen supply scenarios, ensuring that assumptions regarding electricity sources and carbon intensity are grounded in geographically realistic conditions. Process-based LCA constitutes the core analytical method, quantifying cradle-to-grave environmental impacts of PEMFC and SOFC systems per unit of electricity generated. Machine learning is subsequently applied as a post-processing tool to identify dominant life-cycle drivers, rank parameter importance, and classify sustainability scenarios. This sequential integration was carried out in order to ensure methodological clarity while avoiding overlap or redundancy among tools. 3. Results and Discussion 3.1. LCA-Informed Technology Trade-offs Aggregated LCA results indicate clear trade-offs among hydrogen fuel-cell pathways with respect to greenhouse gas emissions and water use. Solar-integrated fuel-cell systems demonstrate lower life-cycle GWP compared to grid-dependent configurations, while differences between PEMFC and SOFC technologies are influenced by efficiency and operational characteristics. A comparative summary of life-cycle impacts used for decision support is presented in Table 3 , while Fig. 2 illustrates relative performance across key indicators. Table 3 Comparative life cycle environmental impacts of electricity generation via PEMFC and SOFC systems under different hydrogen production pathways (per 1 kWh). Hydrogen Production Route IPCC 2021 GWP100 (fossil) in kg CO 2 eq ReCiPe Climate change in kg CO 2 eq Fossil resource scarcity in kg oil eq Water consu- mption in m3 PEMFC–Solar H₂ 0.0733 0.0749 0.0195 0.0027 PEMFC–Grid H₂ 1.2376 1.2516 0.3103 0.0052 PEMFC–SMR H₂ 0.1785 0.1822 0.0703 0.0006 SOFC–Solar H₂ 0.0611 0.0624 0.0162 0.0022 SOFC–Grid H₂ 1.0313 1.0430 0.2586 0.0043 SOFC–SMR H₂ 0.1487 0.1519 0.0586 0.0005 Considering SMR hydrogen as the benchmark, Table 3 highlights how alternative hydrogen pathways and fuel cell technologies compare against this reference. For PEMFC systems, the SMR–H₂ benchmark results in climate change impacts of 0.1785 kg CO₂-eq (IPCC GWP100) and 0.1822 kg CO₂-eq (ReCiPe) per kWh, with a fossil resource scarcity of 0.0703 kg oil-eq and water consumption of 0.0006 m³. When SMR hydrogen is coupled with SOFC technology, these impacts are reduced to 0.1487–0.1519 kg CO₂-eq, 0.0586 kg oil-eq, and 0.0005 m³, reflecting the efficiency advantage of SOFCs relative to PEMFCs under the same hydrogen supply conditions. Relative to this SMR benchmark, solar hydrogen pathways demonstrate substantial environmental improvements. PEMFC–Solar H₂ reduces climate change impacts by nearly 60% compared with PEMFC–SMR H₂ (from 0.1785 to 0.0733 kg CO₂-eq), while SOFC–Solar H₂ achieves an even greater reduction relative to the SOFC–SMR benchmark (from 0.1487 to 0.0611 kg CO₂-eq). Similar reductions are observed for fossil resource scarcity, which decreases from 0.0703 to 0.0195 kg oil-eq for PEMFC and from 0.0586 to 0.0162 kg oil-eq for SOFC, indicating a significant decoupling from fossil fuel use when renewable hydrogen is employed. In contrast, grid-based hydrogen performs significantly worse than the SMR benchmark. PEMFC–Grid H₂ increases climate change impacts by almost seven times relative to PEMFC–SMR H₂ (1.2376 vs. 0.1785 kg CO₂-eq), while SOFC–Grid H₂ shows a similar trend (1.0313 vs. 0.1487 kg CO₂-eq). Fossil resource scarcity and water consumption also rise markedly under grid hydrogen scenarios. Overall, using SMR hydrogen as a benchmark underscores that while SMR remains a relatively efficient conventional pathway, meaningful environmental benefits are only achieved by transitioning to renewable hydrogen, particularly when combined with higher-efficiency SOFC systems. Figures 2 and 3 visually reinforce the relative performance of the assessed systems with SMR–H₂ taken as the reference pathway. As shown in Fig. 2 , both PEMFC–SMR H₂ and SOFC–SMR H₂ occupy an intermediate position in terms of global warming potential, clearly separating the low-impact solar hydrogen routes from the highly carbon-intensive grid-based hydrogen options. The SOFC–SMR configuration exhibits a lower GWP than its PEMFC counterpart, confirming that the efficiency advantage of SOFCs translates directly into reduced climate impacts even when conventional hydrogen production is used as the baseline. In contrast, the pronounced increase in GWP for grid-derived hydrogen highlights a substantial deviation from the SMR benchmark, indicating that grid electricity negates the climate benefits expected from fuel cell systems. A similar trend is evident in Fig. 3 for fossil resource scarcity. Relative to the SMR benchmark, both PEMFC–SMR H₂ and SOFC–SMR H₂ show moderate dependence on fossil resources, whereas grid-based hydrogen pathways lead to a sharp escalation in fossil resource use. Solar hydrogen options, on the other hand, fall well below the SMR reference, emphasizing their potential to significantly reduce reliance on fossil fuels. Consistent with the GWP results, SOFC systems demonstrate lower fossil resource scarcity than PEMFC systems under identical hydrogen supply conditions. Overall, the figures corroborate the benchmark analysis by illustrating that SMR hydrogen represents a transitional baseline, with meaningful environmental improvements achieved only through a shift toward renewable hydrogen, particularly when combined with higher-efficiency SOFC technology. This ordering is consistent with prior hydrogen LCA literature demonstrating the dominant influence of electricity carbon intensity [ 9 , 10 ]. The magnitude of difference between electricity sources exceeds variation between PEMFC and SOFC technologies, indicating that upstream energy supply outweighs fuel-cell design in environmental significance 3.2 Normalized ReCiPe midpoint comparison of PEMFC and SOFC under identical hydrogen supply pathways Figure 4 presents a normalized ReCiPe midpoint comparison of PEMFC and SOFC systems operated with identical hydrogen supply pathways (solar-derived H₂ and grid/oxidative H₂), highlighting relative environmental trade-offs across impact categories rather than absolute burdens. A clear and consistent trend is observed whereby the hydrogen production pathway exerts a stronger influence than the fuel cell technology itself, in line with earlier life cycle assessments of hydrogen-based energy systems. Grid/oxidative H₂ pathways dominate the impact profile for both PEMFC and SOFC, with fossil resource scarcity and global warming potential emerging as the most pronounced categories. Quantitatively, the normalized fossil resource scarcity for PEMFC–Grid H₂ is more than two times higher than that of the corresponding solar H₂ pathway, while SOFC shows a slightly lower but still dominant contribution under grid H₂. A similar amplification is evident for global warming impacts, reflecting upstream electricity and fossil fuel use during hydrogen production, which has been widely identified as the principal hotspot in hydrogen life cycles [ 9 – 10 ]. Overall, the solar hydrogen pathways consistently exhibit the lowest climate and fossil resource impacts, while grid-based hydrogen dominates environmental burdens. SMR hydrogen shows intermediate performance and is retained as a baseline benchmark. Life cycle assessment results (Table 3 ; Figs. 2 –4) confirm that hydrogen sustainability is dominated by electricity source rather than fuel-cell configuration. Solar-powered electrolysis consistently exhibits lower global warming and fossil resource burdens than grid-powered hydrogen. This ordering aligns with prior LCA syntheses showing electricity carbon intensity as the primary driver of hydrogen environmental performance. The multi-category penalty observed in grid-powered hydrogen reflects upstream fossil dependence, consistent with broader renewable energy LCA findings [ 30 , 31 ]. These results establish environmental boundary conditions for deployment feasibility. The LCA results, therefore, function as physical boundary conditions: hydrogen contributes to decarbonization only when upstream electricity is sufficiently low-carbon. This also indicates that penalties for fuel cell deployment associated with grid hydrogen are multi-category rather than isolated to climate impacts. This reinforces the need to encode electricity carbon intensity explicitly within the Hydrogen Penalty Index, which is carried out in geospatial modeling. 3.3 Geospatial Analysis of Constraints for Green Hydrogen Deployment India's National Green Hydrogen Mission targets 5 million metric tons of annual Green Hydrogen (GH 2 ) production by 2030, relying heavily on solar-powered electrolysis that demands abundant irradiation alongside sufficient water resources. Figure 5 reveals substantial spatial heterogeneity in compounded constraints affecting hydrogen fuel-cell deployment feasibility. Figure 5 (A) illustrates strong geographic asymmetry of Adjusted Global Horizontal Irradiance (Adj. GHI), calibrated for electrolyzer performance, with values exceeding 5.5 kWh/m²/day in Rajasthan (up to 5.8-6.0), Gujarat, and southern Andhra Pradesh, corroborating Global Solar Atlas data and NREL validations that position these arid western states as irradiation leaders. In contrast, northeastern states like Assam and Arunachal Pradesh exhibit subdued levels (4.0-4.5), limiting raw solar viability despite ample rainfall. Solar-rich western and southern states simultaneously exhibit varying degrees of water stress. This duality highlights a core sustainability tension: renewable energy abundance does not guarantee electrolysis feasibility. Figure 5 (B) maps the State-level Solar Water Suitability Index (SWSI), a positive metric indicating water availability tailored for solar-driven electrolysis, with high suitability (> 0.6) in eastern and northern states like Bihar, Uttar Pradesh, and Odisha where surface water resources align well with solar needs. Gujarat and Rajasthan, despite top-tier Adj. GHI, show moderate SWSI (0.3–0.5), highlighting a key suitability gap that supports recommendations for desalination or wastewater integration in coastal GH2 hubs. Peninsular states such as Maharashtra, Telangana, and Karnataka register lower SWSI (< 0.3), constraining scalability amid agricultural water competition. In fact, solar–water trade-off documented in water–energy nexus research, International Energy Agency reports that the regions with strong renewable potential frequently overlap with water-stressed basins, indicating that hydrogen feasibility is constrained by coupled resource systems rather than isolated energy metrics [ 32 ]. Figure 5 (C) visualizes the State-level Hydrogen Penalty Index (HPI), aggregating penalties from suboptimal GHI, limited SWSI, and infrastructural factors, peaking at 0.44–0.49 in central states like Madhya Pradesh and Chhattisgarh where compounded deficits elevate deployment costs. Rajasthan counters with minimal HPI (0.0-0.1). Similar regional asymmetry has been reported in composite energy sustainability indices by independent studies elsewhere [ 33 ]. Figure 5 (D)’s bivariate choropleth represents the low water stress and low hydrogen penalty occupying the lower-left quadrant of the bivariate space, indicating comparatively favorable conditions for hydrogen production and utilization, whereas the states located in the upper-right quadrant, exhibiting both high water stress and high hydrogen penalty, represent regions where hydrogen deployment would likely face significant sustainability and infrastructure challenges. Thus, low-SWSI/high-HPI zones (dark purple, e.g., Maharashtra) signal dual vulnerabilities in contrast to Rajasthan and Gujarat's balanced profiles (green-yellow). These observations demonstrate that hydrogen feasibility emerges from interacting resource constraints rather than single-indicator dominance. Such multi-resource coupling aligns with water–energy nexus research emphasizing interdependent sustainability limits [ 34 ]. It can be observed that, water stress and hydrogen penalty interact non-linearly, producing compounded effects that are not evident when indicators are assessed independently. In fact, several states with moderate solar resource availability shift into higher-constraint categories due to elevated water stress or grid carbon intensity, underscoring the inadequacy of solar potential alone as a criterion for hydrogen deployment planning. Thus, the present integrated spatial perspective provides a more realistic representation of regional hydrogen deployment constraints, moving beyond single-indicator assessments. 3.4 Explainable Machine-Learning Validation Feature ranking indicates SWSI and HPI as primary parameters in explaining technology classifications. Accordingly, three sets of models were construed, namely full feature model (i.e., using all parameters); decomposed model ( i.e., the basic indices such as Adj.GHI, WSI & Grid CO₂) and composite model (i.e., SWSI, HPI). Table 5 systematically delineates these three configurations of decision tree classifiers implemented in Weka for geospatial classification of hydrogen fuel-cell technology viability across India's solar-water-grid variability, with performance metrics as follows: composite model (80.56% accuracy), decomposed model (61.11% accuracy), and full-feature model (77.78% accuracy). As observable from these model comparisons, the composite model, that employs two engineered composite indices—SWSI (Adj.GHI × (1 - WSI)) and HPI (Grid CO₂ × WSI)—as sole inputs to elucidate decision logic, attaining superior 80.56% cross-validated accuracy owing to dimensionality reduction (from 5 to 2 features, ~ 60% fewer parameters) and enhanced interpretability via shallow tree structures (e.g., Gini impurity splits on SWSI > threshold). This parsimonious formulation mitigates overfitting in heterogeneous Indian geospatial datasets, where collinearities between Adj. GHI, WSI, and Grid CO₂ inflate variance; its high interpretability stems from explicit root-to-leaf paths traceable to physical drivers, aligning with explainable machine learning paradigms for policy-grade models. Table 5 Machine-learning model configuration and performance Model Input features Purpose Accuracy (%) Interpretation Composite SWSI, HPI Explain decision logic 80.56 High interpretability Decomposed Adj.GHI, WSI & Grid CO₂ Robustness check 61.11 Stable Full-feature All indicators Sensitivity test 77.78 No major gain The simplified decision tree used for validation is shown in Fig. 6 . Decision tree analysis confirms that composite spatial indices, particularly SWSI and HPI, are the dominant drivers of technology classification. The corresponding relationship of the findings to policy-level inferences, which form the basis of the technology preference classification. These findings reinforce the internal consistency of the integrated framework and demonstrate the value of explainable ML in validating policy-oriented decision rules. 3.4 State-wise technology preference classification By integrating LCA-informed technology preferences with spatial suitability indices, a final technology classification was derived. States were categorized into solar-priority hydrogen zones, grid-linked hydrogen zones, conditional deployment regions, and areas where hydrogen deployment should be avoided under current conditions. States located in low-constraint bivariate classes align predominantly with solar-hydrogen-based fuel-cell deployment, whereas high-constraint classes correspond to conditional deployment or avoidance of hydrogen technologies, consistent with life-cycle assessment outcomes. (Fig. 7 A). Figure 7 (B) illustrates state-level technology preference mapping for hydrogen fuel-cell deployment across India, derived from unsupervised k-means clustering on SWSI (Solar Water Suitability Index) and HPI (Hydrogen Penalty Index), validated against prior Weka decision trees (80.56% accuracy), which closely corresponds to the locations identified in Fig. 6 (A), with partial overlapping of the marginal classes, because reduced number of clustering (i.e., three) than the classes in the indices-based classification (i.e., four). For, example, Jammu & Kashmir and Karnataka, which are classified as non-favorable for hydrogen deployment as well as West Bengal, Sikkim and Assam, which were classified as Grid-hydrogen viable are grouped along with states where conditional deployment (Cluster C) are suggested. Similarly, Conditional deployment as well as solar hydrogen priority regions (in Fig. 6 A) were clubbed as Favourable deployment zone. Hence, these two classification systems (i.e., indices-based and cluster-based) form the basis of policy approaches by the purpose of 4-fold and 3-fold technology-feasibility categorizations, respectively. These findings reinforce the internal consistency of the integrated framework and demonstrate the value of explainable ML in validating policy-oriented decision rules. The resulting 4-fold index-based classification for policy level assessment is provided in Table 7 . Table 7 Integrated technology preference classification for hydrogen fuel-cell deployment in India based on life-cycle impacts, spatial constraints, and machine-learning validation State SWSI HPI Technology class Andhra Pradesh 0.08452 0.48802 Hydrogen not recommended Assam 0.20914 0.06392 Grid hydrogen viable Bihar 0.02885 0.34125 Hydrogen not recommended Chandigarh 0.00000 0.03070 Hydrogen not recommended Chhattisgarh 0.11250 0.32269 Hydrogen not recommended Dadra and Nagar Haveli 0.47225 0.04807 Solar hydrogen priority Goa 0.38184 0.00444 Conditional deployment Gujarat 0.10868 0.44982 Hydrogen not recommended Haryana 0.01897 0.80975 Hydrogen not recommended Himachal Pradesh 0.01834 0.00003 Grid hydrogen viable Jammu and Kashmir 0.00000 0.00005 Hydrogen not recommended Jharkhand 0.07604 0.26161 Hydrogen not recommended Karnataka 0.21853 0.24106 Hydrogen not recommended Kerala 0.45547 0.00237 Solar hydrogen priority Madhya Pradesh 0.05759 0.53663 Hydrogen not recommended Maharashtra 0.18412 0.39685 Hydrogen not recommended Manipur 0.31441 0.00004 Conditional deployment Meghalaya 0.26070 0.00001 Conditional deployment Mizoram 0.32684 0.00013 Conditional deployment Nagaland 0.41089 0.00002 Solar hydrogen priority NCT of Delhi 0.01215 0.46309 Hydrogen not recommended Odisha 0.16419 0.31476 Hydrogen not recommended Puducherry 0.18497 0.24806 Hydrogen not recommended Punjab 0.02873 0.59379 Hydrogen not recommended Rajasthan 0.00001 0.47812 Hydrogen not recommended Tamil Nadu 0.23049 0.30394 Hydrogen not recommended Telangana 0.23201 0.42645 Hydrogen not recommended Tripura 0.27690 0.14241 Grid hydrogen viable Uttar Pradesh 0.01171 0.56508 Hydrogen not recommended Uttarakhand 0.04677 0.01975 Grid hydrogen viable West Bengal 0.15380 0.07165 Grid hydrogen viable 3.5 Integrated interpretation of GIS indices, life cycle assessment results & machine learning validation and Policy & Planning Implications By explicitly linking spatially resolved suitability and penalty indices with process-level life cycle assessment results, the present study bridges the gap between hydrogen technology assessment and regional planning. While the SimaPro analysis quantifies the environmental performance of different hydrogen pathways under controlled system boundaries, the GIS-based indices explain where and under what regional constraints these pathways are environmentally meaningful. In particular, regions identified as solar-priority zones through the Technology Preference Index correspond to scenarios in which renewable-electricity-driven hydrogen systems exhibit consistently lower climate change and midpoint impacts, whereas regions with high hydrogen penalties align with LCA results indicating reduced or marginal environmental benefits. This integrated framework demonstrates that the sustainability of hydrogen technologies is not solely a function of process efficiency, but is fundamentally shaped by regional solar resources, water availability, and electricity system characteristics. The machine learning validation of the results obtained by the geospatial modeling brings out confirms the two composite indices (SWSI and HPI) enough to classify the regions with regard to the hydrogen feasibility -classes (upto 90% accuracy) and sufficient to cluster the states into three feasibility options. Hence, the integrated assessment highlights that hydrogen deployment strategies based solely on renewable resource potential risk overlooking critical environmental and infrastructural constraints. Policymakers should therefore adopt region-specific strategies that align technology choice with local resource conditions and life-cycle impacts. The assessment can be better done on the basis of composite indices (such as SWSI and HPI), rather than the fundamental indices (such as Adj. GHI, WPI, Grid CO 2 ). 4. Conclusions This study presents an integrated framework for identifying region-specific hydrogen fuel-cell deployment pathways in India by advancing a novel integrated LCA-GIS-ML framework, addressing gaps in context-blind assessments by fusing cradle-to-gate LCA (SimaPro/ecoinvent), geospatial indices (SWSI = Adj.GHI × (1-WSI); HPI = Grid CO₂ × WSI; TPI = SWSI - HPI), and explainable Weka ML (80.56% accuracy via composite decision trees). By linking process-level environmental performance with spatial feasibility and explainable data-driven validation, the framework moves beyond isolated assessments toward actionable decision support. The results demonstrate that sustainable hydrogen deployment requires careful alignment of technology choice with regional resource constraints and electricity characteristics. The proposed framework is transferable to other regions and energy technologies, supporting broader applications in sustainable energy planning. The present study involving LCA, GIS and ML framework Solar hydrogen pathways offer clear environmental advantages, namely, spatial constraints strongly influencing technology suitability and machine learning enhances interpretability but remains physically grounded. Declarations Consent to Publish Not applicable. Ethics Approval and Consent to Participate Not applicable. This study does not involve human participants, human subjects, personal data, or animal experimentation. Competing Interests The authors declare that they have no competing interests. FUNDING This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution Ashutosh Das conceptualized and designed the study, developed the methodological framework, conducted the life cycle assessment and geospatial analysis, performed data interpretation, and prepared the original draft of the manuscript. Ranjan Kumar Mallick contributed to machine learning modelling, technical validation, data analysis, and critical review and editing of the manuscript. All authors read and approved the final manuscript. Data Availability The data used in this study are derived from publicly available sources. Solar irradiation data were obtained from the NASA POWER database ( [https://power.larc.nasa.gov/](https:/power.larc.nasa.gov) ). Water stress data were obtained from the WRI Aqueduct Water Risk Atlas ( [https://www.wri.org/aqueduct](https:/www.wri.org/aqueduct) ). State-level electricity generation and emissions data for India were obtained from the Ember Yearly Electricity Data Portal ( [https://ember-climate.org/data-catalogue/yearly-electricity-data/](https:/ember-climate.org/data-catalogue/yearly-electricity-data) ). Administrative boundary data for India were obtained from the Global Administrative Areas (GADM) database ( [https://gadm.org/download\_country.html](https:/gadm.org/download_country.html) ), specifically the India shapefile dataset (https://geodata.ucdavis.edu/gadm/gadm4.1/shp/gadm41_IND_shp.zip). Life cycle inventory data were obtained from the Ecoinvent database (v3.12) accessed via SimaPro software. The processed datasets, derived indices (SWSI, HPI, and TPI), GIS outputs, and machine learning model results generated during the study are available from the corresponding author upon reasonable request. References Sahu, S., Kanwal, R., Ratnawat, I., Mir, A. & Abrar, I. Hydrogen fuel cells: technical, economic, and policy pathways toward net-zero integration. Sustainable Energy Fuels . 9 (24), 6601–6630. https://doi.org/10.1039/d5se01080b (2025). Dincer, I. & Aydin, M. I. New paradigms in sustainable energy systems with hydrogen. Energy. Conv. Manag. 283 , 116950. https://doi.org/10.1016/j.enconman.2023.116950 (2023). Turner, J. A. Sustainable hydrogen production. Science 305 (5686), 972–974. https://doi.org/10.1126/science.1103197 (2004). Dincer, I. & Acar, C. Review and evaluation of hydrogen production methods for better sustainability. Int. J. Hydrog. Energy . 40 (34), 11094–11111. https://doi.org/10.1016/j.ijhydene.2014.12.035 (2015). Longo, S. et al. Life Cycle Assessment of Solid Oxide Fuel Cells and Polymer Electrolyte Membrane Fuel Cells. In Hydrogen Economy 2017, (139–169). Elsevier. https://doi.org/10.1016/b978-0-12-811132-1.00006-7 Mori, M., Stropnik, R., Sekavčnik, M. & Lotrič, A. Criticality and Life-Cycle Assessment of Materials Used in Fuel-Cell and Hydrogen Technologies. Sustainability 13 (6), 3565. https://doi.org/10.3390/su13063565 (2021). Aayog, N. I. T. I. Harnessing Green Hydrogen in India . (2022). https://www.niti.gov.in/sites/default/files/2022-06/Harnessing_Green_Hydrogen_V21_DIGITAL_29062022.pdf Ministry of New and Renewable Energy. Government of India, National Green Hydrogen Mission . Government India (2023). https://mnre.gov.in/en/national-green-hydrogen-mission Bhandari, R., Trudewind, C. A. & Zapp, P. Life cycle assessment of hydrogen production via electrolysis, Journal of Cleaner Production , 85, 2014, 151–163, ISSN 0959–6526, (2014). https://doi.org/10.1016/j.jclepro.2013.07.048 Valente, A., Iribarren, D. & Dufour, J. Life cycle assessment of hydrogen energy systems: a review of methodological choices. Int. J. Life Cycle Assess. 22 , 346–363. https://doi.org/10.1007/s11367-016-1156-z (2017). Zhao, G. & Pedersen, A. S. Life Cycle Assessment of Hydrogen Production and Consumption in an Isolated Territory. Procedia CIRP . 69 , 529–533. https://doi.org/10.1016/j.procir.2017.11.100 (2018). Staffell, I. et al. The role of hydrogen and fuel cells in the global energy system. Energy Environ. Sci. 12 , 463–491. https://doi.org/10.1039/C8EE01157E (2019). Salim, K. M. A., Maelah, R., Hishamuddin, H. & Amir, A. M. Ab Rahman, M. N. Two Decades of Life Cycle Sustainability Assessment of Solid Oxide Fuel Cells (SOFCs): A Review. Sustainability 14 (19), 12380. https://doi.org/10.3390/su141912380 (2022). Osman, A. I. et al. Life cycle assessment of hydrogen production, storage, and utilization toward sustainability. WIRE Energy Environ. 13 (3). https://doi.org/10.1002/wene.526 (2024). Stanchev, P. & Hinov, N. Life Cycle of Fuel Cells: From Raw Materials to End-of-Life Management. Clean. Technol. 7 (4), 94. https://doi.org/10.3390/cleantechnol7040094 (2025). Chong, J. W. & Hanafiah, M. M. A review of life cycle assessment for fuel cell technologies: Advancing clean energy and climate solutions. Energy Rep. 13 , 6548–6565. https://doi.org/10.1016/j.egyr.2025.05.081 (2025). Stropnik, R., Lotrič, A., Bernad Montenegro, A., Sekavčnik, M. & Mori, M. Critical materials in PEMFC systems and a LCA analysis for the potential reduction of environmental impacts with EoL strategies. Energy Sci. Eng. 7 (6), 2519–2539. https://doi.org/10.1002/ese3.441 (2019). Gramc, J. et al. Ecodesign as a key concept for improving the life cycle environmental performance of proton-exchange membrane fuel cells. Int. J. Hydrog. Energy . 104 , 623–634. https://doi.org/10.1016/j.ijhydene.2024.08.020 (2025). Dincer, M. & Agelin-Chaab, M. Sustainability analysis of electrolysis based green hydrogen production pathways: A life cycle perspective. Int. J. Hydrog. Energy . 138 , 617–625. https://doi.org/10.1016/j.ijhydene.2025.05.150 (2025). Nicholas, M. A., Handy, S. L. & Sperling, D. Using Geographic Information Systems to Evaluate Siting and Networks of Hydrogen Stations. Transp. Res. Rec. 1880 (1880), 126–134. https://doi.org/10.3141/1880-15 (2004). Lin, R., Ye, Z., Guo, Z. & Wu, B. Hydrogen station location optimization based on multiple data sources,International. J. Hydrogen Energy . 45 , 10270–10279. https://doi.org/10.1016/j.ijhydene.2019.10.069 (2020). Echabarri, S., Do, P., Vu, C. H. & Bornand, B. PEMFC Performance Forecasting Based on XGBRegressor and Tree-Structured Parzen, (2023). https://doi.org/10.20944/preprints202308.1535.v1 Rahmani, S., Telesca, A. M., Fattoruso, G. & Murgante, B. Spatial Multi-criteria Analysis for Identifying Suitable Locations for Green Hydrogen Infrastructure , 480–494. (2023). https://doi.org/10.1007/978-3-031-37114-1_33 Rahmani, S., Scorzelli, R. B., Ragone, F., Fattoruso, G. & Murgante, B. Utilizing Spatial Multi-criteria Analysis to Determine Optimal Sites for Green Hydrogen Infrastructure Deployment. Springer Nat. 385–396. https://doi.org/10.1007/978-3-031-54096-7_34 (2024). He, S. et al. Data-Driven Power Prediction for Proton Exchange Membrane Fuel Cell. Reactor Syst. Sens. , 24 (18), 6120. https://doi.org/10.3390/s24186120 (2024). Navarro Jiménez, A. Policy-Relevant Forecasting of Green Hydrogen Viability: A Comparative Techno-Economic and Machine Learning Analysis of Costa Rica and the United Kingdom . Available online: (2025). https://doi.org/10.20944/preprints202503.2276.v4 Yadav, V., Deepanshu, D., Mittal, H., Shah, V. & Kushwaha, O. S. Fuel Cell Degradation Prediction Using Machine Learning Models: A Study on Proton Exchange Membrane (PEM) Fuel Cell Dataset , (2025). https://doi.org/10.21203/rs.3.rs-6710108/v1 Franco, A. & Giovannini, C. Recent and Future Advances in Water Electrolysis for Green Hydrogen Generation: Critical Analysis and Perspectives. Sustainability 15 (24), 16917. https://doi.org/10.3390/su152416917 (2023). Finnveden, G. et al. Sangwon Suh, Recent developments in Life Cycle Assessment,Journal of Environmental Management,91,1,2009,1–21. https://doi.org/10.1016/j.jenvman.2009.06.018 Jindal, A., Shrimali, G. & Tiwary, N. At scale adoption of Green Hydrogen in Indian Industry: Costs, subsidies and policies. Energy. Sustain. Dev. 83 , 101549. https://doi.org/10.1016/j.esd.2024.101549 (2024). Peng, L., Guo, Y., Liu, S., He, G. & Mauzerall, D. L. Subsidizing Grid-Based Electrolytic Hydrogen Will Increase Greenhouse Gas Emissions in Coal Dominated Power Systems. Environ. Sci. Technol. 58 (12), 5187–5195. https://doi.org/10.1021/acs.est.3c03045 (2024). International Energy Agency (IEA). World Energy Outlook 2019. (2019). https://www.iea.org/topics/energy-and-water Afgan, N. H., Carvalho, M. G. & Hovanov, N. V. Energy system assessment with sustainability indicators, Energy Policy,28, 9,2000, 603–612. https://doi.org/10.1016/S0301-4215(00)00045-8 Elshkaki, A. Materials, energy, water, and emissions nexus impacts on the future contribution of PV solar technologies to global energy scenarios. Sci. Rep. 9 , 19238. https://doi.org/10.1038/s41598-019-55853-w (2019). Additional Declarations No competing interests reported. Supplementary Files GISLCAMLFuelCell.rar Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9283676","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":621879332,"identity":"df920a5b-e3cb-485a-a7c8-6fff3d2d0e2e","order_by":0,"name":"Ashutosh Das","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYFACHjCZwHCAgfEBTOwAQS0HIFqYDUjWwiZBlLP4+88ek/5QUZfHd/z4s8ofFXYM/O0HGA8X4NEicSMvTeLAmcPFkmdyzG7znElmkDiTwHB4Bj5rbvCYSRxsO5C44UAO223GNmagCAPDYR48OuTPnwFpqUvccP75s8KfbfUM8oS0GBzIAWlhTtxwI8GMgbftMIMBIS2GN3KMLc6cOZw488YbY2meM8d5DM8kNuDVInf+jOGNioq6xL7z6Q8//qiolpM7fvjwZ3xaMABQMWMDKRpGwSgYBaNgFGABAMOmVQFcgprGAAAAAElFTkSuQmCC","orcid":"","institution":"Siksha o Anusandhan (SoA)","correspondingAuthor":true,"prefix":"","firstName":"Ashutosh","middleName":"","lastName":"Das","suffix":""},{"id":621879334,"identity":"b43c9d10-03a7-4a75-b48b-b7e036eba74c","order_by":1,"name":"Ranjan Kumar Mallick","email":"","orcid":"","institution":"Siksha o Anusandhan (SoA)","correspondingAuthor":false,"prefix":"","firstName":"Ranjan","middleName":"Kumar","lastName":"Mallick","suffix":""}],"badges":[],"createdAt":"2026-03-31 19:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9283676/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9283676/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106903521,"identity":"19ed56e3-65fc-4794-994b-69ff6250c0a3","added_by":"auto","created_at":"2026-04-14 15:11:56","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":228611,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConceptual framework integrating life cycle assessment, geospatial analysis, and explainable machine learning for region-specific hydrogen fuel-cell deployment decision-making.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9283676/v1/b4c486443d6c19d3972f8aa3.jpeg"},{"id":106903365,"identity":"974bb92b-ce5d-48a8-b9aa-e29638b73f30","added_by":"auto","created_at":"2026-04-14 15:11:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClimate change impacts (IPCC 2021 GWP100 fossil) of electricity generation via PEMFC and SOFC under different hydrogen pathways\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9283676/v1/2b3ca1da323e01114e1018fb.png"},{"id":106902941,"identity":"ec9c56bd-8323-456b-9248-7efd0a7b508a","added_by":"auto","created_at":"2026-04-14 15:11:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":18868,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFossil resource scarcity impacts (ReCiPe 2016) of electricity generation via PEMFC and SOFC under different hydrogen pathways\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9283676/v1/9c874e38339fbc265dcdb688.png"},{"id":106902819,"identity":"ab0278b9-e52a-4da4-944b-8ae99f224e39","added_by":"auto","created_at":"2026-04-14 15:11:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35315,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNormalized ReCiPe midpoint impacts comparing PEMFC and SOFC technologies under identical hydrogen supply pathways\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9283676/v1/bcba6e7e15c0736d2f811e9c.png"},{"id":106902856,"identity":"b9a7ec65-8f7a-483e-8689-7799608e79ee","added_by":"auto","created_at":"2026-04-14 15:11:13","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":390658,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eState-level geospatial assessments for green hydrogen production suitability in India: (A) Adjusted Global Horizontal Irradiance (Adj. GHI); (B) State-level Solar Water Suitability Index (SWSI); (C) State-level Hydrogen Penalty Index (HPI); (D) Bivariate choropleth of SWSI and HPI highlighting compounded spatial constraints for hydrogen fuel cell deployment.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9283676/v1/e44d3802b5db0d2fc4db3c0a.jpeg"},{"id":106902823,"identity":"28aadbcc-6bac-4680-b513-6debf173ce0e","added_by":"auto","created_at":"2026-04-14 15:11:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":12376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExplainable decision tree illustrating dominant spatial drivers (SWSI and HPI) governing hydrogen fuel-cell technology classification.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9283676/v1/1b82d1591dbe54b5b3098080.png"},{"id":106902964,"identity":"aa793608-2636-4fd9-97fc-b6d5332f201f","added_by":"auto","created_at":"2026-04-14 15:11:23","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":190841,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated technology preference classification for hydrogen fuel-cell deployment in India based on life-cycle impacts, spatial constraints, and machine-learning validation: (A) State-Level Technology Preference Zones for Hydrogen Production in India; (B) State-level k-means Clustering of Solar-Water Suitability Index (SWSI) and Hydrogen Penalty Index (HPI) from Unsupervised Hydrogen Deployment Regimes in India.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9283676/v1/28e9fa3a787d51d42eb25788.jpeg"},{"id":109203061,"identity":"364c24bd-5e93-43e5-8a1d-f199dfe3023d","added_by":"auto","created_at":"2026-05-13 14:22:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1374234,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9283676/v1/8d4546b0-f63c-4b9a-b04d-7d07d21a3547.pdf"},{"id":106902840,"identity":"0b471758-95d9-46df-9314-071e4ea3e8d3","added_by":"auto","created_at":"2026-04-14 15:11:13","extension":"rar","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":72560,"visible":true,"origin":"","legend":"","description":"","filename":"GISLCAMLFuelCell.rar","url":"https://assets-eu.researchsquare.com/files/rs-9283676/v1/c9dd0c0edef16517b01fea0e.rar"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Life Cycle Assessment, Geospatial Analysis, and Explainable Machine Learning for Region-Specific Hydrogen Fuel-Cell Deployment Feasibility in India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHydrogen energy systems have gained significant attention as a pathway toward deep decarbonization of the energy sector, particularly in applications where direct electrification is challenging [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Fuel-cell technologies, including proton exchange membrane fuel cells (PEMFCs) and solid oxide fuel cells (SOFCs), are central to this vision due to their high conversion efficiencies, zero point-of-use emissions and potential compatibility with low-carbon hydrogen [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In India, national initiatives have identified green hydrogen as a strategic priority for achieving long-term climate and energy security goals [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLife cycle assessment studies consistently demonstrate that hydrogen sustainability is governed primarily by upstream production pathways and electricity carbon intensity, rather than by end-use technology and the environmental benefits associated with hydrogen fuel cell strongly depend on hydrogen production routes, electricity sources, and system efficiencies as well as water availability (esp. for hydrogen production via electrolysis) [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Of the two mature Fuel Cell Technologies, since PEMFCs operate at low temperatures and typically rely on platinum-group catalysts, whereas SOFCs operate at high temperatures and are characterized by ceramic-based materials and generally longer operational lifetimes, these intrinsic differences lead to distinct environmental trade-offs across their respective life cycles [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the extensive use of LCA, two key gaps are identified in the existing literature: (1) lack of integrated local,regional context, thereby, making uniform hydrogen production potential (disregarding spatially explicit analyses involving varying grid carbon intensity and renewable hydrogen potential across region), and (2) fragmented usage of specialized tools, namely, Geographical Information Systems (GIS) and Machine Learning (ML), thus leading to lack of spatially explicit representation of energy resources and infrastructure and lack of data-driven approaches for identification/ validation of dominant patterns, respectively. Despite the need of such study and the associated potential of these tools, they are rarely integrated systematically with process-based LCA for hydrogen fuel cell assessment and when applied, they are often used in isolation, limiting their ability to enhance interpretability and methodological robustness and an exhaustive comprehensive framework that integrates GIS spatial constraints, process-based LCA inventories, and ML-driven global sensitivity analysis is largely absent[\u003cspan additionalcitationids=\"CR21 CR22 CR23 CR24 CR25 CR26\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study addresses this gap by developing an integrated LCA\u0026ndash;GIS\u0026ndash;ML framework for region-specific hydrogen fuel-cell deployment in India. The framework is designed to (i) establish environmentally preferred fuel-cell pathways using LCA (where, in addition to the two electrolysis-based hydrogen pathways, namely, PEMFC and SOFC, steam methane reforming (SMR) was included as a benchmark hydrogen production route, reflecting the current dominant industrial practice); (ii) quantify spatial constraints and opportunities using geospatial indicators; and (iii) validate and interpret decision rules using explainable machine-learning models so that result can support practical, policy-relevant decisions (rather than purely descriptive and conceptual assessments.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Life Cycle Assessment (LCA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.1. Life Cycle Assessment Framework\u003c/p\u003e\n\u003cp\u003eLife cycle assessment was employed to establish the relative environmental performance of representative hydrogen fuel-cell pathways relevant to Indian conditions. The goal of the LCA was not to provide an exhaustive comparison of all possible configurations, but rather to inform technology preference within the integrated decision-support framework.\u003c/p\u003e\n\u003cp\u003eThe functional unit, system boundary, temporal scope, and geographic assumptions are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The assessment considers hydrogen production, fuel-cell operation, and electricity generation stages relevant to PEMFC and SOFC systems. Aggregated life-cycle impact indicators\u0026mdash;specifically global warming potential (GWP), fossil fuel scarcity and water use\u0026mdash;were selected for integration with spatial analysis due to their relevance for climate mitigation and resource sustainability.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section3\"\u003e\n\u003cdiv class=\"Heading\"\u003e2.1.1 Goal and scope\u003c/div\u003e\n\u003cp\u003eThe goal of the LCA is to compare the environmental impacts of electricity generation via PEMFC and SOFC systems supplied with hydrogen from three production pathways:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eSolar electrolysis (Solar H₂)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGrid electricity\u0026ndash;based electrolysis (Grid H₂)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSteam methane reforming (SMR H₂)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEach hydrogen pathway is coupled with two fuel cell technologies, PEMFC and SOFC, resulting in six scenarios: PEMFC\u0026ndash;Solar H₂, PEMFC\u0026ndash;Grid H₂, PEMFC\u0026ndash;SMR H₂, SOFC\u0026ndash;Solar H₂, SOFC\u0026ndash;Grid H₂, and SOFC\u0026ndash;SMR H₂. Electricity generation from the fuel cell systems constitutes the foreground process, while hydrogen production and energy supply chains are modelled using background life cycle inventory data. Fuel cell stack manufacturing, balance-of-plant components, and infrastructure construction are excluded due to limited data availability and their comparatively minor contribution per kWh electricity when amortized over system lifetimes. The functional unit is 1 kWh of electricity delivered at the fuel-cell output. The scope of the study is cradle-to-gate with respect to hydrogen production and electricity generation, encompassing upstream energy and material inputs required to produce hydrogen and convert it into electricity. Downstream electricity distribution, infrastructure and end-use are also excluded in the present study.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSystem boundaries, functional unit, and key assumptions for PEMFC- and SOFC-based electricity generation under different hydrogen production pathways\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eItem\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDescription\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\u003eGoal of the study\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTo support region-specific hydrogen fuel-cell technology selection in India\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFunctional unit\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 kWh of electricity generated by hydrogen fuel-cell system\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTechnologies assessed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePEMFC (solar/grid), SOFC (solar/grid)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen production route\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWater electrolysis (contextual, non-spatialized)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSystem boundary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCradle-to-gate hydrogen production\u0026thinsp;+\u0026thinsp;fuel-cell electricity generation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGeographic scope\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndia\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTemporal scope\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRepresentative current conditions (\u0026asymp;\u0026thinsp;2020\u0026ndash;2024)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImpact categories used\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlobal warming potential (GWP), water use\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLCA software \u0026amp; database\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSimaPro (13.3.0.1); ecoinvent v3.12 (cut-off system model)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\n\u003cdiv class=\"Heading\"\u003e2.1.2 Life Cycle Inventory Modeling\u003c/div\u003e\n\u003cp\u003eLife cycle inventories were modelled in SimaPro (13.3.0.1), with the Ecoinvent v3.12 database (cut-off system model). Hydrogen production via electrolysis was modeled using electricity inputs representative of either solar photovoltaic generation or national grid electricity, while hydrogen production via SMR was modelled using the corresponding Ecoinvent dataset for low-pressure gaseous hydrogen.\u003c/p\u003e\n\u003cp\u003eFor electrolysis, electricity consumption of 55 kWh per kg of hydrogen was assumed, consistent with reported values for contemporary alkaline and PEM electrolyzers operating at commercial scale [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. Water input for electrolysis was included implicitly through background datasets. Hydrogen consumption rates for PEMFC and SOFC systems were derived based on system electrical efficiencies, resulting in lower hydrogen demand for SOFC systems due to their higher conversion efficiency.\u003c/p\u003e\n\u003cp\u003eElectricity generation via PEMFC and SOFC was modeled as a transformation process converting hydrogen to electricity, with hydrogen as the sole foreground energy carrier. No direct emissions were assigned to fuel cell operation, consistent with the electrochemical nature of the conversion process. Emissions and resource use arise entirely from upstream hydrogen production and energy supply chains.\u003c/p\u003e\n\u003cp\u003eThe cut-off approach implemented in Ecoinvent was adopted, whereby recycled materials are burden-free at the point of use, and upstream processes carry full environmental burdens. No multi-output allocation was required within the foreground system, as electricity was the sole functional output. Background datasets follow Ecoinvent\u0026rsquo;s internal allocation rules.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n\u003cdiv class=\"Heading\"\u003e2.1.3 Impact Assessment Methodology\u003c/div\u003e\n\u003cp\u003eLife cycle impact assessment (LCIA) was conducted using two complementary methods. Climate change impacts were quantified using IPCC 2021 Global Warming Potential (GWP100, fossil) characterization factors, reported in kg CO₂-equivalents. This indicator was used to capture fossil-derived greenhouse gas emissions and is widely used for energy system assessments.\u003c/p\u003e\n\u003cp\u003eTo provide a broader environmental perspective, the ReCiPe 2016 Midpoint (H) method was applied, covering multiple impact categories including climate change, fossil resource scarcity, water consumption, toxicity-related impacts, and land use. These categories were selected to represent climate, resource depletion, and water\u0026ndash;energy coupling effects central to hydrogen sustainability. Normalization was performed using the World 2010 normalization set to facilitate comparison across impact categories with differing units and magnitudes. Data processing and figure preparation were performed using spreadsheet software.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n\u003cdiv class=\"Heading\"\u003e2.1.4 Assumptions and Limitations in carrying out LCA Modeling\u003c/div\u003e\n\u003cp\u003eThe LCA module is used for comparative ranking rather than absolute prediction. Decision-oriented LCA literature emphasizes ranking robustness as more relevant than precise magnitude estimation in policy contexts [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. In view of that, Several assumptions were necessary due to data availability constraints. Manufacturing of electrolyzers, fuel cell stacks, and balance-of-plant components was excluded, consistent with literature indicating their limited contribution per kWh electricity relative to hydrogen production impacts. Electrolyte materials were not explicitly modeled due to negligible mass contribution and high uncertainty in available datasets.\u003c/p\u003e\n\u003cp\u003eThe characteristic values used in the modeling distinct to PEMFC in comparison to SOFC are primarily the electrical efficiency and life time as 0.5 \u0026amp; 30,000 hours for the former, where as 0.6 \u0026amp; 70,000 hours for the latter, respectively, rated power being the same (i.e., 1 kW).\u003c/p\u003e\n\u003cp\u003eGeographical specificity was limited by the availability of regional datasets, and representative global or regional averages were used where country-specific data were unavailable. These assumptions may influence absolute impact values but are unlikely to alter comparative conclusions across scenarios.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 GIS-based spatial analysis\u003c/h2\u003e\n\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.1 Procurement of Base data for State-wise Geospatial Assessment of Hydrogen Fuel Cell Feasibility\u003c/h2\u003e\n\u003cp\u003eSpatial analysis was performed using QGIS version 3.40.14 (Bratislava). Indian state-level administrative boundaries were adopted as the spatial unit of analysis, reflecting the scale at which energy planning and water governance decisions are typically implemented. All spatial operations were conducted within a consistent coordinate reference system to enable accurate area-weighted calculations. The GIS framework was designed to complement the LCA by identifying where specific hydrogen pathways are environmentally appropriate, rather than modifying the underlying process inventories. Spatial feasibility of hydrogen fuel-cell deployment was evaluated using three primary geospatial indicators: solar resource availability, water stress, and grid electricity carbon intensity.\u003c/p\u003e\n\u003cp\u003eNASA POWER NetCDF data were imported into QGIS using CF-compliant latitude\u0026ndash;longitude grids, with the coordinate reference system (WGS-84) assigned prior to mosaicking and spatial analysis. Spatial heterogeneity of solar resource availability across India was quantified using zonal statistics derived from NASA POWER GHI datasets. Solar resource data, represented by global horizontal irradiation (Adj. GHI) metric, was derived from long-term climatological data.\u003c/p\u003e\n\u003cp\u003eSince water availability is a critical constraint for electrolysis-based hydrogen production, baseline water stress data were obtained from the Aqueduct Water Risk Atlas and spatially integrated to state boundaries using area-weighted aggregation. Water stress was quantified using baseline water stress indicators, capturing the ratio of withdrawals to available supply.\u003c/p\u003e\n\u003cp\u003eState-wise grid carbon intensity data (g CO₂ kWh⁻\u0026sup1;) for India were obtained from the Ember Yearly Electricity Data Portal (2024), available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ember-climate.org/data-catalogue/yearly-electricity-data/\u003c/span\u003e\u003c/span\u003e, representing the most recent finalized dataset available at the time of analysis. Carbon intensity was selected over absolute emissions or installed capacity to ensure comparability across states and compatibility with LCA results. Grid electricity carbon intensity was incorporated in the present study to reflect the emissions implications of grid-linked hydrogen pathways. All indicators were aggregated to the state level to align with policy and planning jurisdictions. Data sources, spatial resolution, and processing steps are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGeospatial Indicators used in the Integrated Assessment\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIndicator\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDescription\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eData source\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSpatial resolution\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAggregation level\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\u003eAdj.GHI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdjusted global horizontal irradiation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNASA POWER\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e~\u0026thinsp;1\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eState\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWSI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline water stress\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWRI Aqueduct 4.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBasin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eState\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrid CO₂\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eElectricity carbon intensity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmber (2024)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eState\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eState\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSWSI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolar\u0026ndash;Water Suitability Index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDerived\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eState\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHPI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen Penalty Index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDerived\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eState\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.2. Formulation of Basic Suitability and Penalty Indices\u003c/h2\u003e\n\u003cp\u003eComputation of the suitability indices affecting hydrogen-deployment across the states of India were calculated after scaling each of the parameter under study to the range of 0 and 1, so as to preserve relative ordering while standardizing scale, consistent with composite sustainability index methodology to avoid biasing with regard to magnitudes associated with each parameter.\u003c/p\u003e\n\u003cp\u003eAll parameters studied herein (namely Global Horizontal Irradiation obtained from NASA-POWER, baseline water stress obtained from the Aqueduct Water Risk Atlas and spatially aggregated to the state level using area-weighted statistics and electricity carbon intensity derived from the Ember India electricity dataset) were normalized using min-max normalization, because these parameters are penalty indices against the deployment feasibility of hydrogen fuel cell:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$ {X}_{i}^{norm}=\\frac{{X}_{i}-{X}_{min}}{{X}_{max}-{X}_{min}}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\( {X}_{i}^{norm}\\)\u003c/span\u003e\u003c/span\u003e, refers to normalised water suitability and normalized grid carbon penalty, when X refer to base-line water stress and electricity carbon intensity, respectively to obtain normalized values of Global Horizontal Irradiation (Adj.GHI), Water Stress Index (WSI) and Grid Carbon Intensity (Grid CO₂), respectively.\u003c/p\u003e\n\u003cp\u003eState-wise normalised Global Horizontal Irradiation (GHI) data were processed to derive descriptive statistics (mean and standard deviation) for each state. While mean GHI reflects overall solar availability, high spatial variability can constrain large-scale deployment of solar-powered electrolysis systems.\u003c/p\u003e\n\u003cp\u003eTo account for this effect, an Adjusted Global Horizontal Irradiation (Adj.GHI) indicator was developed by combining normalized mean GHI (GHI\u003csub\u003emean,norm\u003c/sub\u003e) with a penalty for intra-state variability represented by the normalized standard deviation (GHI\u003csub\u003estd,norm\u003c/sub\u003e) :\u003c/p\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equb\" class=\"mathdisplay\"\u003e$$ \\text{Adj. GHI}={\\text{GHI}}_{\\text{mean,norm}}\\times (1-{\\text{GHI}}_{\\text{std,norm}})$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThis formulation was designed to favor regions with both high solar availability and spatial consistency, thereby providing a realistic proxy for deployable solar energy potential relevant to hydrogen production.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.2 Formulation of Composite Suitability Indices\u003c/h2\u003e\n\u003cp\u003eTo integrate multiple spatial constraints into a single evaluative metric, composite indices were developed. A Solar\u0026ndash;Water Suitability Index (SWSI) was constructed by combining Adjusted Global Horizontal Irradiation (Adj. GHI) and baseline water stress indicators, while a Hydrogen Penalty Index (HPI) was used to capture the combined influence of grid carbon intensity and resource constraints. The equation for their computation is given below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSolar\u0026ndash;Water Suitability Index (SWSI)\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equc\" class=\"mathdisplay\"\u003e$$ \\text{S}\\text{W}\\text{S}\\text{I}=\\text{Adj. GHI}\\times (1-{\\text{WSI}}_{\\text{norm}})$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\( \\text{A}\\text{d}\\text{j}. \\text{G}\\text{H}\\text{I} \\)\u003c/span\u003e\u003c/span\u003erepresents standardised solar irradiation and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\( {\\text{W}\\text{S}\\text{I}}_{norm} \\text{r}\\)\u003c/span\u003e\u003c/span\u003eepresents normalized Baseline Water Stress).\u003c/p\u003e\n\u003cp\u003eThis multiplicative approach was designed to remove subjective weighting and ensures that high solar potential is penalized in water-stressed regions, thereby identifying environmentally balanced zones for renewable hydrogen deployment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHPI (Hydrogen Penalty Index)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equd\" class=\"mathdisplay\"\u003e$$ \\text{HPI}={\\text{Grid_CO2}}_{\\text{norm}}\\times {\\text{WSI}}_{\\text{norm}}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\( {Grid{ CO}_{2}}_{norm} \\)\u003c/span\u003e\u003c/span\u003erepresents grid carbon penalty and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\( {\\text{W}\\text{S}\\text{I}}_{norm} \\text{r}\\)\u003c/span\u003e\u003c/span\u003eepresents normalized Baseline Water Stress)\u003c/p\u003e\n\u003cp\u003eTo synthesize opportunity-based and constraint-based indicators, a Technology Preference Index (TPI) was formulated as:\u003c/p\u003e\n\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Eque\" class=\"mathdisplay\"\u003e$$ TPI=SWSI-HPI$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ePositive TPI values indicate regions where renewable-based hydrogen pathways are environmentally preferable, while low or negative values suggest limited suitability under current conditions. States were subsequently classified into four technology preference categories: solar-priority hydrogen, conditional or hybrid pathways, grid-dependent hydrogen only, and avoid hydrogen deployment.\u003c/p\u003e\n\u003cp\u003eThis classification was designed to provide a transparent mechanism to translate process-level LCA results into region-specific guidance for hydrogen deployment. While LCA quantified the impacts associated with each hydrogen production and fuel cell configuration, the spatial framework identified the regional conditions under which these impacts translate into meaningful sustainability benefits.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3. Explainable Machine Learning for Decision Validation\u003c/h2\u003e\n\u003cp\u003eExplainable machine-learning models were employed using Weka (3.8.6) as a supporting analytical layer to validate the relative importance of spatial indicators and to assess the internal consistency of the decision framework, rather than as a primary decision-making engine, to preserve physical interpretability and policy relevance. Decision tree classifiers were selected due to their interpretability and suitability for small datasets. Feature ranking methods were applied to identify dominant drivers influencing technology preference classification.\u003c/p\u003e\n\u003cp\u003eMachine learning was not used to optimize predictive accuracy, but rather to corroborate the decision logic derived from LCA and GIS analysis. State-level indicators derived from GIS analysis\u0026mdash;including the adjusted Global Horizontal Irradiation (Adj GHI), normalized Water Stress Index (WSI_Norm), composite Solar\u0026ndash;Water Suitability Index (SWSI), grid carbon intensity (Grid CO₂), and Hydrogen Penalty Index (HPI)\u0026mdash;were used as input features for ML analysis. The target variable was the rule-based technology preference class derived from integrated LCA\u0026ndash;GIS reasoning.\u003c/p\u003e\n\u003cp\u003eA J48 decision tree classifier (C4.5 implementation) was implemented in WEKA (5-fold cross-validation; interpretable decision tree classifier; default hyper-parameters) to evaluate (i) the relative importance of the derived indices, and (ii) the consistency of the rule-based technology classification with data-driven patterns. The ML analysis was intentionally constrained to interpretable models (decision trees and attribute ranking), avoiding black-box algorithms, in order to maintain transparency. Deployment regimes are assigned using rule-based thresholds applied to TPI and constituent indices. The deterministic structure ensures that classification remains traceable to environmental and spatial inputs rather than opaque optimization.\u003c/p\u003e\n\u003cp\u003eUnsupervised k-mean clustering techniques (k\u0026thinsp;=\u0026thinsp;3; default initialization \u0026amp; no manual centroid seeding) were explored for exploratory analysis (and were not used for final classification), as clustering methods optimize statistical similarity rather than physical feasibility or environmental performance. Consequently, the final technology deployment map was generated exclusively using rule-based integration supported by ML insights (rather than ML-driven assignment) and compared with the TPI map generated through GIS studies.\u003c/p\u003e\n\u003cp\u003eOverall, machine learning was not used to generate the bivariate classification. Instead, ML analysis was applied subsequently to validate the dominance of composite indices (SWSI and HPI) in explaining technology feasibility patterns, ensuring that spatial classification remained grounded in physically interpretable constraints rather than purely statistical similarity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 Visualization of State-level Geospatial Distribution of Indices\u003c/h2\u003e\n\u003cp\u003eUsing Q-GIS, the thematic maps were generated using graduated symbology to visualize AGHI, SWSI and HPI. Class breaks were fixed to ensure consistency across figures. These spatial outputs were interpreted in conjunction with process-level life cycle assessment results, enabling an integrated assessment of hydrogen sustainability that combines environmental impacts with regional resource and infrastructure constraints.\u003c/p\u003e\n\u003cp\u003eTo simultaneously represent the interaction between water stress and hydrogen penalty, a bivariate choropleth classification was implemented using a 3\u0026times;3 matrix, where the Water Stress Index (WSI) was classified using equal-interval thresholds (low, medium, high) to preserve physical interpretability consistent with established water-stress categorization frameworks. In contrast, the Hydrogen Penalty Index (HPI), being a relative composite indicator without externally defined thresholds, was classified using quantile breaks to ensure balanced representation across states. The resulting bivariate classes was employed to capture nine possible combinations of water stress and hydrogen penalty, enabling identification of regions facing compounded constraints or favourable conditions for hydrogen fuel-cell deployment.\u003c/p\u003e\n\u003cp\u003eFinal thematic maps were generated using categorical symbology to visualize technology preference classes and the k-mean clustering of the states obtained by ML study, so as to validate GIS-modelled results to unsupervised clustering results obtained by machine learning.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e2.5 Framework of the Integrated region-specific Feasibility assessment of hydrogen fuel-cell deployment in India\u003c/h2\u003e\n\u003cp\u003eThe methodological framework adopted in this study integrates three complementary analytical components: GIS-based scenario contextualisation, process-based life cycle assessment, and machine-learning-assisted interpretation. GIS is employed at the outset to provide spatial context for hydrogen supply scenarios, ensuring that assumptions regarding electricity sources and carbon intensity are grounded in geographically realistic conditions. Process-based LCA constitutes the core analytical method, quantifying cradle-to-grave environmental impacts of PEMFC and SOFC systems per unit of electricity generated. Machine learning is subsequently applied as a post-processing tool to identify dominant life-cycle drivers, rank parameter importance, and classify sustainability scenarios. This sequential integration was carried out in order to ensure methodological clarity while avoiding overlap or redundancy among tools.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. LCA-Informed Technology Trade-offs\u003c/h2\u003e\n\u003cp\u003eAggregated LCA results indicate clear trade-offs among hydrogen fuel-cell pathways with respect to greenhouse gas emissions and water use. Solar-integrated fuel-cell systems demonstrate lower life-cycle GWP compared to grid-dependent configurations, while differences between PEMFC and SOFC technologies are influenced by efficiency and operational characteristics.\u003c/p\u003e\n\u003cp\u003eA comparative summary of life-cycle impacts used for decision support is presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, while Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates relative performance across key indicators.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparative life cycle environmental impacts of electricity generation via PEMFC and SOFC systems under different hydrogen production pathways (per 1 kWh).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHydrogen Production Route\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIPCC 2021 GWP100 (fossil) in kg CO\u003csub\u003e2\u003c/sub\u003e eq\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReCiPe Climate change in kg CO\u003csub\u003e2\u003c/sub\u003e eq\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFossil resource scarcity in kg oil eq\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWater consu- mption in m3\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\u003ePEMFC\u0026ndash;Solar H₂\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0733\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0749\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0027\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePEMFC\u0026ndash;Grid H₂\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.2376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.2516\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.3103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0052\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePEMFC\u0026ndash;SMR H₂\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1785\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1822\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0703\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSOFC\u0026ndash;Solar H₂\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0611\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0624\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0162\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0022\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSOFC\u0026ndash;Grid H₂\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.0313\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.0430\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.2586\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0043\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSOFC\u0026ndash;SMR H₂\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1487\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1519\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0586\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eConsidering SMR hydrogen as the benchmark, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e highlights how alternative hydrogen pathways and fuel cell technologies compare against this reference. For PEMFC systems, the SMR\u0026ndash;H₂ benchmark results in climate change impacts of 0.1785 kg CO₂-eq (IPCC GWP100) and 0.1822 kg CO₂-eq (ReCiPe) per kWh, with a fossil resource scarcity of 0.0703 kg oil-eq and water consumption of 0.0006 m\u0026sup3;. When SMR hydrogen is coupled with SOFC technology, these impacts are reduced to 0.1487\u0026ndash;0.1519 kg CO₂-eq, 0.0586 kg oil-eq, and 0.0005 m\u0026sup3;, reflecting the efficiency advantage of SOFCs relative to PEMFCs under the same hydrogen supply conditions.\u003c/p\u003e\n\u003cp\u003eRelative to this SMR benchmark, solar hydrogen pathways demonstrate substantial environmental improvements. PEMFC\u0026ndash;Solar H₂ reduces climate change impacts by nearly 60% compared with PEMFC\u0026ndash;SMR H₂ (from 0.1785 to 0.0733 kg CO₂-eq), while SOFC\u0026ndash;Solar H₂ achieves an even greater reduction relative to the SOFC\u0026ndash;SMR benchmark (from 0.1487 to 0.0611 kg CO₂-eq). Similar reductions are observed for fossil resource scarcity, which decreases from 0.0703 to 0.0195 kg oil-eq for PEMFC and from 0.0586 to 0.0162 kg oil-eq for SOFC, indicating a significant decoupling from fossil fuel use when renewable hydrogen is employed.\u003c/p\u003e\n\u003cp\u003eIn contrast, grid-based hydrogen performs significantly worse than the SMR benchmark. PEMFC\u0026ndash;Grid H₂ increases climate change impacts by almost seven times relative to PEMFC\u0026ndash;SMR H₂ (1.2376 vs. 0.1785 kg CO₂-eq), while SOFC\u0026ndash;Grid H₂ shows a similar trend (1.0313 vs. 0.1487 kg CO₂-eq). Fossil resource scarcity and water consumption also rise markedly under grid hydrogen scenarios. Overall, using SMR hydrogen as a benchmark underscores that while SMR remains a relatively efficient conventional pathway, meaningful environmental benefits are only achieved by transitioning to renewable hydrogen, particularly when combined with higher-efficiency SOFC systems.\u003c/p\u003e\n\u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and 3 visually reinforce the relative performance of the assessed systems with SMR\u0026ndash;H₂ taken as the reference pathway. As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, both PEMFC\u0026ndash;SMR H₂ and SOFC\u0026ndash;SMR H₂ occupy an intermediate position in terms of global warming potential, clearly separating the low-impact solar hydrogen routes from the highly carbon-intensive grid-based hydrogen options. The SOFC\u0026ndash;SMR configuration exhibits a lower GWP than its PEMFC counterpart, confirming that the efficiency advantage of SOFCs translates directly into reduced climate impacts even when conventional hydrogen production is used as the baseline. In contrast, the pronounced increase in GWP for grid-derived hydrogen highlights a substantial deviation from the SMR benchmark, indicating that grid electricity negates the climate benefits expected from fuel cell systems.\u003c/p\u003e\n\u003cp\u003eA similar trend is evident in Fig.\u0026nbsp;3 for fossil resource scarcity. Relative to the SMR benchmark, both PEMFC\u0026ndash;SMR H₂ and SOFC\u0026ndash;SMR H₂ show moderate dependence on fossil resources, whereas grid-based hydrogen pathways lead to a sharp escalation in fossil resource use. Solar hydrogen options, on the other hand, fall well below the SMR reference, emphasizing their potential to significantly reduce reliance on fossil fuels. Consistent with the GWP results, SOFC systems demonstrate lower fossil resource scarcity than PEMFC systems under identical hydrogen supply conditions. Overall, the figures corroborate the benchmark analysis by illustrating that SMR hydrogen represents a transitional baseline, with meaningful environmental improvements achieved only through a shift toward renewable hydrogen, particularly when combined with higher-efficiency SOFC technology.\u003c/p\u003e\n\u003cp\u003eThis ordering is consistent with prior hydrogen LCA literature demonstrating the dominant influence of electricity carbon intensity [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. The magnitude of difference between electricity sources exceeds variation between PEMFC and SOFC technologies, indicating that upstream energy supply outweighs fuel-cell design in environmental significance\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Normalized ReCiPe midpoint comparison of PEMFC and SOFC under identical hydrogen supply pathways\u003c/h2\u003e\n\u003cp\u003eFigure 4 presents a normalized ReCiPe midpoint comparison of PEMFC and SOFC systems operated with identical hydrogen supply pathways (solar-derived H₂ and grid/oxidative H₂), highlighting relative\u003c/p\u003e\n\u003cp\u003eenvironmental trade-offs across impact categories rather than absolute burdens. A clear and consistent trend is observed whereby the hydrogen production pathway exerts a stronger influence than the fuel cell technology itself, in line with earlier life cycle assessments of hydrogen-based energy systems. Grid/oxidative H₂ pathways dominate the impact profile for both PEMFC and SOFC, with fossil resource scarcity and global warming potential emerging as the most pronounced categories.\u003c/p\u003e\n\u003cp\u003eQuantitatively, the normalized fossil resource scarcity for PEMFC\u0026ndash;Grid H₂ is more than two times higher than that of the corresponding solar H₂ pathway, while SOFC shows a slightly lower but still dominant contribution under grid H₂. A similar amplification is evident for global warming impacts, reflecting upstream electricity and fossil fuel use during hydrogen production, which has been widely identified as the principal hotspot in hydrogen life cycles [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eOverall, the solar hydrogen pathways consistently exhibit the lowest climate and fossil resource impacts, while grid-based hydrogen dominates environmental burdens. SMR hydrogen shows intermediate performance and is retained as a baseline benchmark. Life cycle assessment results (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;4) confirm that hydrogen sustainability is dominated by electricity source rather than fuel-cell configuration. Solar-powered electrolysis consistently exhibits lower global warming and fossil resource burdens than grid-powered hydrogen. This ordering aligns with prior LCA syntheses showing electricity carbon intensity as the primary driver of hydrogen environmental performance. The multi-category penalty observed in grid-powered hydrogen reflects upstream fossil dependence, consistent with broader renewable energy LCA findings [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. These results establish environmental boundary conditions for deployment feasibility.\u003c/p\u003e\n\u003cp\u003eThe LCA results, therefore, function as physical boundary conditions: hydrogen contributes to decarbonization only when upstream electricity is sufficiently low-carbon. This also indicates that penalties for fuel cell deployment associated with grid hydrogen are multi-category rather than isolated to climate impacts. This reinforces the need to encode electricity carbon intensity explicitly within the Hydrogen Penalty Index, which is carried out in geospatial modeling.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Geospatial Analysis of Constraints for Green Hydrogen Deployment\u003c/h2\u003e\n\u003cp\u003eIndia's National Green Hydrogen Mission targets 5\u0026nbsp;million metric tons of annual Green Hydrogen (GH\u003csub\u003e2\u003c/sub\u003e) production by 2030, relying heavily on solar-powered electrolysis that demands abundant irradiation alongside sufficient water resources. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e reveals substantial spatial heterogeneity in compounded constraints affecting hydrogen fuel-cell deployment feasibility.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e(A) illustrates strong geographic asymmetry of Adjusted Global Horizontal Irradiance (Adj. GHI), calibrated for electrolyzer performance, with values exceeding 5.5 kWh/m\u0026sup2;/day in Rajasthan (up to 5.8-6.0), Gujarat, and southern Andhra Pradesh, corroborating Global Solar Atlas data and NREL validations that position these arid western states as irradiation leaders. In contrast, northeastern states like Assam and Arunachal Pradesh exhibit subdued levels (4.0-4.5), limiting raw solar viability despite ample rainfall. Solar-rich western and southern states simultaneously exhibit varying degrees of water stress. This duality highlights a core sustainability tension: renewable energy abundance does not guarantee electrolysis feasibility.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e(B) maps the State-level Solar Water Suitability Index (SWSI), a positive metric indicating water availability tailored for solar-driven electrolysis, with high suitability (\u0026gt;\u0026thinsp;0.6) in eastern and northern states like Bihar, Uttar Pradesh, and Odisha where surface water resources align well with solar needs. Gujarat and Rajasthan, despite top-tier Adj. GHI, show moderate SWSI (0.3\u0026ndash;0.5), highlighting a key suitability gap that supports recommendations for desalination or wastewater integration in coastal GH2 hubs. Peninsular states such as Maharashtra, Telangana, and Karnataka register lower SWSI (\u0026lt;\u0026thinsp;0.3), constraining scalability amid agricultural water competition. In fact, solar\u0026ndash;water trade-off documented in water\u0026ndash;energy nexus research, International Energy Agency reports that the regions with strong renewable potential frequently overlap with water-stressed basins, indicating that hydrogen feasibility is constrained by coupled resource systems rather than isolated energy metrics [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e(C) visualizes the State-level Hydrogen Penalty Index (HPI), aggregating penalties from suboptimal GHI, limited SWSI, and infrastructural factors, peaking at 0.44\u0026ndash;0.49 in central states like Madhya Pradesh and Chhattisgarh where compounded deficits elevate deployment costs. Rajasthan counters with minimal HPI (0.0-0.1). Similar regional asymmetry has been reported in composite energy sustainability indices by independent studies elsewhere [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e(D)\u0026rsquo;s bivariate choropleth represents the low water stress and low hydrogen penalty occupying the lower-left quadrant of the bivariate space, indicating comparatively favorable conditions for hydrogen production and utilization, whereas the states located in the upper-right quadrant, exhibiting both high water stress and high hydrogen penalty, represent regions where hydrogen deployment would likely face significant sustainability and infrastructure challenges. Thus, low-SWSI/high-HPI zones (dark purple, e.g., Maharashtra) signal dual vulnerabilities in contrast to Rajasthan and Gujarat's balanced profiles (green-yellow). These observations demonstrate that hydrogen feasibility emerges from interacting resource constraints rather than single-indicator dominance. Such multi-resource coupling aligns with water\u0026ndash;energy nexus research emphasizing interdependent sustainability limits [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIt can be observed that, water stress and hydrogen penalty interact non-linearly, producing compounded effects that are not evident when indicators are assessed independently. In fact, several states with moderate solar resource availability shift into higher-constraint categories due to elevated water stress or grid carbon intensity, underscoring the inadequacy of solar potential alone as a criterion for hydrogen deployment planning. Thus, the present integrated spatial perspective provides a more realistic representation of regional hydrogen deployment constraints, moving beyond single-indicator assessments.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 Explainable Machine-Learning Validation\u003c/h2\u003e\n\u003cp\u003eFeature ranking indicates SWSI and HPI as primary parameters in explaining technology classifications. Accordingly, three sets of models were construed, namely full feature model (i.e., using all parameters); decomposed model ( i.e., the basic indices such as Adj.GHI, WSI \u0026amp; Grid CO₂) and composite model (i.e., SWSI, HPI). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e systematically delineates these three configurations of decision tree classifiers implemented in Weka for geospatial classification of hydrogen fuel-cell technology viability across India's solar-water-grid variability, with performance metrics as follows: composite model (80.56% accuracy), decomposed model (61.11% accuracy), and full-feature model (77.78% accuracy).\u003c/p\u003e\n\u003cp\u003eAs observable from these model comparisons, the composite model, that employs two engineered composite indices\u0026mdash;SWSI (Adj.GHI \u0026times; (1 - WSI)) and HPI (Grid CO₂ \u0026times; WSI)\u0026mdash;as sole inputs to elucidate decision logic, attaining superior 80.56% cross-validated accuracy owing to dimensionality reduction (from 5 to 2 features, ~\u0026thinsp;60% fewer parameters) and enhanced interpretability via shallow tree structures (e.g., Gini impurity splits on SWSI\u0026thinsp;\u0026gt;\u0026thinsp;threshold). This parsimonious formulation mitigates overfitting in heterogeneous Indian geospatial datasets, where collinearities between Adj. GHI, WSI, and Grid CO₂ inflate variance; its high interpretability stems from explicit root-to-leaf paths traceable to physical drivers, aligning with explainable machine learning paradigms for policy-grade models.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMachine-learning model configuration and performance\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eInput features\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePurpose\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAccuracy (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eInterpretation\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\u003eComposite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSWSI, HPI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExplain decision logic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh interpretability\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecomposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdj.GHI, WSI \u0026amp; Grid CO₂\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRobustness check\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e61.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStable\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFull-feature\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAll indicators\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSensitivity test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e77.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo major gain\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe simplified decision tree used for validation is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Decision tree analysis confirms that composite spatial indices, particularly SWSI and HPI, are the dominant drivers of technology classification. The corresponding relationship of the findings to policy-level inferences, which form the basis of the technology preference classification. These findings reinforce the internal consistency of the integrated framework and demonstrate the value of explainable ML in validating policy-oriented decision rules.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 State-wise technology preference classification\u003c/h2\u003e\n\u003cp\u003eBy integrating LCA-informed technology preferences with spatial suitability indices, a final technology classification was derived. States were categorized into solar-priority hydrogen zones, grid-linked hydrogen zones, conditional deployment regions, and areas where hydrogen deployment should be avoided under current conditions. States located in low-constraint bivariate classes align predominantly with solar-hydrogen-based fuel-cell deployment, whereas high-constraint classes correspond to conditional deployment or avoidance of hydrogen technologies, consistent with life-cycle assessment outcomes. (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e(B) illustrates state-level technology preference mapping for hydrogen fuel-cell deployment across India, derived from unsupervised k-means clustering on SWSI (Solar Water Suitability Index) and HPI (Hydrogen Penalty Index), validated against prior Weka decision trees (80.56% accuracy), which closely corresponds to the locations identified in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e(A), with partial overlapping of the marginal classes, because reduced number of clustering (i.e., three) than the classes in the indices-based classification (i.e., four). For, example, Jammu \u0026amp; Kashmir and Karnataka, which are classified as non-favorable for hydrogen deployment as well as West Bengal, Sikkim and Assam, which were classified as Grid-hydrogen viable are grouped along with states where conditional deployment (Cluster C) are suggested. Similarly, Conditional deployment as well as solar hydrogen priority regions (in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA) were clubbed as Favourable deployment zone. Hence, these two classification systems (i.e., indices-based and cluster-based) form the basis of policy approaches by the purpose of 4-fold and 3-fold technology-feasibility categorizations, respectively. These findings reinforce the internal consistency of the integrated framework and demonstrate the value of explainable ML in validating policy-oriented decision rules.\u003c/p\u003e\n\u003cp\u003eThe resulting 4-fold index-based classification for policy level assessment is provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eIntegrated technology preference classification for hydrogen fuel-cell deployment in India based on life-cycle impacts, spatial constraints, and machine-learning validation\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eState\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSWSI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHPI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTechnology class\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\u003eAndhra Pradesh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.08452\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.48802\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAssam\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.20914\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06392\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrid hydrogen viable\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBihar\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.02885\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.34125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChandigarh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03070\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChhattisgarh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.11250\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.32269\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDadra and Nagar Haveli\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.47225\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.04807\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolar hydrogen priority\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGoa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.38184\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConditional deployment\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGujarat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.10868\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.44982\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHaryana\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.01897\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.80975\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHimachal Pradesh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.01834\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrid hydrogen viable\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJammu and Kashmir\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJharkhand\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.07604\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.26161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKarnataka\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.21853\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.24106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKerala\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.45547\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00237\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolar hydrogen priority\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMadhya Pradesh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05759\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.53663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaharashtra\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.18412\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.39685\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eManipur\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.31441\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConditional deployment\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMeghalaya\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.26070\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConditional deployment\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMizoram\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.32684\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConditional deployment\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNagaland\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.41089\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolar hydrogen priority\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNCT of Delhi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.01215\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.46309\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOdisha\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.16419\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.31476\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePuducherry\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.18497\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.24806\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePunjab\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.02873\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.59379\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRajasthan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.47812\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTamil Nadu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.23049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.30394\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTelangana\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.23201\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.42645\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTripura\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.27690\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.14241\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrid hydrogen viable\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUttar Pradesh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.01171\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.56508\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHydrogen not recommended\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUttarakhand\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.04677\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.01975\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrid hydrogen viable\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWest Bengal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.15380\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.07165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrid hydrogen viable\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Integrated interpretation of GIS indices, life cycle assessment results \u0026amp; machine learning validation and Policy \u0026amp; Planning Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy explicitly linking spatially resolved suitability and penalty indices with process-level life cycle assessment results, the present study bridges the gap between hydrogen technology assessment and regional planning. While the SimaPro analysis quantifies the environmental performance of different hydrogen pathways under controlled system boundaries, the GIS-based indices explain where and under what regional constraints these pathways are environmentally meaningful. In particular, regions identified as solar-priority zones through the Technology Preference Index correspond to scenarios in which renewable-electricity-driven hydrogen systems exhibit consistently lower climate change and midpoint impacts, whereas regions with high hydrogen penalties align with LCA results indicating reduced or marginal environmental benefits. This integrated framework demonstrates that the sustainability of hydrogen technologies is not solely a function of process efficiency, but is fundamentally shaped by regional solar resources, water availability, and electricity system characteristics. The machine learning validation of the results obtained by the geospatial modeling brings out confirms the two composite indices (SWSI and HPI) enough to classify the regions with regard to the hydrogen feasibility -classes (upto 90% accuracy) and sufficient to cluster the states into three feasibility options. Hence, the integrated assessment highlights that hydrogen deployment strategies based solely on renewable resource potential risk overlooking critical environmental and infrastructural constraints.\u003c/p\u003e\n\u003cp\u003ePolicymakers should therefore adopt region-specific strategies that align technology choice with local resource conditions and life-cycle impacts. The assessment can be better done on the basis of composite indices (such as SWSI and HPI), rather than the fundamental indices (such as Adj. GHI, WPI, Grid CO\u003csub\u003e2\u003c/sub\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis study presents an integrated framework for identifying region-specific hydrogen fuel-cell deployment pathways in India by advancing a novel integrated LCA-GIS-ML framework, addressing gaps in context-blind assessments by fusing cradle-to-gate LCA (SimaPro/ecoinvent), geospatial indices (SWSI\u0026thinsp;=\u0026thinsp;Adj.GHI \u0026times; (1-WSI); HPI\u0026thinsp;=\u0026thinsp;Grid CO₂ \u0026times; WSI; TPI\u0026thinsp;=\u0026thinsp;SWSI - HPI), and explainable Weka ML (80.56% accuracy via composite decision trees). By linking process-level environmental performance with spatial feasibility and explainable data-driven validation, the framework moves beyond isolated assessments toward actionable decision support. The results demonstrate that sustainable hydrogen deployment requires careful alignment of technology choice with regional resource constraints and electricity characteristics. The proposed framework is transferable to other regions and energy technologies, supporting broader applications in sustainable energy planning. The present study involving LCA, GIS and ML framework Solar hydrogen pathways offer clear environmental advantages, namely, spatial constraints strongly influencing technology suitability and machine learning enhances interpretability but remains physically grounded.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConsent to Publish\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e \u003cp\u003eNot applicable. This study does not involve human participants, human subjects, personal data, or animal experimentation.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAshutosh Das conceptualized and designed the study, developed the methodological framework, conducted the life cycle assessment and geospatial analysis, performed data interpretation, and prepared the original draft of the manuscript. Ranjan Kumar Mallick contributed to machine learning modelling, technical validation, data analysis, and critical review and editing of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used in this study are derived from publicly available sources. Solar irradiation data were obtained from the NASA POWER database ( [https://power.larc.nasa.gov/](https:/power.larc.nasa.gov) ). Water stress data were obtained from the WRI Aqueduct Water Risk Atlas ( [https://www.wri.org/aqueduct](https:/www.wri.org/aqueduct) ). State-level electricity generation and emissions data for India were obtained from the Ember Yearly Electricity Data Portal ( [https://ember-climate.org/data-catalogue/yearly-electricity-data/](https:/ember-climate.org/data-catalogue/yearly-electricity-data) ). Administrative boundary data for India were obtained from the Global Administrative Areas (GADM) database ( [https://gadm.org/download\\_country.html](https:/gadm.org/download_country.html) ), specifically the India shapefile dataset (https://geodata.ucdavis.edu/gadm/gadm4.1/shp/gadm41_IND_shp.zip). Life cycle inventory data were obtained from the Ecoinvent database (v3.12) accessed via SimaPro software. The processed datasets, derived indices (SWSI, HPI, and TPI), GIS outputs, and machine learning model results generated during the study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSahu, S., Kanwal, R., Ratnawat, I., Mir, A. \u0026amp; Abrar, I. Hydrogen fuel cells: technical, economic, and policy pathways toward net-zero integration. \u003cem\u003eSustainable Energy Fuels\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e (24), 6601\u0026ndash;6630. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/d5se01080b\u003c/span\u003e\u003cspan address=\"10.1039/d5se01080b\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDincer, I. \u0026amp; Aydin, M. I. New paradigms in sustainable energy systems with hydrogen. \u003cem\u003eEnergy. Conv. Manag.\u003c/em\u003e \u003cb\u003e283\u003c/b\u003e, 116950. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enconman.2023.116950\u003c/span\u003e\u003cspan address=\"10.1016/j.enconman.2023.116950\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner, J. A. Sustainable hydrogen production. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e305\u003c/b\u003e (5686), 972\u0026ndash;974. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1103197\u003c/span\u003e\u003cspan address=\"10.1126/science.1103197\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDincer, I. \u0026amp; Acar, C. Review and evaluation of hydrogen production methods for better sustainability. \u003cem\u003eInt. J. Hydrog. Energy\u003c/em\u003e. \u003cb\u003e40\u003c/b\u003e (34), 11094\u0026ndash;11111. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijhydene.2014.12.035\u003c/span\u003e\u003cspan address=\"10.1016/j.ijhydene.2014.12.035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLongo, S. et al. Life Cycle Assessment of Solid Oxide Fuel Cells and Polymer Electrolyte Membrane Fuel Cells. In Hydrogen Economy 2017, (139\u0026ndash;169). Elsevier. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/b978-0-12-811132-1.00006-7\u003c/span\u003e\u003cspan address=\"10.1016/b978-0-12-811132-1.00006-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMori, M., Stropnik, R., Sekavčnik, M. \u0026amp; Lotrič, A. Criticality and Life-Cycle Assessment of Materials Used in Fuel-Cell and Hydrogen Technologies. \u003cem\u003eSustainability\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (6), 3565. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su13063565\u003c/span\u003e\u003cspan address=\"10.3390/su13063565\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAayog, N. I. T. I. \u003cem\u003eHarnessing Green Hydrogen in India\u003c/em\u003e. (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.niti.gov.in/sites/default/files/2022-06/Harnessing_Green_Hydrogen_V21_DIGITAL_29062022.pdf\u003c/span\u003e\u003cspan address=\"https://www.niti.gov.in/sites/default/files/2022-06/Harnessing_Green_Hydrogen_V21_DIGITAL_29062022.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of New and Renewable Energy. Government of India, \u003cem\u003eNational Green Hydrogen Mission\u003c/em\u003e. \u003cem\u003eGovernment India\u003c/em\u003e (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mnre.gov.in/en/national-green-hydrogen-mission\u003c/span\u003e\u003cspan address=\"https://mnre.gov.in/en/national-green-hydrogen-mission\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhandari, R., Trudewind, C. A. \u0026amp; Zapp, P. Life cycle assessment of hydrogen production via electrolysis, \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, 85, 2014, 151\u0026ndash;163, ISSN 0959\u0026ndash;6526, (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2013.07.048\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2013.07.048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValente, A., Iribarren, D. \u0026amp; Dufour, J. Life cycle assessment of hydrogen energy systems: a review of methodological choices. \u003cem\u003eInt. J. Life Cycle Assess.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 346\u0026ndash;363. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11367-016-1156-z\u003c/span\u003e\u003cspan address=\"10.1007/s11367-016-1156-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, G. \u0026amp; Pedersen, A. S. Life Cycle Assessment of Hydrogen Production and Consumption in an Isolated Territory. \u003cem\u003eProcedia CIRP\u003c/em\u003e. \u003cb\u003e69\u003c/b\u003e, 529\u0026ndash;533. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.procir.2017.11.100\u003c/span\u003e\u003cspan address=\"10.1016/j.procir.2017.11.100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStaffell, I. et al. The role of hydrogen and fuel cells in the global energy system. \u003cem\u003eEnergy Environ. Sci.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 463\u0026ndash;491. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/C8EE01157E\u003c/span\u003e\u003cspan address=\"10.1039/C8EE01157E\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalim, K. M. A., Maelah, R., Hishamuddin, H. \u0026amp; Amir, A. M. Ab Rahman, M. N. Two Decades of Life Cycle Sustainability Assessment of Solid Oxide Fuel Cells (SOFCs): A Review. \u003cem\u003eSustainability\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (19), 12380. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su141912380\u003c/span\u003e\u003cspan address=\"10.3390/su141912380\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsman, A. I. et al. Life cycle assessment of hydrogen production, storage, and utilization toward sustainability. \u003cem\u003eWIRE Energy Environ.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/wene.526\u003c/span\u003e\u003cspan address=\"10.1002/wene.526\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStanchev, P. \u0026amp; Hinov, N. Life Cycle of Fuel Cells: From Raw Materials to End-of-Life Management. \u003cem\u003eClean. Technol.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (4), 94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cleantechnol7040094\u003c/span\u003e\u003cspan address=\"10.3390/cleantechnol7040094\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChong, J. W. \u0026amp; Hanafiah, M. M. A review of life cycle assessment for fuel cell technologies: Advancing clean energy and climate solutions. \u003cem\u003eEnergy Rep.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 6548\u0026ndash;6565. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.egyr.2025.05.081\u003c/span\u003e\u003cspan address=\"10.1016/j.egyr.2025.05.081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStropnik, R., Lotrič, A., Bernad Montenegro, A., Sekavčnik, M. \u0026amp; Mori, M. Critical materials in PEMFC systems and a LCA analysis for the potential reduction of environmental impacts with EoL strategies. \u003cem\u003eEnergy Sci. Eng.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (6), 2519\u0026ndash;2539. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ese3.441\u003c/span\u003e\u003cspan address=\"10.1002/ese3.441\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGramc, J. et al. Ecodesign as a key concept for improving the life cycle environmental performance of proton-exchange membrane fuel cells. \u003cem\u003eInt. J. Hydrog. Energy\u003c/em\u003e. \u003cb\u003e104\u003c/b\u003e, 623\u0026ndash;634. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijhydene.2024.08.020\u003c/span\u003e\u003cspan address=\"10.1016/j.ijhydene.2024.08.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDincer, M. \u0026amp; Agelin-Chaab, M. Sustainability analysis of electrolysis based green hydrogen production pathways: A life cycle perspective. \u003cem\u003eInt. J. Hydrog. Energy\u003c/em\u003e. \u003cb\u003e138\u003c/b\u003e, 617\u0026ndash;625. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijhydene.2025.05.150\u003c/span\u003e\u003cspan address=\"10.1016/j.ijhydene.2025.05.150\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNicholas, M. A., Handy, S. L. \u0026amp; Sperling, D. Using Geographic Information Systems to Evaluate Siting and Networks of Hydrogen Stations. \u003cem\u003eTransp. Res. Rec.\u003c/em\u003e \u003cb\u003e1880\u003c/b\u003e (1880), 126\u0026ndash;134. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3141/1880-15\u003c/span\u003e\u003cspan address=\"10.3141/1880-15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, R., Ye, Z., Guo, Z. \u0026amp; Wu, B. Hydrogen station location optimization based on multiple data sources,International. \u003cem\u003eJ. Hydrogen Energy\u003c/em\u003e. \u003cb\u003e45\u003c/b\u003e, 10270\u0026ndash;10279. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijhydene.2019.10.069\u003c/span\u003e\u003cspan address=\"10.1016/j.ijhydene.2019.10.069\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEchabarri, S., Do, P., Vu, C. H. \u0026amp; Bornand, B. PEMFC Performance Forecasting Based on XGBRegressor and Tree-Structured Parzen, (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.20944/preprints202308.1535.v1\u003c/span\u003e\u003cspan address=\"10.20944/preprints202308.1535.v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahmani, S., Telesca, A. M., Fattoruso, G. \u0026amp; Murgante, B. \u003cem\u003eSpatial Multi-criteria Analysis for Identifying Suitable Locations for Green Hydrogen Infrastructure\u003c/em\u003e, 480\u0026ndash;494. (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-031-37114-1_33\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-37114-1_33\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahmani, S., Scorzelli, R. B., Ragone, F., Fattoruso, G. \u0026amp; Murgante, B. Utilizing Spatial Multi-criteria Analysis to Determine Optimal Sites for Green Hydrogen Infrastructure Deployment. \u003cem\u003eSpringer Nat.\u003c/em\u003e 385\u0026ndash;396. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-031-54096-7_34\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-54096-7_34\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, S. et al. Data-Driven Power Prediction for Proton Exchange Membrane Fuel \u003cem\u003eCell. Reactor Syst. Sens.\u003c/em\u003e, \u003cb\u003e24\u003c/b\u003e(18), 6120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/s24186120\u003c/span\u003e\u003cspan address=\"10.3390/s24186120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNavarro Jim\u0026eacute;nez, A. \u003cem\u003ePolicy-Relevant Forecasting of Green Hydrogen Viability: A Comparative Techno-Economic and Machine Learning Analysis of Costa Rica and the United Kingdom\u003c/em\u003e. Available online: (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.20944/preprints202503.2276.v4\u003c/span\u003e\u003cspan address=\"10.20944/preprints202503.2276.v4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYadav, V., Deepanshu, D., Mittal, H., Shah, V. \u0026amp; Kushwaha, O. S. \u003cem\u003eFuel Cell Degradation Prediction Using Machine Learning Models: A Study on Proton Exchange Membrane (PEM) Fuel Cell Dataset\u003c/em\u003e, (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-6710108/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-6710108/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranco, A. \u0026amp; Giovannini, C. Recent and Future Advances in Water Electrolysis for Green Hydrogen Generation: Critical Analysis and Perspectives. \u003cem\u003eSustainability\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (24), 16917. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su152416917\u003c/span\u003e\u003cspan address=\"10.3390/su152416917\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinnveden, G. et al. Sangwon Suh, Recent developments in Life Cycle Assessment,Journal of Environmental Management,91,1,2009,1\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jenvman.2009.06.018\u003c/span\u003e\u003cspan address=\"10.1016/j.jenvman.2009.06.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJindal, A., Shrimali, G. \u0026amp; Tiwary, N. At scale adoption of Green Hydrogen in Indian Industry: Costs, subsidies and policies. \u003cem\u003eEnergy. Sustain. Dev.\u003c/em\u003e \u003cb\u003e83\u003c/b\u003e, 101549. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.esd.2024.101549\u003c/span\u003e\u003cspan address=\"10.1016/j.esd.2024.101549\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng, L., Guo, Y., Liu, S., He, G. \u0026amp; Mauzerall, D. L. Subsidizing Grid-Based Electrolytic Hydrogen Will Increase Greenhouse Gas Emissions in Coal Dominated Power Systems. \u003cem\u003eEnviron. Sci. Technol.\u003c/em\u003e \u003cb\u003e58\u003c/b\u003e (12), 5187\u0026ndash;5195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.est.3c03045\u003c/span\u003e\u003cspan address=\"10.1021/acs.est.3c03045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Energy Agency (IEA). World Energy Outlook 2019. (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iea.org/topics/energy-and-water\u003c/span\u003e\u003cspan address=\"https://www.iea.org/topics/energy-and-water\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfgan, N. H., Carvalho, M. G. \u0026amp; Hovanov, N. V. Energy system assessment with sustainability indicators, Energy Policy,28, 9,2000, 603\u0026ndash;612. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0301-4215(00)00045-8\u003c/span\u003e\u003cspan address=\"10.1016/S0301-4215(00)00045-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElshkaki, A. Materials, energy, water, and emissions nexus impacts on the future contribution of PV solar technologies to global energy scenarios. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 19238. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-019-55853-w\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-55853-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hydrogen fuel cells, Life cycle assessment, Geospatial analysis, Machine learning, Sustainability assessment, India","lastPublishedDoi":"10.21203/rs.3.rs-9283676/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9283676/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHydrogen fuel cells are increasingly promoted as a cornerstone of India\u0026rsquo;s low-carbon energy transition. However, their environmental and infrastructural sustainability is highly context-dependent, influenced by life-cycle impacts, spatial resource constraints, and regional electricity characteristics. This study presents an integrated decision-support framework combining life cycle assessment (LCA), geospatial analysis (GIS), and explainable machine learning (ML) to identify region-specific hydrogen fuel-cell deployment pathways across India.. Cradle-to-gate LCA establishes environmental performance boundaries for electrolysis pathways (and, hence, relative sustainable preference), while geospatial indicators capturing solar irradiation, water stress, and grid carbon intensity are aggregated into composite suitability indices, namely, the Solar\u0026ndash;Water Suitability Index (SWSI) and Hydrogen Penalty Index (HPI). A directional Technology Preference Index (TPI\u0026thinsp;=\u0026thinsp;SWSI\u0026thinsp;\u0026minus;\u0026thinsp;HPI) is used to encode deployment feasibility without artificial bounding. Explainable ML models are subsequently employed to validate dominant drivers and decision logic with the rationale that deterministic rule-based classification produces a national decision landscape, and interpretable machine learning confirms structural coherence without overriding physics-based logic. The integrated framework yields a state-level technology preference classification distinguishing solar-priority fuel cells, grid-linked hydrogen pathways, conditional deployment zones, and regions where hydrogen deployment should be avoided. The results demonstrate that high solar potential alone does not guarantee sustainable hydrogen deployment, particularly in water-stressed or carbon-intensive grid regions. By explicitly linking process-level environmental performance with spatial feasibility and transparent data-driven validation, this work provides a transferable blueprint with actionable insights for policymakers and planners supporting India\u0026rsquo;s hydrogen mission with consideration of resource-constrained energy transitions.\u003c/p\u003e","manuscriptTitle":"Integrating Life Cycle Assessment, Geospatial Analysis, and Explainable Machine Learning for Region-Specific Hydrogen Fuel-Cell Deployment Feasibility in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 15:09:38","doi":"10.21203/rs.3.rs-9283676/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"69a03bff-ca15-445a-a890-97cf4d54d334","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66173015,"name":"Physical sciences/Energy science and technology"},{"id":66173016,"name":"Physical sciences/Engineering"}],"tags":[],"updatedAt":"2026-04-29T06:26:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 15:09:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9283676","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9283676","identity":"rs-9283676","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}