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

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.