The baseline of fragility and capability: Hedging socio-environmental impacts of Char Dham expressway in Uttarakhand, India

Conventional socio-environmental impact assessments (SEIAs) of linear infrastructure projects are predominantly built upon static, outcome-oriented frameworks that fail to account for the complex, non-linear dynamics of socio-ecological systems. In geographically diverse environments, administrative and planning misalignment induces a state of temporal parallelism where pre-development, active construction, and post-development phases co-occur simultaneously across different stretches of the same corridor. This spatial fragmentation collapses traditional longitudinal baselines. To address this empirical bottleneck, this study introduces a context-informed framework applied to the Char Dham Highway project in the Indian Himalayas. We implement a methodological convergence by interpreting Geographic Information Systems (GIS) based LULC outputs with integrated tabular machine learning architectures (TabNet and TabPFN) to assess relative household capabilities. Weighted across 49 socio-economic indicators surveyed from 462 local households, space-for-time substitution constructs a continuous, cumulative capability baseline through a hypothetical temporal bridge. The findings indicate that the optimized tabular foundation model (TabPFN) outperformed classical machine learning ensembles, classifying multidimensional household capability profiles with a macro-average accuracy and classification score of 86% with ROC-AUC = 0.986. Phase-contingent feature importance extraction revealed a dynamic constraint hierarchy: pre-development capability differentiation is dictated by baseline asset endowments; construction phases flatten into acute hazard exposures while post-development spaces shift toward physical real estate accumulation along the highway axis. Coupling with longitudinal satellite-based earth observations, this framework supports convergence of multiple sources for assessment purposes. It provides environmental managers with an adaptive, cross-phasal decision-support mechanism for anticipating long-term vulnerability trajectories in data-scarce, highly fragile developmental contexts.