Integrating spatial econometrics and remote sensing for pixel-level wind energy corridor identification: evidence from West Bengal, India

Abstract

This study integrates remote sensing and spatial econometric techniques to identify pixel-level wind energy corridors in West Bengal, India. Weibull scale (A) and shape (K) parameter rasters at 100 m resolution were combined with Dynamic World land-cover data to evaluate how landscape characteristics influence wind resource potential. District-level zonal statistics were analysed using Ordinary Least Squares (OLS), Spatial Autoregressive (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). Global Moran’s I (0.723, p < 0.01) revealed significant positive spatial autocorrelation, confirming the importance of explicitly accounting for spatial dependence. Model comparison showed that the Spatial Error Model (SEM) provided the most parsimonious specification based on the Akaike and Bayesian Information Criteria, whereas the Spatial Durbin Model (SDM) achieved the highest overall model fit by capturing additional spatial spillover effects. Building on these results, a pixel-level wind suitability index was developed to delineate contiguous wind energy corridors across the state. The analysis identifies several districts in the southern and coastal regions of West Bengal as possessing relatively high wind energy development potential, demonstrating the value of integrating remote sensing with spatial econometric modelling for renewable energy planning. The proposed framework provides a statistically rigorous planning-support tool; however, engineering feasibility, environmental assessments, transmission infrastructure analysis, and field validation remain essential before implementation.

The authors' abstract, as published at the source. Scientific Reports, 2026 · DOI ↗