India is the third-largest contributor to global energy-use and anthropogenic carbon emissions. India’s urban energy transitions are critical to meet its climate goals due to the country’s rapid urbanization. However, no baseline urban energy-use dataset covers all Indian urban districts in ways that align with national totals and integrate social-economic-infrastructural attributes to inform such transitions. This paper develops a novel bottom-up plus top-down approach, comprehensively integrating multiple field surveys and utilizing machine learning, to model All Urban areas’ Energy-use (AllUrE) across all 640 districts in India, merged with social-economic-infrastructural data. Energy use estimates in this AllUrE-India dataset are evaluated by comparing with reported energy-use at three scales: nation-wide, state-wide, and city-level. Spatially granular AllUrE data aggregated nationally show good agreement with national totals (<2% difference). The goodness-of-fit ranged from 0.78–0.95 for comparison with state-level totals, and 0.90–0.99 with city-level data for different sectors. The relatively strong alignment at all three spatial scales demonstrates the value of AllUrE-India data for modelling urban energy transitions consistent with national energy and climate goals.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Information Systems
- Computer Science Applications
- Statistics, Probability and Uncertainty
- Library and Information Sciences