@article{182626,
  keywords = {India, Energy, NSF Sustainable Healthy Cities Network, Data and Models},
  author = {Kangkang Tong and Ajay Nagpure and Anu Ramaswami},
  title = {All urban areas{\textquoteright} energy use data across 640 districts in India for the year 2011},
  abstract = {<p>India is the third-largest contributor to global energy-use and anthropogenic carbon emissions. India{\textquoteright}s urban energy transitions are critical to meet its climate goals due to the country{\textquoteright}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{\textquoteright} 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 (\&lt;2\% difference). The goodness-of-fit ranged from 0.78{\textendash}0.95 for comparison with state-level totals, and 0.90{\textendash}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.</p>
},
  year = {2021},
  journal = {Scientific Data},
  volume = {8},
  number = {104},
  url = {https://www.nature.com/articles/s41597-021-00853-7},
  language = {eng},
}