CHETNA-Power Sector: High-Resolution Mapping of Power Sector Emissions in Indian Cities: Bridging Data Gaps for Effective GHG Mitigation and Urban Energy Planning

India's power sector plays a pivotal role in the country's greenhouse gas (GHG) mitigation efforts, contributing 45% of national CO₂ emissions, with coal-fired power plants responsible for 72% of CO₂ emissions from fuel combustion in 2022. The sector's dependence on coal and rising electricity demand pose significant challenges to achieving India's targets of reducing GDP emission intensity by 45% and transitioning to 50% non-fossil fuel installed capacity by 2030. Monitoring emissions at the city level is especially critical, as urban areas concentrate electricity demand, with the residential and industrial sectors accounting for 25.77% and 41.16% of total consumption, respectively. High-resolution, city-specific data is essential for identifying emission hotspots, optimizing renewable energy deployment, and prioritizing energy efficiency improvements.

To address the current dataset gaps in India, we conducted a high spatial-temporal resolution analysis of CO₂ and air pollutant emissions for 100 Indian cities. This analysis integrates diverse open-source datasets, including power plant locations, capacities, and fuel types from the Global Energy Monitor (GEM) and OpenStreetMap (OSM); transmission grid data from OSM; industrial factory data from Indian statistical databases and OSM; emission factors from the Central Electricity Authority (CEA) of India; power generation and outage data from the Indian National Power Portal (NPP); and gridded population and land-use data from the Global Human Settlement Layer and Copernicus Global Land Cover Layers.

The power generation time series was first completed using a machine learning model to address missing data. Then, we developed a grid gravity-based power distribution model to analyze power consumption and emissions. This model evaluates the relative "attractiveness" of power consumption for each grid by incorporating large industrial factories, grid population, and cropland areas. An optimization algorithm was employed to allocate power generation, constrained by transmission grid capacity and minimizing the losses. Parameters were fine-tuned using regional monthly electricity consumption data, establishing a robust framework for spatial emission mapping while excluding electricity imports and exports. Using this gridded power distribution model, we generated high-resolution maps of both CO₂ and air pollutant emissions for each city, offering valuable insights into their spatial distribution across urban areas.

This work, part of the CHETNA project (City-wise High-resolution Carbon Emissions Tracking and Nationwide Analysis), leverages artificial intelligence and advanced datasets to deliver near real-time, high-resolution emissions data for over 100 Indian cities.