CHETNA-Overview: City-wise High-resolution Carbon Emissions Tracking and Nationwide Analysis

We present the CHETNA (City-wise High-resolution Carbon Emissions Tracking and Nationwide Analysis) project, an innovative framework designed to generate near real-time high-resolution carbon emissions data for 100 Indian cities across five major sectors: power, traffic, residential, industrial, and aviation. Utilizing advanced technologies including artificial intelligence, large-scale open data scraping, satellite imagery, sophisticated energy models, and field surveys, CHETNA will address critical gaps in emissions tracking and modeling at the city level. CHETNA’s methodologies focus on regions with limited official datasets and inadequate high-resolution data, providing essential insights to support urban planning, climate mitigation, and sustainable urbanization efforts both in India and globally.

India, the world’s third-largest emitter of greenhouse gases (GHGs), plays a pivotal role in global climate mitigation efforts. Its rapidly urbanizing population, expanding economy, and coal-dominated energy structure present both challenges and opportunities for sustainable development. To meet its Paris Agreement commitments, India has pledged to reduce its GHG emissions intensity—emissions per unit of GDP—by 33%–35% by 2030, relative to 2005 levels. However, critical data gaps persist, particularly at the city level, hindering effective city-specific climate action and data-driven decision-making in India’s urban decarbonization. 

To ensure a robust and scalable system for sectoral high-resolution CO₂ emission tracking, CHETNA employs an integrated workflow that combines GHG emission inventories and high-resolution sectoral activity modeling. For sectors such as power, large industrial, and aviation, where reliable national or regional emission inventories are available from open data sources, we developed sophisticated downscaling models to generate gridded emission maps based on those open-source datasets. For sectors lacking comprehensive emission inventories, such as traffic and residential, we adopted a bottom-up approach. Activity models were developed for each sector using machine learning, field-collected data (e.g., traffic sensor and field survey data), and satellite imagery. These activity models were then coupled with advanced emission models. For instance, a fleet-speed-emission model was developed for the traffic sector, while a building-climate-energy model was implemented for the residential sector. In addition to CO₂ emissions, CHETNA provides air pollutant co-emissions by integrating detailed activity data with pollutant-specific emission factors. This approach allows for the assessment of air quality benefits resulting from GHG mitigation efforts, highlighting the co-benefits of reduced air pollutants. 

The dataset generated with the CHETNA project enables policymakers to develop city- and sector-specific strategies, contributing to India's sustainable urban development. Its sectoral high-resolution data would provide insights for guiding urban planning, air pollutant reduction, optimizing transportation systems, enhancing energy efficiency, and implementing effective industrial regulations. Representing a significant advancement in urban GHG emissions monitoring, CHETNA also offers a scalable and replicable framework for other counties or cities facing similar challenges. 

This presentation provides an overview of the CHETNA project, outlining its scope, general concept, workflow design, and simplified methodologies for each sector. At EGU25, we will also present detailed sectoral methodologies and results, including traffic, residential, power, and small industrial sectors.