New Satellite Method Reduces Errors in Coal Plant CO2 Estimates

Researchers from the Chinese Academy of Sciences have developed a satellite-based method that significantly enhances the accuracy of carbon dioxide (CO2) emission estimates from coal-fired power plants. This breakthrough, reported in a study published on July 15, 2025, in *Environmental Science & Technology*, aims to address the challenges of monitoring greenhouse gas emissions critical to global climate change mitigation.

Led by Prof. Cheng Tianhai from the Aerospace Information Research Institute, the new approach improves near-real-time tracking of emissions. Accurate measurement of CO2 emissions is essential for understanding and managing the environmental impact of power generation. Traditionally, estimates are based on average emission factors and plant activity data, which often fail to reflect real-time operational changes or pollution controls.

Current carbon monitoring satellites, such as NASA’s Orbiting Carbon Observatory-2 (OCO-2), have limited observation frequencies. This makes it difficult to capture daily and seasonal variations in emissions. To overcome these limitations, the research team introduced the Pollution-Carbon Synergy Model (PCSM). This innovative model utilizes nitrogen oxides (NOx)—pollutants released alongside CO2 during fossil fuel combustion—as a reliable proxy for estimating CO2 emissions.

Enhanced CO2 Tracking Through Innovative Methodology

The NOx-to-CO2 ratio remains relatively stable for each plant under consistent operational conditions, making it a valuable indicator of carbon output. By integrating near-simultaneous satellite data from the Tropospheric Monitoring Instrument (TROPOMI), which detects NOx, and OCO-2, which measures CO2, the researchers were able to derive plant-specific NOx-to-CO2 emission factors. These factors were paired with daily NOx emissions estimated from TROPOMI overpasses, allowing for much higher temporal resolution in daily CO2 emission calculations.

Testing conducted on 15 major coal-fired power plants in the United States demonstrated that the PCSM method significantly outperformed results obtained through OCO-2 alone. The method reduced average annual CO2 estimation errors from 45.8% (approximately 5.02 million tons) to 13.0% (around 1.43 million tons). The PCSM also surpassed leading global emission inventories, like the Open-Source Data Inventory for Anthropogenic CO2 (ODIAC) and Emissions Database for Global Atmospheric Research (EDGAR), increasing correlation with actual data from the U.S. Continuous Emission Monitoring System (CEMS) by 0.16 to 0.35.

When the PCSM methodology was applied to a total of 38 power plants worldwide, the findings indicated that while overall annual CO2 emissions aligned with existing inventory estimates, a more granular analysis revealed systematic biases. Smaller plants were often overestimated, while larger plants were underestimated in current inventories.

Implications for Climate Commitments

“This method offers a practical, cost-effective way to monitor CO2 emissions from space with much higher accuracy,” stated Prof. Cheng. “It provides essential support for countries aiming to track their emissions and meet their climate commitments under the Paris Agreement.”

The implications of this advancement extend beyond immediate accuracy improvements. As nations strive to meet their climate goals, the enhanced ability to monitor emissions effectively can lead to more informed policy decisions and greater accountability in emission reductions.

For further insights, refer to the research by Donghao Fan et al., titled “Pollution-Carbon Synergy Significantly Enhances the Capability of Tracking Power Plants’ CO2 Emissions from Space” in *Environmental Science & Technology*. The study outlines the detailed methodology and findings of this innovative approach.