Satellite-Based Method Enhances CO₂ Emission Estimates from Coal Plants

A groundbreaking satellite-based methodology developed by researchers from the Chinese Academy of Sciences significantly improves the accuracy of carbon dioxide (CO₂) emissions estimates from coal-fired power plants. This innovative approach, detailed in a study published in the journal Environmental Science & Technology on July 15, 2025, utilizes a novel model that leverages the stable relationship between nitrogen oxides (NOx) and CO₂ emissions, enhancing the near-real-time monitoring capabilities critical for addressing global climate change.

Led by Professor Cheng Tianhai from the Aerospace Information Research Institute, the research team identified the limitations of traditional emission estimation methods, which heavily rely on average emission factors and plant activity data. These traditional estimates often fail to account for real-time operational changes and pollution control measures, leading to significant inaccuracies. Current satellite monitoring technologies, such as NASA's Orbiting Carbon Observatory-2 (OCO-2), also face challenges due to their limited observation frequencies, which hinder the assessment of daily and seasonal variations in emissions.

To overcome these challenges, the team developed the Pollution-Carbon Synergy Model (PCSM). This model employs NOx emissions, which are co-released with CO₂ during the combustion of fossil fuels, as a reliable proxy for estimating CO₂ emissions. The NOx-to-CO₂ ratio remains relatively constant for individual plants under consistent operational conditions, allowing for more precise emission tracking. By integrating satellite data from the Tropospheric Monitoring Instrument (TROPOMI) to measure NOx and OCO-2 for CO₂, the researchers could derive specific NOx-to-CO₂ emission factors for coal plants.

In comparative tests conducted on 15 major coal-fired power plants in the United States, the PCSM method demonstrated a remarkable reduction in average annual CO₂ estimation errors, decreasing from 45.8% (equating to approximately 5.02 million tons) to just 13.0% (1.43 million tons). The model also showed improved correlation with actual emissions data from the U.S. Continuous Emission Monitoring System (CEMS), enhancing correlation coefficients by 0.16 to 0.35 compared to existing global emission inventories, including the Open-Source Data Inventory for Anthropogenic CO₂ (ODIAC) and the Emissions Database for Global Atmospheric Research (EDGAR).

When applied to a global dataset of 38 coal-fired power plants, the PCSM revealed that while total annual CO₂ emissions were consistent with inventory estimates, discrepancies became apparent when examining emissions on a more granular level. Specifically, smaller plants were typically overestimated while larger facilities faced underestimations in traditional inventories.

Professor Cheng emphasized the practical implications of this new methodology, stating, "This method offers a practical, cost-effective way to monitor CO₂ emissions from space with much higher accuracy. It provides essential support for countries aiming to track their emissions and meet their climate commitments under the Paris Agreement."

The development of the PCSM reflects a critical advancement in emissions monitoring technology at a time when accurate data on greenhouse gas emissions is increasingly vital for policymakers and environmental advocates alike. As nations strive to fulfill their climate commitments and mitigate the effects of climate change, methodologies like the PCSM represent significant steps forward in the quest for reliable environmental data. With the capability to provide near-real-time emissions estimates, this innovative approach not only enhances our understanding of coal-fired power generation's environmental impact but also supports global efforts to combat climate change effectively.

Future research may focus on refining the PCSM for broader applications across various emission sources and integrating additional satellite data to further enhance accuracy and reliability in emissions monitoring. As the urgency of climate action intensifies, such technological advancements will be crucial in supporting global sustainability goals.