Satellite-Based Method Dramatically Improves Accuracy of Coal-fired Power Plant CO₂ Emissions Estimates

A  study published in Environmental Science & Technology introduces a satellite-based method that significantly improves the accuracy of carbon dioxide (CO₂) emission estimates from coal-fired power plants. Developed by researchers at the Aerospace Information Research Institute (AIR) of the Chinese Academy of Sciences, the approach enhances our ability to track greenhouse gas emissions in near real-time—crucial for global efforts to combat climate change and achieve national climate goals.

Accurately measuring CO₂ emissions is essential for understanding and managing the environmental impacts of power generation. However, current emission estimates largely rely on inventories built from average emission factors and plant activity data. These traditional methods often fail to reflect real-time changes in emissions caused by varying operational conditions or pollution control measures. Complicating matters further, carbon-focused satellites like NASA's Orbiting Carbon Observatory-2 (OCO-2) offer limited observation frequency, making it difficult to capture daily and seasonal variations in emissions.

To address these challenges, a research team at AIR, led by Professor CHENG Tianhai, developed a new model called the Pollution-Carbon Synergy Model (PCSM). The innovative method uses nitrogen oxides (NOₓ)—pollutants that are co-emitted with CO₂ during fossil fuel combustion—as a reliable proxy to estimate CO₂ emissions. Since the NOₓ-to-CO₂ emission ratio remains relatively stable for each plant under consistent infrastructure, it can serve as a valuable indicator of carbon output.

By combining satellite observations from TROPOMI (which detects NOₓ) and OCO-2 (which measures CO₂) acquired at nearly the same time, the researchers derived plant-specific NOₓ-to-CO₂ emission factors. These factors were then coupled with daily NOₓ emissions estimated from TROPOMI overpasses to calculate daily CO₂ emissions at significantly higher temporal resolution.

When applied to 15 major coal-fired power plants in the United States, the PCSM method significantly improved annual CO₂ emission estimates compared to using OCO-2 alone, reducing the average error from 45.8% (5.02 million tons) to just 13.0% (1.43 million tons). It also outperformed leading global emission inventories such as ODIAC and EDGAR, improving the correlation with actual emission data from the U.S. Continuous Emission Monitoring System (CEMS) by 0.16 to 0.35.

The PCSM method was applied to 38 power plants worldwide and found that, although the overall annual CO₂ emissions were consistent with inventory estimates, a classification by 10 MtCO₂/yr revealed systematic biases—emissions from smaller plants were overestimated, while those from larger plants were underestimated in existing inventories.

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

The PCSM is expected to play a critical role in efforts to achieve synergistic pollution and carbon reduction—an approach that seeks to reduce greenhouse gases and air pollutants together. By offering more frequent and reliable emission data, the method could support smarter regulation, cleaner energy strategies, and more informed climate policy decisions around the world.