Group 1 - The company has leveraged big data in electricity to support local government in optimizing industrial structure, risk prevention, and green development, providing precise data support [1] - In the first half of 2025, the company provided decision-making support to government departments 84 times, representing a 45% year-on-year increase [1] - A specialized digital team was established to conduct dynamic analysis of energy consumption for small and micro enterprises, key industrial chains, characteristic towns, and the private economy [1] Group 2 - The company developed an intelligent monitoring system for enterprise energy consumption, focusing on identifying illegal mining activities through real-time data collection and anomaly detection [1] - As of June 30, the system has completed full monitoring of 179 high-risk enterprises and assisted in the investigation of 4 illegal mining cases [1] - The company created the first "electricity-carbon" analysis model in Shanxi Province, establishing a carbon emission monitoring platform for 115 industrial enterprises [2] Group 3 - The carbon emission monitoring platform includes seven functional modules, visualizing carbon emission distribution and trends, and providing energy-saving strategies [2] - In the first half of the year, the platform helped environmental departments reduce carbon emissions by 52.18 million tons from 9 enterprises through measures like production limits and equipment upgrades [2]

Core Viewpoint - The research team from the Chinese Academy of Sciences has made significant advancements in the precise monitoring and accounting of carbon emissions from large coal-fired power plants, which is crucial under the global "dual carbon" goals of peak carbon emissions and carbon neutrality [1][2]. Group 1: Research Breakthroughs - The research team has developed a new satellite remote sensing solution that enables high-precision dynamic quantification and mapping of carbon dioxide emissions from large coal-fired power plants, marking a significant achievement in the field [1]. - The study was published in the international academic journal "Clean Production," highlighting its importance in providing objective and effective technical means for carbon emission accounting and verification [1][2]. Group 2: Methodological Innovations - The team introduced a model optimization algorithm based on the U.S. Orbiting Carbon Observatory-3, significantly improving the efficiency of background carbon emission value identification [2]. - A dynamic wind direction correction algorithm was developed to enhance the accuracy of smoke plume trajectory inversion, along with a smoke plume elevation model that accurately characterizes the dynamic lifting process of flue gas [2]. Group 3: Implications for Carbon Monitoring - The research results indicate that the improved Gaussian plume model successfully quantified carbon dioxide emissions from 14 large coal-fired power plants, with emissions ranging from 21.54 thousand tons to 82.3 thousand tons per day, demonstrating a significant increase in inversion accuracy [2]. - This research signifies a critical shift from static carbon emission inventories to dynamic management, which is essential for effective carbon monitoring and policy calibration [3].

Core Viewpoint - The article highlights significant advancements in the precise monitoring and accounting of carbon emissions from large coal-fired power plants, which are crucial for achieving global carbon neutrality goals [1][2]. Group 1: Research Breakthroughs - The Chinese Academy of Sciences has achieved a breakthrough in remote sensing and carbon emission estimation, enabling high-precision dynamic quantification and mapping of CO2 emissions from large coal-fired power plants [2]. - The research team developed a new satellite remote sensing approach, marking the first time high-precision dynamic quantification of CO2 emissions from large coal-fired power plants has been realized internationally [2][4]. Group 2: Importance of Carbon Emission Monitoring - Coal-fired power plants account for approximately 50% of global carbon emissions from fossil fuel combustion, making accurate carbon accounting essential for global carbon assessments and the electricity sector [3]. - Traditional methods of calculating emissions rely heavily on self-reported data from power plants, which can lead to discrepancies and lack of comparability due to the absence of unified international accounting standards [3]. Group 3: Methodological Innovations - The research team introduced an innovative optimization algorithm that significantly enhances the efficiency of identifying background carbon emission levels and improves the accuracy of smoke plume trajectory inversion [4]. - The study successfully quantified CO2 emissions from 14 large coal-fired power plants, with emissions ranging from 21.54 thousand tons to 82.3 thousand tons per day, demonstrating a marked improvement in inversion accuracy [4].