Dynamic Coupling Between Atmospheric CO2 Concentration and Land Surface Temperature in Major Urban Agglomerations in China: Insights for Sustainable Urban Development

To provide new insights into the integrated management of carbon and heat for sustainable urban development, this study systematically investigates the complex relationship between atmospheric CO2 concentrations and land surface temperature (LST). Utilizing OCO-2 and OCO-3 satellite observations, combined with meteorological conditions, air pollutants, and spatial characteristics, a high-resolution (0.1 degrees x 0.1 degrees) monthly CO2 column concentration (XCO2) dataset for China spanning 2015 to 2022 was generated using the Random Forest algorithm. The study focuses on urban agglomerations, conducting centroid migration and coupling analyses of XCO2 and LST to elucidate their spatiotemporal distribution patterns and evolution. Results reveal significant seasonal variations in XCO2, which has exhibited a gradual increase over the years. The spatiotemporal distributions of XCO2 and LST in urban agglomerations show a high degree of consistency, with centroids either converging or following similar movement trajectories. Additionally, the degree of coupling and coordination between XCO2 and LST has improved annually, indicating a closer interrelationship. These findings enhance our understanding of climate system dynamics and provide essential scientific evidence and decision-making support for addressing climate change. By clarifying the connection between atmospheric CO2 and LST, this study contributes to the development of more effective strategies for carbon reduction and urban heat island mitigation, thereby advancing cities towards greener, lower-carbon, and more sustainable development pathways.