Calculation of CO₂ Emissions from China at Regional Scales Using Remote Sensing Data

Since industrialization, global carbon dioxide (CO₂) emissions have been rising substantially, playing an increasingly important role in global warming and climate change. As the largest CO₂ emitter, China has proposed an ambitious reduction plan of peaking before 2030 and achieving carbon neutrality by 2060. Calculation of CO₂ emissions inventories at regional scales (e.g., city and county) has great significance in terms of China’s regional carbon policies as well as in achieving the national targets. However, most of the existing emissions data were calculated based on fossil fuel consumptions and were thus limited to the provinces in China, making it challenging to compare and analyze the CO₂ emissions of different cities and counties within a province. Machine learning methods provided a promising alternative but were still suffering from the lack of availability of training samples at city or county scales. Accordingly, this study proposed to use the energy consumption per unit GDP (ECpGDP) and GDP to calculate the effective CO₂ emissions, which are the CO₂ emissions if all consumed energy was generated by standard coal. Random forest models were then trained to establish relationships between the remote sensing night-light data and effective CO₂ emissions. A total of eight predictor variables were used, including the night-light data, the urbanization ratio, the population density, the type of sensors and administrative divisions, latitude, longitude, and the area of each city or county. Meanwhile, the mean value of the five-fold cross-validation model was used as the estimated effective CO₂ emissions in order to avoid overfitting. The evaluation showed a root mean square error (RMSE) of 10.972 million tons and an overall Pearson’s correlation coefficient (R) of 0.952, with satisfactory spatial and temporal consistency. The effective CO₂ emissions of 349 cities and 2843 counties in China during 1992–2021 were obtained, providing a promising dataset for CO₂-emission-related applications.