An approach for analyzing urban carbon emissions using machine learning models

Carbon peaking and carbon neutrality goals have posed great challenges to transforming local economies into low-carbon economies. Hence, establishing an effective carbon management system is urgent. However, the development of the urban carbon management system is hampered by the immaturity of the carbon emission accounting system at the city level. To compensate for the insufficiency of the existing urban carbon emission accounting system and to find the city government in constructing a perfect carbon emission management system as soon as possible, this study used the data science method based on the statistical data of 285 cities in China from 2005 to 2017 to explore the influencing factors of urban carbon emissions, that is, using light gradient boosting machine and the accumulated local effects interpretable models to screen potential influencing factors of urban carbon emissions. Then, an index system for urban carbon management was evaluated and proposed, and a case analysis was conducted with urban industrial electricity consumption as a background. This method can be easily integrated with the existing urban management system, which could reduce the cost of building a carbon management system.