A graph-factor-based random forest model for assessing and predicting carbon emission patterns - Pearl River Delta urban agglomeration

As China actively fulfills its emissions reduction obligations to meet the Paris climate goals, it is crucial to explore carbon reduction policies at the city level, given the important role and potential of cities in China's emissions reduction efforts. In this study, a comprehensive assessment and prediction of carbon emission patterns in the Pearl River Delta urban agglomeration was conducted by developing an integrated model that incorporates graph representation learning, factorial analysis, and random forest methods. The main findings are (1) there is significant heterogeneity in carbon emissions across industries and across time and space; (2) carbon clusters can more accurately characterize the flow of carbon emissions than carbon supply chains; (3) the nonmetallic manufacture, electricity supply and transportation industries play a decisive role in carbon emission reduction; and (4) the prediction results show that the carbon emissions of the Pearl River Delta urban agglomeration will reach 349.2 million tons in 2021, and will then show a declining trend year by year, dropping to a projected 179.8 million tons in 2035. Based on the above findings, it is recommended that the monitoring of key carbon emission clusters should be strengthened, and the monitoring data should be utilized to establish a cross-city and cross-industry cooperation mechanism for emission reduction. In addition, a clear set of action plans and strategies should be formulated and implemented to ensure that the carbon emission targets for 2035 are realized. [Display omitted] •►A graph-factor-based random forest model (GRFM) is developed.•►Carbon emissions have significant sectoral and spatial-temporal heterogeneity.•►Electricity supply is the largest carbon emitter.•►Developed economies such as Guangzhou, Dongguan, Foshan dominate carbon emissions.