Carbon market and emission reduction: evidence from evolutionary game and machine learning - Keyang Zhan; Zhengning Pu (Humanities & Social Sciences Communications)
Abstract The carbon market is a key tool for China to meet its emission reduction targets, but it is still in the early stages of development. More evidence is needed to assess its effectiveness in reducing carbon emissions. This paper establishes an evolutionary game model to analyze the interaction between the government and enterprises and applies the Gradient Boosting Decision Tree (GBDT) algorithm to identify carbon emission reduction effects of the carbon market based on carbon emission data from 2000 to 2019. The theoretical model reveals that the construction of China’s carbon market needs to go through three stages: stages of lack of enthusiasm from both the government and enterprises, government dominance, and market dominance. The empirical results show that the carbon market has a significant carbon emission reduction effect, which affects regional carbon emissions through technological innovation, fiscal, and digitalization effects. Further analysis indicates that the maturity of the carbon market and the readjustment of industrial structure contribute to carbon emission reduction effects. Although carbon emission reduction effects are not achieved by reducing labor employment, a resource curse effect may still emerge. This study deepens the understanding of China’s carbon market construction and offers valuable insights for policy practices aimed at high-quality development.