Content area

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.

Details

Title
Carbon market and emission reduction: evidence from evolutionary game and machine learning
Pages
488
Publication year
2025
Publication date
Dec 2025
Publisher
Springer Nature B.V.
e-ISSN
2662-9992
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3186677028
Copyright
Copyright Palgrave Macmillan Dec 2025