Assessing the Dynamic and Spatial Drivers of CO₂ Emissions in China: The Role of Urbanization, Affluence, and Technology

This study examines the dynamic and spatial drivers of CO 2 emissions across Chinese provinces from 2000 to 2021. Using dynamic spatial panel models, ordinary dynamic panel models, and static spatial panel models, the research applies system generalized method of moments (GMM) and quasi-maximum likelihood (QML) methods to address endogeneity and improve coefficient accuracy. The results indicate that past CO 2 emissions significantly influence current emissions, demonstrating a dynamic inertia effect. The study also identifies substantial spatial spillover effects, revealing that emissions in any one province affect neighboring regions. Urbanization and urban sprawl are found to contribute significantly to CO 2 emissions, primarily through infrastructure development, increased transportation needs, and higher energy consumption. Population growth and rising affluence are positively correlated with emissions, while technological advancements, such as cleaner energy and improved efficiency, are associated with reduced emissions intensity. The analysis supports the Environmental Kuznets Curve (EKC) hypothesis, suggesting that emissions increase with affluence up to a threshold, beyond which technological improvements and better environmental governance lead to reductions. The findings highlight the necessity of regionally coordinated environmental policies to address spatial interdependencies and the need for sector-specific strategies to mitigate emissions in industries like agriculture, construction, and transportation. The study provides valuable insights for designing policies to manage CO 2 emissions effectively.