Within the framework of China's pursuit of green and low-carbon development,Inner Mongolia is characterized by significant carbon emissions,a substantial share of energy-intensive industries,and disparate developm...Within the framework of China's pursuit of green and low-carbon development,Inner Mongolia is characterized by significant carbon emissions,a substantial share of energy-intensive industries,and disparate development levels across its cities,so it faces substantial challenges in attaining the objectives of carbon peak and neutrality.Utilizing the Logarithmic Mean Divisia Index(LMDI)model,this study investigated the drivers and regional differences in carbon emissions.Drawing upon Tapio's decoupling framework,the decoupling status between economic growth and carbon emissions among cities was analyzed in phases.We introduced the Extreme Gradient Boosting(XGBoost)machine learning algorithm to construct a classification model that correlates carbon emission drivers with decoupling states,elucidated by the Shapley Additive exPlanations(SHAP)interpretable model,and performed a spatial analysis of regional differences to assess the significance of industrial energy intensity for achieving strong decoupling in each prefecture-level city.The outcomes revealed two main results.(1)Spatially,regional differences in the influence of driving factors can be classified into four categories:energy intensity-dominant,double-effect negative driven,coexistence of positive and negative effects,and economic growth-driven.(2)Temporally,regional differences in the impact of industrial energy intensity on strong decoupling can be categorized into three types:overall positive,marked fluctuation,and stage stability.Consequently,tailoring emission reduction policies based on regional differences will be instrumental for expediting the achievement of the"dual carbon"targets.展开更多
基金The National Natural Science Foundation of China(71961022)The Natural Science Foundation of Inner Mongolia Autonomous Region(2024MS07012)+3 种基金The Fundamental Research Funds for the Central Universities of Inner Mongolia Autonomous Region(NCYWT23034,NCYWT23043)The Inner Mongolia University of Finance and Economics 2025 High-Quality Research Achievements Cultivation Fund Project(GZCG24247,GZCG2504)The Special Research Project on the Five Major Tasks of Inner Mongolia Autonomous Region by Inner Mongolia University of Finance and Economics(NCXWD2419)The Project of the Regional Digital Economy and Digital Governance Research Center of Inner Mongolia University of Finance and Economics(SZZL202401)。
文摘Within the framework of China's pursuit of green and low-carbon development,Inner Mongolia is characterized by significant carbon emissions,a substantial share of energy-intensive industries,and disparate development levels across its cities,so it faces substantial challenges in attaining the objectives of carbon peak and neutrality.Utilizing the Logarithmic Mean Divisia Index(LMDI)model,this study investigated the drivers and regional differences in carbon emissions.Drawing upon Tapio's decoupling framework,the decoupling status between economic growth and carbon emissions among cities was analyzed in phases.We introduced the Extreme Gradient Boosting(XGBoost)machine learning algorithm to construct a classification model that correlates carbon emission drivers with decoupling states,elucidated by the Shapley Additive exPlanations(SHAP)interpretable model,and performed a spatial analysis of regional differences to assess the significance of industrial energy intensity for achieving strong decoupling in each prefecture-level city.The outcomes revealed two main results.(1)Spatially,regional differences in the influence of driving factors can be classified into four categories:energy intensity-dominant,double-effect negative driven,coexistence of positive and negative effects,and economic growth-driven.(2)Temporally,regional differences in the impact of industrial energy intensity on strong decoupling can be categorized into three types:overall positive,marked fluctuation,and stage stability.Consequently,tailoring emission reduction policies based on regional differences will be instrumental for expediting the achievement of the"dual carbon"targets.
