Exploring the factors driving the decoupling of China’s sulfur dioxide(SO_(2))emissions from economic growth(DEI)is crucial for achieving sustainable development.By analyzing the decoupling indicators and driving fac...Exploring the factors driving the decoupling of China’s sulfur dioxide(SO_(2))emissions from economic growth(DEI)is crucial for achieving sustainable development.By analyzing the decoupling indicators and driving factors at both the generation and treatment stages of SO_(2),more effective targeted mitigation strategies can be developed.We employ the Tapio decoupling model and propose a two-stage method to examine the decoupling issues related to SO_(2).Our findings indicate that:①DEI shows a steady and significant improvement,with SO_(2)emission intensity identified as the primary driver.②for the decoupling of economic growth and SO_(2)generation,energy scale serves as the largest stimulator,while the effect of energy intensity changes from negative to positive,and pollution intensity is first positive and then negative.③For the decoupling of SO_(2)generation and SO_(2)removal,treatment efficiency leads as the largest promoter,followed by treatment intensity.Based on these results,this study recommends that China focuses more on enhancing clean energy utilization and the effectiveness of treatment processes.展开更多
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
The Environmental Kuznets Curve(EKC) model was applied to investigate the relationship between economic growth and water environment quality based on panel data of Taicang during 2010–2017. The typical inversed-U sha...The Environmental Kuznets Curve(EKC) model was applied to investigate the relationship between economic growth and water environment quality based on panel data of Taicang during 2010–2017. The typical inversed-U shaped relationship has been obtained between GDP(gross domestic product) and indicators of ammonia, total nitrogen(TN) and total phosphorus(TP), respectively. The EKC turning point appeared when the GDP per capita was around US$2270, which was much lower than those in some developed countries(US$11,200). However, the decoupling between chemical oxygen demand(COD) and GDP per capita occurred even before this period, which should be attributed to the strict COD emission regulation being implemented since 2010. Further, analysis based on the Tapio decoupling coefficient elasticity model analyzed the ammonia nitrogen and economic development of each industry. We found that the agriculture no-point was strong decoupling in 2011–2014, then came to Recessive decoupling. The domestic wastewater had been in a strong decoupling state;Both urban non-point and industry experienced expansive negative decoupling, due to strict policy that prioritizes the environment over development and the investment in improvement of environment and techniques, both of them gradually came to strong decoupling. The result demonstrated that the EKC turning point could be appear in earlier economic stage and the decoupling coefficient elasticity could be improved through taking strong regulation measures.展开更多
Analyzing the changes in agricultural carbon emissions(ACE)and their influencing factors can provide a sound basis for accurately estimating the carbon balance of agroecosystems.Such analyses can serve as a reference ...Analyzing the changes in agricultural carbon emissions(ACE)and their influencing factors can provide a sound basis for accurately estimating the carbon balance of agroecosystems.Such analyses can serve as a reference for developing policies to mitigate global climate change and promote sustainable agricultural development.Using the carbon emission calculation framework of the Intergovernmental Panel on Climate Change,this study examined the spatiotemporal characteristics of ACE,including total amount,intensity,structure and their influencing factors,in Fujian Province from 2002 to 2022.The logarithmic mean scale index model and Tapio decoupling model were used,with the GM(1,1)model to forecast carbon emissions from 2023 to 2040.The results indicate that both the total emissions and intensity of ACE had fluctuating downward trends and agricultural material inputs were the largest contributors to ACE.Additionally,total ACE was found to have a spatial pattern higher in the west and lower in the east and agricultural production efficiency was the primary factor in reducing ACE.ACE was clearly decoupled from economic development and is projected to continually decline after2023.展开更多
基金the National Natural Science Foundation of China[Grant No.52270183].
文摘Exploring the factors driving the decoupling of China’s sulfur dioxide(SO_(2))emissions from economic growth(DEI)is crucial for achieving sustainable development.By analyzing the decoupling indicators and driving factors at both the generation and treatment stages of SO_(2),more effective targeted mitigation strategies can be developed.We employ the Tapio decoupling model and propose a two-stage method to examine the decoupling issues related to SO_(2).Our findings indicate that:①DEI shows a steady and significant improvement,with SO_(2)emission intensity identified as the primary driver.②for the decoupling of economic growth and SO_(2)generation,energy scale serves as the largest stimulator,while the effect of energy intensity changes from negative to positive,and pollution intensity is first positive and then negative.③For the decoupling of SO_(2)generation and SO_(2)removal,treatment efficiency leads as the largest promoter,followed by treatment intensity.Based on these results,this study recommends that China focuses more on enhancing clean energy utilization and the effectiveness of treatment processes.
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.
基金supported by water pollution control and treatment of the National Major Science and Technology project (No. 2017ZX07106003-002-001)。
文摘The Environmental Kuznets Curve(EKC) model was applied to investigate the relationship between economic growth and water environment quality based on panel data of Taicang during 2010–2017. The typical inversed-U shaped relationship has been obtained between GDP(gross domestic product) and indicators of ammonia, total nitrogen(TN) and total phosphorus(TP), respectively. The EKC turning point appeared when the GDP per capita was around US$2270, which was much lower than those in some developed countries(US$11,200). However, the decoupling between chemical oxygen demand(COD) and GDP per capita occurred even before this period, which should be attributed to the strict COD emission regulation being implemented since 2010. Further, analysis based on the Tapio decoupling coefficient elasticity model analyzed the ammonia nitrogen and economic development of each industry. We found that the agriculture no-point was strong decoupling in 2011–2014, then came to Recessive decoupling. The domestic wastewater had been in a strong decoupling state;Both urban non-point and industry experienced expansive negative decoupling, due to strict policy that prioritizes the environment over development and the investment in improvement of environment and techniques, both of them gradually came to strong decoupling. The result demonstrated that the EKC turning point could be appear in earlier economic stage and the decoupling coefficient elasticity could be improved through taking strong regulation measures.
基金supported by the Humanities and Social Sciences Program of the Ministry of Education(21YJCZH006)the Water Conservancy Science and Technology Program of Fujian Province(MSK202435)the horizontal commissioned project of the Soil and Water Conservation Experimental Station of Fujian Province(Construction of SWAT Model for Soil Erosion Control in Typical Eco-clean Sub-watersheds of the Red Loam Erosion Area and Assessment of the Effectiveness)。
文摘Analyzing the changes in agricultural carbon emissions(ACE)and their influencing factors can provide a sound basis for accurately estimating the carbon balance of agroecosystems.Such analyses can serve as a reference for developing policies to mitigate global climate change and promote sustainable agricultural development.Using the carbon emission calculation framework of the Intergovernmental Panel on Climate Change,this study examined the spatiotemporal characteristics of ACE,including total amount,intensity,structure and their influencing factors,in Fujian Province from 2002 to 2022.The logarithmic mean scale index model and Tapio decoupling model were used,with the GM(1,1)model to forecast carbon emissions from 2023 to 2040.The results indicate that both the total emissions and intensity of ACE had fluctuating downward trends and agricultural material inputs were the largest contributors to ACE.Additionally,total ACE was found to have a spatial pattern higher in the west and lower in the east and agricultural production efficiency was the primary factor in reducing ACE.ACE was clearly decoupled from economic development and is projected to continually decline after2023.
