Macroscopic grasp of agricultural carbon emissions status, spatial-temporal characteristics as well as driving factors are the basic premise in further research on China’s agricultural carbon emissions. Based on 23 k...Macroscopic grasp of agricultural carbon emissions status, spatial-temporal characteristics as well as driving factors are the basic premise in further research on China’s agricultural carbon emissions. Based on 23 kinds of major carbon emission sources including agricultural materials inputs, paddy ifeld, soil and livestock breeding, this paper ifrstly calculated agricultural carbon emissions from 1995 to 2010, as well as 31 provinces and cities in 2010 in China. We then made a decomposed analysis to the driving factors of carbon emissions with logarithmic mean Divisia index （LMDI） model. The results show：（1） The amount of agricultural carbon emissions is 291.1691 million t in 2010. Compared with 249.5239 million t in 1995, it increased by 16.69%, in which, agricultural materials inputs, paddy ifeld, soil, enteric fermentation, and manure management accounted for 33.59, 22.03, 7.46, 17.53 and 19.39%of total agricultural carbon emissions, respectively. Although the amount exist ups and downs, it shows an overall trend of cyclical rise; （2） There is an obvious difference among regions：the amount of agricultural carbon emissions from top ten zones account for 56.68%, while 9.84%from last 10 zones. The traditional agricultural provinces, especially the major crop production areas are the main source regions. Based on the differences of carbon emission rations, 31 provinces and cities are divided into ifve types, namely agricultural materials dominant type, paddy ifeld dominant type, enteric fermentation dominant type, composite factors dominant type and balanced type. The agricultural carbon emissions intensity in west of China is the highest, followed by the central region, and the east zone is the lowest; （3） Compared with 1995, efifciency, labor and structure factors cut down carbon emissions by 65.78, 27.51 and 3.19%, respectively;while economy factor increase carbon emissions by 113.16%.展开更多
Through the matching relationship between land use types and carbon emission items, this paper estimated carbon emissions of different land use types in Nanjing City, China and analyzed the influencing factors of carb...Through the matching relationship between land use types and carbon emission items, this paper estimated carbon emissions of different land use types in Nanjing City, China and analyzed the influencing factors of carbon emissions by Logarithmic Mean Divisia Index（LMDI） model. The main conclusions are as follows： 1） Total anthropogenic carbon emission of Nanjing increased from 1.22928 ×10^7 t in 2000 to 3.06939 × 10^7 t in 2009, in which the carbon emission of Inhabitation, mining ＆ manufacturing land accounted for 93% of the total. 2） The average land use carbon emission intensity of Nanjing in 2009 was 46.63 t/ha, in which carbon emission intensity of Inhabitation, mining ＆ manufacturing land was the highest（200.52 t/ha）, which was much higher than that of other land use types. 3） The average carbon source intensity in Nanjing was 16 times of the average carbon sink intensity（2.83 t/ha） in 2009, indicating that Nanjing was confronted with serious carbon deficit and huge carbon cycle pressure. 4） Land use area per unit GDP was an inhibitory factor for the increase of carbon emissions, while the other factors were all contributing factors. 5） Carbon emission effect evaluation should be introduced into land use activities to formulate low-carbon land use strategies in regional development.展开更多
Within the context of CO_(2)emission peaking and carbon neutrality,the study of CO_(2)emissions at the provincial level is few.Sichuan Province in China has not only superior clean energy resources endowment but also ...Within the context of CO_(2)emission peaking and carbon neutrality,the study of CO_(2)emissions at the provincial level is few.Sichuan Province in China has not only superior clean energy resources endowment but also great potential for the reduction of CO_(2)emissions.Therefore,using logarithmic mean Divisia index(LMDI)model to analysis the influence degree of different influencing factors on CO_(2)emissions from final energy consumption in Sichuan Province,so as to formulate corresponding emission reduction countermeasures from different paths according to the influencing factors.Based on the data of final energy consumption in Sichuan Province from 2010 to 2019,we calculated CO_(2)emission by the indirect emission calculation method.The influencing factors of CO_(2)emissions originating from final energy consumption in Sichuan Province were decomposed into population size,economic development,industrial structure,energy consumption intensity,and energy consumption structure by the Kaya-logarithmic mean Divisia index(LMDI)decomposition model.At the same time,grey correlation analysis was used to identify the correlation between CO_(2)emissions originating from final energy consumption and the influencing factors in Sichuan Province.The results showed that population size,economic development and energy consumption structure have positive contributions to CO_(2)emissions from final energy consumption in Sichuan Province,and economic development has a significant contribution to CO_(2)emissions from final energy consumption,with a contribution rate of 519.11%.The industrial structure and energy consumption intensity have negative contributions to CO_(2)emissions in Sichuan Province,and both of them have significant contributions,among which the contribution rate of energy consumption structure was 325.96%.From the perspective of industrial structure,secondary industry makes significant contributions and will maintain a restraining effect;from the perspective of energy consumption structure,industry sector has a significant contribution.The results of this paper are conducive to the implementation of carbon emission reduction policies in Sichuan Province.展开更多
The scarcity of water resources caused by the unique topography and uneven rainfall distribution in Hainan Island has become a major factor restricting local development. In order to provide effective and scientific r...The scarcity of water resources caused by the unique topography and uneven rainfall distribution in Hainan Island has become a major factor restricting local development. In order to provide effective and scientific reference basis for the overall water resource utilization status and solving this problem, this study calculated the water resource utilization situation of Hainan Island from 2017 to 2021 in detail using methods including water resource ecological footprint analysis. Furthermore, a spatial correlation analysis was conducted to examine the island's water resource utilization characteristics, and the driving factors behind the changes in water resource utilization over the past five years were analyzed using the LMDI model. The results show that:(1) During the study period, the water resource ecological footprint in Hainan Island exhibited a slow growth trend, while the ecological carrying capacity showed a downward tendency.The per capita ecological deficit of water resources remains relatively high, and the water consumption per 10 000 yuan GDP in the whole land continues to decrease, indicating that the overall pressure on water resource demand remains high with significant regional differences accompanied by the efficiency of water resource utilization steadily improving at the same time;(2) Agricultural water use accounts for the highest proportion in the entire water use structure, while ecological water use represents the smallest share, with a year-on-year increase, indicating that Hainan Island highlights the agricultural development and is increasingly conscious of the ecological environment;(3) Significant spatial differentiation in water resource utilization characteristics exists in Hainan Island, with the western region being a hot spot aggregation area for per capita water resource ecological footprint, per capita ecological carrying capacity of water resources, water consumption per 10 000 yuan GDP, while it is a cold spot cluster area for per capita ecological deficit of water resources. The opposite holds true for the eastern region of Hainan Island;(4) Economic and technological factors have a major impact on the changes in water resource ecological footprint within the designated area. Among them, economic factors drive the growth of the water resource ecological footprint in Hainan Island, and exacerbate local water resource consumption, while technological factors negatively contribute to the amount of water resource utilization in Hainan Island, indicating that advanced technology has improved water resource utilization efficiency and significantly reduced water resource consumption.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
Electricity consumption is one of the major contributors to greenhouse gas emissions.In this study,we build a power consumption carbon emission measurement model based on the operating margin factor.We use the decompo...Electricity consumption is one of the major contributors to greenhouse gas emissions.In this study,we build a power consumption carbon emission measurement model based on the operating margin factor.We use the decomposition and decoupling technology of logarithmic mean Divisia index method to quantify six effects(emission intensity,power generation structure,consumption electricity intensity,economic scale,population structure,and population scale)and comprehensively reflect the degree of dependence of electricity consumption carbon emissions on China's economic development and population changes.Moreover,we utilize the decoupling model to analyze the decoupling state between carbon emissions and economic growth and identify corresponding energy efficiency policies.The results of this study provide a new perspective to understand carbon emission reduction potentials in the electricity use of China.展开更多
基金supported by the National Natural Science Foundation of China (71273105)the Fundamental Research Funds for the Central Universities,China (2013YB12)
文摘Macroscopic grasp of agricultural carbon emissions status, spatial-temporal characteristics as well as driving factors are the basic premise in further research on China’s agricultural carbon emissions. Based on 23 kinds of major carbon emission sources including agricultural materials inputs, paddy ifeld, soil and livestock breeding, this paper ifrstly calculated agricultural carbon emissions from 1995 to 2010, as well as 31 provinces and cities in 2010 in China. We then made a decomposed analysis to the driving factors of carbon emissions with logarithmic mean Divisia index （LMDI） model. The results show：（1） The amount of agricultural carbon emissions is 291.1691 million t in 2010. Compared with 249.5239 million t in 1995, it increased by 16.69%, in which, agricultural materials inputs, paddy ifeld, soil, enteric fermentation, and manure management accounted for 33.59, 22.03, 7.46, 17.53 and 19.39%of total agricultural carbon emissions, respectively. Although the amount exist ups and downs, it shows an overall trend of cyclical rise; （2） There is an obvious difference among regions：the amount of agricultural carbon emissions from top ten zones account for 56.68%, while 9.84%from last 10 zones. The traditional agricultural provinces, especially the major crop production areas are the main source regions. Based on the differences of carbon emission rations, 31 provinces and cities are divided into ifve types, namely agricultural materials dominant type, paddy ifeld dominant type, enteric fermentation dominant type, composite factors dominant type and balanced type. The agricultural carbon emissions intensity in west of China is the highest, followed by the central region, and the east zone is the lowest; （3） Compared with 1995, efifciency, labor and structure factors cut down carbon emissions by 65.78, 27.51 and 3.19%, respectively;while economy factor increase carbon emissions by 113.16%.
基金Under the auspices of National Natural Science Foundation of China(No.41301633)National Social Science Foundation of China(No.10ZD&030)+1 种基金Postdoctoral Science Foundation of China(No.2012M511243,2013T60518)Clean Development Mechanism Foundation of China(No.1214073,2012065)
文摘Through the matching relationship between land use types and carbon emission items, this paper estimated carbon emissions of different land use types in Nanjing City, China and analyzed the influencing factors of carbon emissions by Logarithmic Mean Divisia Index（LMDI） model. The main conclusions are as follows： 1） Total anthropogenic carbon emission of Nanjing increased from 1.22928 ×10^7 t in 2000 to 3.06939 × 10^7 t in 2009, in which the carbon emission of Inhabitation, mining ＆ manufacturing land accounted for 93% of the total. 2） The average land use carbon emission intensity of Nanjing in 2009 was 46.63 t/ha, in which carbon emission intensity of Inhabitation, mining ＆ manufacturing land was the highest（200.52 t/ha）, which was much higher than that of other land use types. 3） The average carbon source intensity in Nanjing was 16 times of the average carbon sink intensity（2.83 t/ha） in 2009, indicating that Nanjing was confronted with serious carbon deficit and huge carbon cycle pressure. 4） Land use area per unit GDP was an inhibitory factor for the increase of carbon emissions, while the other factors were all contributing factors. 5） Carbon emission effect evaluation should be introduced into land use activities to formulate low-carbon land use strategies in regional development.
基金financially supported by the National Natural Science Foundation of China(41771535)the National Social Science Foundation Major Project(20&ZD092)。
文摘Within the context of CO_(2)emission peaking and carbon neutrality,the study of CO_(2)emissions at the provincial level is few.Sichuan Province in China has not only superior clean energy resources endowment but also great potential for the reduction of CO_(2)emissions.Therefore,using logarithmic mean Divisia index(LMDI)model to analysis the influence degree of different influencing factors on CO_(2)emissions from final energy consumption in Sichuan Province,so as to formulate corresponding emission reduction countermeasures from different paths according to the influencing factors.Based on the data of final energy consumption in Sichuan Province from 2010 to 2019,we calculated CO_(2)emission by the indirect emission calculation method.The influencing factors of CO_(2)emissions originating from final energy consumption in Sichuan Province were decomposed into population size,economic development,industrial structure,energy consumption intensity,and energy consumption structure by the Kaya-logarithmic mean Divisia index(LMDI)decomposition model.At the same time,grey correlation analysis was used to identify the correlation between CO_(2)emissions originating from final energy consumption and the influencing factors in Sichuan Province.The results showed that population size,economic development and energy consumption structure have positive contributions to CO_(2)emissions from final energy consumption in Sichuan Province,and economic development has a significant contribution to CO_(2)emissions from final energy consumption,with a contribution rate of 519.11%.The industrial structure and energy consumption intensity have negative contributions to CO_(2)emissions in Sichuan Province,and both of them have significant contributions,among which the contribution rate of energy consumption structure was 325.96%.From the perspective of industrial structure,secondary industry makes significant contributions and will maintain a restraining effect;from the perspective of energy consumption structure,industry sector has a significant contribution.The results of this paper are conducive to the implementation of carbon emission reduction policies in Sichuan Province.
基金funded by Guangxi Karst Science and Technology Innovation Fund (KFKT2022001)China Geological Survey Program (DD20230416)。
文摘The scarcity of water resources caused by the unique topography and uneven rainfall distribution in Hainan Island has become a major factor restricting local development. In order to provide effective and scientific reference basis for the overall water resource utilization status and solving this problem, this study calculated the water resource utilization situation of Hainan Island from 2017 to 2021 in detail using methods including water resource ecological footprint analysis. Furthermore, a spatial correlation analysis was conducted to examine the island's water resource utilization characteristics, and the driving factors behind the changes in water resource utilization over the past five years were analyzed using the LMDI model. The results show that:(1) During the study period, the water resource ecological footprint in Hainan Island exhibited a slow growth trend, while the ecological carrying capacity showed a downward tendency.The per capita ecological deficit of water resources remains relatively high, and the water consumption per 10 000 yuan GDP in the whole land continues to decrease, indicating that the overall pressure on water resource demand remains high with significant regional differences accompanied by the efficiency of water resource utilization steadily improving at the same time;(2) Agricultural water use accounts for the highest proportion in the entire water use structure, while ecological water use represents the smallest share, with a year-on-year increase, indicating that Hainan Island highlights the agricultural development and is increasingly conscious of the ecological environment;(3) Significant spatial differentiation in water resource utilization characteristics exists in Hainan Island, with the western region being a hot spot aggregation area for per capita water resource ecological footprint, per capita ecological carrying capacity of water resources, water consumption per 10 000 yuan GDP, while it is a cold spot cluster area for per capita ecological deficit of water resources. The opposite holds true for the eastern region of Hainan Island;(4) Economic and technological factors have a major impact on the changes in water resource ecological footprint within the designated area. Among them, economic factors drive the growth of the water resource ecological footprint in Hainan Island, and exacerbate local water resource consumption, while technological factors negatively contribute to the amount of water resource utilization in Hainan Island, indicating that advanced technology has improved water resource utilization efficiency and significantly reduced water resource consumption.
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This study was sponsored by the National Natural Science Foundation of China(Grant No.72131001).
文摘Electricity consumption is one of the major contributors to greenhouse gas emissions.In this study,we build a power consumption carbon emission measurement model based on the operating margin factor.We use the decomposition and decoupling technology of logarithmic mean Divisia index method to quantify six effects(emission intensity,power generation structure,consumption electricity intensity,economic scale,population structure,and population scale)and comprehensively reflect the degree of dependence of electricity consumption carbon emissions on China's economic development and population changes.Moreover,we utilize the decoupling model to analyze the decoupling state between carbon emissions and economic growth and identify corresponding energy efficiency policies.The results of this study provide a new perspective to understand carbon emission reduction potentials in the electricity use of China.
