The building sector plays a crucial role in the worldwide shift toward achieving net-zero emissions.Building energy efficiency standards(BEESs)are highly effective policies for reducing carbon emissions.Therefore,expl...The building sector plays a crucial role in the worldwide shift toward achieving net-zero emissions.Building energy efficiency standards(BEESs)are highly effective policies for reducing carbon emissions.Therefore,exploring the provincial variations in carbon emission efficiency(CEE)in the building sector and identifying the effect of BEESs on CEE is crucial.This study focuses on commercial buildings in China and applies a difference in differences model to evaluate the impact of BEESs on the CEE of commercial buildings.The slacks-based measure–data envelopment analysis model is employed to assess the CEE of commercial buildings in 30 Chinese provinces from 2000 to 2019.Furthermore,heterogeneous tests are used to explore how climate characteristics and economic conditions affect the efficiency of BEESs.The results indicate that BEESs positively influence the CEE of commercial buildings.Specifically,a 1%increase in the intensity of BEESs causes a 0.1484%increase in the CEE of commercial buildings.Moreover,the impact of BEESs is particularly pronounced in the southern and western provinces.This study provides valuable scientific evidence for governments to enhance BEESs implementation.展开更多
This paper uses an input-output table of China's provinces(2007-2016) to measure carbon emissions of these industries.It employs a Malmquist-Luenberger(ML) index with expected and undesired outputs,and an absolute...This paper uses an input-output table of China's provinces(2007-2016) to measure carbon emissions of these industries.It employs a Malmquist-Luenberger(ML) index with expected and undesired outputs,and an absolute β convergence and a conditional β convergence model,to conduct an in-depth analysis of dynamic changes and spatial convergence.Carbon emission efficiency of forest processing industries in 25 regions,including Shanghai,Chongqing,Zhejiang,and Jiangsu are increasing,whereas those of Tianjin,Liaoning,Heilongjiang,and Tibet are decreasing.The main contributing factors of carbon emission efficiency in three major regions vary over time.Further,carbon emission efficiency in the eastern,central,and western regions all have absolute β convergence and conditional β convergence,indicating that different regions are developing toward their own goals and industry,yet regions with lower efficiency are catching up with those where with more efficient strategies in place.Finally,this paper proposes according recommendations.展开更多
To measure the carbon emission efficiency of China’s pharmaceutical manufacturing industry, explore the factors affecting the carbon emission efficiency of China’s pharmaceutical manufacturing industry, and provide ...To measure the carbon emission efficiency of China’s pharmaceutical manufacturing industry, explore the factors affecting the carbon emission efficiency of China’s pharmaceutical manufacturing industry, and provide reference for improving the carbon emission efficiency of China’s pharmaceutical manufacturing industry and promoting the government to formulate macro policies. Based on the data of the pharmaceutical manufacturing industry in 30 provinces of China from 2010 to 2019, and based on the SBM model and ML (Malmquist-Luenberger) index model, the carbon emission efficiency of the pharmaceutical manufacturing industry was calculated and its dynamic change was investigated, and the Tobit model was further used to explore the influencing factors of the carbon emission efficiency of the pharmaceutical manufacturing industry. The carbon emission efficiency of China’s inter-provincial pharmaceutical manufacturing industry has steadily improved. The carbon emission efficiency of the eastern region is higher than that of the western region, and that of the western region is higher than that of the central region. The eastern region is dominated by technological progress, and there is room for improvement in technological efficiency. The central and western regions are dominated by technological efficiency. Compared with technological efficiency, technological progress needs to be further improved. Environmental regulation, industrial agglomeration and technological innovation level positively affect carbon emission efficiency, while foreign investment level has no significant impact on carbon emission efficiency.展开更多
Grasping the spatial correlation structure of transportation carbon emission efficiency(TCEE)and its influencing factors is significant for promoting high-quality and coordinated development of the transportation indu...Grasping the spatial correlation structure of transportation carbon emission efficiency(TCEE)and its influencing factors is significant for promoting high-quality and coordinated development of the transportation industry and the relevant region.Based on the ideal point cross-efficiency(IPCE)model,the social network analysis method was employed herein to explore the spatial correlation network structure of China’s provincial TCEE and its influencing factors.The results obtained showed the following outcomes.(1)During the study period,China’s provincial TCEE formed a complex and multithreaded network association relationship,but its network association structure was still relatively loose and presented the hierarchical gradient characteristics of dense in the east and sparse in the west.(2)The correlation of China’s TCEE formed a block segmentation based on the regional boundaries,and its factional structure was relatively obvious.The eastern region was closely connected with the central region,and generally connected with the western and northeastern regions.The central region was mainly connected with the eastern and western regions,and relatively less connected with the northeastern region.Besides,the northeastern region was weakly connected with the western region.(3)Shanghai,Beijing,Zhejiang,Guangdong,Jiangsu,Tianjin,and other developed provinces were in the core leading position in the TCEE network,which significantly impacted the spatial correlation of TCEE.However,Heilongjiang,Jilin,Xinjiang,Qinghai,and other remote provinces in the northeast and northwest were at the absolute edge of the network,which weakly impacted the spatial correlation of TCEE.(4)Provincial distance,economic development-level difference,transportation intensity difference,and transportation structure difference had significant negative impacts on the spatial correlation network of China’s provincial TCEE.In contrast,the energy-saving technology level difference had a significant positive impact on it.The regression coefficients of transportation energy structure and environmental regulation differences were positive but insignificant;their response mechanism and effects need to be improved and enhanced.展开更多
Transportation accounts for 80% of open-cut coal mine carbon emissions. With regard to the energy con- sumption and carbon emissions of transportation within an open-cut mine, this paper systematically compared the wo...Transportation accounts for 80% of open-cut coal mine carbon emissions. With regard to the energy con- sumption and carbon emissions of transportation within an open-cut mine, this paper systematically compared the work and energy consumption of a truck and belt conveyor on a theoretical basis, and con- structed a model to calculate the energy consumption of open-cut mine transportation. Life cycle carbon emission factors and power consumption calculation model were established through a Process Analysis- Life Cycle Analysis (PA-LCA). The following results were obtained: (1) the energy consumption of truck transportation was four to twelve times higher than that of the belt conveyor; (2) the C02 emissions from truck transportation were three to ten times higher than those of the belt conveyor; (3) with the increase in the slope angle for transportation, the ratio of truck to belt conveyor for both energy consumption and carbon emissions gradually decreased; (4) based on 2013 prices in China, the energy cost of transportation using a belt conveyor in open-cut coal mines could save 0.6-2.4 Yuan/(t kin) compared to truck transportation.展开更多
基金funded by the National Social Science Foundation of China[Grant No.23CJY018]the Fundamental Research Funds for the Central Universities[Grant No.JBK2406049]+2 种基金the National Natural Science Foundation of China[Grant No.72003151],[Grant No.72173100]the Soft Science Research Program of Sichuan Province[Grant No.2022JDR0227]Projects from the Research Center on Xi Jinping’s Economic Thought,and the Fundamental Research Funds for the“Guanghua Talent Program”of the Southwestern University of Finance and Economics.
文摘The building sector plays a crucial role in the worldwide shift toward achieving net-zero emissions.Building energy efficiency standards(BEESs)are highly effective policies for reducing carbon emissions.Therefore,exploring the provincial variations in carbon emission efficiency(CEE)in the building sector and identifying the effect of BEESs on CEE is crucial.This study focuses on commercial buildings in China and applies a difference in differences model to evaluate the impact of BEESs on the CEE of commercial buildings.The slacks-based measure–data envelopment analysis model is employed to assess the CEE of commercial buildings in 30 Chinese provinces from 2000 to 2019.Furthermore,heterogeneous tests are used to explore how climate characteristics and economic conditions affect the efficiency of BEESs.The results indicate that BEESs positively influence the CEE of commercial buildings.Specifically,a 1%increase in the intensity of BEESs causes a 0.1484%increase in the CEE of commercial buildings.Moreover,the impact of BEESs is particularly pronounced in the southern and western provinces.This study provides valuable scientific evidence for governments to enhance BEESs implementation.
文摘This paper uses an input-output table of China's provinces(2007-2016) to measure carbon emissions of these industries.It employs a Malmquist-Luenberger(ML) index with expected and undesired outputs,and an absolute β convergence and a conditional β convergence model,to conduct an in-depth analysis of dynamic changes and spatial convergence.Carbon emission efficiency of forest processing industries in 25 regions,including Shanghai,Chongqing,Zhejiang,and Jiangsu are increasing,whereas those of Tianjin,Liaoning,Heilongjiang,and Tibet are decreasing.The main contributing factors of carbon emission efficiency in three major regions vary over time.Further,carbon emission efficiency in the eastern,central,and western regions all have absolute β convergence and conditional β convergence,indicating that different regions are developing toward their own goals and industry,yet regions with lower efficiency are catching up with those where with more efficient strategies in place.Finally,this paper proposes according recommendations.
文摘To measure the carbon emission efficiency of China’s pharmaceutical manufacturing industry, explore the factors affecting the carbon emission efficiency of China’s pharmaceutical manufacturing industry, and provide reference for improving the carbon emission efficiency of China’s pharmaceutical manufacturing industry and promoting the government to formulate macro policies. Based on the data of the pharmaceutical manufacturing industry in 30 provinces of China from 2010 to 2019, and based on the SBM model and ML (Malmquist-Luenberger) index model, the carbon emission efficiency of the pharmaceutical manufacturing industry was calculated and its dynamic change was investigated, and the Tobit model was further used to explore the influencing factors of the carbon emission efficiency of the pharmaceutical manufacturing industry. The carbon emission efficiency of China’s inter-provincial pharmaceutical manufacturing industry has steadily improved. The carbon emission efficiency of the eastern region is higher than that of the western region, and that of the western region is higher than that of the central region. The eastern region is dominated by technological progress, and there is room for improvement in technological efficiency. The central and western regions are dominated by technological efficiency. Compared with technological efficiency, technological progress needs to be further improved. Environmental regulation, industrial agglomeration and technological innovation level positively affect carbon emission efficiency, while foreign investment level has no significant impact on carbon emission efficiency.
基金This research was funded by the National Science Foundation under the Project“Synergic evolution mechanism of intercity transportation and metropolitan tourism spatial pattern”[Grant number.41771162]It was also funded by the National First-Class Discipline Development Project in Hunan Province under the category of“Geography”[Grang number.510002].
文摘Grasping the spatial correlation structure of transportation carbon emission efficiency(TCEE)and its influencing factors is significant for promoting high-quality and coordinated development of the transportation industry and the relevant region.Based on the ideal point cross-efficiency(IPCE)model,the social network analysis method was employed herein to explore the spatial correlation network structure of China’s provincial TCEE and its influencing factors.The results obtained showed the following outcomes.(1)During the study period,China’s provincial TCEE formed a complex and multithreaded network association relationship,but its network association structure was still relatively loose and presented the hierarchical gradient characteristics of dense in the east and sparse in the west.(2)The correlation of China’s TCEE formed a block segmentation based on the regional boundaries,and its factional structure was relatively obvious.The eastern region was closely connected with the central region,and generally connected with the western and northeastern regions.The central region was mainly connected with the eastern and western regions,and relatively less connected with the northeastern region.Besides,the northeastern region was weakly connected with the western region.(3)Shanghai,Beijing,Zhejiang,Guangdong,Jiangsu,Tianjin,and other developed provinces were in the core leading position in the TCEE network,which significantly impacted the spatial correlation of TCEE.However,Heilongjiang,Jilin,Xinjiang,Qinghai,and other remote provinces in the northeast and northwest were at the absolute edge of the network,which weakly impacted the spatial correlation of TCEE.(4)Provincial distance,economic development-level difference,transportation intensity difference,and transportation structure difference had significant negative impacts on the spatial correlation network of China’s provincial TCEE.In contrast,the energy-saving technology level difference had a significant positive impact on it.The regression coefficients of transportation energy structure and environmental regulation differences were positive but insignificant;their response mechanism and effects need to be improved and enhanced.
基金supported by the key project of the National Natural Science Foundation of China(No.51034005)the Research Fund for the Doctoral Program of Higher Education(the Specialized Research Fund for the Doctoral Program of Higher Education of China)(No.20100095110019)+1 种基金the National‘‘Twelfth Five-Year’’Plan for Science&Technology Support(No.2014BAC14B00)the National High Technology Research and Development Program of China(No.2012AA062004)
文摘Transportation accounts for 80% of open-cut coal mine carbon emissions. With regard to the energy con- sumption and carbon emissions of transportation within an open-cut mine, this paper systematically compared the work and energy consumption of a truck and belt conveyor on a theoretical basis, and con- structed a model to calculate the energy consumption of open-cut mine transportation. Life cycle carbon emission factors and power consumption calculation model were established through a Process Analysis- Life Cycle Analysis (PA-LCA). The following results were obtained: (1) the energy consumption of truck transportation was four to twelve times higher than that of the belt conveyor; (2) the C02 emissions from truck transportation were three to ten times higher than those of the belt conveyor; (3) with the increase in the slope angle for transportation, the ratio of truck to belt conveyor for both energy consumption and carbon emissions gradually decreased; (4) based on 2013 prices in China, the energy cost of transportation using a belt conveyor in open-cut coal mines could save 0.6-2.4 Yuan/(t kin) compared to truck transportation.