Improving energy efficiency is regarded as a key path to tackling global warming and achieving the Sustainable Development Goals(SDGs).In 2020,the energy consumption of the world's ten major energy-consuming count...Improving energy efficiency is regarded as a key path to tackling global warming and achieving the Sustainable Development Goals(SDGs).In 2020,the energy consumption of the world's ten major energy-consuming countries accounted for 66.8%of the global total.This paper applied data envelopment analysis(DEA)to calculate these ten major energyconsuming countries'total-factor energy efficiency(TFEE)at national and sectoral levels from 2001-2020,and explored the infuencing factors of total-factor energy efficiency with the Tobit regression model.The results showed significant difference in the ten countries'energy efficiency.The United States and Germany topped the list for total-factor energy efficiency,while China and India were at the bottom.Meanwhile,the energy efficiency of the industrial subsector has increased significantly over the past two decades,while that of the other subsectors has been relatively fat.The industrial structure upgrading,per capita GDP,energy consumption structure,and foreign direct investment had significant impacts on energy efficiency with national heterogeneity.Energy consumption structure and GDP per capita were determinative factors of energy efficiency.展开更多
In this study,we developed an evaluation index system for green total-factor water-use efficiency(GTFWUE)which reflected both economic and green efficiencies of water resource utilization.Then we measured the GTFWUE o...In this study,we developed an evaluation index system for green total-factor water-use efficiency(GTFWUE)which reflected both economic and green efficiencies of water resource utilization.Then we measured the GTFWUE of 30 provinces/municipalities/autonomous regions(hereafter provinces)in China(not including Tibet,Hong Kong region,Macao region,Taiwan region of China as no data)from 2000 to 2018 using a minimum distance to the strong frontier model that contained an undesirable output.We further analyzed the regional differences and spatial correlations of GTFWUE using these values based on Global and Local Moran’s I statistics,and empirically determined the factors affecting GTFWUE using a spatial econometric model.The evaluation results revealed that the GTFWUE differed substantially between the regions.The provinces with high and low GTFWUE values were located in the coastal and inland areas of China,respectively.The eastern region had a significantly higher GTFWUE than the central and western regions.The GTFWUEs for all three regions(eastern,central,and western regions)decreased slowly from 2000 to 2011(except 2005),remained stable from 2012 to 2016,and rapidly increased in 2017 before decreasing again in 2018.We found significant spatial correlations between the provincial GTFWUEs.The GTFWUE for most provinces belonged to the high-high or low-low cluster region,revealing a significant spatial clustering effect of provincial GTFWUEs.We also found that China’s GTFWUE was highly promoted by economic growth,population size,opening-up level,and urbanization level,and was evidently hindered by water endowment,technological progress,and government influence.However,the water-use structure had little impact on GTFWUE.This study fully demonstrated that the water use mode would be improved,and water resources needed to be used more efficiently and green in China.Moreover,based on the findings of this study,several policy recommendations were proposed from the aspects of cross-regional cooperation,economy,society,and institution.展开更多
基金supported by the National Natural Science Foundation of China(Nos.71761147001 and 42030707)the International Partnership Program by the Chinese Academy of Sciences(No.121311KYSB20190029)the Fundamental Research Fund for the Central Universities(No.20720210083)。
文摘Improving energy efficiency is regarded as a key path to tackling global warming and achieving the Sustainable Development Goals(SDGs).In 2020,the energy consumption of the world's ten major energy-consuming countries accounted for 66.8%of the global total.This paper applied data envelopment analysis(DEA)to calculate these ten major energyconsuming countries'total-factor energy efficiency(TFEE)at national and sectoral levels from 2001-2020,and explored the infuencing factors of total-factor energy efficiency with the Tobit regression model.The results showed significant difference in the ten countries'energy efficiency.The United States and Germany topped the list for total-factor energy efficiency,while China and India were at the bottom.Meanwhile,the energy efficiency of the industrial subsector has increased significantly over the past two decades,while that of the other subsectors has been relatively fat.The industrial structure upgrading,per capita GDP,energy consumption structure,and foreign direct investment had significant impacts on energy efficiency with national heterogeneity.Energy consumption structure and GDP per capita were determinative factors of energy efficiency.
基金Under the auspices of Chinese Ministry of Education Humanities and Social Sciences Project(No.19YJCZH241)Project of Chongqing Social Science Planning Project of China(No.2020QNGL38)+1 种基金Science and Technology Research Program of Chongqing Education Commission of China(No.KJQN201901143)Humanities and Social Sciences Research Program of Chongqing Education Commission of China(No.20SKGH169)。
文摘In this study,we developed an evaluation index system for green total-factor water-use efficiency(GTFWUE)which reflected both economic and green efficiencies of water resource utilization.Then we measured the GTFWUE of 30 provinces/municipalities/autonomous regions(hereafter provinces)in China(not including Tibet,Hong Kong region,Macao region,Taiwan region of China as no data)from 2000 to 2018 using a minimum distance to the strong frontier model that contained an undesirable output.We further analyzed the regional differences and spatial correlations of GTFWUE using these values based on Global and Local Moran’s I statistics,and empirically determined the factors affecting GTFWUE using a spatial econometric model.The evaluation results revealed that the GTFWUE differed substantially between the regions.The provinces with high and low GTFWUE values were located in the coastal and inland areas of China,respectively.The eastern region had a significantly higher GTFWUE than the central and western regions.The GTFWUEs for all three regions(eastern,central,and western regions)decreased slowly from 2000 to 2011(except 2005),remained stable from 2012 to 2016,and rapidly increased in 2017 before decreasing again in 2018.We found significant spatial correlations between the provincial GTFWUEs.The GTFWUE for most provinces belonged to the high-high or low-low cluster region,revealing a significant spatial clustering effect of provincial GTFWUEs.We also found that China’s GTFWUE was highly promoted by economic growth,population size,opening-up level,and urbanization level,and was evidently hindered by water endowment,technological progress,and government influence.However,the water-use structure had little impact on GTFWUE.This study fully demonstrated that the water use mode would be improved,and water resources needed to be used more efficiently and green in China.Moreover,based on the findings of this study,several policy recommendations were proposed from the aspects of cross-regional cooperation,economy,society,and institution.
