The existing oil import dependence index cannot exactly measure the economic cost or scales, and it is difficult to describe the economical aspect of oil security. To measure the foreign dependence of one country'...The existing oil import dependence index cannot exactly measure the economic cost or scales, and it is difficult to describe the economical aspect of oil security. To measure the foreign dependence of one country's economy and reflect its oil economic security, this paper defines the net oil import intensity as the ratio of net oil import cost to GDP. By using Divisia Index Decomposition, the change of net oil import intensity in five industrialized countries and five newly industrialized countries during 1971—2010 is decomposed into five factors: oil price, oil intensity, oil self-sufficiency, domestic price level and exchange rate. The result shows that the dominating factors are oil price and oil intensity; moreover, the newly industrialized countries have higher net oil import intensity than industrialized countries.展开更多
This paper aims to identify the main driving force for changes of total primary energy consumption in Beijing during the period of 1981-2005.Sectoral energy use was investigated when regional economic structure change...This paper aims to identify the main driving force for changes of total primary energy consumption in Beijing during the period of 1981-2005.Sectoral energy use was investigated when regional economic structure changed significantly.The changes of total primary energy consumption in Beijing are decomposed into production effects,structural effects and intensity effects using the additive version of the logarithmic mean Divisia index (LMDI) method.Aggregate decomposition analysis showed that the major contributor of total effect was made by the production effect fol- lowed by the intensity effect,and the structural effect was rela- tively insignificant.The total and production effects were all posi- tive.In contrast,the structural effect and intensity effect were all negative.Sectoral decomposition investigation indicated that the most effective way to slow down the growth rate of total primary energy consumption (TPEC) was to reduce the production of the energy-intensive industrial sectors and improving industrial en- ergy intensity.The results show that in this period,Beijing's economy has undergone a transformation from an industrial to a service economy.However,the structures of sectoral energy use have not been changed yet,and energy demand should be in- creasing until the energy-intensive industrial production to be reduced and energy intensity of the region reaches a peak.As sequence energy consumption data of sub-sectors are not available, only the fundamental three sectors are considered:agriculture, industry and service.However,further decomposition into secon- dary and tertiary sectors is definitely needed for detailed investi- gations.展开更多
物流业是能源消耗和碳排放的主要产业,碳达峰、碳中和目标的提出将对物流业发展产生深远的影响,因此行业低碳化发展成为亟须解决的问题。运用碳排放系数法测度福建省2012—2021年物流业碳排放总规模,在此基础上利用对数平均迪式指数分解...物流业是能源消耗和碳排放的主要产业,碳达峰、碳中和目标的提出将对物流业发展产生深远的影响,因此行业低碳化发展成为亟须解决的问题。运用碳排放系数法测度福建省2012—2021年物流业碳排放总规模,在此基础上利用对数平均迪式指数分解法(logarithmic mean Divisia index,LMDI)模型分析能源结构、能源效率、产业结构、经济增长和人口因素对福建省物流业运行效率的影响。研究结果显示:经济增长、能源效率和人口因素对福建省物流业碳排放总量增加起促进作用,其中经济增长因素的促进作用最大;能源结构和产业结构因素对福建省物流业碳排放增加起抑制作用。最后,基于实证分析结果,提出推动福建省物流业低碳化发展建议。展开更多
“十四五”时期是中国实现碳达峰的关键时期,也是推动经济高质量发展和生态环境质量持续改善的重要阶段。可拓展的随机性环境影响评估(Stochastic Impacts by Regression on Population,Affluence,and Technology,STIRPAT)模型可以根据...“十四五”时期是中国实现碳达峰的关键时期,也是推动经济高质量发展和生态环境质量持续改善的重要阶段。可拓展的随机性环境影响评估(Stochastic Impacts by Regression on Population,Affluence,and Technology,STIRPAT)模型可以根据研究需要增加自变量,更好地分析相关因素对因变量的影响。以北京市为研究区,通过构建扩展的STIRPAT模型,分析人均地区生产总值(Gross Domestic Product,GDP)、人均汽车保有量、城市化率、第三产业GDP占比、能源消费强度与人均碳排放量的关系,并采用对数平均迪氏指数(Logarithmic Mean Divisia Index,LMDI)分解法分解能源消费强度。结果表明,产业结构和能源消费强度对人均碳排放量均有显著的正向影响。总体来看,要平衡经济发展与碳排放的关系,提高能源利用效率,推广可再生能源,降低能源消耗,减少碳排放。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
基金Supported by the National Natural Science Foundation of China(No.71273027 and No.71322306)
文摘The existing oil import dependence index cannot exactly measure the economic cost or scales, and it is difficult to describe the economical aspect of oil security. To measure the foreign dependence of one country's economy and reflect its oil economic security, this paper defines the net oil import intensity as the ratio of net oil import cost to GDP. By using Divisia Index Decomposition, the change of net oil import intensity in five industrialized countries and five newly industrialized countries during 1971—2010 is decomposed into five factors: oil price, oil intensity, oil self-sufficiency, domestic price level and exchange rate. The result shows that the dominating factors are oil price and oil intensity; moreover, the newly industrialized countries have higher net oil import intensity than industrialized countries.
文摘This paper aims to identify the main driving force for changes of total primary energy consumption in Beijing during the period of 1981-2005.Sectoral energy use was investigated when regional economic structure changed significantly.The changes of total primary energy consumption in Beijing are decomposed into production effects,structural effects and intensity effects using the additive version of the logarithmic mean Divisia index (LMDI) method.Aggregate decomposition analysis showed that the major contributor of total effect was made by the production effect fol- lowed by the intensity effect,and the structural effect was rela- tively insignificant.The total and production effects were all posi- tive.In contrast,the structural effect and intensity effect were all negative.Sectoral decomposition investigation indicated that the most effective way to slow down the growth rate of total primary energy consumption (TPEC) was to reduce the production of the energy-intensive industrial sectors and improving industrial en- ergy intensity.The results show that in this period,Beijing's economy has undergone a transformation from an industrial to a service economy.However,the structures of sectoral energy use have not been changed yet,and energy demand should be in- creasing until the energy-intensive industrial production to be reduced and energy intensity of the region reaches a peak.As sequence energy consumption data of sub-sectors are not available, only the fundamental three sectors are considered:agriculture, industry and service.However,further decomposition into secon- dary and tertiary sectors is definitely needed for detailed investi- gations.
文摘物流业是能源消耗和碳排放的主要产业,碳达峰、碳中和目标的提出将对物流业发展产生深远的影响,因此行业低碳化发展成为亟须解决的问题。运用碳排放系数法测度福建省2012—2021年物流业碳排放总规模,在此基础上利用对数平均迪式指数分解法(logarithmic mean Divisia index,LMDI)模型分析能源结构、能源效率、产业结构、经济增长和人口因素对福建省物流业运行效率的影响。研究结果显示:经济增长、能源效率和人口因素对福建省物流业碳排放总量增加起促进作用,其中经济增长因素的促进作用最大;能源结构和产业结构因素对福建省物流业碳排放增加起抑制作用。最后,基于实证分析结果,提出推动福建省物流业低碳化发展建议。
文摘“十四五”时期是中国实现碳达峰的关键时期,也是推动经济高质量发展和生态环境质量持续改善的重要阶段。可拓展的随机性环境影响评估(Stochastic Impacts by Regression on Population,Affluence,and Technology,STIRPAT)模型可以根据研究需要增加自变量,更好地分析相关因素对因变量的影响。以北京市为研究区,通过构建扩展的STIRPAT模型,分析人均地区生产总值(Gross Domestic Product,GDP)、人均汽车保有量、城市化率、第三产业GDP占比、能源消费强度与人均碳排放量的关系,并采用对数平均迪氏指数(Logarithmic Mean Divisia Index,LMDI)分解法分解能源消费强度。结果表明,产业结构和能源消费强度对人均碳排放量均有显著的正向影响。总体来看,要平衡经济发展与碳排放的关系,提高能源利用效率,推广可再生能源,降低能源消耗,减少碳排放。
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.
