China experienced a decline of water use intensity in the 11th Five Year Plan,but the water use intensity actually increased in 2009.To the best of our knowledge,the index decomposition analysis method was rarely used...China experienced a decline of water use intensity in the 11th Five Year Plan,but the water use intensity actually increased in 2009.To the best of our knowledge,the index decomposition analysis method was rarely used to analyze changes in water use,and no decomposition analysis has investigated the role of regional economy in the decline of water use intensity.In this paper,we use logarithmic mean Divisia index(LMDI)techniques to decompose the change of water use intensity in the period 2006-2010.We find that the change of industrial water use intensity is confirmed as the dominant contributor to the decline in the overall water use intensity;the regional structure effect and the industrial structure effect is positive to the decline of overall water use intensity;the decline of China's water use intensity is mainly attributed to the effect of developed eastern provinces;meanwhile,the effect of central and undeveloped western is also positive to the decline of overall water use intensity;at least one out of three effects is positive to the decline of water use intensity in the different provinces;the intensity effect is positive and the industrial structure effect is positive to the declines of China's water use intensity based on chaining approach except the period 2008-2009,individually;and the deviation of regional structure effect and industrial structure effect between with regional economy and without regional economy in LMDI is 0.9 and2.3 m^3/10~4 RMB,respectively.展开更多
随着经济的快速发展和工业化进程的加快,碳排放问题日益凸显。吐哈地区作为我国西部重要的能源基地,其碳排放量对国家碳减排目标具有重要影响。本研究采用LMDI(Logarithmic Mean Divisia Index)法对吐哈地区碳排放的驱动因素进行分解,...随着经济的快速发展和工业化进程的加快,碳排放问题日益凸显。吐哈地区作为我国西部重要的能源基地,其碳排放量对国家碳减排目标具有重要影响。本研究采用LMDI(Logarithmic Mean Divisia Index)法对吐哈地区碳排放的驱动因素进行分解,分析能源消费碳强度、能源强度、经济发展、交通参与度、车辆密度、土地分配度、城镇化率、人口规模等因素对碳排放的影响,并据此提出相应的减排策略。结果表明:总体上看,能源消费碳强度是最主要的抑制碳排放因素,交通参与度和车辆密度同样展现出抑制效应,分别占总抑制碳排放贡献值的66.9%、29.6%、3.5%;能源强度是主要的促进碳排放因素,土地分配度、经济发展、人口规模、城镇化率等因素均不同程度地推动碳排放增加,分别占总促进碳排放贡献值的57.4%、22.7%、9.5%、7.6%、2.8%。吐哈地区未来可以着重加强能源消费碳强度管理,并着力提高能源效率,从源头上降低碳排放,促进绿色低碳发展。展开更多
以辽宁省交通运输行业为研究对象,根据2010—2021年交通运输公布的数据测算出不同运输方式的能源消耗量与碳排放量。基于Kaya等式,结合辽宁省自身情况并具体考虑生态和城市建设对等式进行扩展,添加碳汇产销率和生态规模影响因素,使用LMD...以辽宁省交通运输行业为研究对象,根据2010—2021年交通运输公布的数据测算出不同运输方式的能源消耗量与碳排放量。基于Kaya等式,结合辽宁省自身情况并具体考虑生态和城市建设对等式进行扩展,添加碳汇产销率和生态规模影响因素,使用LMDI(logarithmic mean divisia index)分解法对其进行分解。研究结果表明,辽宁省的能源强度、结构效应、产业规模、经济产出、碳源碳汇产销率和生态规模对道路交通碳排放具有促进作用,而交通碳排放强度、交通运输强度和人口密度通常对辽宁省道路交通碳排放具有抑制作用。展开更多
基金subsidized by the Central Project of Water Resource Fees[grant number 1261320212020]
文摘China experienced a decline of water use intensity in the 11th Five Year Plan,but the water use intensity actually increased in 2009.To the best of our knowledge,the index decomposition analysis method was rarely used to analyze changes in water use,and no decomposition analysis has investigated the role of regional economy in the decline of water use intensity.In this paper,we use logarithmic mean Divisia index(LMDI)techniques to decompose the change of water use intensity in the period 2006-2010.We find that the change of industrial water use intensity is confirmed as the dominant contributor to the decline in the overall water use intensity;the regional structure effect and the industrial structure effect is positive to the decline of overall water use intensity;the decline of China's water use intensity is mainly attributed to the effect of developed eastern provinces;meanwhile,the effect of central and undeveloped western is also positive to the decline of overall water use intensity;at least one out of three effects is positive to the decline of water use intensity in the different provinces;the intensity effect is positive and the industrial structure effect is positive to the declines of China's water use intensity based on chaining approach except the period 2008-2009,individually;and the deviation of regional structure effect and industrial structure effect between with regional economy and without regional economy in LMDI is 0.9 and2.3 m^3/10~4 RMB,respectively.
文摘随着经济的快速发展和工业化进程的加快,碳排放问题日益凸显。吐哈地区作为我国西部重要的能源基地,其碳排放量对国家碳减排目标具有重要影响。本研究采用LMDI(Logarithmic Mean Divisia Index)法对吐哈地区碳排放的驱动因素进行分解,分析能源消费碳强度、能源强度、经济发展、交通参与度、车辆密度、土地分配度、城镇化率、人口规模等因素对碳排放的影响,并据此提出相应的减排策略。结果表明:总体上看,能源消费碳强度是最主要的抑制碳排放因素,交通参与度和车辆密度同样展现出抑制效应,分别占总抑制碳排放贡献值的66.9%、29.6%、3.5%;能源强度是主要的促进碳排放因素,土地分配度、经济发展、人口规模、城镇化率等因素均不同程度地推动碳排放增加,分别占总促进碳排放贡献值的57.4%、22.7%、9.5%、7.6%、2.8%。吐哈地区未来可以着重加强能源消费碳强度管理,并着力提高能源效率,从源头上降低碳排放,促进绿色低碳发展。
文摘以辽宁省交通运输行业为研究对象,根据2010—2021年交通运输公布的数据测算出不同运输方式的能源消耗量与碳排放量。基于Kaya等式,结合辽宁省自身情况并具体考虑生态和城市建设对等式进行扩展,添加碳汇产销率和生态规模影响因素,使用LMDI(logarithmic mean divisia index)分解法对其进行分解。研究结果表明,辽宁省的能源强度、结构效应、产业规模、经济产出、碳源碳汇产销率和生态规模对道路交通碳排放具有促进作用,而交通碳排放强度、交通运输强度和人口密度通常对辽宁省道路交通碳排放具有抑制作用。
