基于STIRPAT模型的中国典型城市群居民生活用电比较
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摘要
城镇居民生活用电问题是中国城市群城镇化进程中的重要问题,比较与分析经济社会发展水平相近的城市群城镇居民生活电力消费规律,对保障城镇电力供应安全、推动节能减排、完善电网建设有着十分重要的意义。运用STIRPAT模型分析中国京津冀、长三角和珠三角城市群城镇居民生活电力消费的影响因素,结果表明,三大城市群城镇居民用电模式各有特色:京津冀城市群城镇居民用电呈现"产业结构主导型"模式,长三角城市群城镇居民用电呈现"人口主导型"模式,珠三角城市群城镇居民用电呈现"收入主导型"模式。中国应依据不同的城镇居民生活用电模式,有针对性地采取城镇居民生活能源消费结构优化和节能减排措施。
Urban household electricity consumption is an important issue in the process of urbanization of agglomerations in China.The comparison and analysis of urban household electricity consumption of typical city-agglomerations with similar levels of economic and social development is very significant for guaranteeing electricity supply security,promoting energy saving and emission reduction and improving power grid construction.This paper analyzes the factors that influence the urban household electricity consumption of BeijingTianjin-Hebei City-agglomeration,Yangtze-River-Delta City-agglomeration and Pearl-River-Delta City-agglomeration by using STIRPAT model.The result shows that the pattern of urban household electricity consumption is " industrial-structure-oriented" in Beijing-TianjinHebei City-agglomeration,"population-oriented" in Yangtze-River-Delta City-agglomeration,and " income-oriented" in Pearl-RiverDelta City-agglomeration.According to the results,targeted measures about optimizing the structure of urban household energy consumption,energy-saving and emission reduction are proposed.
Urban household electricity consumption is an important issue in the process of urbanization of agglomerations in China.The comparison and analysis of urban household electricity consumption of typical city-agglomerations with similar levels of economic and social development is very significant for guaranteeing electricity supply security,promoting energy saving and emission reduction and improving power grid construction.This paper analyzes the factors that influence the urban household electricity consumption of BeijingTianjin-Hebei City-agglomeration,Yangtze-River-Delta City-agglomeration and Pearl-River-Delta City-agglomeration by using STIRPAT model.The result shows that the pattern of urban household electricity consumption is " industrial-structure-oriented" in Beijing-TianjinHebei City-agglomeration,"population-oriented" in Yangtze-River-Delta City-agglomeration,and " income-oriented" in Pearl-RiverDelta City-agglomeration.According to the results,targeted measures about optimizing the structure of urban household energy consumption,energy-saving and emission reduction are proposed.
引文
[1]陈军,成金华,吴巧生.工业化水平区域差异与中国能源消费[J].中国人口·资源与环境,2007,17(5):59-64.
[2]Holtedahl P,Joutz F L.Residential Electricity Demand in Taiwan[J].Energy Economics,2004,26(2):201-224.
[3]宋炜,顾阿伦,吴宗鑫.城镇居民生活用电需求预测模型[J].中国电力,2006,39(9):67-70.
[4]陈晶,张真.居民生活用电特征与影响机理[J].统计研究,2015,32(5):70-75.
[5]任正委,米红.区域人口城镇化多维特征对居民电力消费的驱动影响---基于浙江省分县数据的实证研究[J].人口研究,2016,40(04):103-112.
[6]相楠,徐峰.城市居民生活用电影响因素和电力消费弹性研究[J].中国人口·资源与环境,2017(S1):207-210.
[7]Chen Y.The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector-a Case Example of Taiwan[J].Sustainability,2017,9(8):1484.
[8]肖勇,王恒山,杨俊保.上海居民用电特点分析及需求侧管理的应对措施[J].上海电力学院学报,2009,25(6):614-618,621.
[9]沈勇,杨荥,俞国勤,等.上海地区居民用电的影响因素和需求预测模型[J].华东电力,2012(10):1763-1766.
[10]Acho C,Schaeffer R.Decomposition Analysis of the Variations in Residential Electricity Consumption in Brazil for the 1980-2007 Period:Measuring the Activity,Intensity and Structure Effects[J].Energy Policy,2009,37(12):5208-5220.
[11]朱凯,尹海涛.中国居民生活用电的分解分析[J].中国电力,2014,47(3):130-134.
[12]国家统计局.中国城市统计年鉴2016[M].北京:中国统计出版社,2016.
[13]安虎森.新区域经济学[M].第3版.大连:东北财经大学出版社,2015.
[14]York R,Rosa E A,Dietz T.STIRPAT,IPAT and ImPACT:Analytic Tools for Unpacking the Driving Forces of Environmental Impacts[J].Ecological Economic,2003,46(3):351-365.
[15]Ehrlich P R,Holdrens J P.The Impact of Population Growth[J].Science,1971,171(3977):1212-1217.
[16]Waggoner P E.Agricultural Technology and its Societal Implications[J].Technology in Society,2004,26(2-3):123-136.
[17]王立猛,何康林.基于STIRPAT模型的环境压力空间差异分析---以能源消费为例[J].环境科学学报.2008(5):1032-1037.
[18]李琦,韩亚芬,陈建永.基于STIRPAT模型的中国能源足迹的区域差异分析[J].安徽农业科学,2010,38(24):13283-13284,13314.
[19]姜磊,季民河.中国区域能源压力的空间差异分析---基于STIRPAT模型[J].财经科学,2011(4):64-70.
[20]高坤.城市化与居民生活用电需求的实证分析[D].成都:西南财经大学,2008.
[21]陈迅,袁海蔚.中国生活能源消费行为影响因素的实证研究[J].消费经济,2008,24(5):47-50.
[22]Cayla J,Maizi N,Marchand C.The Role of Income in Energy Consumption Behaviour:Evidence from French Households Data[J].Energy Policy,2011,39(12):7874-7883.
[23]Dietz T,Rosa E A.Rethinking the Environmental Impacts of Population,Affluence and Technology[J].Human E-cology Review,1994,1(2):277-300.
[24]吴振信,石佳.基于STIRPAT和GM(1,1)模型的北京能源碳排放影响因素分析及趋势预测[J].中国管理科学,2012(11):803-809.
[25]张乐勤,陈素平,王文琴,等.安徽省近15年建设用地变化对碳排放效应测度及趋势预测---基于STIRPAT模型[J].环境科学学报,2013(3):950-958.
[26]张晓平.中国能源消费强度的区域差异及影响因素分析[J].资源科学,2008,30(6):883-889.
[27]林伯强.高级能源经济学[M].北京:中国财政经济出版社,2016.
[28]聂国卿,尹向飞,邓柏盛.基于STIRPAT模型的环境压力影响因素及其演进分析---以湖南省为例[J].系统工程,2012,30(5):112-116.
[29]王立猛,何康林.基于STIRPAT模型分析中国环境压力的时间差异---以1952-2003年能源消费为例[J].自然资源学报,2006(11):862-869.
(1)城市化率由“城镇化率=城镇人口(万人)/年末常住总人口(万人)”计算得出。其中京津冀城市化率为73. 49%,长三角城市化率为73. 81%,珠三角城市化率为68. 71%。
[2]Holtedahl P,Joutz F L.Residential Electricity Demand in Taiwan[J].Energy Economics,2004,26(2):201-224.
[3]宋炜,顾阿伦,吴宗鑫.城镇居民生活用电需求预测模型[J].中国电力,2006,39(9):67-70.
[4]陈晶,张真.居民生活用电特征与影响机理[J].统计研究,2015,32(5):70-75.
[5]任正委,米红.区域人口城镇化多维特征对居民电力消费的驱动影响---基于浙江省分县数据的实证研究[J].人口研究,2016,40(04):103-112.
[6]相楠,徐峰.城市居民生活用电影响因素和电力消费弹性研究[J].中国人口·资源与环境,2017(S1):207-210.
[7]Chen Y.The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector-a Case Example of Taiwan[J].Sustainability,2017,9(8):1484.
[8]肖勇,王恒山,杨俊保.上海居民用电特点分析及需求侧管理的应对措施[J].上海电力学院学报,2009,25(6):614-618,621.
[9]沈勇,杨荥,俞国勤,等.上海地区居民用电的影响因素和需求预测模型[J].华东电力,2012(10):1763-1766.
[10]Acho C,Schaeffer R.Decomposition Analysis of the Variations in Residential Electricity Consumption in Brazil for the 1980-2007 Period:Measuring the Activity,Intensity and Structure Effects[J].Energy Policy,2009,37(12):5208-5220.
[11]朱凯,尹海涛.中国居民生活用电的分解分析[J].中国电力,2014,47(3):130-134.
[12]国家统计局.中国城市统计年鉴2016[M].北京:中国统计出版社,2016.
[13]安虎森.新区域经济学[M].第3版.大连:东北财经大学出版社,2015.
[14]York R,Rosa E A,Dietz T.STIRPAT,IPAT and ImPACT:Analytic Tools for Unpacking the Driving Forces of Environmental Impacts[J].Ecological Economic,2003,46(3):351-365.
[15]Ehrlich P R,Holdrens J P.The Impact of Population Growth[J].Science,1971,171(3977):1212-1217.
[16]Waggoner P E.Agricultural Technology and its Societal Implications[J].Technology in Society,2004,26(2-3):123-136.
[17]王立猛,何康林.基于STIRPAT模型的环境压力空间差异分析---以能源消费为例[J].环境科学学报.2008(5):1032-1037.
[18]李琦,韩亚芬,陈建永.基于STIRPAT模型的中国能源足迹的区域差异分析[J].安徽农业科学,2010,38(24):13283-13284,13314.
[19]姜磊,季民河.中国区域能源压力的空间差异分析---基于STIRPAT模型[J].财经科学,2011(4):64-70.
[20]高坤.城市化与居民生活用电需求的实证分析[D].成都:西南财经大学,2008.
[21]陈迅,袁海蔚.中国生活能源消费行为影响因素的实证研究[J].消费经济,2008,24(5):47-50.
[22]Cayla J,Maizi N,Marchand C.The Role of Income in Energy Consumption Behaviour:Evidence from French Households Data[J].Energy Policy,2011,39(12):7874-7883.
[23]Dietz T,Rosa E A.Rethinking the Environmental Impacts of Population,Affluence and Technology[J].Human E-cology Review,1994,1(2):277-300.
[24]吴振信,石佳.基于STIRPAT和GM(1,1)模型的北京能源碳排放影响因素分析及趋势预测[J].中国管理科学,2012(11):803-809.
[25]张乐勤,陈素平,王文琴,等.安徽省近15年建设用地变化对碳排放效应测度及趋势预测---基于STIRPAT模型[J].环境科学学报,2013(3):950-958.
[26]张晓平.中国能源消费强度的区域差异及影响因素分析[J].资源科学,2008,30(6):883-889.
[27]林伯强.高级能源经济学[M].北京:中国财政经济出版社,2016.
[28]聂国卿,尹向飞,邓柏盛.基于STIRPAT模型的环境压力影响因素及其演进分析---以湖南省为例[J].系统工程,2012,30(5):112-116.
[29]王立猛,何康林.基于STIRPAT模型分析中国环境压力的时间差异---以1952-2003年能源消费为例[J].自然资源学报,2006(11):862-869.
(1)城市化率由“城镇化率=城镇人口(万人)/年末常住总人口(万人)”计算得出。其中京津冀城市化率为73. 49%,长三角城市化率为73. 81%,珠三角城市化率为68. 71%。