基于STIRPAT模型的水利工程建设对碳排放的影响研究
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摘要
为研究水利工程建设对碳排放的影响,在分析水利工程建设对碳排放影响路径的基础上,构建碳排放测度模型和STIRPAT环境压力随机扩展模型。以江苏省水利工程建设为例,采用主成分分析和普通最小二乘回归分析,研究水利工程建设与碳排放之间的线性关系。结果表明:水利工程投资每增加1%,碳排放量增加0. 2157%,水利工程建设前期耗能高,后期低碳效益发挥不足。基于此,对江苏省减少能源消费碳排放量提出政策建议,为江苏省节能减排政策的制定实施提供科学依据,也为其他省市水利工程建设的低碳发展提供借鉴。
In order to study the impact of water conservancy projects on carbon emissions,based on the analysis of the impact path of water conservancy projects on carbon emissions,a carbon emission calculation model and a random expansion model of STIRPAT environmental pressure were constructed. Taking the construction of water conservancy projects in Jiangsu Province as an example,the principal component analysis and ordinary least-squares regression analysis were used to study the linear relationship between water conservancy projects and carbon emissions. The results showed that the investment of water conservancy projects increased by 1%,the carbon emissions increased by 0. 2157%. The energy consumption of the water conservancy projects was high at the early stage,while the low-carbon benefits were insufficient at the later period. Based on this,the policy suggestions were proposed for reducing the carbon emission of energy consumption in Jiangsu Province. It also provided scientific basis for the formulation and implementation of energy saving and emission reduction policies in Jiangsu Province and a reference for the low-carbon development of water conservancy projects in other provinces and cities.
In order to study the impact of water conservancy projects on carbon emissions,based on the analysis of the impact path of water conservancy projects on carbon emissions,a carbon emission calculation model and a random expansion model of STIRPAT environmental pressure were constructed. Taking the construction of water conservancy projects in Jiangsu Province as an example,the principal component analysis and ordinary least-squares regression analysis were used to study the linear relationship between water conservancy projects and carbon emissions. The results showed that the investment of water conservancy projects increased by 1%,the carbon emissions increased by 0. 2157%. The energy consumption of the water conservancy projects was high at the early stage,while the low-carbon benefits were insufficient at the later period. Based on this,the policy suggestions were proposed for reducing the carbon emission of energy consumption in Jiangsu Province. It also provided scientific basis for the formulation and implementation of energy saving and emission reduction policies in Jiangsu Province and a reference for the low-carbon development of water conservancy projects in other provinces and cities.
引文
[1]张靖.镍冶炼废气中重金属产排污系数实测法核算[J].有色金属工程,2013,3(3)∶50-52.
[2]Perkins J L,Padro-martinez L T,Durant J L. Particle Number Emission Factors for an Urban Highway Tunnel[J]. Atmospheric Environment,2013,74(8)∶326-337.
[3]陈超,胡秦镪,邓奕雯,等.基于实测法的下凹式城市地下道路CO,NO_x排放因子研究[J].中国公路学报,2017,30(9)∶116-124.
[4]仲佳爱,陈国俊,张中宁,等.四川盆地气矿天然气开发过程中温室气体的排放特征[J].环境科学研究,2015,28(3)∶355-360.
[5]陈敏,温宗国,杜鹏飞.基于AIM/enduse模型的水泥行业节能减排途径分析[J].中国人口·资源与环境,2012,22(S1)∶234-239.
[6]Sarica K,Tyner W E. Analysis of US Renewable Fuels Policies Using a Modified MARKAL Model[J]. Renewable Energy,2013,50(3)∶701-709.
[7]周伟,米红.中国能源消费排放的CO2测算[J].中国环境科学,2010,30(8)∶1142-1148.
[8]唐德才,刘昊,汤杰新.长三角地区能源消耗与碳排放的实证研究——基于系统动力学模型[J].华东经济管理,2015,29(9)∶63-68.
[9]杜强,陈乔,杨锐.基于Logistic模型的中国各省碳排放预测[J].长江流域资源与环境,2013,22(2)∶143-151.
[10]Cansino J M,Roman R. Main Drivers of Changes in CO2Emissions in the Spanish Economy:A Structural Decomposition Analysis[J].Energy Policy,2016,89(2)∶150-159.
[11]张红丽,沈镭,李艳梅.京津冀经济活动隐含的碳排放转移——基于多区域投入产出模型的分析[J].资源科学,2017,39(12)∶2287-2298.
[12]王强,焦生杰.基于生命周期的工程翻新轮胎碳排放分析与评价[J].江苏大学学报(自然科学版),2018,39(2)∶144-148.
[13]王世进.新型城镇化对我国碳排放的影响机理与区域差异研究[J].现代经济探讨,2017,(7)∶103-109.
[14]方齐云,陶守来.基于人口与城镇化视角的中国碳排放驱动因素探究[J].当代财经,2017,(3)∶14-25.
[15]张腾飞,杨俊,盛鹏飞.城镇化对中国碳排放的影响及作用渠道[J].中国人口·资源与环境,2016,26(2)∶47-57.
[16]李经路,曾天.基于Kaya方法的云南碳排放因素分析[J].科技管理研究,2016,36(19)∶260-266.
[17]Mraihi R,Mraihi T. Carbon Emissions Growth and Road Freight:Analysis of the Influencing Factors in Tunisia[J]. Transport Policy,2015,42(8)∶121-129.
[18]刘险峰.中国工业部门碳排放增长影响因素的动态分析[J].求索,2017,(7)∶86-90.
[19]权丽.我国终端能源消费因素分析及实证研究——基于Laspeyres指数分解技术[J].技术经济,2011,30(8)∶83-86,117.
[20]王丽,欧阳慧,马永欢.经济社会发展对环境影响的再认识——基于IPAT模型的城市碳排放分析[J].宏观经济研究,2017,(10)∶161-168.
[21]Abdallh A A,Abugamos H. A Semi-parametric Panel Data Analysis on the Urbanisation-carbon Emissions Nexus for the MENA Countries[J]. Renewable and Sustainable Energy Reviews,2017,78(10)∶1350-1356.
[22]Zhang N,Yu K,Chen Z F. How Does Urbanization Affect Carbon Dioxide Emissions? A Cross-country Panel Data Analysis[J]. Energy Policy,2017,107(4)∶678-687.
[23]付云鹏,马树才.基于空间计量的碳排放量影响因素分析[J].中南大学学报(社会科学版),2017,23(2)∶103-110.
[24]陈邦丽,徐美萍.中国碳排放影响因素分析——基于面板数据STIRPAT-Alasso模型实证研究[J].生态经济,2018,34(1)∶20-24,48.
[25]Shuai C Y,Shen L Y,Jiao L D,et al. Identifying Key Impact Factors on Carbon Emission:Evidences from Panel and Time-series Data of125 Countries from 1990 to 2011[J]. Applied Energy,2017,187(2)∶310-325.
[26]Xu Q,Dong Y X,Yang R. Urbanization Impact on Carbon Emissions in the Pearl River Delta Region:Kuznets Curve Relationships[J].Journal of Cleaner Production,2018,180(4)∶514-523.
[27]任晓松,赵涛.中国碳排放强度及其影响因素间动态因果关系研究——以扩展型KAYA公式为视角[J].干旱区资源与环境,2014,28(3)∶6-10.
[28]黄蕊,王铮,丁冠群,等.基于STIRPAT模型的江苏省能源消费碳排放影响因素分析及趋势预测[J].地理研究,2016,35(4)∶781-789.
[29]韩梦瑶,刘卫东,唐志鹏,等.世界主要国家碳排放影响因素分析——基于变系数面板模型[J].资源科学,2017,39(12)∶2420-2429.
[30]黄海田,闵毅梅.将低碳经济理念引入水利行业的探讨[J].水利水电科技进展,2011,31(3)∶43-46,83.
[31]王淑军,高翠娟,徐世鹏,等.水利改革发展的低碳经济效益[J].生态经济,2011,(12)∶72-75.
[32]吴硕贤.绿色建筑技术要点及推行绿色建筑的建议措施[J].建筑学报,2011,(9)∶1-3.
[33]焦文献,陈兴鹏.基于IPAT等式的甘肃省能源消费碳排放特征分析及情景预测[J].干旱区资源与环境,2012,26(10)∶180-184.
[34]王世进,马妍妍,耿梦雯.城镇化背景下江苏省区域碳排放的影响因素研究[J].资源开发与市场,2017,33(12)∶1444-1448,1427.
[2]Perkins J L,Padro-martinez L T,Durant J L. Particle Number Emission Factors for an Urban Highway Tunnel[J]. Atmospheric Environment,2013,74(8)∶326-337.
[3]陈超,胡秦镪,邓奕雯,等.基于实测法的下凹式城市地下道路CO,NO_x排放因子研究[J].中国公路学报,2017,30(9)∶116-124.
[4]仲佳爱,陈国俊,张中宁,等.四川盆地气矿天然气开发过程中温室气体的排放特征[J].环境科学研究,2015,28(3)∶355-360.
[5]陈敏,温宗国,杜鹏飞.基于AIM/enduse模型的水泥行业节能减排途径分析[J].中国人口·资源与环境,2012,22(S1)∶234-239.
[6]Sarica K,Tyner W E. Analysis of US Renewable Fuels Policies Using a Modified MARKAL Model[J]. Renewable Energy,2013,50(3)∶701-709.
[7]周伟,米红.中国能源消费排放的CO2测算[J].中国环境科学,2010,30(8)∶1142-1148.
[8]唐德才,刘昊,汤杰新.长三角地区能源消耗与碳排放的实证研究——基于系统动力学模型[J].华东经济管理,2015,29(9)∶63-68.
[9]杜强,陈乔,杨锐.基于Logistic模型的中国各省碳排放预测[J].长江流域资源与环境,2013,22(2)∶143-151.
[10]Cansino J M,Roman R. Main Drivers of Changes in CO2Emissions in the Spanish Economy:A Structural Decomposition Analysis[J].Energy Policy,2016,89(2)∶150-159.
[11]张红丽,沈镭,李艳梅.京津冀经济活动隐含的碳排放转移——基于多区域投入产出模型的分析[J].资源科学,2017,39(12)∶2287-2298.
[12]王强,焦生杰.基于生命周期的工程翻新轮胎碳排放分析与评价[J].江苏大学学报(自然科学版),2018,39(2)∶144-148.
[13]王世进.新型城镇化对我国碳排放的影响机理与区域差异研究[J].现代经济探讨,2017,(7)∶103-109.
[14]方齐云,陶守来.基于人口与城镇化视角的中国碳排放驱动因素探究[J].当代财经,2017,(3)∶14-25.
[15]张腾飞,杨俊,盛鹏飞.城镇化对中国碳排放的影响及作用渠道[J].中国人口·资源与环境,2016,26(2)∶47-57.
[16]李经路,曾天.基于Kaya方法的云南碳排放因素分析[J].科技管理研究,2016,36(19)∶260-266.
[17]Mraihi R,Mraihi T. Carbon Emissions Growth and Road Freight:Analysis of the Influencing Factors in Tunisia[J]. Transport Policy,2015,42(8)∶121-129.
[18]刘险峰.中国工业部门碳排放增长影响因素的动态分析[J].求索,2017,(7)∶86-90.
[19]权丽.我国终端能源消费因素分析及实证研究——基于Laspeyres指数分解技术[J].技术经济,2011,30(8)∶83-86,117.
[20]王丽,欧阳慧,马永欢.经济社会发展对环境影响的再认识——基于IPAT模型的城市碳排放分析[J].宏观经济研究,2017,(10)∶161-168.
[21]Abdallh A A,Abugamos H. A Semi-parametric Panel Data Analysis on the Urbanisation-carbon Emissions Nexus for the MENA Countries[J]. Renewable and Sustainable Energy Reviews,2017,78(10)∶1350-1356.
[22]Zhang N,Yu K,Chen Z F. How Does Urbanization Affect Carbon Dioxide Emissions? A Cross-country Panel Data Analysis[J]. Energy Policy,2017,107(4)∶678-687.
[23]付云鹏,马树才.基于空间计量的碳排放量影响因素分析[J].中南大学学报(社会科学版),2017,23(2)∶103-110.
[24]陈邦丽,徐美萍.中国碳排放影响因素分析——基于面板数据STIRPAT-Alasso模型实证研究[J].生态经济,2018,34(1)∶20-24,48.
[25]Shuai C Y,Shen L Y,Jiao L D,et al. Identifying Key Impact Factors on Carbon Emission:Evidences from Panel and Time-series Data of125 Countries from 1990 to 2011[J]. Applied Energy,2017,187(2)∶310-325.
[26]Xu Q,Dong Y X,Yang R. Urbanization Impact on Carbon Emissions in the Pearl River Delta Region:Kuznets Curve Relationships[J].Journal of Cleaner Production,2018,180(4)∶514-523.
[27]任晓松,赵涛.中国碳排放强度及其影响因素间动态因果关系研究——以扩展型KAYA公式为视角[J].干旱区资源与环境,2014,28(3)∶6-10.
[28]黄蕊,王铮,丁冠群,等.基于STIRPAT模型的江苏省能源消费碳排放影响因素分析及趋势预测[J].地理研究,2016,35(4)∶781-789.
[29]韩梦瑶,刘卫东,唐志鹏,等.世界主要国家碳排放影响因素分析——基于变系数面板模型[J].资源科学,2017,39(12)∶2420-2429.
[30]黄海田,闵毅梅.将低碳经济理念引入水利行业的探讨[J].水利水电科技进展,2011,31(3)∶43-46,83.
[31]王淑军,高翠娟,徐世鹏,等.水利改革发展的低碳经济效益[J].生态经济,2011,(12)∶72-75.
[32]吴硕贤.绿色建筑技术要点及推行绿色建筑的建议措施[J].建筑学报,2011,(9)∶1-3.
[33]焦文献,陈兴鹏.基于IPAT等式的甘肃省能源消费碳排放特征分析及情景预测[J].干旱区资源与环境,2012,26(10)∶180-184.
[34]王世进,马妍妍,耿梦雯.城镇化背景下江苏省区域碳排放的影响因素研究[J].资源开发与市场,2017,33(12)∶1444-1448,1427.