京津冀地区汽车运输碳排放影响因素研究
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摘要
以空间相关性和空间异质性为基础,构建SLM-STIRPAT、SEM-STIRPAT和GWR-STIRPAT模型,对京津冀地区汽车运输碳排放进行测算和影响因素分析.结果表明:京津冀地区汽车运输碳排放存在显著空间相关性和空间异质性.人口对汽车运输碳排放呈正向影响;人均GDP对货运碳排放和总量碳排放呈正向影响,对客运碳排放呈负向影响,城镇化水平对汽车运输碳排放呈负向影响.第三产业增加值对客运碳排放和总量碳排放呈正向影响,对货运碳排放呈负向影响,人口对张家口市汽车运输碳排放影响最为显著;人均GDP对秦皇岛市和沧州市的汽车运输碳排放影响最为显著;城镇化水平对秦皇岛市的汽车运输碳排放影响最为显著;第三产业增加值对秦皇岛市的汽车运输碳排放影响最为显著.
Based on the spatial correlation and spatial heterogeneity, the SLM-STIRPAT model, SEM-STIRPAT model and GWRSTIRPAT model were constructed to measure and analyze the driving factors of vehicle transport carbon emissions in Beijing-Tianjin-Hebei region. The results showed that: There were significant spatial correlation and spatial heterogeneity in vehicle transport carbon emissions in Beijing-Tianjin-Hebei region. The population size and had a positive impact on the vehicle transport carbon emissions. The per capita GDP had a positive impact on the vehicle freight transport and total vehicle transport carbon emissions, had a negative impact on the passenger transport carbon emissions. The urbanization level had a positive impact on the vehicle transport carbon emissions. The added value of the tertiary industry had a positive impact on the vehicle passenger transport and total vehicle transport carbon emissions, had a negative impact on vehicle freight transport carbon emissions. Population size had the most significant impact on vehicle transport carbon emissions in Zhangjiakou. The per capita GDP had the most significant impact on vehicle transport carbon emissions in Qinhuangdao and Cangzhou. The urbanization level had the most significant impact on the vehicle transport carbon emissions in Qinhuangdao. The added value of the tertiary industry had the most significant impact on the vehicle transport carbon emissions in Qinhuangdao.
Based on the spatial correlation and spatial heterogeneity, the SLM-STIRPAT model, SEM-STIRPAT model and GWRSTIRPAT model were constructed to measure and analyze the driving factors of vehicle transport carbon emissions in Beijing-Tianjin-Hebei region. The results showed that: There were significant spatial correlation and spatial heterogeneity in vehicle transport carbon emissions in Beijing-Tianjin-Hebei region. The population size and had a positive impact on the vehicle transport carbon emissions. The per capita GDP had a positive impact on the vehicle freight transport and total vehicle transport carbon emissions, had a negative impact on the passenger transport carbon emissions. The urbanization level had a positive impact on the vehicle transport carbon emissions. The added value of the tertiary industry had a positive impact on the vehicle passenger transport and total vehicle transport carbon emissions, had a negative impact on vehicle freight transport carbon emissions. Population size had the most significant impact on vehicle transport carbon emissions in Zhangjiakou. The per capita GDP had the most significant impact on vehicle transport carbon emissions in Qinhuangdao and Cangzhou. The urbanization level had the most significant impact on the vehicle transport carbon emissions in Qinhuangdao. The added value of the tertiary industry had the most significant impact on the vehicle transport carbon emissions in Qinhuangdao.
引文
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[14]柴建,邢丽敏,周友洪,等.交通运输结构调整对碳排放的影响效应研究[J].运筹与管理,2017,26(7):110-116.
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[19]Deng T,Li X,Ma M.Evaluating impact of air pollution on China’s inbound tourism industry:a spatial econometric approach[J].Asia Pacific Journal of Tourism Research,2017,22(1):1-10.
[20]Rong P,Zhang L,Yang Q,et al.Spatial differentiation patterns of carbon emissions from residential energy consumption in small and medium-sized cities:A case study of Kaifeng[J].Geographical Research,2016,35(8):1495-1509.
[21]李建豹,黄贤金,吴常艳,等.中国省域碳排放影响因素的空间异质性分析[J].经济地理,2015,35(11):21-28.
[22]尹鹏,段佩利,陈才.中国交通运输碳排放格局及其与经济增长的关系研究[J].干旱区资源与环境,2016,30(5):7-12.
[23]袁长伟,张帅,焦萍,等.中国省域交通运输全要素碳排放效率时空变化及影响因素研究[J].资源科学,2017,39(4):687-697.
[24]王锋,秦豫徽,刘娟等.多维度城镇化视角下的碳排放影响因素研究--基于中国省域数据的空间杜宾面板模型[J].中国人口资源与环境,2017,27(9):151-161.
[25]Dietz T,Rosa E A.Effects of population and affluence on CO2emissions[J].Proceedings of the National Academy of Sciences of the United States of America,1997,94(1):175-179.
[26]刘满芝,刘贤贤.基于STIRPAT模型的中国城镇生活能源消费影响因素研究[J].长江流域资源与环境,2017,26(8):1111-1122.
[27]陈操操,刘春兰,汪浩,等.北京市能源消费碳足迹影响因素分析-基于STIRPAT模型和偏小二乘模型[J].中国环境科学,2014,34(6):1622-1632.
[28]刘永伟,闫庆武.基于SLM模型的中国碳排放格网化空间分布模拟[J].地理与地理信息科学,2015,31(3):76-80.
[29]陈操操,蔡博峰,孙粉,等.京津冀与长三角城市群碳排放的空间聚集效应比较[J].中国环境科学,2017,37(11):4371-4379.
[30]岳婷,龙如银.中国省域生活能源碳排放空间计量分析[J].北京理工大学学报(社会科学版),2014,16(2):40-46.
[31]胡艳兴,潘竟虎,王怡睿.基于ESDA-GWR的1997~2012年中国省域能源消费碳排放时空演变特征[J].环境科学学报,2015,35(6):1896-1906.
[32]王雅楠,赵涛.基于GWR模型中国碳排放空间差异研究[J].中国人口资源与环境,2016,26(2):27-34.
[33]中国汽车工业协会.中国汽车工业年鉴(2016)[EB/OL].
[34]海德堡能源与环境研究所.中国交通:不同交通方式的能源消耗与排放研究报告[R].海德堡:海德堡能源与环境研究所,2008.
[35]中国生态环境部.中国机动车污染防治年报(2016)[EB/OL].
[36]中国交通运输部.公路水路交通运输行业发展统计公报(2016)[EB/OL].
[37]国家发展改革委气候司.省级温室气体清单编制指南(试行)[R].北京,2011.
[38]IPCC.2006IPCC Guidelines for national greenhouse gas inventories[C]//Eggleston H S,Buendia L,Miwa K,et al.Prepared by the National Greenhouse Gas Inventories Programme.Japan:IGES,2006.
[39]国家统计局城市社会经济调查司.中国城市统计年鉴2016[J].北京:中国统计出版社,2017.
[40]武义青,赵亚南.京津冀碳排放的地区异质性及减排对策[J].经济与管理,2014,240(5):13-16.
[2]李琳娜.中国客运交通的碳排放地理特征与展望[J].地理研究,2016,35(7):1230-1242.
[3]河北省环保厅.河北省机动车污染防治年报(2016)[EB/OL].
[4]李涛,曹小曙,杨文越.珠江三角洲客货运量位序-规模分布特征及其变化[J].地理科学进展,2016,35(1):108-117.
[5]董宾芳.中国货物运输的时空变化特征及成因分析[J].贵州师范大学学报(自然科学版),2017,35(3):6-14.
[6]马海涛,康雷.京津冀区域公路客运交通碳排放时空特征与调控预测[J].资源科学,2017,39(7):1361-1370.
[7]Shi A.The impact of population pressure on global carbon dioxide emissions,1975-1996:Evidence from pooled cross-country data[J].Ecological Economics,2003,44(1):29-42.
[8]Shahbaz M,Loganathan N,Sbia R,et al.The effect of urbanization,affluence and trade openness on energy consumption:A time series analysis in Malaysia[J].Renewable and Sustainable Energy Reviews,2015,47(11):683-693.
[9]田中华,杨泽亮,蔡睿贤.广东省能源消费碳排放分析及碳排放强度影响因素研究[J].中国环境科学,2015,35(6):1885-1891.
[10]González P F,Landajo M,Presno M J.Tracking European Union CO2Emissions Through LMDI(Logarithmicmean Divisia Index)Decomposition.The Activity revaluation Approach[J].Energy,2014,73(7):741-750.
[11]米红,张田田,任正委,等.城镇化进程中家庭CO2排放的驱动因素分析[J].中国环境科学,2016,36(10):3183-3192.
[12]杨彬,宁小莉.新型城镇化视角下中国交通运输碳排放测度及其空间格局分析[J].内蒙古师范大学学报(自然科学汉文版),2015,44(5):667-679.
[13]孙叶飞,周敏.中国城镇化、产业结构高级化对CO2排放的影响--基于独立效应和联动效应双重视角[J].资源科学,2016,38(10):1846-1860.
[14]柴建,邢丽敏,周友洪,等.交通运输结构调整对碳排放的影响效应研究[J].运筹与管理,2017,26(7):110-116.
[15]Brunsdon C,Fotheringham S,Charlton M.Geographically Weighted Regression-Modelling Spatial Non-Stationarity[J].Journal of the Royal Statistical Society,1998,47(3):431-443.
[16]Fotheringham A S,Brunsdon C,Charlton M.Geographically Weighted Regression[M].New York:Wiley,2002.
[17]Liu Y,Xiao H,Zhang N.Industrial carbon emissions of China’s Regions:A spatial econometric analysis[J].Sustainability,2016,8(3):210-215.
[18]Wei C,Liu C,Gui F.Geographically weight seemingly unrelated regression(GWSUR):a method for exploring spatio-temporal heterogeneity[J].Applied Economics,2017,49(42):1-7.
[19]Deng T,Li X,Ma M.Evaluating impact of air pollution on China’s inbound tourism industry:a spatial econometric approach[J].Asia Pacific Journal of Tourism Research,2017,22(1):1-10.
[20]Rong P,Zhang L,Yang Q,et al.Spatial differentiation patterns of carbon emissions from residential energy consumption in small and medium-sized cities:A case study of Kaifeng[J].Geographical Research,2016,35(8):1495-1509.
[21]李建豹,黄贤金,吴常艳,等.中国省域碳排放影响因素的空间异质性分析[J].经济地理,2015,35(11):21-28.
[22]尹鹏,段佩利,陈才.中国交通运输碳排放格局及其与经济增长的关系研究[J].干旱区资源与环境,2016,30(5):7-12.
[23]袁长伟,张帅,焦萍,等.中国省域交通运输全要素碳排放效率时空变化及影响因素研究[J].资源科学,2017,39(4):687-697.
[24]王锋,秦豫徽,刘娟等.多维度城镇化视角下的碳排放影响因素研究--基于中国省域数据的空间杜宾面板模型[J].中国人口资源与环境,2017,27(9):151-161.
[25]Dietz T,Rosa E A.Effects of population and affluence on CO2emissions[J].Proceedings of the National Academy of Sciences of the United States of America,1997,94(1):175-179.
[26]刘满芝,刘贤贤.基于STIRPAT模型的中国城镇生活能源消费影响因素研究[J].长江流域资源与环境,2017,26(8):1111-1122.
[27]陈操操,刘春兰,汪浩,等.北京市能源消费碳足迹影响因素分析-基于STIRPAT模型和偏小二乘模型[J].中国环境科学,2014,34(6):1622-1632.
[28]刘永伟,闫庆武.基于SLM模型的中国碳排放格网化空间分布模拟[J].地理与地理信息科学,2015,31(3):76-80.
[29]陈操操,蔡博峰,孙粉,等.京津冀与长三角城市群碳排放的空间聚集效应比较[J].中国环境科学,2017,37(11):4371-4379.
[30]岳婷,龙如银.中国省域生活能源碳排放空间计量分析[J].北京理工大学学报(社会科学版),2014,16(2):40-46.
[31]胡艳兴,潘竟虎,王怡睿.基于ESDA-GWR的1997~2012年中国省域能源消费碳排放时空演变特征[J].环境科学学报,2015,35(6):1896-1906.
[32]王雅楠,赵涛.基于GWR模型中国碳排放空间差异研究[J].中国人口资源与环境,2016,26(2):27-34.
[33]中国汽车工业协会.中国汽车工业年鉴(2016)[EB/OL].
[34]海德堡能源与环境研究所.中国交通:不同交通方式的能源消耗与排放研究报告[R].海德堡:海德堡能源与环境研究所,2008.
[35]中国生态环境部.中国机动车污染防治年报(2016)[EB/OL].
[36]中国交通运输部.公路水路交通运输行业发展统计公报(2016)[EB/OL].
[37]国家发展改革委气候司.省级温室气体清单编制指南(试行)[R].北京,2011.
[38]IPCC.2006IPCC Guidelines for national greenhouse gas inventories[C]//Eggleston H S,Buendia L,Miwa K,et al.Prepared by the National Greenhouse Gas Inventories Programme.Japan:IGES,2006.
[39]国家统计局城市社会经济调查司.中国城市统计年鉴2016[J].北京:中国统计出版社,2017.
[40]武义青,赵亚南.京津冀碳排放的地区异质性及减排对策[J].经济与管理,2014,240(5):13-16.