建成环境对城市居民日常出行碳排放的影响——以开封市248个居住区为例
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摘要
居住区是居民日常生活最基本的空间单元,其建成环境对出行碳排放的影响是学术界关注的焦点。基于大样本调查数据核算居民各类出行碳排放,通过POI抓取、空间句法、网络分析等方法识别开封市主城区成规模的248个居住区的建成环境特征,借助核密度和GWR等方法剖析居住区尺度居民各类出行碳排放的空间分异规律。结果表明:(1)城市内部居民出行碳排放空间差异显著,居住区公共服务供给的公平性问题突出,外圈层快速扩张区域应作为城市碳减排工作的关键区域;(2)居住区尺度能较好地揭示建成环境对出行碳排放的影响,路网设计、建筑密度、土地利用混合度等因素对各类出行碳排放的作用机理差异较大,作用强度亦存在不同方向上的空间渐进规律;(3)根据出行碳排放结构及其对应的建成环境指标可识别出外层高密度欠通达低混合型居住区碳排放水平较高,内层低密度高通达高混合型居住区碳排放水平较低。研究结果可为居住区空间重构和城市碳排放的分区规划与治理提供科学依据。
Residential area is the most basic unit of residential daily life, and the impact of its built environment on daily travel carbon emissions is a major concern within academic field.We accounted all kinds of carbon emissions from residential daily travel based on a large number of sample questionnaire data, identified the built environment characteristics of 248 residential areas in the main urban area of Kaifeng city by POI capture, network analysis,spatial syntax and other methods, and analyzed the spatial distribution and the differentiation mechanism of carbon emissions of various types of residents in residential areas by means of nuclear density and GWR. The results show that:(1) There is a significant spatial difference in urban residents' travel carbon emissions, and there is a great difference in the fairness of public service supply in residential areas.(2) The study in residential area scale can better reveal the impact of built environment on travel carbon emissions. Location, accessibility, road network design, building density and land use mix degree, etc. have significant impacts on the residential daily travel carbon emissions. However, for different travel purposes, the impacts of these factors are quite different. What's more, there is a spatial asymptotic difference in the impact intensity in different directions.(3) According to the carbon emission grade combination of various purposes and corresponding built environment indexes, it can be identified that the carbon emission level of outer high-density under-accessible low-mixed residential areas is of high emissions, and that of inner low-density high-accessible high-mixed residential areas is of low emissions. The results can provide a scientific basis for the spatial reconstruction of residential areas and the zoning planning and governance of urban carbon emissions.
Residential area is the most basic unit of residential daily life, and the impact of its built environment on daily travel carbon emissions is a major concern within academic field.We accounted all kinds of carbon emissions from residential daily travel based on a large number of sample questionnaire data, identified the built environment characteristics of 248 residential areas in the main urban area of Kaifeng city by POI capture, network analysis,spatial syntax and other methods, and analyzed the spatial distribution and the differentiation mechanism of carbon emissions of various types of residents in residential areas by means of nuclear density and GWR. The results show that:(1) There is a significant spatial difference in urban residents' travel carbon emissions, and there is a great difference in the fairness of public service supply in residential areas.(2) The study in residential area scale can better reveal the impact of built environment on travel carbon emissions. Location, accessibility, road network design, building density and land use mix degree, etc. have significant impacts on the residential daily travel carbon emissions. However, for different travel purposes, the impacts of these factors are quite different. What's more, there is a spatial asymptotic difference in the impact intensity in different directions.(3) According to the carbon emission grade combination of various purposes and corresponding built environment indexes, it can be identified that the carbon emission level of outer high-density under-accessible low-mixed residential areas is of high emissions, and that of inner low-density high-accessible high-mixed residential areas is of low emissions. The results can provide a scientific basis for the spatial reconstruction of residential areas and the zoning planning and governance of urban carbon emissions.
引文
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[3]Heinonen J,Jalas M,Juntunen J K,et al.Situated lifestyles:I.How lifestyles change along with the level of urbanization and what the greenhouse gas implications are:A study of Finland.Environmental Research Letters,2013,8(2):1-13.
[4]Heinonen J,Jalas M,Juntunen J K,et al.Situated lifestyles:II.The impacts of urban density,housing type and motorization on the greenhouse gas emissions of the middle income consumers in Finland.Environmental Research Letters,2013,8(3):1402-1416.
[5]荣培君,张丽君,杨群涛,等.中小城市家庭生活用能碳排放空间分异:以开封市为例.地理研究,2016,35(8):1495-1509.[Rong Peijun,Zhang Lijun,Yang Quntao.Spatial differentiation patterns of carbon emissions from residential energy consumption in small and medium-sized cities:A case study of Kaifeng.Geographical Research,2016,35(8):1495-1509.]
[6]Shi K,Chen Y,Li L,et al.Spatiotemporal variations of urban CO2emissions in China:A multiscale perspective.Applied Energy,2018,211(12):218-229.
[7]黄经南,高浩武,韩笋生.道路交通设施便利度对家庭日常交通出行碳排放的影响:以武汉市为例.国际城市规划,2015,30(3):97-105.[Huang Jingnan,Gao Haowu,Han Sunsheng.The effect of traffic facilities accessibility on household commuting caused carbon emission:A case study of Wuhan city,China.Urban Planning International,2015,30(3):97-105.]
[8]Xie R,Fang J,Liu C.The effects of transportation infrastructure on urban carbon emissions.Applied Energy,2017,196(2):199-207.
[9]Fremstad A,Underwood A,Zahran S.The environmental impact of sharing:Household and urban economies in CO2emissions.Ecological Economics,2018,145(9):137-147.
[10]申犁帆,王烨,张纯,等.轨道站点合理步行可达范围建成环境与轨道通勤的关系研究:以北京市44个轨道站点为例.地理学报,2018,73(12):2423-2439.[Shen Lifan,Wang Ye,Zhang Chun,et al.Relationship between built environment of rational pedestrian catchment areas and URT commuting ridership:Evidence from 44 URT stations in Beijing.Acta Geographica Sinica,2018,73(12):2423-2439.]
[11]Lee S,Lee B.The influence of urban form on GHG emissions in the U.S.household sector.Energy Policy,2014,68(1):534-549.
[12]Mu?iz I,Sánchez V.Urban spatial form and structure and greenhouse-gas emissions from commuting in the metropolitan zone of Mexico Valley.Ecological Economics,2018,147(2):353-364.
[13]Denant-Boemont L,GaignéCarl,GatéRomain.Urban spatial structure,transport-related emissions and welfare.Journal of Environmental Economics and Management,2018,89(2):29-45.
[14]Zhou C,Wang S.Examining the determinants and the spatial nexus of city-level CO2emissions in China:A dynamic spatial panel analysis of China's cities.Journal of Cleaner Production,2018,171(10):917-926.
[15]Jones C,Kammen D M.Spatial distribution of U.S.household carbon footprints reveals sub-urbanization undermines greenhouse gas benefits of urban population density.Environmental Science&Technology,2014,48(2):895-902.
[16]Holtzclaw J.Using Residential Patterns and Transit to Decrease Auto Dependence and Costs.San Francisco,CA:Natural Resources Defense Council,1994.
[17]Cervero R,Gorham R.Commuting in transit versus automobile neighborhoods.Journal of the American Planning Association,1995,61(2):210-225.
[18]Cervero R,Kockelman K.Travel demand and the 3Ds:Density,diversity,and design.Transportation Research Part DTransport&Environment,1997,2(3):199-219.
[19]Ewing R.Travel and the built environment:A synthesis.Transportation Research Record,2001,1780(1):265-294.
[20]曹小曙,杨文越,黄晓燕.基于智慧交通的可达性与交通出行碳排放:理论与实证.地理科学进展,2015,34(4):418-429.[Cao Xiaoshu,Yang Wenyue,Huang Xiaoyan.Accessibility and CO2emissions from travel of smart transportation:Theory and empirical studies.Progress in Geography,2015,34(4):418-429.]
[21]黄晓燕,刘夏琼,曹小曙.广州市三个圈层社区居民通勤碳排放特征:以都府小区、南雅苑小区和丽江花园为例.地理研究,2015,34(4):751-761.[Huang Xiaoyan,Liu Xiaqiong,Cao Xiaoshu.Commuting carbon emission characteristics of community residents of three spheres:A case study of three communities in Guangzhou city.Geographical Research,2015,34(4):751-761.]
[22]Yang Y,Wang C,Liu W L.Urban daily travel carbon emissions accounting and mitigation potential analysis using surveyed individual data.Journal of Cleaner Production,2018,192(5):821-834.
[23]Ma J,Zhou S H,Mitchell G.CO2emission from passenger travel in Guangzhou,China:A small area simulation.Applied Geography,2018,98(7):121-132.
[24]Cao X S,Yang W Y.Examining the effects of the built environment and residential self-selection on commuting trips and the related CO2emissions:An empirical study in Guangzhou,China.Transportation Research Part D,2017,52(3):480-494.
[25]Alexander R,Christian H R,Joachim S.GHG emissions in daily travel and long-distance travel in Germany:Social and spatial correlates.Transportation Research Part D,2016,49(9):25-43.
[26]杨文越,李涛,曹小曙.广州市社区出行低碳指数格局及其影响因素的空间异质性.地理研究,2015,34(8):1471-1480.[Yang Wenyue,Li Tao,Cao Xiaoshu.The spatial pattern of community travel low carbon index(CTLCI)and spatial heterogeneity of the relationship between CTLCI and influencing factors in Guangzhou.Geographical Research,2015,34(8):1471-1480.]
[27]Tirumalachetty S,Kockelman K M,Nichols B G.Forecasting greenhouse gas emissions from urban regions:Microsimulation of land use and transport patterns in Austin,Texas.Journal of Transport Geography,2013,33:220-229.
[28]Baiocchi G,Creutzig F,Minx J,et al.A spatial typology of human settlements and their CO2emissions in England.Global Environmental Change,2015,34(9):13-21.
[29]杨文越,曹小曙.居住自选择视角下的广州出行碳排放影响机理.地理学报,2018,73(2):346-361.[Yang Wenyue,Cao Xiaoshu.The influence mechanism of travel-related CO2emissions from the perspective of residential self-selection:A case study of Guangzhou.Acta Geographica Sinica,2018,73(2):346-361.]
[30]周素红,宋江宇,宋广文.广州市居民工作日小汽车出行个体与社区双层影响机制.地理学报,2017,72(8):1444-1457.[Zhou Suhong,Song Jiangyu,Song Guangwen.Examining the dual-levels impact of neighbourhood and individual variables on car use on weekdays in Guangzhou.Acta Geographica Sinica,2017,72(8):1444-1457.]
[31]Jain D,Tiwari G.How the present would have looked like?Impact of non-motorized transport and public transport infrastructure on travel behavior,energy consumption and CO2emissions:Delhi,Pune and Patna.Sustainable Cities&Society,2016,22(9):1-10.
[32]Ma J,Mitchell G,Heppenstall A.Exploring transport carbon futures using population microsimulation and travel diaries:Beijing to 2030.Transport Research:Part D,2015,37(5):108-122.
[33]柴彦威,肖作鹏,刘志林.基于空间行为约束的北京市居民家庭日常出行碳排放的比较分析.地理科学,2011,31(7):843-849.[Chai Yanwei,Xiao Zuopeng,Liu Zhiling.Comparative analysis on CO2emission per household in daily travel based on spatial behavior constraints.Scientia Geographica Sinica,2011,31(7):843-849.]
[34]Rong P J,Zhang L J,Qin Y C,et al.Spatial differentiation of daily travel carbon emissions in small-and medium-sized cities:An empirical study in Kaifeng,China.Journal of Cleaner Production,2018,197(6):1365-1373.
[35]秦波,田卉.社区空间形态类型与居民碳排放:基于北京的问卷调查.城市发展研究,2014,21(6):15-20.[Qin Bo,Tian Hui.The impact of neighborhood spatial form on household carbon emissions:Based on the study in Beijing.Urban Development Studies,2014,21(6):15-20.]
[36]Jabbari M,Fonseca F,Ramos R.Combining multi-criteria and space syntax analysis to assess a pedestrian network:The case of Oporto.Journal of Urban Design.2018,23(1):23-41.
[37]满洲,赵荣钦,袁盈超,等.城市居住区周边土地混合度对居民通勤交通碳排放的影响:以南京市江宁区典型居住区为例.人文地理,2018,33(1):70-75.[Man Zhou,Zhao Rongqin,Yuan Yingchao,et al.Impact of land-mixing degree of residential area on carbon emissions of commuting:A case study of typical residential district,Jiangning District,Nan jing.Human Geography,2018,33(1):70-75.]
[38]谢宏,李颖灏,韦有义.浙江省特色小镇的空间结构特征及影响因素研究.地理科学,2018,38(8):1283-1291.[Xie Hong,Li Yinghao,Wei Youyi.Influencing factors and spatial distribution of the characteristic towns in Zhejiang province.Scientia Geographica Sinica,2018,38(8):1283-1291.]
[39]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.环境科学学报,2015,35(6):1896-1906.[Hu Yanxing,Pan Jinghu,Wang Yirui.Spatial-temporal evolution of provincial carbon emission in China from 1997 to 2012 based on ESDA and GWR model.Acta Scientae Circumstantiae,2015,35(6):1896-1906.]
[2]Zhou Q,Leng G,Huang M.Impacts of future climate change on urban flood volumes in Hohhot in northern China:Benefits of climate change mitigation and adaptations.Hydrology&Earth System Sciences,2018,22(1):305-316.
[3]Heinonen J,Jalas M,Juntunen J K,et al.Situated lifestyles:I.How lifestyles change along with the level of urbanization and what the greenhouse gas implications are:A study of Finland.Environmental Research Letters,2013,8(2):1-13.
[4]Heinonen J,Jalas M,Juntunen J K,et al.Situated lifestyles:II.The impacts of urban density,housing type and motorization on the greenhouse gas emissions of the middle income consumers in Finland.Environmental Research Letters,2013,8(3):1402-1416.
[5]荣培君,张丽君,杨群涛,等.中小城市家庭生活用能碳排放空间分异:以开封市为例.地理研究,2016,35(8):1495-1509.[Rong Peijun,Zhang Lijun,Yang Quntao.Spatial differentiation patterns of carbon emissions from residential energy consumption in small and medium-sized cities:A case study of Kaifeng.Geographical Research,2016,35(8):1495-1509.]
[6]Shi K,Chen Y,Li L,et al.Spatiotemporal variations of urban CO2emissions in China:A multiscale perspective.Applied Energy,2018,211(12):218-229.
[7]黄经南,高浩武,韩笋生.道路交通设施便利度对家庭日常交通出行碳排放的影响:以武汉市为例.国际城市规划,2015,30(3):97-105.[Huang Jingnan,Gao Haowu,Han Sunsheng.The effect of traffic facilities accessibility on household commuting caused carbon emission:A case study of Wuhan city,China.Urban Planning International,2015,30(3):97-105.]
[8]Xie R,Fang J,Liu C.The effects of transportation infrastructure on urban carbon emissions.Applied Energy,2017,196(2):199-207.
[9]Fremstad A,Underwood A,Zahran S.The environmental impact of sharing:Household and urban economies in CO2emissions.Ecological Economics,2018,145(9):137-147.
[10]申犁帆,王烨,张纯,等.轨道站点合理步行可达范围建成环境与轨道通勤的关系研究:以北京市44个轨道站点为例.地理学报,2018,73(12):2423-2439.[Shen Lifan,Wang Ye,Zhang Chun,et al.Relationship between built environment of rational pedestrian catchment areas and URT commuting ridership:Evidence from 44 URT stations in Beijing.Acta Geographica Sinica,2018,73(12):2423-2439.]
[11]Lee S,Lee B.The influence of urban form on GHG emissions in the U.S.household sector.Energy Policy,2014,68(1):534-549.
[12]Mu?iz I,Sánchez V.Urban spatial form and structure and greenhouse-gas emissions from commuting in the metropolitan zone of Mexico Valley.Ecological Economics,2018,147(2):353-364.
[13]Denant-Boemont L,GaignéCarl,GatéRomain.Urban spatial structure,transport-related emissions and welfare.Journal of Environmental Economics and Management,2018,89(2):29-45.
[14]Zhou C,Wang S.Examining the determinants and the spatial nexus of city-level CO2emissions in China:A dynamic spatial panel analysis of China's cities.Journal of Cleaner Production,2018,171(10):917-926.
[15]Jones C,Kammen D M.Spatial distribution of U.S.household carbon footprints reveals sub-urbanization undermines greenhouse gas benefits of urban population density.Environmental Science&Technology,2014,48(2):895-902.
[16]Holtzclaw J.Using Residential Patterns and Transit to Decrease Auto Dependence and Costs.San Francisco,CA:Natural Resources Defense Council,1994.
[17]Cervero R,Gorham R.Commuting in transit versus automobile neighborhoods.Journal of the American Planning Association,1995,61(2):210-225.
[18]Cervero R,Kockelman K.Travel demand and the 3Ds:Density,diversity,and design.Transportation Research Part DTransport&Environment,1997,2(3):199-219.
[19]Ewing R.Travel and the built environment:A synthesis.Transportation Research Record,2001,1780(1):265-294.
[20]曹小曙,杨文越,黄晓燕.基于智慧交通的可达性与交通出行碳排放:理论与实证.地理科学进展,2015,34(4):418-429.[Cao Xiaoshu,Yang Wenyue,Huang Xiaoyan.Accessibility and CO2emissions from travel of smart transportation:Theory and empirical studies.Progress in Geography,2015,34(4):418-429.]
[21]黄晓燕,刘夏琼,曹小曙.广州市三个圈层社区居民通勤碳排放特征:以都府小区、南雅苑小区和丽江花园为例.地理研究,2015,34(4):751-761.[Huang Xiaoyan,Liu Xiaqiong,Cao Xiaoshu.Commuting carbon emission characteristics of community residents of three spheres:A case study of three communities in Guangzhou city.Geographical Research,2015,34(4):751-761.]
[22]Yang Y,Wang C,Liu W L.Urban daily travel carbon emissions accounting and mitigation potential analysis using surveyed individual data.Journal of Cleaner Production,2018,192(5):821-834.
[23]Ma J,Zhou S H,Mitchell G.CO2emission from passenger travel in Guangzhou,China:A small area simulation.Applied Geography,2018,98(7):121-132.
[24]Cao X S,Yang W Y.Examining the effects of the built environment and residential self-selection on commuting trips and the related CO2emissions:An empirical study in Guangzhou,China.Transportation Research Part D,2017,52(3):480-494.
[25]Alexander R,Christian H R,Joachim S.GHG emissions in daily travel and long-distance travel in Germany:Social and spatial correlates.Transportation Research Part D,2016,49(9):25-43.
[26]杨文越,李涛,曹小曙.广州市社区出行低碳指数格局及其影响因素的空间异质性.地理研究,2015,34(8):1471-1480.[Yang Wenyue,Li Tao,Cao Xiaoshu.The spatial pattern of community travel low carbon index(CTLCI)and spatial heterogeneity of the relationship between CTLCI and influencing factors in Guangzhou.Geographical Research,2015,34(8):1471-1480.]
[27]Tirumalachetty S,Kockelman K M,Nichols B G.Forecasting greenhouse gas emissions from urban regions:Microsimulation of land use and transport patterns in Austin,Texas.Journal of Transport Geography,2013,33:220-229.
[28]Baiocchi G,Creutzig F,Minx J,et al.A spatial typology of human settlements and their CO2emissions in England.Global Environmental Change,2015,34(9):13-21.
[29]杨文越,曹小曙.居住自选择视角下的广州出行碳排放影响机理.地理学报,2018,73(2):346-361.[Yang Wenyue,Cao Xiaoshu.The influence mechanism of travel-related CO2emissions from the perspective of residential self-selection:A case study of Guangzhou.Acta Geographica Sinica,2018,73(2):346-361.]
[30]周素红,宋江宇,宋广文.广州市居民工作日小汽车出行个体与社区双层影响机制.地理学报,2017,72(8):1444-1457.[Zhou Suhong,Song Jiangyu,Song Guangwen.Examining the dual-levels impact of neighbourhood and individual variables on car use on weekdays in Guangzhou.Acta Geographica Sinica,2017,72(8):1444-1457.]
[31]Jain D,Tiwari G.How the present would have looked like?Impact of non-motorized transport and public transport infrastructure on travel behavior,energy consumption and CO2emissions:Delhi,Pune and Patna.Sustainable Cities&Society,2016,22(9):1-10.
[32]Ma J,Mitchell G,Heppenstall A.Exploring transport carbon futures using population microsimulation and travel diaries:Beijing to 2030.Transport Research:Part D,2015,37(5):108-122.
[33]柴彦威,肖作鹏,刘志林.基于空间行为约束的北京市居民家庭日常出行碳排放的比较分析.地理科学,2011,31(7):843-849.[Chai Yanwei,Xiao Zuopeng,Liu Zhiling.Comparative analysis on CO2emission per household in daily travel based on spatial behavior constraints.Scientia Geographica Sinica,2011,31(7):843-849.]
[34]Rong P J,Zhang L J,Qin Y C,et al.Spatial differentiation of daily travel carbon emissions in small-and medium-sized cities:An empirical study in Kaifeng,China.Journal of Cleaner Production,2018,197(6):1365-1373.
[35]秦波,田卉.社区空间形态类型与居民碳排放:基于北京的问卷调查.城市发展研究,2014,21(6):15-20.[Qin Bo,Tian Hui.The impact of neighborhood spatial form on household carbon emissions:Based on the study in Beijing.Urban Development Studies,2014,21(6):15-20.]
[36]Jabbari M,Fonseca F,Ramos R.Combining multi-criteria and space syntax analysis to assess a pedestrian network:The case of Oporto.Journal of Urban Design.2018,23(1):23-41.
[37]满洲,赵荣钦,袁盈超,等.城市居住区周边土地混合度对居民通勤交通碳排放的影响:以南京市江宁区典型居住区为例.人文地理,2018,33(1):70-75.[Man Zhou,Zhao Rongqin,Yuan Yingchao,et al.Impact of land-mixing degree of residential area on carbon emissions of commuting:A case study of typical residential district,Jiangning District,Nan jing.Human Geography,2018,33(1):70-75.]
[38]谢宏,李颖灏,韦有义.浙江省特色小镇的空间结构特征及影响因素研究.地理科学,2018,38(8):1283-1291.[Xie Hong,Li Yinghao,Wei Youyi.Influencing factors and spatial distribution of the characteristic towns in Zhejiang province.Scientia Geographica Sinica,2018,38(8):1283-1291.]
[39]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.环境科学学报,2015,35(6):1896-1906.[Hu Yanxing,Pan Jinghu,Wang Yirui.Spatial-temporal evolution of provincial carbon emission in China from 1997 to 2012 based on ESDA and GWR model.Acta Scientae Circumstantiae,2015,35(6):1896-1906.]