基于DPSIR模型的中国城市低碳发展水平评价及空间分异
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摘要
基于社会经济统计数据、气象数据、自下而上的碳排放估算数据,构建城市低碳发展水平评价的动力学分析模型——DPSIR模型,全面评价中国288个地级以上城市的低碳发展水平及空间格局。结果表明,中国288个地级以上城市总体处于相对高碳水平,低碳城市具有东中西"梯度化"分异的区域特点,在空间上形成以胡焕庸线为底边、以东南沿海城市群和京津冀城市群为两翼的"△"型结构。低碳驱动力与低碳响应力是决定城市低碳发展水平的重要因素,高低碳驱动力城市主要有研发驱动型、社会经济发展驱动型、清洁能源发展驱动型、紧凑城市发展驱动型,低碳技术与低碳认知响应力远高于低碳制度响应力对城市低碳发展的作用。不同低碳等级结构的城市应根据DPSIR模型提供的低碳发展结构化清单,结合地方特性,选择因地制宜的低碳发展路径。
Much research on the low-carbon city have weakness in systemically and dynamically estimating the low-carbon level of cites in various regions.This study attempts to fill this gap through establishing a comprehensive causal-effect framework analysis framework, which is the interaction of Driving forces-Pressures-State-Impacts-Responses(DPSIR) between human and the environment. The paper used the socioeconomic data, meteorological data, and carbon estimation data from bottom to top, and calculate the low-carbon development level to examine the low-carbon performance and its spatial differentiation. The results reveal that most of the 288 cities at prefecture level and above in China are at a relatively high carbon level. The low-carbon cities display a gradient distribution from the east, the middle to the west.And the low-carbon cities are clustered in Huhuanyong line, the southeast coastal urban agglomeration and the Beijing-Tianjin-Hebei urban agglomeration. Low-carbon driving forces and low-carbon responses are important factors that determine the low-carbon development level of cities. The cities with higher level of low-carbon driving forces are mainly driven by R&D investment, socio-economic development, clean-energy development and/or urban compact development. The responses from low-carbon technology and low-carbon awareness are much more important to low-carbon city than that form low-carbon institution. Not all the low carbon pilot city have low-carbon performance. Cities with various low-carbon levels should select a local low-carbon development path based on the structural DPSIR inventories.
Much research on the low-carbon city have weakness in systemically and dynamically estimating the low-carbon level of cites in various regions.This study attempts to fill this gap through establishing a comprehensive causal-effect framework analysis framework, which is the interaction of Driving forces-Pressures-State-Impacts-Responses(DPSIR) between human and the environment. The paper used the socioeconomic data, meteorological data, and carbon estimation data from bottom to top, and calculate the low-carbon development level to examine the low-carbon performance and its spatial differentiation. The results reveal that most of the 288 cities at prefecture level and above in China are at a relatively high carbon level. The low-carbon cities display a gradient distribution from the east, the middle to the west.And the low-carbon cities are clustered in Huhuanyong line, the southeast coastal urban agglomeration and the Beijing-Tianjin-Hebei urban agglomeration. Low-carbon driving forces and low-carbon responses are important factors that determine the low-carbon development level of cities. The cities with higher level of low-carbon driving forces are mainly driven by R&D investment, socio-economic development, clean-energy development and/or urban compact development. The responses from low-carbon technology and low-carbon awareness are much more important to low-carbon city than that form low-carbon institution. Not all the low carbon pilot city have low-carbon performance. Cities with various low-carbon levels should select a local low-carbon development path based on the structural DPSIR inventories.
引文
[1]Yu L.Low carbon eco-city:New approach for Chinese urbanization.Habitat International,2014,44(10):102-110.
[2]de Jong M,Joss S,Schraven D,et al.Sustainable-smart-resilient-low carbon-eco-knowledge cities;Making sense of a multitude of concepts promoting sustainable urbanization.Journal of Cleaner Production,2015,109(12):25-38.
[3]Price L,Zhou N,Fridley D,et al.Development of a low-carbon indicator system for China.Habitat International,2013,37(1):4-21.
[4]Chen F,Zhu D.Theoretical research on low-carbon city and empirical study of Shanghai.Habitat International,2013,37(1):33-42.
[5]Lin J,Jacoby J,Cui S,et al.A model for developing a target integrated low carbon city indicator system:The case of Xiamen,China.Ecological Indicators,2014,40(5):51-57.
[6]Lo K.China's low-carbon city initiatives:the implementation gap and the limits of the target responsibility system.Habitat International,2014,42(4):236-244.
[7]吴健生,许娜,张曦文.中国低碳城市评价与空间格局分析.地理科学进展,2016,35(2):204-213.
[8]Su M,Li R,Lu W,et al.Evaluation of a low-carbon city:Method and application.Entropy,2013,15(4):1171-1185.
[9]Tan S,Yang J,Yan J,et al.A holistic low carbon city indicator framework for sustainable development.Applied Energy,2017,185:1919-1930.
[10]Zhou N,He G,Williams C,et al.ELITE cities:A low-carbon eco-city evaluation tool for China.Ecological Indicators,2015,48(1):448-456.
[11]Balta-Ozkan N,Watson T,Mocca E.Spatially uneven development and low carbon transitions:Insights from urban and regional planning.Energy Policy,2015,85(10):500-510.
[12]Zhou G,Singh J,Wu J,et al.Evaluating low-carbon city initiatives from the DPSIR framework perspective.Habitat International,2015,50(12):289-299.
[13]OECD.OECD core set of indicators for environmental performancereviews.1994,In:OECDEnvironment Monographs No.83.OECD,Paris,http://www.oecd.org/env/indicators-modellingoutlooks/31558547.pdf
[14]Lewison R L,Rudd M A,Al-Hayek W,et al.How the DPSIR framework can be used for structuring problems and facilitating empirical research in coastal systems.Environmental Science&Policy,2016,56(2):110-119.
[15]Zhang J,Xie Y,Luan B,et al.Urban macro-level impact factors on direct CO2emissions of urban residents in China.Energy and Buildings,2015,107(11):131-143.
[16]张金萍,闫卫阳,孙玮,等.中国低碳发展的类型及空间分异.资源科学,2014,36(12):2491-2499.
[17]Long Y,Li M Y,Liu X J.Bus coverage of Chinese cities.Ranking at Beijing City Lab,2014-09-01,http://www.beijingcity lab.com.
[18]全国城市空气质量实时发布平台.城市空气质量.http://106.37.208.233:20035/.
[19]Fridley D,Zheng N,Qin Y.Inventoryof China’s energy-related CO2emissions in 2008.Berkeley,CA:Lawrence Berkeley National Laboratory(LBNL-4600E).http://china.lbl.gov/sites/china.lbl.gov/files/China_Emissions_Inventory_2008.pdf.
(1)中国低碳试点城市共有81个。因拉萨市、大兴安岭地区,以及济源、共青、敦煌、昌吉、和田、伊宁、阿拉尔等地级以下城市数据可得性限制,本研究未对其进行评价。
[2]de Jong M,Joss S,Schraven D,et al.Sustainable-smart-resilient-low carbon-eco-knowledge cities;Making sense of a multitude of concepts promoting sustainable urbanization.Journal of Cleaner Production,2015,109(12):25-38.
[3]Price L,Zhou N,Fridley D,et al.Development of a low-carbon indicator system for China.Habitat International,2013,37(1):4-21.
[4]Chen F,Zhu D.Theoretical research on low-carbon city and empirical study of Shanghai.Habitat International,2013,37(1):33-42.
[5]Lin J,Jacoby J,Cui S,et al.A model for developing a target integrated low carbon city indicator system:The case of Xiamen,China.Ecological Indicators,2014,40(5):51-57.
[6]Lo K.China's low-carbon city initiatives:the implementation gap and the limits of the target responsibility system.Habitat International,2014,42(4):236-244.
[7]吴健生,许娜,张曦文.中国低碳城市评价与空间格局分析.地理科学进展,2016,35(2):204-213.
[8]Su M,Li R,Lu W,et al.Evaluation of a low-carbon city:Method and application.Entropy,2013,15(4):1171-1185.
[9]Tan S,Yang J,Yan J,et al.A holistic low carbon city indicator framework for sustainable development.Applied Energy,2017,185:1919-1930.
[10]Zhou N,He G,Williams C,et al.ELITE cities:A low-carbon eco-city evaluation tool for China.Ecological Indicators,2015,48(1):448-456.
[11]Balta-Ozkan N,Watson T,Mocca E.Spatially uneven development and low carbon transitions:Insights from urban and regional planning.Energy Policy,2015,85(10):500-510.
[12]Zhou G,Singh J,Wu J,et al.Evaluating low-carbon city initiatives from the DPSIR framework perspective.Habitat International,2015,50(12):289-299.
[13]OECD.OECD core set of indicators for environmental performancereviews.1994,In:OECDEnvironment Monographs No.83.OECD,Paris,http://www.oecd.org/env/indicators-modellingoutlooks/31558547.pdf
[14]Lewison R L,Rudd M A,Al-Hayek W,et al.How the DPSIR framework can be used for structuring problems and facilitating empirical research in coastal systems.Environmental Science&Policy,2016,56(2):110-119.
[15]Zhang J,Xie Y,Luan B,et al.Urban macro-level impact factors on direct CO2emissions of urban residents in China.Energy and Buildings,2015,107(11):131-143.
[16]张金萍,闫卫阳,孙玮,等.中国低碳发展的类型及空间分异.资源科学,2014,36(12):2491-2499.
[17]Long Y,Li M Y,Liu X J.Bus coverage of Chinese cities.Ranking at Beijing City Lab,2014-09-01,http://www.beijingcity lab.com.
[18]全国城市空气质量实时发布平台.城市空气质量.http://106.37.208.233:20035/.
[19]Fridley D,Zheng N,Qin Y.Inventoryof China’s energy-related CO2emissions in 2008.Berkeley,CA:Lawrence Berkeley National Laboratory(LBNL-4600E).http://china.lbl.gov/sites/china.lbl.gov/files/China_Emissions_Inventory_2008.pdf.
(1)中国低碳试点城市共有81个。因拉萨市、大兴安岭地区,以及济源、共青、敦煌、昌吉、和田、伊宁、阿拉尔等地级以下城市数据可得性限制,本研究未对其进行评价。