基于大数据方法和SOFM聚类的中国经济-环境综合分区研究
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摘要
研究使用经济和环境多源大数据,建立包含人口、GDP等经济指标和空气质量等环境指标的中国经济-环境关联体系,识别各指标的热点、冷点时空变化特征,采用人工神经网络聚类方法对中国现阶段经济-环境进行综合分区。研究结果如下:①灯光平均强度较高的省份主要集中在沿海地区,经济以长三角、珠三角和环渤海区域为主要拉动引擎,呈东南高西北低的发展态势,东部沿海地区经济发展优于东北、中部和西南地区。②PM2.5浓度呈现先增后减趋势,高污染区主要集中在华北、华中等区域;东北方向逐步扩散,污染热点地区从辽东半岛、山海关一线向东北扩张;南方地区基本保持稳定态势。③采用自组织特征映射模型对2015年全国各地级市OLS灯光指数、人口、城市自然边界和年均PM2.5浓度4类指标进行聚类,第I类为经济极发达-环境恶化地区,主要位于华北平原和长江三角洲;第II类为经济发达-环境趋恶化地区,主要分布在第I类区域周边,特别是京津冀周边地区;第III类为经济发达-环境良好地区,广东、海南、江西、福建以及重庆等省市多属此类型;第IV类为经济不发达-环境优质地区,主要分布于东北地区北部、内蒙古、甘肃、贵州、新疆、青海、西藏等地。
In the past 40 years, China's economy has developed rapidly but has paid a heavy environmental cost. China's environment and economy have undergone tremendous changes. The imbalance in regional economic development has intensified, while the various kinds of environmental problems have taken place in different area. Therefore, it is necessary to formulate environmental policies in a targeted manner. To this end, big data methods should be applied to reveal the relationship between the environment and economy, which forms the basis of comprehensive zoning. In this study, multi-source big data such as nighttime light remote sensing,spatialized population data, urban natural boundary and simulated PM2.5 concentration was used to establish an economic-environmental linkage system. The time and spatial variation rules of economic and environment hotspots were identified by Getis-Ord index. The provincial administrative areas of China were divided into four groups by using an artificial neural network(Self-Organizing Feature Map, SOFM) clustering of both economic and environmental indicators. The results show that: 1) The provinces with higher average light intensity in China are mainly concentrated in coastal areas. The economy is mainly driven by the Yangtze River Delta, the Pearl River Delta, and the Bohai Rim. It is a developing trend that is low in the southeast, high in the northwest and the eastern coastal areas. The economic development of coastal area is superior to the northeast,central and southwest regions. 2) The concentration of PM2.5 in China shows a trend of increasing first and then decreasing. Highly polluted areas are mainly concentrated in the North China and Central China regions.In northeast China, the pollution hotspots expand from the Liaodong Peninsula and Shanhaiguan to the northeast, while the south China maintains stability. 3) Based on OLS light index, population, urban natural boundary, and annual average PM2.5 concentration in 2015, the prefecture-level cities can be grouped into four types by using the self-organizing feature mapping model. Type I region is highly economic developed and environment deteriorating areas, mainly located in the North China Plain and the Yangtze River Delta. Type II region is the economic developed and environment deteriorating areas, which is mainly distributed in the periphery of the Type I region, especially around Jing-jin-ji area. Type III region is economically developed and environmentally friendly, such as Guangdong, Hainan, Jiangxi, Fujian, and Chongqing. Type IV is economically underdeveloped and high environment quality areas, which are mainly distributed in the north of the Northeast,Inner Mongolia, Gansu, Guizhou, Xinjiang, Qinghai, and Tibet. The research results are hoped to provide reference for balancing economic development and environment conservation in different regions of China.
In the past 40 years, China's economy has developed rapidly but has paid a heavy environmental cost. China's environment and economy have undergone tremendous changes. The imbalance in regional economic development has intensified, while the various kinds of environmental problems have taken place in different area. Therefore, it is necessary to formulate environmental policies in a targeted manner. To this end, big data methods should be applied to reveal the relationship between the environment and economy, which forms the basis of comprehensive zoning. In this study, multi-source big data such as nighttime light remote sensing,spatialized population data, urban natural boundary and simulated PM2.5 concentration was used to establish an economic-environmental linkage system. The time and spatial variation rules of economic and environment hotspots were identified by Getis-Ord index. The provincial administrative areas of China were divided into four groups by using an artificial neural network(Self-Organizing Feature Map, SOFM) clustering of both economic and environmental indicators. The results show that: 1) The provinces with higher average light intensity in China are mainly concentrated in coastal areas. The economy is mainly driven by the Yangtze River Delta, the Pearl River Delta, and the Bohai Rim. It is a developing trend that is low in the southeast, high in the northwest and the eastern coastal areas. The economic development of coastal area is superior to the northeast,central and southwest regions. 2) The concentration of PM2.5 in China shows a trend of increasing first and then decreasing. Highly polluted areas are mainly concentrated in the North China and Central China regions.In northeast China, the pollution hotspots expand from the Liaodong Peninsula and Shanhaiguan to the northeast, while the south China maintains stability. 3) Based on OLS light index, population, urban natural boundary, and annual average PM2.5 concentration in 2015, the prefecture-level cities can be grouped into four types by using the self-organizing feature mapping model. Type I region is highly economic developed and environment deteriorating areas, mainly located in the North China Plain and the Yangtze River Delta. Type II region is the economic developed and environment deteriorating areas, which is mainly distributed in the periphery of the Type I region, especially around Jing-jin-ji area. Type III region is economically developed and environmentally friendly, such as Guangdong, Hainan, Jiangxi, Fujian, and Chongqing. Type IV is economically underdeveloped and high environment quality areas, which are mainly distributed in the north of the Northeast,Inner Mongolia, Gansu, Guizhou, Xinjiang, Qinghai, and Tibet. The research results are hoped to provide reference for balancing economic development and environment conservation in different regions of China.
引文
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[10]李福柱,赵长林.中国经济发展方式的转变动力及其作用途径[J].中国人口·资源与环境,2016,26(2):152-162.[Li Fuzhu,Zhao Changlin.Changing power of China's economic development mode and its pathway.China Population,Resources and Environment,2016,26(2):152-162.]
[11]Kitchin R.Big data,new epistemologies and paradigm shifts[J].Big Data&Society,2014,1(1):1-12.
[12]Song M,Cen L,Zheng Z et al.Improving natural resource management and human health to ensure sustainable societal development based upon insights gained from working within‘Big Data Environments’[J].Journal of Cleaner Production,2015,94:1-4.
[13]Song M,Fisher R,Wang J et al.Environmental performance evaluation with big data:theories and methods[J].Annals of Operations Research,2016:1-14.
[14]Li L,Hao T,Chi T.Evaluation on China's forestry resources efficiency based on big data[J].Journal of Cleaner Production,2016,142:513-523.
[15]Xie H,He Y,Xie X.Exploring the factors influencing ecological land change for China's Beijing-Tianjin-Hebei Region using big data[J].Journal of Cleaner Production,2017,142:677-687.
[16]Wu J,Wang Z,Li W et al.Exploring factors affecting the relationship between light consumption and GDP based on DMSP/OLS nighttime satellite imagery[J].Remote Sensing of Environment,2013,134:111-119.
[17]李德仁,李熙.论夜光遥感数据挖掘[J].测绘学报,2015,44(6):591-601.[Li Deren,Li Xi.An overview on data mining of Nighttime light remote sensing.Acta Geodaetica et Cartographica Sinica,2015,44(6):591-601.]
[18]Clark H,Pinkovskiy M,Sala-i-Martin X,China's GDP growth may be understated[EB/OL].2018-4-23.http://www.nber.org/papers/w23323.pdf.[2018-4-23.]
[19]李德仁,余涵若,李熙.基于夜光遥感影像的“一带一路”沿线国家城市发展时空格局分析[J].武汉大学学报·信息科学版,2017,42(6):711-720.[Li Deren,Yu Hanruo,Li Xi.The spatialtemporal pattern analysis of city development in countries along the Belt and Road Initiative based on nighttime light data.Geomatics and Information Science of Wuhan University,2017,42(6):711-720.]
[20]Dai Z,Hu Y,Zhao G.The suitability of different nighttime light data for GDP estimation at different spatial scales and regional levels[J].Sustainability,2017,9(2):305.
[21]van Donkelaar A,Martin R V,Brauer M et al.Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites,models,and monitors[J].Environmental Science&Technology,2016,50(7):3762.
[22]柯文前,陆玉麒.基于县域的福建省经济空间格局演化[J].经济地理,2011,31(7):1081-1087.[Ke Wenqian,Lu Yulin.Evolvement of spatial pattern of economy at county level in Fujian Province.Economic Geography,2011,31(7):1081-1087.]
[23]李莎,刘卫东.俄罗斯人口分布及其空间格局演化[J].经济地理,2014,34(2):42-49.[Li Sha,Liu Weidong.Spatial distribution of population in Russia and its evolution.Economic Geography,2014,34(2):42-49.]
[24]管卫华,彭鑫,张惠,等.不同空间尺度的江苏省区域经济差异研究[J].长江流域资源与环境,2015,24(12):2003-2011.[Guan Weihua,Peng Xin,Zhang Hui et al.Regional economic inequalities in Jiangsu at different spatial scales.Resources and Environment in the Yangtze Basin,2015,24(12):2003-2011.]
[25]黄姣,高阳,赵志强,等.基于GIS和SOFM网络的中国综合自然区划[J].地理研究,2011,30(9):1648-1658.[Huang Jiao,Gao Yang,Zhao Zhiqiang et al.Comprehensive physiographic regionalization of China using GIS and SOFM neural network.Geographical Research,2011,30(9):1648-1658.]
[26]冯喆,吴健生,高阳,等.基于SOFM网络的景观功能分类--以北京及周边地区为例[J].地球信息科学学报,2012,14(6):800-806.[Feng Zhe,Wu Jiansheng,Gao Yang et al.Classification of landscape functions using SOFM neural network:Acase study from Beijing and its peripheral area.Journal of Geoinformation Science,2012,14(6):800-806.]
[2]王芳,葛全胜.根据卫星观测的城市用地变化估算中国1980~2009年城市热岛效应[J].科学通报,2012,57(11):951-958.[Wang Fang,Ge Quansheng.Estimation of urbanization bias in observed surface temperature change in China from 1980 to2009 using satellite land-use data.Science China Press,2012,57(11):951-958.]
[3]石忆邵.中国“城市病”的测度指标体系及其实证分析[J].经济地理,2014,34(10):1-6.[Shi Yishao.Measurement index system and empirical analysis of China’s urban.Economic Geography,2014,34(10):1-6.]
[4]周元,孙新章.中国城镇化道路的反思与对策[J].中国人口·资源与环境,2012,22(4):56-59.[Zhou Yuan,Sun Xinzhang.Rethinking and countermeasures on China’s urban road.China Population,Resources and Environment,2012,22(4):56-59.]
[5]李芬,李岱青,张林波,等.中日韩城市化过程中的资源环境变化比较研究[J].中国人口·资源与环境,2015,25(4):125-131.[Li Fen,Li Daiqing,Zhang Linbo et al.Comparative study on urbanization process and changes of resource and environment in China,Japan and Korea.China Population,Resources and Environment,2015,25(4):125-131.]
[6]张欢,成金华,陈军,等.中国省域生态文明建设差异分析[J].中国人口·资源与环境,2014,24(6):22-29.[Zhang Huan,Cheng Jinhua,Chen Jun et al.Difference analysis on the ecological civilization construction of China's provinces.China Population,Resources and Environment,2014,24(6):22-29.]
[7]张荣天,焦华富.中国省际城镇化与生态环境的耦合协调与优化探讨[J].干旱区资源与环境,2015,29(7):12-17.[Zhang Rongtian,Jiao Huafu.Coupling and coordination between urbanization and ecological environment in China.Journal of Arid Land Resources and Environment,2015,29(7):12-17.]
[8]成金华,李悦,陈军.中国生态文明发展水平的空间差异与趋同性[J].中国人口·资源与环境,2015,177(5):1-9.[Cheng Jinhua,Li Yue,Chen Jun.Regional difference and convergence of ecological civilization level in China.China Population,Resources and Environment,2015,177(5):1-9.]
[9]杜祥琬,杨波,刘晓龙,等.中国经济发展与能源消费及碳排放解耦分析[J].中国人口·资源与环境,2015,25(12):1-7.[Du Xiangwan,Yang Bo,Liu Xiaolong et al.Decoupling analysis of China's economic development,energy consumption and carbon dioxide emission.China Population,Resources and Environment,2015,25(12):1-7.]
[10]李福柱,赵长林.中国经济发展方式的转变动力及其作用途径[J].中国人口·资源与环境,2016,26(2):152-162.[Li Fuzhu,Zhao Changlin.Changing power of China's economic development mode and its pathway.China Population,Resources and Environment,2016,26(2):152-162.]
[11]Kitchin R.Big data,new epistemologies and paradigm shifts[J].Big Data&Society,2014,1(1):1-12.
[12]Song M,Cen L,Zheng Z et al.Improving natural resource management and human health to ensure sustainable societal development based upon insights gained from working within‘Big Data Environments’[J].Journal of Cleaner Production,2015,94:1-4.
[13]Song M,Fisher R,Wang J et al.Environmental performance evaluation with big data:theories and methods[J].Annals of Operations Research,2016:1-14.
[14]Li L,Hao T,Chi T.Evaluation on China's forestry resources efficiency based on big data[J].Journal of Cleaner Production,2016,142:513-523.
[15]Xie H,He Y,Xie X.Exploring the factors influencing ecological land change for China's Beijing-Tianjin-Hebei Region using big data[J].Journal of Cleaner Production,2017,142:677-687.
[16]Wu J,Wang Z,Li W et al.Exploring factors affecting the relationship between light consumption and GDP based on DMSP/OLS nighttime satellite imagery[J].Remote Sensing of Environment,2013,134:111-119.
[17]李德仁,李熙.论夜光遥感数据挖掘[J].测绘学报,2015,44(6):591-601.[Li Deren,Li Xi.An overview on data mining of Nighttime light remote sensing.Acta Geodaetica et Cartographica Sinica,2015,44(6):591-601.]
[18]Clark H,Pinkovskiy M,Sala-i-Martin X,China's GDP growth may be understated[EB/OL].2018-4-23.http://www.nber.org/papers/w23323.pdf.[2018-4-23.]
[19]李德仁,余涵若,李熙.基于夜光遥感影像的“一带一路”沿线国家城市发展时空格局分析[J].武汉大学学报·信息科学版,2017,42(6):711-720.[Li Deren,Yu Hanruo,Li Xi.The spatialtemporal pattern analysis of city development in countries along the Belt and Road Initiative based on nighttime light data.Geomatics and Information Science of Wuhan University,2017,42(6):711-720.]
[20]Dai Z,Hu Y,Zhao G.The suitability of different nighttime light data for GDP estimation at different spatial scales and regional levels[J].Sustainability,2017,9(2):305.
[21]van Donkelaar A,Martin R V,Brauer M et al.Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites,models,and monitors[J].Environmental Science&Technology,2016,50(7):3762.
[22]柯文前,陆玉麒.基于县域的福建省经济空间格局演化[J].经济地理,2011,31(7):1081-1087.[Ke Wenqian,Lu Yulin.Evolvement of spatial pattern of economy at county level in Fujian Province.Economic Geography,2011,31(7):1081-1087.]
[23]李莎,刘卫东.俄罗斯人口分布及其空间格局演化[J].经济地理,2014,34(2):42-49.[Li Sha,Liu Weidong.Spatial distribution of population in Russia and its evolution.Economic Geography,2014,34(2):42-49.]
[24]管卫华,彭鑫,张惠,等.不同空间尺度的江苏省区域经济差异研究[J].长江流域资源与环境,2015,24(12):2003-2011.[Guan Weihua,Peng Xin,Zhang Hui et al.Regional economic inequalities in Jiangsu at different spatial scales.Resources and Environment in the Yangtze Basin,2015,24(12):2003-2011.]
[25]黄姣,高阳,赵志强,等.基于GIS和SOFM网络的中国综合自然区划[J].地理研究,2011,30(9):1648-1658.[Huang Jiao,Gao Yang,Zhao Zhiqiang et al.Comprehensive physiographic regionalization of China using GIS and SOFM neural network.Geographical Research,2011,30(9):1648-1658.]
[26]冯喆,吴健生,高阳,等.基于SOFM网络的景观功能分类--以北京及周边地区为例[J].地球信息科学学报,2012,14(6):800-806.[Feng Zhe,Wu Jiansheng,Gao Yang et al.Classification of landscape functions using SOFM neural network:Acase study from Beijing and its peripheral area.Journal of Geoinformation Science,2012,14(6):800-806.]