居民活动视角的城市雾霾灾害韧性评估——以南京市主城区为例
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摘要
基于居民活动视角解读城市雾霾灾害韧性潜在的人地关系内涵,以南京市主城区进行韧性评估的实证分析,提出针对性韧性提升策略。结果表明:①主城区雾霾灾害韧性整体处于处于低水平,现状建成环境适应性效果有限。②居民活动扰动性、建成环境适应性分别呈现冷热"多核"圈层集聚"、带状"集聚的空间特征,两者匹配失衡导致霾灾害韧性的空间分异不显著且大部分地区水平较低。③居民活动扰动性主要受雾霾严重性的直接或间接影响,建成环境适应性受生态减霾性、设施避霾性影响均较大,雾霾灾害韧性则更受居民活动扰动性影响。雾霾灾害韧性的建设应针对性地提升建成环境适应性或降低居民活动扰动性,并注重两者在空间优化措施上的匹配。
Under the support of spatial big data, urban haze disaster resilience evaluation from the perspective of residents' activities is of great significance to effectively reduce urban haze disaster risk. Through the interpretation of haze disaster resilience and the interaction between residents' activities and built environment, an evaluation index system is constructed. Taking the main urban area of Nanjing as a case, the spatial characteristics are analyzed, and then the space problems are identified. Finally, the strategy of targeted resilience promotion is put forward. The results show that: 1) The disturbance of residents' activities, the adaptability of built environment and the haze disaster resilience are in the middle, middle and low level respectively, the effect of built environment adaptability is limited now. 2) The residents' activity disturbance and the adaptability of the built environment show the spatial characteristics of the "multi core" circle agglomeration, the "strip" agglomeration, respectively, the imbalance of spatial matching between which causes the spatial differentiation of haze disaster resilience not significant, and low level resilience in most area. 3) The disturbance of residents' activities is directly or indirectly caused by the haze severity, the adaptability of the built environment is greatly influenced by haze reduction of ecological environment and haze avoidance of infrastructure, and haze disaster resilience is more affected by residents' activities. The construction of haze disaster resilience should be targeted to enhance the adaptability of the built environment and reduce the disturbance of residents' activities,and pay attention to the matching degree of the two in the spatial optimization measures.
Under the support of spatial big data, urban haze disaster resilience evaluation from the perspective of residents' activities is of great significance to effectively reduce urban haze disaster risk. Through the interpretation of haze disaster resilience and the interaction between residents' activities and built environment, an evaluation index system is constructed. Taking the main urban area of Nanjing as a case, the spatial characteristics are analyzed, and then the space problems are identified. Finally, the strategy of targeted resilience promotion is put forward. The results show that: 1) The disturbance of residents' activities, the adaptability of built environment and the haze disaster resilience are in the middle, middle and low level respectively, the effect of built environment adaptability is limited now. 2) The residents' activity disturbance and the adaptability of the built environment show the spatial characteristics of the "multi core" circle agglomeration, the "strip" agglomeration, respectively, the imbalance of spatial matching between which causes the spatial differentiation of haze disaster resilience not significant, and low level resilience in most area. 3) The disturbance of residents' activities is directly or indirectly caused by the haze severity, the adaptability of the built environment is greatly influenced by haze reduction of ecological environment and haze avoidance of infrastructure, and haze disaster resilience is more affected by residents' activities. The construction of haze disaster resilience should be targeted to enhance the adaptability of the built environment and reduce the disturbance of residents' activities,and pay attention to the matching degree of the two in the spatial optimization measures.
引文
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[32]张童,姚士谋,胡伟平,等.基于交通可达性的广佛都市区城市扩展的模拟与分析[J].地理科学,2018,38(5):737-746.[Zhang Tong, Yao Shimou, Hu Weiping et al. Simulation of urban expansion in guangzhou-foshan metropolitan area under the influence of accessibility. Scientia Geographica Sinica,2018,38(5):737-746.]
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[35] Saaty T. The analytic network process:Decision making with dependence and feedback[J]. International,2001,95(2):129-157.
[2] World Economic Forum. The global risks report 2018[EB/OL].https://cn.weforum.org/reports/global-risks-report-2018.2018-01-08/2018-04-16.
[3]杨敏行,黄波,崔翀,等.基于韧性城市理论的灾害防治研究回顾与展望[J].城市规划学刊,2016(1):48-55.[Yang Minhang,Huang Bo, Cui Chong et al. Review and prospect:Urban disaster resilience. Urban Planning Forum,2016(1):48-55.]
[4]黄建毅,苏飞.城市灾害社会脆弱性研究热点问题评述与展望[J].地理科学,2017,37(8):1211-1217.[Huang Jianyi, Su Fei.The review and prospect on the hot issues of urban social vulnerability to disasters. Scientia Geographica Sinica,2017,37(8):1211-1217.]
[5]徐耀阳,李刚,崔胜辉,等.韧性科学的回顾与展望:从生态理论到城市实践[J].生态学报,2017,38(15):1-8.[Xu Yaoyan-g, Li Gang, Cui Shenghui et al. Retrospect and prospect of resilience science:From ecological theory to urban practice. Acta Zoology Sinica,2017,38(15):1-8.]
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[7] Folke C. Resilience:The emergence of a perspective for socialecological systems analyses[J].Global Environmental Change,2006,16(3):253-267.
[8]李彤玥.韧性城市研究新进展[J].国际城市规划,2017,32(5):15-25.[Li Tongyue. New progress in study on resilie-nt cities.Urban Planning International,2017,32(5):15-25.]
[9] Mileti D S. Disasters by design:A reassessment of natural hazards in the united states, natural hazards and disasters[M].Washington D C:Joseph Henry Press,1999:45-49.
[10] The World Bank. World development report 2009[M]. Washington D C:The World Bank,2008:73-78.
[11]刘婧,史培军,葛怡,等.灾害恢复力研究进展综述[J].地球科学进展,2006,21(2):211-218.[Liu Jing, Shi Peijun, Ge Yi et al. The review of disaster resilience research. Advances in Earth Science,2006,21(2):211-218.]
[12]费璇,温家洪,杜士强,等.自然灾害恢复力研究进展[J].自然灾害学报,2014,23(6):19-31.[Fei Xuan, Wen Jiahong, Du Shiqiang et al. Progress in research on natural disaster resilience.Journal of Natural Disasters,2014,23(6):19-31.]
[13]李彤玥,牛品一,顾朝林.弹性城市研究框架综述[J].城市规划学刊,2014(5):23-31.[Li Tongyue, Niu pinyi, Gu Chaolin. A reviewon research framework of resilient cities. Urban Planning Forum,2014(5):23-31.]
[14]曹莉萍,周冯琦.纽约弹性城市建设经验及其对上海的启示[J].生态学报,2018,38(1):86-95.[Cao Liping, Zhou Fengqi. Enlightenment due to elastic construction in New York City for Shanghai. Acta Zoology Sinica,2018,38(1):86-95.]
[15]邴启亮,李鑫,罗彦.韧性城市理论引导下的城市防灾减灾规划探讨[J].规划师,2017,33(8):12-17.[Bing Qiliang, Li Xin, Luo Yan. Urban disaster prevention plan with resilient city theory.Planners,2017,33(8):12-17.]
[16]李亚,翟国方.我国城市灾害韧性评估及其提升策略研究[J].规划师,2017,33(8):5-11.[Li Fang, Zhai Guofang. China’s urban disaster resilience evaluation and promotion.Planners,2017,33(8):5-11.]
[17] Bruneau M, Reinhorn A. Exploring the concept of seismic resilience for acute care facilities[J]. Earthquake Spectra,2007,23(1):41-62.
[18] Goldstein B E. Skunkworks in the embers of the cedar fire:Enhancing resilience in the aftermath of Disaster[J]. Human Ecology,2008,36(1):15-28.
[19] Ouyang M, Due?as-Osorio L. Multi-dimensional hurricane resilience assessment of electric power systems[J]. Structural Safety,2014,48:15-24.
[20] Cutter S L, Barnes L, Berry M et al. A place-based model for understanding community resilience to natural disasters[J].Global Environmental Change,2008,18(4):598-606.
[21] Prashar S K, Shaw R. Urbanization and hydro-meteorological disaster resilience:the case of Delhi[J]. Intern-ational Journal of Disaster Resilience in the Built Environment,2012,3(1):7-19.
[22] Sun H, Cheng X, Dai M. Regional flood disaster resilience evaluation based on analytic network process:A case study of the Chaohu Lake Basin, Anhui Province, China[J]. Natural Hazards,2016,82(1):39-58.
[23]秦萧,甄峰,熊丽芳,等.大数据时代城市时空间行为研究方法[J].地理科学进展,2013,32(9):1352-1361.[Qin Xiao,Zhen Feng, Xiong Lifang et al. Methods in urban temporal and spatial behavior research in the Big Data Era.Progress in Geography,2013,32(9):1352-1361.]
[24]甄峰,秦萧,王波.大数据时代的人文地理研究与应用实践[J].人文地理,2014(3):1-6.[Zhen Feng, Qin Xiao, Wang Bo. Human geography research and practical application in big data Era. Human geography,2014(3):1-6.]
[25]甄峰,王波.“大数据”热潮下人文地理学研究的再思考[J].地理研究,2015,34(5):803-811.[Zhen Feng, Wang Bo. Rethinking human geography in the Age of Big Data. Geographical Research,2015,34(5):803-811.]
[26]邵帅,李欣,曹建华,等.中国雾霾污染治理的经济政策选择——基于空间溢出效应的视角[J].经济研究,2016(9):73-88.[Shao Shuai, Li Xin, Cao Jianhua et al. China’s economic policy choices for governing smog pollution based on spatial spillover effects. Economic Research Journal,2016(9):73-88.]
[27]马丽梅,张晓.中国雾霾污染的空间效应及经济、能源结构影响[J].中国工业经济,2014(4):19-31.[Ma Limei, Zhang Xiao.The spatial effect of china’haze pollution and the impact from economic change and energy structure. China Industrial Economics,2014(4):19-31.]
[28]姜磊,周海峰,柏玲.外商直接投资对空气污染影响的空间异质性分析——以中国150个城市空气质量指数(AQI)为例[J].地理科学,2018,38(3):351-360.[Jiang Lei, Zhou Haifeng, Bai Ling. Spatial Heterogeneity analysis of impacts of foreign direct investment on air pollution:Empirical evidence from 150cities in China based on AQI. Scientia Geographica Sinica,2018,38(3):351-360.]
[29]南京市规划局.南京市城市总体规划(2011~2020)[EB/OL].http://www.njghj.gov.cn/ngweb/Page/Detail.aspx?Info Guid=8d1b97a9-5d83-4cfe-bd7f-dad74a5b39be,2016-05-01/2018-04-20.[Nanjing City Planning Bureau. Nanjing city master plan(2011-2020). http://www.njghj.gov.cn/ngweb/Page/Detail.aspx?InfoGuid=8d1b97a9-5d83-4cfe-bd7f-ad74a5b39be, 2016-05-01/2018-04-20.]
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[31]漆信贤,黄贤金,赖力.基于Meta分析的中国森林生态系统生态服务功能价值转移研究[J].地理科学,2018,38(4):522-530.[Qi Xinxian, Huang Xianjin, Lai Li. An empirical study of meta-analytical value transfer of forest ecosystem services in China. Scientia Geographica Sinica,2018,38(4):522-530.]
[32]张童,姚士谋,胡伟平,等.基于交通可达性的广佛都市区城市扩展的模拟与分析[J].地理科学,2018,38(5):737-746.[Zhang Tong, Yao Shimou, Hu Weiping et al. Simulation of urban expansion in guangzhou-foshan metropolitan area under the influence of accessibility. Scientia Geographica Sinica,2018,38(5):737-746.]
[33]高德地图,交通运输部科学研究院,阿里云.2017年度中国主要城市交通分析报告[EB/OL]. http://report.amap.com/share.do?id=8a38bb8660f9109101610835e79701bf,2018-01-18/2018-04-20.[Amap, China Academy of Transportation Sciences, Alibaba Cloud Computing. Traffic analysis report of China’s major cities in 2017. http://report.amap.com/share.do?id=8a38bb8660-0f9109101610835e79701bf,2018-01-18/2018-04-20.]
[34]滴滴出行.2017年度城市交通出行报告[EB/OL].http://www.199it.com/archives/684474.html,2018-01-25/2018-04-20.[Didi Dache.Urban traffic travel report for 2017.http://www.199it.com/archives/684474.html,2018-01-25/2018-04-20.]
[35] Saaty T. The analytic network process:Decision making with dependence and feedback[J]. International,2001,95(2):129-157.