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Article(id=1149789603286184389, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149768563956826506, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2405650, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1722009600000, receivedDateStr=2024-07-27, revisedDate=1741968000000, revisedDateStr=2025-03-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1752060803480, onlineDateStr=2025-07-09, pubDate=1749312000000, pubDateStr=2025-06-08, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752060803480, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752060803480, creator=13701087609, updateTime=1752060803480, updator=13701087609, issue=Issue{id=1149768563956826506, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='16', pageStart='6587', pageEnd='7021', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752055787314, creator=13701087609, updateTime=1768456850262, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1218559607937618069, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149768563956826506, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1218559607937618070, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149768563956826506, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=6993, endPage=7003, ext={EN=ArticleExt(id=1149789603441373638, articleId=1149789603286184389, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Spatiotemporal Evolution and Influencing Factors of Urban Natural Disaster Resilience in Shaanxi Province, columnId=1156262729993277777, journalTitle=Science Technology and Engineering, columnName=Papers·Environmental and Safe Science, runingTitle=null, highlight=null, articleAbstract=

The concept of “resilience” is introduced as a new research direction for cities to withstand uncertain risks in the face of complex challenges such as global environmental changes, accelerated urbanization, and frequent epidemics. To explore the spatiotemporal evolution and obstacle factors of urban natural disaster resilience in Shaanxi Province to enhance resilience against natural disasters. The entropy weight-technique for order preference by similarity to ideal solution(TOPSIS) method was used to assess resilience levels across four dimensions: economy, society, infrastructure, and ecological environment. The spatiotemporal evolution characteristics of resilience in Shaanxi Province from 2018 to 2022 were examined using a combination of GIS, Theil index, and center-standard deviation ellipse. An obstacle degree diagnosis model was employed to analyze influencing factors. The results indicate that over time, the overall resilience level against natural disasters in cities, except for Xianyang, shows an upward trend. Overall resilience, social resilience, and infrastructure resilience increase, while ecological environment resilience slightly declines. Spatially, the resilience in Shaanxi Province follows a pattern of “Guanzhong region > Northern Shaanxi region > Southern Shaanxi region.” The major influencing factors are infrastructure resilience and ecological environment resilience, with the length of urban drainage pipelines and greening coverage area identified as the top obstacles restricting urban resilience. The research findings are expected to provide theoretical references for regional natural disaster management and resilient urban planning in Shaanxi Province.

, correspAuthors=Xue-zhao ZHENG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Xue-zhao ZHENG, Yan-ling XIONG, Xin TONG, Xin-yi ZHANG, Hai-jiao SU), CN=ArticleExt(id=1149789624740049811, articleId=1149789603286184389, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=陕西省城市自然灾害韧性时空演变与影响因素, columnId=1156262730140078420, journalTitle=科学技术与工程, columnName=论文·环境科学、安全科学, runingTitle=null, highlight=null, articleAbstract=

在面临全球环境变化、城市化加快以及流行病频发等复杂挑战的背景下,“韧性”一词为城市抵御不确定风险提供了新的研究方向。为探讨陕西省城市自然灾害韧性的时空演变及障碍因子,提高自然灾害城市韧性水平。基于熵权-逼近理想解的排序方法(technique for order preference by similarity to ideal solution,TOPSIS)从经济、社会、基础设施和生态环境4个维度评估韧性水平,结合GIS、泰尔指数和重心-标准差椭圆探究陕西省2018—2022年各城市韧性水平时空演变特征,运用障碍度诊断模型分析其影响因素。结果表明:时序演变上,研究期内,除咸阳市外其他城市自然灾害韧性水平总体呈现上升趋势,从研究的4个维度来看,经济韧性、社会韧性和基础设施韧性总体为上升趋势,生态环境韧性略有降低;空间格局上,陕西省自然灾害韧性总体呈现“关中地区>陕北地区>陕南地区”的分布格局;影响因素来看,自然灾害韧性主要受基础设施韧性和生态环境韧性影响。研究成果可为陕西省制定区域自然灾害管理政策和韧性城市规划提供理论参考。

, correspAuthors=郑学召, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=qtINZm6ddBx89CMQwfQftg==, magXml=qsTV6jlq5kOJfzo3tfMulA==, pdfUrl=null, pdf=vplhaMiaQgKIJgbpRsdd7g==, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=6vhhMa9EEJV5F4UYkoEqJw==, mapNumber=null, authorCompany=null, fund=null, authors=

郑学召(1977—),男,汉族,新疆巴音郭楞蒙古自治州人,博士,教授。研究方向:应急技术与管理、灾害应急救援。E-mail:。

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郑学召(1977—),男,汉族,新疆巴音郭楞蒙古自治州人,博士,教授。研究方向:应急技术与管理、灾害应急救援。E-mail:。

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郑学召(1977—),男,汉族,新疆巴音郭楞蒙古自治州人,博士,教授。研究方向:应急技术与管理、灾害应急救援。E-mail:。

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journalId=1146123166801305609, articleId=1149789603286184389, language=CN, orderNo=4, keyword=泰尔指数), Keyword(id=1177977304102880087, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, language=CN, orderNo=5, keyword=障碍度诊断)], refs=[Reference(id=1177977307097613176, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, doi=null, pmid=null, pmcid=null, year=2024, volume=56, issue=11, pageStart=1, pageEnd=19, url=null, language=null, rfNumber=[1], rfOrder=0, authorNames=张宝生, 杨晓婷, 王晓红, journalName=情报科学, refType=null, unstructuredReference=张宝生, 杨晓婷, 王晓红. 面向突发自然灾害事件的政务微博舆情动态预警机制研究[J]. 情报科学, 2024, 56(11): 1-19., articleTitle=面向突发自然灾害事件的政务微博舆情动态预警机制研究, refAbstract=null), Reference(id=1177977307168916345, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, doi=null, pmid=null, pmcid=null, year=2024, volume=56, issue=11, pageStart=1, pageEnd=19, url=null, language=null, 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A Rid Land Geography, 2018, 41(2): 306-313., articleTitle=Assessment and regionalization of flood disaster risk in Shaanxi Province at the county scale, refAbstract=null)], funds=[Fund(id=1177977306833372022, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, awardId=2022R058, language=CN, fundingSource=陕西省社会科学基金(2022R058), fundOrder=null, country=null), Fund(id=1177977306900480887, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, awardId=2023HZ1554, language=CN, fundingSource=陕西省哲学社会科学研究专项项目(2023HZ1554), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1177977301544354606, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, xref=1, ext=[AuthorCompanyExt(id=1177977301552743215, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, companyId=1177977301544354606, language=EN, country=null, province=null, city=null, 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基于自然资源部标准地图服务网站GS(2019)3333号的标准地图制作,底图边界无修改

, figureFileSmall=CDI+YX1IUE981Qrkxce0OA==, figureFileBig=mGHdTQ7ML1XrLYCO+vLhsg==, tableContent=null), ArticleFig(id=1177977305562497896, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, language=EN, label=Fig.9, caption=Standard deviation ellipse and center of gravity locus, figureFileSmall=PsgwS67/dkUDzzVO77va8Q==, figureFileBig=QQtehEN3kS5UDXcyCGjoDg==, tableContent=null), ArticleFig(id=1177977305629606761, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, language=CN, label=图9, caption=标准差椭圆与重心轨迹, figureFileSmall=PsgwS67/dkUDzzVO77va8Q==, figureFileBig=QQtehEN3kS5UDXcyCGjoDg==, tableContent=null), ArticleFig(id=1177977305688327018, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, language=EN, label=Fig.10, caption=Criterion level obstacle degree, figureFileSmall=reV6yUdasM+N2UeWjYIyig==, figureFileBig=mUZUou46s/HAK3seDq2iOg==, tableContent=null), ArticleFig(id=1177977305759630187, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, language=CN, label=图10, caption=准则层障碍度, figureFileSmall=reV6yUdasM+N2UeWjYIyig==, figureFileBig=mUZUou46s/HAK3seDq2iOg==, tableContent=null), ArticleFig(id=1177977305822544748, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, language=EN, label=Fig.11, caption=Frequency of the top 6 obstacle factors, figureFileSmall=SYzxRCl43JndJEHdXh4CUQ==, figureFileBig=g15zDg9xUZ2Tn5yM9FQ3rw==, tableContent=null), ArticleFig(id=1177977305889653613, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, language=CN, label=图11, caption=前6位障碍因子频次, figureFileSmall=SYzxRCl43JndJEHdXh4CUQ==, figureFileBig=g15zDg9xUZ2Tn5yM9FQ3rw==, tableContent=null), ArticleFig(id=1177977305944179566, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, language=EN, label=Table 1, caption=

Calculation results of the 2022 TOPSIS evaluation

, figureFileSmall=null, figureFileBig=null, tableContent=
城市 D i + D i - Ci
西安 0.015 3 0.038 0 0.714
铜川 0.033 0 0.017 0 0.344
宝鸡 0.032 0 0.016 0 0.336
咸阳 0.032 0 0.017 8 0.347
渭南 0.034 0 0.016 0 0.324
延安 0.032 0 0.016 0 0.331
汉中 0.034 0 0.014 0 0.294
榆林 0.024 0 0.034 0 0.589
安康 0.038 0 0.012 0 0.240
商洛 0.040 0 0.013 0 0.249
), ArticleFig(id=1177977306015482735, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, language=CN, label=表1, caption=

2022年TOPSIS评价计算结果

, figureFileSmall=null, figureFileBig=null, tableContent=
城市 D i + D i - Ci
西安 0.015 3 0.038 0 0.714
铜川 0.033 0 0.017 0 0.344
宝鸡 0.032 0 0.016 0 0.336
咸阳 0.032 0 0.017 8 0.347
渭南 0.034 0 0.016 0 0.324
延安 0.032 0 0.016 0 0.331
汉中 0.034 0 0.014 0 0.294
榆林 0.024 0 0.034 0 0.589
安康 0.038 0 0.012 0 0.240
商洛 0.040 0 0.013 0 0.249
), ArticleFig(id=1177977306099368816, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, language=EN, label=Table 2, caption=

Toughness index attribute statistics

, figureFileSmall=null, figureFileBig=null, tableContent=
统计量 年份
2018 2019 2020 2021 2022
平均值 0.383 0.395 0.403 0.419 0.433
标准差 0.100 0.091 0.090 0.089 0.080
变异系数 0.260 0.223 0.224 0.212 0.185
), ArticleFig(id=1177977306179060593, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, language=CN, label=表2, caption=

韧性指数属性统计

, figureFileSmall=null, figureFileBig=null, tableContent=
统计量 年份
2018 2019 2020 2021 2022
平均值 0.383 0.395 0.403 0.419 0.433
标准差 0.100 0.091 0.090 0.089 0.080
变异系数 0.260 0.223 0.224 0.212 0.185
), ArticleFig(id=1177977306267140978, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789603286184389, language=EN, label=Table 3, caption=

Toughness level classification standard

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韧性水平 低度韧性 较低韧性 中度韧性 较高韧性 高度韧性
分级标准 [0.246 2,0.267 4) [0.267 4,0.326 9) [0.326 9,0.396 5) [0.396 5,0.492 7) [0.492 7,0.618 3)
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韧性水平分级标准

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韧性水平 低度韧性 较低韧性 中度韧性 较高韧性 高度韧性
分级标准 [0.246 2,0.267 4) [0.267 4,0.326 9) [0.326 9,0.396 5) [0.396 5,0.492 7) [0.492 7,0.618 3)
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Parameters of standard deviation ellipse

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年份 椭圆面积/km2 重心坐标 短半轴/m 长半轴/m 扁率/m 方位角/(°)
2018 115 709.80 108.79°E,34.898°N 61 767.26 149 092.94 87 325.68 15.20
2019 117 284.53 108.82°E,34.849°N 61 862.16 150 890.49 89 028.33 13.99
2020 116 179.02 108.83°E,34.825°N 62 080.29 148 942.54 86 862.25 14.37
2021 117 436.41 108.80°E,34.828°N 62 256.98 150 127.36 87 870.37 14.99
2022 120 200.05 108.78°E,34.784°N 63 695.32 150 189.74 86 494.42 14.74
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标准差椭圆参数

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年份 椭圆面积/km2 重心坐标 短半轴/m 长半轴/m 扁率/m 方位角/(°)
2018 115 709.80 108.79°E,34.898°N 61 767.26 149 092.94 87 325.68 15.20
2019 117 284.53 108.82°E,34.849°N 61 862.16 150 890.49 89 028.33 13.99
2020 116 179.02 108.83°E,34.825°N 62 080.29 148 942.54 86 862.25 14.37
2021 117 436.41 108.80°E,34.828°N 62 256.98 150 127.36 87 870.37 14.99
2022 120 200.05 108.78°E,34.784°N 63 695.32 150 189.74 86 494.42 14.74
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陕西省城市自然灾害韧性时空演变与影响因素
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郑学召 1, 2 , 熊艳玲 1 , 童鑫 1 , 张心怡 1 , 苏海蛟 1
科学技术与工程 | 论文·环境科学、安全科学 2025,25(16): 6993-7003
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科学技术与工程 | 论文·环境科学、安全科学 2025, 25(16): 6993-7003
陕西省城市自然灾害韧性时空演变与影响因素
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郑学召1, 2 , 熊艳玲1, 童鑫1, 张心怡1, 苏海蛟1
作者信息
  • 1 西安科技大学安全科学与工程学院, 西安 710054
  • 2 国家矿山救援西安研究中心, 西安 710054

    • 郑学召(1977—),男,汉族,新疆巴音郭楞蒙古自治州人,博士,教授。研究方向:应急技术与管理、灾害应急救援。E-mail:。

Spatiotemporal Evolution and Influencing Factors of Urban Natural Disaster Resilience in Shaanxi Province
Xue-zhao ZHENG1, 2 , Yan-ling XIONG1, Xin TONG1, Xin-yi ZHANG1, Hai-jiao SU1
Affiliations
  • 1 College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China
  • 2 Xi'an Research Center of National Mine Rescue, Xi'an 710054, China
出版时间: 2025-06-08 doi: 10.12404/j.issn.1671-1815.2405650
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摘要
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在面临全球环境变化、城市化加快以及流行病频发等复杂挑战的背景下,“韧性”一词为城市抵御不确定风险提供了新的研究方向。为探讨陕西省城市自然灾害韧性的时空演变及障碍因子,提高自然灾害城市韧性水平。基于熵权-逼近理想解的排序方法(technique for order preference by similarity to ideal solution,TOPSIS)从经济、社会、基础设施和生态环境4个维度评估韧性水平,结合GIS、泰尔指数和重心-标准差椭圆探究陕西省2018—2022年各城市韧性水平时空演变特征,运用障碍度诊断模型分析其影响因素。结果表明:时序演变上,研究期内,除咸阳市外其他城市自然灾害韧性水平总体呈现上升趋势,从研究的4个维度来看,经济韧性、社会韧性和基础设施韧性总体为上升趋势,生态环境韧性略有降低;空间格局上,陕西省自然灾害韧性总体呈现“关中地区>陕北地区>陕南地区”的分布格局;影响因素来看,自然灾害韧性主要受基础设施韧性和生态环境韧性影响。研究成果可为陕西省制定区域自然灾害管理政策和韧性城市规划提供理论参考。

自然灾害  /  城市韧性  /  时空演变  /  泰尔指数  /  障碍度诊断

The concept of “resilience” is introduced as a new research direction for cities to withstand uncertain risks in the face of complex challenges such as global environmental changes, accelerated urbanization, and frequent epidemics. To explore the spatiotemporal evolution and obstacle factors of urban natural disaster resilience in Shaanxi Province to enhance resilience against natural disasters. The entropy weight-technique for order preference by similarity to ideal solution(TOPSIS) method was used to assess resilience levels across four dimensions: economy, society, infrastructure, and ecological environment. The spatiotemporal evolution characteristics of resilience in Shaanxi Province from 2018 to 2022 were examined using a combination of GIS, Theil index, and center-standard deviation ellipse. An obstacle degree diagnosis model was employed to analyze influencing factors. The results indicate that over time, the overall resilience level against natural disasters in cities, except for Xianyang, shows an upward trend. Overall resilience, social resilience, and infrastructure resilience increase, while ecological environment resilience slightly declines. Spatially, the resilience in Shaanxi Province follows a pattern of “Guanzhong region > Northern Shaanxi region > Southern Shaanxi region.” The major influencing factors are infrastructure resilience and ecological environment resilience, with the length of urban drainage pipelines and greening coverage area identified as the top obstacles restricting urban resilience. The research findings are expected to provide theoretical references for regional natural disaster management and resilient urban planning in Shaanxi Province.

natural disasters  /  urban resilience  /  spatiotemporal evolution  /  Theil index  /  impairment diagnosis
郑学召, 熊艳玲, 童鑫, 张心怡, 苏海蛟. 陕西省城市自然灾害韧性时空演变与影响因素. 科学技术与工程, 2025 , 25 (16) : 6993 -7003 . DOI: 10.12404/j.issn.1671-1815.2405650
Xue-zhao ZHENG, Yan-ling XIONG, Xin TONG, Xin-yi ZHANG, Hai-jiao SU. Spatiotemporal Evolution and Influencing Factors of Urban Natural Disaster Resilience in Shaanxi Province[J]. Science Technology and Engineering, 2025 , 25 (16) : 6993 -7003 . DOI: 10.12404/j.issn.1671-1815.2405650
正文
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中国由于地理环境与气候系统复杂多样,常受到自然灾害的侵袭,是世界上自然灾害较为严重、发生频率较高的国家之一[1]。2023年,中国主要受洪涝、台风、地震和地质灾害等自然灾害的影响,共造成9 544.4万人受不同层次伤害,直接经济损失达3 454.5亿元[2]。这些灾害的发生,不仅导致城市遭受巨大损失,还会产生经济社会不稳定和生态失衡等问题。为有效减轻自然灾害带来的影响,需加强自然灾害韧性城市建设,建立高质量的城市生态系统和安全系统[3]。
城市韧性是指城市在受到扰动时维持、恢复并适应的能力[4]。为应对自然灾害给城市带来的各种风险,“城市韧性”逐步成为学术界一个重要的研究方向[5]。学者们针对城市自然灾害韧性开展了大量研究。Cutter等[6]从社会、经济、社区等6个方面构建指标体系评价城市韧性,并分析不同地区韧性的差异。Zhou等[7]从方法论的角度概述了自然灾害应急决策的理论和方法。Zhang等[8]使用了支持向量机和Delphi-层次分析法来评估深圳市滑坡韧性水平。付家能等[9]用组合赋权逼近理想解的排序方法(technique for order preference by similarity to ideal solution,TOPSIS)对江西省城市韧性开展时空演变及障碍因子分析。王会权等[10]从PPRR(prevention、preparation、response、recovery)的理论视角下以新乡市洪涝灾害为例研究自然灾害应急处置。陈述等[11]采用组合权重TOPSIS从时空演变和影响因素评估湖北省雨涝灾害韧性。随着学者对城市韧性研究的深入,使用方法也逐渐多样化。学者们多采用优劣解距离法[12]、主成分分析法[13]、反向传播(back propagation,BP)神经网络[14]、SBM-DEA(slacks-based measure data envelopment analysis)模型[15]等评价方法和用障碍因子诊断模型[16]、多元线性回归模型[17]等模型分析影响因素。
综上可知,上述研究为城市韧性提供了理论基础和现实参考。然而,现有研究仍存在一些不足:一是多数研究集中于单一城市或区域,缺乏跨区域、跨时间的系统比较分析;二是对于韧性评价中的动态变化和时间序列数据的利用不足。当前研究已初步构建较为完善的评价体系,并在方法的应用上取得重要进展,但要全面提升城市自然灾害韧性水平,仍需在跨区域、跨时间的综合研究等方面进一步探索。鉴于此,以陕西省10座城市为研究对象,从经济、社会、基础设施和生态环境4个维度建立陕西省城市自然灾害韧性指标体系,采用熵权-TOPSIS对各城市自然灾害韧性水平进行时序分析,运用泰尔指数和重心-标准差椭圆探究其空间演变规律,建立障碍因子诊断模型分析城市自然灾害韧性的主要影响因素,从而表征各城市对自然灾害的综合应对能力,为陕西省各城市自然灾害韧性建设提供依据。
1 城市自然灾害韧性评价体系
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1.1 评价维度分析
陕西省地处中国西北地区,其地形复杂多样,旱、涝和地质灾害等多种自然灾害在该地时有发生。据分析,陕西省自然灾害特征包括:一是发生时间聚集强,在某时间段发生频率较高;二是某些灾害间联系密切,易形成灾害链;三是自然灾害与周边城市相关性较强。面对陕西省多发的自然灾害,从城市韧性角度建立评价体系能更有效地做好城市自然灾害风险管理和规划,提高城市抗灾和应对能力,实现城市的可持续发展。
综上,结合陕西省自然灾害特征与城市韧性的内涵,推进陕西省城市自然灾害韧性水平建设,从以下4个维度展开评价。
(1)经济韧性。经济韧性指城市经济系统能有效应对灾害造成的压力,并迅速恢复的能力。
(2)社会韧性。社会韧性指城市在遭遇社会风险时能自我调整和恢复的能力。
(3)基础设施韧性。指城市基础设施在面临突发事件时保障城市正常运转和及时恢复的能力。
(4)生态环境韧性。指城市应对内外环境压力时的承载能力和应变能力。
1.2 城市自然灾害韧性评价体系建立
城市自然灾害韧性评价的目的是提高城市抵御自然灾害能力,选取合理的指标是准确、客观评价城市韧性的关键。根据文献[18-20]的研究成果,以及《安全韧性城市评价指南》(GB/T 40947—2021)《城市和社区可持续发展 韧性城市指标》(GB/T 43652—2024),基于全面性、科学性、可行性等指标选取原则[21],从经济、社会、基础设施和生态环境维度选取22个评价指标,如图1所示。
2 评估模型的建立
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2.1 熵权-TOPSIS
(1)熵权法确定指标权重。熵权法是一种客观赋值的方法,根据各指标数据的分散程度,利用信息熵计算各指标的熵权,得到较为客观的指标权重。其评价结果主要是依据客观资料,几乎不受主观因素的影响。具体计算步骤如下。
步骤1 归一化处理。
Yij= X i j - X m i n X m a x - X m i n
式(1)中:Xij为原矩阵X的元素;Yij为归一化后矩阵Y的元素;Xmax和Xmin为同一指标的最大值和最小值。
步骤2 计算熵值。
Pij= Y i j ∑ i = 1 m Y i j
Ej=- 1 l n m ∑ i = 1 mPijlnPij
式中:Pij为比重;m为评价数;Ej为熵值。
步骤3 计算指标权重。
Wj= 1 - E j ∑ j = 1 n E j
式(4)中:Wj为指标权重。
(2)利用TOPSIS法确定综合得分。TOPSIS法是一种常用的综合评价方法,又称为优劣解距离法。根据熵权法计算出的权重值构建加权矩阵,得到正负理想解,能更精确的反应各个指标之间的优劣与差距并进行排序。具体计算步骤如下。
步骤1 构建加权矩阵Z的元素Zij。
Zij=WjYij
步骤2 确定正理想解 Z i +和负理想解 Z i -。
Z i +=max{Zij j=1,2,…,m}
Z i -=max{Zij j=1,2,…,m}
步骤3 计算指标的欧氏距离。
D i += ∑ j = 1 n ( Z i j - Z i + )
D i -= ∑ j = 1 n ( Z i j - Z i - )
式中: D i +为最优距离; D i -为最劣距离。
步骤4 计算相对贴近度Ci。
Ci= D i - D i + + D i -
2.2 泰尔指数
泰尔指数是衡量区域间和区域内的差异以及两者对总差异的影响程度,取值在0~1,越接近0表示差异性越小,越接近1表示差异性越大[22]。TOPSIS结果只能进行简单的排序,不能进行差异程度的衡量,使用泰尔指数能进一步计算区域间和区域内差异以及两者对总差异的影响程度,对陕西省自然灾害韧性进行空间分析,计算公式为
T= 1 n ∑ i = 1 n C i C - i l n C i C - i
Tp= 1 n ∑ i = 1 n p C p i C - p l n C p i C - p
Tw= ∑ p = 1 n n p n C - p CTp
Tb= ∑ p = 1 n n p n C - p Cln C - p C
式中:n为城市总数;Ci为韧性指数; C - i为韧性指数的均值;T为总体泰尔指数;Tp为地区泰尔指数;np为地区的城市数;Cpi为p地区i城市的韧性指数;Cp为p地区的韧性指数; C - p为p地区的韧性指数的均值;C为全部城市韧性指数均值;Tw、Tb为区域内和区域间泰尔指数。
2.3 重心-标准差椭圆
重心-标准差椭圆是解释地理要素空间分布特征的空间统计方法[23]。重心分析对研究不平衡分布的空间变化规律有重要参考价值,标准差椭圆能够全面的分析自然灾害韧性迁移情况。将TOPSIS所得韧性指数进行重心-标准差椭圆分析,能进一步研究陕西省城市自然灾害韧性的空间演化特征和趋势方向。
2.4 障碍因子诊断模型
通过熵权法所得各指标权重引入障碍因子诊断模型[24],研究影响城市韧性水平的主要障碍因子,为提升城市韧性水平路径指明方向。
(1)因子贡献度。
Fi=Wj W j *
式(15)中:Fi为因子贡献度; W j *为指标所属准则层的权重。
(2)计算偏离度。
Iij=1-Xij
式(16)中:Iij为指标偏离度。
(3)计算障碍度。
Qij= F j I i j ∑ j F j I i j×100%
式(17)中:Qij为指标障碍度,障碍度越高,该指标的阻碍作用越强。
采用熵权-TOPSIS、泰尔指数、重心-标准差椭圆和障碍因子诊断模型4种方法构建城市自然灾害韧性水平评估模型,通过熵权-TOPSIS计算各指标权重和自然灾害韧性指数,对城市自然灾害韧性水平进行时序分析,在此基础上引入泰尔指数和重心-标准差椭圆探究空间演变规律,同时通过熵权法所得权重计算各指标障碍度,分析影响城市自然灾害韧性的主要因素。具体评估流程如图2所示。
3 实例分析
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陕西省地处105°29'E~111°15'E,31°42'N~39°35'N,地形复杂,地势呈南北高,中间低,气候表现为春冬少雨干旱,夏秋多雨湿润。因横跨3个气候带,自然灾害事件常有发生[25]。以陕西省10座城市为例展开评价。
3.1 数据来源
数据来源于2019—2023年《中国城市统计年鉴》、2019—2023年《陕西省统计年鉴》、陕西省各市2019—2023年统计年鉴、《国民经济和社会发展统计公报》以及相关网站获取的统计数据。
3.2 评价指标权重确定
采用熵权法确定指标权重,先计算各年的权重,再得平均权重值,如图3所示。
3.3 评价结果与分析
3.3.1 城市自然灾害韧性时序分析
使用熵权-TOPSIS法计算陕西省各城市自然灾害韧性指数。以2022年一级指标经济韧性为例,首先通过熵权法式(1)~式(4)计算出A1、A2、A3、A4的权重分别为2.16%、9.05%、4.75%、2.95%,再使用TOPSIS法即式(5)~式(10)得到各指标正理想解和负理想解,从而得到各市的最优距离和最劣距离,进一步计算相对贴进度,结果如表1所示。同理,计算出2018年—2022年陕西省各城市自然灾害韧性指数和各分项韧性指数,计算结果分别如图4和图5所示。
(1)各城市自然灾害韧性评价。在研究期内陕西省绝大部分城市的自然灾害韧性指数有一定幅度的增加。由于各城市的经济发展水平和生态环境等条件存在着差异,各城市的自然灾害韧性指数都存在着不同的变化。西安和汉中两座城市的自然灾害韧性水平呈稳步直线上升趋势,尤其是汉中市的自然灾害韧性水平幅度最大,究其原因,汉中在其他指标正常变动情况下,A1和C7显著增加。但咸阳市韧性指数从2018年的0.493降到2022年的0.464,从指标分析,咸阳市的D1、B3和C6等指标都大幅度下降,其他指标变化未能抵消这几个指标降低造成的影响。其他城市的韧性指数由于C2、C5和C7等指标升高而呈上升的趋势。为刻画研究期内陕西省韧性指数的分异特征和分异程度,进一步分析,如表2所示。2018—2022年韧性指数均值从0.383上升到0.433,表明研究期内韧性水平有所提高。标准差在[0.080,0.100]范围,反映陕西省城市韧性离散程度不大,各城市的韧性发展相对平衡。同时,变异系数呈现出缩小的趋势,表明陕西省韧性水平随着时间的变化差异呈现缩小趋势, 2018—2022年城市韧性区域协同发展效果显著。总体来看,研究期内各城市韧性水平呈上升趋势,且区域之间发展逐渐趋于平衡。
(2)自然灾害韧性各维度时序评价。研究期内陕西省综合、社会和基础设施韧性明显上升,经济韧性指数在0.362~0.377波动,上升幅度不大。生态环境韧性为降低趋势,在0.278~0.352波动,下降幅度较大,从指标来看,安康、商洛和西安等城市的D4逐年降低,且其权重为5.65%,对韧性水平影响较大,再加上渭南、延安和榆林等大部分城市的D3也在不断减弱,多种影响共同作用造成生态环境韧性水平有所降低。具体来看,经济韧性增长3.99%,社会韧性上升19.17%,基础设施韧性指数上升20.02%,生态环境韧性下降14.42%。原因分析:2020年爆发新冠疫情,虽加大公共预算投入,但人均生产总值和人均可支配收入降低,导致经济韧性水平提升速度较慢;近年来防灾减灾规划的提出和完善,各城市不断加大防灾减灾资金投入、调整产业结构,在一定程度上促进社会韧性水平上升;在新冠疫情的影响下,各市不断健全医疗卫生服务体系、发展邮电通信业和交通运输业等公共服务,促使基础设施韧性水平迅速发展;但随着近年来城镇化和工业化迅速推进,生态环境受到一定程度破坏,造成绿化、绿地面积逐年减少,生态环境韧性水平逐年下降。
3.3.2 陕西省城市韧性空间格局演变特征
(1)城市韧性的空间差异分析。根据陕西省各市的地理位置、空间分布和经济发展情况,将10座城市划分为关中、陕北和陕南地区,由各城市韧性指数,结合式(12)~式(14),计算各区域泰尔指数分析其空间差异,如图6所示。
可以看出,研究期内,总体泰尔指数的波动范围是0.014 6~0.030 4,呈下降趋势,说明各城市间的韧性差异在不断减小。具体到指标,各市B4、B6和C1等指标间的差距逐年减少,在一定程度上降低各城市间的差异,韧性水平趋于平衡。就贡献度来看,区域内差异贡献率保持在50%以上,且城市自然灾害韧性区域内部差异明显且一直高于区域间差异,说明陕西省城市韧性的区域差异主要来自区域内部。分区域来看,关中的泰尔指数明显高于陕南和陕北地区,这表明在关中各市间存在着更大的不平衡现象。在研究期内,西安市的自然灾害城市韧性指数均值为0.59,而关中地区其他城市的韧性指数均值都不超过0.48,两极分化问题较为突出,区域内协调性较弱。
陕西省各城市的经济韧性、社会韧性、基础设施韧性以及生态环境韧性的区域差异如图7所示。
如图7(a)所示,各城市经济韧性泰尔指数波动下降,说明研究期内经济韧性差异在不断缩小。区域上看,关中和陕南地区城市差异逐年减少,陕北地区城市差异不断扩大,且关中地区的总体差异大于陕南和陕北,在一定程度上表现出关中地区的经济韧性存在着较为严重的不平衡性。同时,陕西省城市经济韧性的区域内差异贡献度高达80%,这揭示区域内差异是制约经济韧性水平发展不平衡的关键因素。
如图7(b)所示,社会韧性差异总体下降,反映各城市间差异不断缩小。从地区来看,关中的差异指数明显高于陕南和陕北地区,泰尔指数在0.061 8~0.093 6波动,说明关中地区社会韧性的不平衡是影响总体泰尔指数的主要因素。其中社会韧性的区域内差异贡献度远远高于区域间,因此,在未来要加强各区域内协同发展。
如图7(c)所示,陕西省城市基础设施韧性的总体泰尔指数从0.146 2降至0.023 8,说明研究期内各城市基础设施韧性差异不断缩小,但受经济增长、政策推出等多类因素的影响,各城市基础设施建设水平高低不一,基础设施韧性仍存在一定差异。分区域看,关中泰尔指数始终高于陕南和陕北地区,说明关中地区韧性差异最大。陕南基础设施韧性差异虽较小但呈扩大态势,未来发展要纳入考虑。2018—2022年基础设施韧性区域内差异的贡献率远远高于区域间,说明区域内差异是造成总体差异的主要制约因素。
如图7(d)所示,生态环境韧性存在着明显的区域差异性,总体差异由2018年的0.107 5上升到2020年的0.244 9再下降到2022年的0.227 2,总体为波动上升趋势。从指标看,各市生态环境韧性指标除D2其他指标间的差值逐渐增大,造成差异变大。就区域来看,陕南的泰尔指数高于陕北和关中地区,且除陕北其余地区差异扩大。2018—2022年,区域内和区域间的贡献率均上升,但区域间贡献率远小于区域内,表明区域内差异仍是影响生态环境韧性差异的重要原因。
综上,陕西省自然灾害韧性的泰尔指数总体呈缩小趋势,城市间差异不断减小,逐渐趋于平衡;从各维度分析,各市的经济、社会和基础设施韧性差距逐渐减小,但生态环境韧性有扩大趋势,未来有必要从生态环境着手,促进各市生态环境的协同发展,降低各城市自然灾害韧性的差异性;研究期内,区域内差异远高于区域间差异贡献率,说明区域内差异是造成总体差异的主要因素,在未来要着重加强区域内部的协同合作。
(2)城市韧性的空间动态推进。根据韧性指数评价结果,通过GIS(地理空间软件)中的Jenks将其划分为5类,如表3所示。绘制各城市韧性等级空间分布图,如图8所示。
2018年,各城市韧性指数范围在0.267~0.548,主要是中韧性、低韧性、较低韧性,分别占30%、20%和30%;2019年无低度韧性城市,中度韧性城市较2018年增加1个;2020年较高韧性城市数增加为4个,较低度韧性城市减少为1个;2021年高韧性城市增加到2个,榆林为高韧性城市,较高韧性城市3个,其余城市均为中等韧性和较低度韧性城市;2022年,高韧性城市是榆林和西安较低韧性和低度韧性城市全部消失。
陕西省城市自然灾害韧性总体特征如下:城市韧性水平呈上升趋势,城市间的差异随着时间的推移逐渐减少,与泰尔指数结果一致;研究期内,一直处于高度韧性水平的城市是省会城市西安,后发展成高韧性城市的是经济发展较好的榆林市,余下城市均低于高韧性水平;城市自然灾害韧性水平逐渐呈现以省会城市为中心向周边辐射的溢出效应;陕西省自然灾害韧性总体呈现出“关中地区>陕北地区>陕南地区”的空间分布格局。
通过重心-标准差椭圆探究城市自然灾害韧性的时空动态演进过程,如图9所示。标准差椭圆相关参数如表4所示。
2018—2022年,陕西省城市韧性标准差椭圆表现为东北—西南格局,椭圆面积扩大4 490.25 km2。就半轴看,短半轴长度为增长趋势;长半轴2020年较2019年有所将下降,其余年份较前一年均上升。
长轴约为短轴的2倍,说明韧性水平在西南-东北方向的离散性大于西北-东南,但扁率逐渐减小,方向性逐渐减弱,差异变小,与泰尔指数评估结果一致。2018—2019年方位角为逆时针旋转,表明该阶段北部城市对城市韧性的空间格局的塑造力逐渐增强,而2019—2022年呈顺时针旋转,该阶段南部城市对标准差椭圆的拉动作用逐渐强于北部。从重心看,坐标范围在108.78°E~108.83°E,34.79°N~34.90°N,从铜川逐渐转向咸阳市。原因在于:在新冠疫情的影响下,我国提出防灾减灾政策和发展多种公共服务业,西安、咸阳和宝鸡等城市的医疗设备配备、通信覆盖率和卫生健康投入等指标相较于其他城市增长幅度更大,且西安为省会城市,经济较其他城市增长更快,废气废水处理能力更强。因此,韧性水平重心由铜川市逐渐向咸阳市转移。
3.3.3 城市自然灾害韧性障碍因子分析
运用障碍度诊断模型筛选制约陕西省城市自然灾害韧性水平提升的关键因素。
(1)准则层障碍度分析。通过熵权法所得指标权重,结合式(15)~式(17),计算陕西省城市自然灾害韧性准则层障碍度,结果如图10所示。
准则层障碍度由高到低为基础设施韧性(46.7%)、生态环境韧性(25.7%)、社会韧性(14.7%)和经济韧性(12.9%)。从时序看,经济、社会、生态环境韧性的障碍度上升,幅度分别是7.7%,2.1%,19.7%。基础设施韧性障碍度下降了29.5%。这表明基础设施韧性障碍度下降在一定程度上提升了韧性水平,但其障碍度仍最大,是陕西省未来提升城市韧性水平的关键切入点;经济、生态环境和社会障碍度逐年升高,对韧性水平阻碍作用逐渐加大,且生态环境韧性障碍度上升幅度较大,形势较为紧迫,在未来发展中要格外重视。
(2)指标层障碍度分析。考虑涉及指标较多,为体现出关键性,仅列出各年各城市障碍度前6的因子,并统计频次,从高到低依次为:C2(17.63%)、D1(11.78%)、A2(6.67%)、D4(1.85%)、C5(5.55%)、B6(5.56%),如图11所示。
除A2和B6外,其他因子均属于基础设施和生态环境韧性两个维度,与前文所提的基础设施和生态环境是影响自然灾害韧性的重要准则层吻合。究其原因,随着城镇化迅速推进,城市排水管道建成长度难以满足建成区的面积增长速度,同时城市化需要更多的土地建设商业、住宅等设施,导致道路面积和园林绿化面积减少。工业化逐渐扩大,废水治理设施处理能力也受到影响。因此,要因地制宜地加强基础设施方面建设和改善生态环境。A2和B6所属的经济韧性和社会韧性贯穿城市防灾减灾全过程,是生态环境韧性和基础设施韧性水平提升的重要保障。因此,要有效地提升韧性水平,必须在保证经济的同时加强各市基础设施的建设,促进各市间交流合作,注重生态环境工作,实现可持续发展。
具体到各市,西安市统计的30个障碍因子中只有7个是排名前6的因子,与其余市相差较大,其他城市的影响因素大同小异,多是前6个主要障碍因子。因此,西安在研究期内一直是高度韧性水平。障碍度较小的指标,各城市不完全相同,出现频次最高的前4个是B2、C8、D2、B5,因此在未来城市韧性发展中要注重经济结构的调整和优化,加强交通、通信等方面的投资建设,合理规划住宅等建筑的建设。
4 结论
收起
从经济、社会、基础设施和生态环境4个维度建立陕西省自然灾害韧性指标体系,从时序演变、空间格局、影响因素3个方面对陕西省10个城市的自然灾害韧性进行评价,得出以下结论。
(1)在时序演变上,陕西省绝大多数城市自然灾害韧性总体呈上升趋势,咸阳市韧性水平略有降低,从2018年的0.493降到2022年的0.464;4个维度上看,研究期内陕西省经济韧性、社会韧性和基础设施韧性总体为上升趋势,生态环境韧性水平下降14.42%。
(2)在空间格局上,陕西省自然灾害韧性总体空间格局呈现出关中地区>陕北地区>陕南地区;关中地区各城市自然灾害韧性水平差异最大,泰尔指数为0.064;区域内部差异是造成陕西省各区域自然灾害韧性水平发展不平衡的关键因素。
(3)在障碍因子方面,影响陕西省城市自然灾害韧性的因素主要集中在基础设施(障碍度46.7%)和生态环境(障碍度25.7%)两个方面,特别是城市排水管道长度、园林绿化覆盖面积和一般公共预算收入是障碍因子出现频率最高的3个指标。因此,其是未来提升陕西省自然灾害韧性水平的重要途径。
基金
收起
  • 陕西省社会科学基金(2022R058)
  • 陕西省哲学社会科学研究专项项目(2023HZ1554)
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2025年第25卷第16期
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doi: 10.12404/j.issn.1671-1815.2405650
  • 接收时间：2024-07-27
  • 首发时间：2025-07-09
  • 出版时间：2025-06-08
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  • 收稿日期：2024-07-27
  • 修回日期：2025-03-15
基金
陕西省社会科学基金(2022R058)
陕西省哲学社会科学研究专项项目(2023HZ1554)
作者信息
    1 西安科技大学安全科学与工程学院, 西安 710054
    2 国家矿山救援西安研究中心, 西安 710054
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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