Constructing a comprehensive disaster resilience index: The case of Italy

English version

Autoři: Sepehr Marzi ^aff001; Jaroslav Mysiak ^aff001; Arthur H. Essenfelder ^aff001; Mattia Amadio ^aff001; Silvio Giove ^aff002; Alexander Fekete ^aff003
Působiště autorů: Centro Euro-Mediterraneo sui Cambiamenti Climatici and Università Ca' Foscari Venezia, via della Libertà, Venice Marghera, Italy ^aff001; Department of Economics, Università Ca' Foscari Venezia, Cannaregio 873 –Fondamenta San Giobbe, Venice, Italy ^aff002; Institute of Rescue Engineering and Civil Protection, TH Köln (University of Applied Sciences), Betzdorfer Straße 2, Cologne, Germany ^aff003
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0221585

Souhrn

Measuring disaster resilience is a key component of successful disaster risk management and climate change adaptation. Quantitative, indicator-based assessments are typically applied to evaluate resilience by combining various indicators of performance into a single composite index. Building upon extensive research on social vulnerability and coping/adaptive capacity, we first develop an original, comprehensive disaster resilience index (CDRI) at municipal level across Italy, to support the implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030. As next, we perform extensive sensitivity and robustness analysis to assess how various methodological choices, especially the normalisation and aggregation methods applied, influence the ensuing rankings. The results show patterns of social vulnerability and resilience with sizeable variability across the northern and southern regions. We propose several statistical methods to allow decision makers to explore the territorial, social and economic disparities, and choose aggregation methods best suitable for the various policy purposes. These methods are based on linear and non-liner normalization approaches combining the OWA and LSP aggregators. Robust resilience rankings are determined by relative dominance across multiple methods. The dominance measures can be used as a decision-making benchmark for climate change adaptation and disaster risk management strategies and plans.

Klíčová slova:

Engineering and technology – Social sciences – People and places – Population groupings – Computer and information sciences – Network analysis – Geographical locations – Europe – Economics – Labor economics – Earth sciences – Geography – Atmospheric science – European Union – Ethnicities – European people – Italian people – Italy – Human geography – Management engineering – Risk management – Climatology – Climate change – Network resilience – Human mobility – Human capital – Economics of training and education

Zdroje

1. European Commission. The post-2015 Hyogo Framework for Action: managing risks to achieve resilience. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions [Internet]. 2014. Available: https://www.ipcc.ch/report/ar5/wg2/

2. European Commission. Action Plan for Resilience in Crisis Prone Countries 2013–2020. Staff Working Document [Internet]. SWD(2013) 227 final. 2013. Available: http://ec.europa.eu/echo/files/policies/resilience/com_2013_227_ap_crisis_prone_countries_en.pdf

3. Poljanšek K, Marin Ferrer M, De Groeve T, Clark I. Science for disaster risk management 2017: knowing better and losing less. EUR 28034 EN, Publ Off Eur Union. 2017; doi: 10.2788/688605, JRC102482

4. IPCC. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In: Field CB, Barros V.R. Dokken DJ, Mach KJ, Mastrandrea MD, Bilir T.E Chatterjee M, Ebi KL, et al., editors. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press; 2014. p. 1132. Available: https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-PartA_FINAL.pdf

5. UNISDR. Sendai Framework for Disaster Risk Reduction 2015–2030 [Internet]. United Nations Office for Disaster Risk Reduction (UNISDR); 2015. Available: https://www.unisdr.org/we/inform/publications/43291

6. IPCC. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Group s I and II of the Intergovernmental Panel on Climate Change [Internet]. Field CB, Barros V, Stocker TF, Qin D, Dokken D.J., Ebi KL, et al., editors. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press; 2012. Available: http://www.ipcc.ch/report/srex/

7. World Bank. The Sendai report: managing disaster risks for resilient future. Washington DC; 2012.

8. Breil M, Downing C, Kazmierczak A, Mäkinen K, Romanovska L. Social vulnerability to climate change in European cities–state of play in policy and practice [Internet]. Bologna; 2018. doi: 10.25424/CMCC/SOCVUL_EUROPCITIES

9. Mysiak J, Surminski S, Thieken A, Mechler R, Aerts J. Brief communication: Sendai framework for disaster risk reduction—Success or warning sign for Paris? Nat Hazards Earth Syst Sci. 2016; doi: 10.5194/nhess-16-2189-2016

10. EC. An EU Strategy on adaptation to climate change COM (2013) [Internet]. Brussels; 2013. Available: http://ec.europa.eu/transparency/regdoc/rep/1/2013/EN/1-2013-216-EN-F1-1.Pdf

11. EC. Building a resilient Europe in a globalised world [Internet]. Brussels; 2015. Available: https://ec.europa.eu/jrc/sites/jrcsh/files/jrc-building-resilient-europe-summary-report_en.pdf

12. DRMKC. European Commision. Disaster Risk Management Knowledge Centre [Internet]. 2017 [cited 7 Aug 2018]. Available: https://drmkc.jrc.ec.europa.eu/

13. Frazier TG, Thompson CM, Dezzani RJ, Butsick D. Spatial and temporal quantification of resilience at the community scale. Appl Geogr. 2013;42: 95–107. doi: 10.1016/J.APGEOG.2013.05.004

14. Cutter SL, Ash KD, Emrich CT. The geographies of community disaster resilience. Glob Environ Chang. 2014;29: 65–77. doi: 10.1016/J.GLOENVCHA.2014.08.005

15. Parsons M, Glavac S, Hastings P, Marshall G, McGregor J, McNeill J, et al. Top-down assessment of disaster resilience: A conceptual framework using coping and adaptive capacities. Int J Disaster Risk Reduct. 2016;19: 1–11. doi: 10.1016/j.ijdrr.2016.07.005

16. Bakkensen LA, Fox-Lent C, Read LK, Linkov I. Validating Resilience and Vulnerability Indices in the Context of Natural Disasters. Risk Anal. 2017;37: 982–1004. doi: 10.1111/risa.12677 27577104

17. Sherrieb K, Louis CA, Pfefferbaum RL, Betty Pfefferbaum JD, Diab E, Norris FH. Assessing community resilience on the US coast using school principals as key informants. Int J Disaster Risk Reduct. 2012;2: 6–15. doi: 10.1016/J.IJDRR.2012.06.001

18. Sherrieb K, Norris FH, Galea S. Measuring Capacities for Community Resilience. Soc Indic Res. 2010;99: 227–247. doi: 10.1007/s11205-010-9576-9

19. Tierney K. Disaster Governance: Social, Political, and Economic Dimensions. Annu Rev Environ Resour. 2012;37: 341–363. doi: 10.1146/annurev-environ-020911-095618

20. Aldrich DP. Building resilience: social capital in post-disaster recovery. Chicago: University of Chicago Press; 2012.

21. Rose A. Economic resilience to natural and man-made disasters: Multidisciplinary origins and contextual dimensions. Environ Hazards. 2007;7: 383–398. doi: 10.1016/j.envhaz.2007.10.001

22. Alawiyah T, Bell H, Pyles L, Runnels RC. Spirituality and Faith-Based Interventions: Pathways to Disaster Resilience for African American Hurricane Katrina Survivors. J Relig Spiritual Soc Work Soc Thought. 2011;30: 294–319. doi: 10.1080/15426432.2011.587388

23. Khazai B, Anhorn J, Burton CG. Resilience Performance Scorecard: Measuring urban disaster resilience at multiple levels of geography with case study application to Lalitpur, Nepal. Int J Disaster Risk Reduct. 2018;31: 604–616. doi: 10.1016/J.IJDRR.2018.06.012

24. Bates S, Angeon V, Ainouche A. The pentagon of vulnerability and resilience: A methodological proposal in development economics by using graph theory. Econ Model. 2014;42: 445–453. doi: 10.1016/J.ECONMOD.2014.07.027

25. Beccari B. A Comparative Analysis of Disaster Risk, Vulnerability and Resilience Composite Indicators. PLoS Curr. 2016; doi: 10.1371/currents.dis.453df025e34b682e9737f95070f9b970 27066298

26. Smith N., O’Keefe P. Geography, max and the concept of nature. In: Agnew J, Livingstone D, Roger A, editors. Human geography: an essential anthology. Blackwell Publishing; 1996. pp. 283–95.

27. Cimellaro GP, Reinhorn AM, Bruneau M. Framework for analytical quantification of disaster resilience. Eng Struct. 2010;32: 3639–3649. doi: 10.1016/J.ENGSTRUCT.2010.08.008

28. Cutter SL, Barnes L, Berry M, Burton C, Evans E, Tate E, et al. A place-based model for understanding community resilience to natural disasters. Glob Environ Chang. 2008;18: 598–606. doi: 10.1016/J.GLOENVCHA.2008.07.013

29. Cutter SL, Burton CG, Emrich CT. Disaster Resilience Indicators for Benchmarking Baseline Conditions. J Homel Secur Emerg Manag. 2010;7. doi: 10.2202/1547-7355.1732

30. Adger WN, Vincent K. Uncertainty in adaptive capacity. Comptes Rendus Geosci. 2005;337: 399–410. doi: 10.1016/j.crte.2004.11.004

31. Folke C. Resilience: The emergence of a perspective for social–ecological systems analyses. Glob Environ Chang. 2006;16: 253–267. doi: 10.1016/J.GLOENVCHA.2006.04.002

32. Brenkert AL, Malone EL. Modeling Vulnerability and Resilience to Climate Change: A Case Study of India and Indian States. Clim Change. 2005;72: 57–102. doi: 10.1007/s10584-005-5930-3

33. Adger WN. Vulnerability. Glob Environ Chang. 2006;16: 268–281. doi: 10.1016/J.GLOENVCHA.2006.02.006

34. Rose A. Economic resilience to disasters: toward a consistent and comprehensive formulation. In: Paton D, Johnston D, editors. Disaster resilience: An integrated approach. Springfield, IL: Charles C. Thomas; 2006. pp. 275–303.

35. Tierney K, Bruneau M. Conceptualizing and measuring resilience: a key to disaster loss reduction. TR News. 2007; 14–17. Available: https://trid.trb.org/view/813539

36. Norris FH, Stevens SP, Pfefferbaum B, Wyche KF, Pfefferbaum RL. Community Resilience as a Metaphor, Theory, Set of Capacities, and Strategy for Disaster Readiness. Am J Community Psychol. 2008;41: 127–150. doi: 10.1007/s10464-007-9156-6 18157631

37. Linkov I, Fox-Lent C, Read L, Allen CR, Arnott JC, Bellini E, et al. Tiered Approach to Resilience Assessment. Risk Anal. 2018;38: 1772–1780. doi: 10.1111/risa.12991 29694670

38. USEPA. Risk assessment forum white paper: Probabilistic risk assessment methods and case studies. EPA/100/R-09/001A. Washington, DC: Risk Assessment Forum, Office of the Science Advisor, USEPA; 2014.

39. Williams PR, Nolan M, Panda A. Disaster resilience scorecard for cities. UNISDR. 2014; Available: https://www.unisdr.org/2014/campaign-cities/Resilience%25%0A20Scorecard%2520V1.5.pdf

40. Linkov I, Trump BD. The Science and Practice of Resilience [Internet]. Cham: Springer International Publishing; 2019. doi: 10.1007/978-3-030-04565-4

41. Moser SC, Ekstrom JA. A framework to diagnose barriers to climate change adaptation. Proc Natl Acad Sci U S A. 2010;107: 22026–31. doi: 10.1073/pnas.1007887107 21135232

42. Schultz MT, Smith ER. Assessing the Resilience of Coastal Systems: A Probabilistic Approach. J Coast Res. 2016;321: 1032–1050. doi: 10.2112/JCOASTRES-D-15-00170.1

43. Gao J, Barzel B, Barabási A-L. Universal resilience patterns in complex networks. Nature. 2016;530: 307–312. doi: 10.1038/nature16948 26887493

44. Foster K. In search of regional resilience. In: Weir M, Pindus N, Wial H, Wolman H, editors. Building Regional Resilience: Urban and Regional Policy and Its Effects. Washington, DC: Brookings Institute Press; 2012.

45. Peacock W, Brody S, Seitz W, Merrell W, Vedlitz A, Zahran S, et al. Advancing Resilience of Coastal Localities: Developing, Implementing, and Sustaining the Use of Coastal Resilience Indicators: A Final Report. Hazard Reduction and Recovery Center. 2010.

46. Graziano P, Rizzi P. Vulnerability and resilience in the local systems: The case of Italian provinces. Sci Total Environ. 2016;553: 211–222. doi: 10.1016/j.scitotenv.2016.02.051 26925733

47. Graziano P, Provenzano V. Rischio, vulnerabilità e resilienza territoriale: il caso delle province italiane. In: Mazzola F, Musolino D, editors. no, P, Provenzano, V, 2014 Rischio, vulnerabilità e resilienza territoriale: il caso delle province itaReti, nuovi settori e sostenibilità Prospettive per l’analisi e le politiche regionali. Milano: Franco Angeli; 2014. pp. 243–270.

48. Rizzi P, Graziano P. Vulnerabilità e resilienza in Emilia Romagna. Ecoscienza. 2013;6: 17–19.

49. Dallara A, Rizzi P. Geographic Map of Sustainability in Italian Local Systems. Reg Stud. 2012;46: 321–337. doi: 10.1080/00343404.2010.504703

50. Neil Adger W, Arnell NW, Tompkins EL. Successful adaptation to climate change across scales. Glob Environ Chang. 2005;15: 77–86. doi: 10.1016/J.GLOENVCHA.2004.12.005

51. Birkmann J. Risk and vulnerability indicators at different scales: Applicability, usefulness and policy implications. Environ Hazards. 2007;7: 20–31. doi: 10.1016/J.ENVHAZ.2007.04.002

52. Marzi S, Mysiak J, Santato S. Comparing adaptive capacity index across scales: The case of Italy. J Environ Manage. 2018;223: 1023–1036. doi: 10.1016/j.jenvman.2018.06.060 30096743

53. Hinkel J. “Indicators of vulnerability and adaptive capacity”: Towards a clarification of the science–policy interface. Glob Environ Chang. 2011;21: 198–208. doi: 10.1016/J.GLOENVCHA.2010.08.002

54. Roder G, Sofia G, Wu Z, Tarolli P, Roder G, Sofia G, et al. Assessment of Social Vulnerability to Floods in the Floodplain of Northern Italy. Weather Clim Soc. 2017;9: 717–737. doi: 10.1175/WCAS-D-16-0090.1

55. Frigerio I, Carnelli F, Cabinio M, De Amicis M. Spatiotemporal Pattern of Social Vulnerability in Italy. Int J Disaster Risk Sci. 2018; 1–14. doi: 10.1007/s13753-018-0168-7

56. Frigerio I, Ventura S, Strigaro D, Mattavelli M, De Amicis M, Mugnano S, et al. A GIS-based approach to identify the spatial variability of social vulnerability to seismic hazard in Italy. Appl Geogr. 2016;74: 12–22. doi: 10.1016/J.APGEOG.2016.06.014

57. ISTAT. Indice di vulnerabilità sociale e materiale. In: ISTAT [Internet]. 2018 [cited 16 Aug 2018]. Available: http://ottomilacensus.istat.it/documentazione/

58. ISTAT. L ‘indice di vulnerabilità sociale e materiale [Internet]. 2017. Available: http://ottomilacensus.istat.it/fileadmin/download/Indice_di_vulnerabilità_sociale_e_materiale.pdf

59. Fernandez P, Mourato S, Moreira M. Social vulnerability assessment of flood risk using GIS-based multicriteria decision analysis. A case study of Vila Nova de Gaia (Portugal). Geomatics, Nat Hazards Risk. 2016;7: 1367–1389. doi: 10.1080/19475705.2015.1052021

60. Aroca-Jimenez E, Bodoque J, Garcia J. Construction of an Integrated Social Vulnerability Index in urban areas prone to flash flooding. Nat Hazards Earth Syst Sci. 2017;17: 1541. doi: 10.5194/nhess-17-1541-201

61. Fekete A. Validation of a social vulnerability index in context to river-floods in Germany. Nat Hazards Earth Syst Sci. 2009;9: 393–403. doi: 10.5194/nhess-9-393-2009

62. Kienberger S, Contreras D, Zeil P. Spatial and Holistic Assessment of Social, Economic, and Environmental Vulnerability to Floods—Lessons from the Salzach River Basin, Austria. Assess Vulnerability to Nat Hazards. 2014; 53–73. doi: 10.1016/B978-0-12-410528-7.00003–5

63. Carreño M-L, Cardona OD, Barbat AH. Urban Seismic Risk Evaluation: A Holistic Approach. Nat Hazards. 2007;40: 137–172. doi: 10.1007/s11069-006-0008-8

64. Haddow GD, Bullock JA, Coppola DP. Introduction to Emergency Management. Butterworth-Heinemann. Burlington, MA; 2011.

65. UNDP. Sustainable Development Goals [Internet]. 2017 [cited 10 Apr 2017]. Available: http://www.undp.org/content/undp/en/home/sustainable-development-goals/goal-16-peace-justice-and-strong-institutions/targets/

66. ESPON. ESPON CLIMATE-Climate Change and Territorial Effects on Regions and Local Economies. 2011;

67. Denton F, Wilbanks TJ, Abeysinghe AC, Burton I, Gao Q, Lemos MC, et al. Climate-resilient pathways: adaptation, mitigation, and sustainable developmen. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea M.D. Bilir TE, Chatterjee M, et al., editors. Climate Change 2014: Impacts, Adaptation, and Vulnerability Part A: Global and Sectoral Aspects Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press; 2014. pp. 1101–113. Available: https://www.ipcc.ch/pdf/assessment-report/ar5/wg2/WGIIAR5-Chap20_FINAL.pdf

68. Bellini E, Nesi P. Exploiting Smart Technologies to Build Smart Resilient Cities. Routledge Handbook of Sustainable and Resilient Infrastructure. 1st ed. Routledge; 2018. doi: 10.4324/9781315142074-35

69. Barca F, Casavola P, Lucatelli S. A strategy for Inner Areas in Italy: definition, objectives, tools and governance. Mater Uval Ser. 2014;31. Available: http://www.agenziacoesione.gov.it/opencms/export/sites/dps/it/documentazione/servizi/materiali_uval/Documenti/MUVAL_31_Aree_interne_ENG.pdf

70. ENRD. Strategy for Inner Areas Italy. In: European Network for Rural Development [Internet]. 2018. Available: https://enrd.ec.europa.eu/sites/enrd/files/tg_smart-villages_case-study_it.pdf

71. Smit B, Pilifosova O. Adaptation to climate change in the context of sustainable development and equity. Sustain Dev. 2003;8. Available: http://www.start.org/Program/advanced_institute3_web/download/Smit_etal_IPCCwg2_ch18.pdf

72. Bowen A, Cochrane S, Fankhauser S. Climate change, adaptation and economic growth. Clim Change. 2012;113: 95–106. doi: 10.1007/s10584-011-0346-8

73. Rufat S, Tate E, Burton CG, Maroof AS. Social vulnerability to floods: Review of case studies and implications for measurement. Int J Disaster Risk Reduct. 2015;14: 470–486. doi: 10.1016/J.IJDRR.2015.09.013

74. Larsen CA. Social cohesion: Definition, measurement and developments. Inst Statskundskab, Aalborg Univ. 2014; Available: http://www.forskningsdatabasen.dk/en/catalog/2262055869

75. Hooghe M, Stiers D. Elections as a democratic linkage mechanism: How elections boost political trust in a proportional system. Elect Stud. 2016;44: 46–55. doi: 10.1016/J.ELECTSTUD.2016.08.002

76. Diamanti I. Rapporto gli italiani e lo stato [Internet]. 2017 [cited 20 Aug 2018]. Available: http://www.demos.it/rapporto.php

77. Kaufmann D, Kraay A, Mastruzzi M. The Worldwide Governance Indicators: Methodology and Analytical Issues. Hague J Rule Law. 2011;3: 220–246. doi: 10.1017/S1876404511200046

78. Nifo A, Vecchione G. Do institutions play a role in skilled migration? The case of Italy. Reg Stud. 2014;48: 1628–1649. doi: 10.1080/00343404.2013.835799

79. Ludy J, Kondolf GM. Flood risk perception in lands “protected” by 100-year levees. Nat Hazards. 2012;61: 829–842. doi: 10.1007/s11069-011-0072-6

80. Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B. A Social Vulnerability Index for Disaster Management. J Homel Secur Emerg Manag. 2011;8. doi: 10.2202/1547-7355.1792

81. Townshend I, Awosoga O, Kulig J, Fan H. Social cohesion and resilience across communities that have experienced a disaster. Nat Hazards. 2015;76: 913–938. doi: 10.1007/s11069-014-1526-4

82. Patel RB, Gleason KM. The association between social cohesion and community resilience in two urban slums of Port au Prince, Haiti. Int J Disaster Risk Reduct. 2018;27: 161–167. doi: 10.1016/J.IJDRR.2017.10.003

83. Vandermotten C, Van Hamme G. Research for REGI Committee–Indicators in Cohesion Policy [Internet]. European Parliament, Policy Department for Structural and Cohesion Policies. Brussels; 2017. Available: http://www.bbc.com/persian

84. Appleby-Arnold S, Brockdorff N, Jakovljev I, Zdravković S. Applying cultural values to encourage disaster preparedness: Lessons from a low-hazard country. Int J Disaster Risk Reduct. 2018;31: 37–44. doi: 10.1016/J.IJDRR.2018.04.015

85. Thomas DSK, Phillips BD, Lovekamp WE, Fothergill A. Social Vulnerability to Disasters [Internet]. 2nd ed. Boca Raton: CRC Press; 2013. doi: 10.1201/b14854

86. Frigerio I, De Amicis M. Mapping social vulnerability to natural hazards in Italy: A suitable tool for risk mitigation strategies. Environ Sci Policy. 2016;63: 187–196. doi: 10.1016/J.ENVSCI.2016.06.001

87. Araya-Muñoz D, Metzger MJ, Stuart N, Wilson AMW, Alvarez L. Assessing urban adaptive capacity to climate change. J Environ Manage. 2016;183: 314–324. doi: 10.1016/j.jenvman.2016.08.060 27604755

88. De Groeve T, Poljansek K, Vernaccini L. Index for Risk Management—INFORM. JRC Sci Policy Reports—Eur Comm. 2015; 96. doi: 10.2788/636388

89. Juhola S, Kruse S. A framework for analysing regional adaptive capacity assessments: challenges for methodology and policy making. Mitig Adapt Strateg Glob Chang. 2015;20: 99–120. doi: 10.1007/s11027-013-9481-z

90. Annoni P, Dijkstra L, Gargano N. EU Regional Competitiveness Index 2016 [Internet]. European Commission; 2017. Report No.: WP 02/2017. Available: http://ec.europa.eu/regional_policy/sources/docgener/work/201701_regional_competitiveness2016.pdf

91. World Economic Forum. The Global Competitiveness Report 2017–2018 [Internet]. Geneva; 2017. Available: https://www.weforum.org/reports/the-global-competitiveness-report-2017-2018

92. Sietchiping R. Applying an index of adaptive capacity to climate change in north-western Victoria, Australia. Appl GIS. 2006;2: 1–16. Available: http://www.epress.monash.edu/ag/ag060016.pdf

93. Tol RSJ, Yohe GW. The weakest link hypothesis for adaptive capacity: An empirical test. Glob Environ Chang. 2007;17: 218–227. doi: 10.1016/J.GLOENVCHA.2006.08.001

94. Barr R, Fankhauser S, Hamilton K. Adaptation investments: a resource allocation framework. Mitig Adapt Strateg Glob Chang. 2010;15: 843–858. doi: 10.1007/s11027-010-9242-1

95. Mitchell D, Myers M, Grant D. Land valuation: a key tool for disaster risk management. L Tenure J. 2014;1.

96. Roy F, Ferland Y. Land-use planning for disaster risk management. L tenure J. 2015;1.

97. Vallecillo S, Polce C, Barbosa A, Perpiña Castillo C, Vandecasteele I, Rusch GM, et al. Spatial alternatives for Green Infrastructure planning across the EU: An ecosystem service perspective. Landsc Urban Plan. 2018;174: 41–54. doi: 10.1016/J.LANDURBPLAN.2018.03.001

98. Suckall N, Tompkins EL, Nicholls RJ, Kebede AS, Lázár AN, Hutton C, et al. A framework for identifying and selecting long term adaptation policy directions for deltas. Sci Total Environ. 2018;633: 946–957. doi: 10.1016/j.scitotenv.2018.03.234 29602126

99. ISTAT. Population Census [Internet]. 2015 [cited 20 Aug 2018]. Available: https://www.istat.it/en/archive/population+census

100. ISTAT. 8milaCensus [Internet]. 2015 [cited 20 Aug 2018]. Available: https://www.istat.it/it/archivio/160823

101. Dipartimento delle Finanze. Statistiche sulle dichiarazioni [Internet]. 2018 [cited 20 Aug 2018]. Available: http://www1.finanze.gov.it/finanze3/analisi_stat/index.php?search_class%5B0%5D=cCOMUNE&opendata=yes

102. Raffinetti E, Aimar F. GiniWegNeg: Computing the Gini-Based Coefficients for Weighted and Negative Attributes. R Packag version 101. 2016; Available: https://cran.r-project.org/package=GiniWegNeg

103. Agenzia Entrate. Stock catastale [Internet]. 2013 [cited 21 Aug 2018]. Available: https://www.agenziaentrate.gov.it/wps/content/Nsilib/Nsi/Schede/FabbricatiTerreni/omi/Banche+dati/Stock+catastale/?page=fabbricatiterreniimp

104. ISTAT. Matrici di contiguità, distanza e pendolarismo [Internet]. 2013 [cited 21 Aug 2018]. Available: https://www.istat.it/it/archivio/157423

105. Dipartimento dei Vigili del Fuocco. Corpo Nazionale dei Vigili del Fuoco [Internet]. 2009 [cited 21 Aug 2018]. Available: http://www.vigilfuoco.it/aspx/PDI_VVF/TomTom.aspx

106. EEA. Nationally designated areas (CDDA). In: European Environment Agency [Internet]. 2017 [cited 8 Apr 2018]. Available: https://www.eea.europa.eu/data-and-maps/data/nationally-designated-areas-national-cdda-12

107. EEA. Natura 2000 data—the European network of protected sites. In: European Environment Agency [Internet]. 2017 [cited 8 Apr 2018]. Available: https://www.eea.europa.eu/data-and-maps/data/natura-9

108. Copernicus. Green Linear Elements [Internet]. 2018 [cited 20 Aug 2018]. Available: https://land.copernicus.eu/local/riparian-zones/green-linear-elements-gle-image?tab=mapview

109. OECD. Handbook on constructing composite indicators. OECD Publ. 2008; Available: http://www.oecd-ilibrary.org/economics/handbook-on-constructing-composite-indicators_533411815016

110. JRC. 10 Step Guide | COIN [Internet]. 2018 [cited 4 Sep 2018]. Available: https://composite-indicators.jrc.ec.europa.eu/?q=10-step-guide

111. University Yale. Environmental Performance Index [Internet]. 2016. Available: http://epi.yale.edu/

112. Nardo M, Saisana M, Saltelli A, Tarantola S. Tools for Composite Indicators Building. Eur Comm Jt Res Cent. 2005;EUR 21682.

113. García-Sánchez I-M, Almeida TA das N, Camara RP de B. A proposal for a Composite Index of Environmental Performance (CIEP) for countries. Ecol Indic. 2015;48: 171–188. doi: 10.1016/J.ECOLIND.2014.08.004

114. Damioli G. The identification and treatment of outliers. COIN 2017–15th JRC Annual Training on Composite Indicators & Scoreboards. Ispra, Italy: European Commission; 2017. Available: https://composite-indicators.jrc.ec.europa.eu/sites/default/files/COIN 2017 Step 3 Outliers_0.pdf

115. Saisana M, Domínguez-Torreiro M, Vértesy D. Joint Research Centre Statistical Audit of the 2018 Gobal Innovation Index. Global Innovation Index 2018. Ithaca, Fontainebleau, and Genev; 2018. pp. 71–88. Available: http://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2018.pdf

116. Avkiran NK, Ringle CM. Partial Least Squares Structural Equation Modeling: Recent Advances in Banking and Finance [Internet]. Cham, Switzerland: Springer; 2018. doi: 10.1007/978-3-319-71691-6

117. Greco S, Ishizaka A, Tasiou M, Torrisi G. On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Soc Indic Res. 2018; 1–34. doi: 10.1007/s11205-017-1832-9

118. Yager RR. On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans Syst Man Cybern. 1988;18: 183–190.

119. Fullér R. OWA operators in decision making. Exploring the Limits of Support Systems. 1996. pp. 85–104.

120. Zabeo A. A decision support system for the assessment and management of surface waters [Internet]. Ca’Foscari University of Venice. 2011. Available: http://dspace.unive.it/handle/10579/1093

121. Mysiak J, Torresan S, Bosello F, Mistry M, Amadio M, Marzi S, et al. Climate risk index for Italy. Philos Trans A Math Phys Eng Sci. 2018;376: 20170305. doi: 10.1098/rsta.2017.0305 29712797

122. Jin L, Kalina M, Qian G. Discrete and continuous recursive forms of OWA operators. Fuzzy Sets Syst. 2017;308: 106–122. doi: 10.1016/J.FSS.2016.04.017

123. Chaji A, Fukuyama H, Khanjani Shiraz R. Selecting a model for generating OWA operator weights in MAGDM problems by maximum entropy membership function. Comput Ind Eng. 2018;124: 370–378. doi: 10.1016/J.CIE.2018.07.040

124. Pinar M, Cruciani C, Giove S, Sostero M. Constructing the FEEM sustainability index: A Choquet integral application. Ecol Indic. 2014;39: 189–202. doi: 10.1016/J.ECOLIND.2013.12.012

125. Dujmović J, Cordeliers L. A comparison of andness/orness indicators. Proceedings of the 11th Information Processing and Management of Uncertainty international conference (IPMU 2006). 2006. Available: http://www.math.s.chiba-u.ac.jp/~yasuda/open2all/Paris06/IPMU2006/HTML/FINALPAPERS/P467.PDF

126. Belles-Sampera J, Merigó JM, Guillén M, Santolino M. Indicators for the characterization of discrete Choquet integrals. Inf Sci (Ny). 2014;267: 201–216. doi: 10.1016/J.INS.2014.01.047

127. Bendanillo FE, Yurong RR, Roble ND, Yee JC, Sotto FB. Species Composition, Abundance and Distribution of Seawater Bugs (Order Hemiptera: Class Insecta) in Badian, Cebu, Philippines. J Aquat Sci Vol 4, 2016, Pages 1–10. 2016;4: 1–10. doi: 10.12691/JAS-4-1-1

128. EEA. National monitoring, reporting and evaluation of climate change adaptation in Europe (No. 20/2015) [Internet]. Pringle P, Karali E, Klostermann JEM, Mäkinen K, Prutsch A, Hildén M, et al., editors. Luxembourg: European Environment Agency; 2015. doi: 10.2800/629559

Constructing a comprehensive disaster resilience index: The case of Italy

Souhrn

Klíčová slova:

Zdroje

PLOS One

Aktuální možnosti diagnostiky a léčby litiáz