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Natural Hazards

, Volume 62, Issue 1, pp 57–70 | Cite as

The impacting factors of vulnerability to natural hazards in China: an analysis based on structural equation model

  • Le–Le Zou
Original Paper

Abstract

The vulnerability to natural hazard is one of the inner characteristics of social economic system, which is determined and impacted by various factors from almost all the aspects of the system. Although it is widely agreed that the economic development, the migration, and the equity of resource allocation are among the most important impacting factors in forming and developing of the vulnerability, their interrelationship is still unclear enough. The study in this paper employs the method of structural equation model to discover the interrelationship between different latent impacting factors, as well as their contributions to the vulnerability of the system to natural hazards. It is found that, among the factors relating social and economic development, the equity of income allocation is a key point. Also the impacts from industrialization are different for different development levels. The conclusions are expected to be helpful references for the decision consideration of the local development strategies.

Keywords

Vulnerability Impacting factors Structural equation model Development Industrialization level 

Notes

Acknowledgments

The Financial support from the National Natural Science Foundation of China (NSFC) under the grant Nos. 40971276, and the National Key Projects from the Ministry of Science and Technology of China (grants 2008BAC44B04), is gratefully acknowledged.

References

  1. Adger WN, Kelly PM (1998) Social vulnerability to climate change and the architecture of entitlements. Paper presented at the IPCC workshop on adaptation to climatic variability and change, San Jose, Costa Rica, 29 March-1 AprilGoogle Scholar
  2. Adger WN, Brooks N, Bentham G, Agnew M, Eriksen S (2004) New indicators of vulnerability and adaptive capacity. vol technical report 7. Tyndall Centre for Climate Change Research, NorwichGoogle Scholar
  3. Adger WN, Hughes TP, Folke C, Carpenter SR, Rockstrom J (2005) Social-ecological resilience to coastal disasters. Science 309(5737):1036–1039CrossRefGoogle Scholar
  4. Alberini A, Chiabai A, Muehlenbachs L (2006) Using expert judgement to assess adaptive capacity to climate change: evidence from a conjoint choice survey. Glob Environ Change 16:123–144CrossRefGoogle Scholar
  5. Anbalagan R, Singh B (1996) Landslide hazard and risk assessment mapping of mountainous terrains-a case study from Kumaun Himalaya, India. Engg Geol 43(4):237–246Google Scholar
  6. Bandyopadhyay S (1997) Natural environmental hazards and their management: a case study of Sagar Island, India. Singap J Trop Geogr 18(1):20–45CrossRefGoogle Scholar
  7. Bayard B, Jolly C (2007) Environmental behavior structure and socio-economic conditions of hillside farmers: a multiple-group structural equation modeling approach. Ecol Econ 62:433–440CrossRefGoogle Scholar
  8. Bhagavan MR, Virgin I (2004) Generic aspects of institutional capacity development in developing countries. SEI working paper. Stockholm Environment Institute, StockholmGoogle Scholar
  9. Brenkert AL, Malone EL (2005) Modeling vulnerability and resilience to climate change: a case study of India and Indian states. Clim Change 72:57–102CrossRefGoogle Scholar
  10. Brooks N (2003) Vulnerability, risk and adaptation: a conceptual framework. Tyndall Centre for climate change research working paper No. 38. Tyndall Centre for Climate Change Research and Centre for Social and Economic Research on the Global Environment (CSERGE) School of Environmental Sciences, University of East Anglia, NorwichGoogle Scholar
  11. Chan NW (1997) Increasing flood risk in Malaysia: causes and solutions. Disaster Prev Manage 6(2):72–86CrossRefGoogle Scholar
  12. Chen Y, Lin L-S (2010) Structural equation-based latent growth curve modeling of watershed attribute-regulated stream sensitivity to reduced acidic deposition. Ecol Model 221(17):2086–2094CrossRefGoogle Scholar
  13. Chou C-P, Bentler PM (2002) Model modification in structural equation modeling by imposing constraints. Comput Stat Data Anal 41(2):271–287CrossRefGoogle Scholar
  14. Crighton EJ, Elliott SJ, Meer J, Small I, Upshur R (2003) Impacts of an environmental disaster on psychosocial health and well-being in Karakalpakstan. Soc Sci Med 56(3):551–567Google Scholar
  15. Curran PJ, Hussong AM (2002) Structural equation modeling of repeated measures data: latent curve analysis. In: Moskowitz DS, Hershberger SL (eds) Modeling intraindividual variability with repeated measures data: methods and applications. Lawrence Erlbaum Associates, MahwahGoogle Scholar
  16. Cutter SL (1995) The forgotten casualties: women, children, and environmental change. Glob Environ Change 5(3):181–194CrossRefGoogle Scholar
  17. Downing TE, Patwardhan A (2004) Assessing vulnerability for climate adaptation. In: Lim B, Spanger-Siegfried E (eds) Adaptation policy frameworks for climate change: developing strategies, policies and measures. Cambridge University Press, CambridgeGoogle Scholar
  18. Fussel H-M (2007) Vulnerability: a generally applicable conceptual framework for climate change research. Glob Environ Change 17:155–167CrossRefGoogle Scholar
  19. Füssel H-M (2007) Vulnerability: a generally applicable conceptual framework for climate change research. Glob Environ Change 17(2):155–167CrossRefGoogle Scholar
  20. Fyhri A, Klæboe R (2009) Road traffic noise, sensitivity, annoyance and self-reported health: a structural equation model exercise. Environ Int 35(1):91–97CrossRefGoogle Scholar
  21. Gertler PJ (1988) A latent-variable model of quality determination. J Bus Econ Stat 6(1):97–107CrossRefGoogle Scholar
  22. Golob TF (2003) Structural equation modeling for travel behavior research. Transp Res B 37(1):1–25CrossRefGoogle Scholar
  23. Herbert RD, Bell RD (1997) Visualization in the simulation and control of economic models. Comput Econ 10(2):107–118CrossRefGoogle Scholar
  24. Iacobucci D (2010) Structural equations modeling: fit indices, sample size and advanced topics. J Consum Psychol 20(1):90–98CrossRefGoogle Scholar
  25. Imamura F, To DV (1997) Flood and typhoon disasters in Vietnam in the half century since 1950. Nat Hazards 15:71–87CrossRefGoogle Scholar
  26. Ionescu C, Klein RJT, Hinkel J, Kumar KSK, Klein R (2005) Towards a formal framework of vulnerability to climate change. NeWater working paper potsdam institute for climate impact research, PotsdamGoogle Scholar
  27. Joreskog KG, Goldberger AS (1972) Factor analysis by generalized least squares. Psychometrika 37:243–260CrossRefGoogle Scholar
  28. Jöreskog K, Sörbom D (2001) LISREL 8: user’s reference guide. Scientific Software International, LincolnGoogle Scholar
  29. Kasperson JX, Kasperson RE, Turner BL II, Schiller AA, Hsieh W (2005) Vulnerability to global environmental change. In: Kasperson JX, Kasperson RE (eds) Social contours of risk, vol. II: risk analysis, corporations & the globalization of risk, vol Chapter 14. Earthscan, London, pp 245–285Google Scholar
  30. Klein RJT, Nicholls RJ (1999) Assessment of coastal vulnerability to sea-level rise. Ambio 28:182–187Google Scholar
  31. Kline RB (1998) Software review: software programs for structural equation modeling: Amos, EQS, and LISREL. J Psychoedu Assess 16(4):343–364Google Scholar
  32. Krishnakumar J, Ballon P (2008) Estimating basic capabilities: a structural equation model appplied to Bolivia. World Dev 36(6):992–1010CrossRefGoogle Scholar
  33. Leary N, Conde C, Kulkarni J, Nyong A, Pulhin J (2008) Climate change and vulnerability. Earthscan, LondonGoogle Scholar
  34. Lebel L, Garden P, Imamura M (2005) The politics of scales, position, and place in the governance of water resources in the Mekong Region. Ecol Soc [online] 10(2):18Google Scholar
  35. Lindskog E, Dow K, Axberg GN, Miller F, Hancock A (2005) When rapid changes in environmental, social and economic conditions converge: challenges to sustainable livelihoods in Dak Lak. Stockholm environment institute report. Stockholm Environment Institute, Stockholm, VietnamGoogle Scholar
  36. Metzger MJ, Rounsevell MDA, Acosta-Michlik L, Leemans R, Schröter D (2006) The vulnerability of ecosystem services to land use change. Agric Ecosyst Environ 114(1):69–85CrossRefGoogle Scholar
  37. Mushtaque A, Chowdhury R, Bhuyia AU, Choudhury AY, Sen R (1993) The Bangladesh cyclone of 1991: why so many people died. Disasters 17(4):291–303CrossRefGoogle Scholar
  38. Nunnally JC (1974) Introduction to statistics for psychology and education. McGraw-Hill, New YorkGoogle Scholar
  39. O’Brien K, Eriksen S, Nygaard LP, Schjolden A (2007) Why different interpretations of vulnerability matter in climate change discourses. Clim Policy 7:73–88CrossRefGoogle Scholar
  40. Roesch SC, Weiner B (2001) A meta-analytic review of coping with illness: do causal attributions matter? J Psychosom Res 50(4):205–219CrossRefGoogle Scholar
  41. Schader M, Schmid F (1994) Fitting parametric lorenz curves to grouped income distributions—A critical note. Empir Econ 19(3):361–370Google Scholar
  42. Tang N-S, Chen X, Fu Y-Z (2009) Bayesian analysis of non-linear structural equation models with non-ignorable missing outcomes from reproductive dispersion models. J Multivar Anal 100(9):2031–2043CrossRefGoogle Scholar
  43. Thompson PM, Sultana P (1996) Distributional and social impacts of flood control in Bangladesh. Geogr J 162(1)Google Scholar
  44. Tol RSJ, Yohe GW (2007) The weakest link hypothesis for adaptive capacity: an empirical test. Glob Environ Change 17:218–227CrossRefGoogle Scholar
  45. Torres-Vera MA, Canas JA (2003) A lifeline vulnerability study in Barcelona, Spain reliability. Engg Syst Saf 80(2):205–210Google Scholar
  46. Turner BL, Kasperson RE, Matson PA, McCarthy JJ, Corell RW, Christensen L, Eckley N, Kasperson JX, Luers A, Martello ML, Polsky C, Pulsipher A, Schiller A (2003) A framework for vulnerability analysis in sustainability science. www.pnas.org/cgi/doi/10.1073/pnas.1231335100
  47. Ülengin F, Kabak Ö, Önsel S, Ülengin B, Aktas E (2010) A problem-structuring model for analyzing transportation-environment relationships. Eur J Oper Res 200:844–859CrossRefGoogle Scholar
  48. Vazquez C, Cervellon P, Perez-Sales P, Vidales D, Gaborit M (2001) Positive emotions in earthquake survivors in El Salvador. Anxiety Disorder 19:313–328Google Scholar
  49. Williams NL, Shahar G, Riskind JH, Joiner JTE (2005) The looming maladaptive style predicts shared variance in anxiety disorder symptoms: further support for a cognitive model of vulnerability to anxiety. J Anxiety Disord 19(2):157–175CrossRefGoogle Scholar
  50. Zou L–L, Wei Y-M (2009) Impact assessment using DEA of coastal hazards on social-economy in Southeast Asia. Nat Hazards 48:167–189CrossRefGoogle Scholar
  51. Zou L–L, Wei Y-M (2010) Driving factors for social vulnerability to coastal hazards in Southeast Asia: results from the meta-analysis. Nat Hazards 54:901–929CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Institute of Policy and Management, Chinese Academy of SciencesBeijingChina

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