Skip to main content

Advertisement

SpringerLink
Log in

Climate change vulnerability and resilience: current status and trends for Mexico

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

Climate change alters different localities on the planet in different ways. The impact on each region depends mainly on the degree of vulnerability that natural ecosystems and human-made infrastructure have to changes in climate and extreme meteorological events, as well as on the coping and adaptation capacity toward new environmental conditions. This study assesses the current resilience of Mexico and Mexican states to such changes, as well as how this resilience will look in the future. In recent studies (Moss et al. in Vulnerability to climate change: a quantitative approach. Pacific Northwest National Laboratory, Washington DC, 2001; Brenkert and Malone in Clim Change 72:57–102, 2005; Malone and Brenkert in Clim Change 91:451–476, 2008), the Vulnerability–Resilience Indicators Model (VRIM) is used to integrate a set of proxy variables that determine the resilience of a region to climate change. Resilience, or the ability of a region to respond to climate variations and natural events that result from climate change, is given by its adaptation and coping capacity and its sensitivity. On the one hand, the sensitivity of a region to climate change is assessed, emphasizing its infrastructure, food security, water resources, and the health of the population and regional ecosystems. On the other hand, coping and adaptation capacity is based on the availability of human resources, economic capacity, and environmental capacity. This paper presents two sets of results. First, we show the application of the VRIM to determine state-level resilience for Mexico, building the baseline that reflects the current status. The second part of the paper makes projections of resilience under socioeconomic and climate change and examines the varying sources and consequences of those changes. We used three tools to examine Mexico’s resilience in the face of climate change, i.e., the baseline calculations regarding resilience indices made by the VRIM, the projected short-term rates of socioeconomic change from the Boyd–Ibarrarán computable general equilibrium model, and rates of the IPCC-SRES scenario projections from the integrated assessment MiniCAM model. This allows us to have available change rates for VRIM variables through the end of the twenty-first century.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. In general, varying input parameter best-estimate values 2% and propagating the variances around the parameters through a model is a way of testing the structure of a model. Mean output values resulting from such a tests are, by definition, very similar to the deterministic output. The effects of model structure can be analyzed by regressing the output values as dependent variables, against the sampled input parameters as independent variables (e.g., Rose et al. 1991). Those parameters explaining most of the variance of the output can thus be identified. Stratified Latin Hypercube sampling of the parameters ensures that each of the input parameters has its total predefined sampling range represented, because the procedure consists of dividing the range of each parameter into N strata of equal marginal probability 1/N and sampling once from each stratum with N = 1,000 in our case. Each of the N samples from each of the parameter values are then combined in a random manner and the indicators calculated a thousand times. This type of sampling avoids spurious correlations among parameters. When parameters are sampled from distributions representing their estimated actual uncertainty, i.e., from a variance larger than the 2% coefficient of variation, their impacts on the final model outputs change and different parameters contribute more or less to the uncertainty of the outputs depending both on model structure and uncertainties of the parameters. This, again, can be analyzed through ordinary least-squares regression (e.g., Gardner et al. 1983; Moss et al. 2001).

References

  • Ávila García, P. (2007). Las cuencas hidrológicas de México y su vulnerabilidad socio ambiental por el agua, Sustentabilidad y Desarrollo ambiental. Agenda para el Desarrollo, 14, 133–161.

    Google Scholar 

  • Blaikie, P., Cannon, T., Davis, I., & Wisner, B. (1996). Vulnerabilidad: el Entorno Social, Político y Económico de los Desastres. LA RED/ITDG, Perú. Bogotá: Tercer Mundo Editores.

    Google Scholar 

  • Boyd, R., & Ibarrarán, M. E. (2009). Extreme climate events and adaptation: An exploratory analysis of drought in Mexico. Environmental and Development Economics, 14, 371–395. doi:10.1017/S1355770X08004956. Cambridge University Press.

    Article  Google Scholar 

  • Brenkert, A. L., & Malone, E. L. (2005). Modeling vulnerability and resilience to climate change: A case study of India and Indian States. Climatic Change, 72, 57–102.

    Article  CAS  Google Scholar 

  • Brooks, N., Adger, W. N., & Kelly, P. M. (2005). The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation. Global Environmental Change, 15, 151–163.

    Article  Google Scholar 

  • Carstairs, V., & Morris, R. (1989). Deprivation, mortality and resource allocation. Community Medicine, 11, 364–372.

    CAS  Google Scholar 

  • Coombes, M., Raybould, S., Wong, C., & Openshaw, S. (1994). Towards an index of deprivation: A review of alternative approaches. London: UL Department of the Environment, HMSO.

    Google Scholar 

  • Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social vulnerability to environmental hazards. Social Science Quarterly, 84(2), 242–261.

    Article  Google Scholar 

  • Florescano, E. (1980). Una historia olvidada: La sequía en México. Nexos, 32, 9–18.

    Google Scholar 

  • Folke, C. (2006). Resilience: The emergence of a perspective for social-ecological analyses. Global Environmental Change, 16, 253–267.

    Article  Google Scholar 

  • García Acosta, V. (Ed.). (2005). Construcción Social de Riesgos y el Huracán Paulina. México: Publicaciones de la Casa Chata, CIESAS.

    Google Scholar 

  • Gardner, R. H., Roder, B., & Bergstrom, U. (1983). PRISM: A systematic method for determining the effect of parameter uncertainties on model predictions. Nykoping, Sweden: Studsvik Energiteknik AB, report/NW-83/555.

  • Gay, C., Conde, C., Eakin, H., Seiler, R., Vinocur, M., Wehbe, M. (2006). Vulnerability and adaptation to climate change: The case of farmers in Mexico and Argentina. Final report project no. LA 29. http://www.aiaccproject.org.

  • Hewitt, K. (1995). Excluded perspectives in the social construction of disaster. International Journal of Mass Emergencies and Disasters, 13, 317–339.

    Google Scholar 

  • Holling, C. S. (1973). Resilience and stability of ecological systems. Annual Review of Entomology, 6, 163–182.

    Article  Google Scholar 

  • Ibarrarán, M. E., & Boyd, R. (2006). Hacia el Futuro: Energy, economics and the environment in 21st century Mexico. The Netherlands: Springer.

    Google Scholar 

  • IPCC (Intergovernmental Panel on Climate Change). (1991). Common methodology for assessing vulnerability to sea-level rise. Report of the coastal zone management subgroup, IPCC response strategies working group. The Hague: Ministry of Transport, Public Works and Water Management.

    Google Scholar 

  • IPCC (Intergovernmental Panel on Climate Change]. (2007). Climate change 2007—The physical science basis. Working group I contribution to the fourth assessment report of the IPCC. Cambridge: Cambridge University Press.

    Google Scholar 

  • Kim, S. H., Edmonds, J., Lurz, J., Smith, S. J., & Wise, M. (2006). The Objects Framework for Integrated Assessment. The Energy Journal, Special Issue No. 2:2006, 63–91.

  • Liverman, D. M. (1990). Drought and agriculture in Mexico: The case of Sonora and Puebla in 1970. Annals of the Association of American Geographers, 80(1), 49–72.

    Article  Google Scholar 

  • Liverman, D. M. (1994). Vulnerability to global environmental change. In S. Cutter (Ed.), Environmental risks and hazards. USA: Prentice Hall.

    Google Scholar 

  • Luers, A. L., Lobell, D. B., Sklar, L. S., Addams, C. L., & Matson, P. A. (2003). A method for quantifying vulnerability, applied to the agricultural system of the Yaqui Valley, Mexico. Global Environmental Change, 13, 255–267.

    Article  Google Scholar 

  • Malone, E. L., & Brenkert, A. L. (2008). Uncertainty in resilience to climate change in India and Indian States. Climatic Change, 91, 451–476.

    Article  Google Scholar 

  • Malone, E. L., & Brenkert, A. L. (2009). Vulnerability, sensitivity, and coping/adaptive capacity worldwide. In M. Ruth & M. E. Ibarraran (Eds.), The distributional effects of climate change and disasters: Concepts and cases. UK: Edward Elgar Publishing.

    Google Scholar 

  • McCarthy, J. J., Canziani, O. F., Leary, N. A., Dokken, D. J., & White, K. S. (Eds.). (2001). Climate change 2001: Impacts, adaptation, and vulnerability. Cambridge: Cambridge University Press.

    Google Scholar 

  • Moss, R. H., Brenkert, A. L., & Malone, E. L. (2001). Vulnerability to climate change: A quantitative approach. PNNL-SA-33642. Washington DC: Pacific Northwest National Laboratory.

  • Nakicenovic, N., & Swart, R. (2000). Special report on emissions scenarios (SRES). Cambridge: Cambridge University Press.

    Google Scholar 

  • Parry, M., Canziani, O., & Palutikof, J. (2008). Climate change 2007: Impacts, adaptation and vulnerability. Workgroup II contribution to the fourth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press.

    Google Scholar 

  • Parson, E. A., & Fisher-Vanden, K. (1997). Integrated assessment models of global climate change. Annual Review of Energy and the Environment, 22, 589–628.

    Article  Google Scholar 

  • Pratschke, J., & Haase, T. (2000). Structures of disadvantage: Spatial and theoretical aspects of index construction. In Fifth international conference on logic and methodology. Cologne, Germany, Oct 3–6, 2000.

  • Reyes, M., & Flores, R. (2007). Causas y persistencias de la desigualdad en México 1984–2004. Documento de trabajo, Campo Estratégico de Pobreza y Exclusión (CEAPE). México: Universidad Iberoamericana Puebla.

  • Rose, K. A., Smith, E. P., Gardner, R. H., Brenkert, A. L., & Bartell, S. M. (1991). Parameter sensitivities, Monte Carlo filtering, and model forecasting under uncertainty. Journal of Forecasting, 10, 117–133.

    Article  Google Scholar 

  • Sánchez-Rodriguez, R. (2002). Cities and global environmental change: Challenges and opportunities for a human dimension perspective. Newsletter of the international human dimensions programme on global environmental change, number 3/2002.

  • Sánchez-Rodríguez, R., Seto, K. C., Simon, D., Solecki, W. D., Kraas, F., & Laumann, G. (2005). Science plan urbanization and global environmental change. International human dimensions programme on global environmental change, report no. 15.

  • Satterthwaite, D., Ha, S., Pelling, M., Reid, H., & Romero Lankao, P. (2007). Adapting to climate change in urban areas: The possibilities and constraints in low- and middle-income nations. IIED-Human settlements dicussion paper series: Climate change and cities (Vol. 1). London: International Institute for Environment and Development.

  • Townsend, P., Phillimore, P., & Beattie, A. (1988). Health and deprivation: Inequality and the north. London: Croom Helm.

    Google Scholar 

  • Vera Cortés, G. (2005). Vulnerabilidad social y expresiones del desastre en el distrito de Pochutla, Oaxaca. In V. García Acosta (Ed.), Construcción Social de Riesgos y el Huracán Paulina. México: Publicaciones de la Casa Chata, CIESAS.

    Google Scholar 

  • Villegas, C. (2005). Recuperando el paraíso perdido: el proceso de reconstrucción de la ciudad de Acapulco. In V. García Acosta (Ed.), Construcción Social de Riesgos y el Huracán Paulina. México: Publicaciones de la Casa Chata, CIESAS.

    Google Scholar 

  • Watson, R. T., Zinyowera, M. C., & Moss, R. H. (1996). Climate change 1995: Impacts, adaptations and mitigation of climate change: Scientific-technical analyses. Cambridge: Cambridge University Press.

    Google Scholar 

  • Watson, R. T., Zinyowera, M. C., Moss, R. H., & Dokken, D. J. (1998). The regional impacts of climate change, an assessment of vulnerability. A special report of IPCC working group II. Cambridge: Cambridge University Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universidad Iberoamericana Puebla, Puebla, Mexico

    María E. Ibarrarán

  2. Joint Global Change Research Institute, College Park, MD, USA

    Elizabeth L. Malone & Antoinette L. Brenkert

Authors
  1. María E. Ibarrarán
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elizabeth L. Malone
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antoinette L. Brenkert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to María E. Ibarrarán.

Additional information

Readers should send their comments on this paper to BhaskarNath@aol.com within 3 months of publication of this issue.

Appendix

Appendix

See Table 3.

Table 3 Indicators used in the VRIM México study
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ibarrarán, M.E., Malone, E.L. & Brenkert, A.L. Climate change vulnerability and resilience: current status and trends for Mexico. Environ Dev Sustain 12, 365–388 (2010). https://doi.org/10.1007/s10668-009-9201-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10668-009-9201-8

Keywords

Search

Navigation