Vulnerability is a multidimensional concept associated with high uncertainty in measurement and classification. Developing a vulnerability index from the diverse and often incommensurate data that form the basis of vulnerability assessments is often a core challenge of vulnerability research. Problematically, many vulnerability indices are based on the implicit or explicit assumption that each indicator of vulnerability is of equal importance. In this paper we propose a procedure to engage constructively with the inherent subjectivity and uncertainty of assigning weights to disparate indicators used in vulnerability assessments, using common tools of multicriteria decision analysis (MCDA) and fuzzy logic. To illustrate our proposed methodology, we present a case study of rural livelihood vulnerability in the state of Tamaulipas, México. In our case study, we combine a livelihoods framework with MCDA to weigh household attributes according to their relative importance in driving household vulnerability. This approach requires the explicit articulation of the relationship of each indicator to the umbrella concept (vulnerability) as well as of each indicator to every other indicator. In recognition of the inherent uncertainties involved in assigning any particular unit of analysis to a specific vulnerability class, we use fuzzy logic to create the final categories of household livelihood vulnerability to climatic risk. Our analysis reveals how different structures of livelihood assets and activities contributes to household sensitivity and capacities in a region characterized by variable climatic conditions, stagnant incomes, increasing market stress and declining farm productivity.
- Adaptive capacity
- Multicriteria decision analysis
ASJC Scopus subject areas
- Global and Planetary Change
- Geography, Planning and Development
- Management, Monitoring, Policy and Law