Different indicator methods of measuring vulnerability to climatic shocks
| Index | Authors (year) | Assumption | Limitation |
|---|---|---|---|
| Social vulnerability | Lee (2014) | Indicator based (in terms of capital) study Zero-mean normalization was applied to standardize the indicator values | All indicators (variables) showed same (positive) direction to vulnerability Considered only single hazard (flood) |
| Social vulnerability index (SVI) | Ge et al. (2013) | Application of projection pursuit cluster (PPC) model. Hazard-loss assessment by using economic variables (GDP and PCI) | Absence of exposure indicator(s) No algebraic solution of PPC and hence no global optimal solution |
| Climate vulnerability index (CVI) | Pandey and Jha (2012) | Primary data-based index Useful tool for assessing spatio-temporal scale differences in vulnerability | Suitable only for mountainous areas Weightage of different sub-components were data sensitive |
| Vulnerability index | Gbetibouo et al. (2010) | Large spatial base (nine South African provinces) for data collection Principal component analysis for weighing indicators | Likelihood of paradoxical weight assigning to indicators due to poor data structure |
| Livelihood effect index (LEI) | Urothody and Larsen (2010) | Primary data were used Comparison between LVI and LEI | Perception on climate change and assigning importance (weights) to contributing factors by the illiterate respondents might not be accurate |
| LVI | Hahn et al. (2009) | Good dataset/primary data Diversified components were considered for vulnerability | Equal weights for all components is not feasible |
| Vulnerability as expected poverty | Deressa et al. (2009) | Measures farmers’ vulnerability to drought, floods and other climatic extremes Estimates the probability that a household’s consumption will fall below a minimum level due to the occurrence of a climatic shock | Measures only the tendency to be poor (vulnerability) in future due to climatic extremes and not current vulnerability |
| Social vulnerability index (SVI) | Vincent (2004) | Different weights were used for different sub-indices Multi-country analysis data problem due to us age of secondary data | For multi-country analysis the relative importance(weights) of sub-indices were likely to be different Missing data problem due to usage of secondary data |
| Social vulnerability index (SVI) | Cutter et al. (2008) | County-level socio-economic and demographic data were used Principal component analysis was applied for data reduction | Variables related to exposure to natural hazard were ignored Likelihood of not considering important variable after extraction of principal components due to data structure |
| Index | Authors (year) | Assumption | Limitation |
|---|---|---|---|
| Social vulnerability | Indicator based (in terms of capital) study | All indicators (variables) showed same (positive) direction to vulnerability | |
| Social vulnerability index (SVI) | Application of projection pursuit cluster (PPC) model. | Absence of exposure indicator(s) | |
| Climate vulnerability index (CVI) | Primary data-based index | Suitable only for mountainous areas | |
| Vulnerability index | Large spatial base (nine South African provinces) for data collection | Likelihood of paradoxical weight assigning to indicators due to poor data structure | |
| Livelihood effect index (LEI) | Primary data were used | Perception on climate change and assigning importance (weights) to contributing factors by the illiterate respondents might not be accurate | |
| LVI | Good dataset/primary data | Equal weights for all components is not feasible | |
| Vulnerability as expected poverty | Measures farmers’ vulnerability to drought, floods and other climatic extremes | Measures only the tendency to be poor (vulnerability) in future due to climatic extremes and not current vulnerability | |
| Social vulnerability index (SVI) | Different weights were used for different sub-indices | For multi-country analysis the relative importance(weights) of sub-indices were likely to be different | |
| Social vulnerability index (SVI) | County-level socio-economic and demographic data were used | Variables related to exposure to natural hazard were ignored |
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