Vulnerability Indices: Limitations and Effectiveness
A vulnerability index is “a measure of the exposure of a population to some hazard” which is usually presented in the form of a “composite of multiple quantitative indicators that via some formula, delivers a single numerical result” (UNESCWA).
Some examples of climate-related vulnerability indices are the U.S. Social Vulnerability Index, the Climate Vulnerability Index which assesses risk to world heritage, the Multidimensional Vulnerability Index for small island developing states, the gender vulnerability index developed by Balikoowa et al in 2019, and the University of Notre Dame Global Adaptation Index (ND-GAIN’).
Vulnerability indices can be useful tools for governments to analyse and understand large sets of data with the objective of allocating resources more effectively where it is most needed.
The ND-GAIN, for example, is constructed by using 74 variables to aggregate 45 core indicators that measure a country’s vulnerability to climate disruptions and its readiness to leverage investment for adaptive actions (UND). Vulnerability is defined as a function of exposure, sensitivity and adaptive capacity, and is also assessed by looking at the sectors of food, water, health, ecosystem services, human habitat, and infrastructure (UND). Readiness is determined by economic, governance, and social components (UND).
A country’s ND-GAIN score is computed as follows (UND):-
Data is first collected from source or computed from underlying data.
Then, baseline minimum and maximum values are selected for the raw data and, if applicable, proper reference data points are then set for the measures.
The raw data is then scaled to score, ranging from 0 to 1, and the score for each sector is computed by taking the arithmetic mean of its 6 constituent indicators. This produces the overall readiness score.
To calculate a country’s overall vulnerability score, one takes the arithmetic mean of the 6 sector scores.
The ND-GAIN score is then arrived at by subtracting the vulnerability score from the readiness score for each country, and scaling the scores to give a value from 0 to 100.
The ND-GAIN is an example of the outcome of using scientific framing, or outcome vulnerability, to approach and interpret the problem of climate change, with vulnerability as the end point of a sequence of analyses (O’Brien et al, 2007). Under this modality, the collection and computation of empirical data, vulnerability is quantified and measured in the form of a numerical score, with the result of mitigation and adaptation measures proposed in order to improve scores by limiting negative outcomes.
There are a number of limitations inherent in the ND-GAIN, as identified by the authors themselves –the ND-GAIN explicitly excludes Gross Domestic Product (‘GDP’) per capita, as well as “data on the impact of recent climate-related disasters” (UND).
The authors explain that GDP has been excluded in order not to “doubly penalize many developing countries,” since “[it] is well known that less developed countries also have low adaptive capacity and readiness, and high sensitivity” (UND). A lower GDP is undeniably a factor influencing a country’s vulnerability to climate change, as well as its readiness, given that GDP can be seen as both a cause and an effect of several of 45 core indicators used in the ND-GAIN. For example, child nutrition (a component of adaptive capacity within the food sector of ND-GAIN) is both the result of a low GDP by reason of lack of access to food and resources, and could also be the cause of a low GDP by reason of a smaller or less productive national workforce. Similarly, electricity access (a component of adaptive capacity within the infrastructure sector of ND-GAIN), can be seen as a cause of a lower GDP because of its effect on industry and productivity, as well as an effect of lower GDP due to less funds available for investment into such infrastructure and services.
Climate disasters have far-reaching and often long-term impacts requiring the mobilisation of often huge amounts of resources – including manpower and finance – leading to the diversion of public and private funds toward rehabilitation and reconstruction and away from less pressing matters. This would have an impact on how the affected country’s scores in, for example, innovation (an indicator within the social readiness component of ND-GAIN) and engagement in international environmental conventions (an adaptive capacity component within the ecosystems services component of ND-GAIN). Additionally, the recent occurrence of a climate disaster is a highly relevant factor with deep impacts across the economic, social, political, and cultural levels. For example, a country that is reliant on agriculture for subsistence or export may suffer from droughts or flooding which severely reduces its capacity to sustain itself or to export those products. These circumstances would have a direct effect on the country's food import dependency (a sensitivity component within the food sector of ND-GAIN) at the time of scoring. That country's score prior to the occurrence of the climate disaster would presumably be vastly different had the disaster not occurred.
Given the huge amounts of time and resources invested in the development of vulnerability indices, it is important that we create an environment for these tools to be used as effectively as possible. This requires, amongst others, the engagement of government and non-governmental stakeholders and the willingness – by individuals, groups, communities, and nations – to give serious consideration to the outcomes presented by these indices, in order to assist and uplift the most vulnerable amongst us.
References:
United Nations Economic and Social Commission for Western Asia ('UNESCWA'). ‘Vulnerability Index’. https://archive.unescwa.org/vulnerability-index
Balikoowa, K., Nabanoga, G., Tumusiime, D.M., and Mbogga, M.S. (2019) ‘Gender Differentiated Vulnerability to Climate Change in Eastern Uganda’, Climate and Development 11(10) 2019, pp.839–849.
University of Notre Dame (‘UND’). ‘ND-GAIN Notre Dame Global Adaptation Initiative’. https://gain.nd.edu/our-work/country-index/matrix/
O’Brien, K., Eriksen, S., Nygaard, L.P., and Schjolden, A.N.E. (2007) ‘Why Different interpretations of Vulnerability Matter in Climate Change Discourses’, Climate Policy 7(1) 2007, pp.73–88