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Socio-economic indicators of vulnerability to climate hazards in Nepal

This work on socio-economic indices was supporting research for the larger ACCCA project managed by UNITAR, which aimed to increase capacity to adapt to climate change in Nepal.
Multiple Authors

Composite socio-economic vulnerability indices

Project Information

This work on socio-economic indices was supporting research for a pilot action that was part of the larger ACCCA project to increase capacity to adapt to climate change, managed by UNITAR. weADAPT provides technical support to various ACCCA pilot action teams through its different partners. Details of other pilot actions done through the ACCCA project can be found in the ACCCA knowledge base.

Summary of Project

Recovery from weather-related disasters is a great challenge for the Nepalese Government and any future increase in these disasters from enhanced climate variability and change will certainly add to this challenge. Studies indicate that collective or community-based disaster insurance could be one of the options for post-disaster loss sharing measures in Nepal and has the potential to contributing to poverty alleviation through distributing the impacts of disasters more evenly.


A methodology was developed to provide quantitative indicators of vulnerability for the study site. The aim was to link the indicators to the specific concerns recognised by the exposure matrix. In this case the dimensions have been divided into human, natural, economic and social indices. For each of these the concerns related to the climate hazards identified are listed and possible indicators identified.

The approach to the use of indicators would vary depending on the question being addressed. In a local context the most vulnerable livelihoods and/or households could be identified with the community by focusing using the approach in the exposure table on their specific livelihoods and concerns. If the assessment is trying to represent the entire country then a range of profiles for the representative major livelihoods groups and regions (chosen by the country study group) could be conducted to represent the country.

Key messages/ What has been learnt

There are a range of aggregation techniques beyond summing up standard scores (whether weighted or not). Critical failures and the concatenation of risks might be captured by only counting low scores. Targeting to specific socioeconomic, vulnerable groups might imply a choice of “indicators”, for instance food security among pastoralists is not well represented by maize yields. Validation of indices is very difficult but the mapped vulnerability indices should at least be discussed with experts and also in the case of localised studies mapped information can be presented and information be exchanged back and forth with communities groups involved.

In our example many indices were tabled, grouped into social, natural, human or economic (based on the approach of Thornton, P., Mapping climate poverty and vulnerability in Africa, 2006), then standardised to values from 0 to 5, 5 being the most vulnerable. When all the indices were then mapped and compared the regions with the highest vulnerability (higher that 2.5) for all factors are the mountain and hill regions of the Far-west and West and two further border mountain districts in the Central and East regions.

Project leader and Institutional Partners:

Partner: SEI Oxford

Contact: Ruth Butterfield

Overall project manager:

Dr. Sharad P. Adhikary, Himalayan Climate Centre

Department of Hydrology and Meteorology

CBDP Unit through Nepal Red Cross Society

Contact: Archana Shrestha

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