Abstract
Poor communities in high risk areas are disproportionately affected by disasters compared to their wealthy counterparts; yet, there are few analyses to guide public decisions on pro-poor investments in disaster risk reduction. This paper illustrates an application of benefit-cost analysis (BCA) for assessing investments in structural flood proofing of low-income, high-risk houses. The analysis takes account of climate change, which is increasingly viewed as an important consideration for assessing long-term investments. Specifically, the study focuses on the Rohini river basin of India and evaluates options for constructing non-permanent and permanent residential structures on a raised plinth to protect them against flooding. The estimates show a positive benefit-cost ratio for building new houses on a raised plinth, while the ratio is less than one for demolishing existing houses to rebuild on a raised plinth. Climate change is found to significantly affect the BCA results. From a policy perspective, the analysis demonstrates the potential economic returns of raised plinths for ‘building back better’ after disasters, or as a part of good housing design practice.
Highlights
During the past decade there has been a marked improvement in the availability of risk information and analyses for disaster risk reduction (DRR), but many of the poorest and most vulnerable countries have been left behind (World Bank 2016)
This paper illustrates an application of benefit-cost analysis (BCA) for assessing investments in structural flood proofing of lowincome, high-risk houses
The methodology begins by identifying the options for mitigating risk, after which the risk without DRR is estimated with either a forward-looking or backward-looking approach depending on whether risks are estimated with a catastrophe model or with a statistical analysis of past events, respectively (Kull et al 2013)
Summary
During the past decade there has been a marked improvement in the availability of risk information and analyses for disaster risk reduction (DRR), but many of the poorest and most vulnerable countries have been left behind (World Bank 2016). The World Bank (2016) has brought attention to the proprietary nature of risk assessment models in developing countries, and subsequently another challenge is acquiring data on the hazard, exposure to the hazard and vulnerability of exposed assets and people (Handmer et al 2017). Still another challenge is how climate change will alter these hazards in the future, especially given the lack of data from weather monitoring stations in developing countries needed to downscale global climate models to the local level (Hochrainer et al 2009). Because of the methodological and data challenges, there have been limited applications of climate-sensitive probabilistic BCA for DRR in the developing world and in rural and poor areas (Kull et al 2013). We discuss limitations of our approach as well as possible ways forward and, summarize and discuss policy implications
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