Adapting WASH to climate change
Water is predicted to be the main channel through which the impacts of climate change will be felt by people, ecosystems and economies. However, predicting impacts on the availability and quality of water resources, and consequently water supply and sanitation services, remains difficult. Drawing on case studies from Malawi, Sierra Leone and Tanzania, a new ODI-OPM report, in collaboration with Richard Carter & Associates, assesses the risks of climate change to WASH programmes and sets out a cost-benefit analysis approach for evaluating possible solutions.
The report looks at various risk-screening approaches that could be applied by WASH programme designers and implementers to identify and mitigate risks of climate change, and how cost-benefit analysis (CBA) can be used to appraise and prioritise adaptation options.
The value of CBA lies in its ability to narrow the scope for ‘pure judgement’, providing a more secure and transparent basis for investment decision-making. However, robust CBA requires reasonable data on what would happen to WASH interventions and outcomes ‘with’ and ‘without’ adaptation. Since there is little hard data linking climate to WASH outcomes, the examples provided in the report are indicative. The main aim is to show how CBA could be used as an appraisal tool, alongside risk screening approaches.
One of the report’s key conclusions is that clear opportunities exist to increase the resilience of WASH, and that adaptation should start with the measures that tackle existing climate risks. Many of these measures, such as improved siting, design and construction of water points, or changes in latrine design, are relatively simple, if capacity exists to implement. CBA suggests that such measures are likely to bring positive returns, even over short time periods. Perhaps more importantly, the report illustrates how CBA can be used to compare the costs and benefits of different adaptation measures, and how sensitivity analysis can be applied to see how results change under different scenarios and assumptions.