Community-based adaptation costing: An integrated framework for the participatory costing of community-based adaptations to climate change in agriculture
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Sova C, Chaudhury A, Helfgott A, Corner-Dolloff C. 2012. Community-based adaptation costing: An integrated framework for the participatory costing of community-based adaptations to climate change in agriculture. CCAFS Working Paper 16. Copenhagen, Denmark: CCAFS.
Permanent link to cite or share this item: https://hdl.handle.net/10568/21076
Understanding the cost associated with climate change adaptation interventions in agriculture is important for mobilizing institutional support and providing timely resources to improve resilience and adaptive capacities. Top-down national estimates of adaptation costs carry a risk of mismatching the availability of funds with what is actually required on the ground. Consequently, global and national policies require credible evidence from the local level, taking into account microeconomic dynamics and community-appropriate adaptation strategies. These bottom-up studies will improve adaptation planning (the how) and will also serve to inform and validate top-down assessments of the total costs of adaptation (the how much). Participatory Social Return on Investment (PSROI) seeks to provide a pragmatic, local-level planning and costing framework suitable for replication by government and civil society organizations. The ‘PSROI Framework’ is designed around a participatory workshop for prioritizing and planning community-based adaptation (CBA) strategies, followed by an analysis of the economic, social and environmental impacts of the priority measures using a novel cost-benefit analysis framework. The PSROI framework has been applied in three separate pilot initiatives in Kochiel and Othidhe, Kenya, and Dodji, Senegal. This working paper seeks to outline the theoretical and methodological foundations of the PSROI framework, provide case-study results from each pilot study, and assess the strengths and weaknesses of the framework according to its robustness, effectiveness and scalability