The Adjohoun, Bonou and Dangbo (ABD) health zone in the lower Ouémé valley of south-eastern Benin is highly vulnerable to seasonal and extreme flooding, rising temperatures and humidity. They lead to multiple climate-related health impacts, most notably vector-borne diseases such as malaria, and non-communicable conditions including acute respiratory infections (ARIs) and cardiovascular diseases (CVDs). Flood events exacerbate challenges by damaging health infrastructure and equipment, disrupting essential health services, and restricting access for vulnerable populations. Women, children, persons with disabilities, and the elderly are among the groups most at risk.

Benin’s health sector is underfunded and ill-equipped to deal with how climate change affects people’s health. The country’s public resources are stretched thin, and there is limited flexibility to invest in building a climate-resilient health sector. Additional barriers include the lack of knowledge and awareness of climate-health linkages, limited health technologies, infrastructure, and early warning systems, reduced access of populations to health facilities in climatic events, and limited participation of vulnerable groups in decision-making processes.

The project seeks to address climate-sensitive health risks by strengthening the resilience of public health systems and vulnerable communities. This will be pursued through the integration of climate information into disease surveillance systems to improve health early warning systems; enhancing the operational capacities of health personnel to manage emergencies during extreme weather events; climate-proofing health infrastructure and providing essential equipment; supporting climate-resilient community livelihoods; and raising local awareness.

The project’s comprehensive, community-based model aims to address the critical nexus of climate change and health. Its multi-pronged design targets high-risk groups while strengthening local capacities and systems, positioning the project as an innovative model for Locally Led Adaptation.