Participatory Modelling of Wellbeing Trade-offs in Coastal Kenya (P-Mowtick)

The project focussed on coastal ecosystem services in the Mombasa area of Kenya and combined participatory wellbeing research, participatory system mapping and ecological foodweb models in a processes of participatory modelling and scenario development. The models and scenarios demonstrated the tradeoffs resulting from the interlinked social-ecological systems and supported stakeholders and researchers to learn about and reflect on these tradeoffs. 

 

Ecosystem services became popular for understanding linkages between human needs and ecosystems, but their practical application for sustainable poverty alleviation have been limited.  Ecosystem services frameworks have struggled to take account of the complexity of social-ecological systems limiting our understanding of how ecosystem services ultimately translate into poverty alleviation. Ecosystem services benefits depend on institutions and markets, but also on capital, knowledge, expertise, technology and labour - something that poor groups often lack. It is also important to understand the inter-linkages between different aspects of well-being. These have serious implications for the effectiveness of using an ecosystem services approach for poverty alleviation in the face of trade-offs of ecosystem services benefits to different groups.

 

The framework was tested in a case study of the coastal environment of Kenya. Quantitative and semi-quantitative models were developed to explore the impacts of scenarios on ecosystem services and wellbeing of particular groups. Linkages in the social-ecological system were modelled based on quantitative and qualitative knowledge about the interactions and responses between the social and ecological components held by stakeholders and scientists. Interspersed with model development and data collation, one workshop in UK and five workshops in East Africa were conducted with scientists, managers and stakeholders to: develop conceptual and methodological tools, plan scenarios, obtain model parameters, conduct multi-criteria analysis, and reflect and disseminate results. The methods, experiences and learning were disseminated amongst scientists and policy makers to support the ESPA programme, and described in several peer-reviewed publications.