Decision-makers are faced with the task to translate the science of future climate change impacts to set policy goals and plans based on their capacities and contexts. However, there is a lack in support tools that translate the preferences and constraints of stakeholders to assess the viability of goals and strategies for adaptation planning. In this study, we introduce a decision-support model that simulates adaptation pathways using a multi-objective optimization algorithm. The model has been applied to find optimal adaptation pathways for reducing heat related morbidity in Seoul, South Korea under Representative Concentration Pathway (RCP) 8.5. We analyzed the effects of six hard and soft adaptation strategies from 2020 to 2100. Decision-maker preference scenarios based on three budget levels, two goal setting approaches and two investment delay plans were evaluated. The results show that after 2065, current adaptation strategies cannot reduce the impacts of heat mortality even with high budgets. A low budget limits adaptation for both ambitious and conservative goal settings while a higher budget did lead to greater adaptation but was not necessary for the conservative goal setting suggesting that efficient pairing of budget level based on the adaptation goal can be beneficial. Further, the longer the delay in investment toward adaptation results in irrecoverable reduction in adaptation. These results imply that different planning approaches are necessary for the desired adaptation effect and level of cost efficiency. This study is significant in that the methodology can be expanded to include other sectors and applied to various locations of different scales to help stakeholders develop more effective long-term adaptation plans based on their needs and constraints.
Keywords: Adaptation pathways; Adaptation planning; Climate change; Decision-making under uncertainty; Heat mortality; Maladaptation.
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