首页|Now or later? Optimal timing of mangrove rehabilitation under climate change uncertainty
Now or later? Optimal timing of mangrove rehabilitation under climate change uncertainty
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NSTL
Elsevier
Climate change poses a variety of threats to ecosystems, biodiversity, and human lives. Mangrove forest is one of the key nature-based solutions that address climate change and its impacts while providing socio-economic and ecological services. With the increasing frequency of extreme weather events, various national and local governments are considering mangrove rehabilitation as a natural defense to these threats. This study aims to assess the value of mangrove rehabilitation by focusing on the ecological goods and services it provides as well as the cost of the project at different implementation periods. We apply a real options-based trinomial tree model to evaluate the optimal timing of making an investment decision for the rehabilitation project under uncertainty in climate change damage cost. Validating the model using the Philippines as a case, the results found that the economic benefits from the mangrove forest, including the market value of forestry and fishery products, protection from climate-related damages, carbon sequestration, and ecotourism, exceeded the investment cost for the rehabilitation project. Considering the uncertainty in damage costs from natural disasters, the real option valuation gave a higher total investment value which described a more optimal decision to postpone the implementation of the mangrove rehabilitation. However, the results found conditions in the model to invest immediately when the frequency of natural disasters and their associated damage costs significantly increased. The implications of the results recommend government policies to enable the realization of mangrove rehabilitation as a nature-based solution addressing the Paris Agreement, Sustainable Development Goals, and the Disaster Risk Reduction global agendas of transition to low-carbon and climate-resilient economies.
NSTL
Elsevier
