Abstract
Extreme natural disasters caused by climate change and socio-economic development, such as floods, are likely to occur more frequently and impose an increasing threat on the safety of people‘s lives and property around the world. The infrastructure investment of adaptation projects can effectively prevent and mitigate these climate impacted disaster risks, as well as reducing the catastrophic losses. In this paper, we propose an economic evaluation framework to incorporate risk preferences and investment timing for making climate adaptation. We use an extended loss distribution approach (LDA) to estimate the expected economic loss (EEL) of flood events with different levels, in which the Bayesian inference is integrated to estimate the parameters of loss severity distributions. The modeling framework is applied to a case study of flood risk management in Nanjing City, China. We find that the appropriate delay of investment timing can significantly increase the value of the adaptation project. The optimal investment timing is earlier, and the project value under optimal investment timing is higher, when the risk aversion coefficient of insurance company or the asset growth rate becomes higher. A lower discount rate or a lower investment cost will accelerate the optimal investment timing. Interestingly, we also find that the greater the difference between the discount rate and the asset growth rate, the higher the value of the adaptation project under the optimal investment timing. These findings are expected to provide useful information on flood disasters prevention for the investment decision of adaptation infrastructure which can be economically effective to integrate the impacts of climate change and socio-economic development.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.