Damage to infrastructure caused by anthropogenic climate change can cause dramatic economic loss and social disruption. According to the Intergovernmental Panel on Climate Change, climate change may cause an increase in the intensity of droughts, the frequency of floods, and increasing coastal vulnerability to tropical cyclones, storm surges and sea-level rise. This paper uses the stochastic technique Monte Carlo technique to explore the time-dependent damage due to climate change, considering

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... he hazard as a stochastic variable. Three different time periods were explored. The analysis investigated no climate change and with climate change scenarios that affect the probabilistic model of hazard occurrence, and vulnerability functions that represent the potential damage for a given hazard. The probabilistic model and vulnerability models used in this study are relatively simple, but they can help to generate a framework of the time-dependent climate change damage and understand its economic behaviour. Parametric studies are heavily used in this study to explore a range of possible conclusions. The sensitivity of the change of parameters such as threshold h T , discount rate r, and time T are considered. The results are given in terms of mean cumulative annual loss. The discount rate was found to be a key parameter affecting cumulative damage prediction. Since damage often only occurs once a threshold level of hazard has occurred (such as a flood level exceeding the floor level of a house), then the influence of this threshold value on damage is significant. Damage/impact models utilising probabilistic hazard were used to verify the robustness of results. Results are very sensitive to threshold. Time-dependent predictions of damage can help decisionmakers assess the impact of climate change and the economic viability of climate change adaptation strategies.