As one of the most vulnerable countries to climate change, understanding climate risks and impact to Indonesia is of great importance. Risks from climate change are split into two groups: physical risks, risks originating from natural disasters, and transition risks, risks that come from transitioning away from usage of greenhouse gasses. Both of these risks are going through an unprecedented pace, giving nonlinear and disproportionate impacts that are also ultimately transmitted to the economy and financial system. Thus, this research tries to understand and explore the transmission channels and possible outcomes from various scenarios using Bayesian networks. By modelling these risks and transmissions through Bayesian networks, we capture the probabilistic dependencies and interactions among macroeconomic variables, climate risks, and the financial system. This approach enables the uncertainty regarding climate risks to be represented. Results show the possible impacts of various risks and scenarios, with low risks contributing to low change to the economy while high risks lead to high change. These findings underscore the need for urgent and effective policy tools to mitigate the negative effects of climate change to the economy while also ensuring sustainable economic growth and stability. This study contributes by developing transmission channels from climate risks to Indonesia's economy and assessing those risks through exercises using Bayesian Networks, offering a new method for climate risk assessments.

Keywords: Climate risk, stress test, scenario analysis, Bayesian Networks.