Optimal carbon emissions mitigation plan for a company under a transition scenario

Climate stress-tests aim at projecting the financial impacts of climate change, covering both transition and physical risks under given macro scenarios. However, in practice, transition risk has been the main focus of supervisory and academic exercises, and existing tools to downscale these macroeconomic projections to the firm level remain limited. We develop a methodology to downscale sector-level trajectories into firm-level projections for credit risk stress-tests. The approach combines probabilistic modeling with stochastic control to capture firm-level uncertainty and optimal decision-making. It can be applied to any transition scenario or sector and highlights how firm-level characteristics such as initial intensity, abatement cost, and exposure to uncertainty shape heterogeneous firm-level responses to the transition. The model explicitly incorporates firm-level business uncertainty through stochastic dynamics on relative emissions and sales, which affect both optimal decisions and resulting financial projections. Firms’ rational behavior is modeled as a stochastic minimization problem, solved numerically through a method we call Backward Sampling. Illustrating our method with the NGFS transition scenarios and three types of companies (Green, Brown and Average), we show that firm-specific intensity reduction strategies yield significantly different financial outcomes compared to assuming uniform sectoral decarbonisation rates. Moreover, investing an amount equivalent to the total carbon tax paid at a given date is limited by its lack of a forward-looking feature, making it insufficient to buffer against future carbon shocks in a disorderly transition. This highlights the importance of firm-level granularity in climate risk assessments. By explicitly modeling firm heterogeneity and optimal decision-making under uncertainty, our methodology complements existing approaches to granular transition risk assessment and contributes to the ongoing development of scenario-based credit risk projections at the firm level.