Scenario analysis is a comprehensive approach to assess the impact of climate-related transition risk on businesses. Environmental, social, and governance (ESG) scores are popular tools with financial institutions (FI’s) for green-scoring practices and since they characterise a company’s performance from an ESG perspective, they have been criticised for enabling “greenwashing” when used within the context of climate risk. Commercially available ESG scores are also available for listed entities, while FI counterparties are often unlisted. This study develops a methodology for creating in-house environmental scores (E-scores), which are then used to effectively choose appropriate transition pathways to be used in company-specific forward-looking scenario analysis. Such scenario analysis can be used to forecast the company’s financial position, including the cost of its greenhouse gas (GHG) emissions, and quantify the impact of transition climate risk on specified metrics. The choice of metrics depends on what the results of the analysis are used for. Two metrics are identified for being useful for risk management and credit decisions: future profitability and weighted average carbon intensity. Finally, the study demonstrates how this process can be implemented with a practical worked example, using only publicly available data.

This paper presents a macroeconomic approach to calculating Climate Value-at-Risk (CliVaR) for financial institutions, addressing critical limitations in existing commercial solutions and historical data availability. This methodology leverages the Network for Greening the Financial System (NGFS) scenarios to derive implied forward-looking means and volatilities from scenarios mapped to macroeconomic variables (MEVs), circumventing the reliance on insufficient historical data. Through regression analysis, we identify statistically significant relationships between climate-sensitive macroeconomic variables and bank equity values, based on the premise that climate risk is transmitted to bank balance sheets via its impact on the general economy. It is recognised that MEVs alone cannot explain the full variance in equity values and the regression of MEVs to equity is inherently inefficient. However, the purpose of the regression is to determine statistically significant MEVs and not to predict the share price. Along with the NGFS scenarios, this enables the Monte Carlo simulation and the calculation of CliVaR. To account for the regression inefficiency, a Post-Model Adjustment (PMA) equation is developed. The methodology is demonstrated in a practical case study, by calculating a CliVaR based climate risk Pillar 2A capital requirement for Standard Bank Group. This proof-of-concept demonstrates the feasibility of transparent, in-house CliVaR calculations.