Propagation of a carbon price in a credit portfolio through macroeconomic factors

TL;DR

This paper develops a stochastic model to analyze how carbon pricing influences credit risk measures in a portfolio, considering sectoral emissions costs and short-term effects of climate transition scenarios.

Contribution

It introduces a novel stochastic multisectoral framework that captures short-term carbon price impacts on credit risk, incorporating uncertainties and sectoral relationships.

Findings

Carbon price increases credit risk and bank provisions.

It distorts firm value distribution and reduces bank profitability.

Sensitivity analysis improves climate transition risk assessment.

Abstract

We study how the climate transition through a low-carbon economy, implemented by carbon pricing, propagates in a credit portfolio and precisely describe how carbon price dynamics affects credit risk measures such as probability of default, expected and unexpected losses. We adapt a stochastic multisectoral model to take into account the greenhouse gases (GHG) emissions costs of both sectoral firms' production and consumption, as well as sectoral household's consumption. GHG emissions costs are the product of carbon prices, provided by the NGFS transition scenarios, and of GHG emissions. For each sector, our model yields the sensitivity of firms' production and households' consumption to carbon price and the relationships between sectors. It allows us to analyze the short-term effects of the carbon price as opposed to standard IAM (such as REMIND), which are deterministic and only capture long-term trends. Finally, we use a DCF methodology to compute firms' values which we then combine with a structural credit risk model to describe how the carbon price impacts credit risk measures. We obtain that the carbon price distorts the distribution of the firm's value, increases banking fees charged to clients (materialized by the bank provisions), and reduces banks' profitability (translated by the economic capital). In addition, the randomness we introduce provides extra flexibility to take into account uncertainties on the productivity and on the different transition scenarios. We also compute the sensitivities of the credit risk measures with respect to changes in the carbon price, yielding further criteria for a more accurate assessment of climate transition risk in a credit portfolio. This work provides a preliminary methodology to calculate the evolution of credit risk measures of a credit portfolio, starting from a given climate transition scenario described by a carbon price.