  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.
