Using credit risk as an empirical basis for the development of Brown taxonomies

Green taxonomies are designed to highlight investment opportunities for a transition to a low-carbon economy. While useful for many purposes, they fail to capture risks. Banking supervisors are now pressing for the development of dirty taxonomies as a way of quantifying potential stresses in the financial system associated with climate change.

Our project considers how integration of climate risk assessment and credit risk assessment can form a transparent and replicable methodology for accomplishing this goal. A useful dirty taxonomy should be able to identify assets and firms whose adaptive capacity limits their ability to navigate physical and transition risks. The analysis will be forward-looking and rely on scenarios. Perhaps most crucially, the outputs of the taxonomy should be clearly traceable to major input assumptions. In contrast to green taxonomies, one cannot say a priori what is dirty. Much depends on an unknowable set of policy interventions, technological developments, and localised events that will occur in the future.

To gain a transparent and replicable dirty taxonomy, we will proceed by organising asset- and firm-level impacts into three major categories: adaptation risks, mitigation risks, and natural capital risks. These risks will be analysed within an integrated assessment model that we have combined with a structural economic model. We adopt three transition scenarios that are examined across three time horizons. Testing our methodology on firms in the European energy sector, the outputs establish the materiality of risk factors. Tracing clearly from inputs to outputs allows for changes in credit quality to be observed for individual firms. The work will reveal whether events such as the weakening of coal producers and the utilities sector over the past decade are idiosyncratic, or representative of a ‘new normal’ under a transition to a low-carbon economy.