Does the green property reduce bank loan risk?

To address whether credit risk is lower for banks’ green assets, we perform a granular analysis on the risk implications of the green property of bank loans in the context of a comprehensive international bank loan dataset, Thomson Reuters’ DealScan. We address three key research questions related to the discussion of the adoption of a ‘green supporting factor’:

  • Is the risk of banks’ green loans lower globally?
  • If the risk is lower, what are the possible channels/mechanisms through which the green property exerts this effect?
  • How can the green impact be calibrated into the green supporting factor and how can the empirical effect of the green supporting factor on banking systemic risk be quantified at the country level?

We conduct an empirical analysis, utilising the detailed global loan-level data from the LoanConnector database. Based on the empirical findings, we compose a single-period model to analyse the impact of a ‘green supporting factor’ (GSF) on bank risk profiles. We use the calibration to address our third research question and to gauge the extent to which a GSF changes bank loan risk at the end of the period where the equilibrium is reached. The calibration results quantify the impact of a GSF on bank loan risk profiles and the stability of financial markets as a whole.

Our expected findings will provide a systematic understanding of the linkage between green loans and their associated risk, which will help policymakers to quantify a green supporting factor and to understand the sensitivity of this factor to varying lender and borrower characteristics.

Estimating the impact of physical climate risks on the probability of default (PD) of mortgage loans in the coastal cities of China

Climate-related physical risks, such as typhoons, floods, and heat waves, will result in considerable damages and losses to the real economy and to the financial sector that provides financing for economic activities. Against this backdrop, the international financial community has been calling for attention and actions to integrate climate-related physical risks into financial decision-making by financial institutions. To manage environmental and climate risks, the primary step is to quantify these risks. However, literature that quantifies the implication of climate-related physical risks for the financial sector is very limited.

We present an analytical framework for measuring the impact of climate-related physical risks on the default risk of bank loans. We applied this method to quantify the increase in the probability of default of mortgage loans for properties in China’s coastal cities, caused by the increased intensity and frequency of typhoons under four commonly used climate scenario defined by the Intergovernmental Panel on Climate Change.

The preliminary findings of this INSPIRE study show that future typhoon events exacerbated by climate change along the coast of China could potentially lead to a considerable increase in the probability of default for mortgage loans, with a possible accumulation of incremental probability of default of more than 5%. This analytical framework can also be applied to many other scenarios of environmental and climate risk analysis for banks if the data required are available, such as impacts of floods and water shortages on credit risk of loans to sectors that are sensitive to such risks.

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.

Assessing the Impact of Climate-Related Transition on Default Probabilities of Thermal Power Companies.

In this chapter, we present an analytical framework and the methodologies for measuring the impact of climate-related physical risks on the default risks of bank loans. The framework consists of the setting of climate scenarios and a suite of catastrophe models and financial models. For the case study, we analyzed the impacts of climate change on typhoons’ intensity and frequency and on credit risk metrics (e.g. PD and LGD) of mortgage loans in China’s coastal cities. The model shows that, under an extreme scenario (RCP8.5 with extreme exacerbation effect on typhoons), the expected annual credit loss of mortgage loans could rise nearly three fold in 2050 compared with the baseline scenario which assumes no change in typhoons’ occurrence pattern. This framework can also be applied to estimate potential climate exacerbated impacts of other natural disasters including floods, heatwaves, drought and wildfires on financial risk metrics such as default probability and valuation of assets.