Climate transition risk in determining credit risk: evidence from firms listed on the STOXX Europe 600 index

The COVID pandemic, declared in January 2020, has led to an unprecedented reduction in economic activity, which has consequently triggered a significant reduction in greenhouse gas emissions, this decline being six times greater than that seen after the financial crisis of 2009 (International Energy Agency 2020).¹ This can be explained by the fact that economic growth is linked to an increase in carbon emissions (Mardani et al. 2019). Therefore, due to concerns about climate change, the challenge for economic agents, especially policymakers, is to stimulate economic activity to recover the economy and employment levels but without increasing emissions, in order to meet the goals established in the Paris Agreement (UNFCCC 2016).²

In this context, a significant number of initiatives and bodies have emerged with the aim of promoting the transition from a carbon-intensive economy to a green, low-emissions economy, compatible with the goals of the Paris Agreement.

In the banking sector specifically, the Basel Committee on Banking Supervision (BCBS) has established a working group, known as the Task Force on Climate-related Financial Risks (TFCR), dedicated to developing initiatives to address the integration of the climate dimension into risk management (BCBS 2020). Moreover, different financial supervisors and central banks have developed a network, the so-called Network for Greening the Financial System (NGFS), whose main objective is to tackle climate risk and encourage financial institutions to include the analysis of this risk across all their management frameworks and processes (NGFS 2017). There is also the UNEP FI initiative (UNEP FI 2018), whose task is to assess existing methodologies in order to share best practices identified among participating entities.

All these bodies have been working on defining the theoretical frameworks for the discussion of the transmission channels and main drivers of climate risk (BCBS 2021b; NGFS 2019), but also on an analysis of the available methodologies, including an assessment of the main gaps in current practices within the financial sector (BCBS 2021a; UNEP FI 2019).

The financial sector is a key player in this transformation, not only because of its exposure to climate-related events, but also because of its role as the main conduit of funds. Institutions are expected to incorporate climate risk management into their management framework in the same way as they do for other financial risks. This will affect their business strategy as well as their balance sheets and income statements, both due to transition and for physical reasons (Feridun and Güngör 2020; NGFS, 2019; BCBS 2021).

For example, in the case of the European framework (ECB, 2020), institutions are expected to identify and integrate climate-related risks into default risk quantification, as well as to incorporate such assessment into both management and pricing processes. They are also expected to include the climate dimension in collateral evaluation and in the monitoring of portfolios through stress test exercises and sectoral and/or geographical concentration analyses.

This regulatory concern is partly due to the fact that climate events can be a real source of default risk. As shown in Table 1, several authors have assessed the correlation between sustainability factors and different financial performance indicators, especially in relation to the premium between green and conventional bonds. Some authors have also focused their analyses on the impact that sustainability factors have specifically on credit risk. One common approach is to use carbon emissions to explain changes in distance to default (Capasso et al. 2020; Kabir et al. 2021) or in credit rating (Safiullah et al. 2021).³ Another way to assess this impact is to use corporate social rating (Zanin 2022) or environmental and social rating (Drago et al. 2019) as an explanatory variable. However, other relevant factors remain largely unexplored. For example, Clarkson et al. (2015) found that both regulatory and market factors are relevant when analysing the impact of carbon emissions on company value.

This paper, therefore, contributes to climate risk factor analysis in credit risk by considering two substantial innovations. The first is the use of a factorial model based on the disparities between the different components of the STOXX Europe 600 index, to observe if there are any co-movements that explain the changes in Merton’s distance to default once various sectoral and financial factors have been considered.

The second new approach, consistent with the framework employed by Clarkson et al. (2015), is the definition of a factor climate variable based on the Climate Change Commercial Risks Opportunities indicator to recognise potential differences in the regulatory frameworks and markets and assess differences in credit risk.

For this purpose, a panel data approach is used. It includes financial and sectoral control variables and also a factorial climate variable based on the differences in the evolution of credit risk depending on the classification of the company as having a green or a brown label under the Climate Change Commercial Risks Opportunities label system.

As a result, it can be observed that the climate factor is significant after but also before the signing of the Paris Agreement. It can also be seen that this agreement does not particularly affect the relevance of this factor.

Hence, this paper is organised as follows. Section 2 sets out the literature related to the analysis between environmental variables and credit risk performance indicators, along with a review of the different climate transition risk impact frameworks. In Sect. 3, the main methodological assumptions linked to credit risk measurement approaches and green and brown labelling criteria, as well as the proposed models, are presented. Section 4 contains a discussion of the different outcomes obtained. Finally, Sect. 5 examines the implications of the key findings stemming from this paper.

The existing literature covers the analysis of the relationship between environmental issues and credit risk performance indicators, such as cost of equity (Sharfman and Fernando 2008), loan contract conditions (Nandy and Lodh 2012), cost of debt (Chava 2014) and credit spreads (Oikonomou et al. 2014), showing in all cases that a better environmental performance is associated with better credit performance indicators.

Since 2013, when the first green bond databases became available, many authors have focused their analysis on the interest rate premium differences between green and conventional bonds. Some of them, such as Agliardi and Agliardi (2019), Baker et al. (2022), Ehlers and Packer (2017) and Zerbib (2019), have found a positive green premium, while others such as Hachenberg and Schiereck (2018), Harrison (2019) and Tang and Zhang (2020) have found mixed effects or even no green premium at all. MacAskill et al. (2021) carried out a systematic review of this precise topic, finding that at least a 1–9 green basis point premium exists in the secondary market.

Nevertheless, fewer analyses have focused on the underlying causes that explain such spreads. In most cases, default risk is used as a control variable and, therefore, the assessment of the default risk in green bonds can be considered a field of study with potential for improvement (Löffler et al. 2021).

Most recently, specifically after the signing of the Paris Agreement, some of the interest in the credit risk field has shifted from the green bond premium discussion to the firm-level analysis. This is because even if differences in bonds may be a primary source of analysis, transition risk is more closely related to emissions at the firm-level, since green bond labels are not associated with low carbon emissions at a company level (Ehlers et al. 2020).

Many analyses have focused on the development of a framework, built within the context of the IPCC objectives,⁴ that is able to transpose the general equilibrium and macro-economic scenarios into profit and loss and balance sheet impacts, through the use of a driver that links the evolution of socio-economic and emissions factors to impacts on financial variables (UNEP FI 2018; Monnin 2018). Some simplified versions of these frameworks are based on sectoral shocks, considering a reduction of the equity value or cash flows in specific sectors, as presented in Battiston et al. (2017) and Vermeulen et al. (2018, 2021). These papers, while presenting non-homogeneous approaches with a low level of sophistication, still show that these combined fossil fuel exposures can also be amplified indirectly, by using shocks to asset values and GDP at the sectoral level.

Recently, supervisors have promoted more sophisticated approaches through the incorporation of innovations such as dynamic balance, which is required when events are considered over such a long time horizon; and by means of the use of macro–micro approaches, which combine climate scenarios affecting macro magnitudes with entity-level shocks (ECB 2021a).

Considering the results of these stress exercises, other authors suggest the importance of revising the current regulatory framework for the calculation of credit risk capital in the medium-term, both by proposing capital-based macroprudential policies to incorporate the climate dimension in the assessment of capital (ECB 2021b) and through climate-related additional capital requirements (ECB 2021c).

Other authors have taken advantage of new data sources and approaches, using more sophisticated hypotheses and models that relate climate behaviour to the performance of financial variables, within a context in which the paradigm has considerably shifted after the Paris Agreement.For instance, Clarkson et al. (2015) use a firm-level carbon emissions breakdown to assess the impact of these emissions on company value. The author found that investors consider not only the current amount of carbon emissions, but also the company’s carbon efficiency compared to sectoral peers and the ability to pass on carbon costs to end consumers. In the same line, Jung et al. (2018) develop a carbon risk metric to assess the impact of carbon risk management on the cost of debt, defined using recent carbon emissions data and the CDP questionnaires. The results show that a firm’s cost of debt will increase with its historical carbon risk profile but demonstrate that carbon-related risks awareness in CDP questionnaires will serve to mitigate the penalty.

Table 1 summarises the articles that link environment-related variables and credit risk and shows that, in general, recent papers concerning the environmental impact on credit risk synthesise the transition risk considering scope 1 and 2 carbon emissions, qualitative metrics or environmental scores. However, this limited definition is only appropriate for specific sectors or particular cases in which the main source of transition risk arises from such direct emissions, since it ignores the findings in Clarkson et al. (2015).

Additionally, in some cases, there are biases in the available emissions data. For example, in Safiullah et al. (2021) an initial dataset of 2500 firms was selected, but after filtering by the availability of carbon emissions information, only 574 companies were finally used.

Other authors rely on asset pricing theories to ensure the inclusion of certain differences in agents’ preferences or the technology available. For example, in Daniel et al. (2016), a model that assumes that the atmosphere is an asset with negative payoffs and that includes uncertainty about CO₂ prices and climate change damage effects is resolved over time by discounting the impact of marginal benefits as a consequence of reducing emissions. Moreover, some authors consider that transition risk stems from the possibility of a significant amount of stranded assets due to changes in the market, regulatory environment or shifts in consumer preferences that directly affect companies’ values. Accordingly, investors deduct the negative impact of this risk by considering the available information when deciding on their actions. Different authors have reached this conclusion through the analysis of differences in portfolio betas (Monasterolo & de Angelis 2020; Ramelli et al. 2018; Wagner et al. 2018). In the same vein, in Görgen et al. (2019) and Roncalli et al. (2021), a new Brown-Minus-Green factor⁵ is followed to explain the differences in expected returns. In Görgen et al. (2019) a composite indicator, considering (i) current emissions, (ii) public perception of emissions and iii) companies’ mitigation strategies, is used. Since this composite index uses several variables and may be difficult to reproduce, Roncalli et al. (2021) propose two carbon intensity alternatives based on the three scopes defined by both the GHG protocol and the MSCI carbon emissions exposure score.⁶ The findings obtained in both cases indicate that carbon intensity is not the only factor that the market considers in relation to climate transition risk.

Therefore, we can conclude that different methods are used nowadays in both academia and industry to measure the transition risk depending on various additional factors, such as companies’ carbon efficiencies compared to their sectoral peers, the ability to pass on carbon costs to end consumers and awareness of carbon-related risks in general terms.

When assessing the impact of a climate risk factor on credit risk measurement, some assumptions that affect the following elements must be considered:

The assumptions discussed above are explained in more detail in the following subsections.

Interest in analysing the climate dimension of risk has grown recently in both the financial and academic fields. This attention is particularly focused on understanding the impact of the climate dimension on investors' perceptions of risk and how this risk translates into specific metrics, in terms of both financial performance and financial risks.

This paper extends the current credit risk-focused analyses by assessing whether market perception can be evaluated to measure the credit risk that investors discount in the market. It provides empirical evidence on the relationship between a climate factor after the Paris Agreement and the distance to default.

Considering the results of the three stated hypotheses, the first conclusion is that investors consider transition risk, and this can be linked to differences in credit risk. The second conclusion is that these differences cannot be captured by classical financial indicators. Although there is substantial evidence in the academic literature about the impact of emission behaviour on financial performance, climate transition risk is beyond this relationship, requiring additional variables to measure its impact.

The main conclusion is that since transition risk scenarios are known and can be discounted by the markets, they may be observable in the implicit market default rate. Therefore, this approach could be useful as a method to incorporate transition risk-related market expectations. This would involve adapting transition default rate determination to include the effects of the value chain or the technological capacity to reduce or mitigate emissions under a transition scenario, possible differences in regulatory frameworks and consumer responses. Moreover, the more the regulation is acknowledged, the fewer transition scenarios there are and the easier it is to assign a probability of occurrence to them. Consequently, markets will be more capable of discounting the effects on the value of companies.

Finally, some elements can only be incorporated through market-based variables or scenarios, particularly those related to the existence of less polluting technological alternatives as well as those whose origin is reputational or market-based, even when some assumptions regarding the absence of mispricing and symmetrical information are necessary.

In view of the above, expanded emissions-based approaches with variables that capture consumer and market preferences, as well as differences in regulatory frameworks, do not address all the limitations of emission factoring approaches but provide some improvements that can be incorporated into existing frameworks.

However, several challenges still remain in terms of both data availability and methodological refinement. In relation to data, one of the main issues is the lack of standardisation between “green” and “brown” companies and also the broad definition of climate risk and the multiple channels through which it affects credit risk. This is one of the most important limitations in the comparison of results or the standardisation of analyses, along with the availability of databases, including scope 1, 2 and 3 emissions for a larger set of companies and having consistent calculations across sectors and over time.

In terms of the methodology, additional refinements are required. Examples of such challenges are a more sophisticated consideration of the impact of regulatory framework changes over time, for example by assigning a different timing of impacts after the Paris Agreement depending on the sector, relaxing the assumptions regarding market efficiency and a joint analysis of market and emissions variables.