Regulators Ask Banks To Assess Climate-Related Risks From Largest Counterparties, but Data Gaps Persist

With regulators seeking to determine how climate change will affect the banking industry, new demands for data are spreading worldwide.  Currently, with no common standards in place, corporates are fielding different questions from different banks, and banks are fielding different demands from different regulators.  Without some agreement on common standards, we risk a climate change Tower of Babel, with assessments based on what language is being spoken rather than real risk, and with no ability to translate the results for one firm to another.


As regulators ask banks to undertake climate scenario analysis, one of the key requirements for such an exercise is data on each bank’s largest corporate counterparties. For example, the Bank of England’s Climate Biennial Exploratory Scenario (CBES) for 2021 is directing banks to do a detailed analysis of at least their top 100 non-financial corporate counterparties based on exposures.  In addition, banks are directed to include their three largest corporate counterparties in each of the most carbon-intensive sectors (e.g., airlines, oil and gas, car manufacturers) and their top five largest financial counterparties. The analysis typically requires projections of probability of default and loss given default for each counterparty every five years over a 30-year horizon.

For banks to assess the impact of climate on the credit risk of their largest corporate counterparties by exposure, they need to collect additional data from those counterparties:  for example, how each counterparty plans to change its cost structure to account for changes in carbon pricing in accordance with the scenarios and product mix over the scenario time horizon. The Bank of England is directing each bank to engage directly with each counterparty to collect this data.  Initial baseline template questionnaires by sector suggest a vast amount of granular information is necessary for this task:  for example, detailed Scope 1, 2 and 3 emissions information;[1] planned capital expenditures and operating expenses across different physical and transition risks that might materialize; revenue, asset and production volume breakdowns across high-emitting subsectors of business; detailed information on large asset holdings (e.g., value and location); and details on investment activity over five- and 10-year time horizons.  The Bank of England also wants banks to gather more qualitative information relating to governance, board oversight and reporting, information on corporate policies related to physical and transition risks and information on corporate scenario analysis and strategy.

The theory for this data collection is that it could allow banks to assess the probability of default of their counterparties due to climate change over the horizon of the exercise and to estimate portfolio alignment in relation to the Paris Agreement temperature targets.[2]  Thus, we note that the collection of information from counterparties is complex and needs to be standardized to facilitate the comparison of risk assessments for the same or similar counterparties across banks.

Defining the Task

To embark on these exercises in a meaningful manner, banks and regulators need to know the answers to the following three questions: 1) which counterparties are in scope (e.g., based on the bank’s aggregate exposure vs. the counterparty’s total emissions); 2) what type of counterparty-level data is the bank required to gather; and 3) what data are counterparties able to provide in a standardized fashion.  A fourth, related issue is understanding the different use needs of climate-related data by different parties and how that impacts any disclosure requirements.  

With regard to the first question, the BoE is asking U.K. banks to focus their analysis on their top 100 counterparties by the size of their financial exposure. While this would be the normal approach taken by regulators undertaking a financial stress exercise, it may not be the most efficient approach in the context of climate.  Since the objective of the exercise is to assess the financial impacts from climate change, choosing counterparties based on their financial exposure (i.e., loan size or exposure at default) rather than carbon footprint would likely result in a nonoptimal sample of counterparties because the size of the exposure is not necessarily a good predictor of carbon emissions, and therefore, the physical and transitions risks of some of the largest counterparties could be low. Further, since the analysis is cumbersome and requires significant resources, it would be better for the counterparty-level analysis to be done for the top 10 firms as measured by CO2 emissions across the major high-risk sectors (i.e., automotive, building and construction, chemicals, metals and mining, oil and gas and power and utilities) as opposed to the top 100 counterparties by financial exposure. 

With regard to the second question, neither regulators nor industry has reached consensus on what the relevant data are for such analysis, and a standardized template for such data collection has yet to be developed.  This inevitably leads to variability—in extremis, two banks could be asking the same corporate very different questions, receiving two different answers and coming to two different risk assessments for the same types of exposures to the same counterparty.  This lack of standardization prevents meaningful comparison across banks.  In contrast, when dealing with credit risk, banks use balance sheet information that is publicly available and therefore consistent. Furthermore, it is very inefficient for each bank to create its own questionnaire and thus for a corporate to complete multiple questionnaires from multiple banks. As noted by John Coates, SEC Acting Director, Division of Corporate Finance, “The status quo is costly for companies, and increasingly so over time. Companies face higher costs in responding to … demand for ESG information because there is no consensus ESG disclosure system. Rather, they are faced with numerous, conflicting and frequently redundant requests for different information about the same topics.”[3] 

It would seem that this question could easily be solved by either a standardized set of questions for each sector developed either by the authority requiring the exercise or by a common approach established by the private sector (and perhaps administered by a utility established for this purpose).[4]

Assuming that consistency in questions can be achieved, a third question is whether corporates can actually answer the questions they are being asked, or whether relevant information is publicly disclosed.  Currently, the answer is unequivocally no.  While certain sectors are more advanced in the granularity with which they disclose climate-related data, disclosure is certainly not uniform across sectors or even within sectors, thereby making the information of use to banks and investors challenging at times.  Furthermore, we have already seen that some of the questionnaires that are being developed are seeking a level of granularity well beyond what corporates provide in a quarterly filing for investors or in an annual sustainability report. Also, the horizon of the scenario analysis often requires information projected well into the future—far beyond any typical securities disclosure.  This requires each bank to make ad hoc judgements regarding how its clients will adjust their business models in response to risks and opportunities tied to a transition to a low-carbon economy.

A key conceptual issue that flows from the above discussion on corporate disclosures, is how such data gaps should be addressed given the different data uses and needs by different financial market players.  For example, securities regulators appear predominantly focused on disclosures that will allow investors to decide whether to hold or buy a company’s stock or invest in a particular fund; they do not appear to be focused on disclosure for purposes of credit risk analysis and portfolio alignment metrics, which is what banks are being asked to do by prudential regulators.  At a high level one would expect that there will be some overlap in the information that is being sought by investors and banks (e.g., Scope 1, 2 and 3 emissions data), but inevitably banks will require more granular information and possibly more frequently than quarterly for purposes of assessing their credit exposures in aggregate across clients.    

What this suggests is that even with mandated disclosure requirements, there will be data gaps for banks in performing detailed credit evaluation and scenario analysis.  This could potentially be solved through industry initiatives to develop a common set of metrics that are most relevant for credit and prudential risk analysis as well as further development of top-down models that could be used to analyze risks within a sector, albeit noting the limitations of models for purposes of individualized credit decisions. While these data issues are not intractable, they are complex and should be considered carefully in the context of who needs data and for what purposes, before deciding that generalized mandatory disclosure requirements will satisfy all needs.

[1] In general, “scope 1 emissions” covers direct emissions from owned or controlled sources; scope 2 covers indirect emissions from the generation of purchased electricity, steam, heating and cooling consumed by the reporting company; and scope 3 includes all other indirect emissions that occur in a company’s value chain.

[2] Again, one can debate how reliable such an assessment would be, especially given an assumption that the bank would maintain a flat balance sheet over the period; one could also question the focus on probability of default rather than loss given default; however, we take the exercise as a given.

[3] Statement Published in Connection with Remarks at the 33rd Annual Tulane Corporate Law Institute, March 11, 2021.

[4] For example, similar approaches have been developed in the context of cyber risk exercises (see and through third-party vendor management companies (e.g., TruSight).