Why banks need to evolve their approach to climate and ESG risk

Banks, regulators, consumers, investors and others increasingly emphasize the importance of managing a range of environmental, social and governance (ESG) risks. Yet conversations about an ESG framework among risk and compliance communities elicit seemingly conflicting responses.

Compliance officers say things like:

• “ESG has too much emphasis on climate risk and greenhouse emissions."
• Or “ESG should be more about reporting than risk management.”

Risk managers tend to say:

• “ESG is misleading in terms of measuring the actual risk.”
• Or “ESG does not tell you how to measure climate risk.”

All this feedback is valid and reflects the individual’s understanding of environmental, social and governance issues. Regardless of how an individual perceives the topic, ESG performance is a factor in credit risk evaluations.

To thrive, organizations will have to evolve their risk management practices – including those affected by ESG risk. This entails accounting for climate risk factors not necessarily covered under traditional ESG frameworks.

ESG factors, ratings and frameworks: A peek behind the scenes

Risk managers often look for a high ESG rating when making credit risk decisions. But this is not a straightforward endeavor.

ESG ratings encompass a broad set of factors, including equal gender opportunity, equality, waste treatment, water usage, energy usage, governance risks, greenhouse gas emissions (GHGs) and more. In addition to the diverse factors behind an ESG rating, different ESG frameworks rely on different criteria and weights.

The same company might get different ESG ratings, depending on the framework they use. And those ratings still might not reflect the actual risks to which the company is exposed.

It’s imperative to understand the underlying framework of an ESG rating before using this information to support investment decisions. That can be a struggle because behind-the-scenes details may not be readily available.

Risk and compliance professionals need greater transparency into what an ESG framework covers. They must also ensure that newer measures, such as climate risk factors, are sufficiently accounted for.

ESG: Too big for a single bucket

Consider the example of an oil company. Based on the framework used and the weight of each ESG aspect, an oil company may have a high ESG rating – even though it presents a high climate transition risk and consequent high long-term credit risk.

How can we change this situation? The answer is to avoid throwing everything together in the same ESG assessment bag.

For example, having a board of directors with a diverse ethnic or gender background does not compensate for elevated GHG emissions. At the same time, low GHG emissions will not compensate for child labor or inadequate waste management.

All these topics are too important to be consolidated under a single rating.

Comparing the “e” of environmental risk to climate risk

It seems as if climate risk would be covered by the “e” of “environment” under the ESG umbrella. But there are several significant differences between traditional environmental risks and climate risks.

It’s important to consider the differences in how environmental risks and climate risks affect credit risk – and how these risks can be managed.

Traditional ESG and credit risk

Traditional ESG influences the financial sector through the risk that a customer’s operations could present to the environment and society – such as flaws that damage their reputation, affect business continuity and result in fines. Customers could have an environmental issue like a toxic spill. Or they could employ a contractor that, unknown to them, uses slave labor.

Such flaws might prevent a customer from paying back loans and bonds. Firms can prevent this by adhering to existing compliance procedures, such as “know your customer” (KYC) policies and collecting and assessing documentation. Usually, it is a binary assessment – your customer either falls within a lending policy or does not.

Traditional ESG exposure can be managed through governance, risk and compliance (GRC) policies. Best practices include:

• Monitoring processes, key performance indicators (KPIs) and key risk indicators (KRIs).
• Collecting incidents and ESG issues.
• Identifying problems and causes.
• Creating action plans, controls and tests.
• Auditing all these aspects.

To evaluate credit risk using traditional ESG measures, ask questions like these:

• Do companies we are lending to have the appropriate governmental labor certificates?
• Are they present on non-governmental organizations (NGOs) blacklists?
• Did a reputable auditing company audit them?
• Can they be affected by ESG risks from others in their supply chain?

Climate risk and credit risk: Broad scope, far-reaching measures

From a credit perspective, climate risk measures exposure to rising temperatures associated with GHG emissions – and derived effects, such as rising oceans and increasing frequency of extreme weather events. Credit risk assessments also incorporate the increased cost of mitigating these emissions.

Climate risk received an increased focus from the United Nations through the Conference of Parts (COP). In response to COP, several financial regulators worldwide are formalizing frameworks. Also, several banks are adopting voluntary targets for GHG emissions, not only from their institutions but also from their loan portfolios. This reflects a new mindset around climate risk.

To manage climate risk, financial institutions must account for three elements:

• Net zero GHG emissions.
• Physical risk.
• Transition risk.

Net-Zero GHG emissions

The Net-Zero Coalition is a voluntary commitment to cutting GHGs as close to zero as possible. In keeping with these goals, organizations (including banks) can improve their operations to cut GHGs or compensate for emissions through projects that capture greenhouse gases (or prevent further emissions) and generate equivalent carbon credits.

At first glance, it appears that financial institutions should have minimal emissions because they primarily process data – and that would be easy to mitigate by improving energy efficiency and consumption. But most of a bank’s emissions – also the hardest to cut – derive from its loans.

For example, when a bank processes a car loan application, it should consider all emissions from the vehicle during its lifespan – because the loan enabled a car sale that wouldn´t have happened otherwise. Consequently, the loan interest rate should include a “carbon spread” to cover the acquisition costs of carbon credits to mitigate these emissions. This would make the loan “carbon-free.”

The inclusion of accounting for “carbon spread” has a vast scope.

Several financial institutions have already committed to cutting emissions by 50% by 2030 and reaching net zero by 2050. But all economic activities would have different spreads based on the magnitude of their greenhouse emissions.

As accounting for carbon spread becomes the standard for the global financial system, we’ll see how it drives essential economic changes. Lending to sectors of the economy with high carbon ratings may become expensive and ultimately infeasible from an economic standpoint.

From a bank’s perspective, optimizing the portfolio requires tools that can simulate new risk factors, such as emissions from customers and their loans. Such tools will allow financial institutions to simulate different portfolio strategies and scenarios with fluctuating carbon credit prices.

Over time, the carbon credit price will become a risk factor as important as the interest rate and will affect accounting for carbon spread. Effectively measuring these risks will help banks develop a strategy for long-term sustainability.

Generating carbon credits

Generating carbon credits is not straightforward either. A project that reduces the carbon footprint does not necessarily generate carbon credits. It only does so if its economic feasibility is dependent on the additional revenue from the generated carbon credits.

For example, if a city invests in electric buses because they offer the best economic option, it does not generate carbon credits. Doing what you would do anyway does not generate carbon credits. In this case, buying electric buses would be the base scenario.

On the other hand, if electric buses are more expensive than traditional buses, then the base scenario is buying the traditional buses. Given that electric buses would reduce emissions in an alternate scenario, the city may issue carbon credit bonds to acquire the additional revenue to buy the electric buses. The difference in emissions between the two scenarios would be the base for the value of these bonds.

All carbon credits must be audited to compare the base scenario with the carbon credits scenario. This can be challenging since no standardized methodology for calculating the difference exists. Plus, there is always room for interpretation, greenwashing and improper certification processes.

Physical risk

When a company´s operations are disrupted due to extreme weather events or changing climate patterns, it’s known as physical risk. Examples include events like:

• Drought.
• Floods and hurricanes.
• Desertification (affecting changes in water availability).
• Changes to average temperature and impacts in agriculture (as in crops).
• Rising oceans.

Globally, credit models already indirectly incorporate extreme weather event risks. When lending to an agricultural customer, banks consider that the customer may be unable to pay back due to a drought. And for real estate loans, banks often require insurance to cover the likelihood of climate-related events, like floods.

The bigger challenge is forecasting changes over time in the probability of these extreme events. Most experts anticipate that rising temperatures will increase their frequency.

The starting point is to review available data, identify past credit losses and associate them with extreme weather events. Accurate public weather records from governmental and research institutes provide deviations from the average rain and wind expected for each region per season. Data scientists can correlate credit losses to these deviations, then create:

• An internal definition of extreme events.
• Model distribution curves to represent frequency and severity.

Data scientists can also identify trends in frequency over time based on an analysis that’s granular enough to consider the vulnerability of each specific location.

The two models (frequency and severity) can be combined using traditional operational risk forecasting methodologies. From there, a firm can calculate the unexpected operational loss within a certain confidence interval – which results in a calculation of the required capital to cover the risk and the required spread in the loans.

But changing weather patterns and rising oceans are hard to predict because we are talking about very long horizons and relatively new science. Here, firms will need to resort to simulations and "what-if" analysis.

• What if rising temperatures force a farmer to grow grapes instead of sugar cane?
• Should a bank grant a 30-year real estate loan for a site that may be underwater within 30 to 50 years?
• Is a customer likely to stop making payments if they know their property may be worthless by the time it’s paid for?

Transition risk

Transition risk derives from a company´s operations being disrupted due to changing regulatory requirements and peoples’ changing behaviors and values.

It is hard to predict all the impacts of transition risk.

Some European countries have already set new rules prohibiting the sale of fossil fuel vehicles after 2030, for example. What will happen to the motor industries in these countries? What about gas stations and service providers? They will have to adapt or cease trade. Mechanics will have to retrain and specialize in electric vehicles (EVs), gas stations may become battery swap stations and so on.

A financial institution should consider these changes when lending because they might not get their money back from companies with uncertain futures.

In these cases, the focus should be on “what-if” analysis. A bank should be able to simulate its portfolio under different regulations:

• What if the legal area allowed for planting is reduced?
• What if there is an extra fee for energy derived from fossil fuels?
• What if there is a tax exemption for clean energy generation?
• What if mineral coal is banned?

Lending banks should employ a platform that can simulate their portfolio under each scenario – from changing weather patterns to new regulations. This will enable the bank to choose the exposure that it is most comfortable with, even in an unlikely worst-case scenario.

To date, most stress testing and other climate risk analytical exercises have been done standalone. In the future, regulators will expect them to be done within existing regulatory analytics frameworks – meaning the data will need to be properly governed and integrated with existing data, models will need to be audited and validated, and all downstream calculations and decisions will be subject to regulatory and legal requirements.

To ensure successful integration, financial institutions must:

• Envision how climate risk analytics fits into this process.
• Identify all gaps.
• Be prepared to address the gaps.

An integrated way to tackle traditional ESG and climate risk

With a qualitative, integrated risk management and governance framework, firms are better positioned to understand and manage the nuances of ESG disclosures while supporting evolving climate-related financial risk management.

Using SAS^®, your bank can monitor processes, validate compliance and audit policies from start to finish. And data scientists can more easily model and simulate the impacts of emissions, climate and new regulations.

SAS helps you simulate different portfolio compositions with GHG emissions, credit policies and what-if scenarios. This allows you to estimate ideal portfolio compositions based on different carbon credit prices and design an optimal strategy to reduce carbon emissions derived from the loan portfolio. You can even test this ideal portfolio composition against various changing climate patterns and transition scenarios.