The European Central Bank (ECB) issued a new warning to lenders stating that their failure to respond to financial risks emerging from climate change in the next two years will have serious ramifications, such as an increase in capital requirements and imposition of fines. The ECB wants banks to include climate risks in their “governance, strategy and risk management” by the end of 2023 and meet all supervisory expectations by the end of 2024. This move comes as the ECB has recognised shortcomings in efforts taken by banks to address climate risks.

Mapping of transition and physical risk for euro area firms. Source: ECB

Mapping of transition and physical risk for euro area firms. Source: ECB

Results of the ECB's thematic review revealed that banks significantly lag behind in managing climate and environmental risks. The review found "blind spots" at 96 per cent of the banks in identifying risks.

Frank Elderson, vice-chair of the ECB's supervisory board, stated in a blog: "Most banks have thus not yet answered the question of what they will do with clients who may no longer have sustainable revenue sources because of the green transition. In other words, too many banks are still hoping for the best while not preparing for the worst."

There are clear regulatory expectations from banks and firms to further their green agendas and ramp up efforts to understand climate risks better. ECB’s move here reflects these expectations.

Climate Risk and its Impact on Banks

These regulations come against the backdrop of increased financial risks stemming from climate change as Chief Risk Officers have asserted that climate change is the biggest emerging risk they face today. The major climate risks faced by banks are:

  • Credit risks: Climate change poses credit risks as it affects the borrower's ability to repay or the banks' ability to recover losses caused by default on payment.

  • Market risks: Market risks emerge due to the decreased financial value of assets, the repricing of sovereign debts and the repricing of securities owing to strict climate regulations.

  • Liquidity risks: Challenges for banks in accessing stable sources of funding due to the change in market conditions.

  • Operational risks: are caused by legal and compliance risks with regard to climate-sensitive investments.

According to new research, the United States' six largest banks - Bank of America, JPMorgan Chase, Citi, Morgan Stanley, Goldman Sachs, and Wells Fargo face above-average loan risk related to climate change. Other experts believe that climate change threatens to impact US banks more than what they are willing to disclose.

Thus, as the risks posed by climate change on banks become increasingly severe, it is imperative for banks to identify, analyse, and mitigate these challenges.

While proposing a new set of guidelines, the Office of the Comptroller of the Currency, a top banking regulator stated: "Weaknesses in how banks identify, measure, monitor and control the potential physical and transition risks associated with a changing climate could adversely affect a bank's safety and soundness, as well as the overall financial system".

Innovative components of the ECB climate stress test. Source: ECB

Innovative components of the ECB climate stress test. Source: ECB

Recognising this, banking authorities have ramped up risk analysis initiatives to measure the impact of climate change on the operation of banks. These efforts include risk models, stress testing and other such tools to help them measure the impact of climate risks and aid them in making lending and investment decisions. You can read more about global banking efforts to tackle climate risk in our blog here. In the case of ECB, the first milestone listed for lenders is to ensure that they categorise climate risks appropriately and carry out a full assessment of its impact on their businesses. The success of these initiatives, however is dependent on one key factor: Data.

The Data Challenge

Reliable data is one of the crucial factors in estimating climate-related financial risks. For instance, a bank will need essential elements such as a model, locational data, and credit or market exposure to arrive at an accurate capital charge. However, accessing this data is a considerable challenge in the banking sector today.

Speaking of these challenges, Richenda Connell, chief technical officer and co-founder of U.K.-based climate risk modeling firm Acclimatise states: "In order to evaluate physical climate risks, banks need to know a lot of information about the unit that is being affected: what are the borrowers’ physical assets, where are they located, how do they operate, where do they source their supply, where do they sell products…Generally speaking, bank data doesn’t have the level of granularity to make that assessment."

In addition, lack of quality data also impacts risk assessment models, giving an incomplete and inaccurate picture of the risks as a climate model is only as good as its inputs are. For instance, several studies have found that the recent extreme climatic events have surpassed predictions made by experts. Investigations on climate modeling, one of the key tools in risk management, have also exposed significant gaps in its risk assessments. One of the major factors responsible for this is the lack of credible data, which is the cornerstone of a reliable climate model.

In this regard, some of the key characteristics of data that can enable efficient risk analysis are:

  • High frequency: Climate modelling without high frequency data (daily/weekly/monthly) for a particular latitude or longitude or an asset stretching over several lat-longs, has very limited utility as the results will be severely curtailed, since data gathered a few times a year cannot paint a clear picture of climate risks. High-frequency data from a credible third party source is extremely important to gain better insights and enable higher statistical precision in climate modelling.

  • Global coverage: Data across wide geographical regions is important to monitor risks effectively, as climate disasters are transboundary in nature.

  • Wide scope: To make sound estimates, it is imperative to have access to a wide range of physical climate risks, such as emissions, wildfires, floods and other climate disasters. Monitoring a single variable does not provide an overall understanding of the climate crisis, as most climate phenomena are interlinked. All the essential variables need to be assessed for a holistic understanding of our planetary systems.

  • Data history: Another crucial factor is the provider's data history. The duration of the data history (3 years to 5 years) plays a critical role in the modelling of Climate & Environmental risks.

  • Credible data: Credible data is a crucial factor, as it is the data inputs that will determine the accuracy of any risk assessment model. Complete and accurate data without any subjective elements are imperative for efficient risk analysis.

Satellite- derived climate intelligence - the solution?

Satellite-derived climate intelligence in this regard has emerged as a promising option to source data from credible sources. Providing high frequency data, having wide scope and global coverage, it ensures credibility, thus helping gain a good picture of climate risks. In addition, large-scale climate datasets aggregated from satellites and sensors can also provide data at a granular level, helping get a deeper understanding of climate risks. While historically, it has been challenging to develop climate intelligence for asset-level analysis of climate-related risks, the rapid breakthrough in climate science, machine learning and artificial intelligence, where satellite data acts as a raw material similar to crude oil before it is refined, is now making this possible and firms like Blue Sky Analytics are at the forefront of producing climate intelligence for the world.

This along with sophisticated processing systems and analytical skills, has revolutionised climate intelligence and promises to play a vital role for banks and firms to capture climate risks effectively.

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