Nature’s turn: With a potential UK GDP impact greater than COVID-19, nature-related risks deserve more attention by the financial services industry

It is becoming clearer that banks need to make further progress in quantifying nature-related risks. A recent European Central Bank (ECB) update on banks’ Climate and Environmental risk management progress states  “Material financial risks are not only limited to climate change but also concern broader nature-related risks that can no longer be ignored.” And a recent report from the Green Finance Institute (GFI), reinforces the potential importance of nature risks, estimating that the impact of nature is material for the UK economy and could lead to up to 12% loss in GDP as soon as 2030. To put this into context, this is worse than the 2008 / 2009 financial recession and the COVID-19 pandemic where UK GDP fell by 5% and 11% respectively.

In this article we provide our point of view on the progression of quantification of nature-related risks, provide examples of how nature-related risks can be quantified using the GFI report and provide an overview of Deloitte methodologies. Finally, we provide a regulatory perspective.

Why is nature important?

Nature is important to any economy. It provides essential ecosystem services that support economic activities and human well-being. These services include for example provision of clean air and water, pollination of crops, regulation of climate, and the maintenance of
biodiversity. Without these services many industries, including the financial services sector, could be negatively impacted.

Furthermore, nature plays a crucial role in climate change mitigation and adaptation. Natural ecosystems, such as forests and wetlands act as carbon sinks, absorbing and storing carbon dioxide from the atmosphere. This helps to mitigate greenhouse gas emissions and combat climate change.

According to the report, at least half of global GDP is moderately, or highly, directly dependent on nature. Furthermore, the report states that the UK is already one of the most nature-depleted countries in the world and, as is evident from the severity of their results, this is already having a negative impact on our economy.

Progressing the quantification of nature-related risks

The banking sector globally has made progress toward building its climate scenario analysis capabilities. However, limited progress has been made on quantifying nature-related risks. This is driven by several factors such as, limited regulatory expectations, resource constraints, a lack of nature-related risk scenarios like those provided by the NGFS for climate risk, and the complexities associated with quantifying nature-related risks. In our view, the work done by the GFI has the potential to change the current state for several reasons set out below.

Firstly, the GFI report highlights that the risks associated with nature-related losses could potentially have material consequences to the UK economy. Inherently this implies heighted risks for banks that need to be correctly identified, and then measured and managed appropriately.  Raising the profile of nature-related risks should also encourage further guidance from regulators as discussed further below.

Secondly, the work done by the GFI shows that it is possible to quantify the risk and provides several accelerators that banks can use in their own risk assessments and scenario analysis. For example, the materiality assessments (per items 1 & 2 in the appendix below) can be used as a starting point to understand where a bank’s portfolio is vulnerable to nature-related risks and to support reporting under TNFD, for example. In our experience, materiality assessments are a crucial first step and require careful consideration of the range of nature-related risks and how they might impact bank balance sheets. The importance of materiality assessments was also emphasised in the ECB’s recent statement: “…materiality assessments are a precondition for sound risk management”.

Thirdly, the lack of historical precedents for nature-related risk means that, similar to climate risk, forward-looking scenario analysis will be a key tool for quantification of nature-related risks.  The GFI work provides a set of scenario narratives together with associated impacts on macro-economic variables (see item 4 & 5 in the appendix below) that can be used by banks as an input into their own scenario analysis.  In our conversations with banks, the current lack of available predefined, commonly used and consistent scenarios has been a constraint on their ability to quantity nature risks.  The GFI scenarios represent a real step forward.

While the GFI report contains very helpful accelerators, banks will have to develop additional quantification capabilities to perform robust risk measurement. Similar to climate risk, a more robust nature scenario analysis capability should consider idiosyncratic risks at a customer level that are not considered by the GFI project. Examples of the types of factors that needs to be considered are outlined in the next section.

Overview of modelling approaches developed by Deloitte

At Deloitte we have developed credit risk quantification methods for the Agricultural sector that are applied at a customer level. We measure the impact on agricultural yields due to a decline in pollination services, and soil and water quality. Ultimately our approach can be used to estimate nature-risk adjusted probabilities of default.

To measure the risk of declining pollination services delivered by bees we need to consider three key factors.  First, we need to identify the farm boundary, which we do using spatial data.  Second, we need to understand the type of crops farmed because some crops are more dependent on pollination services than others. For example, watermelons, pumpkins and almond nuts are heavily dependent, whereas wheat and maize are less dependent. Third, we need to assess the type of surrounding land and whether it provides an appropriate habitat – bees are dependent on food and a place to nest. For example, an orchard is a better habit for bees compared to a grassland. Considering these three factors allow us to derive an estimate of pollinator abundance for a given farm. This then allows us to estimate the total crop yield dependent on bees’ pollination services which can then be used to estimate the potential decline in the crop yield and subsequent financial position of the borrower under different scenarios.

Declining soil quality and water quality are key risks to farmers. Soil and water quality can differ between different farms depending on their location. Soil quality is also impacted by the types of farming methods used. For example, tillage is the preparation of soil by mechanical agitation which creates soil disturbances that reduces soil quality.  Using location specific metrics like Soil Organic Carbon¹ for soil we estimate the potential decline in crop yields due to declining soil quality. For water quality we use a Biological Oxygen Demand² metric to estimate the reduction in fish populations for a fish farmer due to declining water quality.

Based on our experience developing these types of approaches, modellers will experience similar (and even exacerbated) challenges compared to building climate risk models. For example, due to the complexity of nature-related risks, a wealth of additional counterparty data and third-party vendor data is required (as demonstrated by the methodologies explained above), while requiring several assumptions based on expert judgement.

We note that the scenarios that have been developed by the GFI are severe – the GFI acknowledge this within their report. Nonetheless, they are a useful starting point especially in the absence of any other nature risk scenarios. Furthermore, given the uncertainty of the outcome of nature-related risks, it is important to consider severe but plausible scenarios.

A regulatory perspective

Many regulators around the globe, including the Bank of England, the ECB and the Federal Reserve Board, have performed climate scenario exercises in the past few years. These exercises, in conjunction with implementation of supervisory expectations such as SS3/19, encouraged banks to develop climate risk management capabilities. In our view a similar journey is required for banks to invest in building capabilities to progress the understanding, management and measurement of nature-related risks.

Based on our industry insights, resource constraints (both human and financial) are a key challenge for banks. Explicit supervisory expectations would support risk management teams to secure more budget (and therefore resources) to further improve their risk management capabilities on this topic. The work done by the GFI could be a useful reference point for regulators as they develop their own regulatory nature scenarios. 

As mentioned earlier, the report suggests that the potential consequences of nature-related risks are significant and therefore to ensure the stability of the financial system we expect regulators to increase their focus on nature-related risks. The ECB has been explicit about its expectation that banks manage broader environmental risks, in addition to climate risks. In their most recent update they state that “...turning a blind eye to the materiality of (environmental) risks is clearly no longer compatible with sound risk management.”

Summary

In summary, the work led by the GFI suggests that impacts of nature-related risks are potentially very material to the UK economy and financial services. Limited progress has been made in quantifying nature-related risks and the analysis presented in the GFI report provides useful accelerators for banks and regulators to start quantifying the impacts of nature-related risks. Due to the complexity of nature-related risks, modellers will experience several challenges as they embark on this journey. Deloitte has developed quantification methodologies for the Agricultural sector that provides examples of the additional models, data and metrics that are required.

Appendix: High level overview of the work led by GFI

 The objective of this GFI led project was to assess the materiality of nature-related risks to the UK financial sector both in the short-term and the long-term. The analysis was performed in different stages (per Figure 1) and our understanding of the analysis for each of the numbered items is summarised below at a high-level.

1. Nature-related Risk Inventory (NRRI):

The UK NRRI identifies the UK’s most material nature-related economic risks e.g. water shortages, critical resource supply chain disruption, business impacts due to biodiversity policies, etc. Each of the risks are classified as low, medium or high within two dimensions: likelihood and impact.

As a first step, the project team identified a long list of 29 nature-related risks. Secondly, plausible worst-case scenarios for each risk's impact on the UK economy were developed, accompanied by evidence statements based on literature review and expert judgement. Industry experts were consulted to assess the likelihood and potential impact of these scenarios materialising over the next three decades.

The results suggest that the risks with the highest impact and likelihood to the UK economy and financial system up to 2050 include:

• Global food security repercussions; 
• Anti-microbial resistance (AMR); 
• Zoonotic disease (diseases transmitted from animals to humans);
• and Soil health decline.

2. Exposure to Nature-related Risk

A key step in measuring impacts to financial services, is understanding the sectoral exposure to nature-related risks. The authors estimated the dependency of UK financial services on ecosystem services (e.g. pollination, surface water, soil quality) at a sector level, leveraging the ENCORE dataset. The results indicate that there is a significant exposure of UK banks and insurers to ecosystem services. They also explored the nature-related transition risk (e.g. changes in demand for meat, changes in soft commodity supply chains) of the largest UK banks using the WWF Biodiversity Risk Filter. The results indicate that between 8% and 53% of the portfolios of the seven largest UK banks are exposed to transition risks.

3. Value-at-Risk Analysis:

Chapter 3 of the report discusses how dependencies and exposure to ecosystem services can be translated into economic risks to sectors, using a Nature Value-at-Risk (nVaR) analysis. The nVAR analysis considers the likelihood and scale of impacts and reveals that potentially trillions of GBP are at risk of natural capital depletion. Based on a 1-in-100 year value-at-risk, water scarcity and climate regulation posing the highest risks. The Agricultural sector faces the highest risk relative to economic output, while the Services sector faces the most significant financial risk in monetary terms.

4 – 5: Scenario development

Nature scenario analysis requires the use of scenarios that contain both chronic and acute nature risks. The scenarios were identified and developed based on a long-list of individual nature-related risks from the UK-NRRI (see item 1 above) as well as in reference to previous scenario families (e.g. NGFS). The authors developed three scenarios:

• Domestic (UK focused) scenario setting out the impact of chronic degradation of key environmental factors. 
• International (supply chain) scenario setting out the broader impact of environmental degradation having knock on impact on supply of key products and shortage of key agricultural commodities.
• Health scenario setting out the impact of overuse of antimicrobials leading to more resistant diseases leading to increased human and animal population mortality rates that impact key macro variables.

The qualitative scenarios were translated into quantitative inputs that could be used for NiGEM3 in order to produce impacts on key macroeconomic variables such as GDP, inflation and public expenditure.

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Reference:

¹ Soil Organic Carbon helps us understand how much carbon is stored in soil. Carbon improves overall soil quality as it increases microbial activity, increases water storage and protect against erosion, for example.

² Biological Oxygen Demand is a way to gauge how polluted or clean water is based on how much oxygen is used by bacteria to break down organic waste in it. High BOD levels suggest more pollution and lower oxygen levels.

³ A macro-economic model used by a wide range of central banks and supervisors globally, including the NGFS.