Climate Change Crisis: Not Just about CO₂ Emissions

Discussions on climate change, and whether it is indeed an emergency or real, are not new and have been around for decades. However, at the time of writing, 97% of climate scientists agree that climate change exists.³ Research on the impacts of climate change shifted to forward-looking scenarios based on projected emissions grouped as greenhouse gases. Greenhouse gas emissions are a byproduct of both the extraction and burning of fossil fuels. It has an impact on the climate, that is, increasing the atmospheric temperature, but it also impacts human health.⁴

More specifically, polluting facilities that process and distribute fossil fuels increase the levels of greenhouse gases in the atmosphere, leading to a warming effect. The main culprits are CO₂ and other pollutants such as methane, but also include nitrous oxide and fluorinated gases.⁵ These facilities create 80% of CO₂ and 30% of methane pollution.⁶ Accumulated evidence has suggested that other contributors to climate change are human activities such as natural gas drilling, transportation, farming, deforestation, and fertilizers.⁷

Research indicates that human activities contribute to the association between greenhouse gas emissions and the average rise in the planet's temperature observed in the last century.⁸ More dramatically, in recent years, since 1975, the planet's temperature has risen by 2.3 to 2.7 degrees Fahrenheit or 1.5 to 1.8 degrees Celsius on average.⁹

However, climate change is more than global warming: it is defined as “a significant variation of average weather conditions”: becoming warmer, wetter, or drier—over decades or more.¹⁰ There are also natural causes of climate variation such as volcanic eruptions, variation in solar radiation, the movement of crustal plates, and oscillation of the ocean and atmospheric system under El Nino–Southern Oscillation (ENSO),¹¹ however, it is anticipated that the frequency and severity of variations in weather patterns are increasing.

In this case, the longer-term trend differentiates climate change from natural weather variability.

Other impacts of human activity on the climate range from:

• Ambient temperature oscillations between hot and cold:
  • The most recent example is the extreme heat and long-term dry conditions that contributed to the unprecedented bushfires in Australia in 2019–2020. Bushfires burned through 46 million hectares (72,000 square miles).¹² There is no doubt that climate change is associated with the more frequent occurrence of bushfires: 2019 was the warmest year recorded since temperature recordings started.
  • Ice formation and heavy snowstorms.
• Landfill releasing methane and nitrous oxide as it breaks down.
• Increased precipitation from frequent and more intense rainfalls, leading to chemical-based weathering (carbonic acid that creates erosion along coastal regions and watercourses).
• Floods and high-water flow due to the expansion of human settlements and construction of infrastructure.
• Deforestation and the removal of carbon sinks via logging or clearcutting.

We need to appreciate that human civilization flourishes in a stable climate and that the planet is potentially accelerating toward instability based on the increased frequency and severity of extreme weather events. The socioeconomic impacts and hazards associated with extreme weather events are dramatic in terms of loss of human life, exemplified by the 2010 Russian heatwave that killed 55,000 people. Monetary losses of Hurricane Harvey equated to $125 billion (about $380 per person in the United States lost in 2017). As mentioned, climate change has worsened the hazards of extreme weather events; for instance, the Russian heatwave was estimated to be three times more likely due to global climate change.¹³

A global group of 11,000 scientists have endorsed 40 years of research on a range of measures that supports the hypothesis that the world is now facing a climate emergency; the research suggests that the following scenarios are plausible:¹⁴

• Twenty years from now, heavily populated parts of South Asia will more regularly face life-threatening heatwaves greater or equal to 34°C.
• The Sahara Desert will cross the Mediterranean Sea by 2030. This will mean that the heavily populated coast of Africa will be squeezed between the desert and the rising sea levels, impacting millions of people.
• Flammability of the Amazon will increase significantly based on a change in the fire season from 2021 to 2050 (using 1971–2000 as baseline for comparison).
• The coasts of China, Japan, and Korea will experience an increased frequency of what would have been rare rain events in the past, such as typhoons. Later in the century, systemic floods will present serious issues.

United Nations and Climate Change

Since 1992, the United Nations has been recognizing that changes to global climate patterns, and those at regional levels, pose serious issues to the world.¹⁵ The recognition has created notable accords in negotiation with participating countries. Table 9.1 provides a summarized list of the talks and accords to date.

Scientific evidence on climate change and its impending impacts over the past 28 years has directed the accords. The Paris Agreement is no exception.

In 2015, the Paris Agreement set a target for the net increase in global temperatures, to be limited to between 1.5 and 2 degrees Celsius. In general, there are two ways for participating countries to reach their agreed emissions targets. The first is to reduce emissions gradually over time, and the second, and more drastic approach, is to eliminate emissions by shutting down polluting facilities. The latter can be achieved by replacing fossil fuel production with renewable alternatives. Before we look at these options, let us look at the scientific reasons behind why emissions targets should be met.

Limitations of the Paris Accord Target

Even a temperature increase of less than 2 degrees Celsius will affect lives and livelihood: it will likely lead to flooding caused by an increase in frequency of extreme rainfall, an increase in sea levels, and an increased extent of wildfires. In addition, to meet the Paris targets, participating countries will need to make and sustain changes in industry sectors and energy usage to reduce emissions by at least 6% annually until 2030.¹⁶ It has been reported that a new record in daily average carbon levels in the atmosphere was reached in 2021: it spiked to 421.21 parts per million, for the first time exceeding 420 parts per million. Such an increase, never before recorded in human history, confirmed that, according to sources, the planet's temperature increased by more than 2 degrees Celsius, compared to the Industrial Revolution.¹⁷ Keep in mind, even if greenhouse gases were to reduce significantly overnight, the planet's temperature is expected to continue to increase due to the historical accumulation of CO₂ in the atmosphere.

Some research studies on climate change are suggesting that the Paris targets are not reasonably achievable, and that a 3 to 4 degrees Celsius warming is a more likely outcome.¹⁸

In its Fifth Assessment Report, the Intergovernmental Panel on Climate Change (IPCC) identified a set of representative concentration pathways (RCPs) that depict the greenhouse gas concentration (not emissions) trajectories under a range of warming scenarios.¹⁹ Thus, RCP is a way to show how greenhouse gas concentration may develop over time. However, they do not account for the social and economic drivers. The Shared Socioeconomic Pathways (SSPs) were created to complete this picture.²⁰ Importantly, RCP and SSPs allow parallel approaches to better understand climate change scenarios, and the associated likely output warming. The IPCC has since published a Sixth Assessment Report that documents the individual contributions of three working groups focusing on physical sciences, climate change impacts (including adaptions and vulnerability), and mitigation. The report is a stock-take of current progress and provides the best- and worst-case scenarios of impacts on the environment and human society.²¹

Regulatory Guidance and Compliance Measures

In most cases, financial institutions are not waiting for governments to drive the large-scale action needed to mitigate financial risks. They are responding to signals from institutional investors (such as the IIGCC²⁹) and the public to decarbonize their portfolios, announcing net-zero targets, and diversifying away from thermal coal lending.³⁰ In addition, they are creating sustainable finance and managing the financial and nonfinancial risks associated with climate instability.

Central banks and regulators are releasing forward-looking climate stress tests and disclosure expectations, mostly based on the recommendations by the Task Force on Climate-Related Financial Disclosures (TCFD).³¹ The Financial Stability Board formed the TCFD to help companies understand what types of disclosures financial markets need on climate-related risks. Without this information, investors may incorrectly evaluate or price assets that can lead to a misallocation of capital. While this will support convergence and standardization, the climate stress tests and TCFD recommendations need calibration and adjustment by countries to be relevant in each jurisdiction.

To assess the impacts of climate change, many organizations are extending their scenario analysis and stress testing frameworks to include climate risk scenarios. This is also the approach adopted by many central banks. Scenario analysis is a helpful tool to, firstly, comply with climate-related regulatory stress testing and secondly, allow various stakeholders to better understand the shorter- and longer-term implications under a range of forward-looking scenarios, and plan accordingly.

Running climate-related stress tests is a good starting point. Keep in mind that, for financial institutions, in general, the narratives of regulatory stress testing may not be sufficient to capture the unique impacts of climate change. Regulatory stress tests rely on only a handful of scenarios and broad-brush model assumptions that are not necessarily specific to a firm's risk profile. Responses are heavily reliant on human judgment.

Although there are plans to make regulatory and industry scenarios more granular, the scenarios currently available do not capture the full range of potential pathways. In addition, these do not account for policy changes or mitigating actions: for example, mitigating events such as technological improvements in renewable energy to avoid the physical risks before financial losses are realized.³²

In most cases, the stress testing models in use today do not account for propagation channels through micro- and macroeconomic drivers, nor the nonlinear relationships between risk factors, mostly due to the popular use of linear modeling techniques.³³

In addition to facilitating a more granular level of analysis of complex, nonlinear relationships, i.e., help ascertain the direct and indirect influences of physical and transition risks on the micro- and macroeconomy, and subsequently on financial balance sheets, at the individual and interrelated level,³⁴ the use of AI and machine learning can help to better quantify the uncertainty of climate-based events.

Stress Testing: Getting a Foot in the Door

Once the fundamentals are in place, the simplest way for a bank to build their climate risk management capacity is to extend their stress testing framework to incorporate climate risk scenarios and simulations. The extension will require to run scenarios with much longer time horizons (to 2050 and even 2100). As mentioned earlier, regulators and other industry bodies are releasing forward-looking climate stress tests to help ease the burden on financial institutions having to design their own scenarios. Of these, the Bank of England's Prudential Regulatory Authority (PRA) has published one of the most comprehensive climate stress tests to date.³⁵ The developed 2021 biennial exploratory scenario (BES) has drawn from the lessons learned from climate scenarios in their 2019 Insurance Stress Tests. In addition, the NGFS has collaborated with climate scientists and published a set of climate scenarios. We will next describe both the BES and NGFS climate scenarios.

Firms Can Start by Strengthening Their Analytics Frameworks

With all this in mind, it is no wonder that organizations are not sure where to start.

Although most central banks have issued guidelines on how regulated entities, including banks, can apply prudent practices for climate risk management, in many jurisdictions a lack of clarity remains on:

• The key data and parameter requirements for different stress testing scenarios
• Endorsement of credible and commonly used scenarios apart from those provided by the NGFS
• Details on how institutions should conduct short- and long-term scenario analysis

To not be caught off guard, for organizations a good starting point is to review their capability to see if it meets the fundamentals of a robust analytic ecosystem (Figure 9.2):

1. Data sourcing and its management, including data validation and quality assurance.
2. A risk-modeling platform for data-derived insights leveraging quantitative techniques and procedures.
3. Risk calculations and simulation capability.
4. Integration with impact assessments, business decision-making, and reporting.

Organizations will need granular physical and transition risk data, and that data will require use of advanced data-quality procedures to assure data quality. High-performance, parallel-processing engines can accommodate the granular data requirements and frequency of forward-looking risk simulation cycles.

As we know, data is the foundation of any analytics-derived outcome. Without the quality, validation, and assurance measures of data, the outcome is likely to be inferior. Provided that climate risk modeling is a relatively new topic, climate risk data for measuring financial impacts is not that readily available, especially loan-level information, and not yet of adequate quality to support large-scale, robust model development and simulation.

To better address the limitations, organizations will need to focus on the following areas to achieve robustness in their climate risk management efforts:

• Ensure data quality:
  • Establish a robust data-quality framework consistent with BCBS (Basel Committee on Bank Supervision) 239 principles for internal and third-party climate-related data.
  • Supply full transparency, such as source of the data, methods used to transform variables, and geospatial level of granularity.
• Expand scenario coverage:
  • Strengthen scenario analysis capability.
  • Streamline the management of multiple scenarios.
  • Incorporate flexibility for portfolio growth to reflect business strategy and run-off assumptions.
  • Quickly and efficiently meet the expanding challenges of supervisory stress testing to include longer time horizons and expanded risk factors.
  • Expand capabilities to support scenario-based business planning.
• Improve modeling:
  • Put in place a more sophisticated modeling environment that can be used to develop models for physical and transition risk, to also enrich models such as those used for expected credit losses (ECL) and risk-weighted assets (RWA).
  • Ensure that the modeling environment can be extended to include modern AI and machine learning models.
• Improve process efficiency:
  • Accelerate model implementation through a more simplified model deployment mechanism for adjustment and recalibration.
    • Automate manual processes and the documentation of adjustments and overrides of analytic-derived outcomes.
• Increase collaboration.
  • Centrally orchestrate the entire climate risk management process.
  • Ensure consistency processes across the enterprise.
  • Ensure climate risk forms part of the enterprise risk governance framework and is integrated with existing risk systems.

Climate Risk Management Solution

Recognizing the need for an enterprise climate risk solution, a Tier 1 bank in Asia Pacific extended their stress testing solution to quantify the impacts of climate risk following a bottom-up analytical approach. They employed scenario analysis with supporting internal and external reports. A climate risk model assesses the impacts of each industry or subindustry's decline in asset values and quantifies the climate risk at exposure level. It links the bank's portfolio strategy with its finance and auditing processes for business planning and policy decision-making.

Environmental, Social, and Governance Application in APAC-Based Financial Companies

For financial institutions, assessing the carbon emissions of counterparties presents practical challenges in data collection. In this case, the firm employed AI and machine learning to upload and process sustainability reports and counterparty information on a cloud-based platform. Geo-location data, scenario, and other risk data from third-party providers are uploaded through APIs. By modeling the real estate data, disaster prediction data, and financial data of the counterparty, the degree of financial impact and the impact on collateral value are projected. The information is then used to assess the impacts of climate risk on the counterparty's balance sheet and P&L.

Sustainability Investment Screening

An analytics company in the Nordics created a sustainability investment screening solution to help their clients. By utilizing its web interface, financial investors can select companies that are more sustainable than others. The solution performs investment screening and monitoring based on the use of unstructured data, machine learning algorithms, and an intelligent triage process.

Specifically, the solution incorporates the corporate entities of existing investments. The application downloads the annual reports of each company that the client's financial investors are considering for investing, via the Danish registry, in the form of unstructured textual information. In addition, it uses sustainability reports from the UN Global Database via Robotic Process Automation (RPA). In this case, RPA was needed because an API was unavailable. The RPA performs screen-scraping, extracts the information, and stores it in a structured dataset. The sustainability reports include sustainability development goals (SDGs), as well as human rights, labor rights, the environment, and anti-corruption information.

AI and machine learning are used to improve the process by identifying climate-specific information using new types of data, generating SDG scores, and analyzing trends and variations.