The Challenges of Quantifying Climate-Related Risks
a calendar icon
March 7, 2023
min read

The Challenges of Quantifying Climate-Related Risks

Learn about the challenges of quantifying climate-related risks and how to address them in this insight blog post by 15Rock.


$21 Trillion worth of companies globally failed to report their environmental impacts in 2021. According to CDP’s rating, out of 17,000 companies, 200 companies, or 2% received an A-grade. HP, Lenzing AG, L’Oreal and others scored triple-A, only 4 more than the prior year’s 10 have increased their efforts to slow the global damage of climate change. Why are the remaining 98% struggling?

A challenge for many is understanding the extent and impact of ESG. ESG data has been increasingly scrutinized for the lack of uncertainty, nonlinearity and internal factors, making identification quantitative values challenging. In this article I will explore ways ESG can address these challenges: risk management tools, scenario analysis, and sensitivity analysis.

Risk Management Tools

Risks are managed by two tools: scenario analysis and sensitivity analysis. These tools remediate the challenges of internal influences and uncertainty when managing ESG data.

Scenario Analysis

Scenario analysis involves mapping out potential future states based on assumptions leading to those stats. These hypotheticals explore the best and worse case, and alternative strategies towards a particular outcome.

Scenarios help ESG identify areas where risks may occur and create strategies for mitigating those risks. Additionally, investors are given insight on how and what stressors may impact business revenues. Financial impact assessment and performance projections would make an existing company ESG disclosures more reputable.

Using scenario analysis for investment decisions, the benefits can be easily over or underestimate the actual risks. Scenario analysis predicts the impact of climate, weather and their impacts. To test the impact and severity on an ESG portfolio is not well researched, especially the sustainability and governance aspects. Additionally, the resulting model may be completely unexpected or even predicted.

The information allows ESG managers to tactically position. Investing assets in active management as opposed to the passive strategic asset allocation appeals to more than shareholders but also competitors, employees, and clients. Proactive tactics raise the industry standard, recruiting and retaining employees that share corporate values.

Sensitivity Analysis

Leading experts use a sensitivity analysis tool to identify which variable has the largest impact on the outcome of a model. ESG and financial modeling are perfect examples since both have complex inputs and outputs often requiring a “what-if” analysis to predict outcomes of an action.

Data complexity is best understood as a “Black Box Processes” where only the inputs and outputs are observed, without knowing the interior workings. The findings indicate which independent variables affect a specific dependent variable. Additionally, variable can be flexible to determine severity. Sensitivity analysis enhances forecast accuracy when managing ESG risks, providing a sense of assurance to decision making.

Without our Tools, Calamity Ensues.

To better understand why we need these tools when looking at ESG data, we must first understand the concept of uncertainty, nonlinearity, and internal factors. Uncertainty makes prediction difficult due to incomplete or lack of data; nonlinearity is when small factors have large influences; and internal factors occurs when an entity is affected by factors internal to itself. When portfolios lack sufficient data, there is an underlining aspect of gambling investments. Predictive measures deny a sense of security knowing certain variables are missing. The lack of data means there is uncertainty in whether an internal or external variable can influence the entire portfolio.

These tools enable actionable understanding for investors and management. Especially tools combing external data with machine learning algorithms to create missing data, providing insight on investor and company behaviour.


Individuals struggle to quantify climate related risks due to insufficient data and uncertain influences may impact variables. Resulting in doubtful forecasts and growing portfolio concerns. Scenario and sensitivity analysis are two tools which remediate these challenges. They enable a sense of assurance in ESG reporting and financial management by identifying variable impacts and severity and potential futuristic events.

I hope this provides insight on the difficulties that lie ahead. Feel free to leave suggestions, ideas, or concerns. Come work with us for greater impact on the world!

If you have any suggestions, ideas, or concerns, leave a message! Or, come work with us for greater impact on the world!