An illustration: Translating climate risk into financial outcomes

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Quantifying the financial impacts of climate risk

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The example below illustrates how climate signals can be translated into EV using the farming examples in the previous articles.

1. Probability of business impact

A. Establish the relationship between climate condition, business impact and sensitivity

For example, higher ambient heat → higher cooling load → increased electricity consumption electricity use → margin compression in an energy-intensive model.

The next question is whether the persistence of this condition would materially affect costs, margins, or operational reliability. For a typical vertical farm, key sensitivities include:

• Cooling loads that increase non‑linearly with higher temperatures
• Energy costs that are variable and market‑linked
• Operating margins that are sensitive to increases in energy‑related costs

Taken together, these factors indicate that the business is financially sensitive to sustained increases in heat stress. At this stage, three critical elements have been established:

• the relevant climate condition,
• the business’s exposure, and
• its sensitivity to that condition.

B. Establish the directional shift in climate conditions

Using Singapore’s Climate Projection V3 as an example, the report describes Singapore ambient heat development as “temperatures will rise under all emissions scenarios,” “very hot days and warm nights will become more frequent,” “extreme rainfall intensity is projected to increase,” and “dry periods may become more severe”.

These statements represent high‑confidence directional signals. While they are not probabilities, they clearly indicate the direction in which the climate system is evolving. Directional signals provide an essential foundation for understanding where climate‑related stresses are likely to intensify.

C. Establish the intensity of the climate condition vis-à-vis the baseline 

Singapore Climate Projection V3 describes ambient temperature changes as follows: “Occurrences of high heat stress (WBGT ≥ 33 °C) are projected to become significantly more frequent by end‑century under all emissions scenarios, with high heat stress days exceeding moderate heat stress days under the high‑emissions scenario.”

From this statement, it is reasonable to infer a baseline condition that Singapore’s operating environment will be materially hotter for a greater proportion of the year.

D. Derive a business‑impact likelihood band

In this example:

• the climate projection states that high heat stress becomes “significantly more frequent,”
• the language does not suggest the outcome is possible, rare, or highly uncertain, and
• the business exhibits both high exposure and high sensitivity to the projected condition.

Together, these factors support a qualitative‑to‑quantitative translation, allowing the company to assign a likelihood band to the probability of material business impact, even though the underlying climate scenario itself is not probabilistic.

In this instance, purely for illustration, we assume that the farm adopts a business‑impact likelihood band in the following table. 

To get to an indicative range, map projection language to the likelihood bands to derive an indicative probability range.

Consequently, for other risks such as rainfall flood where risk is expressed in less “severe” terms, the likelihood band is nearer to 20-35% to business impact based our business-impact likelihood table, which is essentially “Low-to-medium” likelihood that rainfall flood driven cost pressure will materially affect operations.

This exercise should be repeated across all material climate risk faced by the company within the time horizon that it has chosen as a time frame for reference.

2. Financial value of business impact

Next, estimate the financial value of that impact should the climate condition materialise.

For illustrative purposes, we focus on the financial impact of water supply variability of the ASEAN farmer featured earlier. In this scenario, water variability is expected to affect crop yields (assumed here to be grain), which in turn introduces volatility in production volumes and, consequently, revenue from grain farming.

Now, apply planning assumptions to translate climate variability into quantified financial impacts.

A. Anchor to a baseline (Year 0)

In this case, baseline metrics such as the amount of water supply, planted area, average crop yield, average crop price (revenue) and baseline cost structure can be used as a basis to extrapolate given the climate outcome (water supply) in the future. 

B. Ascertain the climate variability 

In this case, water supply variability is the driver. From the regional climate data, we assumed, for the purpose of this study, we can get rainfall variability projection data – e.g. Sumatra and Java show an increase in dry‑spell length “of up to 40%” in the near future.

C. Translate climate variability into operational data 

To translate climate varaibility into operational data, we often refer to peer-reviewed sources or internal expert data. In this instance, according to major studies, a 5% water shortfall is likely to result in a 3.5% in effective irrigation shortfall. The shortfall in turn will cause a 3.6% reduction in crop yield (assuming that yield response factor to irrigation shortfall is approximately 1.1x.

Effective irrigation shortfall (%) = Water supply variability (%) x Water‑to‑irrigation transmission factor 

Yield reduction (%) = Effective irrigation shortfall (%) × 1.1 

D. Translate operational data into financial impact

Revenue impact channels

• Yield loss on planted area
• Lost production from unplanted area

Revenue impact (%) = (1−Yield reduction (%)) × (1 − Area loss) - 1

E. Summary impact

Assumptions are illustrative and should be replaced with crop‑ and region‑specific coefficients. What this illustration strives to do is to provide guidance on how to approach translating climate risk to financial impact. 

However, the above example is an example on how to model the financial impact of water variability on an ASEAN farm. Different climate risk had a different relationship with crop yield (e.g. wind, heat etc.) but most can be modelled reasonably using public / academic resources (e.g. yield response factor).

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Identifying climate risks and their impact on business performance

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