IFRS S2 Scenario Analysis: A Proportionate Approach to Climate Resilience

A first-time reporter cannot satisfy IFRS S2 by stating that scenario analysis is “not yet mature” and omitting the resilience assessment. Equally, the Standard does not require a new reporter with limited exposure and capability to commission a complex bespoke model merely to appear sophisticated. The required outcome is a decision-useful assessment of climate resilience supported by an approach that fits the entity’s circumstances.

Proportionality is not a permanent exemption. Scenario analysis is an iterative practice. As exposure, data, skills and resources develop, the approach is expected to strengthen. An entity with high climate exposure and access to the necessary capabilities is required to use a more advanced quantitative approach.

Figure 1. IFRS S2 proportionality matrix for climate-related scenario analysis.

Use one or more scenarios with a reasonable and supportable basis. Public, freely available international or regional scenarios from authoritative sources can be used.

Consider whether the selected set represents a diverse range of climate-related scenarios rather than minor variations of one central case.

Consider physical-risk and transition-risk dimensions relevant to the identified exposures.

Include, among the scenarios used, a scenario aligned with the latest international agreement on climate change, and explain its relevance to the resilience assessment.

Align time horizons with the entity’s definitions, strategic planning, asset lives and financial planning, while recognising that climate effects can extend beyond the normal budget cycle.

Define the operational scope: relevant locations, business units, assets, products, suppliers, customers or portfolios.

Retain the source, version and date for scenarios, variables and external data, and identify internally developed inputs separately.

The ten steps below are an LRA implementation pathway, not a sequence prescribed by IFRS S2. The required outcome is a proportionate, evidence-based scenario analysis and resilience disclosure.

1. Confirm the reporting entity, governance owner, strategic planning cycle and the purpose of the resilience assessment.

2. Use the climate risk and opportunity register to identify the exposures that should drive scenario selection and analytical scope.

3. Assess exposure and available skills, capabilities and resources, including reasonable access to external expertise.

4. Select a diverse and defensible set of scenarios and define time horizons, locations, business units, assets and value-chain areas in scope.

5. Choose an initial qualitative, hybrid or quantitative method and document why it is commensurate with the entity’s circumstances.

6. Set controlled assumptions for policy, macroeconomics, regional variables, energy, technology and management responses.

7. Analyse vulnerabilities, thresholds, response options, financial-effect channels and implications for strategy and business model.

8. Challenge the results with finance, risk, operations, strategy and relevant technical specialists; record disagreements and uncertainty.

9. Prepare the paragraph 22 disclosures: resilience assessment results, capacity to adapt, uncertainties, scenario inputs, assumptions, timing and scope.

10. Approve a capability-development plan and define the trigger and timetable for more advanced analysis in later cycles.

A hypothetical regional freight operator has high exposure to fuel policy, vehicle technology and extreme heat, but limited internal modelling capability. It uses three structured scenarios: a faster transition with tighter emissions policy and rapid electric-vehicle adoption; a delayed transition with higher later policy shock; and a high-physical-risk pathway with more frequent heat and flood disruption. It adds quantitative sensitivities for fuel cost, vehicle replacement, downtime and selected depot adaptation, while supplier and customer-demand effects remain qualitative.

The approach is proportionate because it concentrates available data on the most decision-relevant drivers, discloses the limitations and produces strategy implications. However, high exposure means the entity cannot treat the hybrid method as a permanent endpoint. Its approved roadmap adds route-level physical modelling, fleet residual-value analysis and more granular demand scenarios over the next two planning cycles.

Treating scenario analysis as a forecast or prediction rather than a structured exploration of plausible futures.

Selecting scenarios before identifying the entity’s material climate exposures and decision needs.

Using temperature labels without describing the policy, technology, physical and economic assumptions that matter.

Assuming a qualitative method requires no evidence, scope or internal consistency.

Using “without undue cost or effort” as a reason to ignore information already available to management.

Confusing limited current capability with an indefinite right not to improve the analysis.

Failing to test management responses, resource constraints, asset flexibility and financial capacity.

Reporting model outputs without implications for strategy, business model and resilience.

Rerunning scenario analysis annually for formality while failing to update the annual resilience conclusion meaningfully.