Section 9 – Portfolio management and disclosure

Module 21 – Scenario analysis

Scenario planning should be structured and iterative to enable proactive planning despite uncertainty. Scenario-based risk assessment is recommended as best practice under IFRS S2 (see Module 23), and can be carried out at the sector, segment or portfolio level, with insights aggregated from the bottom up.

Scenarios are not intended to predict the future but offer plausible narratives and pathways that test assumptions and inform strategic decision-making.

In financial risk management, Scenario planning is particularly helpful in identifying and assessing materiality of climate-related risks and opportunities.

• Scenarios analysis and traditional financial risk management
  Relies heavily on established methods, precedents and historical data, such as actuarial models and statistical projections, often within short planning horizons of three months to three years.

• Scenario analysis and climate change

  • Climate change is characterised by significant uncertainty and non-linear impacts, meaning past data is no longer a reliable guide for future expectations.
  • The rapidly evolving regulatory landscape, and the potential for physical and transition shocks, also makes conventional approaches less effective, or even counter-productive, as they may limit the agility needed to respond to the scale and complexity of transition dynamics.
  • Climate risks also unfold over longer timescales, typically 30 years or more for physical risks, and five to ten years for transition risks. Therefore, short-term decision frameworks risk becoming maladaptive, locking in vulnerabilities that increase long-term exposure.

The purpose of scenario analysis is to test assumptions, reveal potential vulnerabilities and strengthen strategic agility. In many cases, a qualitative assessment can provide a broader and equally valuable perspective as a purely quantitative one.

Practical tips for scenario selection and risk assessment

Using reference scenarios helps ensure internal consistency and enables transparent external benchmarking of climate risk assessments. Reference scenarios offer several key benefits:

• Benefits of reference scenarios

  Credibility

  Scenarios developed by recognised institutions, such as the International Panel on Climate Change (IPCC) or Network for Greening the Financial System (NGFS) are based on transparent, peer-reviewed science and policy analysis.

  Comparability

  Using industry-standard scenarios allows for meaningful benchmarking across institutions, sectors and jurisdictions.

  Aggregability

  Common scenario frameworks make it possible to combine individual asset-level assessments with portfolio-wide insights, supporting effective governance and capital allocation.

Recommended providers of reference scenarios include the NGFS, IPCC, International Energy Agency (IEA) and the Principles for Responsible Investment (PRI). The World Business Council for Sustainable Development (WBCSD) has consolidated several scenario datasets (including those mentioned above) in a single data portal.

Diagram 6a and 6b

Diagram 6a shows how the different scenario archetypes compare in terms of their levels of physical and transition risk.

Diagram 6b highlights the main factors that shape different scenario outcomes. It illustrates how varying policy reactions and technological developments influence the severity of risks faced by economies and, in turn, financial institutions.

These minimum recommended scenario archetypes for climate risk analysis are:

• Below 2°C
  This scenario, or other high transition risk equivalents, can help stress-test transition readiness and regulatory exposure. It assumes ambitious and rapid global decarbonisation with strong policy action and high technological change.
• Delayed transition
  This scenario, or other disorderly transition equivalents, can assess vulnerability to policy shocks and market dislocation. It assumes limited near-term action followed by abrupt policy tightening and market shifts.
• Current policies
  This scenario, or other high physical risk equivalents, can identify chronic physical vulnerabilities and resilience gaps. It assumes limited climate action with a rapid escalation of physical risks.

Guidance 19: Segment/portfolio level scenario analysis

Guidance 19 presents a structured process for conducting climate scenario analysis at the segment or portfolio level. It can help FIs move from granular project-level insights to strategic portfolio-wide decisions. By linking scenario analysis to practical decision points, such as risk appetite and resilience target-setting, the framework supports the integration of climate considerations across both risk management and business strategy.

Identifying early warning indicators for monitoring

Early warning indicators (EWIs) are measurable signals or metrics that provide advance notice of emerging risks, vulnerabilities or opportunities. They help decision-makers to act pre-emptively to mitigate impacts before issues escalate.

EWIs build on the outputs of scenario analysis. Once potential risks or opportunities have been identified, the next step is to “how will we know if this is beginning to materialising in the real world?” The aim is to identify observable signals that can indicate when a particular risk or opportunity is emerging, allowing for timely and proportionate actions.

• Key selection criteria for EWIs:

  Accessibility

  Data for tracking indicators should be easy to obtain, up to date and sourced from trusted internal or external providers.

  Accuracy

  Indicators should be reliable and minimise the risk of false positives or negatives.

  Actionability

  The thresholds for taking specific actions should be clearly defined.

  Accountability

  Each indicator should have an assigned person or team responsible for monitoring and responding to it.

Guidance 20: Early warning climate indicator examples

Guidance 20 shows examples of EWIs that can be used to anticipate and manage emerging climate-related risks across portfolios. They combine practical data sources with suggested thresholds and indicative management actions. They can be adapted to suit their regional context, institutional priorities and data availability.

Advanced analytics with reference scenario datasets (for Advanced FIs)

Higher climate risk maturity can help FIs move beyond basic scenario mapping to developing tailored, quantitative and dynamic analytics that drive decision-making and strategic planning.

Reference scenario providers such as NGFS supply datasets containing sector, micro, and macroeconomic projections of key indicators, including GDP growth, country emissions by sector, carbon price trajectories, and estimated GDP losses from chronic physical risks. These are often available at the country level, and can be supplemented by geospatial hazard maps, sector-specific emissions pathways, and other third-party datasets.

Advanced FIs can combine these datasets with their own project, segment and portfolio-level risk assessments to quantify financial risks across their portfolio. Scenario variables, such as changes in commodity prices, energy mix or demand growth rates, can be converted into explicit input assumptions for detailed financial modelling at the project level.

• For example, modelling a 30% reduction in water availability under the IEA’s Current Policies scenario, which implies high physical risk, could curtail production volumes, reduce revenues and subsequently increase the default risk of a leather manufacturer in South Africa.
• Another example would be using the NGFS Disorderly Transition scenario to estimate how the EU’s carbon pricing in 2040 could affect the revenues of a cement manufacturer and, in turn, influence downstream asset valuations.

The ability to use scenario data in bespoke models, such as climate-adjusted credit risk modelling or probability of default, and to produce credible and actionable insights, requires significant analytical sophistication. This approach is best suited to Advanced FIs with high confidence in their risk modelling capabilities.

Even so, FIs should recognise the inherent uncertainties of climate-related risks and opportunities, and the unpredictability of regional transition trajectories. where quantitative data is limited, expert judgement remains essential for translating macro-scenario projections into project-level financial outcomes.

Despite these challenges, this approach can greatly enhance strategic decision-making by supporting dynamic balance sheet projections, testing key assumptions and assessing baseline and tail risks across multiple climate scenarios.

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