Climate scenario analysis for physical and transition risks helps organisations prepare for an uncertain future shaped by climate change. By exploring 1.5°C, 2°C, and 3°C global temperature pathways, businesses can assess how climate related developments (from regulation and technology shifts to physical climate impacts) may affect strategy, operations and financial performance.
Scenario analysis is a key component of climate risk management and disclosure frameworks such as the Task Force on Climate-related Financial Disclosures (TCFD) and the ISSB’s IFRS S2 standard. It enables decision makers to test resilience and identify areas for adaptation under a range of plausible climate futures.
Understanding 1.5°C, 2°C, and 3°C Climate Scenarios
Climate scenarios represent alternative pathways for how global efforts to mitigate climate change might unfold.
1.5°C scenario: Reflects a rapid, coordinated transition toward net zero by mid-century. It involves significant transition risks including policy tightening, carbon pricing, and technological disruption but limits physical climate risks such as extreme weather and sea-level rise.
2°C scenario: Illustrates a moderate transition. Some climate impacts become unavoidable but mitigation efforts still reduce the most severe risks. Both transition and physical risks are balanced in this scenario.
3°C (or higher) scenario: Describes a world with limited climate action. Physical risks dominate with more frequent heatwaves, storms, droughts, and coastal flooding, while transition risks are lower due to weaker policy responses.
Each scenario offers a lens for understanding potential business exposure and resilience.
Physical and Transition Climate Risks Explained
Scenario analysis considers two main categories of climate risk:
Transition risks: These arise from the global shift to a low-carbon economy, including changes in regulation, market preferences, technology, and investor expectations. For instance, carbon pricing or new disclosure requirements can affect business models, supply chains and asset values.
Physical risks: These stem from the direct effects of climate change. Acute risks include floods, wildfires, and severe storms, while chronic risks involve longer term changes such as rising temperatures, altered rainfall patterns and sea-level rise.
Assessing both helps organisations identify which operations, assets, or markets are most exposed under different climate projections.
How to Conduct a Climate Scenario Analysis Step by Step
1. Define scope and objectives
Set clear boundaries for the analysis — which geographies, operations, or assets will be included, and over what time horizon (e.g. 2030, 2050). Select recognised reference scenarios such as those from the IEA or IPCC to maintain credibility and comparability.
2. Identify key drivers and variables
Determine which factors have the most influence — such as carbon pricing, energy mix, consumer demand, or exposure to physical hazards.
3. Develop scenario narratives and assumptions
Translate the chosen climate pathways into clear assumptions about policy settings, technology adoption and physical impacts. Describe how each scenario could affect the broader operating environment.
4. Assess impacts and resilience
Analyse how each scenario might affect financial performance, operations and strategy. This could involve quantitative stress-testing or qualitative assessments of business model resilience.
5. Integrate findings into decision-making
Use insights to inform strategic planning, investment priorities and risk management. Incorporate results into sustainability disclosures, and review regularly as new information emerges.
Practical Benefits of Scenario Analysis
Improved risk understanding: Clarifies exposure to both transition and physical risks across different futures.
Strategic resilience: Supports long term planning and adaptive decision making.
Regulatory alignment: Meets emerging disclosure expectations under UK SRS S2.
Enhanced transparency: Builds stakeholder confidence through clear, evidence based analysis.
Common Challenges and How to Overcome Them
Data uncertainty: Use transparent assumptions and note limitations. Focus on material drivers over weaker information.
Complexity: Start with a manageable scope, expanding as internal capability grows.
Fragmentation: Involve finance, risk, and sustainability teams to ensure findings inform core strategy.
Static analysis: Treat scenario analysis as a dynamic process, updated as science, regulation and markets evolve.
Integrating Scenario Analysis into Broader ESG and Risk Strategy
Scenario analysis complements other sustainability tools, such as double materiality assessments, risk mapping, and ESG performance reviews. Linking these processes creates a more coherent view of how environmental, social and governance factors interact which improves both reporting quality and strategic decision making.
Conclusion
Climate scenario analysis for physical and transition risks provides a structured way to explore how different climate futures may affect an organisation’s resilience. By examining 1.5°C, 2°C, and 3°C scenarios, businesses can identify vulnerabilities, test strategic assumptions and strengthen decision making.
The goal is not to predict the future but to ensure that, whatever future unfolds, the organisation is prepared to respond effectively.
Frequently Asked Questions
Climate scenario analysis is a structured method for assessing how different climate futures — such as 1.5°C, 2°C, and 3°C pathways — may impact an organisation through physical and transition risks.
Physical risks result from climate impacts like heatwaves, storms, and flooding. Transition risks stem from policy, market, or technology shifts in the move toward a low-carbon economy.
These scenarios align with internationally recognised climate models (e.g., IPCC, IEA) and represent distinct levels of global mitigation effort — from rapid transition to limited action.
It should be reviewed regularly, ideally every one to two years, or when new regulatory, technological, or physical developments materially change underlying assumptions.
