Inputs
How to use this calculator
- Set exposure and vulnerability based on assets, revenue, and dependencies.
- Choose confidence and materiality to reflect data quality and priority.
- Weight drivers so the heatmap matches your risk narrative.
- Enter horizon values (0–10) for physical and transition drivers.
- Increase adaptive capacity where planned controls reduce impacts.
- Enable scenarios, assign probabilities, and add multipliers.
- Submit, review hotspots, then download CSV or PDF.
Formula used
Example data table
Illustrative inputs for a coastal manufacturing site with moderate adaptation.
| Item | 2030 | 2050 | 2100 |
|---|---|---|---|
| Heat stress | 5 | 6 | 7 |
| Flood risk | 4 | 5 | 6 |
| Drought risk | 4 | 5 | 6 |
| Wildfire | 3 | 4 | 5 |
| Transition drivers | |||
| Policy/regulation | 4 | 5 | 6 |
| Carbon price pressure | 4 | 5 | 6 |
| Technology disruption | 3 | 4 | 5 |
| Market/demand shift | 3 | 4 | 5 |
| Adaptive capacity | 5 | 6 | 7 |
Scenario Inputs and Scope
Enter exposure and vulnerability on a 0–10 scale to represent asset concentration, revenue dependency, and supply criticality. Confidence (0.50–1.00) reflects data quality, while materiality (0.50–1.50) expresses strategic importance. The calculator evaluates three horizons—2030, 2050, and 2100—so you can compare near‑term operational stress with long‑tail strategic risk under multiple pathways. Typical baselines use exposure 5–7, vulnerability 4–6.
Driver Weights and Normalization
Physical and transition drivers are combined using normalized weights. Within each group, weights are scaled to sum to 1, preventing accidental over‑counting when you adjust a single driver. For example, if heat is weighted 35 and flood 25, heat contributes 35/(35+25+25+15)=0.35 of the physical base. This makes driver tuning consistent across teams and versions. Transition drivers can be tuned to mirror regulatory, pricing, and demand signals.
Heatmap Scores Across Horizons
Each cell score is mapped to a 0–100 range and colored by thresholds you set (for example, Low ≤30, Medium ≤70, High >70). Scores rise when hazards intensify and when context is high: Exposure×Vulnerability×(1−Adaptation). Increasing adaptive capacity from 5 to 7 reduces the context factor by 20%, which typically shifts borderline hotspots down one band in the heatmap. In the example table, heat moves from 5 to 7 by 2100.
Expected Value and Decisions
Scenario multipliers adjust physical and transition components separately. A delayed transition can increase transition pressure via mT>1, while an extreme warming pathway can increase physical stress via mP>1. Scenario probabilities are normalized so the expected score for each horizon becomes a single decision metric. Use expected 2050 to prioritize capital plans and expected 2030 to refine resilience. A probability change from 10% to 25% can materially re-rank hotspots.
Controls, Monitoring, Reporting
Use the downloadable CSV to feed dashboards, and the PDF to document governance decisions. Track changes in inputs over time: hazard updates, policy signals, and control maturity. For assurance, keep a log of data sources and confidence assumptions. When reporting, cite the highest‑scoring drivers, their weights, and the adaptation actions that reduce context across horizons. Review assumptions quarterly and after site or policy changes.
FAQs
What does a 0–100 score represent?
It estimates relative risk intensity for each horizon by combining driver severity, exposure, vulnerability, adaptation, confidence, and materiality. It supports prioritization; it is not a prediction of financial loss.
How should I choose driver weights?
Start with equal weights, then align them to your risk narrative and sector. If heat stress is the main operational constraint, increase its weight. Keep a brief rationale so changes can be reviewed and audited later.
What are scenario multipliers used for?
Multipliers adjust physical and transition components per scenario. Use mP to reflect higher or lower hazard outcomes, and mT to reflect policy, pricing, technology, and demand pressures. They help differentiate pathways without changing base inputs.
How do I set Low, Medium, and High bands?
Set thresholds to match your governance appetite. Many teams begin with 30 and 70, then recalibrate after comparing results to historical incidents or expert judgement. Consistent bands make trends and hotspots easier to communicate.
What is the difference between Scenario and Expected views?
Scenario view shows a score for each pathway and horizon. Expected view blends scenarios using their probabilities, producing one score per horizon for planning and reporting. Use scenario results for stress tests and expected results for prioritization.
Can I use the exports in reporting packs?
Yes. The CSV supports dashboards and further analysis, while the PDF provides a snapshot for committees. Store the exported file with the input assumptions and date to support traceability and year‑over‑year comparisons.