See transition hotspots by sector and region. Adjust weights, scenarios, and timelines for transparency easily. Export results for reviews, audits, and board discussions now.
| Company | Sector | Region | Scenario | Policy | Carbon price | Emissions intensity | Revenue at risk | Preparedness |
|---|---|---|---|---|---|---|---|---|
| Delta Cement | Manufacturing | South Asia | Delayed Policy Shock | 4 | 120 | 0.95 | 18 | 2 |
| Urban Transit Co. | Transport | Europe | Disorderly Transition | 3 | 90 | 0.35 | 10 | 3 |
| GreenGrid Power | Utilities | North America | Orderly Transition | 2 | 75 | 0.40 | 8 | 4 |
| Coastal Retail Group | Retail | East Asia | High Carbon Price | 3 | 150 | 0.20 | 6 | 3 |
Step 1: Normalize inputs. Each metric is scaled to 0–1. For 1–5 ratings: (value−1)/(5−1). For numeric fields, conservative caps are used.
Step 2: Exposure index. A weighted average of normalized pressure indicators, then scaled to 0–100.
Step 3: Preparedness index. A weighted average of normalized readiness indicators, scaled to 0–100.
Step 4: Risk score. Risk = Exposure × (1 − Preparedness) × Scenario Multiplier. Output is clipped to 0–100.
Likelihood blends policy, regulation, stakeholders, and technology factors. Impact blends revenue at risk, emissions intensity, carbon price, supply chain, and brand.
Preparedness reduces both likelihood and impact to reflect mitigation actions.
Transition exposure summarizes how quickly external forces can change your cost base and revenue model. Policy stringency and regulatory exposure capture tightening standards, reporting duties, and enforcement risk. Stakeholder pressure reflects lender, investor, customer, and employee expectations. Technology disruption reflects substitution risk from low‑carbon alternatives. Use the 1–5 scale to keep scoring consistent across sites, business units, and suppliers. Document assumptions, data sources, and scoring dates to maintain comparability over time.
Carbon price, emissions intensity, and revenue at risk translate transition drivers into financial materiality. A higher carbon price amplifies compliance costs and product margin pressure. Emissions intensity acts as a proxy for exposure per unit of economic output, making peer comparisons easier. Revenue at risk captures demand shifts, product obsolescence, and contract churn. Combining these inputs helps stakeholders see where mitigation can protect cash flow.
Preparedness reflects your ability to respond through strategy, capital allocation, data maturity, and innovation capacity. Strategy readiness covers targets, governance, and accountability. Capital readiness reflects funded roadmaps, financing access, and project pipelines. Data readiness covers inventory quality, scope mapping, and assurance processes. Innovation readiness reflects low‑carbon products, process redesign, and partnerships. In this calculator, higher preparedness reduces both likelihood and impact to represent mitigation effects.
Weights make the model auditable by showing what you considered material. Heavy industry often increases emissions intensity and carbon price weights. Consumer sectors may raise brand sensitivity and stakeholder weights. Regions with fast policy tightening may increase policy and regulatory weights. Start with equal weights, then adjust based on risk registers, peer benchmarks, and board priorities. Keep a record of why weights changed for governance reviews.
The heatmap places results into four likelihood bands and four impact bands, producing an intuitive hotspot view. A marker in high likelihood and high impact suggests urgent mitigation, financing, and operational changes. Moderate cells can be managed through monitoring and staged investments. Low cells still require evidence, especially for disclosure. Use the CSV export for registers and the PDF for committees, audit trails, and stakeholder communications.
The score summarizes transition exposure and preparedness under your selected scenario. It ranges from 0 to 100 and is intended for screening, prioritization, and documentation rather than precise forecasting of audited financial impacts.
Select the scenario that best matches your planning narrative and policy expectations, then align the horizon with investment cycles. Use multiple runs for sensitivity analysis and keep outputs for committee review.
Weights show which drivers you consider material, making results transparent. They help adapt the model to sector and geography differences and support governance by explaining why two assessments may differ.
Revenue at risk, emissions intensity, carbon price, and supply chain exposure typically drive impact. If your products face rapid substitution or contract churn, revenue at risk should be reviewed carefully.
Use the heatmap as a visual summary for risk registers, management discussion, and transition planning narratives. Pair it with documented assumptions, governance notes, and any mitigation actions to improve traceability.
Yes, if you keep consistent scales, data definitions, and scoring dates. Use the example table format to standardize inputs, then export CSV outputs to consolidate results across units and review outliers.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.