Inputs
Tip: Start with inherent likelihood and impact. Then set exposure and controls to reflect your current state.
Example data table
| Scenario | E (L×I) | E exposure | S (L×I) | S exposure | G (L×I) | G exposure | Controls | Outcome |
|---|---|---|---|---|---|---|---|---|
| Flood-prone facility | 4×5 | 85% | 3×3 | 40% | 2×3 | 35% | 30–40% | Environmental dominates; prioritize physical resilience. |
| Labor audit gap | 3×3 | 45% | 4×4 | 80% | 3×3 | 55% | 35–55% | Social risk elevated; tighten supplier monitoring. |
| Compliance weakness | 2×3 | 35% | 3×3 | 50% | 4×4 | 75% | 20–35% | Governance hotspot; strengthen oversight and controls. |
These examples show how exposure and controls shift residual outcomes.
Formula used
Inherent score estimates baseline risk before adjustments:
Inherent = Likelihood × ImpactLikelihood and impact use a 1–5 scale, giving a 1–25 inherent range.
Residual score adjusts inherent risk with context:
Residual = Inherent × Exposure × (1 − Control) × ConfidenceFactor × DriverFactorExposure and control are percentages; confidence and drivers apply small multipliers.
Overall score blends category residual scores:
Overall = (wE×E) + (wS×S) + (wG×G)Weights are normalized to total 100% if needed.
How to use this calculator
- Enter organization, sector, geography, horizon, and appetite.
- Set E/S/G weights to match stakeholder priorities.
- For each category, choose likelihood and impact from 1–5.
- Estimate exposure percentage for your assets and value chain.
- Set control effectiveness and data confidence realistically.
- Adjust driver scores to reflect leading indicators and trends.
- Click Generate heatmap to see results above the form.
- Use CSV or PDF export buttons for reporting and reviews.
Informational output only; confirm decisions with qualified experts.
Mapping ESG exposure across portfolios
An ESG heatmap helps teams compare heterogeneous risks using consistent scales. For each category, the calculator multiplies likelihood and impact (1–5) to create an inherent score from 1–25, then adjusts it for exposure and control strength. This structure makes it practical to rank facilities, suppliers, or business lines and to surface concentration risk. Many organizations treat residual risk at 15+ as priority for mitigation and for reporting to leadership.
Turning qualitative drivers into scores
Risk drivers are often narrative: policy tightening, community pressure, or governance maturity. Translating them into numeric indicators creates a repeatable process. The driver sliders in this tool nudge residual outcomes by small multipliers, which helps when evidence is directional. For example, a higher transition-policy score can raise environmental residual risk when carbon pricing or disclosure rules are accelerating. Pair driver scoring with documented assumptions for auditability.
Heatmap thresholds and action bands
A heatmap is most useful when colors correspond to actions. Many programs define bands such as Low (≤6), Moderate (6–12), High (12–18), and Severe (≥18). These bands can trigger predefined responses: monitoring, mitigation plans, or escalation to a risk committee. Use confidence to flag where the score is less reliable; a High result with low confidence should prioritize data collection before spending is approved.
Using weights to reflect materiality
Environmental, social, and governance issues are not equally material in every context. Weighting lets you align scoring with sector exposure and stakeholder expectations. Energy-intensive sectors may emphasize environmental impacts, while consumer brands may weight social performance and supply-chain practices more heavily. The calculator normalizes weights to 100% so comparisons remain consistent. Revisit weights annually or after major changes, such as a new geography or updated materiality assessment.
Review cadence and governance
Heatmaps should be living tools tied to governance. A common cadence is quarterly refresh for high-volatility risks and semiannual review for stable categories. Track movement, not just level: a Moderate score trending upward can be more urgent than a static High. Combine exports with KRIs, incident logs, and assurance findings to support board reporting. Clear ownership and follow-up dates turn the heatmap into accountable risk management.
FAQs
What does the residual score represent?
It estimates remaining ESG risk after adjusting inherent likelihood×impact for exposure, control effectiveness, confidence, and selected drivers. Use it to prioritize mitigation and track improvement over time.
How do I set the exposure percentage?
Use the share of assets, revenue, or spend exposed to the scenario. For example, percentage of production in a region, suppliers in a tier, or customers under a regulation.
What should I enter for control effectiveness?
Enter how strongly current policies, processes, and safeguards reduce the risk, as a percent. Higher values mean better prevention, detection, and response capability; validate with audits or performance evidence.
Why do E, S, and G weights matter?
Weights reflect materiality. Changing them shifts the overall score toward the categories that matter most for your sector, stakeholders, and strategy, while still keeping the same underlying category calculations.
What is the role of data confidence?
Confidence does not erase risk; it signals how reliable the inputs are. Low confidence increases the multiplier slightly and flags the result for data improvement or sensitivity testing.
How can I use the CSV and PDF exports?
Use CSV to aggregate results across sites or scenarios and build dashboards. Use PDF for meeting packs, audit trails, and approvals, keeping a timestamped snapshot of assumptions and scores.