Confidence Scoring
A claim with strong documentation and cross-checks isn't the same as a claim with a single source. Confidence scoring quantifies that difference so you can make better decisions.
- Confidence score = how much you should trust this claim, given the evidence
- Sources, tests, and cross-references all feed into the score
- High confidence = act. Low confidence = verify or flag for review.
- The score doesn't replace judgment—it informs it
- Useful for environmental claims, supplier data, and anything with greenwashing risk
Real-world example
Two suppliers claim '50% recycled content'
Supplier A sent a PDF from 2019 with no third-party audit. Supplier B sent a 2024 LCA with traceable batch numbers and verified by a known lab.
- Supplier A: Single source, dated, no audit trail → Low confidence. Flag for verification.
- Supplier B: Recent LCA, traceable, third-party verified → High confidence. Safe to use in reporting.
- Same claim, different confidence. The number is identical; the defensibility is not.
- Without scores, you're guessing. With scores, you're deciding with evidence.
Confidence scoring turns "we have some data" into "we know how much we can trust it."
- Source quality: Primary source vs. second-hand? Verified vs. self-declared?
- Recency: How old is the data? Does it reflect current processes?
- Traceability: Can you trace back to original measurement?
- Cross-checks: Does it align with reference databases, industry norms, or other sources?
- Completeness: Are critical fields present? Gaps reduce confidence.
High confidence: Use in decisions, reports, and external claims. Document the score and why it's high.
Medium confidence: Use with caveats, or flag for review. Good for internal planning, risky for public claims.
Low confidence: Don't act on it without verification. Treat as a hypothesis, not a fact.
Greenwashing risk comes from overclaiming on weak evidence. Confidence scoring forces you to distinguish between "we have a number" and "we can defend this number." Auditors and regulators care about the latter.
- Treating all data the same—one source isn't the same as five
- Ignoring low-confidence data instead of flagging it for improvement
- Using confidence scores as a substitute for judgment, not a support for it
- Not documenting why a score is high or low—you need the audit trail
See it in action
Need confidence scoring for your environmental claims?