Transparency
Scoring Algorithm
How the composite ESG score is calculated from raw survey responses — fully documented for auditability.
Composite Score Formula
40%
Environmental
Energy, GHG, Water, Waste
30%
Social
Labour, Safety, Gender
30%
Governance
Compliance, Certifications
Scores are expressed on a 0–100 scale. The Environmental pillar carries a higher weight reflecting the sector's primary impact area: energy-intensive manufacturing with significant Scope 1 and 2 emissions.
Pillar Score Derivation
Environmental Score (E)
| Metric | Sub-weight | Direction |
|---|---|---|
| Energy intensity (kWh/unit) | 35% | ↓ Lower better |
| Renewable energy share | 25% | ↑ Higher better |
| GHG intensity (kgCO₂e/unit) | 20% | ↓ Lower better |
| Water intensity (L/unit) | 10% | ↓ Lower better |
| Waste diversion rate | 10% | ↑ Higher better |
Social Score (S)
| Metric | Sub-weight | Direction |
|---|---|---|
| Wage compliance rate | 30% | ↑ Higher better |
| Workplace safety score | 30% | ↑ Higher better |
| Gender ratio (female %) | 20% | ↑ Higher better |
| Labour rights compliance | 20% | ↑ Higher better |
Governance Score (G)
| Metric | Sub-weight | Direction |
|---|---|---|
| Compliance section scores (avg) | 60% | ↑ Higher better |
| Certifications held (count/max) | 25% | ↑ Higher better |
| Permit validity rate | 15% | ↑ Higher better |
Normalization Method
Each metric is normalized to a 0–100 score using min-max scaling within the sector peer group:
For "lower is better" metrics (energy intensity, GHG, water), the formula is inverted so that the most efficient factory scores 100.
Metrics with fewer than 3 data points in the peer group are excluded from scoring and flagged as insufficient_data.
Peer Group Benchmarking
Factories are benchmarked within peer groups determined by factory type (Cut-to-Pack vs Vertically Integrated) and size band. The peer group selection follows a two-level fallback:
- Type + size band (if n ≥ 10 factories)
- Type only (if n ≥ 10 but size band too small)
- All factories (last resort fallback)
A minimum of n = 10 factories is required before computing any percentile rank, to prevent de-anonymization through inference.