Jira progress: loading…

NORM

Normalization Engines

1. Overview

Normalization Engines (NORM) are micro-engines that rescale or normalize values to enable comparability across entities, time periods, or benchmarks.

NORM engines preserve underlying meaning while adjusting representation for analysis.

2. Design Principles

1. Comparability Over Absolutes
   NORM engines exist to support relative analysis, not raw reporting.

2. Explicit Reference Bases
   All normalization is relative to a declared baseline (e.g. revenue, FTE, area).

3. Traceable Scaling
   Normalized outputs always reference their source values.

3. Scope of Responsibility

3.1. What NORM Engines Do

• Convert absolute metrics to intensities
• Scale values relative to benchmarks
• Normalize metrics for peer comparison

Examples:

• tCO₂e / revenue
• energy per m²
• emissions per unit produced

4. What NORM Engines Do Not Do

• ❌ Compute primary values (CALC)
• ❌ Aggregate entities (AGGR)
• ❌ Estimate missing data (SEM)
• ❌ Apply policy decisions

5. Inputs

• Validated and possibly aggregated values
• Normalization bases (denominators)
• Benchmark or peer group references

6. Outputs

• Normalized value payload
• Reference metadata
• Provenance links to source values

7. Canonical Identification

• Engine Type: NORM
• USO Code: NORM
• Category: Micro Engine (MICE)

---

Status: Stable
Owner: Computation Hub / MICE