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S2-CON

Scope 2 Consolidation

1. Identity​

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Depends on module:

Canonical computation and modeling domain for ESG calculations, simulations, extrapolation, aggregation, normalization, and decision-grade metric synthesis. Provides governed, auditable compute services to other modules (e.g., Reporting, Risk, Net Zero, ZARA).
Domain:
computation-hub
Category:
analytics-modeling
Classification:
module
Lifecycle status:
active
Semver:
1.0.0
Introduced in:
v0.3
Governance
AI risk level:
high
Trust threshold:
0.9
Human review required:
true
Verifier involved:
false
Audit required:
true
Ownership
Primary owner:
Platform
Architecture board:
true
White-label allowed:
true
Entrypoints
Docs:
/computation-hub
UI:
/app/computation-hub
API:
/api/computation-hub
Dependencies
Modules
  • sis
  • input-hub
Unresolved tokens
  • BUME
  • SEM
  • VTE
Engines (declared)
  • DICE
  • DaVE
  • VTE
  • SEM
  • BUME
Micro-engines (from registry)
Micro-engines (declared)
None
Signals
USO
  • DATA.COMPUTATION
  • DATA.VALIDATION
  • DATA.EXTRAPOLATION
  • MODEL.SIMULATION
  • MODEL.UNCERTAINTY
CSI
  • CSI_COMPUTATION_HUB
SSSR tags
  • computation
  • modeling
  • simulation
  • monte-carlo
  • bayesian
  • extrapolation
  • aggregation
  • normalization
  • validation
  • mice
Workflows & Outputs
Workflows
  • MicroEngineRouting
  • RuleBasedComputeDispatch
  • ScenarioModeling
  • UncertaintyQuantification
  • AggregationAndRollups
  • NormalizationAndBenchmarking
  • ExtrapolationAndGapFilling
  • ComputeAuditAndReplay
Outputs
  • computed_metrics
  • scenario_ranges
  • confidence_intervals
  • normalized_values
  • validation_findings
  • trust_scored_compute_outputs
Audit
Ledger:
ALTD
Replay supported:
true
PII policy:
no_pii_in_omr
Tags

Purpose
Normalizes and consolidates Scope 2 emissions into canonical outputs for:

  • location-based Scope 2 (scope2_location)
  • market-based Scope 2 (scope2_market) when available/derivable

This engine is a wrapper/entry point that ensures Scope 2 values are consistent and, when needed, delegates methodological conversion to MEID_CALC_GHG_SCOPE2_ADJ.

2. Contract References (ZAR)​

2.1. Input Schema​

ZAR Address: schema.compute.ghg.scope2.inputs.v1_0_0

Required conceptual fields:

  • scope2_location: scalar or time-series (recommended as authoritative)
  • scope2_market: optional scalar or time-series
  • target_method: LOCATION | MARKET | BOTH (default BOTH)
  • alignment: BY_YEAR | BY_INDEX (default BY_YEAR)

Optional (needed for derivation):

  • electricity_consumption
  • instrument_portfolio

2.2. Options Schema​

ZAR Address: schema.compute.ghg.scope2.options.v1_0_0

Common options:

  • derive_market_if_missing: boolean (default false)
  • residual_mix_policy: DEFAULT_GRID | COUNTRY_RESIDUAL | ERROR
  • missing_policy: ERROR | SKIP
  • rounding: optional digits

2.3. Output Schema​

ZAR Address: schema.compute.ghg.scope2.output.v1_0_0

Outputs include:

  • ghg_scope2_location
  • ghg_scope2_market (if available/derived)
  • ghg_scope2_selected (depends on target_method)
  • metadata (derivation flags, source method, coverage)

3. Accepted Input Shapes​

A. Location only

{
  "scope2_location": [[2025, 800]],
  "target_method": "LOCATION"
}

B. Location + market provided

{
  "scope2_location": [[2025, 800]],
  "scope2_market": [[2025, 600]],
  "target_method": "BOTH"
}

C. Derive market from electricity + instruments

{
  "scope2_location": [[2025, 800]],
  "electricity_consumption": [[2025, 100000]],
  "instrument_portfolio": [
    { "type": "EAC", "volume_mwh": 40000, "emission_factor": 0.0 }
  ],
  "target_method": "MARKET"
}

4. Compute Semantics (Normative)​

For each period tt:

4.1 Location value​

S2loc(t)=scope2_location(t)S2_{loc}(t) = scope2\_location(t)

4.2 Market value​

  • If scope2_market is provided:
S2mkt(t)=scope2_market(t)S2_{mkt}(t) = scope2\_market(t)
  • Else if derive_market_if_missing = true and sufficient inputs are provided:
    • Delegate to MEID_CALC_GHG_SCOPE2_ADJ to derive S2mkt(t)S2_{mkt}(t).
    • Record derivation details in metadata.

4.3 Selected output​

If target_method = LOCATION:

S2sel(t)=S2loc(t)S2_{sel}(t) = S2_{loc}(t)

If target_method = MARKET:

S2sel(t)=S2mkt(t)S2_{sel}(t) = S2_{mkt}(t)

If target_method = BOTH, S2_selected MUST be omitted or set to null, and both series are returned.

5. Validation & Error Model​

Invariants​

  • Location-based input is required (v1 default governance)
  • Values must be finite
  • Market derivation requires electricity consumption and either instruments and/or residual mix availability

Error codes (suggested)​

  • SCOPE2_LOCATION_REQUIRED
  • SCOPE2_MARKET_MISSING_AND_NOT_DERIVABLE
  • SCOPE2_ALIGNMENT_MISMATCH
  • SCOPE2_NON_FINITE_VALUE

6. Dependencies​

  • MEID_CALC_GHG_SCOPE2_ADJ (optional, for derivation)
  • Schema resolver (ZAR)
  • Residual mix datasets (if deriving)

Declared via ZAR dependencies.

7. Methods Served (v1)​

  • GHG.scope2.abs (selected method)
  • GHG.scope2.location.abs
  • GHG.scope2.market.abs


GitHub RepoRequest for Change (RFC)