layers.gold.decision_metrics

layers.gold.decision_metrics

Decision-useful metrics for investment, operations, and compliance.

Functions

Name	Description
calculate_compliance_metrics	Calculate compliance risk, reporting obligations, and audit readiness.
calculate_efficiency_benchmarks	Calculate efficiency scores and performance categories vs industry benchmarks.
calculate_marginal_abatement_cost	Calculate marginal abatement cost, NPV, and payback for reduction scenarios.
calculate_regulatory_risk_score	Calculate regulatory risk score from emissions scale, intensity, and profile.
create_decision_dashboard	Combine all decision metrics into comprehensive dashboard.
identify_reduction_opportunities	Identify reduction opportunities by source (venting, flaring, fuel).
normalize_score	Normalize a score series to 0-100 range.

calculate_compliance_metrics

layers.gold.decision_metrics.calculate_compliance_metrics(
    operator_emissions: pd.DataFrame,
    reporting_threshold_kt: float = 100.0,
)

Calculate compliance risk, reporting obligations, and audit readiness.

Args: operator_emissions: Operator emissions DataFrame reporting_threshold_kt: Regulatory reporting threshold (kt CO2e)

Returns: DataFrame with compliance metrics

calculate_efficiency_benchmarks

layers.gold.decision_metrics.calculate_efficiency_benchmarks(
    operator_emissions: pd.DataFrame,
)

Calculate efficiency scores and performance categories vs industry benchmarks.

calculate_marginal_abatement_cost

layers.gold.decision_metrics.calculate_marginal_abatement_cost(
    operator_emissions: pd.DataFrame,
    carbon_price_per_tonne: float = 170.0,
    capex_reduction_pct: float = 0.25,
)

Calculate marginal abatement cost, NPV, and payback for reduction scenarios.

Args: operator_emissions: Operator emissions DataFrame carbon_price_per_tonne: Carbon price ($/tCO2e) capex_reduction_pct: Assumed reduction percentage

Returns: DataFrame with MAC, NPV, and payback metrics

calculate_regulatory_risk_score

layers.gold.decision_metrics.calculate_regulatory_risk_score(
    operator_emissions: pd.DataFrame,
)

Calculate regulatory risk score from emissions scale, intensity, and profile.

create_decision_dashboard

layers.gold.decision_metrics.create_decision_dashboard(
    operator_emissions: pd.DataFrame,
)

Combine all decision metrics into comprehensive dashboard.

Warning: The composite_score and audit_readiness_score metrics use simplified calculations that exclude placeholder dimensions: - data_quality_score is not modeled (set to NaN) - documentation_score is not modeled (excluded from audit_readiness_score) - risk_growth is not modeled (set to NaN, excluded from regulatory_risk_score)

These metrics should be considered experimental until the data quality validation system is integrated and multi-year data is available for growth risk modeling.

Returns: DataFrame with investment, operations, compliance, and composite scores

identify_reduction_opportunities

layers.gold.decision_metrics.identify_reduction_opportunities(
    operator_emissions: pd.DataFrame,
)

Identify reduction opportunities by source (venting, flaring, fuel).

normalize_score

layers.gold.decision_metrics.normalize_score(
    series: pd.Series,
    method: str = 'percentile',
)

Normalize a score series to 0-100 range.

Args: series: Series to normalize method: Normalization method (‘percentile’, ‘minmax’, or ‘zscore’)

Returns: Normalized series with values in [0, 100] range