A Reducing-CO2 scenario maximises carbon reduction, not SAP 🟩

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

domain/modelling/optimisation/optimiser.py

@@ -244,6 +244,7 @@ def optimise_package_fabric_first(
     budget: Optional[float],
     target_sap: Optional[float],
     dependencies: Sequence[MeasureDependency] = (),
+    objective: Callable[[Score], float] = sap_rating,
 ) -> OptimisedPackage:
     """Select the Optimised Package under the Fabric First constraint: optimise
     the fabric measures (``FABRIC_MEASURE_TYPES``) first with the full budget;
@@ -271,6 +272,7 @@ def optimise_package_fabric_first(
         budget=budget,
         target_sap=target_sap,
         dependencies=dependencies,
+        objective=objective,
     )
     if (
         target_sap is not None

domain/modelling/scoring/scoring.py

@@ -15,7 +15,7 @@ truthful. The whole-package re-score (role 2) is `PackageScorer.score` directly.
 """
 
 from dataclasses import dataclass
-from typing import Sequence
+from typing import Callable, Sequence
 
 from datatypes.epc.domain.epc_property_data import EpcPropertyData
 from domain.modelling.scoring.package_scorer import PackageScorer, Score
@@ -113,3 +113,30 @@ def independent_option_impacts(
             scored.append((option.overlay, cached))
         impacts.append(cached)
     return impacts
+
+
+def independent_option_signals(
+    scorer: PackageScorer,
+    baseline: EpcPropertyData,
+    options: Sequence[MeasureOption],
+    objective: Callable[[Score], float],
+) -> list[float]:
+    """Each Option's independent-vs-baseline gain **in the objective's
+    currency** (role 1 — the optimiser's approximate input signal, ADR-0062):
+    SAP points for an Increasing-EPC goal, kg CO2 saved for Reducing CO2, £
+    saved for Energy Savings. Each distinct Simulation Overlay is scored once
+    (Options sharing an overlay reuse the result); results follow the input
+    order."""
+    base_value: float = objective(scorer.score(baseline, []))
+    scored: list[tuple[EpcSimulation, float]] = []
+    signals: list[float] = []
+    for option in options:
+        cached: float | None = next(
+            (signal for overlay, signal in scored if overlay == option.overlay),
+            None,
+        )
+        if cached is None:
+            cached = objective(scorer.score(baseline, [option.overlay])) - base_value
+            scored.append((option.overlay, cached))
+        signals.append(cached)
+    return signals

orchestration/modelling_orchestrator.py

@@ -20,6 +20,7 @@ from domain.modelling.optimisation.optimiser import (
     ScoredOption,
     optimise_package,
     optimise_package_fabric_first,
+    sap_rating,
 )
 from domain.modelling.scoring.package_scorer import PackageScorer, Score
 from domain.modelling.plan import Plan, PlanMeasure
@@ -29,7 +30,7 @@ from domain.modelling.scenario import Scenario
 from domain.modelling.scoring.scoring import (
     MeasureImpact,
     cascade_scores,
-    independent_option_impacts,
+    independent_option_signals,
     marginals_from_scores,
 )
 from domain.modelling.generators.wall_recommendation import recommend_cavity_wall
@@ -50,10 +51,12 @@ from repositories.solar.solar_repository import SolarRepository
 from repositories.unit_of_work import UnitOfWork
 
 # The PortfolioGoal value that targets a SAP band (cf.
-# backend.app.db.models.portfolio.PortfolioGoal.INCREASING_EPC). Other goals
-# (Energy Savings, Reducing CO2 emissions) don't yet set a SAP repair target —
-# the optimiser just maximises SAP gain within budget for them (later slice).
+# backend.app.db.models.portfolio.PortfolioGoal.INCREASING_EPC). The
+# goal-aligned goals (ADR-0062) set no target: they maximise their own metric
+# within the Scenario budget.
 _INCREASING_EPC_GOAL: Final[str] = "Increasing EPC"
+_REDUCING_CO2_GOAL: Final[str] = "Reducing CO2 emissions"
+_ENERGY_SAVINGS_GOAL: Final[str] = "Energy Savings"
 
 # Best-practice install sequence for the role-3 attribution cascade (ADR-0016):
 # walls → roof → ventilation → floor, per the legacy `Recommendations` class.
@@ -176,6 +179,10 @@ class ModellingOrchestrator:
         considered: Optional[frozenset[MeasureType]] = combine_considered_measures(
             scenario.considered_measures(), considered_measures
         )
+        # The Optimiser speaks the goal's currency (ADR-0062): group signals,
+        # dependency pricing and repair marginals are all measured by this
+        # objective — SAP by default, carbon reduction for a Reducing-CO2 goal.
+        objective: Callable[[Score], float] = _objective_for(scenario)
         groups: list[list[ScoredOption]] = _scored_candidate_groups(
             scorer,
             effective_epc,
@@ -183,6 +190,7 @@ class ModellingOrchestrator:
             planning_restrictions,
             solar_potential,
             considered,
+            objective,
         )
         # Forced Measure Dependencies (ventilation) are excluded from the pool
         # but injected into the package before the re-score (ADR-0016).
@@ -202,6 +210,7 @@ class ModellingOrchestrator:
             budget=scenario.budget,
             target_sap=_target_sap(scenario),
             dependencies=dependencies,
+            objective=objective,
         )
 
         # Role-3 attribution: re-apply the *selected* set in best-practice order
@@ -395,9 +404,11 @@ def _scored_candidate_groups(
     planning_restrictions: PlanningRestrictions,
     solar_potential: Optional[SolarPotential],
     considered_measures: Optional[frozenset[MeasureType]],
+    objective: Callable[[Score], float] = sap_rating,
 ) -> list[list[ScoredOption]]:
     """One group per Recommendation: each Option scored independently against
-    the baseline (role-1 warm-start signal, ADR-0016)."""
+    the baseline (role-1 warm-start signal, ADR-0016), in the goal objective's
+    currency (ADR-0062)."""
     # The SAP design heat loss sizes the ASHP to the dwelling (ADR-0049); read it
     # off a baseline score, which the group scoring computes anyway.
     baseline_result = scorer.score(effective_epc, []).sap_result
@@ -414,18 +425,33 @@ def _scored_candidate_groups(
         design_heat_loss_kw,
     ):
         options = list(recommendation.options)
-        impacts: list[MeasureImpact] = independent_option_impacts(
-            scorer, effective_epc, options
+        signals: list[float] = independent_option_signals(
+            scorer, effective_epc, options, objective
         )
         groups.append(
             [
-                ScoredOption(option=option, sap_gain=impact.sap_points)
-                for option, impact in zip(options, impacts, strict=True)
+                ScoredOption(option=option, sap_gain=signal)
+                for option, signal in zip(options, signals, strict=True)
             ]
         )
     return groups
 
 
+def _carbon_reduction(score: Score) -> float:
+    """The Reducing-CO2 objective: annual kg CO2 below zero-point, negated so
+    higher is better (a saved kg scores +1)."""
+    return -score.co2_kg_per_yr
+
+
+def _objective_for(scenario: Scenario) -> Callable[[Score], float]:
+    """The metric the Scenario's goal maximises (ADR-0062), as an Optimiser
+    objective (higher is better). Goals without an aligned metric optimise
+    SAP, as every goal did before."""
+    if scenario.goal == _REDUCING_CO2_GOAL:
+        return _carbon_reduction
+    return sap_rating
+
+
 def _target_sap(scenario: Scenario) -> Optional[float]:
     """The SAP rating the Optimiser repairs toward — the floor of the goal
     band for an INCREASING_EPC goal, else None (no SAP target)."""