Strategic Transition Intelligence

Carbon & Sustainability Intelligence

Continuously connect carbon price, emissions, sustainability performance, transition risk, assets, and capital decisions.

Back to Future Finance suite Open Continuous Close proof

Core decision question

How do carbon exposure, emissions, and transition risk change enterprise decisions?

Every signal, context package, evidence pack, policy result, recommendation, human approval, action, replay, and learning artifact should serve this question.

North Star KPIs

Carbon exposure visibilityTransition risk reductionEmissions forecast accuracyCapital transition alignmentSustainability performance

Agent workforce

Carbon Intelligence AgentEmissions Forecast AgentTransition Risk AgentSustainability Performance AgentCarbon Scenario Agent

Enterprise integrations

Carbon systemsProduction systemsAsset systemsTrading systemsSAP S/4HANAMarket feedsDatabricksFuture UDP

Monitored domains

Carbon exposureCarbon pricingEmissions forecastingSustainability performanceTransition riskLow-carbon investments

Reference signal ticker

Signal visibility must be more than the word signal. Each mission shows reference signal counts, severity, scenario REF-TICK ID, and current blocked/recommended state. These are scenario proof shapes until connected to live traversal.

Reference signal generated from the scenario library; REF-TICK IDs show the expected production trace shape and are not attested live events.

3535 reference signals / scenario

66 transition-risk movements

ScenarioScenario event REF-TICK-12044 at 14:10

$140M$140M carbon exposure shift

Reference signals and recommended responses

Signal	Source	Impact	Recommended response
Carbon price increased	Carbon market feed	$140M exposure shift	Run portfolio impact
Emissions forecast worsened	Asset system	Target variance risk	Recommend mitigation
Transition scenario changed	Scenario feed	Portfolio risk shift	Re-rank investments

Demo sequence

Carbon cost increases
Asset and portfolio impact recalculates
Transition risk changes
Capital decisions re-ranked
Evidence and scenario replay generated

Source-system traceability for major numbers

Every major value is tied to a source system, API object, data product, or source transaction. This answers where the number came from before BP asks.

Context pack: business unit: Group Finance · cost center: CC-1042 · entity code: BP-GROUP · owner: mission runtime.

Number / metric	Source system or data product	Record / API object	Business context
$140M exposure shift	Reference carbon market feed + asset model	scenario_carbon_event=REF-TICK-12044	Owner: Sustainability Finance
Target variance risk	Asset emissions system	asset=EMISS-NS-440	Policy: Carbon target
Investment ranking shift	Scenario engine + capital model	scenario=TRANS-LOW-18	Owner: Strategy

Evidence, policy, replay, learning, and action artifacts

Decision memory

Runtime artifact	Identifier	Credibility proof
Evidence pack	EP-CARB-12044	data-evidence="pack" · source records and supporting documents
Policy ID	FIN-CARB-2026-06	Policy runtime gate evaluated before recommendation
Replay ID	REPLAY-CARB-12044	Replay available from signal through human judgment and action
Learning	patterns learned: 2	Decision memory updates after accepted/rejected outcome
Action payload	Carbon scenario approval workflow	Next action: governed execution after approval limit $500K and human override check

Alternative actions table

Alternative actions table. This is the concrete recommendation surface: bp Sphere shows what it recommends, what it rejected, the expected value, the risk, and the action path.

Alternative action	Confidence	Expected value / impact	Risk / tradeoff	Action path
Re-rank low-carbon investment	82%	+0.2 transition score	Capital tradeoff	Strategy review
Mitigate emissions variance	85%	$24M cost avoided	Operational dependency	Asset action
Update carbon price assumption	91%	$140M exposure governed	Forecast sensitivity	FP&A approval

Runtime injection console

Use these controls during the workshop to show the mission changing state. Injection creates a named runtime event, updates mission KPI/readiness, adds a replay row, and opens the trace from signal to learning.

Mission KPI

Carbon exposure visibility

Awaiting scenario injection.

Mission readiness

Baseline

No injected blockers or opportunities yet.

Latest replay event

None

Click an injection below to create a replayable runtime event.

Time	Event	Scenario	Decision impact
--:--:--	Waiting	No scenario injected	No mission state change

Decision portfolio

Decision	Impact	Owner	Status
Re-rank low-carbon investment	+0.2 transition score	Strategy Lead	Recommended
Mitigate asset emissions variance	$24M cost avoided	Asset Lead	Draft action
Update carbon price assumption	$140M exposure	FP&A Lead	Governed

Vertical operating-model audit

This validates that the mission is useful from frontline work through enterprise steering: Agent Runtime → Analyst → Supervisor → Function Leader/Controller → FB&T Leader → CFO.

Role layer	What this layer can do	Executive credibility proof
Agent Runtime	Carbon, emissions forecast, transition risk, sustainability, and carbon scenario agents monitor transition impact.	Carbon trace with asset, emissions, carbon price, policy, and capital effect.
Analyst	Review emissions, carbon price, and asset-level signals.	Can explain why carbon exposure changed.
Supervisor	Monitor sustainability actions and evidence gaps.	Can manage unresolved carbon-risk work.
Function Leader / Sustainability Lead	Optimize transition-risk response and sustainability performance.	Can compare asset and portfolio exposure.
FB&T Leader	Understand carbon impact on finance missions, capital, and performance.	Can orchestrate transition tradeoffs.
CFO	Steer carbon, cash, earnings, risk, and capital tradeoffs.	Can defend strategic transition decisions.

Executive credibility scenarios

These are the scenarios BP can challenge live. The mission must show signal, context, evidence, policy, decision, action, replay, learning, and enterprise impact for each.

Scenario	Inject	Expected runtime proof
Carbon price increase	Carbon market price rises	Asset, portfolio, capital, and transition impact update.
Emissions forecast worsens	Asset emissions variance	Target risk, mitigation actions, and evidence are visible.
Transition scenario changes	Transition risk model changes	Investment ranking and board scenario update.

12+ dimension BP credibility audit

Dimension	Visible proof requirement	This mission's answer
Signal	Business event or market movement is visible and linked to a mission decision.	Carbon price increased · Scenario event REF-TICK-12044 at 14:10
Context	Entity, business unit, owner, system object, policy domain, and history are resolved.	Resolved through enterprise context graph: owner, system object, business unit, history, and policy domain.
Evidence	Source records, documents, approvals, and source-system snapshots are inspectable.	Evidence pack includes source system record, curated data snapshot, policy reference, and supporting document where applicable.
Policy	Authority, control, risk, accounting, tax, treasury, or investment policies are referenced.	Policy/runtime gate is evaluated before recommendation or action.
Decision	The mission produces a named decision object, not just a metric or chart.	Re-rank low-carbon investment
Recommendation	Recommended response and alternatives are shown with impact.	Re-rank low-carbon investment · confidence 82% · impact +0.2 transition score
Human Override	Human approval, rejection, override, or escalation path is explicit.	Approval, rejection, override reason, and escalation owner are retained in replay.
Action	Action payload is traceable to workflow, system update, notification, hold, hedge, or approval.	Recommended
Replay	Event, context, evidence, policy, recommendation, human judgment, action, and outcome can be replayed.	Replay captures signal, context, evidence, policy, agent output, human judgment, action, outcome, and learning.
Learning	Accepted/rejected outcomes are captured as future pattern memory.	Outcome becomes pattern memory after human decision and realized impact are known.
Business Impact	Financial, risk, cash, control, or value impact is quantified.	+0.2 transition score
Enterprise Integration	Systems of record, data products, APIs, and future UDP context layer are named.	Carbon systems · Production systems · Asset systems · Trading systems · example: Reference carbon market feed + asset model
Source Drill-down	Users can drill from decision to source transaction, document, or curated data product.	Source drill-down points to SOR/API record, Databricks/UDP data product, document, or market feed behind the decision.
Confidence	The decision carries confidence and uncertainty drivers.	Confidence is shown in decision formation and decomposed into data freshness, evidence completeness, policy clarity, and model uncertainty.
Alternatives	At least two alternative actions or scenarios are available.	Re-rank low-carbon investment \| Mitigate emissions variance \| Update carbon price assumption
Audit Trail	Every material step is retained with timestamp, actor/agent, evidence, and policy result.	Timestamped audit trail spans agent, human, policy, evidence, and action events.
Executive Narrative	The mission can produce an executive answer: what happened, why, what to do, and expected outcome.	Narrative answers what happened, why, recommended action, value, risk, and next owner.
Mission KPI	A mission-level KPI changes when the scenario changes.	Carbon exposure visibility
Readiness	Readiness or confidence updates when blockers, policies, or evidence status change.	Mission readiness/confidence updates when signals, evidence, policies, or blockers change.
Enterprise Impact	The mission shows enterprise-level consequence beyond one transaction.	How do carbon exposure, emissions, and transition risk change enterprise decisions?

Runtime architecture

SignalBusiness event, market movement, policy breach, or performance variance.

ContextEntity, asset, customer, project, policy, historical pattern, and owner resolution.

Evidence + PolicySource records, documents, controls, authority, and data-quality checks.

Decision + ActionAlternatives, confidence, financial impact, owner, action payload, and approval path.

Replay + LearningFull trace, outcome, accepted/rejected recommendation, and pattern memory.