Optimize carbon cost performance portfolio
Construct a dependency-, exclusion-, budget-, and capacity-feasible portfolio frontier across investment, expected and CVaR total cost including scenario carbon price, residual emissions, and performance capacity, with explicit interactions and exact-or-disclosed heuristic search.
What it's for
Gives CTOs, CEOs, investors, and infrastructure leaders one inspectable efficient frontier for spend, tail cost, emissions, and performance—without hiding tradeoffs in a synthetic ESG or efficiency score.
What you give it
Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.
| Field | Type | Role | Required |
|---|---|---|---|
| implementation_capacity_units | number ≥ 0 | Your calibration | Yes |
| initiatives | array of objects (8 fields) ≥ 1 item | Evidence | Yes |
| interactions | array of objects (6 fields) | Evidence | Yes |
| investment_budget | number ≥ 0 | Your calibration | Yes |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| maximum_cvar_total_cost | number ≥ 0 | Your calibration | Optional |
| maximum_exact_states | integer ≥ 2, ≤ 65536 | Numerical control | Optional |
| maximum_expected_emissions_tonnes | number ≥ 0 | Your calibration | Optional |
| maximum_expected_total_cost | number ≥ 0 | Your calibration | Optional |
| maximum_frontier_states | integer ≥ 10, ≤ 100000 | Your calibration | Optional |
| minimum_expected_performance_capacity | number ≥ 0 | Your calibration | Optional |
| risk_aversion | number ≥ 0 | Your calibration | Optional |
| scenarios | array of objects (6 fields) ≥ 2 items | Evidence | Yes |
| tail_probability | number ≥ 0.001, ≤ 0.5 | Your calibration | Optional |
Each initiatives
record
| Field | Type | Required |
|---|---|---|
| dependency_ids | array of string | Yes |
| emissions_reduction_tonnes_scenarios | array of number (≥ 2 items) | Yes |
| exclusion_ids | array of string | Yes |
| id | string (non-empty) | Yes |
| implementation_capacity_units | number (≥ 0) | Yes |
| investment_cost | number (≥ 0) | Yes |
| operating_cost_reduction_scenarios | array of number (≥ 2 items) | Yes |
| performance_capacity_gain_scenarios | array of number (≥ 2 items) | Yes |
{
"implementation_capacity_units": 2,
"initiatives": [
{
"dependency_ids": [],
"emissions_reduction_tonnes_scenarios": [
50,
50
],
"exclusion_ids": [],
"id": "efficient-compute",
"implementation_capacity_units": 1,
"investment_cost": 10,
"operating_cost_reduction_scenarios": [
40,
40
],
"performance_capacity_gain_scenarios": [
10,
10
]
},
{
"dependency_ids": [],
"emissions_reduction_tonnes_scenarios": [
80,
80
],
"exclusion_ids": [],
"id": "low-carbon-region",
"implementation_capacity_units": 1,
"investment_cost": 15,
"operating_cost_reduction_scenarios": [
10,
10
],
"performance_capacity_gain_scenarios": [
2,
2
]
}
],
"interactions": [],
"investment_budget": 25,Truncated for display — the full payload is 67 lines.
What you get back
This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.
{
"assumptions": [
"Every initiative and interaction uses the same coherent operating-cost, emissions, performance and carbon-price scenario columns, horizon, system boundary and counterfactual; reductions are incremental and jointly reconciled rather than independently claimed benefits added together.",
"Investment and operating cost share one currency and present-value basis; emissions use a governed lifecycle boundary and comparable tonnes; performance capacity is decision-relevant and additive only as encoded. Carbon price is a declared scenario value, not a universal social-cost estimate.",
"Exact mode certifies the frontier only over all feasible submitted portfolios. Heuristic mode returns a supported set without a global certificate; missing technologies, nonlinear load shifts, rebound, embodied carbon, grid constraints, location and reliability can change the frontier.",
"The selected portfolio minimizes the declared risk-adjusted total cost among evaluated target-clearing portfolios. It is not a sustainability assurance, regulatory disclosure, procurement authority, migration order, vendor score or staffing instruction."
],
"configuration": {
"implementation_capacity_units": 2,
"investment_budget": 25,
"maximum_cvar_total_cost": 250,
"maximum_expected_emissions_tonnes": 75,
"maximum_expected_total_cost": null,
"minimum_expected_performance_capacity": 50,
"reductions_capped_at_scenario_baselines": true,
"risk_aversion": 0,
"tail_probability": 0.5
},
"decision": "select_risk_adjusted_carbon_cost_performance_portfolio",
"method": "constrained_carbon_cost_performance_pareto_portfolio_v1",
"pareto_frontier": [
{
"cvar_total_cost": 245,
"expected_performance_capacity": 57,
"expected_residual_emissions_tonnes": 35,
"expected_total_cost": 160,
"implementation_capacity_units": 2,
"investment_cost": 25,
"pareto_efficient": true,
"passes_cvar_cost_target": true,
"passes_emissions_target": true,
"passes_expected_cost_target": true,
"passes_performance_target": true,
"risk_adjusted_total_cost": 160,
"selected_initiative_ids": [
"efficient-compute",
"low-carbon-region"
]
},
{
"cvar_total_cost": 320,
"expected_performance_capacity": 55,
"expected_residual_emissions_tonnes": 100,
"expected_total_cost": 220,Truncated for display — the full payload is 153 lines.
How it works
Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.
- 1 Freeze coherent baseline operating cost, emissions, performance and carbon-price scenarios, then encode each eligible infrastructure initiative's investment, implementation capacity, scenario effects, dependencies, exclusions and all material pair interactions.
- 2 Enumerate every feasible portfolio inside the exact-state boundary or build a disclosed multiweight dependency-closure supported set, cap reductions at scenario baselines, and price residual emissions inside total scenario cost without collapsing physical tonnes or performance into one hidden score.
- 3 Remove portfolios dominated across investment, expected cost, CVaR cost, emissions and performance, apply governed target gates, and select the lowest declared risk-adjusted total cost only among evaluated target-clearing portfolios.
Before you trust it
Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.
Assumptions & guardrails
- Objectives use commensurable locally governed value units, constraints reflect real feasibility, and uncertainty covers plausible adverse inputs.
- Cost, emissions, performance and carbon-price scenarios share one system boundary, lifecycle horizon and counterfactual; effects and interactions are incremental; dependencies and constraints are complete; carbon and performance units remain visible and decision-relevant.
- The recommendation is optimal only for its stated objective, feasible set, evidence, and solver guarantee; it is not a universal management optimum.
- The frontier is conditional on submitted technologies and scenario accounting; heuristic mode is not globally complete, carbon price is not a universal social cost, and selection is not sustainability assurance, regulatory disclosure, procurement or migration authority.
Minimum evidence
- initiatives: at least 1 rows/items
- scenarios: at least 2 rows/items
- interactions: required and organization-defined
- investment_budget: required and organization-defined
- implementation_capacity_units: required and organization-defined
How to validate it
Backtest the chosen action against simple feasible baselines on held-out scenarios, sweep costs/constraints/risk tolerance, and require constraint feasibility under adverse inputs.
Calibrating it to your org
Same for everyone
The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.
Specific to you
- joint prospective infrastructure scenario model with finance-reconciled present-value cost, sustainability-approved emissions boundary, decision-relevant performance capacity, causal or engineering effect ranges, common load/grid/price shocks and overlap reconciled to one status quo
- system and lifecycle boundary, eligible initiatives, effect evidence, scenario law, currency/horizon, emissions and performance units, carbon-price semantics, dependencies/interactions, investment budget, implementation capacity, expected/CVaR/emissions/performance targets, risk aversion, solver boundary, and accountable approvals
Calibration workflow
- 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
- 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
- 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
- 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
- 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
- 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.
Call it from your AI
You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.
gitrevio_capabilities_search
{ "q": "construct a dependency exclusion budget and" }
→ finds "optimize_carbon_cost_performance_portfolio"
gitrevio_capability_describe
{ "capability_id": "optimize_carbon_cost_performance_portfolio" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "optimize_carbon_cost_performance_portfolio", "arguments": { ... } }
→ returns the result shown aboveWorks in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.
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