api

Look up a single greenhouse-gas emission factor by its canonical key. Returns the value plus an audit envelope: provenance (publisher, exact source reference, retrieval date, LICENCE and whether it may be redistributed, with the attribution the licence requires) and verification (whether the per-gas components sum to the headline, the GWP set, and the source note stating what the publisher did NOT provide). If the key does not exist you get candidate keys back rather than a dead end. Use search_factors first if you do not know the key.

{
  "type": "object",
  "properties": {
    "key": {
      "type": "string",
      "description": "Canonical factor key, e.g. \"grid.gbr.electricity.location_based\"."
    }
  },
  "required": [
    "key"
  ]
}

Find emission factors by section, key prefix, or free text. Returns each match with its key, name, section, VALUE, unit and gas — enough to choose between them or read a few numbers without a second call. For one factor's full audit envelope (publisher, exact source reference, retrieval date, licence, verification) call lookup_factor; for several, call lookup_factors.

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Free-text search over factor names/keys."
    },
    "section": {
      "type": "string",
      "description": "Restrict to a section, e.g. \"grid\", \"fuels\", \"freight\"."
    },
    "key_prefix": {
      "type": "string",
      "description": "Restrict to keys starting with this prefix."
    },
    "limit": {
      "type": "number",
      "description": "Max results (default 20)."
    }
  }
}

Look up SEVERAL emission factors by key in one call, each with the same audit envelope lookup_factor returns — provenance (publisher, exact source reference, retrieval date, licence, redistribution) and verification. Use this instead of calling lookup_factor in a loop: a portfolio or a multi-country comparison is one call, not one per factor. Keys that do not exist come back in `not_found` rather than failing the batch.

{
  "type": "object",
  "properties": {
    "keys": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Canonical factor keys, e.g. [\"grid.gbr.electricity.location_based\", \"grid.fra.electricity.location_based\"]. Maximum 25 per call."
    }
  },
  "required": [
    "keys"
  ]
}

Turn activity data into greenhouse-gas emissions: emissions = activity × factor. Give an amount + unit and a factor key; the unit engine converts to the factor basis (MWh→kWh, tonne→kg, gallon→litre, mile→km) and returns the emissions with the working, the GHG Protocol scope, and the source. Use search_factors / lookup_factor to find the factor key.

{
  "type": "object",
  "properties": {
    "activity": {
      "type": "object",
      "description": "{ \"value\": <number>, \"unit\": \"<unit e.g. kWh, MWh, litres, tonne, km>\" }."
    },
    "factor_key": {
      "type": "string",
      "description": "Canonical emission-factor key."
    }
  },
  "required": [
    "activity",
    "factor_key"
  ]
}

Whole-life embodied carbon for materials, per EN 15978. Give a material key + quantity (+ optional boundary A1-A3 / A1-A4 / A1-A5 / A1-C); it assembles the declared lifecycle modules (A1-A3, B, C1-C4, D) into stages, totals the boundary, reports module D separately, and flags any missing stage as not-assessed (never zero). Find material keys via search_factors (section "materials").

{
  "type": "object",
  "properties": {
    "materials": {
      "type": "array",
      "description": "Each: { material_key, quantity:{value,unit e.g. m3/kg/tonne/m2}, boundary? }.",
      "items": {
        "type": "object"
      }
    }
  },
  "required": [
    "materials"
  ]
}

Compute PCAF Part A financed emissions for a portfolio. Returns each holding's attribution factor and financed emissions, the portfolio total, the outstanding-weighted data-quality score, and the audit trail (formula + PCAF source).

{
  "type": "object",
  "properties": {
    "asset_class": {
      "type": "string"
    },
    "holdings": {
      "type": "array",
      "description": "Each: { outstanding_amount, denominator:{type:\"evic\"|\"equity_plus_debt\", value}, company_emissions:{value,unit?} OR estimate_from_spend:{amount_usd, sector_key}, data_quality_score? }.",
      "items": {
        "type": "object"
      }
    }
  },
  "required": [
    "holdings"
  ]
}

GHG Protocol Scope 2 for purchased electricity, both methods. Always returns location-based (grid-average) emissions; also returns market-based when you supply a contractual supplier_factor (e.g. a green tariff / REC = 0) or a market_factor_key (residual mix). Find grid keys via search_factors (section "grid").

{
  "type": "object",
  "properties": {
    "consumption": {
      "type": "object",
      "description": "{ \"value\": <number>, \"unit\": \"kWh|MWh|GWh\" }."
    },
    "location_factor_key": {
      "type": "string",
      "description": "Grid-average factor key, e.g. grid.gbr.electricity.location_based."
    },
    "market_factor_key": {
      "type": "string",
      "description": "Optional: residual-mix / supplier grid factor key."
    },
    "supplier_factor": {
      "type": "object",
      "description": "Optional contractual factor { value, unit } (wins over market_factor_key)."
    }
  },
  "required": [
    "consumption",
    "location_factor_key"
  ]
}

Freight & logistics emissions (Scope 3 Cat 4 & 9), GLEC framework. THE FACTOR'S DENOMINATOR PICKS THE METHOD — check the factor's unit before choosing the input. Per tonne-km: emissions = tonnes × km × factor, send "mass". Per TEU-km (the sea-container family, e.g. freight_detailed.sea.container.trans_suez.dry): emissions = TEU × km × factor, send "teu" — a TEU is a twenty-foot container slot, not a mass, and a forty-foot box is 2 TEU. Sending mass against a per-TEU-km factor is refused (422), not converted. Find mode factors via search_factors (section "freight" or "freight_detailed"), e.g. freight.road_hgv.tonne_km.

{
  "type": "object",
  "properties": {
    "mass": {
      "type": "object",
      "description": "{ \"value\": <number>, \"unit\": \"tonne|kg|lb\" }. Required for a per-tonne-km factor; omit for a per-TEU-km one."
    },
    "teu": {
      "type": "object",
      "description": "{ \"value\": <number> } — container slots. Required for a per-TEU-km factor; omit for a per-tonne-km one."
    },
    "distance": {
      "type": "object",
      "description": "{ \"value\": <number>, \"unit\": \"km|mi|nmi\" }."
    },
    "factor_key": {
      "type": "string",
      "description": "Freight factor key. Its unit says whether to send mass (per tonne-km) or teu (per TEU-km)."
    }
  },
  "required": [
    "distance",
    "factor_key"
  ]
}

Spend-based (EEIO) Scope 3 screening: emissions = spend × economic-intensity factor. Spend must be in the factor's own currency/year (no FX). Find sector factors via search_factors (section "spend_based"), e.g. spend_based.us.naics6.541511.custom_computer_programming_services.

{
  "type": "object",
  "properties": {
    "spend": {
      "type": "object",
      "description": "{ \"value\": <number>, \"currency\": \"USD|GBP|EUR|SGD\" }."
    },
    "factor_key": {
      "type": "string",
      "description": "Spend-based (EEIO) sector factor key."
    }
  },
  "required": [
    "spend",
    "factor_key"
  ]
}

Business travel emissions (Scope 3 Cat 6), distance method: emissions = km × passengers × factor. Air factors come in with_rf / without_rf (radiative forcing) variants. Find factors via search_factors (section "business_travel").

{
  "type": "object",
  "properties": {
    "distance": {
      "type": "object",
      "description": "{ \"value\": <number>, \"unit\": \"km|mi\" }."
    },
    "passengers": {
      "type": "number",
      "description": "Optional, defaults to 1."
    },
    "factor_key": {
      "type": "string",
      "description": "Per-passenger-km travel factor key."
    }
  },
  "required": [
    "distance",
    "factor_key"
  ]
}

Find the best emission-factor key(s) for a plain-language description — the hardest step is picking the right key out of ~16,000. Returns ranked matches, each carrying the key, value, unit and a confidence score; feed the chosen key to a calculate_* tool or lookup_factor. Prefer this over guessing a key. PUT THE COUNTRY IN THE DESCRIPTION. Geography is read from the description text itself, not from a separate field — "diesel per litre" and "diesel per litre France" resolve differently, and omitting the country will quietly return a factor from somewhere else marked "geo_match":"proxy". ACT ON THE LABEL. Every candidate carries label: "accept" or "review", plus "why". "accept" means confidence >= 0.85 and no demotion applied — right about nine times in ten. "review" means the answer may be usable but something is off (low confidence, only one term matched, a proxy country, or a gate demoted it); confirm it before adopting the number rather than using it silently. Roughly half of CORRECT answers are also flagged "review" — that is the intended trade, so treat "review" as "check this", not "discard this". A MISS MAY EXPLAIN ITSELF. When nothing matches, or the only matches are from the wrong country, the response may carry an "absence" object saying WHY. classification "structural" means no publisher issues this anywhere — STOP, do not retry with reworded queries and do not substitute a different country without saying so. "not_yet_sourced" means a publisher exists and we have not ingested it (the publisher is named). "refused" means we found the data and declined it, with the reason. "coupled" means this reads empty only because a related family is empty. Use explain_absence to ask the same question directly.

{
  "type": "object",
  "properties": {
    "description": {
      "type": "string",
      "description": "What you need a factor for, INCLUDING the country if it matters, e.g. \"UK grid electricity\", \"diesel per litre France\", \"hotel stay Japan\". Geography is parsed from this string."
    },
    "section": {
      "type": "string",
      "description": "Optional section filter, e.g. \"fuels\", \"grid\", \"freight\"."
    },
    "limit": {
      "type": "number",
      "description": "Optional, default 5."
    }
  },
  "required": [
    "description"
  ]
}

Ask why a factor is NOT in the corpus. The reasoned counterpart to coverage: where a country reports zero rows for an inventory family, this says whether that is the world's limit or our backlog. Five classifications: "structural" (no publisher issues this anywhere — stop looking, and do not silently substitute another country), "not_yet_sourced" (a publisher exists and is named; it is our backlog), "refused" (we found it and declined — the reason is stated), "held_not_counted" (we DO hold it — see we_hold for the key), "coupled" (empty only because another family is empty). Each record names the publisher checked, the route tried, a confidence and a review date, and reports stale:true once past review. These are authored judgements about what the world publishes, not values derived from our data. Call this before concluding that a gap is permanent, and before telling a user to look elsewhere.

{
  "type": "object",
  "properties": {
    "country": {
      "type": "string",
      "description": "ISO-3166 alpha-3 code, e.g. \"can\"."
    },
    "family": {
      "type": "string",
      "description": "Inventory family, e.g. \"water\", \"wtt\", \"travel\", \"spend\", \"heat\"."
    },
    "classification": {
      "type": "string",
      "description": "Filter: structural | not_yet_sourced | refused | held_not_counted | coupled."
    }
  }
}