"""
Carbon Footprint Calculator — Scope 1/2/3 emissions estimation.
Estimates corporate carbon footprints using EPA emission factors,
energy consumption data, and supply chain multipliers.
Free data sources: EPA GHG factors, EIA energy data.
"""
import json
from typing import Dict, List, Optional
from datetime import datetime
# EPA emission factors (kg CO2e per unit)
EMISSION_FACTORS = {
# Scope 1: Direct emissions
"natural_gas": {"factor": 53.06, "unit": "kg CO2e/MMBtu"},
"diesel": {"factor": 10.21, "unit": "kg CO2e/gallon"},
"gasoline": {"factor": 8.887, "unit": "kg CO2e/gallon"},
"propane": {"factor": 5.72, "unit": "kg CO2e/gallon"},
"coal_bituminous": {"factor": 93.28, "unit": "kg CO2e/MMBtu"},
"fuel_oil": {"factor": 10.21, "unit": "kg CO2e/gallon"},
# Scope 2: Electricity (US average)
"electricity_us_avg": {"factor": 0.386, "unit": "kg CO2e/kWh"},
"electricity_eu_avg": {"factor": 0.276, "unit": "kg CO2e/kWh"},
"electricity_china": {"factor": 0.555, "unit": "kg CO2e/kWh"},
"electricity_india": {"factor": 0.708, "unit": "kg CO2e/kWh"},
"electricity_uk": {"factor": 0.212, "unit": "kg CO2e/kWh"},
# Scope 3: Supply chain multipliers (kg CO2e per $1000 spend)
"manufacturing": {"factor": 420, "unit": "kg CO2e/$1000"},
"transportation": {"factor": 580, "unit": "kg CO2e/$1000"},
"professional_services": {"factor": 120, "unit": "kg CO2e/$1000"},
"it_services": {"factor": 180, "unit": "kg CO2e/$1000"},
"construction": {"factor": 350, "unit": "kg CO2e/$1000"},
"agriculture": {"factor": 680, "unit": "kg CO2e/$1000"},
"mining": {"factor": 820, "unit": "kg CO2e/$1000"},
}
# GHG Protocol sector benchmarks (tonnes CO2e per $M revenue)
SECTOR_BENCHMARKS = {
"technology": 25,
"financial_services": 15,
"healthcare": 45,
"manufacturing": 180,
"energy_oil_gas": 550,
"utilities": 800,
"retail": 65,
"transportation": 320,
"real_estate": 40,
"agriculture": 280,
"mining": 450,
"construction": 120,
"telecom": 35,
"media": 20,
}
def calculate_scope1(
fuel_consumption: Dict[str, float]
) -> Dict[str, float]:
"""
Calculate Scope 1 (direct) emissions from fuel consumption.
Args:
fuel_consumption: Dict mapping fuel type to quantity consumed.
e.g. {"natural_gas": 1000, "diesel": 500}
Returns:
Breakdown of emissions by fuel type + total in kg CO2e.
"""
breakdown = {}
total = 0.0
for fuel, quantity in fuel_consumption.items():
if fuel in EMISSION_FACTORS:
factor = EMISSION_FACTORS[fuel]["factor"]
emissions = quantity * factor
breakdown[fuel] = {
"quantity": quantity,
"factor": factor,
"unit": EMISSION_FACTORS[fuel]["unit"],
"emissions_kg_co2e": round(emissions, 2),
}
total += emissions
return {
"scope": 1,
"description": "Direct emissions from owned/controlled sources",
"breakdown": breakdown,
"total_kg_co2e": round(total, 2),
"total_tonnes_co2e": round(total / 1000, 2),
}
def calculate_scope2(
electricity_kwh: float,
region: str = "us_avg",
steam_mmbtu: float = 0,
cooling_kwh: float = 0,
) -> Dict:
"""
Calculate Scope 2 (indirect energy) emissions.
Args:
electricity_kwh: Annual electricity consumption in kWh.
region: Grid region for emission factor.
steam_mmbtu: Purchased steam in MMBtu.
cooling_kwh: Purchased cooling in kWh equivalent.
Returns:
Scope 2 emissions breakdown.
"""
elec_key = f"electricity_{region}"
elec_factor = EMISSION_FACTORS.get(elec_key, EMISSION_FACTORS["electricity_us_avg"])["factor"]
elec_emissions = electricity_kwh * elec_factor
steam_emissions = steam_mmbtu * 53.06 # natural gas equivalent
cooling_emissions = cooling_kwh * elec_factor * 0.3 # COP adjustment
total = elec_emissions + steam_emissions + cooling_emissions
return {
"scope": 2,
"description": "Indirect emissions from purchased energy",
"electricity": {
"kwh": electricity_kwh,
"region": region,
"factor": elec_factor,
"emissions_kg_co2e": round(elec_emissions, 2),
},
"steam": {
"mmbtu": steam_mmbtu,
"emissions_kg_co2e": round(steam_emissions, 2),
},
"cooling": {
"kwh": cooling_kwh,
"emissions_kg_co2e": round(cooling_emissions, 2),
},
"total_kg_co2e": round(total, 2),
"total_tonnes_co2e": round(total / 1000, 2),
}
def calculate_scope3(
supply_chain_spend: Dict[str, float],
employee_commute_km: float = 0,
business_travel_km: float = 0,
waste_tonnes: float = 0,
) -> Dict:
"""
Calculate Scope 3 (value chain) emissions using spend-based method.
Args:
supply_chain_spend: Dict mapping category to annual spend in $1000s.
employee_commute_km: Total annual employee commute km.
business_travel_km: Total annual business travel km (air).
waste_tonnes: Annual waste generated in tonnes.
Returns:
Scope 3 emissions breakdown.
"""
breakdown = {}
total = 0.0
# Purchased goods & services (spend-based)
for category, spend_k in supply_chain_spend.items():
if category in EMISSION_FACTORS:
factor = EMISSION_FACTORS[category]["factor"]
emissions = spend_k * factor
breakdown[category] = {
"spend_k_usd": spend_k,
"factor": factor,
"emissions_kg_co2e": round(emissions, 2),
}
total += emissions
# Employee commuting (avg 0.21 kg CO2e/km for mixed mode)
commute_emissions = employee_commute_km * 0.21
if employee_commute_km > 0:
breakdown["employee_commute"] = {
"km": employee_commute_km,
"emissions_kg_co2e": round(commute_emissions, 2),
}
total += commute_emissions
# Business travel (avg 0.255 kg CO2e/km for air)
travel_emissions = business_travel_km * 0.255
if business_travel_km > 0:
breakdown["business_travel"] = {
"km": business_travel_km,
"emissions_kg_co2e": round(travel_emissions, 2),
}
total += travel_emissions
# Waste (avg 460 kg CO2e/tonne for mixed waste)
waste_emissions = waste_tonnes * 460
if waste_tonnes > 0:
breakdown["waste"] = {
"tonnes": waste_tonnes,
"emissions_kg_co2e": round(waste_emissions, 2),
}
total += waste_emissions
return {
"scope": 3,
"description": "Value chain emissions (upstream + downstream)",
"method": "spend-based + activity-based hybrid",
"breakdown": breakdown,
"total_kg_co2e": round(total, 2),
"total_tonnes_co2e": round(total / 1000, 2),
}
def full_carbon_footprint(
fuel_consumption: Optional[Dict[str, float]] = None,
electricity_kwh: float = 0,
region: str = "us_avg",
supply_chain_spend: Optional[Dict[str, float]] = None,
employee_commute_km: float = 0,
business_travel_km: float = 0,
waste_tonnes: float = 0,
revenue_millions: Optional[float] = None,
sector: Optional[str] = None,
) -> Dict:
"""
Calculate complete carbon footprint across all scopes.
Returns total emissions with sector benchmark comparison.
"""
scope1 = calculate_scope1(fuel_consumption or {})
scope2 = calculate_scope2(electricity_kwh, region)
scope3 = calculate_scope3(
supply_chain_spend or {},
employee_commute_km,
business_travel_km,
waste_tonnes,
)
total_tonnes = scope1["total_tonnes_co2e"] + scope2["total_tonnes_co2e"] + scope3["total_tonnes_co2e"]
result = {
"timestamp": datetime.utcnow().isoformat(),
"scope1": scope1,
"scope2": scope2,
"scope3": scope3,
"total_tonnes_co2e": round(total_tonnes, 2),
"scope_percentages": {
"scope1": round(scope1["total_tonnes_co2e"] / max(total_tonnes, 0.01) * 100, 1),
"scope2": round(scope2["total_tonnes_co2e"] / max(total_tonnes, 0.01) * 100, 1),
"scope3": round(scope3["total_tonnes_co2e"] / max(total_tonnes, 0.01) * 100, 1),
},
}
if revenue_millions and sector and sector in SECTOR_BENCHMARKS:
benchmark = SECTOR_BENCHMARKS[sector]
intensity = total_tonnes / revenue_millions
result["benchmark"] = {
"sector": sector,
"sector_avg_tonnes_per_m_revenue": benchmark,
"company_tonnes_per_m_revenue": round(intensity, 2),
"vs_benchmark": f"{'above' if intensity > benchmark else 'below'} sector average",
"percentile_estimate": round(min(100, max(0, (1 - intensity / benchmark) * 50 + 50)), 0),
}
return result
def get_emission_factors() -> Dict:
"""Return all available emission factors with units."""
return EMISSION_FACTORS
def sector_benchmark_comparison(
total_tonnes: float, revenue_millions: float
) -> List[Dict]:
"""
Compare a company's carbon intensity against all sector benchmarks.
Returns ranked list of sectors with intensity comparison.
"""
intensity = total_tonnes / max(revenue_millions, 0.01)
results = []
for sector, benchmark in sorted(SECTOR_BENCHMARKS.items(), key=lambda x: x[1]):
results.append({
"sector": sector,
"benchmark_tonnes_per_m": benchmark,
"company_intensity": round(intensity, 2),
"ratio": round(intensity / benchmark, 2),
"status": "cleaner" if intensity < benchmark else "dirtier",
})
return results
