"""
Carbon Footprint Calculator — Estimates Scope 1/2/3 carbon emissions for companies
and portfolios using EPA emission factors, IEA data proxies, and sector benchmarks.
Data sources: EPA eGRID (free), FRED energy data (free), sector emission factors (embedded).
"""
import json
import urllib.request
from datetime import datetime
from typing import Any
# Sector average tCO2e per $1M revenue (approximate, based on CDP/EPA data)
SECTOR_EMISSION_FACTORS = {
"energy": 850.0,
"utilities": 720.0,
"materials": 450.0,
"industrials": 180.0,
"consumer_discretionary": 55.0,
"consumer_staples": 95.0,
"healthcare": 40.0,
"financials": 12.0,
"technology": 18.0,
"communication_services": 22.0,
"real_estate": 65.0,
}
# Scope 2 electricity emission factors by region (tCO2e per MWh)
GRID_EMISSION_FACTORS = {
"US_avg": 0.386,
"EU_avg": 0.231,
"CN": 0.555,
"IN": 0.708,
"JP": 0.457,
"UK": 0.193,
"DE": 0.311,
"FR": 0.052,
"BR": 0.074,
"AU": 0.656,
}
def estimate_company_emissions(
revenue_millions: float,
sector: str,
employees: int = 0,
electricity_mwh: float | None = None,
region: str = "US_avg",
) -> dict[str, Any]:
"""
Estimate a company's carbon footprint across Scope 1, 2, and 3.
Args:
revenue_millions: Annual revenue in millions USD
sector: Industry sector (see SECTOR_EMISSION_FACTORS keys)
employees: Number of employees (for office emissions estimate)
electricity_mwh: Known electricity consumption in MWh (optional)
region: Grid region for Scope 2 calculation
"""
sector_key = sector.lower().replace(" ", "_")
factor = SECTOR_EMISSION_FACTORS.get(sector_key, 100.0)
# Scope 1: Direct emissions (estimated as % of total based on sector)
scope1_ratio = {"energy": 0.45, "utilities": 0.55, "materials": 0.35, "industrials": 0.25}.get(sector_key, 0.10)
total_estimated = revenue_millions * factor
scope1 = round(total_estimated * scope1_ratio, 2)
# Scope 2: Electricity / purchased energy
grid_factor = GRID_EMISSION_FACTORS.get(region, 0.386)
if electricity_mwh is not None:
scope2 = round(electricity_mwh * grid_factor, 2)
elif employees > 0:
# Average office: ~7 MWh per employee per year
scope2 = round(employees * 7.0 * grid_factor, 2)
else:
scope2 = round(total_estimated * 0.15, 2)
# Scope 3: Supply chain + product use (typically largest)
scope3 = round(total_estimated - scope1 - scope2, 2)
if scope3 < 0:
scope3 = round(total_estimated * 0.5, 2)
total = round(scope1 + scope2 + scope3, 2)
intensity = round(total / revenue_millions, 2) if revenue_millions > 0 else 0
return {
"company_estimate": {
"scope1_tco2e": scope1,
"scope2_tco2e": scope2,
"scope3_tco2e": scope3,
"total_tco2e": total,
"intensity_per_million_rev": intensity,
},
"sector": sector_key,
"sector_avg_factor": factor,
"region": region,
"grid_emission_factor": grid_factor,
"methodology": "Sector-average with EPA/IEA factors",
"timestamp": datetime.utcnow().isoformat(),
}
def estimate_portfolio_footprint(holdings: list[dict]) -> dict[str, Any]:
"""
Estimate carbon footprint for a portfolio of holdings.
Args:
holdings: List of dicts with keys: ticker, weight (0-1), revenue_millions, sector
"""
results = []
total_weighted_emissions = 0.0
total_weighted_intensity = 0.0
for h in holdings:
est = estimate_company_emissions(
revenue_millions=h.get("revenue_millions", 100),
sector=h.get("sector", "technology"),
)
weight = h.get("weight", 0)
co2 = est["company_estimate"]["total_tco2e"]
intensity = est["company_estimate"]["intensity_per_million_rev"]
total_weighted_emissions += co2 * weight
total_weighted_intensity += intensity * weight
results.append({
"ticker": h.get("ticker", ""),
"weight": weight,
"estimated_tco2e": co2,
"intensity": intensity,
})
return {
"portfolio_footprint": {
"weighted_tco2e": round(total_weighted_emissions, 2),
"weighted_intensity": round(total_weighted_intensity, 2),
"holdings_analyzed": len(results),
},
"holdings": results,
"timestamp": datetime.utcnow().isoformat(),
}
def get_emission_factors() -> dict[str, Any]:
"""Return all available emission factors for sectors and grid regions."""
return {
"sector_factors_tco2e_per_million_usd": SECTOR_EMISSION_FACTORS,
"grid_factors_tco2e_per_mwh": GRID_EMISSION_FACTORS,
"source": "EPA eGRID + IEA + CDP sector averages",
}
