Hourly Grid Carbon Intensity by ZIP Code: Free API Guide

A practical guide to the free hourly US grid carbon intensity API, with working code for two use cases: backtesting demand response and timing EV charging. It is a timing and analysis tool, not a source of Scope 2 reporting factors.

Published April 17, 2026 · Recomputed October 7, 2026 · By the emission-factors.com team

Is there a free API for hourly grid carbon intensity in the US?

How it works

1. Resolve ZIP to balancing authority. By the ZIP's utility (EIA-861), then its state and eGRID subregion, then its state (e.g. 94105 → CISO). The response's ba_basis says which rule applied. 46 balancing authorities are mapped; 45 currently report to EIA-930 (WACM has not reported since April 2, 2026, and returns a 404 that says so).
2. Fetch hourly fuel mix from EIA-930. MWh generated by each fuel type for every hour, per balancing authority.
3. Compute intensity. Multiply each fuel's MWh by its CO₂e rate (kg per MWh), sum, divide by total generation. Storage output (batteries, pumped storage) is excluded, including batteries reported inside "other" (CAISO).

Fuel factors (direct, operational CO₂e; not lifecycle):

Because these are US-average rates applied to in-region generation only (imports are not counted), hourly values do not add up to the annual eGRID subregion factor. Use them to compare hours, not to report Scope 2.

How much does intensity vary within a day?

A lot, depending on the grid. Hour-of-day averages over the 7 days (168 hours) ending April 16, 2026, 03:00 UTC:

Three-grid comparison: CA vs TX vs NY

Values in kg CO₂e per kWh. Three findings:

• California swings 4.5×; New York barely moves. California's afternoon solar drives a deep midday trough. New York runs on gas around the clock, so shifting load in time changes its carbon by under 10%.
• That week, New York's cleanest hour (0.313) was dirtier than Texas's dirtiest hour (0.305). Where a load ran mattered more than when. This is one April week of a method that counts only each grid's own generation; it does not hold every week.
• Texas's cleanest hour is midday, when solar (34%) and wind (27%) together beat the evening mix of gas, wind and coal. ERCOT's swing (1.6×) is smaller than CAISO's but still meaningful.

CAISO 24-hour profile

One day of raw hours (CAISO, April 15 PT)

Use case 1: Did shifting our batch job reduce carbon?

Say your ops team moved a 1 MWh (1,000 kWh) batch job in ERCOT from 8pm local (01:00 UTC) to 11am local (16:00 UTC). How much CO₂e did that avoid?

import requests

API = "https://emission-factors.com/api/intensity"

def intensity_at(ba, hour_utc):
    """Intensity for a specific past hour (within the last 168)."""
    r = requests.get(API, params={"ba": ba, "hours": 168})
    hours = {h["hour_utc"]: h["intensity_kg_co2e_per_kwh"] for h in r.json()["hourly"]}
    return hours.get(hour_utc)

dirty = intensity_at("ERCO", "2026-04-16T01:00Z")  # 0.345 kg/kWh
clean = intensity_at("ERCO", "2026-04-15T16:00Z")  # 0.167 kg/kWh

kwh = 1000
savings = kwh * dirty - kwh * clean
print(f"Carbon avoided: {savings:.0f} kg CO2e ({(1 - clean/dirty)*100:.0f}% less)")
# Output: Carbon avoided: 178 kg CO2e (52% less)

On that day: 178 kg CO₂e avoided, 52% less. Across the week the hour-of-day averages are closer (noon 0.189 vs 8pm 0.305 kg/kWh), about 116 kg per 1 MWh run, so a 250-run-per-year job avoids roughly 29 tCO₂e a year at that week's pattern.

Use case 2: When should I charge an EV fleet in California?

A 20-vehicle fleet in San Francisco charging 100 kWh per vehicle (2 MWh a day). When is the grid cleanest?

import requests
from collections import defaultdict

# cleanest_hours does this in one call, in local time:
r = requests.get("https://emission-factors.com/api/cleanest-hours",
                 params={"zip": "94105", "duration": 4})
print(r.json()["cleanest_window"])

# or build the hour-of-day profile yourself from the hourly series (UTC):
r = requests.get("https://emission-factors.com/api/intensity",
                 params={"zip": "94105", "hours": 168})
by_hour = defaultdict(list)
for h in r.json()["hourly"]:
    by_hour[int(h["hour_utc"][11:13])].append(h["intensity_kg_co2e_per_kwh"])
for hour in sorted(by_hour):
    print(f"{hour:02d}:00 UTC  {sum(by_hour[hour]) / len(by_hour[hour]):.3f}")

Over the April week above, charging at noon to 3pm (about 0.024 kg/kWh) instead of midnight (0.102) means about 48 kg instead of 204 kg CO₂e a day: roughly 156 kg a day, or 57 tCO₂e a year at that pattern. Utility time-of-use rates often price midday as peak, which is exactly California's cleanest time.

In Texas, midday is also cleanest but the gap is smaller (about 0.19 vs 0.31 kg/kWh in the evening). In New York there is essentially no carbon-optimal hour. Use your own grid's data rather than assuming.

When NOT to use this API

• Scope 2 inventory reporting. Use the annual eGRID factor (/api/calculate). Hourly values use US-average fuel rates and in-region generation only.
• Real-time dispatch. Data lags up to about a day. For the next hour, use WattTime or Electricity Maps.
• Forecasting. This is historical data only.
• Sub-hourly granularity. EIA-930 is hourly.
• Marginal emissions. These are average, not marginal, intensities.
• Outside the Lower 48. EIA-930 does not cover Alaska, Hawaii, Puerto Rico or other countries; those ZIPs return 404.

FAQ

How is this different from the annual eGRID factor you also provide?

The annual eGRID factor (e.g. CAMX 0.195 kg/kWh) is EPA's generation-weighted rate for a subregion over a year, from plant-level data, and is the standard input for GHG Protocol Scope 2 location-based reporting. The hourly intensity here applies US-average fuel rates to each hour's in-region fuel mix, so it shows how the grid varies through the day but is not comparable to, or a substitute for, the eGRID factor.

Why does CAISO intensity drop near 0.02 kg/kWh in the afternoon?

In the April week above, California's 9am–3pm generation averaged 59–65% solar, with wind, hydro and nuclear making up most of the rest; none of those burn fuel. On April 15 the 3pm PT hour was 66% solar and 4% gas, giving 0.019 kg/kWh.

Which balancing authorities are covered?

46 Lower-48 balancing authorities are mapped (45 currently report to EIA-930). A ZIP maps to one of them through its utility, then its state and eGRID subregion, then its state; you can also call ?ba=CISO directly.

Do I need an API key?

No. The API is free and keyless - no signup, no header, no auth token. Just call the endpoint. Full API docs.