⛽ US EPA Fuel Economy Scraper

🚀 Export EPA fuel economy data for every US vehicle in seconds. Pull city, highway, and combined MPG, CO2 emissions, annual fuel cost, engine config, and drivetrain for any model year from 1984 onward. No API key, no signup, no manual look-ups.

🕒 Last updated: 2026-05-13 · 📊 30 fields per record · ⛽ 1984-present coverage · 🚗 Makes, models, vehicles, and by-ID modes

The US EPA Fuel Economy Scraper queries the official fueleconomy.gov REST API and returns up to 30 fields per record, including the EPA vehicle ID, year, make, model, engine and transmission config, vehicle class, fuel type (primary and alternate for flex-fuel and dual-fuel vehicles), city/highway/combined MPG, alternative-fuel MPG, annual fuel cost, tailpipe CO2 grams per mile, drivetrain, engine displacement, cylinder count, electric range, and start-stop equipment. The underlying dataset is the federally mandated efficiency database every US-market vehicle is tested against.

The catalog covers every model year from 1984 to today, every make sold in the US (including electric, hybrid, plug-in, flex-fuel, and diesel variants), and every engine/transmission configuration the EPA tests separately. This Actor exposes four traversal modes (vehicles, models, makes, byId) and an optional model-level aggregation that picks the best-MPG variant per make+model. Export as CSV, Excel, JSON, or XML in under a minute. All filtering and aggregation run server-side.

🎯 Target Audience	💡 Primary Use Cases
Auto journalists, dealerships, sustainability teams, EV analysts, fleet managers, researchers, comparison-shopping apps	MPG and CO2 comparisons, fleet planning, tax-credit eligibility checks, dealer inventory enrichment, EV vs ICE studies, year-over-year emissions trends

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📋 What the EPA Fuel Economy Scraper does

Four traversal modes in a single Actor:

• 🚗 Vehicles mode. Year [+ make + model] -> every matching vehicle with full MPG, CO2, and cost detail.
• 📋 Models mode. Year + make -> all models that year (lightweight, no per-trim detail).
• 🏭 Makes mode. Year -> every make sold that model year.
• 🔍 By ID mode. Look up a single vehicle by its EPA vehicle ID for the full spec sheet.

For vehicles mode, an extra aggregation toggle controls row granularity. engineConfig (default) returns one row per engine/transmission/drivetrain combination (raw EPA structure). model collapses to one row per make+model and keeps the best-MPG variant, adding a configCount field showing how many configs were considered.

Each record includes the EPA vehicle ID, year, make, model, engine config string (e.g. "Auto (S8), 4 cyl, 2.5 L"), vehicle class, primary and alternate fuel types, city/highway/combined MPG (and alt-fuel variants for flex-fuel cars), annual fuel cost, tailpipe CO2 g/mi, drivetrain, transmission, displacement in liters, cylinder count, EV range (city, highway, combined), and start-stop equipment.

💡 Why it matters: the EPA endpoint returns positional XML wrappers nested across menus and detail records, with ten-plus alternate fields for hybrids and FFVs that you have to decode manually. This Actor walks the make -> model -> trim -> detail tree, normalizes alt-fuel fields, and emits one clean record per vehicle, plus optional model-level aggregation so dashboards don't drown in 20 Camry rows.

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🎬 Full Demo

🚧 Coming soon: a 3-minute walkthrough showing how to go from sign-up to a downloaded dataset.

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⚙️ Input

Input	Type	Default	Behavior
maxItems	integer	10	Records to return. Free plan caps at 10, paid plan at 1,000,000.
mode	string	"vehicles"	One of vehicles, models, makes, byId.
year	integer	2024	Model year (1984-2030). Required in vehicles, models, makes modes.
make	string	empty	Optional manufacturer name (e.g. Toyota). Required in models mode.
model	string	empty	Optional model name. Used as a case-insensitive prefix match in vehicles mode.
vehicleId	integer	null	EPA vehicle ID (required in byId mode).
aggregateBy	string	"engineConfig"	Vehicles mode only. engineConfig = one row per config. model = one row per make+model (best-MPG variant).

Example: every 2024 Toyota Camry config with full MPG detail.

{
    "maxItems": 25,
    "mode": "vehicles",
    "year": 2024,
    "make": "Toyota",
    "model": "Camry",
    "aggregateBy": "engineConfig"
}

Example: best-MPG variant per Toyota model in 2024.

{
    "maxItems": 100,
    "mode": "vehicles",
    "year": 2024,
    "make": "Toyota",
    "aggregateBy": "model"
}

Example: all makes available in 2024.

{
    "maxItems": 100,
    "mode": "makes",
    "year": 2024
}

Example: look up vehicle ID 42011.

{
    "mode": "byId",
    "vehicleId": 42011
}

⚠️ Good to Know: the EPA encodes trim variants in the model name itself ("Camry", "Camry LE/SE", "Camry XSE", "Camry Hybrid LE" are separate rows). When you specify a model, the Actor treats it as a case-insensitive prefix so "Camry" matches every Camry variant. Vehicles mode traverses makes -> models -> trims -> detail and stops as soon as maxItems is reached, so wide queries on all makes are practical.

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📊 Output

Each vehicle record can contain up to 30 fields. Optional fields appear only when the EPA has data for them. Download the dataset as CSV, Excel, JSON, or XML.

🧾 Schema

Field	Type	Example
🆔 id	number | null	47085
🔗 url	string	"https://www.fueleconomy.gov/feg/noframes/47085.shtml"
📅 year	number | null	2024
🏭 make	string | null	"Toyota"
🚗 model	string | null	"Camry"
🛠️ engineConfig	string | null	"Auto (S8), 6 cyl, 3.5 L"
🔢 configCount	number (optional)	3
🏷️ vehicleClass	string | null	"Midsize Cars"
⛽ fuelType	string | null	"Regular"
⛽ fuelType1	string | null	"Regular Gasoline"
⛽ fuelType2	string (optional)	"E85"
🏙️ cityMpg	number | null	22
🛣️ highwayMpg	number | null	33
📊 combinedMpg	number | null	26
🏙️ cityMpgAlt	number (optional)	18
🛣️ highwayMpgAlt	number (optional)	26
📊 combinedMpgAlt	number (optional)	21
💵 annualFuelCostUsd	number | null	2300
💵 annualFuelCostUsdAlt	number (optional)	3100
🌫️ co2TailpipeGpm	number | null	338
🌫️ co2TailpipeGpmAlt	number (optional)	420
🛞 drive	string | null	"Front-Wheel Drive"
⚙️ transmission	string | null	"Automatic (S8)"
🔧 displacementL	number (optional)	3.5
🔧 cylinders	number (optional)	6
🔋 rangeMiles	number (optional)	350
🔋 rangeCityMiles	number (optional)	400
🔋 rangeHwyMiles	number (optional)	300
🔋 rangeAltMiles	number (optional)	380
🔁 startStop	string | null	"Y"
🕒 scrapedAt	ISO 8601	"2026-05-12T23:06:24.431Z"

📦 Sample records

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✨ Why choose this Actor

	Capability
🇺🇸	Authoritative source. Direct from fueleconomy.gov, the federal vehicle-efficiency database.
📅	40+ years of coverage. Every model year from 1984 to present.
🧩	Four traversal modes. Makes, models, vehicles, and by-ID, so you can query at the level you need.
🔁	Smart aggregation. Collapse trim explosions to best-MPG variant per make+model with one input toggle.
⛽	Hybrids and flex-fuel handled. Alt-fuel MPG, cost, and CO2 fields included when present.
⚡	Fast. A typical make-year query (under 100 vehicles) finishes in 30-90 seconds.
🚫	No authentication. Federal open data, no API key needed.

📊 The EPA fuel-economy database is the single most-cited source for US vehicle efficiency. Clean, normalized records are the starting point for every comparison shopping app, fleet model, and sustainability dashboard.

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📈 How it compares to alternatives

Approach	Cost	Coverage	Aggregation	Setup
⭐ US EPA Fuel Economy Scraper (this Actor)	$5 free credit, then pay-per-use	1984-present, every US model	engineConfig or model	⚡ 2 min
Raw fueleconomy.gov API	Free	All	Manual XML decode	🐢 Days
EPA bulk CSV downloads	Free	All historical	Manual joins	🕒 Hours
Commercial vehicle-spec APIs	$500+/month	All US + global	Vendor-specific	⏳ Account setup

Pick this Actor when you want the EPA database ready to query with traversal modes and aggregation already wired up.

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🚀 How to use

1. 📝 Sign up. Create a free account with $5 credit (takes 2 minutes).
2. 🌐 Open the Actor. Go to the US EPA Fuel Economy Scraper page on the Apify Store.
3. 🎯 Set input. Pick a mode (vehicles, models, makes, byId), set a year (and optionally make+model), choose aggregation.
4. 🚀 Run it. Click Start and let the Actor walk the EPA tree.
5. 📥 Download. Grab your results in the Dataset tab as CSV, Excel, JSON, or XML.

⏱️ Total time from signup to downloaded dataset: 3-5 minutes. No coding required.

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💼 Business use cases

🚗 Auto comparison-shopping apps MPG and annual-cost comparisons across trims Best-in-class hybrid or EV picks per segment Inventory enrichment with EPA-verified numbers Year-over-year efficiency callouts	🌱 Sustainability and ESG Fleet CO2 reporting with tailpipe g/mi per vehicle Carbon-tax modeling using annual fuel cost EV-vs-ICE transition planning Scope-1 emissions for company-car programs
🚙 Dealerships and fleet managers Detail-page MPG widgets with engine-config breakouts Total-cost-of-ownership estimators Tax-credit eligibility checks (EV, plug-in) Spec-sheet enrichment for VDPs	📰 Journalism and research Decadal efficiency trend stories Hybrid market-share studies Flex-fuel and E85 availability analysis Datasets for transportation economics research

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🔌 Automating EPA Fuel Economy Scraper

Control the scraper programmatically for scheduled runs and pipeline integrations:

• 🟢 Node.js. Install the apify-client NPM package.
• 🐍 Python. Use the apify-client PyPI package.
• 📚 See the Apify API documentation for full details.

The Apify Schedules feature lets you trigger this Actor on any cron interval. A yearly run when the new model year is released keeps your catalog current. Daily runs on a specific make refresh inventory enrichment in dealer portals.

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🌟 Beyond business use cases

EPA fuel-economy data powers more than commercial workflows. The same structured records support research, education, and personal projects.

🎓 Research and academia Transportation economics and emissions research Multi-decade efficiency trend studies Teaching datasets for environmental policy classes Reproducible vehicle-cost comparisons	🎨 Personal and creative Personal car-shopping spreadsheets Hobbyist car-spec databases Sustainability-themed blog posts DIY comparison widgets
🤝 Non-profit and civic Open-data exercises for civic-tech groups Investigative journalism on emissions standards Educational content for sustainability programs Open EV-adoption transparency dashboards	🧪 Experimentation Train ML models on vehicle features vs MPG Prototype carbon-budget calculators Test alerting agents for new model-year releases Validate UI mockups with real spec data

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🤖 Ask an AI assistant about this scraper

Open a ready-to-send prompt about this ParseForge actor in the AI of your choice:

• 💬 ChatGPT
• 🧠 Claude
• 🔍 Perplexity
• 🅒 Copilot

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❓ Frequently Asked Questions

🧩 How does it work?

Pick a mode and set the relevant inputs. In vehicles mode the Actor walks the EPA menu tree (make -> model -> trim -> detail), pulling the full record per trim. Models, makes, and byId modes hit a single menu or detail endpoint each and return correspondingly lightweight rows.

📏 How accurate is the data?

The data is the same EPA-certified record used on fueleconomy.gov. Every US-market vehicle since 1984 is in the source database.

🔁 How often is the dataset refreshed?

The EPA adds new vehicles continuously through each model year. Every Actor run fetches live data.

📅 Which years are supported?

1984 to present. The input schema allows 1984 through 2030 to accommodate future-year additions.

🛠️ What's the difference between engineConfig and model aggregation?

engineConfig (default) returns one row per engine/transmission/drivetrain combination. One Camry can have ten rows for V6 vs 4-cyl, FWD vs AWD, hybrid vs gas, base vs trim packages. model collapses those down to one row per make+model and keeps the variant with the highest combined MPG, adding a configCount field showing how many configs were merged.

⛽ Are hybrids, EVs, and flex-fuel vehicles included?

Yes. EVs surface electric range in the rangeMiles family of fields. Flex-fuel vehicles populate fuelType2, cityMpgAlt, highwayMpgAlt, and the corresponding cost and CO2 alt fields.

🔍 What is byId mode for?

When you already know an EPA vehicle ID, byId fetches the full record directly without a menu walk. Useful for refreshing a known vehicle in a downstream database.

⏰ Can I schedule regular runs?

Yes. Apify Schedules supports any cron interval. A common pattern is a weekly refresh on the current model year and an annual full pull when a new model year drops.

⚖️ Is this data legal to use?

Yes. The EPA publishes the fuel economy database as US Government open data. It is freely usable for any purpose including commercial use.

💼 Can I use this data commercially?

Yes. EPA fuel-economy data is public-domain federal data and is widely embedded in commercial auto products, comparison apps, and dealer portals.

💳 Do I need a paid Apify plan to use this Actor?

No. The free Apify plan is enough for testing and small runs (10 records per run). A paid plan lifts the limit and unlocks scheduling.

🆘 What if I need help?

Our support team is here to help. Contact us through the Apify platform or use the Tally form linked below.

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🔌 Integrate with any app

EPA Fuel Economy Scraper connects to any cloud service via Apify integrations:

• Make - Automate multi-step workflows
• Zapier - Connect with 5,000+ apps
• Slack - Get run notifications in your channels
• Airbyte - Pipe vehicle specs into your warehouse
• GitHub - Trigger runs from commits and releases
• Google Drive - Export datasets straight to Sheets

You can also use webhooks to trigger downstream actions when a run finishes. Push fresh vehicle data into your inventory backend, or alert your team in Slack when a new model year is fully loaded.

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💡 Pro Tip: browse the complete ParseForge collection for more automotive and reference-data scrapers.

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🆘 Need Help? Open our contact form to request a new scraper, propose a custom data project, or report an issue.

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⚠️ Disclaimer: this Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by the US Environmental Protection Agency, the US Department of Energy, or fueleconomy.gov. All trademarks mentioned are the property of their respective owners. Only publicly available federal open data is collected.