# Car Fuel Economy Scraper: MPG, EV Range & CO2 by Model (`scrapemint/car-fuel-economy-scraper`) Actor Look up official US EPA fuel economy for any car, 1984 to today. Paste "year make model" lines and get city/highway/combined MPG, fuel type, EV range, CO2 per mile, annual fuel cost and transmission per variant. For dealers, car apps and fleet buyers. No API key needed. - **URL**: https://apify.com/scrapemint/car-fuel-economy-scraper.md - **Developed by:** [Ken M](https://apify.com/scrapemint) (community) - **Categories:** Business, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Car Fuel Economy Scraper: MPG, EV Range & CO2 by Model Look up official fuel economy for any car sold in the US since 1984. Paste **"year make model"** lines and get one clean row per variant: city, highway and combined MPG, fuel type, EV range, CO2 per mile, annual fuel cost, engine and transmission — straight from the US EPA's official test data. Built for **car dealers, listing sites, fleet buyers and car apps**. Turn an inventory list into complete fuel economy specs in one run, with no signup and no API key. ### What you get for each vehicle variant - **cityMpg**, **highwayMpg**, **combinedMpg** (MPGe for electric vehicles) - **fuelType** and **electricType** (hybrid, plug-in hybrid, EV, diesel, flex fuel) - **evRangeMiles** for electric vehicles - **co2GramsPerMile** and **ghgScore**: emissions for sustainability reporting - **annualFuelCostUsd**: the EPA's estimated yearly fuel cost - **cylinders**, **displacementLiters**, **drive**, **transmission**, **vehicleClass** - **detailUrl**: the vehicle's official fueleconomy.gov page ### Example output ```json { "input": "2023 Toyota Camry", "found": true, "year": 2023, "make": "Toyota", "model": "Camry", "variant": "Automatic (S8), 6 cyl, 3.5 L", "fuelType": "Regular", "cityMpg": 22, "highwayMpg": 33, "combinedMpg": 26, "co2GramsPerMile": 338, "annualFuelCostUsd": 2400, "detailUrl": "https://www.fueleconomy.gov/feg/Find.do?action=sf1&id=45719" } ```` ### How to write vehicle lines - **"2023 Toyota Camry"**: returns every Camry variant that year (Camry, Camry AWD, Camry Hybrid...). - **"2022 Tesla Model 3"**: trims count as models; this returns Long Range, Performance and RWD. - **"2023 Honda"**: leave the model off to get every Honda model that year. - Set **max rows per vehicle line** to control how many variants each line returns (default 10). ### Pricing **$0.003 per vehicle variant found.** Lines that match nothing are **free**, and the first 2 rows of every run are free. Speccing a 500-car inventory costs about $1.50. ### How to run it via API ```bash curl -X POST "https://api.apify.com/v2/acts/scrapemint~car-fuel-economy-scraper/run-sync-get-dataset-items?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"vehicles": ["2023 Toyota Camry", "2022 Tesla Model 3"], "maxRowsPerVehicle": 10}' ``` ### Frequently asked questions **Where does the data come from?** The US EPA's [fueleconomy.gov](https://www.fueleconomy.gov) vehicle database, the official source of US fuel economy test results. Coverage runs from model year 1984 to the newest releases. **Does it cover cars outside the US?** Only vehicles sold in the US market. European and Asian market versions of the same model can have different figures. **Why does one line return several rows?** The EPA tests each engine, transmission and drive combination separately, and lists trims like "Model 3 Long Range AWD" as their own models. Each row is one tested variant. **What are MPGe and EV range?** For electric and plug-in vehicles the MPG fields hold MPGe (miles per gallon equivalent), and `evRangeMiles` holds the official electric range. ### More tools from Scrapemint - [Car Info & Safety Check](https://apify.com/scrapemint/car-safety-check): VIN decode, recalls and complaints for any US vehicle. - [Product Recall Finder](https://apify.com/scrapemint/product-recall-finder): US food, drug and product recalls by keyword. - [Currency Exchange Rates Scraper](https://apify.com/scrapemint/currency-exchange-rates-scraper): live and historical exchange rates. # Actor input Schema ## `vehicles` (type: `array`): One vehicle per line as "year make model", like "2023 Toyota Camry" or "2022 Tesla Model 3". Leave the model off ("2023 Honda") to get every model of that make. Trim names like "F150 Pickup 4WD" also work. ## `maxRowsPerVehicle` (type: `integer`): A line can match several models and trim variants (engine, transmission, drive). Stop after this many rows per input line. ## `maxRows` (type: `integer`): Stop after this many rows in total. ## Actor input object example ```json { "vehicles": [ "2023 Toyota Camry", "2022 Tesla Model 3", "2021 Ford F150" ], "maxRowsPerVehicle": 10, "maxRows": 1000 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "vehicles": [ "2023 Toyota Camry", "2022 Tesla Model 3", "2021 Ford F150" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapemint/car-fuel-economy-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "vehicles": [ "2023 Toyota Camry", "2022 Tesla Model 3", "2021 Ford F150", ] } # Run the Actor and wait for it to finish run = client.actor("scrapemint/car-fuel-economy-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "vehicles": [ "2023 Toyota Camry", "2022 Tesla Model 3", "2021 Ford F150" ] }' | apify call scrapemint/car-fuel-economy-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapemint/car-fuel-economy-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Car Fuel Economy Scraper: MPG, EV Range & CO2 by Model", "description": "Look up official US EPA fuel economy for any car, 1984 to today. Paste \"year make model\" lines and get city/highway/combined MPG, fuel type, EV range, CO2 per mile, annual fuel cost and transmission per variant. For dealers, car apps and fleet buyers. No API key needed.", "version": "0.1", "x-build-id": "x6gSwu0WvgLiM8g2f" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapemint~car-fuel-economy-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapemint-car-fuel-economy-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapemint~car-fuel-economy-scraper/runs": { "post": { "operationId": "runs-sync-scrapemint-car-fuel-economy-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapemint~car-fuel-economy-scraper/run-sync": { "post": { "operationId": "run-sync-scrapemint-car-fuel-economy-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "vehicles": { "title": "Vehicles (year make model)", "type": "array", "description": "One vehicle per line as \"year make model\", like \"2023 Toyota Camry\" or \"2022 Tesla Model 3\". Leave the model off (\"2023 Honda\") to get every model of that make. Trim names like \"F150 Pickup 4WD\" also work.", "items": { "type": "string" } }, "maxRowsPerVehicle": { "title": "Max rows per vehicle line", "minimum": 1, "maximum": 50, "type": "integer", "description": "A line can match several models and trim variants (engine, transmission, drive). Stop after this many rows per input line.", "default": 10 }, "maxRows": { "title": "Max rows", "minimum": 1, "maximum": 50000, "type": "integer", "description": "Stop after this many rows in total.", "default": 1000 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```