# Ember Electricity Data Scraper - Generation, CO2 Intensity (`jungle_synthesizer/ember-electricity-data-scraper`) Actor Pull country-level electricity data from Ember — generation TWh by fuel, capacity GW, CO2 intensity gCO2/kWh, demand across ~250 countries. Yearly (1985+) and monthly (2010+). The leading free substitute for IEA's paywalled Electricity Information dataset. No auth. - **URL**: https://apify.com/jungle\_synthesizer/ember-electricity-data-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Business, Developer tools, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 ## Ember Electricity Data Scraper Pull country-level electricity-sector data from [Ember](https://ember-energy.org) — the leading independent global electricity think tank. Covers generation by fuel, capacity, CO2 emissions, CO2 intensity, and demand for ~250 countries. Yearly data back to 1985, monthly data back to 2010. The closest free substitute for the IEA's paywalled Electricity Information product. **No API key, no proxy, no captcha.** Data is published under CC-BY 4.0 by Ember. --- ### What data is available Ember publishes four public CSV datasets: | Dataset | Coverage | Records | Updated | |---------|----------|---------|---------| | Global yearly | ~250 countries, 1985–present | ~300,000 rows | Monthly | | EU yearly | EU + wider Europe, 1985–present | ~60,000 rows | Monthly | | Global monthly | ~80 countries, 2010–present | ~1.3M rows | Monthly | | EU monthly | EU + wider Europe, 2010–present | ~250,000 rows | Monthly | Each row is one **country × period × fuel × variable** record, e.g.: - Germany / 2023 / Solar / Generation → 59.7 TWh - Germany / 2023 / — / CO2 intensity → 385 gCO2/kWh - France / 2023-07 / Nuclear / Share of generation → 67.4 % --- ### Fields returned | Field | Type | Description | |-------|------|-------------| | `country` | string | Country or region name (e.g. `Germany`, `European Union`) | | `country_iso3` | string | ISO-3 code (e.g. `DEU`) | | `continent` | string | Continent | | `ember_region` | string | Ember's regional grouping | | `period_type` | string | `yearly` or `monthly` | | `year` | integer | Year (e.g. `2023`) | | `month` | integer | Month 1–12 for monthly rows; `null` for yearly | | `category` | string | `Electricity generation`, `Capacity`, `Electricity demand`, `Emissions`, `Imports` | | `subcategory` | string | Fuel: `Coal`, `Gas`, `Nuclear`, `Solar`, `Wind`, `Hydro`, `Bioenergy`, `Other fossil`, `Clean`, `Fossil` | | `variable` | string | Metric: `Generation`, `Share of generation`, `Capacity`, `CO2 intensity`, `Demand`, … | | `unit` | string | `TWh`, `GW`, `gCO2/kWh`, `MtCO2`, `%`, `MWh` | | `value` | number | Value in the indicated unit | | `source_dataset` | string | `ember-yearly-global` \| `ember-yearly-eu` \| `ember-monthly-global` \| `ember-monthly-eu` | --- ### Input options | Field | Type | Default | Description | |-------|------|---------|-------------| | `periodType` | select | `yearly` | `yearly`, `monthly`, or `both` | | `scope` | select | `global` | `global`, `europe`, or `both` | | `countries` | string[] | all | Country names or ISO-3 codes to include | | `yearFrom` | integer | `2010` | Earliest year (yearly data starts 1985; monthly 2010) | | `yearTo` | integer | `2024` | Latest year (inclusive) | | `variables` | string[] | all | Filter by variable name (e.g. `CO2 intensity`, `Generation`) | | `maxItems` | integer | `10` | Maximum records to return (0 = unlimited) | #### Example: Scope 2 carbon intensity by country (2023) ```json { "periodType": "yearly", "scope": "global", "variables": ["CO2 intensity"], "yearFrom": 2023, "yearTo": 2023, "maxItems": 300 } ```` Returns one row per country with the grid CO2 intensity (gCO2/kWh) — exactly what Scope-2 location-based GHG accounting requires. #### Example: Germany full electricity mix (monthly, last 2 years) ```json { "periodType": "monthly", "scope": "global", "countries": ["Germany"], "yearFrom": 2023, "yearTo": 2024, "maxItems": 500 } ``` #### Example: EU solar generation history ```json { "periodType": "yearly", "scope": "europe", "variables": ["Generation"], "yearFrom": 2010, "yearTo": 2024, "maxItems": 0 } ``` *** ### Use cases - **Scope-2 GHG reporting** — location-based grid carbon intensity by country x year, as required by the GHG Protocol. - **Corporate sustainability benchmarking** — compare grid decarbonisation across supplier countries. - **Climate equity research** — multi-decade fuel mix and emissions by country for academic and Bloomberg Green / FT-style analysis. - **LCA / EPD providers** — background electricity data for product carbon footprints (replaces manual IEA downloads). - **Energy policy analysis** — track solar/wind penetration, coal retirement, and clean share by country. - **Data journalism** — verified reference dataset for carbon intensity stories. *** ### Data source and attribution Data sourced from [Ember](https://ember-energy.org), published under [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). When using this data, attribute as: *"Source: Ember (ember-energy.org)"*. Ember publishes updates monthly. Run this actor after each Ember release to keep your dataset current. *** ### Sister actors - [ENTSO-E European Power Market Scraper](https://apify.com/jungle_synthesizer/entsoe-european-power-market-scraper) — sub-hourly EU market prices, cross-border flows, and actual generation by bidding zone (complements Ember's annual/monthly country aggregates). - [EnergiDataService Nordic Power Scraper](https://apify.com/jungle_synthesizer/energidataservice-nordic-power-scraper) — Nordic balancing market data. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `periodType` (type: `string`): Yearly is global + EU back to 1985; monthly is global + EU back to 2010. ## `scope` (type: `string`): Global covers ~250 countries; Europe covers the EU and wider European region with more granular data. ## `countries` (type: `array`): Country names or ISO-3 codes to include. Leave empty for all countries. ## `yearFrom` (type: `integer`): Earliest year to include. Yearly data starts 1985; monthly starts 2010. ## `yearTo` (type: `integer`): Latest year to include (inclusive). ## `variables` (type: `array`): Filter by Ember variable name (e.g. 'CO2 intensity', 'Generation', 'Share of generation'). Leave empty for all. ## `maxItems` (type: `integer`): Maximum number of records to return. Set to 0 for no limit. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "periodType": "yearly", "scope": "global", "countries": [], "yearFrom": 2010, "yearTo": 2024, "variables": [], "maxItems": 10 } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "periodType": "yearly", "scope": "global", "countries": [], "yearFrom": 2010, "yearTo": 2024, "variables": [], "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/ember-electricity-data-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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "periodType": "yearly", "scope": "global", "countries": [], "yearFrom": 2010, "yearTo": 2024, "variables": [], "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/ember-electricity-data-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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "periodType": "yearly", "scope": "global", "countries": [], "yearFrom": 2010, "yearTo": 2024, "variables": [], "maxItems": 10 }' | apify call jungle_synthesizer/ember-electricity-data-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/ember-electricity-data-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Ember Electricity Data Scraper - Generation, CO2 Intensity", "description": "Pull country-level electricity data from Ember — generation TWh by fuel, capacity GW, CO2 intensity gCO2/kWh, demand across ~250 countries. Yearly (1985+) and monthly (2010+). The leading free substitute for IEA's paywalled Electricity Information dataset. No auth.", "version": "0.1", "x-build-id": "apSVZZhfQ0K8COPvu" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~ember-electricity-data-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-ember-electricity-data-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/jungle_synthesizer~ember-electricity-data-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-ember-electricity-data-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/jungle_synthesizer~ember-electricity-data-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-ember-electricity-data-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", "required": [ "sp_intended_usage", "sp_improvement_suggestions" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "periodType": { "title": "Period Type", "enum": [ "yearly", "monthly", "both" ], "type": "string", "description": "Yearly is global + EU back to 1985; monthly is global + EU back to 2010." }, "scope": { "title": "Geographic Scope", "enum": [ "global", "europe", "both" ], "type": "string", "description": "Global covers ~250 countries; Europe covers the EU and wider European region with more granular data." }, "countries": { "title": "Country Filter", "type": "array", "description": "Country names or ISO-3 codes to include. Leave empty for all countries.", "items": { "type": "string" } }, "yearFrom": { "title": "Year From", "type": "integer", "description": "Earliest year to include. Yearly data starts 1985; monthly starts 2010." }, "yearTo": { "title": "Year To", "type": "integer", "description": "Latest year to include (inclusive)." }, "variables": { "title": "Variable Filter", "type": "array", "description": "Filter by Ember variable name (e.g. 'CO2 intensity', 'Generation', 'Share of generation'). Leave empty for all.", "items": { "type": "string" } }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of records to return. Set to 0 for no limit.", "default": 10 } } }, "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 } } } } } } } } } } ```