# SBTi Target Dashboard Scraper (`jungle_synthesizer/esg-rating-disclosure-scraper`) Actor Scrapes the SBTi (Science Based Targets initiative) public dataset. Returns 14,000+ companies with net-zero commitments, temperature alignment (1.5°C/2°C), target scope, ISIN, sector, and SBTi status. No login required. - **URL**: https://apify.com/jungle\_synthesizer/esg-rating-disclosure-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Business, Education, Automation - **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 ## SBTi Target Dashboard Scraper Scrape the public [Science Based Targets initiative (SBTi)](https://sciencebasedtargets.org/target-dashboard) target dashboard. Returns 14,000+ companies with net-zero commitments — temperature alignment (1.5°C, well-below 2°C, 2°C), near-term and long-term target classifications, scope coverage, sector, region, ISIN/LEI, and full target language. No login required. --- ### SBTi Scraper Features - Extracts 24 fields per company including ISIN, LEI, and SBTi internal ID - Returns near-term, long-term, and net-zero target statuses as separate columns - Captures temperature alignment (1.5°C, well-below 2°C, 2°C) per target horizon - Returns target scope coverage as a structured string (`1+2`, `1+2+3`) - Filter by SBTi status (Committed, Targets set, Removed) or region (Europe, Asia, Americas, Africa, Oceania) - Source data is SBTi's public XLSX export — no login, no scraping ToS gray area, no proxy --- ### Who Uses SBTi ESG Data? - **ESG analysts and rating agencies** — Cross-check corporate climate commitments against SBTi's validated dataset - **Sustainable investment funds** — Screen portfolios for companies with validated near-term and net-zero targets - **Climate disclosure consultants** — Benchmark client commitments against sector peers - **Procurement and supply-chain teams** — Identify suppliers already aligned with science-based targets, which simplifies your Scope 3 reporting - **Academic and policy researchers** — Track temperature-alignment adoption rates by sector and region over time --- ### How the SBTi Scraper Works 1. **Configure filters** — Pick a status (Committed, Targets set, Removed, or All) and a region. Leave both at `All` to get everything. 2. **Fetch the dataset** — The actor downloads SBTi's public XLSX target export, the same one the dashboard front-end consumes. 3. **Parse and filter** — Each row is normalized into a flat record with separate columns per target horizon. Filters apply during parsing. 4. **Export** — Records land in your Apify dataset as JSON, one company per record. --- ### Input #### All committed companies ```json { "status_filter": "Committed", "region_filter": "All", "maxItems": 0 } ```` #### European companies with validated targets ```json { "status_filter": "Targets set", "region_filter": "Europe", "maxItems": 500 } ``` #### Full dataset ```json { "status_filter": "All", "region_filter": "All", "maxItems": 0 } ``` | Field | Type | Default | Description | |-------|------|---------|-------------| | status\_filter | string | `All` | `All`, `Committed`, `Targets set`, or `Removed`. | | region\_filter | string | `All` | `All`, `Europe`, `Asia`, `Americas`, `Africa`, or `Oceania`. | | maxItems | integer | 15 | Cap on records returned. `0` = all. | *** ### SBTi Scraper Output Fields ```json { "sbti_id": 8741, "company": "Unilever PLC", "isin": "GB00B10RZP78", "lei": "549300MKFYEKVRWML317", "organization_type": "Corporate", "country": "United Kingdom", "region": "Europe", "sector": "Consumer Staples", "disclosure_type": "SBTi", "near_term_status": "Targets set", "near_term_target_classification": "1.5°C", "near_term_target_year": 2030, "long_term_status": "Targets set", "long_term_target_classification": "1.5°C", "long_term_target_year": 2039, "net_zero_status": "Targets set", "net_zero_target_year": 2039, "sbti_status": "Targets set", "target_scopes": "1+2+3", "temperature_alignment": "1.5°C", "target_count": 3, "target_types": "Near-term, Long-term, Net-zero", "full_target_language": "Unilever PLC commits to reduce absolute scope 1 and 2 GHG emissions 100% by FY2030.", "date_updated": "2025-03-12", "source_url": "https://sciencebasedtargets.org/target-dashboard" } ``` | Field | Type | Description | |-------|------|-------------| | sbti\_id | integer | SBTi internal company identifier | | company | string | Company name | | isin | string | ISIN security identifier when available | | lei | string | Legal Entity Identifier when available | | organization\_type | string | `Corporate`, `SME`, `Financial Institution`, etc. | | country | string | Country of registration | | region | string | `Europe`, `Asia`, `Americas`, `Africa`, `Oceania` | | sector | string | GICS-based industry sector | | disclosure\_type | string | Always `SBTi` | | near\_term\_status | string | Near-term commitment status | | near\_term\_target\_classification | string | Near-term temperature classification | | near\_term\_target\_year | integer | Near-term target achievement year | | long\_term\_status | string | Long-term commitment status | | long\_term\_target\_classification | string | Long-term temperature alignment | | long\_term\_target\_year | integer | Long-term target year | | net\_zero\_status | string | Net-zero commitment status | | net\_zero\_target\_year | integer | Net-zero target year | | sbti\_status | string | Summary status: Committed, Targets set, or Removed | | target\_scopes | string | Scopes covered, e.g. `1+2` or `1+2+3` | | temperature\_alignment | string | Highest temperature alignment achieved | | target\_count | integer | Number of validated targets for this company | | target\_types | string | Comma-separated target types | | full\_target\_language | string | Full text description of the company's targets | | date\_updated | string | Date the SBTi record was last updated | | source\_url | string | URL of the SBTi target dashboard | *** ### FAQ #### How do I scrape SBTi target dashboard data? SBTi Target Dashboard Scraper handles it. Pick a status filter (or `All`) and a region (or `All`), set `maxItems`, and run. The actor pulls SBTi's public XLSX export and emits one record per company with each target horizon broken out. #### How much does this actor cost to run? SBTi Target Dashboard Scraper uses pay-per-event pricing on the `default_2603_basic` profile at a 1.5x coefficient. No proxy fees. The full 14,000-company dataset costs a few dollars in platform fees. #### Can I filter by sector or country? SBTi Target Dashboard Scraper exposes status and region filters at input. For sector or country, every record carries those fields explicitly — a single downstream filter on `sector = 'Consumer Staples'` or `country = 'Germany'` gets you the slice. #### Does this include net-zero target years? SBTi Target Dashboard Scraper returns near-term, long-term, and net-zero target years as separate integer columns, alongside the temperature classification for each horizon. Useful if you want to chart commitment timelines by sector. #### Does this actor need proxies? SBTi Target Dashboard Scraper runs proxy-free. SBTi publishes the XLSX export at a stable public URL with no rate limiting in normal use. *** ### Need More Features? Need CDP, GRI, or TCFD disclosure data alongside SBTi, or scheduled re-runs when the dashboard updates? [Open an issue](https://console.apify.com/actors/issues) or get in touch. ### Why Use SBTi Target Dashboard Scraper? - **Three horizons, separate columns** — Near-term, long-term, and net-zero targets each get their own status / classification / year fields. No string parsing required. - **Joinable on ISIN and LEI** — When SBTi publishes them, you get them. That covers the path from climate commitment to security master data. - **No scraping fragility** — Uses SBTi's official XLSX export, not the dashboard HTML. The schema is stable across SBTi front-end changes. # 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. ## `maxItems` (type: `integer`): Maximum number of company records to return. Set to 0 for all records. ## `status_filter` (type: `string`): Filter by SBTi near-term commitment status. Options: All, Committed, Targets Set, Removed ## `region_filter` (type: `string`): Filter by company region. Options: All, Europe, Asia, Americas, Africa, Oceania ## 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...", "maxItems": 15, "status_filter": "All", "region_filter": "All" } ``` # 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...", "maxItems": 15, "status_filter": "All", "region_filter": "All" }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/esg-rating-disclosure-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...", "maxItems": 15, "status_filter": "All", "region_filter": "All", } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/esg-rating-disclosure-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...", "maxItems": 15, "status_filter": "All", "region_filter": "All" }' | apify call jungle_synthesizer/esg-rating-disclosure-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/esg-rating-disclosure-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "SBTi Target Dashboard Scraper", "description": "Scrapes the SBTi (Science Based Targets initiative) public dataset. Returns 14,000+ companies with net-zero commitments, temperature alignment (1.5°C/2°C), target scope, ISIN, sector, and SBTi status. No login required.", "version": "0.1", "x-build-id": "k2iVwrrEd8dDqIr6M" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~esg-rating-disclosure-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-esg-rating-disclosure-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~esg-rating-disclosure-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-esg-rating-disclosure-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~esg-rating-disclosure-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-esg-rating-disclosure-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": [ "maxItems" ], "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." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of company records to return. Set to 0 for all records.", "default": 15 }, "status_filter": { "title": "Status Filter", "enum": [ "All", "Committed", "Targets set", "Removed" ], "type": "string", "description": "Filter by SBTi near-term commitment status. Options: All, Committed, Targets Set, Removed", "default": "All" }, "region_filter": { "title": "Region Filter", "type": "string", "description": "Filter by company region. Options: All, Europe, Asia, Americas, Africa, Oceania", "default": "All" } } }, "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 } } } } } } } } } } ```