# esg-csrd-scraper (`korobz/esg-csrd-scraper`) Actor Automate CSRD compliance. Extract Scope 1, 2, 3 emissions and ESG metrics from corporate reports. Perfect for Carbon Accounting & Supply Chain analysis. - **URL**: https://apify.com/korobz/esg-csrd-scraper.md - **Developed by:** [Korobz Korobz](https://apify.com/korobz) (community) - **Categories:** Automation - **Stats:** 1 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $20.00/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## 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 ## CSRD & ESG Data Extractor API (Scope 1, 2, 3) **Automate the extraction of sustainability metrics from complex Annual Reports (PDF & HTML). Designed for ESG Consultants, Carbon Accounting Platforms, and Financial Analysts.** --- ### 🚀 Why use this Actor? With the **CSRD (Corporate Sustainability Reporting Directive)** deadline approaching, extracting data manually from 200+ page PDF reports is slow, expensive, and error-prone. This Actor is an **enterprise-grade extraction engine** that navigates corporate websites, downloads Sustainability/Annual Reports, and uses AI to extract structured **Scope 1, 2, and 3 emissions data** with high precision. #### Key Features * **📄 Advanced PDF Parsing:** Unlike simple HTML scrapers, this actor downloads and processes heavy PDF files (OCR capabilities included for scanned tables). * **🛡️ Anti-Blocking Technology:** Built on top of Puppeteer with **Residential Proxies** and stealth plugins to bypass Cloudflare and strict corporate firewalls. * **🎯 Scope 1, 2, 3 Granularity:** Extracts specific emission figures, units (tCO2e), and reporting years. * **🔍 Audit Trail & Citations:** Every extracted data point includes the **source context** (text snippet or page reference) so you can verify the numbers for compliance. * **📊 Reliability Scoring:** Returns a confidence score and reasoning for every extraction, flagging potential data gaps. --- ### 🛠️ How it works 1. **Input:** You provide the `domain` (e.g., `volvocars.com`) and the `company_name`. 2. **Discovery:** The actor scans the website to find the latest "Sustainability Report", "Non-Financial Statement", or "Annual Report". 3. **Processing:** It downloads the document (handling PDF or HTML). 4. **Extraction:** Using LLM-powered analysis, it identifies ESG tables and relevant paragraphs. 5. **Output:** You receive a clean JSON with the structured data. --- ### 📥 Input Parameters The input of this actor should be JSON. | Field | Type | Description | | :--- | :--- | :--- | | `domain` | String | **Required.** The website of the company (e.g., `volvocars.com`). | | `company_name` | String | **Required.** The full name of the company to aid the search. | | `reporting_year` | String | **Optional.** Specific year to target (e.g., `2023`). Defaults to the latest available. | | `force_pdf_processing` | Boolean | **Optional.** If true, prioritizes PDF documents over HTML pages. Default: `true`. | #### Example Input ```json { "domain": "volvocars.com", "company_name": "Volvo Car Corporation", "reporting_year": "2023", "force_scrape": true } ```` *** ### 📤 Output Example The results are stored in the default dataset associated with the run. Note how the actor distinguishes between market-based Scope 2 and the massive Scope 3 categories typical of automotive companies. ```json [ { "domain": "volvocars.com", "company_name": "Volvo Car Corporation", "reporting_year": 2023, "status": "success", "data": { "emissions": { "scope_1": { "value": 38000, "unit": "tCO2e", "context": "Direct GHG emissions from manufacturing and operations (Page 182, Sustainability Notes)", "confidence": "High" }, "scope_2": { "value": 12000, "type": "market-based", "unit": "tCO2e", "context": "Indirect emissions from purchased electricity, heating and cooling (market-based). Location-based was 85,000 tCO2e.", "confidence": "High" }, "scope_3": { "value": 42500000, "unit": "tCO2e", "categories_included": ["Purchased goods and services", "Use of sold products", "Upstream transportation"], "confidence": "High", "notes": "Includes lifecycle emissions from sold vehicles." } }, "reliability_score": 0.98, "reliability_reasoning": "Data extracted explicitly from the 'GRI Content Index' and 'Greenhouse Gas Emissions' tables in the Annual Report 2023." }, "source_url": "https://investors.volvocars.com/annual-report-2023.pdf", "scraped_at": "2024-05-20T14:30:00Z" } ] ``` *** ### 💰 Pricing & Cost Efficiency This actor is designed to be significantly cheaper than manual data entry. - **Manual Entry:** An analyst takes ~30-60 minutes to find and transcribe Scope 1-3 data per report. Cost: **~$50/report** (labor). - **Apify Actor:** Takes ~1-3 minutes. Cost: **Fraction of manual labor**. *Recommended for bulk usage. If you need to process 100+ companies, please contact me via the Issues tab for a custom solution.* *** ### ⚠️ Known Limitations - **Scanned PDFs:** While OCR is supported, extremely low-quality scans (images of text without a text layer) may result in lower confidence scores. - **Language:** Currently optimized for **English** and **Italian** reports. Support for German, French, and Spanish is in beta. *** #### Support & Feedback If you encounter any issues, have feature requests, or need a custom integration for your enterprise pipeline, please create an issue in the tab above. # Actor input Schema ## `domain` (type: `string`): The website of the company (e.g., google.com) ## `company_name` (type: `string`): Legal name of the entity (e.g., Alphabet) ## `reporting_year` (type: `string`): Optional: Specific year to extract. ## `force_scrape` (type: `boolean`): If true, ignores cache and scrapes again. ## Actor input object example ```json { "force_scrape": false } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("korobz/esg-csrd-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("korobz/esg-csrd-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 '{}' | apify call korobz/esg-csrd-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=korobz/esg-csrd-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "esg-csrd-scraper", "description": "Automate CSRD compliance. Extract Scope 1, 2, 3 emissions and ESG metrics from corporate reports. Perfect for Carbon Accounting & Supply Chain analysis.", "version": "0.0", "x-build-id": "GSICRqF4nkEGEfW2K" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/korobz~esg-csrd-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-korobz-esg-csrd-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/korobz~esg-csrd-scraper/runs": { "post": { "operationId": "runs-sync-korobz-esg-csrd-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/korobz~esg-csrd-scraper/run-sync": { "post": { "operationId": "run-sync-korobz-esg-csrd-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "domain", "company_name" ], "properties": { "domain": { "title": "Target Domain", "type": "string", "description": "The website of the company (e.g., google.com)" }, "company_name": { "title": "Company Name", "type": "string", "description": "Legal name of the entity (e.g., Alphabet)" }, "reporting_year": { "title": "Reporting Year", "type": "string", "description": "Optional: Specific year to extract." }, "force_scrape": { "title": "Force Fresh Scrape", "type": "boolean", "description": "If true, ignores cache and scrapes again.", "default": false } } }, "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 } } } } } } } } } } ```