# 🌱 Open-Data ESG Scoring Inputs (`taroyamada/esg-enhanced-scoring`) Actor Compile directional ESG signals using public data from OSHA, the EPA, FEC, and SEC. Augment existing models with structured US regulatory enforcement data. - **URL**: https://apify.com/taroyamada/esg-enhanced-scoring.md - **Developed by:** [ε€ͺιƒŽ ε±±η”°](https://apify.com/taroyamada) (community) - **Categories:** Business, Other - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN 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.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 ## ESG Enhanced Scoring Evaluating corporate sustainability and governance requires looking beyond marketing claims to actual legal and regulatory footprints. The Open-Data ESG Scoring Inputs scraper delivers a completely transparent, public-record approach to environmental, social, and governance evaluation. Instead of relying on costly black-box ratings from legacy financial data providers, this tool extracts structured enforcement and disclosure data directly from US government portals, including the EPA ECHO system, OSHA establishment databases, SEC EDGAR filings, and FEC contribution records. Data scientists, investment researchers, and corporate compliance teams use this web scraper to systematically track the operational reality of public and private entities. By scheduling regular crawls of these federal databases, users can automatically monitor shifts in a company’s risk profile. It is an ideal solution for alternative data ingestion, providing the raw material needed to construct custom composite scores or augment existing financial intelligence dashboards. Extracted details include specific workplace safety violation types, environmental penalty amounts, governance filing dates, and categorized political expenditure signals. Each output row connects a target company to its empirical regulatory history. This allows teams to extract unbiased, high-fidelity data to accurately assess supply chain risks, validate corporate responsibility claims, and build robust, evidence-based ESG assessment frameworks. ### Status Scaffolded as part of **Wave 17 Batch S β€” Tier 3 (strategic / emerging platforms + governance)**. Domain logic lives in `src/workflow.js`. ### Feasibility **High β€” US government open-data portals (EPA ECHO, SEC EDGAR, OSHA establishment search, FEC political-donation API) expose enforcement, filings, and violations without authentication. Private-sector ESG scores (MSCI, Sustainalytics) remain out of scope.** ### V1 scope Public US government open-data only: EPA ECHO enforcement, SEC EDGAR climate-related filings, OSHA establishment search, FEC committee lookups. Per company: environmental violations, governance filings, workplace-safety citations, political-spend signals, composite E/S/G directional score. OUT OF SCOPE: MSCI / Sustainalytics / Refinitiv proprietary scores, non-US regulators, carbon-accounting (Scope 1/2/3), supply-chain ESG (covered by sibling actor). ### Extraction surfaces - EPA ECHO: https://echo.epa.gov/tools/web-services/facility-search-all-data - SEC EDGAR: https://data.sec.gov/submissions/CIK{cik}.json - SEC EDGAR search: https://efts.sec.gov/LATEST/search-index?q={term} - OSHA establishment: https://www.osha.gov/pls/imis/establishment.search - FEC OpenFEC: https://api.open.fec.gov/v1/committees (requires free DEMO_KEY) ### Known limitations and explicit warnings - ESG scoring is directional, not a certified rating β€” this actor surfaces SIGNALS that feed into a score, not a final rating to publish. - Coverage is US-centric (EPA, SEC, OSHA) β€” international subsidiaries are underrepresented. - Company name β†’ facility / establishment matching is fuzzy; exact CIK is always preferred. - EPA ECHO data lags real enforcement actions by 1-3 months. - SEC EDGAR climate filings (10-K Item 1A risk factors) are text-heavy; the actor surfaces the filing references, not full NLP-extracted metrics. - OSHA establishment search is establishment-level, not consolidated at parent-company level. - FEC DEMO_KEY is shared and rate-limited; consumers should supply their own api.data.gov key for volume use. - Composite E/S/G numeric scores are simple normalized signal counts β€” NOT comparable to MSCI or ISS ratings. - Historical trend data depends on each source's retention policy and is not back-filled by this actor. - Positive ESG actions (disclosures, green-bond issuance) are NOT weighted as heavily as negative signals in V1 β€” V2 plans a balanced scoring rubric. ### Input - Company identifiers (name / ticker / CIK / EIN) - Delivery mode (dataset or webhook) - Dry-run support for local validation ### Output - Normalized `scores` array - `meta` section with implementation status, feasibility note, V1 scope, warnings, and notes ### Local run ```bash npm test npm start ```` ### ⭐ Was this helpful? If this actor saved you time, please [**leave a β˜… rating**](https://apify.com/taroyamada/esg-enhanced-scoring/reviews) on Apify Store. It takes 10 seconds, helps other developers discover it, and keeps updates free. Bug report or feature request? Open an issue on the [Issues tab](https://apify.com/taroyamada/esg-enhanced-scoring/issues) of this actor. # Actor input Schema ## `companies` (type: `array`): Company names, tickers, or SEC CIK numbers (e.g. 'Apple Inc.', 'AAPL', '0000320193'). ## `secCik` (type: `array`): Exact 10-digit zero-padded SEC CIK numbers when known, for precise matching. ## `includeEnvironmental` (type: `boolean`): Include EPA ECHO environmental enforcement signals. ## `includeSocial` (type: `boolean`): Include OSHA workplace-safety citation signals. ## `includeGovernance` (type: `boolean`): Include SEC EDGAR filings and FEC political-donation signals. ## `delivery` (type: `string`): Where to send results: dataset or webhook. ## `webhookUrl` (type: `string`): Webhook URL to POST results to when delivery=webhook. ## `dryRun` (type: `boolean`): Run without saving results to the dataset. ## Actor input object example ```json { "companies": [ "Apple Inc." ], "secCik": [], "includeEnvironmental": true, "includeSocial": true, "includeGovernance": true, "delivery": "dataset", "dryRun": 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 = { "companies": [ "Apple Inc." ], "secCik": [] }; // Run the Actor and wait for it to finish const run = await client.actor("taroyamada/esg-enhanced-scoring").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "companies": ["Apple Inc."], "secCik": [], } # Run the Actor and wait for it to finish run = client.actor("taroyamada/esg-enhanced-scoring").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "companies": [ "Apple Inc." ], "secCik": [] }' | apify call taroyamada/esg-enhanced-scoring --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=taroyamada/esg-enhanced-scoring", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "🌱 Open-Data ESG Scoring Inputs", "description": "Compile directional ESG signals using public data from OSHA, the EPA, FEC, and SEC. Augment existing models with structured US regulatory enforcement data.", "version": "0.1", "x-build-id": "xIPzKZ5b6LaRscAms" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/taroyamada~esg-enhanced-scoring/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-taroyamada-esg-enhanced-scoring", "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/taroyamada~esg-enhanced-scoring/runs": { "post": { "operationId": "runs-sync-taroyamada-esg-enhanced-scoring", "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/taroyamada~esg-enhanced-scoring/run-sync": { "post": { "operationId": "run-sync-taroyamada-esg-enhanced-scoring", "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": { "companies": { "title": "Company Identifiers", "type": "array", "description": "Company names, tickers, or SEC CIK numbers (e.g. 'Apple Inc.', 'AAPL', '0000320193').", "items": { "type": "string" } }, "secCik": { "title": "SEC CIKs (optional)", "type": "array", "description": "Exact 10-digit zero-padded SEC CIK numbers when known, for precise matching.", "items": { "type": "string" } }, "includeEnvironmental": { "title": "Include Environmental", "type": "boolean", "description": "Include EPA ECHO environmental enforcement signals.", "default": true }, "includeSocial": { "title": "Include Social", "type": "boolean", "description": "Include OSHA workplace-safety citation signals.", "default": true }, "includeGovernance": { "title": "Include Governance", "type": "boolean", "description": "Include SEC EDGAR filings and FEC political-donation signals.", "default": true }, "delivery": { "title": "Delivery", "enum": [ "dataset", "webhook" ], "type": "string", "description": "Where to send results: dataset or webhook.", "default": "dataset" }, "webhookUrl": { "title": "Webhook URL", "type": "string", "description": "Webhook URL to POST results to when delivery=webhook." }, "dryRun": { "title": "Dry Run", "type": "boolean", "description": "Run without saving results to the dataset.", "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 } } } } } } } } } } ```