Block 1 - Schedule Trigger
- Type / Role
- n8n-nodes-base.scheduleTrigger - scheduleTrigger
- Config choices
- Version 1.2
This workflow is provided as-is. Please review and test before using in production.
Carbon Footprint Tracker with ScrapeGraphAI Analysis and ESG Reporting Automation Target Audience Sustainability managers and ESG officers Environmental compliance teams Corporate social respons...
n8n-nodes-base.scheduletrigger, n8n-nodes-scrapegraphai.scrapegraphai, n8n-nodes-base.code, n8n-nodes-base.googledrive, n8n-nodes-base.stickynote
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by vinci-king-01.
Original n8n.io sourceManual carbon footprint calculation and ESG reporting is complex, time-consuming, and often inaccurate due to fragmented data sources and outdated emission factors. This template solves the challenge of automatically collecting environmental data, calculating accurate carbon footprints, identifying reduction opportunities, and generating comprehensive ESG reports using AI-powered data collection and automated sustainability workflows.
This workflow automatically collects energy and transportation data using ScrapeGraphAI, calculates comprehensive carbon footprints across all three scopes, identifies reduction opportunities, and generates automated ESG reports for sustainability compliance and reporting.
The template calculates and tracks the following emission categories:
| Emission Scope | Category | Data Sources | Calculation Method | Example Output |
|---|---|---|---|---|
| Scope 1 (Direct) | Natural Gas | EPA emission factors | Consumption Γ 11.7 lbs CO2/therm | 23,400 lbs CO2 |
| Scope 1 (Direct) | Fleet Fuel | EPA fuel economy data | Miles Γ· MPG Γ 19.6 lbs CO2/gallon | 11,574 lbs CO2 |
| Scope 2 (Electricity) | Grid Electricity | EPA emission factors | kWh Γ 0.92 lbs CO2/kWh | 46,000 lbs CO2 |
| Scope 3 (Indirect) | Employee Commute | EPA transportation data | Miles Γ 0.77 lbs CO2/mile | 19,250 lbs CO2 |
| Scope 3 (Indirect) | Air Travel | EPA aviation factors | Miles Γ 0.53 lbs CO2/mile | 26,500 lbs CO2 |
| Scope 3 (Indirect) | Supply Chain | Estimated factors | Electricity Γ 0.1 multiplier | 4,600 lbs CO2 |
Estimated setup time: 25-30 minutes
# Install required community nodes
npm install n8n-nodes-scrapegraphai
Common Issues:
Support Resources:
This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
| Workflow | Carbon footprint tracker with ScrapeGraphAI analysis and Google Drive ESG reports |
|---|---|
| Complexity | advanced |
| Nodes | 16 |
| Categories | Document Extraction, AI Summarization |
| Author | vinci-king-01 |
| Published | 29 Jul 2025 |
Use the JSON export at /data/workflows/6643/6643.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
Carbon Footprint Tracker with ScrapeGraphAI Analysis and ESG Reporting Automation Target Audience Sustainability managers and ESG officers Environmental compliance teams Corporate social respons...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Document Extraction, AI Summarization use case.