Carbon Footprint Optimization Engine (CfoE) is a multi-agent AI system designed to monitor environmental risks, predict regulatory fines, and optimize corporate compliance strategies.
The system focuses on CAFE (Corporate Average Fuel Efficiency) norms in India, enabling organizations to proactively detect potential violations and avoid large post-market penalties.
- Traditional compliance audits are slow (1–2 years) and reactive
- ESG violations and emission breaches are often detected after damage occurs
- Companies lack real-time visibility into fleet emissions and regulatory risk
- CAFE fines are imposed 1–2 years after vehicle sales, making prediction critical
CfoE transforms compliance into a real-time, predictive, and automated system using a hybrid AI architecture:
- Monitors external signals (news, policies, market trends)
- Calculates fleet-level CO₂ emissions
- Predicts CAFE norm violations and penalties
- Provides actionable recommendations
- Ensures safety using Human-in-the-Loop (HITL)
MonitorAgent → CalculationAgent → PolicyAgent → HITL → ReportingAgent
- Collects:
- EV adoption trends
- SUV / ICE sales signals
- Regulatory updates
- Emission-related news
- Computes:
- Fleet CO₂ emissions
- CAFE target comparison
- Fine estimation
- Applies rules:
- SAFE (< 0.5)
- MONITOR (0.5–0.8)
- CRITICAL (≥ 0.8)
- Triggers Human Approval if critical
- Ensures safe decision-making
- Prevents automated high-risk actions
- Generates:
- ESG compliance reports
- Risk summaries
- Recommended actions
- Framework: Google ADK (Agent Development Kit)
- Language: Python
- LLM: GPT / Gemini / Claude
- Tools:
- Web Search APIs (Serper / News API)
- Custom scoring functions
- UI (Optional): Streamlit
- Storage: JSON / SQLite / PostgreSQL
Fleet_CO2 = Σ (Model_CO2 × Units_Sold) / Total_Units
If Fleet_CO2 > Govt_Target → Violation
Fine = Excess_CO2 × Penalty_Rate × Vehicles_Sold
- EV Sales %
- SUV / ICE Sales %
- Vehicle Weight Trends
- Engine Efficiency
- Government CAFE Targets
- CAFE Breach Probability
- Estimated Fine Amount
- Risk Classification (SAFE / MONITOR / CRITICAL)
- Preventive Action Recommendations
The system models time-delayed regulatory impact:
| Event | Timeline |
|---|---|
| Vehicle Sales | Year 0 |
| Emission Measurement | FY |
| Audit | +6–18 months |
| Fine Announcement | +1–2 years |
- Increase EV / Hybrid adoption
- Reduce SUV-heavy portfolio
- Optimize vehicle weight
- Improve engine efficiency
- Adjust pricing and product mix
- Multi-Agent Architecture
- Deterministic Risk Scoring
- Real-Time External Monitoring
- Human-in-the-Loop Safety
- Predictive Compliance Engine
- Enterprise-Ready Workflow
- Automotive OEM compliance monitoring
- ESG risk assessment platforms
- Supply chain sustainability tracking
- Regulatory risk forecasting
- Real-time data ingestion (ERP / PLM integration)
- Parallel agent execution
- Risk trend visualization dashboard
- Automated alert system
- Multi-region regulatory support
- Focused on real-world regulatory impact (CAFE norms)
- Combines LLM intelligence + deterministic logic
- Designed for enterprise-scale compliance systems
- Enables proactive risk mitigation instead of reactive audits
This project is intended for educational and research purposes.
View your app in AI Studio: https://ai.studio/apps/ddec9226-e2c9-49c4-a86f-8c7bef7a79a9
Prerequisites: Node.js
- Install dependencies:
npm install - Set the
GEMINI_API_KEYin .env.local to your Gemini API key - Run the app:
npm run dev