Data-Driven Energy Emissions Estimation & Carbon Credit Analysis
EnerCast is an end-to-end machine learning pipeline that estimates national CO₂ emissions from energy consumption data. The project demonstrates production-grade ML engineering practices including:
- Rigorous data pipeline design with validation and quality checks
- Leakage-safe feature engineering with centralized governance
- Baseline-first modeling philosophy for principled ML adoption
- Time-series aware evaluation to prevent temporal data leakage
⚠️ EnerCast is designed as a decision-support and analysis system, not a regulatory or policy-grade emissions calculator.
Accurately estimating carbon dioxide (CO₂) emissions is essential for understanding climate impact and evaluating emission-reduction strategies. Traditional emissions accounting relies on fixed emission factors and aggregated rules—transparent but often coarse.
- How well can CO₂ emissions be estimated from energy consumption patterns?
- Where do simple rule-based (physics-inspired) models fail?
- Can machine learning models learn systematic deviations beyond fixed emission factors?
- How do different modeling approaches compare in accuracy and interpretability?
| Component | Implementation | Skills Demonstrated |
|---|---|---|
| Data Pipeline | Country-year normalization, overlap analysis | ETL, Data Validation, Pandas |
| Feature Engineering | Leakage-safe policy with banned/approved columns | Feature Governance, ML Best Practices |
| Baseline Model | Physics-inspired emission factor estimator | Domain Knowledge Integration |
| ML Models | Linear Regression with time-based splits | Scikit-Learn, Model Evaluation |
| Evaluation | MAE, RMSE, R² with baseline comparison | Metrics Design, Statistical Analysis |
- Energy-related CO₂ emissions estimation
- Fossil fuel and energy mix effects analysis
- Historical data analysis and forecasting diagnostics
- Official greenhouse gas inventories replacement
- Non-energy emissions (land use, agriculture)
- Carbon credit registry or compliance systems
The project uses publicly available global datasets:
| Dataset | Features |
|---|---|
| Energy Consumption | Country-level annual consumption by source |
| Fossil Fuels | Coal, oil, gas usage metrics |
| Renewables | Low-carbon and renewable energy indicators |
| Emissions | National CO₂ emissions (kilotons, per capita) |
All datasets are merged using country–year composite keys and validated for coverage and consistency.
Raw Data → Normalization → Overlap Analysis → Merged Dataset
↓
Country/Year Keys
↓
merged_energy_emissions.csv
- Country and year standardization with manual mapping for edge cases
- Explicit overlap and label-coverage analysis
- Reproducible merged dataset generation
To prevent label leakage, EnerCast enforces a centralized feature policy:
# Feature categories
BANNED_COLUMNS = ["emissions_derived_*", "co2_*"] # Direct leakage
SUSPICIOUS_COLUMNS = ["gdp_emissions_*"] # Indirect leakage
APPROVED_COLUMNS = ["coal_twh", "oil_twh", ...] # Safe featuresAll ML experiments must pass through this feature filter before training.
A transparent baseline estimator using:
- Fossil fuel consumption (coal, oil, gas)
- Global-average emission factors
- Unit-safe conversions:
TWh → kWh → kg → kilotons
This baseline represents a physics-inspired lower bound that ML models must beat.
Linear Regression serves as the diagnostic model to evaluate:
- Whether linear relationships already explain emissions
- Which features dominate predictions
- Where ML improves or fails relative to baseline
Models use time-based train/test splits to prevent temporal information leakage.
| Metric | Purpose |
|---|---|
| MAE | Average prediction error magnitude |
| RMSE | Penalizes large errors more heavily |
| R² | Variance explained (secondary metric) |
Primary emphasis is on error reduction relative to baseline, not absolute accuracy.
Outcomes where ML does not outperform the baseline are considered valid and informative.
EnerCast prioritizes:
- ✅ Explainability over black-box accuracy
- ✅ Honest failure analysis over cherry-picked results
- ✅ Robustness to feature changes over overfitting
- ✅ Engineering reasoning over accuracy chasing
EnerCast/
├── data/
│ ├── raw/ # Original datasets
│ └── processed/ # Merged, validated dataset
│
├── notebooks/ # Exploration and analysis
│
├── src/
│ ├── feature_safety.py # Leakage-safe feature policy
│ ├── baseline.py # Rule-based emission estimator
│ └── models.py # ML models and experiments
│
├── results/ # Metrics and analysis outputs
│
├── README.md
└── requirements.txt
| Decision | Rationale |
|---|---|
| Baseline before ML | ML must justify its computational cost |
| Time-based splits | Prevents temporal data leakage |
| Centralized feature governance | Leakage prevention is enforced, not assumed |
| Transparency over complexity | Simple models preferred until justified |
| Limitation | Impact |
|---|---|
| Global-average emission factors | Country-specific variations not captured |
| Non-energy emissions excluded | Underestimates total emissions |
| Annual aggregation | Seasonal dynamics ignored |
| Diagnostic models only | Not production-tuned |
These limitations are acknowledged by design to maintain project scope.
- GDP and feature ablation studies
- Non-linear models (tree-based regressors, XGBoost)
- Regional error analysis and visualization
- Uncertainty estimation and confidence intervals
- Scenario simulations for energy transition analysis
- Language: Python 3.8+
- Data Processing: Pandas, NumPy
- Machine Learning: Scikit-Learn
- Visualization: Matplotlib, Seaborn
- Development: Jupyter Notebooks
EnerCast is an educational and analytical project. It should not be used for regulatory reporting, compliance, or financial decision-making.
Developed as a machine learning and data engineering project demonstrating end-to-end ML system design with emphasis on:
- Production-grade data pipeline architecture
- Leakage-aware feature engineering
- Baseline-first modeling methodology
- Principled evaluation frameworks