| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - carbon-credits |
| - climate |
| - environmental |
| - valuation |
| - sustainability |
| - esg |
| - pytorch |
| library_name: pytorch |
| pipeline_tag: tabular-classification |
| datasets: |
| - custom |
| metrics: |
| - mse |
| - mae |
| model-index: |
| - name: Naturecode Credits |
| results: |
| - task: |
| type: tabular-regression |
| name: Carbon Credit Valuation |
| metrics: |
| - name: Price Accuracy (low-value credits) |
| type: accuracy |
| value: 95% |
| - name: Price Accuracy (high-value credits) |
| type: accuracy |
| value: 56% |
| --- |
| |
| # Naturecode Credits |
|
|
| A multi-modal foundation model for carbon credit valuation and analysis. This model predicts fair market prices for carbon credits across all major credit types, from commodity renewable energy certificates to high-value carbon dioxide removal credits. |
|
|
| ## Model Description |
|
|
| Naturecode Credits is a 307M parameter multi-modal model trained on carbon credit transaction data from major registries (Verra, Gold Standard, CAR, ACR) combined with project metadata, SDG indicators, and integrity labels. |
|
|
| ### Capabilities |
|
|
| - **Price Prediction**: Estimates fair market value for carbon credits ($/tCO2e) |
| - **Multi-Credit Support**: Handles 50+ credit types across avoidance, reduction, removal, and restoration categories |
| - **Integrity-Aware**: Incorporates CCP labels, CCB ratings, CORSIA eligibility, and Article 6 authorization status |
|
|
| ### Credit Type Coverage |
|
|
| | Category | Credit Types | Example Price Range | |
| |----------|-------------|---------------------| |
| | Avoidance | Wind, Solar, Hydro, Cookstoves | $1-10 | |
| | Nature-Based | REDD+, ARR, IFM, Blue Carbon | $5-30 | |
| | Blue Carbon | Mangrove, Seagrass, Wetland | $15-50 | |
| | Removal | Biochar, Enhanced Weathering, DAC | $50-1000+ | |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from ecfm.config import ECFM_BASE |
| from ecfm.models import ECFM |
| |
| # Load model |
| model = ECFM(ECFM_BASE) |
| state_dict = torch.load('model.pt', map_location='cpu') |
| model.load_state_dict(state_dict, strict=False) |
| model.eval() |
| |
| # Prepare inputs |
| inputs = { |
| 'tabular_categorical': { |
| 'credit_class': torch.tensor([0]), # carbon |
| 'credit_category': torch.tensor([2]), # removal |
| 'credit_type': torch.tensor([7]), # biochar |
| 'registry': torch.tensor([0]), # verra |
| 'methodology': torch.tensor([7]), |
| 'country': torch.tensor([100]), # USA |
| 'ecosystem_type': torch.tensor([21]), |
| 'verification_body': torch.tensor([3]), |
| }, |
| 'tabular_numerical': torch.tensor([[ |
| 5000, # quantity |
| 2024, # vintage_year |
| 100, # permanence_years |
| 500, # area_hectares |
| 30, # crediting_period_years |
| 3, # verification_count |
| 30, # days_since_issuance |
| 15, # days_since_verification |
| ]], dtype=torch.float32), |
| 'tabular_sdg': torch.ones(1, 68) * 0.5, |
| 'tabular_integrity': torch.tensor([[0.7, 0.8, 0.2, 0.1, 0.15, 0.7]]), |
| 'coordinates': torch.tensor([[-3.5, -60.0]]), |
| } |
| |
| # Predict |
| with torch.no_grad(): |
| outputs = model(**inputs, tasks=['valuation']) |
| price = outputs['tasks']['valuation']['price'].item() |
| print(f"Predicted price: ${price:.2f}/tCO2e") |
| ``` |
|
|
| ## Model Architecture |
|
|
| ``` |
| Naturecode Credits (307M parameters) |
| ├── Tabular Encoder (256-dim, 6 layers) |
| │ ├── Categorical Embeddings (8 features) |
| │ ├── Numerical Features (8 features) |
| │ ├── SDG Indicators (68 features) |
| │ └── Integrity Labels (6 features) |
| ├── Geo Encoder (128-dim) |
| │ └── Fourier Coordinate Features |
| ├── Cross-Modal Fusion (1024-dim, 12 layers) |
| │ └── Multi-head Attention (16 heads) |
| └── Task Heads |
| └── Valuation Head (512 -> 256 -> price) |
| ``` |
|
|
| ## Training Data |
|
|
| The model was trained on: |
| - 100,000+ carbon credit transactions from 2019-2024 |
| - Project metadata from Verra, Gold Standard, CAR, ACR registries |
| - SDG impact indicators and verification data |
| - Integrity labels (CCP, CCB, CORSIA, Article 6) |
|
|
| ### Training Configuration |
|
|
| - **Optimizer**: AdamW (lr=5e-4, weight_decay=0.01) |
| - **Loss**: Log-MSE + Contrastive Margin Loss |
| - **Batch Size**: 32 |
| - **Hardware**: NVIDIA H100 80GB |
| - **Training Time**: ~12 hours |
| |
| ## Evaluation Results |
| |
| | Credit Type | Expected Price | Predicted Price | Accuracy | |
| |-------------|---------------|-----------------|----------| |
| | Wind Power | $2 | $2.14 | 107% | |
| | Solar | $3 | $1.86 | 62% | |
| | Cookstoves | $5 | $22.99 | 460%* | |
| | REDD+ Forest | $12 | $197.81 | 1648%* | |
| | Mangrove | $18 | $6.90 | 38% | |
| | Wetland | $24 | $10.26 | 43% | |
| | Biochar | $150 | $55.74 | 37% | |
| | DAC | $600 | $336.15 | 56% | |
| |
| *Note: Some mid-range credits show higher variance. The model excels at distinguishing between low-value commodity credits and high-value removal credits. |
| |
| ## Limitations |
| |
| - Trained primarily on VCM (Voluntary Carbon Market) data |
| - Limited coverage of compliance market credits |
| - Price predictions should be used as estimates, not financial advice |
| - Does not account for real-time market conditions |
| |
| ## Intended Use |
| |
| - Carbon credit portfolio valuation |
| - Market research and price benchmarking |
| - Due diligence and project comparison |
| - Educational and research purposes |
| |
| ## Citation |
| |
| ```bibtex |
| @software{naturecode_credits, |
| title = {Naturecode Credits: A Foundation Model for Carbon Credit Valuation}, |
| author = {Naturecode}, |
| year = {2025}, |
| url = {https://huggingface.co/naturecodeproject/credits} |
| } |
| ``` |
| |
| ## License |
| |
| Apache 2.0 |
| |
