Plantd vs OSB — Screening LCA Workflow

Overview

This project develops a structured and reproducible workflow for a screening-level life cycle assessment (LCA) comparing:

• Plantd structural panels
• Conventional OSB (Oriented Strand Board) structural panels

The workflow integrates Excel-based data extraction with Python-driven data cleaning, modeling, and visualization. Outputs are structured to support future integration with openLCA.

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Objective

The objective is to assess whether a bio-based structural panel (Plantd) may offer lower embodied carbon compared to conventional OSB, which serves as the appropriate structural benchmark.

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Functional Unit

1 m² of structural panel used for wall or roof sheathing, assuming equivalent functional performance.

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System Boundary

Cradle-to-gate (screening-level boundary)

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Methodological Approach

Data Sources

• Plantd structural panel technical report
• LCA literature on bio-based construction panels
• Comparative studies of conventional building materials

Workflow

1. Data Extraction
Data were extracted from technical documents and literature and compiled into a structured Excel dataset.

2. Data Cleaning and Structuring
src/02_clean_openlca_inputs.py

• Standardized variable names
• Structured inputs for modeling
• Exported cleaned dataset

3. Screening Model Development
src/03_build_screening_model.py

• Established OSB baseline scenarios
• Developed Plantd emission estimates
• Calculated comparative performance

4. Visualization
src/04_make_chart.py

• Generated comparative emissions chart

5. LCA Process Structuring
src/05_build_lca_structure.py

• Translated extracted inputs into LCA-style flows
• Constructed a simplified foreground process model

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Results (Screening Level)

Scenario	GWP (kg CO2e/m²)
OSB (low)	20
OSB (mid)	25
OSB (high)	30
Plantd (low)	10
Plantd (mid)	14
Plantd (high)	18

Estimated reduction relative to the OSB midpoint scenario is approximately 30% to 60%.

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Foreground LCA Process Structure

A simplified foreground process was constructed to represent the Plantd panel using the following input categories:

• Biomass feedstock (plant strands)
• Binder or resin
• Electricity for manufacturing
• Thermal energy for drying and pressing
• Transport of biomass
• Packaging

Where primary data were unavailable, placeholder values were introduced and explicitly flagged. This preserves model structure while maintaining transparency and enabling future refinement.

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Outputs

• Cleaned dataset: outputs/openlca_input_table_clean.csv
• Screening model results: outputs/screening_gwp_results.csv
• LCA structure file: outputs/plantd_lca_structure.csv
• Visualization: outputs/plantd_vs_osb_screening_chart.png

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Limitations

This analysis is a screening-level assessment and does not constitute a full LCA.

The Plantd technical documentation provides structural and performance data but does not include a complete life cycle inventory (LCI). Key missing elements include:

• Material composition and mass per functional unit
• Binder/resin type and proportion
• Manufacturing energy consumption
• Transport distances and logistics
• End-of-life assumptions

All results should therefore be interpreted as preliminary estimates.

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Path to Full LCA

To develop a complete and verifiable LCA, the following steps are required:

• Incorporate an OSB Environmental Product Declaration (EPD) or database dataset
• Develop a detailed Plantd foreground inventory
• Integrate with an LCA database (e.g., ecoinvent) in openLCA
• Apply a recognized LCIA method (e.g., IPCC GWP 100a)

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Project Structure

plantd-osb-lca/
│
├── data/
│   └── Plantd_openLCA_extracted_dataset.xlsx
│
├── outputs/
│   ├── openlca_input_table_clean.csv
│   ├── screening_gwp_results.csv
│   ├── plantd_lca_structure.csv
│   └── plantd_vs_osb_screening_chart.png
│
├── src/
│   ├── 01_read_excel.py
│   ├── 02_clean_openlca_inputs.py
│   ├── 03_build_screening_model.py
│   ├── 04_make_chart.py
│   └── 05_build_lca_structure.py
│
├── docs/
│   └── methodology.md
│
├── README.md
├── requirements.txt
└── .gitignore

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Key Takeaways

This project demonstrates:

• Structuring incomplete sustainability data into a usable analytical format
• Developing a transparent and reproducible screening model
• Explicitly handling data gaps in LCA modeling
• Preparing datasets for integration into professional LCA tools

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Disclaimer

This project is intended for analytical and educational purposes. Results should not be interpreted as verified environmental performance claims.