Life Cycle Assessment of Engineered Wood Products in the Building Sector: A Review
Bibliographic record
Abstract
Engineered wood products have become key sustainable alternatives to conventional building materials, offering strong potential for reducing climate impacts in the construction sector. This review systematically assesses recent life cycle assessment studies on engineered wood products to compare their environmental performance and support low-carbon building practices. The peer-reviewed literature published over the past decade was analyzed for publication trends, geographic focus, and methodological approaches, including goal and scope definition, life cycle inventory, and life cycle impact assessment. Comparative analyses examined climate change impact and key parameters influencing environmental outcomes. Results indicate a steady growth of research in this field, led by China, the United States, and Europe. Volume-based functional units (e.g., 1 m3) are predominant in structural wood studies, while mass-based units are more common for composites. Cradle-to-gate boundaries are most frequently used, and data are primarily drawn from Ecoinvent, Environmental Product Declarations, and regional databases such as GaBi and CLCD. Common impact assessment methods include CML-IA, ReCiPe, and TRACI, with climate change identified as the core impact category. Cross-laminated timber and glue-laminated timber consistently show lower and more stable climate change impacts, while fiberboards exhibit higher and more variable results due to adhesive content and energy-intensive manufacturing. Key factors influencing environmental outcomes include service life, wood species, and material sourcing. The review highlights the need for standardized methodologies and further exploration of emerging products, such as nail-laminated and dowel-laminated timber and laminated bamboo, to improve comparability and inform sustainable design practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".