Quantification of Biodiversity Loss in Building Life Cycle Assessment: Insights Towards Regenerative Design
Bibliographic record
Abstract
This study examines the incorporation of biodiversity loss into the Life Cycle Assessment (LCA) of buildings, with a specific focus on the Danish construction sector. Motivated by the ecological crisis reflected in the Planetary Boundaries and the Kunming-Montreal Global Biodiversity Framework, it addresses regulatory gaps that prioritise climate indicators, such as Global Warming Potential (GWP), while largely ignoring biodiversity. The study analyses 73 Danish building cases for GWP and a custom method linking material quantities to ReCiPe 2016 endpoint data for biodiversity loss. The findings indicate key methodological issues include the quality of Environmental Product Declarations (EPDs), the regional relevance of assessment methods, and differences in European standards. While average GWP levels mostly meet upcoming Danish limits, variability, especially in Office and Other building categories, supports the need for differentiated regulations. Results show embodied impacts mainly drive GWP, while biodiversity loss is split between embodied and operational impacts. Detached and Terraced houses, which use more bio-based materials, have low embodied GWP but higher biodiversity loss, highlighting trade-offs in regenerative design. The shift in GWP impacts to end-of-life phases stresses the need to consider forest dynamics. Operational impacts rank similarly, despite differences in the data. The study concludes that progress toward regenerative design requires addressing climate and biodiversity together to avoid shifting environmental burdens.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".