Are the factors recommended by UNEP-SETAC for evaluating biodiversity in LCA achieving their promises: a case study of corrugated boxes produced in the US
Bibliographic record
Abstract
Abstract Purpose We tested the effectiveness of the global and ecoregion-based average characterization factors (CFs) for “Potential Species Loss” recommended by the UNEP-SETAC Life Cycle Initiative to identify hotspots and improvement opportunities compared to using a land competition indicator for a product for which the predominant life cycle use of land is forest management. Methods For a case study of average corrugated boxes produced in the US, system boundaries were defined to encompass all life cycle stages from forest management to disposal. Fiber procurement was regionalized to US ecoregions, and (Chaudhary et al. Environ Sci Technol 49:9987–9995, 2015) ecoregion-specific CFs were applied. US-average CFs were applied to other background processes. Hotspots were identified using contribution analyses, and improvement opportunities were evaluated using scenarios. We compared the results with those from applying a land competition indicator, often used as a proxy for biodiversity in LCA. Results and discussion Forest management was identified as the activity within the life cycle of corrugated boxes that uses the greatest amount of land, allowing the definition of two potential improvement opportunities: reducing fiber consumption and intensifying forest management. By applying the recommended CFs, fiber procurement was also identified as the main contributor to “Potential Species Loss.” The CFs also allowed to identify ecoregions in which species were potentially the most affected by forest management and related potential improvement opportunities. Tradeoffs between taxonomic groups were discussed. In some cases, the results contradicted those from applying a land competition indicator, and in many cases, we were unable to reconcile the results obtained with existing scientific knowledge on species diversity and forest management. Conclusions and recommendations The results obtained by applying the recommended CFs could not always be reconciled with existing scientific knowledge on the effect of forest management on species diversity, significantly impairing the usefulness of these factors for assessing improvement opportunities and increasing the risk of counterproductive decisions. The local effect on species of forest management is likely to be misrepresented by the average number of species in a given ecoregion. Successful consideration of biodiversity response in the context of forest management would require the integration of other approaches, such as site-specific studies. Potential improvements to the proposed method include further spatialization of the CFs, defining a range of forest management practices for which CFs would be defined, considering forest productivity, and defining CFs using a baseline that would encourage better practices even within a given existing management regime.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".