Bibliographic record
Abstract
Abstract Purpose Life cycle assessment (LCA) has established itself as part of the sustainability toolkit of the private sector, informing environmental decision-making and improving environmental performance. However, we know less about its use in the public sector. To what extent and how do governments refer to LCA in their public policies? We review the literature on the use of LCA in public policy and gauge how LCA has penetrated public policymaking through its incorporation in different policy instruments across various sectors. We then discuss the politics of LCA use in policymaking. Methods We review the literature on LCA from a public policy and social science perspective (1) and back our argument with information from a dozen interviews with LCA experts from government, consultancy, and academia in France, Germany, Switzerland, and the EU (2). Results We show that, along with the growing importance of target setting and science-based environmental and climate policymaking, LCA has penetrated the realm of public policy in OECD countries in different policy sectors. Our understanding of the politics of LCA use in policymaking is however deficient, which leads us to outline a research agenda. Conclusions With the growing importance of LCA in public policy, societal values, public/private governance, state capacity, and political agency should be addressed in further research.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".