Life cycle assessment in corporate sustainability reporting: Global, regional, sectoral, and company‐level trends
Bibliographic record
Abstract
Abstract Large companies now commonly release corporate sustainability (CS) reports in which they describe their approach to handle sustainability challenges. To guide environmental sustainability efforts in the industry, the life cycle assessment (LCA) methodology has been recognized as an important tool by researchers and policy makers. But to what extent has the LCA methodology been present in companies' narratives through their CS reports up to now? To answer this question, we map references to the LCA methodology in CS reports over the past two decades at geographical, sectoral, and company levels through keyword searching within an extensive database (~45,000 CS reports), analyze trends, and highlight challenges, opportunities, and recommendations to strengthen the presence of LCA in CS reports. The results show that LCA generally remains weakly present in CS reporting, with some geographical and sectoral variations. Recommendations to strengthen LCA presence in CS reports are derived for method developers, policy makers, and companies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".