Highly cited articles in life cycle assessment research in 21st century: a systematic and visualised analysis
Bibliographic record
Abstract
This paper aims to examine the trends and various aspects of life cycle assessment (LCA) research using bibliometric analysis to gain a deeper perspective on the various avenues of LCA research. Using the Scopus database, the relationship between countries, journals, authors, and keywords was analysed, and the results were visualised using the VOSviewer software. According to the findings, during this time frame, 1,669 highly cited papers were published in 160 journals focusing on LCA with the highest output coming from the Journal of Cleaner Production. 4,155 authors from a variety of countries have contributed to the subject at hand. The top five contributing countries have been highlighted in this paper among which the USA served as the main research hub. Additionally, the co-occurrence network of keywords identifies four focal points for LCA research, highlighting the topics that have garnered the most attention in recent years and those that will continue to grow rapidly in the future.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.141 | 0.138 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".