Ecosystem Services Research Trends: A Bibliometric Analysis from 2000–2020
Bibliographic record
Abstract
The study of ecosystem services (ES) is becoming increasingly popular, as it plays an important role in human wellbeing, economic growth, and livelihoods. The primary goal of this research is to investigate the global trend in ES research using a rigorous systematic review of highly cited articles. The articles for this study were extracted from Science Citation Index Expanded (SCI-E), Emerging Sources Citation Index (ESCI), and Social Sciences Citation Index (SSCI) databases of Web of Science Core Collection (WoSCC) covering the period from 2000 to 2020. This study was limited to SCI-E, ESCI, and SSCI databases of the Web of Science. The term “ecosystem service/s” has been used as a research term to filter the study sample and eliminate other databases from the analysis. A citation level equal to or greater than 200 was used to further filtration of articles. This query could restrict to 128 articles that are highly cited in the selected period. Bibliometric analysis results show that, according to the author’s keywords, the “ecosystem service/s” keyword is highly connected to the “biodiversity”, “valuation”, “marine spatial planning”, and “conservation planning”. The U.S.A., Canada, China, France, and Australia are the leading countries in the cumulative number of highly cited articles and networks of co-authors. The U.S.A. is a strong contributor to ES research with China, Canada, and France. The most productive universities linked to the United States were the University of Minnesota, the University of California-Santa Barbara (UC Santa Barbara), and the Chinese Academy of Science. The most significant and compelling author is Halpern S Benjamin, who represents UC Santa Barbara. He has earned international recognition for a model he developed to analyze global data sets of anthropogenic drivers of ecological change in marine environments. The most accessed and studied fields in the ES are terrestrial, urban, and marine environments.
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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 | 0.016 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.155 | 0.208 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".