Frontier and Hot Topics in Watershed Ecological Compensation Based on Bibliometric Visualization Analysis
Bibliographic record
Abstract
Watershed Ecological Compensation (WEC) has emerged as an essential strategy for advancing sustainable management of water resources and environmental protection. This research offers a thorough evaluation of performance, identifies current research hotspots, and predicts future trends in WEC. The bibliometric analysis of 2,189 entries from the Web of Science core database evaluates publication performance, contributions by countries and institutions, research hotspots, and emerging trends using CiteSpace and VOSviewer tools. The findings reveal a significant rise in publications on watershed eco-compensation since 2007, with leading contributions from China, the USA, England, Brazil, Germany, and Canada. Institutions such as the Chinese Academy of Sciences and Beijing Normal University are at the forefront of this research area. Hotspot analysis highlights themes such as payments for ecosystem services, poverty, willingness to pay, compensation standards, and models as focal areas. Emerging trend analysis indicates that research on WEC in Latin America and China is expected to expand, with a growing emphasis on environmental justice and integrated watershed strategies. This study highlights current research hotspots, suggests future directions, and enriches the existing literature, enabling researchers to stay updated on the latest advancements.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.142 | 0.144 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".