The Comparative Bibliometric Analysis of Watershed Ecological Protection and Restoration in the Context of Territorial Spatial Planning: An Overview of Global Research Trends
Bibliographic record
Abstract
Research on watershed ecological protection and restoration within the framework of territorial spatial planning serves as a critical approach to ensuring national ecological security and plays a vital role in enhancing ecosystem stability. In recent years, scholarly interest in this topic has grown significantly. However, development trends and optimization strategies remain unclear, especially regarding comparative insights between Chinese and English research articles within the territorial spatial planning paradigm. A comprehensive review is therefore needed to bridge this gap. This study utilizes bibliometric analysis with CiteSpace, based on publications from the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases, to visualize and compare Chinese and English research articles on watershed ecological protection and restoration. By combining quantitative and qualitative approaches, this study identified research hotspots and trajectories and provided directions for future research. The main findings are as follows: (1) A quantitative analysis indicates that the number of publications has increased significantly since 1998, with growing interdisciplinary and cross-sector collaboration. (2) The qualitative analysis reveals three fundamental theoretical principles: holistic management, multi-scale interactions, and dynamic coordination. (3) The Chinese Academy of Sciences led in research output, while other institutions showed wider geographic coverage, stronger collaboration networks, and a decentralized, multi-core structure. (4) Keyword clustering highlights three major themes: evaluation methodologies for ecological protection and restoration, spatiotemporal evolution and driving mechanisms, and integrated governance system development. (5) Within the territorial spatial planning paradigm, future researchers should employ big data analytics and monitoring technologies to better diagnose and address ecological challenges.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.169 | 0.241 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".