Mapping Hotspots and Emerging Trends in Global Wetlands Research: A Scientometric Analysis (2002–2022)
Bibliographic record
Abstract
Recent studies have focused on wetlands due to their benefits for human spiritual satisfaction and mental health. This paper conducted a scientometric analysis of 2,388 studies published in the Web of Science database between 2002 and 2022. Using VOSviewer, Origin, and CiteSpace software, this study identified research hotspots and emerging trends in wetland research. The analysis revealed an upward trend in global wetland research publications, with notable contributions from researchers in the United States, China, Australia, Canada, and India. Network keyword co-occurrence analysis highlighted primary research themes, including constructed wetlands, climate change, wastewater treatment, phytoremediation, restoration, and hydrology. The United States emerged as the central hotspot for wetland research, with China, Canada, Australia, and other countries following. Given the growing recognition of wetlands' importance, wetland research is expected to gain even more global attention. Moreover, improvements in the quality of wetland tourism research are recommended, as most related publications have low citation rates. This paper provides a methodological overview of scientometric techniques applicable to global wetland research, offering scholars a framework for using scientometric analysis to enhance their future research. The increasing recognition of wetlands' crucial role in human well-being, encompassing both spiritual satisfaction and mental health, has led to a surge in research interest in this field. This study presents a comprehensive scientometric analysis of 2,388 wetland-related publications indexed in the Web of Science database between 2002 and 2022. Employing VOSviewer, Origin, and CiteSpace software, we mapped research hotspots and identified emerging trends within global wetland research. Our analysis reveals a significant upward trend in the volume of publications, highlighting the growing international attention to wetland ecosystems. The United States, China, Australia, Canada, and India have emerged as leading contributors to this research landscape. A network keyword co-occurrence analysis identified core research themes such as constructed wetlands, climate change, wastewater treatment, phytoremediation, ecological restoration, and hydrology. The United States is a central hub for wetland research, with China, Canada, and Australia also demonstrating substantial research activity. Given the escalating importance of wetlands in addressing global challenges, this research area is expected to attract further scholarly attention. We recommend a greater emphasis on enhancing the quality and impact of wetland tourism research, which currently exhibits low citation rates. Furthermore, this paper provides a methodological framework, demonstrating the application of scientometric techniques in global wetland research, thus empowering scholars to utilise such analytical approaches to refine their research. Our study offers a valuable and comprehensive overview of the key research areas, emerging topics, and influential contributors within the field of global wetland research.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.027 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".