A global development and dynamics of peatland restoration: a bibliometric analysis
Bibliographic record
Abstract
Peatland ecosystems play a critical role in conservation of biodiversity and climate regulation, but face ongoing degradation from land-use change, mining, and infrastructure development. In response, peatland restoration has gained global attention. This bibliometric study analyzed 448 publications from the Web of Science (1996–2024) to identify global research trends, influential contributors, collaborative networks, technological developments, and implementation challenges in peatland restoration. The results demonstrate the rapid increase of Peatland restoration studies since the 2010 s, driven by global initiatives (Paris Agreement and REDD + ). Institutions from Canada, the UK, and Indonesia are among the most prolific, which also showed a strong collaboration profile. Keyword and co-citation analyses illustrated an evolutionary topic from ecological and hydrological studies to policy-driven research addressing emissions, biodiversity, and sustainable land use. The emergence of terms such as “carbon sequestration”, paludiculture”, and “remote sensing” reflects a shift toward integrative restoration strategies with ecological, economic, and technological dimensions. Challenges include technical uncertainties in carbon dynamics and hydrological modeling, policy inconsistencies, and limited community engagement. Significant knowledge gaps include long-term carbon monitoring, standardized mapping methods, hydrological model accuracy, and biodiversity restoration mechanisms. Future research should prioritize multi-decadal carbon assessments, machine learning-enhanced hydrological models, and biodiversity-focused strategies like paludiculture. Integrating advanced technologies, such as synthetic aperture radar, with interdisciplinary collaboration can enhance evidence-based restoration, supporting ecosystem conservation and climate mitigation.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.166 | 0.233 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".