Forest fire consequences under the influence of changing climate: a systematic review with bibliometric analysis in the context of sustainable development goals
Bibliographic record
Abstract
Forest fires have become a significant research subject among national and international communities due to increasingly favorable climate conditions characterized by prolonged periods conducive to forest fires. However, several studies have been developed worldwide to assess forest fires' conditions, frequency, and intensity. Still, the literature is not transparent globally for forest fires impacting climate or the climate affecting the forest fires. Therefore, the primary objective of this paper is to conduct a comprehensive review using bibliometric analysis and synthesis of the effects of climate change on forest fire activity throughout 2003–2023. The bibliometric data has been collected from the Web of Science (WoS) Core Collection. The results reveal that the USA and Canada occupy the leading positions in maximum production of scientific output with the highest documents and contain the most productive authors' institutes and well-known scholars or authors. Co-authorship overlay analysis showed that Flannigan, M. D. (2.08%) and Bergeron, Y. (1.18%) were the most productive authors with maximum citations and the highest H-index (15 and 11). In addition, we investigated the research strengths of Sustainable Development Goals (SDGs) and their sub-areas at the country and institute levels. Also, we conducted a comprehensive analysis to determine the percentage of literature that contributes to addressing forest fires and climate change in advancing the Sustainable Development Goals (SDGs) throughout the study period. The observation suggests that the existing body of literature significantly contributes to SDGs related to environmental sustainability and climate resilience. Also, the assessment of the literature's impact on SDGs achievement and awareness spans from 2005 to September 2023. Most earlier studies in this domain demonstrated a significant commitment to SDG 13 (Climate Action), constituting 83.049% of contributions with 886 published documents.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.066 | 0.066 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".