Production and application of biochar: a bibliometric–bibliographic study from 2004 to 2023
Bibliographic record
Abstract
Biochar is a carbon-rich material that has attracted considerable interest and has generated an important literature across various academic disciplines in the past decade. Environmental applications of biochar include climate change mitigation, waste management, soil fertility improvement, and contaminant remediation. In this study, a bibliometric analysis was performed to visualise the current research status and emerging trends in the field of biochar production and application research. A total of 33 718 documents related to the topic of biochar were collected from the Scopus database, covering the period from 2004 to December 2023. These publications were analysed using the bibliometric tools provided by the Bibliometrix package R and VOSviewer. The analysis focused on publication output, subject areas, authors, journals, institutions, and countries. The leading subject area is Environmental Science, and most articles are published in the Journal of Environmental Science and Pollution Research. China and the USA are the leading countries contributing in the number of publications, with China producing 17 096 publications and the USA 4255 publications. Based on the keyword co-occurrence analyses, in the last 5 years, searches on biochar are mainly focused on ‘biochar for adsorption’, ‘Biochar for global warming mitigation’, and ‘Biochar for heavy metals immobilisation’. Finally, the literature review highlighted several underexplored areas in biochar research, mainly using artificial intelligence to determine optimal production parameters for specific biochar applications and the scarcity of thorough assessment of the entire biochar life cycle. This review offers essential insights for stakeholders and researchers on the most extensively studied topics related to biochar production and applications, helping to identify areas that have received less attention.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.134 | 0.231 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".