Trends and Advancements in Utilization of Biomass Waste for Gasification: A Bibliometric Review
Bibliographic record
Abstract
ABSTRACT Biomass waste gasification is widely recognized as a sustainable and efficient method for converting organic waste into valuable energy, making it a focal point in global research. This study conducts a bibliometric analysis of publications related to this field, focusing on articles indexed in the Elsevier Scopus database from 1977 to 2023 using search terms “biomass waste,” “biomass residue,” “waste biomass,” and “gasification”. Initially, 981 articles were identified, with subsequent refined analyses narrowing the focus to 592 publications, using VOSviewer for in‐depth examination. The analysis revealed that the year 2023 saw the highest publication count with 73 articles, followed by the year 2022 and the year 2020, with 61 and 54 articles, respectively. China, the USA, and India emerged as the leading contributors, accounting for 9.68%, 7.07%, and 6.75% of the total publications, respectively. Top institutions by citations are the University of Saskatchewan (259), Hamad Bin Khalifa University (169), and Paul Scherrer Institut (113). The most prolific researchers in the field include Gulyurtlu, I., Cabrita, I., and Dalai, Ajay K., with citation counts of 1296, 1290, and 1020, respectively. The journals Energy , Fuel , and Energies were identified as authors' most preferred publishing choice with 26, 23, and 22 publications, respectively. The keywords “Gasification,” “Biomass,” and “Syngas” were the most frequently occurring, with 194, 147, and 52 occurrences. Keyword analysis also revealed five thematic clusters. These findings offer a detailed overview of the research landscape in biomass waste gasification, emphasizing key contributors, emerging trends, and thematic areas, providing valuable insights for guiding future research in this domain.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.114 | 0.147 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".