Green Economy Research Trends and Mapping in SMEs: A Bibliometric Analysis
Bibliographic record
Abstract
The application of the green economy to SMEs is still experiencing obstacles, so that it affects competitiveness, especially in the global market.This study aims to determine the trend and map the application of the green economy in SMEs.Data analysis used bibliometric analysis with the Scopus database.The number of articles analyzed is 350, published from 1997 to 2022.The research technique is divided into three stages: planning, implementation, and reporting.Data analysis employs bibliometric analysis, with forms of analysis ranging from co-authorship analysis to co-occurrence analysis to citation analysis.The results show that India and the United Kingdom are countries with a great influence on this topic.There are 5 clusters found in this study, with the topic of sustainable development being the most relevant topic in research related to the green economy.There are relatively few strategies for adopting the green economy in SMEs.Some of the emerging strategies concern the deployment of green image, green manufacturing, and environmental social responsibility techniques.This study adds to the quiet literature on themes that have significant potential for additional investigation, particularly those connected to the strategy of implementing the green economy in SMEs.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.160 | 0.202 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| 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".