Mapping the landscape of carbon trading & carbon offset research: A global and Indonesian perspective
Bibliographic record
Abstract
The escalating annual increase in carbon emissions has posed a significant threat to the environment and human life. In recent years, numerous countries have implemented carbon trading schemes to combat climate change, encourage global cooperation, and promote reductions in emissions. Here, we aimed to explore and delve into the evolving landscape of carbon trading research through in-depth bibliometric and content analysis methods, identifying promising avenues for future research. We identified and retrieved publications on carbon trading and offset from 1993 to 2023 from the Scopus database. By examining 1, 994 articles indexed with the keywords ‘carbon trading’ or ‘carbon offsets, ’ this study offers valuable insights for policymakers, researchers, and practitioners working to mitigate climate change. Our findings revealed four primary clusters: Cluster 1 entailed carbon management and climate change mitigation, cluster 2 entailed innovations and policies in carbon management and sustainable energy, cluster 3 was related to policies related to carbon trading and markets, and cluster 4 was related to integrated energy systems, carbon trading mechanisms, and strategies for achieving a low-carbon economy. Globally, China stands out as a dominant contributor in carbon trading research, followed by the USA, UK, Australia, and Canada. Moreover, Indonesia (as the authors’ country) demonstrates increasing involvement, evidenced by 19 publications and collaborations with 12 countries. These findings underscore the need for further, more in-depth research to identify the most effective carbon trading mechanisms specific to Indonesia’s unique context. Thematic evolution analysis revealed that carbon sequestration and neutrality were prominent research topics in 2023. A new topic that has emerged is carbon trading policy, which indicates that much research on carbon trading is needed to regulate this matter.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.065 | 0.123 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".