Recent advances in the relationship between economic development and carbon emissions
Bibliographic record
Abstract
Purpose With the rapid development of the economy, carbon emissions have also risen sharply. This study explores the relationship between the two by combining the literature of relevant fields and maps the analytical framework from the knowledge base to the research frontier model using CiteSpace. Design/methodology/approach Using CiteSpace and data statistical tools, we conducted a bibliometric and visual analysis of nearly ten thousand research papers on carbon emissions and economic development published in the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases from 1991 to 2021. Findings It shows that research on economic development and carbon emissions is developing steadily and involves a wide range of fields. Notably, keywords such as “carbon emissions,” “economic growth,” and “energy consumption” had high frequency, centrality, and persistence. “carbon emissions,” “economic growth,” and “energy consumption” had high frequency, centrality, and persistence. Research institutions in the USA and China have made great contributions to research on economic development and carbon emissions. The authors should continue to enrich and improve research on related subjects and concerns to reasonably plan the path of carbon emission reduction and economic development. Originality/value The study analyzes the evolution of the relationship between carbon emissions and economic growth to provide scholars a more comprehensive and in-depth understanding of the relationship from an international perspective.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".