Systematic literature review of сarbon-neutral economy concept
Bibliographic record
Abstract
The purpose of the article is to study trends in the development of scientific interest in the issue of implementing a carbon-neutral economy. The authors performed a bibliometric analysis of the publications indexed in the Scopus database using VOSviewer 1.6.16 software and Microsoft Excel. This analysis contributes to determining prospects for transitioning to a carbon-neutral economy and priority areas requiring special attention to accelerate this process. The obtained results of the study of the patterns of the development of the carbon-neutral economy indicate that this concept has a dynamic development, which traces the prerequisites for the formation of a powerful scientific school that investigates the influence of changes in the level of energy efficiency in the process of carbon-neutral development of the national economy. Analysis of the dynamics of publication activity demonstrates a reasonably rapid growth in the number of publications. From 1992 to March 2023, the average growth rate of published research results in the analyzed field was 28.50%. A comparison of subject areas in the study of the carbon-neutral economy shows the predominance of energy, ecology, and socio-economic studies. As a result of the bibliometric analysis of 654 publications indexed by the scientometric database Scopus during 1992-2023 on the topic of the carbon-neutral economy using the software VOSViewer v. 1.6.10, six scientific clusters were identified, which study the possibilities of building an economic system with minimal carbon emissions, taking into account the technical, technological, climatic, social and economic aspects of this issue. However, there is an urgent need to study the organizational and economic mechanisms of replacing non-renewable energy resources with renewable ones in the conditions of rapid changes in the global energy market. The study of the geographical affiliation of the authors of scientific works in this area showed that scientists from China, the USA, Great Britain, Germany, India, Canada, Italy, Japan, Switzerland, and Spain published the most significant number of publications. The share of publications by geographical affiliation to these countries is 75%. At the same time, the number of publications in the Scopus database published by domestic authors is relatively small, which once again confirmed the lack of research and timeliness of the study carried out in Ukraine.
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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 | 0.015 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.049 | 0.040 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".