Citation-based systematic literature review of energy-growth nexus: An overview of the field and content analysis of the top 50 influential papers
Bibliographic record
Abstract
This study is a systematic survey of literature on the energy-growth nexus, which has been carried out with a view to identifying the leading sources of knowledge in the forms of the most influential journals, authors, and papers. This study not only recognizes and classifies the well-known methodologies used in the energy-growth nexus analysis but also reveals intriguing content-based findings, with quantitative measures for the top 50 papers ranked according to the highest average citations per year. This survey is unique in that the process of selecting articles is entirely objective, allowing the research community's opinions to take the lead in the process rather than any subjective judgments of the authors. In this way, we examine 1041 peer-reviewed articles that specifically focused on the energy-growth nexus. We found that, as of the end of 2017, with 200 articles, Energy Policy is the leading journal publishing on this area while Energy Economics, with a total of 25,352 citation counts, holds the highest impact on this field of research. In addition, the most frequently cited article by the scholastic community in terms of average citations per year has been a literature survey conducted by Ozturk (2010). Our study's main conclusion, based on a thorough content analysis, is that the nexus results of previous studies are generally inconclusive, with conflicting policy implications. This is not helpful and to a large extent is due to a lack of an appropriate theory. This, we contend, is essentially a methodological weakness and could be addressed by incorporating an appropriate testable economic/environmental theory.
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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 | 0.027 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.067 | 0.072 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".