Exploring the economic and non-economic determinants of investments in renewable energy
Bibliographic record
Abstract
ABSTRACT Amid growing concerns about climate change and fossil fuel depletion, the global shift toward sustainable energy has become increasingly urgent. Despite significant investments in renewable energy (RE), there remains a large gap in meeting global sustainability targets. This study is the first to analyze the key factors driving RE investments, using a comprehensive panel dataset of 36 countries over 21 years (2000-2020), comparing developed and developing/emerging nations. The results show that in developed countries, factors like industrial growth, environmental taxes, social globalization, and climate vulnerability drive RE investments, while inflation and political instability hinder progress. In contrast, developing countries benefit from environmental taxes, social globalization, environmental technologies, and climate vulnerability, but industrial growth and oil prices negatively impact RE investments. These differences underscore the need for tailored strategies. Quantile-based assessments further highlight variations across countries, offering a nuanced understanding of the determinants of RE investments. This research provides valuable insights for policymakers, helping to shape more effective strategies to accelerate the transition to RE and meet sustainability goals worldwide.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".