The socio-economic and technological dimensions of energy transition: Do financial mechanisms enhance renewable energy generation?
Bibliographic record
Abstract
The transition to renewable energy generation (REG) is a critical priority for emerging economies aiming to meet the 2030 Sustainable Development Goals. This study investigates the key drivers of REG across the MINT countries (Mexico, Indonesia, Nigeria, and Turkey) using a multidimensional framework that integrates socio-economic factors, financial mechanisms, and technological enablers. Employing advanced panel estimation techniques, including Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL), Dynamic Common Correlated Effects (DCCE), and Augmented Mean Group (AMG) estimators, the analysis covers the period from 1995 to 2022. The results revealed that while economic growth significantly promotes REG, trade openness and unemployment are negatively associated with clean energy advancement. In the financial dimension, both green finance and financial development support REG, whereas foreign direct investment exerts an inverse effect. Technological innovation, information and communication technology (ICT), and the digital economy are identified as key accelerators of REG progress. This study advances energy transition theory by integrating multidimensional drivers, socio-economic, financial, and technological factors into a unified empirical framework for emerging economies. These findings underscore the need for an integrated policy framework that simultaneously strengthens macroeconomic structures, enhances green financing systems, and promotes technological innovation to facilitate an inclusive and sustainable clean energy transition in emerging markets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".