The socio-economic and technological dimensions of energy transition: Do financial mechanisms enhance renewable energy generation?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it