Regime Switching Model Estimates of the Impact of Financial Development on Renewable Energy Consumption: The Role of Geopolitical Risk in the Case of Emerging Economies
Bibliographic record
Abstract
Financial development and geopolitical risks are crucial for understanding the consumption of renewable energies (CREs), given the sustainability challenges of the global economy. This research examines the nonlinear impact of financial development on adopting renewable energies in 10 emerging countries facing geopolitical risks between 1985 and 2022. By employing two regime‐switching approaches (panel threshold autoregressive (PTAR) and panel smooth transition autoregression (PSTAR)), the results indicate nonlinear correlations between domestic bank credit granted to the private sector and the adoption of renewable energies. The optimal threshold value of the PTAR model is 40.171, while those of the PSTAR model are 35.705 and 122.9. Below the thresholds of the PTAR and PSTAR models, financial development decreases the CREs. Beyond these thresholds, financial development promotes CRE through specific financial incentives, such as low‐interest loans and tax credits for renewable energy projects, as well as the creation of specialized financial instruments, such as green bonds. Geopolitical issues have prompted governments to diversify their energy sources and intensify their investments in renewable energy to strengthen energy security and reduce dependence on the volatile fossil fuel markets. Therefore, in terms of policy implications, financial education, the mitigation of geopolitical risks, and renewable energy goals must be priorities in national development efforts to promote sustainable economic growth. JEL Classification: C23, C54, K32, Q43
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".