Succeeding in Energy Transition in Sub-Saharan Africa: Does Institutional Quality Matter?
Bibliographic record
Abstract
The objective of this article is to examine the effect of the quality of institutions on energy transition in 19 sub-Saharan African (SSA) countries over the period 1996-2016. To achieve this, we proceed in two steps. We first use the principal component analysis (PCA) to construct a composite indicator of institutional quality, from Kaufmann’s (1996) six indicators of governance. Then, we estimate an autoregressive distributive lag model (ARDL) on panel data using the pooled mean group (PMG) estimation technique. Our results show that the quality of institutions determines the energy transition in SSA. The associated coefficient is positive and statistically significant. In addition, our results show that economic growth and trade openness promote energy transition. On the other hand, it emerges that CO2 emissions hinder energy transition, due to the high dependence of the countries considered on fossil fuels. We suggest an improvement in the quality of institutions and the implementation of political incentives favorable to the adoption of new technologies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".