Natural resources, renewable energy, and governance: A path towards sustainable development
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
Abstract Based on data for 48 African countries for the period 2000–2020, we analyse the effects of natural resources on renewable energy development and the mediating effects of governance on that relationship. For this purpose, the Ordinary Least Squares method was used to develop a baseline regression model, and the Generalized Method of Moments (GMM) approach was used for the dynamic model regression. Quantile regression was used for robustness checking across the various distributions of renewable energy. First, we find that natural resources enhance renewable energy development in Africa and that the results are robust across alternative specifications of natural resources and governance, except for forest resources, which have a negative effect on renewable energy development. When robustness is checked through a quantile regression analysis, the results show that the positive effect depends on the conditional distribution of natural resources and the type of natural resource under consideration. The negative effect of total natural resources becomes weaker as we move towards higher quantiles. Second, governance interacts with natural resource rents to generate positive effects across different governance specifications and natural resources, except for coal rent. We thereby derive some relevant implications for renewable energy financing in the Global South.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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