Natural resources, renewable energy, and governance: A path towards sustainable development
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".