Energy transition and pollution emissions in developing countries: are renewable energies guilty?
Bibliographic record
Abstract
Purpose This study aims to examine the effects of energy transition on pollution emissions in Africa. In addition, it explores the indirect channels through which energy consumption impacts environmental quality. Design/methodology/approach The study uses system Generalised Method of Moments approach for a panel of 51 developing African countries over the 1996–2020 period. Findings The results show that fossil fuel and renewable energy consumption increase pollution emissions. The environment-degrading effect of renewable energy in Africa is however counter-intuitive, though the results are robust across regional economic blocks and income groups except for upper-middle-income countries where energy consumption is environment enhancing. Moreover, the results show that the environmental impacts of non-renewable energy consumption are modulated through financial development and information and communication technology (ICT) adoption, leading to respective positive net effects of 0.04460796 and 0.07682873. This is up to respective policy thresholds of 203.265 and 137.105 of financial development and ICT adoption, respectively, when the positive net effects are nullified. Practical implications Contingent on the results, the study suggests the need for African countries to develop sound financial systems and encourage the use of green technologies, to ensure that energy transition effectively contributes to emissions reduction. Policymakers in Africa should also be aware of the critical levels of financial development and ICT, beyond which complementary policies are required for non-renewable energy consumption to maintain a negative impact on environmental degradation. Originality/value Firstly, extant studies on the nexus between energy transition and environmental degradation in Africa are very sparse. Therefore, this study fills the existing research gap by comprehensively examining the effects of energy transition on pollution emissions across 51 African economies. Additionally, besides accounting for the direct environmental effects of energy transition, the current study accounts for the indirect channels through which the environmental impacts of energy transition are modulated. Hence, this study provides critical thresholds for the policy modulating variables, which enlighten policymakers on the necessity of designing complementary policies once the modulating variables attain the established thresholds.
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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 | 0.001 |
| 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) | 0.002 | 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".