The Importance of Green Energy Consumption and Agriculture in Reducing Environmental Degradation: Evidence From Sub-Saharan African Countries
Bibliographic record
Abstract
In recent period of energy focus countries have paid more consideration to the argumentative topic of green energy in both developed and developing economies. Renewable energy is also called green energy. It is described as the energy that is collected by renewable bases of wind, waves, geothermal, sunlight, heat and rain etc. and gives least harm to the nature and environment. The rapid placing of green energy is providing a noteworthy economic profit, energy security and environment change mitigation. Therefore, this current research investigates the association of green energy consumption with environmental degradation by utilizing panel data of 35 sub-Saharan African countries from 1995 to 2017. Moreover, we utilize the advanced panel techniques to investigate the cross-section independence. We also apply CIPS unit root test, Westerlund (2007) bootstrap cointegration, Panel Pedroni and Kao co-integration, FMOLS, DOLS and heterogeneous panel causality methods. The results confirm that all factors are connected in the long-term period. The outcomes also explain that the green energy utilization has a negative impact on environmental hazards and support to decrease environmental hazards. Likewise, globalization has a positive and significant effect on environmental hazards. Also, the agriculture productions also play a significant and positive impact on environmental degradation. Finally, the heterogeneous panel causality confirms a bi-directional causal relationship between green energy consumption and environmental degradation in all sub-Saharan African countries. This current research offers valuable strategy suggestions for the management and the policymakers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".