Mortality rate, carbon emissions, renewable energy and per capita income nexus in Sub-Saharan Africa
Bibliographic record
Abstract
This study exclusively contributes to the health-environment discourse by using mortality rates, carbon emissions (proxy for environmental degradation), renewable energy and real per capita income to investigate these intrinsic relationships. This study uses an unbalanced sample of 47 Sub-Saharan African countries from 2005-2019 to reveal that: (1) both carbon emissions and renewable energy are associated with higher mortality rates; (2) real per capita income is associated with reducing mortality rates; (3) per capita income attenuates the effect of renewable energy on mortality rates, (4) persistency in mortalities exist; and (5) the health-environment-energy-income dynamics differ across income groups. Additionally, this study submits that the interaction of renewable energy and real per capita income dampens the positive effect of renewable energy on mortality rates and supports the argument that income levels lessen the extent of mortalities. Besides, these results vividly show that real per capita income reduces the devastating effect of renewable energy on infant and under-5 mortality rates from 0.942% to 0.09%, 2.42% to 0.55%, 1.04% to 0.09% and 2.8% to 0.64% for high and middle-income countries, respectively. This is a novel and significant contribution to the health-environment literature. Hence, real per capita income is a crucial determinant of mortality rate. Policy recommendations are discussed.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".