Mathematical Modelling of Public Health Expenditure and Carbon Footprint in Nigeria
Why this work is in the frame
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Bibliographic record
Abstract
Most major rising economies are seeing a rise in health problems as a result of airborne smog caused by carbon dioxide discharges. The situation in Nigeria is getting too concerning, as the expense of healthcare services continues to rise as a result of environmental problems. The purpose of this research is to determine the extent to which investments in healthcare and social welfare have altered Nigeria's carbon footprint. Dependent variable in this study is CO2 emissions captured by the World Bank Development Indicators in metric tonnes, whereas the independent variables are public healthcare spending and social welfare cost. The data for these variables are kept in the Central Bank of Nigeria Statistical Bulletin from 2006 to 2020. Several statistical tests are being used in the study to confirm model stability, appropriateness, and normalcy. As a consequence, the unit root is validated at the level, and additional diagnostic tests show that the multiple regression model used in this work is free of distortion, serial correlation, and hetroskedacity. As a result, the data reveal that the predictor factors have a substantial and positive correlation with Nigeria's carbon footprint. Further data show that healthcare costs have a considerable and beneficial influence on carbon footprint, but social welfare spending is insignificant in this regard. The report recommends the use of green technologies to minimize carbon emissions and enhance the overall health of the population. As a solution, both people and the government's health-care costs will be significantly reduced in the absence of air pollution.
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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 | 0.001 | 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 it