Mathematical Modelling of Public Health Expenditure and Carbon Footprint in Nigeria
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".