Fiscal Decentralization and Environmental Pollution Control
Bibliographic record
Abstract
Fiscal decentralization is one of the strategies applied to involve both the national and local governments in environmental management. Although, this study tries to examine its effectiveness in Nigeria which has been ambiguous. Using a multiple regression method, the study examines the effect of revenue fiscal structure on CO2 emission management in Nigeria from 2007 to 2020. Controlling pollution through the fiscal system is exceedingly difficult. According to the t-statistic results, it is only the central government that has a significant favorable influence on pollution management. State and local governments have a minimal impact on CO2 emissions reduction. This outcome leads to a suggestion that resource accumulation powers should be equitable with a higher consideration to the state and local governments which have a greater burden of controlling pollution in the rural areas where majority of the citizens have their abodes. The government at all levels should guarantee that the country's environmental policies and regulations are effectively implemented in order to reduce carbon emissions and other types of environmental pollution.
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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 | 0.000 | 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".