Does Fiscal Decentralization Drive CO2 Emissions? A Quantile Regression Analysis
Bibliographic record
Abstract
Achieving sustainable models is a crucial challenge today, where government actions play a fundamental role. Therefore, this study aims to analyze the impact of fiscal decentralization on CO2 emissions in 40 economies between 2000 and 2020. To this end, an unbalanced panel was constructed, and the Method of Moments Quantile Regression (MMQR) was employed. As a robustness check, Driscoll and Kraay’s standard errors approach was used. The MMQR results indicate that fiscal decentralization has a positive and significant effect across all quantiles of CO2 emissions. Additionally, it was found that revenue-side decentralization has a greater impact on the lower quantiles of CO2 emissions, while expenditure-side decentralization has a stronger effect on the upper quantiles. The findings also reveal that renewable energy mitigates CO2 emissions, whereas economic growth, resource rents, and information and communication technologies increase them, although the latter with lower statistical significance. These findings are expected to serve as a basis for public policy formulation aimed at improving environmental quality.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".