Does fiscal decentralization and <scp>eco‐innovation</scp> promote sustainable environment? A case study of selected fiscally decentralized countries
Bibliographic record
Abstract
Abstract This study highlights the importance of fiscal decentralization in promoting a sustainable environment. The literature on the importance of fiscal decentralization in affecting environmental quality is scant, and thus, this study attempts to fill the gap by incorporating the linear and nonlinear terms of fiscal decentralization as possible determinants for CO 2 emissions. Particularly, we utilize data from seven highly fiscally decentralized countries, that is, Australia, Austria, Belgium, Canada, Germany, Spain, and Switzerland, over the period 1990–2018. For empirical analysis, advanced panel data econometric tools are used that can deal with both heterogeneous coefficients and dependence of cross‐sections. The findings confirm that linear and nonlinear terms of fiscal decentralization improve the environment by reducing CO 2 emissions. Moreover, gross domestic product (GDP) increases, while eco‐innovation and renewable energy usage reduce CO 2 emissions. This study recommends that any policy that targets green growth will affect CO 2 emissions. Moreover, policies targeting fiscal decentralization, GDP, eco‐innovation, and renewable energy will play the role in more than 1 year, namely in the long run.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".