Does fiscal decentralization foster renewable electricity generation? A panel data study of OECD countries
Bibliographic record
Abstract
As the global community intensifies efforts to transition toward sustainable energy systems, the role of institutional and fiscal arrangements in fostering renewable energy has gained increasing attention. This study aims to assess whether fiscal decentralization contributes to the expansion of renewable electricity generation in OECD countries by analyzing panel data and identifying the direction and significance of this relationship. Utilizing a panel dataset of 34 countries spanning 2000–2023, the analysis employs a fixed-effects regression model with Driscoll-Kraay standard errors. It includes a one-year lag of fiscal variables to ensure robustness. The findings reveal a statistically significant but modest negative relationship between the share of subnational revenues in GDP and the share of renewables in electricity generation, suggesting that greater fiscal decentralization may not automatically incentivize renewable energy adoption. More specifically, the fixed-effects model corrected for heteroskedasticity and autocorrelation indicates that the coefficient for lagged subnational revenue (as a percentage of GDP) is negative and marginally significant (p ≈ 0.057), hinting at a potential delayed inhibitory effect. Additionally, country-level fixed effects demonstrate substantial heterogeneity, with nations like Iceland, Norway, and Canada showing systematically higher renewable electricity shares, regardless of fiscal structure. These results underscore the importance of complementary institutional frameworks and national coordination mechanisms to ensure that decentralization effectively supports climate policy goals. AcknowledgmentThis study was carried out within the framework of a research grant awarded by the Swiss National Science Foundation (grant no. IZURZ1_224119/1) and funded by the European Union grant “NextGenerationEU through the Recovery and Resilience Plan for Slovakia” (No. 09I03-03-V01-00130) and project VEGA – 1/0392/23 “Changes in the approach to the creation of companies’ distribution management concepts influenced by the effects of social and economic crises caused by the global pandemic and increased security risks”.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".