Assessing the Environmental Impact of Fiscal Consolidation in OECD Countries: Evidence from the Panel QARDL Approach
Bibliographic record
Abstract
Concerns about ensuring a sustainable environment are growing, attracting major attention from policy professionals worldwide. Therefore, this study investigates the nonlinear impacts of fiscal consolidation on CO2 emissions in 17 OECD countries from 1978 to 2020. To probe the short- and long-term connections across various quantiles of CO2 emissions, we adopted panel QARDL frameworks. The Granger non-causality test was used to investigate the variables’ association with CO2 emission. The study’s main findings confirm the overall beneficial effect of fiscal consolidation on carbon emissions. It reduces CO2 emissions at almost all quantiles in the short run. By contrast, in the long run, the effect is positive at lower quantiles and turns negative at upper quantiles. Furthermore, a causality analysis identified a bidirectional causal relationship between fiscal consolidation and CO2 emissions, confirming the existence of mutual influence. While Keynesian theory links fiscal consolidation to economic recession, our findings support the non-Keynesian view, showing that such policy can foster economic growth and thereby contribute to reducing CO2 emissions in the short run. Thus, OECD countries are orienting public spending and carbon taxation toward environmentally friendly practices while ensuring environmental protection and deficit reduction. Nonetheless, the identified mixed effect in the long run highlights the need for sustained consolidation policies by enhancing expenditure efficiency and adopting targeted taxation measures to achieve lasting emission reductions and support the transition to cleaner energy, even when emissions are relatively low.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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".