On the interaction between fiscal policy and CO<sub>2</sub>emissions in G7 countries: 1875–2016
Bibliographic record
Abstract
This study examines the impact of fiscal policy and economic growth on CO2 emissions employing a bootstrap causality test in the frequency domain. Analysing a long time series of data from 1875 to 2016 for G7 countries, we mainly aim to investigate the validity of the environmental Kuznets curve (EKC) hypothesis and whether fiscal policy affects the environment. The findings of causality from government expenditures to CO2 emissions are time-varying. However, the causality from economic growth to CO2 emissions follows a stable path and does not change over time in all countries except Canada. Since causal relations follow a consistent line and do not confirm an inverted U-shaped relationship between economic growth and environmental pollution, the EKC hypothesis does not hold in the G7 countries, implying that environmental problems are not automatically solved. The results also suggest that fiscal policy can contribute to climate change mitigation at different points in time.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".