Environmentally related taxes and their influence on decarbonization of the economy
Bibliographic record
Abstract
Environmental taxes ensure sustainable development, but their fiscal and environmental effectiveness differs for countries with different socio-economic characteristics. This study aims to compare the impact of environmental tax revenues on economy’s decarbonization (measured through carbon productivity – the ratio of GDP to carbon dioxide emissions) in different countries, considering their green technologies development and carbon emissions. The paper analyzed OECD and World Bank statistical data for 38 OECD countries for 2002–2021 using linear panel regression models with fixed and random effects (using Hausman test and STATA 18). To identify explicit and latent patterns of this influence, which are common to certain countries, this analysis did not consider each country separately but targeted clusters, distinguished by Ward and Sturges methods based on the effective tax rate on carbon emissions, total environmental tax revenues, total carbon emissions, and carbon productivity. The positive influence of environmental tax revenues on the economy’s decarbonization level has been confirmed for 29 countries (four from six clusters). The effect is the largest for the USA (an increase in tax revenues by 1% leads to an increase in carbon productivity by 0.9% on average) and the smallest – for the cluster including Austria, Belgium, Canada, Costa Rica, Czechia, Estonia, France, Germany, Hungary, Iceland, Korea, Lithuania, New Zealand, Poland, Portugal, Slovakia, Spain, and the Great Britain (increase – 0.1%). The negative impact was confirmed for nine countries (two from six clusters): Denmark, Finland, Israel, Latvia, and Sweden (decrease – 0.3%) and Greece, Italy, the Netherlands, and Slovenia (decrease – 0.21%).
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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 | 0.004 |
| 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 | 0.001 |
| 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".