Did ETS Coverage and Free Allowances Affect Economic Performance and GHG Emissions in the EU?: Evidence from a Panel of EU Sectors
Bibliographic record
Abstract
This study analyzes the impacts of the European Union Emissions Trading System and free allowances on sectoral value added, gross output, and greenhouse gas emissions in the European Union for the period 1995-2020. Since the European Union Emissions Trading System inherently covers firm-level emissions, most studies in this area have been conducted at the firm level. However, a sectoral analysis allows understanding how sectors as a whole respond to the carbon pricing mechanism in terms of carbon reductions, competitiveness and sectoral output growth. It can also reveal how changes differ across sectors subject to different regulations. Controlling for sectoral employment, intermediate input use, and time effects, the results show that European Union Emissions Trading System coverage has a negative impact on both value added and gross output, but does not lead to a significant reduction in greenhouse gas emissions. The findings indicate that more labor-intensive and less input-intensive production can reduce emissions. Furthermore, the study draws attention to the competitive losses caused by compliance costs in sectors within the scope of the European Union Emissions Trading System and shows that the impact of free allowances on performance is insufficient. These results highlight the importance of coherent and inclusive approaches in policy design to more effectively manage the economic and environmental impacts of the European Union Emissions Trading System. It is recommended to develop more targeted and flexible strategies, taking into account sectoral differences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".