Sustainability, emission trading system and carbon leakage: An approach based on neural networks and multicriteria analysis
Bibliographic record
Abstract
Two transitions, green and digital, are changing the operations and strategies of industrial systems. At the same time, businesses are challenged to be globally competitive. Europe has a very ambitious agenda as it aims to be the first climate-neutral continent in 2050. The European Emissions Trading Scheme (EU ETS) has proven to have facilitated the reduction of significant amounts of greenhouse gas emissions, but the risk of carbon leakage is present. This work seeks to explore these issues and their relationships. Through the use of a long short-term memory (LSTM) neural network, a model is built to determine the price of European Union Allowance (EUA) as a function of different financial energy futures. The results show that the model is very robust and the EUA tends to vary between 78 and 91 €/tCO2. In addition, a Multi-Criteria Decision Analysis (MCDA) is applied to identify the best policy alternatives to enable businesses subject to the EU ETS to be competitive in global markets. The analysis is carried out with the help of academic and industrial experts and it emerges that the criteria considered most relevant are two: (i) public expenditure and its expected benefits and (ii) the industrial ecosystem. The policy implications identify that bonuses should be provided to businesses for innovative solutions that protect both the energy and raw material components. The framework of the 3E (Energy Efficiency, Renewable Energy, and Circular Economy) are critical to businesses' long-term strategies, flanked by digital development.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".