The Drivers behind Gas Market Liberalization: Diversity of Gas Sources, Market Structure and Gas Prices
Bibliographic record
Abstract
This paper studies how European Union members’ individual gas market characteristics, i.e. source diversification, incumbent firms’ market share and gas prices, measured at the start of liberalization process, influenced the full market opening timetable. A linear regression model is proposed with the time lag to the introduction of liberalization since 1998 (the first EU gas directive) as a dependent variable and the market characteristics as independent variables. The model is applied to cross-sectional data for 13 European countries. The results confirm the statistically significant impact of the market characteristics on the liberalization schedule. Our model explains 90% variation in the dependent variable. The more concentrated initial gas import structure and the higher import dependence were, the later the full market opening was scheduled. The more competitive gas market structure and the higher the average gas prices at the start of deregulation were, the sooner the gas sector was open to competition. The conclusions of the paper can be important for a better understanding of the liberalization process in the European Union and for application of EU deregulation experience to other countries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".