Allocative Efficiency towards Energy Transition: The Cases of Natural Gas and Electricity Markets
Bibliographic record
Abstract
Conventional economic theory indicates that the free market contributes to allocative efficiency. However, specific energy markets present network industry characteristics which distance them from perfect competition. These markets, therefore, need effective regulation. The liberalizing reforms which took place in the Organization for Economic Cooperation and Development (OECD) and emerging countries from the 1990s onwards have reduced the share of state ownership in the energy sector, but not its functions of regulation, coordination and planning. It is also worth noting the expansion of the government’s agenda due to the energy transition that has unequivocally imposed itself in the 21st century. This article uses the Slacks-Based Measure of the Data Envelopment Analysis (SBM-DEA) methodology to investigate the relationship between market liberalization and sustainability in a low-carbon energy transition context. Taking the cases of the natural gas and electricity markets, we verify whether liberalization contributes to the progress of the energy transition, driven by the emergency need to tackle climate change. The results show that the most advanced markets, in their processes of opening up, tend to be positively associated with a more vigorous energy transition. European nations, such as the United Kingdom and Norway, have experienced a relatively more advanced market liberalization leading to an efficient path toward energy transition. Chile, Canada and Colombia also have efficient scores regarding their energy transitions. For low performing countries, such as Brazil, the study suggests some calls for action that should be pursued to improve their energy market indicators, resulting in a stronger energy transition towards renewables, more competitive energy prices and a larger participation of natural gas in the energy mix, which will contribute to decreasing its external dependency.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".