Financial Convergence or Decoupling in Electricity and Energy Markets? A Dynamic Study of OECD, Latin America and Asian Countries
Bibliographic record
Abstract
The motivation for this theoretical paper is to “shed further light” on electricity market liberalisation. The major influences on electricity prices in each country are local supply and demand conditions, which include costs of renewables and/or regulatory effects on pricing. This also includes effects of public or private monopoly pricing. However, many countries from a representative sample of groups of economies, show long-term equilibrium relationships in their electricity and energy stock market sectors. In these countries in the short-term, exogeneity lies with the energy sectors in the EMU, the UK, New Zealand, the Philippines, Hong Kong and Thailand. In the cases of the US and India the electricity markets are exogenous, which is probably due to the sheer size of those markets. Where there is evidence of cointegration the nexus between electricity and energy sectors remains and the strength of this relationship is indicative of greater progress in electricity market liberalisation. This is because their electricity prices are influenced to a significant degree by global fossil fuel supply costs. In those cases domestic factors such as cost of regulatory environments are less important.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".