CONVERGENCE OF WHOLESALE ELECTRICITY PRICES ACROSS EUROPEAN MARKETS: ARE WE THERE YET?
Bibliographic record
Abstract
European electricity market integration has been a research focus, especially after the third electricity directive emphasizing the internal market. Electricity markets are characterized by immediate consumption with storage and transmission constraints, leading to unique price dynamics. Price convergence in European markets can lead to competitive pricing, advocating market integration. This research examines whether European wholesale spot electricity prices converge. Prior literature uses sigma and beta-convergence to test price convergence. We have examined the stochastic convergence of prices across 19 European nations from 2013 to 2021 utilizing monthly data. For this analysis, we employed LM tests incorporating trend breaks grounded on the RALS regression framework. RALS-LM tests are more powerful than linear tests when structural breaks and nonnormal errors are present. LM and RALS-LM unit root tests with two trend breaks resulted in rejection of the unit root hypothesis for all countries, suggesting relative wholesale prices are stationary. Thus, shocks to relative prices are transient, supporting price convergence at the wholesale level. This result should be considered carefully as it contradicts the results from recent literature that examines retail electricity prices using beta- sigma- and club convergence. The policy implications of this finding can provide a foundation for establishing policies that promote a harmonized and integrated European electricity market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".