The nexus between fossil energy markets and the effect of the COVID-19 pandemic on clustering structures
Bibliographic record
Abstract
The main purpose of this paper is to analyze price returns series to investigate causality between international fossil energy markets and the effect of the COVID-19 pandemic on their clustering structures. The sample period covers August 1993 to June 2023. The empirical results from Granger causality applied to tests show ( i ) no evidence of causality in both directions between Australian coal and Brent, and between Dubai crude oil and Australian coal, ( ii ) evidene of 52 unidirectional causal relationships across international fossil energy markets, and ( iii ) evidence of bidirectional causality between US gasoline and Brent, South African coal and Australian coal, Indonesian natural gas and Australian coal, Russian natural gas and Australian coal, and between South African coal and Russian natural gas. Besides, results from agglomerative hierarchical clustering show that the COVID-19 pandemic affected the structures in the clusters in fossil energy markets and increased the similarity between them. Overall, we provide insights about the connectedness and clustering among major international fossil energy markets to highlight important system dynamics that could be helpful for policy makers, traders and investors.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".