Can U.S. strategic petroleum reserves calm a tight market exacerbated by the Russia–Ukraine conflict?
Bibliographic record
Abstract
Recent changes in global petroleum markets have driven the debate regarding the use of strategic petroleum reserves (SPRs) as a price management tool during periods marked by extreme price volatility. We examine the price management role of the U.S. SPR under typical market conditions and in extreme emergencies. Furthermore, we discuss the White House's hypotheses that (a) boosted Organization of the Petroleum Exporting Countries' (OPEC) production and releases from the U.S. SPR result in a negative pressure on U.S. gasoline inflation, and (b) crude oil releases from the U.S. SPR helps balance the global oil market. The threshold cointegration results indicate that U.S. SPR releases impact neither OPEC production nor imported input prices. We apply a hybrid open-economy Phillips curve to model gasoline inflation, accounting for backward- and forward-looking price settings, domestic and global slackness, and energy security. We distinguish between normal-, super-, and hyper-backwardation and -contango oil markets using threshold cointegration and regression techniques. Our results demonstrate that SPR releases and OPEC output increases generally decrease inflation, with a crucial exception being the hyper-backwardation market, as seen in 2021–2022. This period was characterized by severely constrained global supply buffers, including OPEC's spare capacity, exacerbated by the Russia–Ukraine conflict. For this period, we conclude that (1) the impact of OPEC production changes on gasoline inflation would be negligible, (2) excess domestic demand relative to domestic supply raises concerns about domestic energy security, and (3) the unprecedentedly large SPR drawdowns are likely to have caused the market to panic and contributed to gasoline price increases, contrary to arguments suggesting that the 2022 releases eased domestic gasoline prices. We conclude that the SPR is an ineffective price control mechanism during crises and may not have the strategic value previously thought in an extremely tight oil market.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".