The Russia-Ukraine conflict, soaring international energy prices, and implications for global economic policies
Why this work is in the frame
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Bibliographic record
Abstract
This study examines the economic impact of soaring international energy prices during the Russia-Ukraine conflict from February 23, 2022, to May 31, 2022. Notably, by applying a CGE model, this study offers insights into energy policies at both macroeconomic and industrial levels, emphasizing the model's utility in analyzing complex economic interactions under geopolitical stress. Findings indicate that: (1) Russia, a critical energy-producing country , faced severe economic setbacks due to sanctions, with its GDP contracting by 5.5 %, household income decreasing by 4 %, and consumer spending dropping by 3.5 %. This was accompanied by a significant reduction in domestic investment by 6 %, a decline in output by 5 %, and a decrease in societal welfare indicators. (2) Other energy-producing countries or regions , such as the Middle Eastern oil-producing countries, Australia, Canada, Mexico, and Southeast Asia, experienced economic benefits from the global energy market's "crowding-out effect." These regions saw an increase in GDP ranging from 2 % to 4.5 %, output growth by 3 %–6 %, and household income and consumption improvements by approximately 3 %–5 %. However, these benefits were tempered by a 1 %–2.5 % decline in domestic investment due to rising local energy costs. (3) Developed and developing regions, suffered adverse impacts, including the US, UK, EU, Japan, China, South Asia, Middle Eastern non-oil-producing countries, and Africa. These regions reported a decrease in GDP by 0.5 %–3 %, a decline in household income by 2 %–4 %, and lower consumption rates by 1.5 %–3.5 %. The economic strain was further exacerbated by an inflation increase of up to 2 % across these economies. This research offers valuable insights for governments and policymakers globally to address the challenges posed by the Ukraine crisis-induced energy crisis, underscoring the need for strategic energy policy adjustments and economic resilience planning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it