Linking geopolitical risk, load capacity factor, income, labor, population, and trade on natural resources: Evidence from top oil‐producing countries
Bibliographic record
Abstract
Abstract The global economy has been witnessing increasing geopolitical risk (GPR) in recent years. The rise in GPR has several consequences, and the impact of this situation on natural resource rent (NR) has not yet been analyzed for the major oil‐producing countries. Given this deficiency, this study analyzes the impact of GPR, gross domestic product, the labor force (LBR), load capacity factor, population density, and trade openness on the NR for the five major oil‐producing countries (namely, Canada, China, Russia, Saudi Arabia, and the United States of America). To this end, the study analyzes data for the period 1995/Q1‐2021/Q4 by using a cross‐sectional Autoregressive Distributed Lag approach. The results demonstrate that (i) an increase in GPR and load capacity factor declines the NR; (ii) an increase in gross domestic product stimulates the NR; and (iii) a rise in population density, labor force, and trade openness has a stimulating impact on the NR. Overall, the research shows that all variables substantially impact the NR. Based on the results, various policy options are discussed, such as assessing geopolitical tensions as leverage to sustainably regulate the natural resource market and growth to prevent negative impact from managing the NR effectively for the five major oil‐producing countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".