Dynamic linkage of Renewable Energy, Technology Innovation and Mineral Resource Demand in Resource Rich Economies
Bibliographic record
Abstract
This investigates the relationship of renewable energy, technology innovation, economic growth, and the interaction between renewable energy and technology innovation with mineral resource demand in resource-rich economies (Russia, The USA, Brazil, Canada, Iran, Saudi Arabia, Australia, Venezuela, China, & Iraq). The research employs the dynamic common correlated effect (DCCE) method for the period from 1995 to 2021. The findings show that coefficients for RENR, TINO, TR, and EG are all statistically significant, indicating their significant positive long-term impact on the mineral resource demand. Increases in RENR, TINO, TR, and EG are associated with positive changes in the outcome variable over the long term. However, the coefficient of INTERM has a significant negative influence on the mineral resource demand in the long run. The findings provide valuable insights into the dynamic interrelationships between the model's variables. Policymakers should focus on policies that promote and enhance RENR, TINO, TR, and EG, as they positively influence the outcome variable in the long run. Additionally, policymakers should be cautious of the potential negative effects of changes in INTERM on the mineral resource demand over the long term and carefully assess policies affecting this variable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".