Renewable Energy Challenges and Opportunities in the Kingdom of Saudi Arabia
Bibliographic record
Abstract
This paper seeks to introduce the advantages of investing in renewable energy in Saudi Arabia. It concludes that investment in renewable energy is a promising strategy for creating more sustainable jobs for Saudi citizens and promoting the domestic economic diversification. The Saudi renewable energy sector shall increase the Saudi non-oil private sector’s contribution to the total Saudi economic activities. This research paper uses Leontief’s method to estimate the impact of investment in renewable energy through three main scenarios (investment of 25, 50, and 85 billion Saudi Riyal) over 5 years (2020-2025). The total value added, an additional expected growth in the gross domestic product (GDP) during the period from 2020 to 2025, is estimated to be around 2.7, 4.7, and 6.0 percent of the investment of 25, 50, and 80 billion Saudi Riyal in renewable energy respectively. The expected number of new jobs that would be generated in all three scenarios are 44,000, 90,000, and 150,000 thousand jobs. Moreover, further development of the Saudi renewable energy sector should encourage domestic energy consumption to be more efficient and less polluted. However, challenges typically thwart progress in the renewable energy sector. These challenges include technical problems, cost issues, and lack of financial sources. This paper proposes some solutions that should help circumvent these particular challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".