Implications of Transition towards Manufacturing on the Environment: Saudi Arabia’s Vision 2030 Context
Bibliographic record
Abstract
This study is based on the idea that Saudi Arabia’s Vision 2030 considered the achievement of economic diversification is very crucial for the economy. In turn, this target requires a sustained increase in the contribution of the manufacturing sector in Gross Domestic Product (GDP). At the same time, the transition towards industrialization might trigger high rates of CO2 emissions, due to the escalated manufacturing demand for primary energy consumption (specifically fossil fuel). Ultimately, the high rates of CO2 emissions would have severe environmental consequences, such as environmental degradation. These environmental consequences might be more dangerous in a country extensively dependent on oil, such as Saudi Arabia. The study aims to investigate the manufacturing and environment nexus in an attempt to explore the validity of the inverted U-shaped curve, the so-called Kuznets hypothesis, during 1971–2021. Applying the econometric model autoregressive distributed lag (ARDL), the findings of the study do not show evidence supporting the validity of an inverted U-shaped Kuznets function in Saudi Arabia during the period of the study. Furthermore, the short-term results do not confirm the impact of increasing manufacturing on CO2 emissions. However, there are indications of positive effects, although limited, in the long-term.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".