Factors Affecting the Willingness to Adopt Residential Rooftop Solar Panels: Evidence from Saudi Arabia
Bibliographic record
Abstract
This study investigates the willingness to adopt rooftop solar panels across diverse geographic scales in Saudi Arabia—specifically, Riyadh City, Buraydah City, and the rural area of Al-Qassim Province. Drawing upon an online survey of 1 647 respondents, we employ the chi-square test of association to analyze the relationships between willingness to adopt and several related determinants from the literature, including various socio-economic factors, variations in the built environment, social information networks, and the institutional and pricing context. The survey data reveal a strong willingness to adopt rooftop solar panels across all three study areas, with distinct geographical variations in the associated variables. The chi-square results show statistically significant associations between willingness to adopt and various environmental beliefs, financial incentives, and prior expectations and perceptions, especially concerning solar panel costs and benefits. The findings underscore the importance of tailoring solar renewable energy policies to local contexts in Saudi Arabia.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".