Determinants of uptake the residential solar photovoltaic system and its impact on environmental sustainability: Evidence from PLS-SEM and fuzzy sets (fsQCA)
Bibliographic record
Abstract
In response to the escalating global CO2 emissions and the urgent need to reduce dependence on fossil fuels, this study diverges from prior research that predominantly focuses on intentions or attitudes towards renewable energy. It investigates the actual uptake of residential solar photovoltaic (PV) systems in regions rich in solar radiation, where, despite the potential, renewables remain a minor part of the energy mix. Incorporating psychological and functional factors and employing the innovation resistance theory (IRT), the study comprehensively examines solar PV technology’s resistance aspects. Utilizing a robust methodological framework that uses partial least squares-structural equation modeling (PLS-SEM) with fuzzy set qualitative comparative analysis (fsQCA), the research evaluates responses from a comprehensive questionnaire survey of 758 households. The advantages of this method lie in its ability to capture both symmetric and asymmetric relationships, thereby offering a richer and more detailed analysis compared to traditional single-method approaches. PLS-SEM results identify significant barriers: image barriers (β = −0.131, t = 3.418, p < 0.001), traditional barriers (β = −0.084, t = 2.143, p < 0.05), and risk barriers (β = −0.124, t = 4.172, p < 0.001). Positive influences include environmental benefits (β = 0.166, t = 3.108, p < 0.001), environmental concern (β = 0.364, t = 6.341, p < 0.001), and government incentives (β = 0.159, t = 2.767, p < 0.01). Conversely, usage barriers and value barriers appeared non-influential. Conversely, fsQCA revealed that all factors may have a role in the uptake of residential solar PV systems. The novelty of this research is evident in its application of IRT to the context of solar PV adoption and the use of a hybrid analytical method, which together provide new insights into consumer behavior and policy implications. These findings offer actionable recommendations for policymakers and practitioners to promote the adoption of residential solar PV systems.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".