The social acceptance of agrivoltaics: Exploring the cognitive and affective outcomes of technological, contextual, and personal beliefs

Agrivoltaics -the co-siting of photovoltaic energy and agronomic production- has been promoted as a win-win solution to reconcile renewables expansion with food security, yet its deployment has raised ambivalence and public opposition. Existing studies have largely focused on farmers and experts, leaving lay citizens’ views underexplored. This paper addresses that gap by examining the determinants of agrivoltaics’ acceptance among non-experts. Acceptance was assessed through the integrated Sustainable Energy Technology Acceptance framework, further extended by introducing outcome evaluation as both a cognitive (perceived efficacy) and an affective (positive emotions) pathway to acceptability, and by incorporating anticipatory solastalgia, understood as present distress over anticipated landscape change. A convenience sample of Italian citizens (N = 849), mostly residing in urban or semi‑urban areas, completed an online questionnaire during the early phase of APV implementation in Italy, thus providing anticipatory evaluations. Hierarchical regressions and path analyses show that technological beliefs, especially perceptions of global and local sustainability, are the strongest predictors of agrivoltaics’ acceptability. Contextual beliefs, notably legitimacy, fairness, and trust in agrivoltaics’ technical-scientific actors, further enhance acceptability. Personal beliefs contribute as well: injunctive norms encourage acceptability, whereas anticipatory solastalgia significantly hinders it. The findings provide a quantitative baseline against which context-specific case studies can be interpreted as installations spread across the territory in the future. Findings suggest that policy and communication should highlight agrivoltaics’ environmental and socio-economic benefits and engage local experts rather than political actors; implications concerning landscape framing are advanced as hypotheses to be tested with affected populations.