The deployment of solar photovoltaic (PV) technology has consistently outpaced expectations over the past decade. However, long-term prospects for PV remain deeply uncertain, as recent global scenarios span two orders of magnitude in installed PV capacity by 2050. Here we systematically compile an ensemble of 1,550 scenarios from peer-reviewed and influential grey literature, including IPCC and non-IPCC scenarios, and apply a statistical learning framework to link scenario characteristics with foreseen PV outcomes. We show that a large portion of the uncertainty in the global scenarios is associated with general features such as the type of organization, energy model and policy assumptions, without referring to specific techno-economic assumptions. IPCC scenarios consistently project lower PV adoption pathways and higher capital costs than non-IPCC scenarios. We thus recommend increasing the diversity of models and scenario methods included in IPCC assessments to represent the multiple perspectives present in the PV scenario literature.

在过去十年中，太阳能光伏 (PV) 技术的部署一直超出预期。然而，光伏的长期前景仍然非常不确定，因为最近的全球情景到 2050 年光伏装机容量将跨越两个数量级。在这里，我们系统地汇编了来自同行评审和有影响力的灰色文献的 1,550 个情景的集合，包括 IPCC 和非-IPCC 情景，并应用统计学习框架将情景特征与可预见的 PV 结果联系起来。我们表明，全球情景中的很大一部分不确定性与组织类型、能源模型和政策假设等一般特征相关，而没有参考具体的技术经济假设。与非 IPCC 情景相比，IPCC 情景始终预测较低的光伏采用途径和更高的资本成本。因此，我们建议增加 IPCC 评估中包含的模型和情景方法的多样性，以代表光伏情景文献中存在的多种观点。