Data-driven complementary policy development for photovoltaic industry facing resource constraints: A case study of Zhejiang Province in China

Accelerating renewable energy development is critical for climate goals, with photovoltaic (PV) power emerging as a leading technology. However, PV's land-intensive nature triggers a “green-green dilemma” between climate targets and natural resource conservation, that is a globally prevalent challenge particularly acute in Zhejiang Province, China, where land constraints hamper large-scale PV expansion. To address this issue, this study proposes six PV-industry complementary modes including mountain-based agrophotovoltaics and offshore wind-solar hybrids, etc. Furthermore, we introduce the data-driven complementary development (DCD) framework, an innovative approach integrating K-means clustering, Mann–Whitney U test, and significance-based tier-searching (STS) algorithm to quantify subregional development potential for each mode. Unlike traditional clustering methods, the DCD framework enhances statistical robustness in potential identification. Applied to 90 subregions in Zhejiang, this framework reveals that most areas are suited for at least one complementary mode, providing actionable insights for local policymakers. This study makes three key contributions: enriching PV complementary development literature, proposing a replicable decision-support tool for resource-constrained regions, and offering targeted strategies to balance renewable energy growth and natural resource preservation in Zhejiang.