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. • Develop six complementary modes in renewable energy economics that take photovoltaic (PV) power generation and other industries based on the land resources into account. • Propose a data-driven complementary development (DCD) that integrates K-means algorithm to group subregions into clusters, Mann-Whitney U test for the significance of differences between subregion clusters, and significance-based tier-searching (STS) algorithm for potential identification. • Apply the DCD framework to identify the protentional of regions for the complementary between photovoltaics and other industries. • Provide an alternative perspective applicable to the development of renewable energy economics subject to land resource constraints.

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Publication Details

Journal
Energy Strategy Reviews
Published
2026-02-13
DOI
https://doi.org/10.1016/j.esr.2026.102128
Primary Topic
Photovoltaic Systems and Sustainability
Type
article
Field-Weighted Citation Impact
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article

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

Enjing Jiang, Yijia Chen, Yun Zhou, Yuxiu Si et al.
Energy Strategy Reviews
Photovoltaic Systems and Sustainability
article

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

Enjing Jiang, Yijia Chen, Yun Zhou, Yuxiu Si, Jie Cao, Xuanlan Huang, Xinran Yin
article en

Abstract

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. • Develop six complementary modes in renewable energy economics that take photovoltaic (PV) power generation and other industries based on the land resources into account. • Propose a data-driven complementary development (DCD) that integrates K-means algorithm to group subregions into clusters, Mann-Whitney U test for the significance of differences between subregion clusters, and significance-based tier-searching (STS) algorithm for potential identification. • Apply the DCD framework to identify the protentional of regions for the complementary between photovoltaics and other industries. • Provide an alternative perspective applicable to the development of renewable energy economics subject to land resource constraints.

Energy Strategy ReviewsVol. 64
Duke University (US), University of Chinese Academy of Sciences (CN), Zhejiang University of Technology (CN)
Openalex Percentile: Top 62%
Photovoltaic Systems and Sustainability
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