Driven by the carbon neutrality agenda, the collaborative development of the photovoltaic (PV) industry chain faces dual challenges: behavioral heterogeneity among multiple stakeholders and the absence of a decentralized trust mechanism. Existing research exhibits notable limitations in the completeness of participant modeling, the quantification of blockchain effects, and the dynamic adaptability of incentive mechanisms. To address these gaps, this paper constructs a blockchain-enabled cooperative governance framework encompassing four core actors: PV power generation enterprises, power grids, users, and government entities. A four-party evolutionary game model is developed, integrating blockchain-based trust mechanisms and cost constraints. The model innovatively endogenizes default penalties, trust enhancement, and behavioral preferences into the payoff structure, and designs a multi-dimensional incentive system including subsidies, rewards, and penalties. Numerical simulations demonstrate that the blockchain platform significantly enhances the stability of cooperation and accelerates strategic convergence. Furthermore, appropriately calibrated subsidies and dynamic performance-based rewards effectively incentivize collaboration, while user power supply preferences and default penalties are identified as key variables influencing system evolution. Compared with a traditional three-party model, the proposed four-party model exhibits superior performance in terms of convergence speed and system stability. This study provides theoretical support for building a trustworthy, efficient, and sustainable governance system for the PV industry, and offers actionable policy recommendations in areas such as cost regulation, tiered incentives, differentiated tariffs, and smart contract implementation. • A blockchain-enabled PV governance model integrating four core stakeholders. • A four-party evolutionary game with incentive, penalty, and trust mechanisms. • Blockchain adoption and cost strongly shape multi-agent strategy evolution. • Simulations identify optimal subsidy–penalty settings for stable cooperation. • Policy insights support enhanced PV collaboration and system resilience.
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Wencheng Chen
Jinhua Wang
Anhui Jianzhu University
Jinhua Wang
Anhui Jianzhu University
Energy
Fuzhou University
Nanjing University of Science and Technology
Nanjing University of Information Science and Technology
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Chen et al. (Thu,) studied this question.
synapsesocial.com/papers/69a766e6badf0bb9e87dedda — DOI: https://doi.org/10.1016/j.energy.2026.140345
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