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<p>To address the prevalent problems of blind model selection, unfair income distribution, and insufficient cooperation stability in the shareholding system of rural collective construction land, this study constructs a scientific quantitative decision-making framework. It designs a three-step process of “diagnosis-modeling-decision-making,” comprehensively utilizing the AHP-TOPSIS model for model comparison, the Sharpe ratio and Monte Carlo simulation to assess risk-return, the Shapley value method to calculate fair distribution, and evolutionary game theory to analyze cooperation stability. Through empirical testing in three types of regions—City CE (strong collective), City B (weak collective but strong government), and City CA (weak foundation)—the model successfully matches the characteristics of each region: AHP-TOPSIS validates the rationality of the model selection in each case, the Shapley value method quantifies multi-party contributions, and evolutionary game theory reveals that government adjustment funds and supervision are key to maintaining cooperation (raising the stability probability to above 0.85). This framework effectively transforms experience-based decision-making into data-driven precise decision-making, providing a practical tool of “<i>identify the type, select the model, determine the mode</i>” for regions with different resource endowments.</p>

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A quantifiable decision-making framework for selecting models of rural collective construction land valuation and shareholding in China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月13日

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  • Jiwei Chen,
  • Xiaofeng Xu

摘要

To address the prevalent problems of blind model selection, unfair income distribution, and insufficient cooperation stability in the shareholding system of rural collective construction land, this study constructs a scientific quantitative decision-making framework. It designs a three-step process of “diagnosis-modeling-decision-making,” comprehensively utilizing the AHP-TOPSIS model for model comparison, the Sharpe ratio and Monte Carlo simulation to assess risk-return, the Shapley value method to calculate fair distribution, and evolutionary game theory to analyze cooperation stability. Through empirical testing in three types of regions—City CE (strong collective), City B (weak collective but strong government), and City CA (weak foundation)—the model successfully matches the characteristics of each region: AHP-TOPSIS validates the rationality of the model selection in each case, the Shapley value method quantifies multi-party contributions, and evolutionary game theory reveals that government adjustment funds and supervision are key to maintaining cooperation (raising the stability probability to above 0.85). This framework effectively transforms experience-based decision-making into data-driven precise decision-making, providing a practical tool of “identify the type, select the model, determine the mode” for regions with different resource endowments.