Intense market competition has driven small- and medium-sized enterprises (SMEs) in the manufacturing sector to collaborate and form supply chain coalitions, which can improve the information flow and resource sharing and significantly enhance supply chain management efficiency. However, the distribution of cooperative benefits poses a core challenge for the long-term stability of coalitions. This paper addresses the impact of dynamic changes in complex business environments by utilizing triangular fuzzy numbers to represent the value of coalition, effectively depicting the uncertainty and ambiguity in the cooperation process. Compared to traditional models (which do not use triangular fuzzy numbers), this model is better suited to dynamic changes, offering flexible response mechanisms that ensure adaptability and fairness in the decision-making process. In addition, considering the influence of each member’s weight in the coalition, the fuzzy comprehensive evaluation method is used to determine the weights. With the goal of minimizing the dissatisfaction of enterprises in benefit distribution, a least square contribution with triangular fuzzy numbers is constructed to replace the marginal contribution of the classical Banzhaf value, and an improved Banzhaf value based on the player’s triangular fuzzy number-weighted excess contribution is proposed to arrive at a fair and reasonable benefit allocation strategy in order to enhance the long-term stability and cooperative benefits of coalition. By analyzing an example of the supply chain coalition, the effectiveness of the proposed improved Banzhaf value is verified, which satisfies the uniqueness, the individual rationality, and the group rationality. It not only promotes the level of risk management and decision making under the uncertainty conditions of complex business, but also deepens the theoretical foundation of cooperative game theory and expands its possibilities in practical applications and future development.

With the rapid development of the prefabricated building industry, the green supply chain of prefabricated buildings has become a key driver of sustainable development and efficiency improvement in the industry. However, the issue of benefit distribution arising from cooperation has become the main challenge affecting the long-term stability of the supply chain. To address this, this study proposes an improved TFN-TOPSIS-Banzhaf value model, which optimizes the benefit distribution in the green supply chain of prefabricated buildings using cooperative game theory. This approach enhances both the fairness and accuracy of the distribution. The model integrates a combination of subjective and objective weighting methods based on triangular fuzzy numbers and the M-TOPSIS method for multi-factor evaluation, resulting in the corrected weight coefficients. By combining the weighting coefficients and least squares contributions, the improved Banzhaf value based on players’ weighted least squares contributions is constructed. The effectiveness and robustness of the model are verified through a case analysis, which significantly enhances the model’s ability to handle supply chain synergies and achieves a more fair and precise benefit distribution. This research provides an effective benefit distribution tool for the prefabricated building industry, promoting the continuous development of green building practices and supply chain cooperation.

As global energy demand grows and the focus on environmental sustainability intensifies, wind energy, as a form of clean energy, plays a pivotal role in the global energy transition. The public–private partnership (PPP) model, by integrating resources from both the public and private sectors, effectively propels the implementation of wind energy projects. However, these projects face a myriad of risks during both development and operation, making effective risk management crucial to project success. This paper, through literature analysis and System Dynamics methodology, develops a risk diversification indicator system that covers the entire project lifecycle. In addition, by combining the improved G1 weighting method and the entropy method, a dynamic risk model is established. Furthermore, through numerical simulation and sensitivity analysis, the risk levels of each subsystem and the key boundary risk factors are identified, and a set of highly adaptable risk diversification strategies is proposed. These strategies will enhance the resilience of wind energy PPP projects, foster trust among stakeholders, help participants effectively respond to and predict risk evolution, improve the project’s risk tolerance, and ensure its long-term sustainable operation.