In an increasingly complex decision-making environment, the decision information we obtain contains the decision makers’ professional cognition, psychological preference, risk attitude and other hidden uncertainty factors. These hidden inherent factors may cause evaluations being better or worse than their actual level. In order to reduce the bias brought by these factors, they should be identified and eliminated prior to decision making. Hence, a preference determination model is proposed and a risk model is established to eliminate these factors by analyzing the possible impact. Moreover, a more practical probabilistic linguistic model–2-tuple probabilistic linguistic term set is proposed and its related concepts are defined. Furthermore, an integrated decision-making method is provided. Finally, considering the application background of double carbon economy, an example by selecting the best design of electric vehicles charging station is conducted to illustrate the proposed method, and the feasibility and effectiveness are verified.

在日益复杂的决策环境中，我们获得的决策信息包含了决策者的专业认知、心理偏好、风险态度等隐藏的不确定因素。这些隐藏的内在因素可能会导致评估比实际水平更好或更差。为了减少这些因素带来的偏差，应该在决策之前识别并消除它们。因此，提出了偏好确定模型，并建立了风险模型，通过分析可能的影响来消除这些因素。此外，提出了一种更实用的概率语言模型——二元概率语言术语集，并定义了其相关概念。此外，还提供了一种综合决策方法。最后，