Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a commonly used decision model in multi-attribute group decision making (MAGDM), and a probabilistic linguistic term set (PLTS) is the linguistic variable that can effectively express the fuzziness of decision makers’ (DMs’) preference. However, in actual decision use, PLTS type decision preference needs to be processed before use, which can distort the decision results. The randomness of DM’s preference which also affects the final decision making is often ignored. Therefore, in order to better serve the MAGDM problem, this paper proposes an asymmetric probabilistic linguistic cloud TOPSIS (ASPLC-TOPSIS) method. First, the basic theories of linguistic variables and cloud model (CM) are introduced. Second, the conversation model between linguistic variables and CM is defined along with the operation formula of ASPLC. Third, considering the importance of the DMs’ subjective weights, a DM trust network is established to calculate the DMs’ weights. Finally, the decision process of ASPLC-TOPSIS is proposed and the superiority of this method is proved through experimental studies.

Social networks (SNs) have become popular as a medium for disseminating information and connecting like-minded people. They play a central role in decision-making by correlating the behaviors and preferences of connected agents. However, it is difficult to identify social influence effects in decision-making. In this article, we propose a framework of how to describe the uncertain nature of the social network group decision-making (SN-GDM) process. Social networks analysis (SNA) and quantum probability theory (QPT) are combined to construct a decision framework considering superposition and interference effects in SN-GDM scenarios. For the first time, we divide interference effects into symmetry and asymmetry. We construct an influence diagram, which is a quantum-like Bayesian network (QLBN), to model group decisions with interactions. We identify symmetry interference terms from Shapley value and asymmetry interference terms from trust value, respectively. The probability of an alternative is calculated through quantum probability theory in our influence diagram. The combination of QLBN model and social network could gain an understanding of how the group preferences evolve within SN-GDM scenarios, and provide new insights into SNA. Finally, an overall comparative analysis is performed with traditional SNA and other quantum decision models.

This paper uses Chinese provincial panel data from 2011 to 2019, measures CO2 emissions of provinces in China using the IPCC method, and explores the impact of digital finance on CO2 emissions through the SAR model and SDM. Empirical study shows that digital finance significantly reduces CO2 emissions. Digital finance reduces CO2 emissions by promoting energy industrial structure transformation and spreads to surrounding areas through spillover effects, contributes to increasing green patents granted and thus reduces regional CO2 emissions, advances the green technological progress and therefore inhibits CO2 emissions, but reduces the green technological progress in surrounding areas and increases CO2 emissions due to the siphon effect. With the development of digital finance itself, the higher the level of financial regulation, green development and the green finance index, the better the effect of digital finance on CO2 emission reduction. Additionally, digital finance significantly reduces CO2 emissions in the south of China.