Multi-attribute group decision making method with dual comprehensive clouds under information environment of dual uncertain Z-numbers

A Relative Projection-Based Multiattribute Group Decision-Making Model With Noncooperative Behavior Management and Application to NEV Supplier Selection

This article reports a novel consensus model where a group of internal and external experts evaluate alternatives under multiple attributes and provide mutual evaluations. First, different from previous studies, the cognitive and interest conflicts of internal and external experts are considered simultaneously. But interest conflict is emphasized for internal experts, and cognitive conflict is mainly considered for external experts. Second, we explore the categorization and management methods of noncooperative behaviors (NCBs) of experts. The relative projection-based indexes are proposed for the first time to measure the degrees of cognitive and interest conflicts by using multiattribute preference matrices (MAPMs) and the weight vectors of attributes. A group of experts are divided into three categories and the corresponding management strategies are developed. Third, we investigate the consensus mechanism among experts with cognitive and interest conflicts. For reaching an acceptable consensus level, an adjustment process is proposed to revise some local entries in MAPMs and mutual evaluation matrix (MEM). A penalty mechanism is further established to dynamically update the weights of experts. An algorithm is designed to capture the consensus reaching process in multiattribute group decision making, where internal and external experts are distinguished by proposing a parameter. Finally, the high-performance battery supplier selection of new energy vehicle is studied to illustrate the proposed model. The results reveal that the efficiency of reaching consensus can be enhanced by using the developed model with effective management of NCBs.

Evaluation mechanism of sustainable development level for the cities in the Yangtze River Economic Belt under the dual-carbon policy

Low carbon sustainable development (LCSD) has become an inevitable choice, for which China has put forward a "dual-carbon" policy. The purpose of this study is to capture the interaction between the environment and the economy in the context of this goal, thus evaluating LCSD level from a systematic perspective. This paper proposes a super slack based measurement (SBM) model with a non-equal weight structure to assess the LCSD level. Firstly, a maximum influence minimum redundancy (MIMR) index selection algorithm is designed to establish input and output index systems, which avoids redundancy indexes. Secondly, the objective function of the original super SBM employs an equal weight structure, which leads results inadequately reflect the research preferences. Therefore, the weights of indexes are introduced to form an improved super SBM. Finally, 40 cities along the Yangtze River Economic Belt (YREB) are selected for empirical analysis. Results show that (1) the LCSD level of YRBE decreases from downstream to upstream to midstream; (2) Jiangsu, Zhejiang, and Sichuan provinces have higher LCSD levels, while Hunan and Jiangxi provinces have lower levels; and (3) up to 2021, there are 32 effective cities and 8 ineffective cities. The research implies that balancing the economy-environment relationship is crucial for higher efficiency. The LCSD evaluation method not only reflects the coordination level between the economy and the environment, but also integrates the research preference into the results, providing decision support for the government to formulate carbon reduction policies and allocate resources.

Forecasting the carbon emissions in Hubei Province under the background of carbon neutrality: a novel STIRPAT extended model with ridge regression and scenario analysis

The impact of global greenhouse gas emissions is increasingly serious, and the development of green low-carbon circular economy has become an inevitable trend for the development of all countries in the world. To achieve emission peak and carbon neutrality is the primary goal of energy conservation and emission reduction. As the core province in central China, Hubei Province is under prominent pressure of carbon emission reduction. In this paper, the future development trend of carbon emissions is analyzed, and the emission peak value and carbon peak time in Hubei Province is predicted. Firstly, the generalized Divisia index method (GDIM) model is proposed to determine the main influencing factors of carbon emissions in Hubei Province. Secondly, based on the main influencing factors identified, a novel STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) extended model with ridge regression is established to predict carbon emissions. Thirdly, the scenario analysis method is used to set the variables of the STIRPAT extended model and to predict the emission peak value and carbon peak time in Hubei Province. The results show that Hubei Province's carbon emissions peaked first in 2025, with a peak value of 361.81 million tons. Finally, according to the prediction results, the corresponding suggestions on carbon emission reduction are provided in three aspects of industrial structure, energy structure, and urbanization, so as to help government establish a green, low-carbon, and circular development economic system and achieve the industry's cleaner production and sustainable development of society.

A VIKOR-Based Linguistic Multi-Attribute Group Decision-Making Model in a Quantum Decision Scenario

Quantum decision theory has been successfully applied to multi-attribute group decision-making (MAGDM) to model decision-makers’ interference and superposition effects in recent years. Existing quantum models assume that interference effects among decision-makers are symmetric. However, asymmetric interference effects have been ignored. We propose a VIKOR-based linguistic distribution assessments (LDAs) model considering asymmetric interference effects in a quantum decision scenario. Firstly, we combine VIKOR with LDAs to obtain a compromise solution in a fuzzy multi-attribute decision scenario with conflicting attributes. Secondly, an aggregation framework based on quantum probability theory is constructed to explore group preferences considering asymmetric interference effects among decision-makers. Finally, the model is compared with other methods to confirm its validity and stability.