A hybrid outranking method for greenhouse gas emissions’ institution selection with picture 2-tuple linguistic information

An appropriate greenhouse gas (GHG) emissions’ institution largely affects long-term development of cement corporates. The primary objective of this paper is to investigate a scientific method for selecting an optimal institution for a cement corporate. The paper mainly makes the following contributions to effectively resolving this selection problem. Given the capacity of picture 2-tuple linguistic sets (P2TLSs) in depicting voting information, they are used as evaluation representation in this case. Another contribution is that this paper newly proposes a prospect theory-based score function to rank picture 2-tuple linguistic numbers (P2TLNs). The proposed score function effectively overcomes the limitations in the existing score function by considering the voting potential of those decision-makers (DMs) who hold “abstain” opinion. Meanwhile, this proposed score function also adequately considers the rational-bounded behavior of DMs, thereby making the evaluation more approximate to the reality. Furthermore, this paper investigates a hybrid outranking method to select institution in the context of P2TLSs. It takes different types of preference functions and the Elimination and Choice Translating Reality (ELECTRE) method into account simultaneously. Compared with other similar type methods, this hybrid method can flexibly choose criteria type and provide specific values to represent quantitative dominance relation rather than qualitative judgment. Finally, an illustrative example and comparative analyses are provided to certify the availability and reliability of the proposed method.