An integrated TOPSIS-ORESTE-based decision-making framework for new energy investment assessment with cloud model

New energy investment assessment has a significant effect on facilitating the new energy industry's development. Reasonable evaluation methods can effectively reduce the risk of new energy investment and push forward the sustainable development of new energy resources. However, there are few research results in this area, especially in the complex decision-making environment. Thus, to make up for that, an integrated multi-attribute group decision-making (MAGDM) framework is developed to assess the new energy investment problems. First, considering the complex decision-making environment of new energy investment, hesitant fuzzy dual linguistic term sets (HFDLTSs) are applied to represent the new energy alternative evaluation values. Due to the cloud models can reflect the fuzziness and uncertainties of linguistic information, an algorithm is proposed to realize the quantitative transformation from HFDLTSs to cloud models. Second, in view of the heterogeneity of decision makers (DMs) regarding to different attributes, a cloud model similarity-based method is proposed to derive the weight information of DMs. Then, an integrated TOPSIS-ORESTE method with cloud model is established to rank the new energy investment alternatives. The proposed method could overcome the information loss due to Besson's ranks. In addition, the global preference score considers the ideal and negative solutions relative to the traditional ORETSE method. Finally, a case study on new energy investment of Shandong province, China is conducted to illustrate the application of the established MAGDM model. The effectiveness and superiority of the introduced method are further demonstrated through the sensitivity analysis of parameters and the comparison with other representative methods.