## 1 Introduction  
To remove the dependence on fossil energy, the development of renewable energy has been emerging rapidly on a global scale. The onshore wind energy has reached technological maturity and is cost-effective [1]. However, onshore wind energy projects are limited by the disadvantages of land use, noise, and visible impacts. Therefore, the researchers and managers of renewables have gradually turned their attention to offshore projects [2]. Offshore wind power stations allow for the deployment of larger wind turbines and alleviate transport constraints. On the other hand, ocean energy studies are also developing at an early stage. Wave or tidal energy has great potential for development. Therefore, technologies that integrate offshore wind and wave energy are being developed vigorously [3]. In 2020, a good review about the future of offshore wind-wave integrated energy conversion systems was provided by [PERSON] et al. The key finding of this study is the potential for greater energy generation and the reduced energy dispersion of integrated conversion systems [2]. Although WWSs are a solution to simultaneously exploit the wind and wave energy potential of the ocean, this type of integrated renewable energy is limited by high installation and operation and maintenance costs. Several attempts to optimize the cost of WWSsby the researchers have been found [4; 5; 6]. The primary purpose of integrated wind and wave energy studies is to minimize the cost and maximize the energy yield of offshore stations [7]. In addition to technological developments, determining the appropriate location is an important decision for the efficiency and sustainability of offshore wind and wave energy stations (WWS) [8; 9]. Integrated multi-criteria decision-making (MCDM) approaches are increasingly being applied to the renewable energy site selection problems [8]. However, most renewable energy site selection studies use either expert-based assessments or quantitative analyses. In addition, decision-making processes are influenced by the psychological behavior of the decision maker, such as risk aversion, opinions on gains/losses [10]. Therefore, they leave an academic research gap for methods that combine expert-based assessment and quantitative assessment that consider psycho-behavior in the problem of location selection. According to the International Energy Agency (IEA), Vietnam is leading the growth of renewable energy capacity of ASEAN with a contribution of 40% of renewable energy capacity [1]. Vietnam's wind power capacity is planned to share 1.5% in 2025 and 2.1% in 2030 of the total national electricity production [11]. World Bank studies forecast that Vietnam's offshore wind power will produce between 203 TWh and 433 TWh of electricity in 2035 [12]. However, offshore wind projects, either in the early stages or already in operation, are mostly nearshore wind power projects. Therefore, there is still a practical research gap on assessing suitable locations for offshore integrated wave and wind energy projects in Vietnam. With the aim of narrowing the academic and practical research gaps discussed, the objective of this study is to analyze and evaluate the efficiency and sustainability of WWS locations in Vietnam. For this purpose, as theoretical contribution, this study proposes a novel behavioral dual-side spherical fuzzy approach. On the one side, the efficiency of WWS locations is evaluated based on quantitative indicators by a behavioral Data Envelopment Analysis (B-DEA) model, which is refined according to prospect theory principles. On the other side, the sustainability of locations is evaluated by an integrated MCDM method. The proposed MCDM method is composed of spherical fuzzy extensions of the DEMATEL method and EDAS method. Based on efficiency and sustainability rankings, this study classifies locations for WWS along the coastline of Vietnam as the practical contribution.