## 4 Results
### Review Papers and Application Fields
#### 4.6.1 MCDA and Energy Management
[66], while concluding that MCDA is widely used in renewable energy, also identified challenges in improving expert consultation methodologies and enhancing stakeholder participation in renewable energy policy development, while academics and practitioners using MCDA are increasingly considering social criteria for evaluating relevant policies and projects to integrate expert and stakeholder perspectives. Regarding stakeholder engagement, [PERSON] et al. [82] argued \begin{table}
\begin{tabular}{c c} \hline \hline
**Subcategories in the Energy Sector** & **References** & **Total Number** \\ \hline Sustainable and renewable energy & [49; 66; 69; 70; 72; 73; 76; 77; 78; 80; 85; 87; 89] & 17 \\ Efficiency evaluation & [64; 71] & 2 \\ Policymaking & [47; 63] & 2 \\ Power plant selection & [75; 86] & 2 \\ Urban energy system & [74; 79] & 2 \\ Energy investments & [67] & 1 \\ Management problems & [65] & 1 \\ Storage systems & [88] & 1 \\ Oil and gas decommissioning & [68] & 1 \\
**Total** & & **29** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Review papers related to energy sector. that hybrid MCDA is quite effective for related problems, integrating stakeholder perspectives. [PERSON] et al. [68], focusing on oil and gas decommissioning decisions, concluded that integrating their perspectives is quite common in the applications they studied. A comprehensive literature review on the combination of MCDA with life cycle analysis (LCA) by [PERSON] et al. [69] showed that MCDA is the most common tool integrated with LCA to support decision making in the sustainable assessment of energy systems, arguing that hybrid models lead to more appropriate results. [PERSON] et al. [80] also focused on LCA, but only in combination with data envelopment analysis (DEA), and concluded that this hybrid approach leads to a number of advantages over other MCDA strategies in terms of practicality and soundness. [PERSON] et al. [77] investigated the use of DEA in performance evaluation of renewable energy studies and found that, depending on the research objective and the complexity and accuracy of the data issues, DEA can and should be an appropriate tool for performance evaluation of renewable energy studies. [PERSON] et al. [71] focused on DEA applications for energy efficiency assessment and concluded that DEA is a powerful tool by presenting a variety of examples of applications, as there are many DEA models and many of these methods are applicable to solve problems related to energy efficiency assessment. While a similar review paper showed that many academics are increasingly improving their models from various perspectives, while the accuracy of the energy efficiency assessment is also continuously increasing [64]. [PERSON] et al. [63] conducted a study on the application of MCDA in energy policymaking problems, and they found that it effectively facilitated the determination of the importance of different energy alternatives, scenario analysis, systems, project plans, and investment decisions. Therefore, they concluded that this tool is suitable for tackling energy policy- and decision-making problems. The same conclusions were also reached by [PERSON] et al. [47] who focused on the use of fuzzy MCDA. [PERSON] et al. [75] showed interest in selecting renewable energy sites and summarised the exclusion and evaluation criteria in five phases: criteria selection, data normalization, criteria weighting, alternative evaluation, and result validation. In each phase, they distinguished the method that is most widely applicable. [PERSON] and [PERSON] [86] conducted a study on the selection of power plant sites and found that using a hybrid MCDA approach to identify criteria and rank alternatives is a reliable technique. On the other hand, [PERSON] et al. [87] noted that employing different decision-making methods may lead to different conclusions. [PERSON] et al. [81] argued that the use of MCDA in the bioenergy industry so far has lacked balance in terms of the potential application of these methodologies.