## 4 Results
### Review Papers and Application Fields
#### 4.6.1 MCDA and Energy Management  
The sustainable energy sector must balance energy production and consumption, minimising negative environmental impacts within tolerance limits, while enabling a country to efficiently implement its social and economic activities [83]. In other words, meeting the growing demand for energy based on sustainable development requires the energy sector to \begin{table}
\begin{tabular}{c c c} \hline \hline
**Application Field** & **Number** & **Percentage (\%)** \\ \hline Energy & 29 & 36.25\% \\ Waste management & 11 & 13.75\% \\ Water management & 9 & 11.25\% \\ Forestry & 7 & 8.75\% \\ Sustainability & 6 & 7.50\% \\ Environmental sciences & 5 & 6.25\% \\ Agriculture & 4 & 5.00\% \\ Nature conservation & 3 & 3.75\% \\ Engineering & 3 & 3.75\% \\ Fisheries & 1 & 1.25\% \\ Green supply chains & 1 & 1.25\% \\ Transportation & 1 & 1.25\% \\
**Total** & **80** & **100.00\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification of reviewed papers by main application. consider the required political, social, economic, and environmental aspects [85]; thus, MCDA can address the challenges of sustainable energy management [66; 73]. The 29 review papers in the energy sector were further separated based on their focus (Table 4). Sustainable and renewable energy is the dominant topic, accounting for about 58% of all review papers. More specifically, [PERSON] and [PERSON] [78] focused on sustainable energy planning and found that it is common practice to validate results using various MCDA methods, interactive decision support systems, and fuzzy approaches to deal with data uncertainties. [PERSON] et al. [83] developed a similar review and found that multi-attribute decision making is gaining popularity in sustainable energy decision making and that criteria weights have a direct impact on the decision outcomes of energy project alternatives. Another review on the same topic was conducted by [PERSON] et al. [70], who distinguished that AHP is becoming increasingly popular for solving various renewable energy problems; also, they stated that fuzzy sets and hybrid approaches have been extensively incorporated in the literature. [PERSON] et al. [72] also concluded that AHP is the most commonly used method for wind farm site suitability models. Simultaneously, solar and wind studies have increased over time, while hydro, geothermal, and biofuels seem to be less studied. [PERSON] et al. [84] focused on the use of grey-based methods in MCDA for the assessment of sustainable energy systems and concluded that it is a powerful combination, as validating the results with MCDA to address data uncertainty increases the applicability and power of grey-based analysis. [PERSON] et al. [49], who focused on the use of fuzzy models in renewable energy system applications, also noted that they have been widely used in recent years for site assessment, photovoltaic/solar farm installation, photovoltaic/solar farm power point monitoring, and optimisation among conflicting criteria. [PERSON] et al. [76] examined quality functions for offshore renewable energy using MCDA. AHP is the most commonly used method, while other MCDA methodologies have gained popularity as decision-making tools. Fuzzy approaches, such as F-AHP, F-TOPSIS, and F-VIKOR, have been shown to significantly improve MCDA methodologies used in quality function deployment. [PERSON] et al. [73] reviewed the ability of MCDA to address sustainable energy development issues and concluded that they are very popular and widely used in practice; they also reported that there is a strong interest in technology assessment [89], while the AHP method is commonly used for energy policy/project selection issues and fuzzy AHP is mainly used for impact analysis. [PERSON] et al. [85] also reviewed MCDA applications in renewable energy development and argued that MCDA methods should include multiple stakeholders and scenarios to find the optimal solution that overcomes all environmental and local issues in real-time implementation. [PERSON] et al.