## 3 Results
### Geospatial Analysis  
The study areas were defined using GIS data for the region of Palawan, the Philippines. The boundaries of the three off-grid island communities (Araceli, Balabac, and Cuyo Islands) were extracted for use as a mask for all criteria. The data were uploaded to QGIS, including base criteria such as DEM, solar radiation, wind speed, aspect, and slope. Then, the application of the qualifiers layer was implemented to assess the land quality of the areas being investigated. The preparation of the maps before the exclusion criteria and constraints were implemented is presented in Figure S1. Balabac has a GHI of 4.98 kWh/m\({}^{2}\)/day, and Cuyo has a GHI of 5.35 kWh/m\({}^{2}\)/day [139]. The slopes are extracted from DEM in the QGIS slope tool, and a slope of more than 5\({}^{\circ}\) was excluded because it affects the reception of solar radiation. The aspect was also extracted from DEM using the aspect tool in QGIS. The solar and wind farm locations should be near electric networks and road infrastructures to avoid transmission losses and high economic costs [30,140]. The Euclidian distance was utilized to calculate the nearest source based on straight-line distance. Proximity to built-up areas and water bodies were also reclassified using the raster calculator in QGIS. After applying all of the exclusion criteria and constraints set in Table 2, the result of the spatial analysis is shown in Figure S4. * Spatial Decision Support  
Fuzzy AHP-TOPSIS was applied to eliminate infeasible areas to map suitable solar PV plant and wind farm locations. A hierarchical structure (Figure 6) was made to show how the criteria are used for evaluation to reach the common goal. There are eight criteria for solar PV and seven for wind farm mapping. Therefore, the sizes of PCM are 64 (solar PV) and 49 (wind). The comparison matrix of the criteria using a numerical scale was converted into TFNs. The fuzzy weights were obtained using Equations (3)-(6), and through the defuzzification process, the crisp weight was calculated. Then, each crisp weight was divided by the sum of all crisp weights to obtain the normalized matrix. The maximum eigenvalue and Consistency Index (CI) for solar PV were calculated as 8.602 and 0.086, respectively, and 7.428 and 0.071 for wind energy. With that, the CR values for the two RETs are 0.061 and 0.054. Since all the values of CR for both solar PV and wind are less than 0.10, the value judgments are considered acceptable [140]. Table 7 shows the summary of the results for the Fuzzy AHP method. In QGIS, the final normalized weights were used to identify the degree of importance of each criterion and the rasters of the exclusion criteria and constraints were used as input layers. The final rasters of the suitability map (SM) for solar PV farms (5) and wind farms (6) were calculated using the following expressions:  
\[\text{SM}_{\text{S}}=0.316\text{C1S}+0.203\text{C2}+0.189\text{C3}+0.098\text{ C4}+0.071\text{C5}+0.055\text{C6}+0.033\text{C7}+0.044\text{C8} \tag{18}\]  
\[\text{SM}_{\text{W}}=0.327\text{C1}+0.267\text{C2}+0.121\text{C4}+0.105\text{ C5}+0.089\text{C6}+0.045\text{C7}+0.046\text{C8} \tag{19}\]  
The final suitability map (Figures 7 and 8) is represented by binary values 0 and 1. \"0\" represents those areas that are not suitable, and \"1\" shows the best possible location for installing solar PV and wind power farms. Figure 6: Hierarchical structure for Solar PV and wind farm site selection. Figure 8: Final suitability map of wind energy: **(a)** Araceli; and **(b)** Cuyo.