## 4 Numerical Results
### Effectiveness Determination by SfRDMA  
For effectiveness, first, a group of ten experts was established with qualifications as shown in Table A3. In step 1, because of differences in expertise, the weights of experts \((\Psi_{k})\) were determined according to Equation (22), as illustrated in Figure 3. As suggested by experts and references, ten criteria were identified to evaluate the effectiveness of countries, including construction, installation costs (EFC-1) [47; 48], diversity of transportation services (EFC-2) [47; 49; 50], labor costs (EFC-3) [47; 49; 50], human resources availability (EFC-4) [47; 49; 50], political stability (EFC-5) [47], environmental management systems (EFC-6) [47; 48], logisticscosts (EFC-7) [47; 49], land costs (EFC-8) [47; 50], government policies (EFC-9) [47], and climate (EFC-10) [48]. In step 2, linguistic pairwise comparisons of influence across criteria were provided by each expert. Then, the linguistic terms were converted into the scale SFNs, as shown in Table 3. As a result, the individual SF direct-influence matrices were established. Based on expert weights, the SF direct influence matrix was aggregated using the spherical weight arithmetic mean as Equation (7) in step 3. The SF direct-influence matrix is shown in Table A4. Based on that matrix, in step 4 and step 5, the SF total-influence matrix was determined according to Equations (25)-(32), as shown in Table A5. In step 6, as shown in Table 5, the defuzzied total influence matrix was formed according to Equation (14). According to Equations (34) and (35), the criteria weights were determined and are illustrated in Figure 4. From the results, government policy (EFC-9), political stability (EFC-5), and human resources availability (EFC-4) were the three most important criteria, with weights of 0.128, 0.120, and 0.109, respectively. The weight of the environmental management  
Figure 3: Experts’ weights. Figure 2: Super-SBM efficiency map. system criterion (EFC-6) had the lowest importance, with a weight of 0.073. Meanwhile, the remaining criteria had no significant difference in weight. In step 7, experts provided linguistic assessments of the alternatives corresponding to the criteria. These linguistic assessments were converted into the individual SF decision matrices, as shown in Table 3. Once again, the spherical weight arithmetic mean was used for the aggregation of individual matrices. As a result, the SF decision matrix was constructed as shown in Table A6. In steps 8-10, the SF decision matrix was then defuzzied, according to Equation (14). The defuzzied results are shown in Table A7. According to Equations (39) and (43), with the suggested risk-aversion coefficient \((\varphi=0.88)\) and regret-aversion coefficient \((\lambda=0.3)\)[51], the utility matrix, the regret matrix, and the overall utility matrix were established, as shown in Tables 6-8. Based on the overall utility matrix, as shown in Table 8, the average solution vector was determined according to Equations (44) and (45) in step 11. In step 12, the positive and negative distance from average solution matrices was calculated as Equations (46) and (47). Finally, according to Equation (48) in step 13, the effectiveness score was determined, as shown in Table A8 and illustrated in Figure 5.