## 4 Results
### Agent-Based Simulations of Global-Local Surveys
#### 4.2.3 Statistical Analysis and MCDA  
After each round of the survey, emerging discrepancies can be statistically analyzed and presented to the decision makers in various ways--e.g., as images, as illustrated above--to help them to identify the relevant factors and to better understand why citizens' opinions differ from their general attitudes toward SETs. As we do not have access to the socioeconomic data of the different households, we solely focused on citizens' locations to illustrate our methodology. As we have already seen, the distance to the next potential wind turbine or photovoltaic plant influences opinions on local implementation of SETs. Decision makers can now consider these insights when consolidating aggregated global and local surveys in each survey round. They could acknowledge these discrepancies and, for example, weight local surveys higher than global surveys, or rate global opinions--in this example, more positive opinions--higher, and take the discrepancies into account when weighting criterion \(\mathcal{CC}\). Technological mixes are also implicitly associated with a particular implementation, and a high weighting of \(\mathcal{CC}\) may preclude area-wide implementations in the immediate vicinity. For this reason, we weighted global surveys higher than local surveys--in part because global attitudes toward SETs are generally positive, but favor a particular technology. In this context, further visual support can help decision makers to capture the participants' opinions and personal prerequisites, as exemplarily provided for wind power, illustrating distributions of global and local opinions towards wind power, discrepancies, and potential distances to nearest wind turbines (Figure 5). Figure 6 shows that higher discrepancies tend to be associated with greater distances. Figure 4: Deviations and discrepancies in citizens’ attitudes towards wind turbines (**left**) and photovoltaic plants (**right**) between the first local and global surveys. Values range from 1 (green; no discrepancy) to 5 (dark red; strongest discrepancy). The blue dots in the left-hand image are wind turbine locations, and the cyan dots in the right-hand image are photovoltaic plant locations. In both images, the tendency of citizens to change their opinion if they are personally affected becomes visible, caused by the proximity of entities. After the final round of surveys, the findings on citizens' opinions of SETs are aggregated over time, and provide values for the criterion \(\mathcal{CC}\) of the subsequent MCDA. The MCDA is carried out on the basis of the decision alternatives and criteria presented in Table 2. In this study, the scores of a decision alternative with respect to a specific criterion are based on a qualitative assessment of the different technologies with regard to the criterion  
Figure 5: The panels ‘windPowerGlobal’ and ‘windPowerLocal’ show the distribution of global and local voting values for wind power on a qualitative scale from 1 to 5. The panels ‘discrepancyWind’ and ‘distanceWind’ illustrate the distribution of discrepancies between global and local voting values and the distribution of potential distances (km) to the nearest wind turbine. Figure 6: Scatterplot of discrepancies between global and local votes for wind power and potential distances (km) to the nearest wind turbine. The greater the discrepancy, the more the mean distance decreases. in question. The assessment is set out as follows: Profitability, scalability, and relative land use are significantly better rated for photovoltaics than for wind power. The reaction time and safety receive a medium-to-positive assessment--similar for both technologies, but better for photovoltaic. Location dependency is positively assessed for both technologies, but better for wind power than for photovoltaic power. Volatility is rated negatively for both technologies, with wind power scoring slightly better.