D4.3 Multi-Criteria Satisfaction Analysis (b)

HORIZON EUROPE PROGRAMME – TOPIC HORIZON-CL5-2021-D3-03-05 Wind energy in the natural and social environment Research and Innovation action (RIA) WIMBY Wind in My Backyard: Using holistic modelling tools to advance social awareness and engagement on large wind power installations in the EU Grant Agreement No. 101083460 Starting date: 1st January 2023 – Duration: 36 months Deliverable D4.3 Multi-Criteria Satisfaction Analysis (b) Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 2 of 46 DOCUMENT INFORMATION Deliverable number D4.3 Deliverable title Multi-Criteria Satisfaction Analysis (b) Work Package WP4 Deliverable type R Dissemination level PU Due date 30.6.2025 (Month 30)/ 31.08.025 (M32) Pages 46 Document version 3.0 Lead author(s) He Huang, Paul Scherrer Institute Contributors Peter Burgherr, PSI Eleftherios Siskos, PSI The WIMBY project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101083460. This document reflects only the author’s view, and the Commission is not responsible for any use that may be made of the information it contains. Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 3 of 46 DOCUMENT CHANGE HISTORY Version Date Author Description DRAFT 0.1 07.04.2025 H. Huang, PSI Creation 0.2 05.05.2025 H. Huang, PSI Section completement 0.3 26.05.2025 E. Siskos, TUC, P. Burgherr, PSI Internal suggestion FIRST PEER REVIEW 1.0 13.06.2025 M. Bucha, KIE Proofreading and peer review 1.1 16.06.2025 C. Mikovits, BOKU T. Schauppenlehner, BOKU Proofreading and peer review 1.2 20.06.2025 H. Huang, PSI Consolidation of input from reviewers COORDINATOR APPROVAL 2.0 26.06.2025 Luis Ramirez Camargo, UU Coordinator review 2.1 29.08.2025 H. Huang, PSI Consolidation of input from coordinator 2.2 01.09.2025 S. Arapoglou (VUB) Coordinator approval FINAL VERSION 3.0 01.09.2025 S. Arapoglou (VUB) Format review, version ready for submission Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 4 of 46 SHORT ABSTRACT FOR DISSEMINATION PURPOSES Abstract This deliverable presents a step-by-step guide for implementing a framework based on the Multi-Criteria Satisfaction Analysis (MCSA) method to evaluate satisfaction for wind power plant projects. The framework proposes a systematic procedure to collect and translate the preferences of residents into quantifiable satisfaction levels for wind power production installations in their vicinity. Building on the inputs from D4.1 and D4.2, a tailor-made and transparent satisfaction analysis system has been developed that incorporates specific criteria, indicators, and factors influencing wind energy satisfaction. Satisfaction functions of participants are determined using a preference disaggregation decision analysis model called the MUlticriteria Satisfaction Analysis (MUSA) method, with the help of questionnaires. The aggregated preferences of participants across multiple criteria yield result in the satisfaction levels, which are then categorised per demographic groups to support targeted strategies for improving satisfaction. The detailed guidelines provided in this deliverable support local authorities, analysts, and anyone else, seeking to apply a satisfaction analysis in their respective areas. The potential practitioners can also benefit and inspire themselves from the actual satisfaction questions, created as part of the WIMBY project. The satisfaction analysis framework and the developed questionnaire have been validated across four pilot sites in three countries, the results of which are presented and analysed in this deliverable. Insights from the case studies will inform recommendations in WIMBY D4.6. Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 5 of 46 TABLE OF CONTENTS 1. INTRODUCTION .............................................................................................. 10 2. SATISFACTION ANALYSIS FRAMEWORK AND GUIDELINES ........................ 12 2.1 MUSA METHOD ......................................................................................... 12 2.2 MCSA FRAMEWORK ................................................................................ 15 3. MUSA IMPLEMENTATION FOLLOWING THE GUIDELINES ............................. 21 4. PILOT SITE RESULTS ........................................................................................ 26 5. OUTLOOK ......................................................................................................... 33 6. CONCLUSIONS ................................................................................................ 35 REFERENCES ........................................................................................................... 37 ANNEX ..................................................................................................................... 39 Questionnaire designed in WIMBY ................................................................ 39 MUSA Results .................................................................................................... 45 Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 6 of 46 LIST OF PARTNERS N o Logo Name Short Name Country 1 VRIJE UNIVERSITEIT BRUSSEL VUB Belgium 2 DANMARKS TEKNISKE UNIVERSITET DTU Denmark 3 INTERNATIONALES INSTITUT FUER ANGEWANDTE SYSTEMANALYSE IIASA Austria 4 UNIVERSITAET FUER BODENKULTUR WIEN BOKU Austria 5 UNIVERSITETET I OSLO UiO Norway 6 NAZKA MAPPS BVBA NAZKA Belgium 7 KELSO INSTITUTE EUROPE GEMEINNUTZIGE GMBH KIE Germany 8 DEEP BLUE SRL DEEP BLUE Italy 9 UNIVERSITEIT UTRECHT UU Netherlands 10 POLITECNICO DI TORINO POLITO Italy 11 UNIVERSITA DEGLI STUDI DI PALERMO UNIPA Italy 12 APREN-ASSOCIACAO PORTUGUESA DE ENERGIAS RENOVAVEIS APREN Portugal 13 MULTICONSULT NORGE AS MCN Norway 14 EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH ETH Zürich Switzerland 15 PAUL SCHERRER INSTITUT PSI Switzerland 16 UNIVERSITY COLLEGE LONDON UCL United Kingdom Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 7 of 46 ABBREVIATIONS Acronym Description ADI Average Demanding Index AII Average Improvement Index ASI Average Satisfaction Index GDPR General Data Protection Regulation KM Kilometre MCDA Multi-Criteria Decision Analysis MCSA Multi-Criteria Satisfaction Analysis (Framework) MUSA Multicriteria Satisfaction Analysis (Method) MW Megawatt Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 8 of 46 LIST OF FIGURES Figure 1 Structure of a MUSA problem. Respondents provide ordinal judgements at three levels: a global judgement (top), main criteria (middle row), and for each criterion, a set of subcriteria (bottom row). ............................. 13 Figure 2 Error variables for the 𝒋𝒕𝒉 participant. The horizontal axis shows the declared satisfaction category on an 𝜶 point ordinal scale. The residual between the observed score and the model prediction is split into the nonnegative error variables 𝝈𝒋+ and 𝝈𝒋−. ................................................................................... 14 Figure 3 Satisfaction analysis workflow across five phases: Problem structuring, survey preparation, data collection, data analysis, conclusion, and recommendation ........................................................................................................................ 16 Figure 4 Different questions and scales for sub-criteria, criteria, and global satisfaction ................................................................................................................................................ 23 Figure 5 Survey distribution activities at the pilot sites: (a) Pantelleria, (b) Styria (Irdning and Gröbming), (c) Viana do Castelo, and (d) Torres Vedras. All individuals pictured provided consent for their photographs to be used. ............................................................................................................................................................................ 25 Figure 6 Regional global satisfaction function diagram ............................................ 27 Figure 7 Regional action diagram (a) and regional improvement diagram (b) .................................................................................................................................................................... 30 Figure 8 Global satisfaction function and criteria satisfaction functions on five pilot sites ............................................................................................................................................ 45 Figure 9 Sub criteria satisfaction functions on five pilot sites ................................ 46 LIST OF TABLES Table 1 Set of satisfaction criteria ...............................................................................................22 Table 2 MCSA survey overview and age statistics ......................................................... 26 Table 3 Global satisfaction and demanding across pilot sites ............................ 28 Table 4 Main criteria indices. ASI is obtained based on the average satisfaction value from the participants’ satisfaction level. ADI is obtained based on the satisfaction function obtained from the optimization model. AII is the composite index based on weight and ASI. ......................................................... 28 Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 9 of 46 EXECUTIVE SUMMARY Public acceptance is a critical challenge for wind energy deployment. Authorities and developers need a rigorous yet practical way to measure how residents perceive benefits and impacts, and to turn those insights into targeted actions. D4.3 (i) validates the guidelines for implementing the Multi-Criteria Satisfaction Analysis (MCSA) framework, (ii) tests their transferability across contrasting contexts, and (iii) presents common and site-specific public satisfaction/acceptance of wind energy on different aspects. Building on D4.1–D4.2, we applied MCSA, which is centred on the preferencedisaggregation method Multicriteria Satisfaction Analysis (MUSA), at four pilot sites in three countries: Pantelleria in Italy, Styria (Ennstal: Gröbming & Irdning) in Austria, Viana do Castelo and Torres Vedras in Portugal. A harmonised, consortium-validated questionnaire captured satisfaction/acceptance across twelve sub-criteria grouped into three dimensions (environmental, community, individual). MUSA produced criterion weights, satisfaction functions, and summary indices, visualised via action and improvement diagrams. Fieldwork relied on in-person surveys adapted to each context. Through MCSA, we find the overall acceptance is strong and demand is low at all sites. Community aspects are most influential in Austria and Portugal, and personal aspects dominate in Pantelleria. Applying the full workflow across four contrasting settings demonstrated the guidelines’ usability and adaptability. From problem structuring through survey localisation, data processing, and interpretation, it provides a consistent pathway from raw responses to decision-ready evidence. D4.3 delivers a ready-to-use set of guidelines for integrating MCSA into planning and permitting. Results feed directly into D4.6 for the synthesis analysis. All task objectives were successfully achieved within the planned timeline. Moreover, the scope of the study expanded significantly from the original plan, evolving from one to four pilot sites across three countries. . Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 16 of 46 MUSA’s raw mathematical outputs into actionable insights that planners and policymakers can apply directly. Also, there is a need to account for divergent interests that may arise due to varying socio-demographic characteristics of participants. These variations are particularly relevant to this study. For example, the proximity of wind turbines to residents' homes can significantly influence their opinions. Residents living closer to wind turbines may have distinct preferences compared to those living further away, necessitating a tailored analysis. To address this, the satisfaction analysis framework captures these nuanced differences, focusing on the satisfaction levels of local residents regarding wind power plants. In the previous deliverable D4.2, we introduced the MCSA framework based on the MUSA method, which is organised into several key steps. We detail the workflow of the satisfaction analysis framework, outlining the step-bystep process from problem structuring to data analysis. The workflow presented in Figure 3 serves as a practical guideline, ensuring consistency, accuracy, and relevance in applying the framework across diverse contexts. Figure 3 Satisfaction analysis workflow across five phases: Problem structuring, survey preparation, data collection, data analysis, conclusion, and recommendation Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 17 of 46 • Problem structuring: The foundation of the MCSA framework begins with a clear understanding of the problem and its context. o Problem identification: The first step is to clearly define the study's key objective. This involves pinpointing the specific subject about which satisfaction needs to be measured and analysed. In this study, this subject is satisfaction with wind power installations in the vicinity. o Target group identification: The identification of target groups is a crucial step in designing a satisfaction analysis, as it ensures that the study focuses on the perspectives of the most relevant stakeholders. In our study, the primary target group is local residents, as they are the most directly impacted by wind turbine installations in their vicinity. These residents may experience changes in their environment, such as visual or noise impacts, which significantly influence their satisfaction with the project. Additionally, secondary stakeholder groups may include tourists and visitors who may also have valuable perspectives. o Criteria definition: This step establishes the specific factors/criteria that are accounted for and included in the MCSA that affect the satisfaction levels of the participants. There is no strict maximum number of criteria for MUSA; however, it is important to maintain a balance between exhaustiveness and the level of detail. On the one hand, the criteria should be comprehensive to capture all relevant aspects, and on the other hand, they should be streamlined to avoid unnecessarily extending the answering time, which could reduce participants' willingness to engage. • Survey development and questionnaire preparation: A wellstructured survey is essential for capturing meaningful and actionable data. As such, a well-developed survey can help structure a comprehensive questionnaire that will be the means for the extraction of the required data by participants. o Instructions Design: Develop clear and concise instructions for survey participants to ensure consistency in understanding and responses. Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 18 of 46 o MUSA Questionnaire Design: Compile clear and straightforward questions that explicitly translate into each criterion and provide well-defined response options. These questions are fully aligned with the MUSA method within the MCSA framework and ensure consistent measurement of satisfaction across the complete set of criteria. Another important aspect is the definition of an appropriate point scale for the answers to each question. Oddand even-point response scales serve different purposes: A five-point (odd) scale is best for bidirectional questions, because it offers two positive options, two negative options, and a neutral midpoint; A four-point (even) scale, which omits the neutral choice, is better suited to unidirectional questions that range only from low to high intensity. For example, noise impact might use a 4-point scale ranging from “No Impact” to “High Degree of Impact,” while aesthetic integration could use a 5-point scale from “Strongly Negative” to “Strongly Positive.” This is because noise pollution is assessed using a unidirectional question, where a 4-point scale suffices to capture varying degrees of disturbance (best case, no impact). In contrast, aesthetic integration is evaluated through a bidirectional question, acknowledging that it can have both positive and negative impacts. Therefore, a 5-point scale is employed, including a neutral option, with two positive and two negative choices to accurately reflect respondents' perceptions. o Socio-Demographic Question Design: Incorporate questions to capture participants' socio-demographic attributes, such as age, education, income, and living location, to enable categorisation and clustering. These questions must comply with General Data Protection Regulation (GDPR) principles by collecting only the minimum data necessary to identify patterns and differences in opinions, while ensuring the essential information required for a demographic analysis is obtained. o Localization: Adapt the survey to the specific context of the pilot site region, considering cultural, linguistic, and geographic nuances. Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 19 of 46 • Data collection and implementation: Effective outreach and data collection methods are critical for obtaining a good quantity of highquality data. o Choice of Reaching Approach: Decide on the mode of distribution, such as online questionnaires, in-person interviews, or community workshops, based on the target group's accessibility and preferences. o Choice of Format: The choice of format for data collection should align with the specific characteristics of the location and the selected outreach approach. Depending on these factors, the method can range from structured questionnaires and polls to more in-depth extended interviews. Questionnaires and polls are ideal for efficiently collecting standardized data from a larger group of participants, while extended interviews allow for deeper exploration of individual perceptions and nuanced concerns. In both formats, the MUSA questionnaire is essential, but the interview is optional. For example, in Pantelleria, some participants were approached in a café, and since they had adequate time to spare, they were also interviewed to provide further feedback. However, in Styria, where the survey took place over a single day in a museum, participants were less willing to answer additional questions, so only the questionnaire option was appropriate. Selecting a rational outreach format ensures that the data collection process is both effective and contextually relevant. • Data analysis: Collected data is processed and analysed to derive meaningful insights. More details on how the collected data is verified, cleaned, and curated can be found in D4.1. o Data Quality Check: Review the raw data to ensure accuracy, completeness, and consistency. All incomplete survey responses were excluded from the analysis. o Raw Data Processing: Convert raw responses into a usable format for statistical and MUSA analysis, such as coding responses and normalizing scales. o Statistical Analysis: Conduct descriptive and inferential analyses to understand response patterns and identify significant trends across socio-demographic groups. Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 20 of 46 o MCSA Analysis: Apply the MUSA method to quantify satisfaction and derive detailed insights into the satisfaction of wind turbines. The analysis includes identifying satisfaction levels for individual criteria and their contributions to overall satisfaction. • Conclusions and recommendations: Upon completing the MCSA analysis, the final step is to transform the indices and value functions into clear, actionable conclusions and tailored recommendations. These findings are translated into site-specific guidance. In the WIMBY case, the outputs will serve as inputs to D4.6. By packaging the results into technical reports for planners, executive summaries for policymakers, and digestible briefs for local stakeholders, this step closes the loop between analysis and action, ensuring that the satisfaction analysis framework drives both practical improvements on the ground and informs wider policy and research through forthcoming scientific publications. Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 21 of 46 3. MUSA IMPLEMENTATION FOLLOWING THE GUIDELINES After finalizing the guidelines, we applied the MCSA framework across four pilot sites in three countries: Pantelleria (Italy), Styria (Austria), Viana do Castelo (Portugal), and Torres Vedras (Portugal). These locations were selected to reflect a diversity of geographical, socio-economic, and planning contexts, providing a robust testing ground for the framework's applicability and adaptability: • Pantelleria is a remote volcanic island located 110 km off the coast of Sicily. The island's electricity grid is primarily powered by diesel generators, but due to its location in one of the windiest areas in Italy, wind energy is considered a viable option. There are no operational wind turbines currently on the island. • Styria is a federal state of Austria, and our on-site surveys were conducted in the towns of Gröbming and Irdning. They were situated in a mountainous region of Styria, representing a rural context where landscape aesthetics and ecological conservation are prominent concerns. Public opinion here is often shaped by strong place attachment and cultural heritage values[7]. What should be noted is that, because Gröbming and Irdning lie only a few kilometres apart and share the same Styrian context, we treat them as a single pilot site. In contrast, the two Portuguese locations exhibit distinct settings and stakeholder profiles, so they are analysed as separate pilot sites. • Viana do Castelo (Portugal) is a coastal municipality with existing wind farms. The region is home to the Alto Minho Wind Farm with a capacity of 240 megawatt(MW), Europe's largest onshore wind farm at the time of completion[8]. Additionally, the WindFloat Atlantic project, located 20 kilometres (km) off the coast, is Continental Europe's first floating offshore wind farm. • Torres Vedras (Portugal), located closer to the Lisbon metropolitan area, reflects a semi-urban setting with diverse stakeholder groups. The region has several operational wind farms, including the Catefica wind farm, with a total nominal power of 18 MW. To capture local residents’ satisfaction regarding wind energy infrastructures, we defined a comprehensive set of criteria that spans environmental, community, and individual dimensions. Each dimension aggregates closely related sub-criteria to ensure that every major facet of Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 22 of 46 wind energy impact is systematically assessed. Table 1 summarizes the identified dimensions and sub-criteria. Table 1 Set of satisfaction criteria Dimension (Criterion) Sub-criterion Environmental Undesired land use changes Impact on biodiversity Reduce Greenhouse gas emissions (GHG) Community Economic impact on the community Negative effect on community lifestyle Safety risks to people and infrastructures Raise social awareness and political engagement Long-term maintenance and decommissioning plans Individual Impact on personal finances Negative effect on the landscape’s aesthetics Disturbance from noise pollution Disturbance from shadow flicker These criteria were selected through a combination of literature review, e.g., [9], [10], [11], [12], [13], and expert consultation, ensuring that they reflect both international best practice and site-specific concerns. Each survey question is carefully tailored with a scale type and set of response descriptions that best fit the nature of the underlying criterion, as illustrated in Figure 4. The complete, non-localised questionnaire (in English language), including all MUSA questions and socio-demographic questions, is provided in the Annex. Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 23 of 46 Figure 4 Different questions and scales for sub-criteria, criteria, and global satisfaction We developed localized survey instruments tailored to the specific social and geographic contexts of each pilot site. For instance, since Pantelleria has no turbines at present, we could not measure residents’ postinstallation satisfaction; instead, we assessed their ex-ante acceptance of a proposed wind-turbine project. To ensure direct engagement with local communities and to capture both quantitative and qualitative insights, we conducted offline, in-person surveys. This method allowed us to explain questions in real-time, build trust with participants, and gather more nuanced perspectives on wind energy satisfaction. However, we should also acknowledge that, because participation depended on who happened to be present and willing during our short field visits, the sample may overrepresent socially active or curious residents while missing less accessible or time-pressed groups. In-person interviews can also introduce interviewer Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 24 of 46 effects, where respondents tailor their answers—consciously or not—to what they think the surveyor expects. At each site, the survey team spent a minimum of two days in the location, with the data collection strategy adapted to suit local circumstances. Our extended stay in Pantelleria provided the opportunity to build closer contact with residents. Given the island’s social dynamics and the popularity of local bars as gathering spaces, we conducted many interviews in these venues. This setting encouraged relaxed conversations and allowed us to probe deeper into individual attitudes, yielding detailed qualitative information in addition to the survey responses. In contrast, data collection in Gröbming and Irdning posed different challenges. Located in a natural, less densely populated region, there were fewer opportunities for extended one-on-one interviews. We focused our efforts on areas with greater foot traffic. We were fortunate to receive support from a local museum, which kindly provided indoor space for survey distribution and participant engagement. It should be noted that although no turbines are currently operating in Gröbming and Irdning, many survey respondents came from surrounding areas where there are wind turbines. We therefore retained the term “satisfaction” rather than “acceptance”. Meanwhile, in Viana do Castelo and Torres Vedras, we adopted a broader outreach strategy. In addition to distributing surveys in public spaces such as streets and squares, we targeted shopping centres, where we approached not only shoppers but also store owners and staff. Figure 5 includes photos from our fieldwork across the pilot sites, capturing moments of interaction with participants and illustrating the diverse settings in which data collection took place. Overall, the guidelines ensured that the MCSA framework was applied consistently, while respecting each community’s local realities and social dynamics. Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 25 of 46 Figure 5 Survey distribution activities at the pilot sites: (a) Pantelleria, (b) Styria (Irdning and Gröbming), (c) Viana do Castelo, and (d) Torres Vedras. All individuals pictured provided consent for their photographs to be used. Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 32 of 46 In summary, however, the survey samples are small relative to the studyarea populations and should not be treated as statistically representative. Even in Pantelleria, where the site with the highest coverage, respondents comprise only about 1% of permanent residents. The findings therefore provide indicative insights into residents’ views rather than a complete picture of all subgroups. Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 33 of 46 5. OUTLOOK With the framework now validated across four pilot sites, we have established a transferable approach for measuring social satisfaction with wind energy. The step‑by‑step guidelines contained in this deliverable allow planners, developers, and researchers elsewhere to adopt the method without repeating the trial‑and‑error phase. In general, a community wishing to gauge attitudes toward a new repowering program can adapt the questionnaire template, run the MUSA algorithm, and obtain a complete set of satisfaction, demand, and improvement indices within a few weeks using the same terminology. This harmonised workflow promises two distinct benefits. It accelerates local decision-making by turning stakeholder/resident sentiment into quantitative evidence. It is important to mention here that the developed satisfaction analysis framework has already been reproduced in a case study, outside the WIMBY project. Specifically, the WIMBY questionnaire was adapted, translated into Greek, and distributed on the Greek island of Evia (Euboea) in a master’s thesis study [16]. This approach aimed to utilize tools developed within the WIMBY project to measure residents' satisfaction with wind turbines. Evia was chosen because it is a region with substantial wind energy development, with over 500 MW of installed wind power capacity, making it a relevant case for assessing local perceptions of wind energy projects. The 80 completed questionnaires from Evia indicated lower satisfaction levels compared to the four pilot sites of the WIMBY project. This difference could be attributed to the extensive wind energy development already present on the island, which has led to growing frustration among parts of the local population. Additionally, this lower satisfaction may stem from widespread distrust in the incentives and motives of the government, along with broader sociopolitical factors that influence public attitudes toward renewable energy initiatives. These findings underscore the importance of understanding local community perceptions and highlight the need for transparent communication and participatory planning in expanding renewable energy infrastructure. In summary, the deliverable reports only the top-line results necessary to validate the framework. It does not delve into correlational analysis between sociodemographic attributes and MUSA outcomes yet, nor does it test the robustness of the indices under alternative weighting schemes or scale normalizations. These deeper statistical investigations are essential for Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 34 of 46 unpacking questions such as whether age or income systematically influences the importance residents assign to personal versus community benefits, or how the index behaves in regions with very different turbine densities. Addressing such questions will require more extensive analyses. Moreover, we are currently exploring how demographics of a population, as well as other external factors (such as level of visibility and density of installed wind turbines), influence the satisfaction of citizens. To do that, we are combining the knowledge about the satisfaction of citizens, with the aid of the MUSA method, together with the demographic data of the citizens, using statistical tools. The results coming from the Greek case study of the island of Evia are also included in the analysis, together with the results from the four pilot sites of WIMBY. This synthesis will be presented in Deliverable D4.6 and will be published in a scientific journal. Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 35 of 46 6. CONCLUSIONS This deliverable demonstrates that the Multi-Criteria Satisfaction Analysis (MCSA) framework, built around the data-driven MUSA method and supported by the guidelines developed in this project, provides a practical and transparent way to elicit stakeholder satisfaction with wind-energy projects. A single, neutral questionnaire was co-designed and validated by the full consortium; its wording was carefully cross-checked to ensure the same criteria apply in Austria, Portugal, and Italy. MUSA relies solely on respondents’ answers, without any analyst-defined weights or other subjective inputs. It treats every response equally and keeps the entire evaluation model fully traceable. Deploying the workflow in three countries and four pilot sites confirmed that the framework captures both broad patterns (such as consistently high global satisfaction) and site-specific priorities (for example, high personal perspective priority in Pantelleria, high community perspective priority in Austria and Portugal). MUSA’s statistical process, based on ordinal regression, produces outputs which represent residents’ preferences: criterion weights, value functions, and the ASI, ADI, and aII indices detailed in D4.1. The resulting action and improvement diagrams convert these metrics into site-tailored priorities, giving planners and developers an evidencebased roadmap for enhancing stakeholder satisfaction with current and future wind-energy projects. Equally important, applying the guidelines from problem structuring to survey localisation and data processing demonstrated that they can be adapted to diverse cultural and geographic settings. Although this first validation was performed by the framework’s own developers, it confirms the guidelines’ technical workability and sets the stage for external authorities and analysts to test, refine, and standardise satisfaction studies across Europe. At the same time, the study’s scope was intentionally pragmatic: it focused on establishing method viability rather than exhaustively exploring all statistical nuances. The deeper investigations, such as the correlation analysis of residents’ satisfactions with socio‑demographic variables and the classification of respondents are in progress and will be presented in D4.6 and a forthcoming peer‑reviewed paper. In summary, the work fulfils Task 4.2’s objectives by (i) validating the MCSA framework in real‑world settings, (ii) producing a practitioner‑oriented Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 36 of 46 guideline, and (iii) delivering initial empirical insights that inform WIMBY tasks T4.5 and T4.6. Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 37 of 46 REFERENCES [1] R. Umit and L. M. Schaffer, “Wind Turbines, Public Acceptance, and Electoral Outcomes,” Swiss Political Science Review, vol. 28, no. 4, pp. 712– 727, 2022, doi: 10.1111/spsr.12521. [2] R. 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Trigo, “The influence of the main large-scale circulation patterns on wind power production in Portugal,” Renewable Energy, vol. 102, pp. 214–223, Mar. 2017, doi: 10.1016/j.renene.2016.10.002. [15] N. Schneider and A. Rinscheid, “The (de-)construction of technology legitimacy: Contending storylines surrounding wind energy in Austria and Switzerland,” Technological Forecasting and Social Change, vol. 198, p. 122929, Jan. 2024, doi: 10.1016/j.techfore.2023.122929. [16] I. Spanos, “Measuring acceptance of wind farms by citizens using the MUSA method.” Technical University of Crete, 2025, Master Thesis in progress (In Greek). Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 39 of 46 ANNEX Questionnaire designed in WIMBY The following section shows the questionnaire developed for the Styria pilot site. Please note that it is presented here in English and has not yet been translated into German. Survey on the satisfaction with wind turbines in Styria Part I: Introduction Purpose The aim of this survey is to establish personal opinions on wind turbines in order to investigate the satisfaction and opinions of the population with regard to the expansion of wind turbines in Styria. The survey is part of the WIMBY (Wind in my Backyard) research project funded by the EU Horizon Program and has no connection with specific wind power projects. The full survey will take approximately 5 minutes to complete. Confidentiality Your answers will be used exclusively for research purposes. All results will be published in an aggregated format, fully anonymized, ensuring that individual responses cannot be traced back to any participant. They will be treated confidentially, and under no circumstances will they be shared with third parties or companies outside the WIMBY initiative. The data processing policy is available in full on our website at: https://wimby.eu/privacy-policy/. Instructions The survey is divided into three sections, focusing on: ◘ Section A: Environmental aspects of wind energy ◘ Section B: Community aspects of wind energy ◘ Section C: Your individual opinion At the end of the questionnaire, we ask you to provide personal information (sociodemographic data). For each question, select the answer that comes Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 40 of 46 closest to your opinion or experience. Now, please look at the image of the wind turbine on the Sommeralm north of Graz. This image gives you a visual impression of what wind farms normally look like in Styria. Sector A – Environmental aspects of wind energy A1 Do you think that the installation of wind turbines in Styria will cause undesired land use changes? ◘ To a high degree ◘ To a moderate degree ◘ Maybe a bit ◘ Not at all A2 Are you concerned that wind turbines will harm biodiversity in Styria? ◘ To a high degree ◘ To a moderate degree ◘ Maybe a bit ◘ Not at all A3 Do you think that wind turbines can help produce clean energy in Styria? ◘ Rather negatively ◘ Slightly negatively ◘ No effect ◘ Slightly positively ◘ Rather positively Complete guidelines on the MCSA and case study results | V 3.0 | Dissemination level [P] Page 41 of 46 Summarizing question: Environmental aspects A4 How do you assess the potential environmental impact of the construction of wind turbines in Styria? ◘ Rather negatively ◘ Slightly negatively ◘ No effect ◘ Slightly positively ◘ Rather positively Sector B – Community aspect of wind turbines B1 How do you think the installation of wind farms in Styria will affect the regional economy? ◘ Rather negatively ◘ Slightly negatively ◘ No effect ◘ Slightly positively ◘ Rather positively B2 Are you concerned that the installation of wind turbines in Styria will negatively affect the community lifestyle? ◘ To a high degree ◘ To a moderate degree ◘ Maybe a bit ◘ Not at all B3 Do you expect that wind turbines can pose a safety risk of people and infrastructure? ◘ To a high degree ◘ To a moderate degree ◘ Maybe a bit ◘ Not at all B4 Do you agree that wind turbines in Styria can increase the social awareness and political commitment of the population? ◘ Strongly disagree ◘ Disagree