Worldviews as predictors of wind and solar energy support in Austria: Bridging social acceptance and risk perception research

Sposato, Robert Gennaro; Hampl, Nina

doi:10.1016/j.erss.2018.03.012

Cited by 41 publications

(18 citation statements)

References 63 publications

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“…At the moment, no major resistance to wind power can be identified; this is true for the sample of mountain visitors used in this study, but also for the general population sample in Sposato and Hampl (2018). Policy makers therefore should focus on maintaining the positive image of the technology and on communicating the benefits and reliability of wind farms, as respective perceptions are a main predictor for acceptance.…”

Section: Policy Implicationsmentioning

confidence: 96%

“…Nonetheless, location is only one of the factors explaining acceptance or resistance to wind farms: The NIMBY ("not in my backyard") phenomenon has long been used as the prime explanation for the discrepancy between acceptance for the technology and resistance to concrete projects, but is increasingly being criticized in academic literature. Rand and Hoen (2017) criticize the NIMBY framework as too simplistic and unable to explain the complex process leading to negative attitudes; in a similar vein Sposato and Hampl (2018) see limited value in the NIMBY framework. NIMBY resistance might rather be a result of negative attitudes, but not an explanation for them (Rand and Hoen 2017).…”

Section: Theoretical Background: Public Acceptance Of Wind Powermentioning

confidence: 99%

“…A positive attitude toward renewable energy usually predicts high acceptance levels, while technology skepticism and belief on fossil fuel reliance reduce acceptance levels (Sposato and Hampl 2018). Attitude toward climate change and climate change concerns are often related to renewable energy acceptance (Guo et al 2015;Tatchley et al 2016).…”

Section: Theoretical Background: Public Acceptance Of Wind Powermentioning

confidence: 99%

“…Therefore, full representativity cannot be assumed. However, an acceptance study with a representative Austrian sample has been already done by Sposato and Hampl (2018); the present study instead focuses on acceptance of wind parks in the Alps by visitors of the Alps and investigates factors that influence acceptance levels in this stakeholder group.…”

Section: Limitationsmentioning

confidence: 99%

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Not in my hiking trail? Acceptance of wind farms in the Austrian Alps

Brudermann

Zaman

Posch

2019

Clean Techn Environ Policy

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Large-scale wind power projects are one of the bearers of hope for a transition toward low-carbon electricity systems. The question of social acceptance of such projects near residential areas, or acceptance of the technology in general, has received significant attention in the scientific literature. Less attention has been placed on acceptability of wind farms in sparsely inhabited mountain areas; the focus of this paper therefore is on acceptance of wind farms in the Austrian Alps from the perspective of tourists and day trippers. We conducted a quantitative survey with visitors of alpine regions (n = 137) in proximity to recently constructed wind farms and identified drivers of (non-)acceptance by means of bivariate correlations and multiple linear regressions. Results indicate a high acceptance of wind technology in general and fairly high acceptance for the existing projects. Acceptance levels, however, are slightly, but significantly lower when respondents were asked to rate acceptability of wind farms in the Alps in general. Perceived benefits and reliability of wind power is the strongest predictor variable for higher acceptance levels, while annoyance through visual impact and noise is the strongest predictor variable for lower acceptance levels. Interestingly, factors like degree of information, concern regarding environmental impacts, trust in decision makers and climate change concern do not significantly affect acceptance levels. At the moment, no major opposition to wind power can be identified among tourists. Policy makers therefore should emphasize benefits of wind farms, as respective perceptions are a main predictor for acceptance. Operators should take annoyance concerns seriously, as this factor is predominant in predicting non-acceptance. Graphic abstractElectronic supplementary material The online version of this article (https ://doi.org/10.

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Section: Policy Implicationsmentioning

confidence: 96%

Section: Theoretical Background: Public Acceptance Of Wind Powermentioning

confidence: 99%

Section: Theoretical Background: Public Acceptance Of Wind Powermentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

See 2 more Smart Citations

Not in my hiking trail? Acceptance of wind farms in the Austrian Alps

Brudermann

Zaman

Posch

2019

Clean Techn Environ Policy

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“…Through a Simple Stochastic Model, Pedro G. Lind et al [33] proposed a procedure to estimate the fatigue loads on wind turbines, based on a recent framework used for reconstructing data series of stochastic properties measured at wind turbines. Ana Russo et al [34] presented a simple neural network and data pre-selection framework, discriminating the most essential input data for accurately forecasting the concentrations of PM10, based on observations for the years between 2002 and 2006 in the metropolitan region of Lisbon, Portugal. Robert Gennaro Sposatoa and Nina Hampla [35] presented worldviews as predictors of wind and solar energy support in Austria.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-Criteria Decision Making (MCDM) for Renewable Energy Plants Location Selection in Vietnam under a Fuzzy Environment

et al. 2018

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In the context of increasing energy demands in Vietnam, and as a result of the limited supply of domestic energy (oil/gas/coal reserves are exhausted), the potential for renewable energy sources in Vietnam is significant. Thus, building wind power plants in Vietnam is necessary. Access to this type of renewable energy not only contributes to society’s energy supply but also helps to save energy and reduce environmental pollution. Although some works have reviewed applications of the Multi-Criteria Decision Making (MCDM) model in wind power plant site selection, little research has focused on this problem in a fuzzy environment. This is the reason why a hybrid Fuzzy Analytic Hierarchy Process (FAHP) and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are developed for wind power plant site selection in Vietnam. In the first stages of this research, an FAHP model is proposed for determining the weight of each potential location for building a wind power plant, based on qualitative and quantitative factors. A TOPSIS is applied for ranking all potential alternatives in the final stage. The authors collected data from seven locations, which have good conditions for investment in a wind power plant. The results indicate that Binh Thuan (Binh Thuan Province is located on coast of South Central Vietnam) is the best place for building a wind power plant in Vietnam. The contributions of this work proposed an MCDM approach under fuzzy environments for wind power plant location selection in Vietnam. This paper also resides in the evolution of a new approach that is flexible and practical for a decision-maker. This work also provides a useful guideline for wind power plant location selection in others countries.

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Defining the Main Risk Factors for Solar Energy Companies with Fuzzy Entropy

Etı

Çelebi

2021

Financial Strategies in Competitive Markets

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Worldviews as predictors of wind and solar energy support in Austria: Bridging social acceptance and risk perception research

Cited by 41 publications

References 63 publications

Not in my hiking trail? Acceptance of wind farms in the Austrian Alps

Not in my hiking trail? Acceptance of wind farms in the Austrian Alps

A Multi-Criteria Decision Making (MCDM) for Renewable Energy Plants Location Selection in Vietnam under a Fuzzy Environment

Defining the Main Risk Factors for Solar Energy Companies with Fuzzy Entropy

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