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Modelling perception and attitudes towards renewable energy technologies

handle: 1822/70678
Abstract While renewable energy technologies (RET) increase their share in power generation systems worldwide, some questions remain open, namely those concerning the opinion of the populations on new projects of these technologies. Given the long period of planning and large capital sums required by RET and, in some cases, the fact of being subsidized, it is desirable for decision-makers to acknowledge the public opinion and at least perceive if the opinions are rooted on biased perceptions. In this paper we propose a methodology for public perception and awareness assessment, involving an initial phase of data collection by means of a survey, followed by a phase of regression models construction resulting in predictive models of expected perceptions and attitudes towards RET. The models were translated in a free and easy to use computational Excel application and its usefulness was demonstrated for the case of four electricity RET in Portugal: hydro, wind, biomass and solar.
- University of Minho Portugal
Ordered logistic regression, Science & Technology, Renewable energy technologies, Binary logistic regression, Excel simulation tool, Public opinion
Ordered logistic regression, Science & Technology, Renewable energy technologies, Binary logistic regression, Excel simulation tool, Public opinion
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).33 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10% visibility views 1 - 1views
Data source Views Downloads Universidade do Minho: RepositoriUM 1 0

