Modelling perception and attitudes towards renewable energy technologies

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.

[1]  Paolo Polinori,et al.  Assessing the Determinants of Renewable Electricity Acceptance Integrating Meta-Analysis Regression and a Local Comprehensive Survey , 2015 .

[2]  Maria Madalena T. de Araújo,et al.  Sustainability assessment of electricity production using a logic models approach , 2013 .

[3]  Esther Salazar,et al.  Applying models for ordinal logistic regression to the analysis of household electricity consumption classes in Rio de Janeiro, Brazil , 2008 .

[4]  J. Richard Eiser,et al.  Identifying predictors of attitudes towards local onshore wind development with reference to an English case study , 2009 .

[5]  Scott R. Beach,et al.  Differing opinions about natural gas drilling in two adjacent counties with different levels of drilling activity , 2013 .

[6]  A. Menegaki.  Valuation for renewable energy: A comparative review , 2008 .

[7]  Fausto Cavallaro,et al.  A multicriteria approach to evaluate wind energy plants on an Italian island , 2005 .

[8]  Valentin Bertsch,et al.  What drives people's opinions of electricity infrastructure? Empirical evidence from Ireland , 2017 .

[9]  E. Sardianou,et al.  Which factors affect the willingness of tourists to pay for renewable energy , 2011 .

[10]  Maksym Polyakov,et al.  Consumers’ Willingness to Pay for Renewable Energy: A Meta-Regression Analysis , 2015 .

[11]  Paula Varandas Ferreira,et al.  Public opinion on renewable energy technologies in Portugal , 2014 .

[12]  Konstantinos P. Tsagarakis,et al.  The willingness of hoteliers to adopt proactive management practices to face energy issues , 2012 .

[13]  W. Fichtner,et al.  Public acceptance and preferences related to renewable energy and grid expansion policy: Empirical insights for Germany , 2016 .

[14]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[15]  Paul D. Allison,et al.  Logistic Regression Using the SAS System : Theory and Application , 1999 .

[16]  Yong Zhang,et al.  Analyzing public awareness and acceptance of alternative fuel vehicles in China: The case of EV , 2011 .

[17]  Alan Agresti,et al.  Statistical models for ordinal variables , 1994 .

[18]  G. D. Garcia,et al.  Ordinal Regression , 2009, Data Visualization and Analysis in Second Language Research.

[19]  Helen Theodoropoulou,et al.  Socioeconomic and demographic factors that influence publics' awareness on the different forms of renewable energy sources , 2014 .

[20]  L. Steg,et al.  Psychological factors influencing sustainable energy technology acceptance: A review-based comprehensive framework , 2012 .

[21]  Johan Martinsson,et al.  Energy saving in Swedish households. The (relative) importance of environmental attitudes , 2011 .