The role of NIMBY in opposing the siting of wind farms: evidence from Greece

Abstract

This paper aims to assess the relative importance of a NIMBY (‘Not-In-My-Back-Yard’) stance on an individual's opposition to the siting of a wind farm vis-à-vis other predictors, such as perceived effects (costs, risks and benefits associated with the project), perceived fairness of the siting decision and societal trust. Data originate from two case studies, a small wind farm of just two wind turbines in southern Greece and a mega-project of 153 turbines on the Greek island of Lesvos (aggregate N = 334). We use structural equation modelling (SEM) for testing the theoretically-suggested relations between the various constructs. We find that NIMBY is not the most important predictor of opposition while it is strongly correlated with other predictors, such as the perceived unfairness of the siting decision as well as the risks and costs associated with the wind farm. These latter findings undermine the common-sense idea that wishing a wind farm out of one's vicinity (‘Not-In-My-Back-Yard’) is an example of mere ‘free-riding’. Since the fit of the SEM models was found to be moderate, we discuss the limitations of our study and the implications of our findings as well as suggesting pathways for future research.

Keywords:

• NIMBY
• wind farms
• unfairness
• Greece
• structural equation modelling

Acknowledgements

The authors would like to thank Prof. Maarten Wolsink for sharing with them yet unpublished material and for his comments on a much earlier draft of this paper. Sincere thanks also go to the anonymous reviewers for their critical reading of the manuscript and for their useful comments. Any remaining errors are those of the authors.

Notes

1. With regard to the remaining factors, five items load on the second one (20.9% of variance explained) which taps into the importance of balancing costs vs. benefits as well as private vs. public concerns before taking a position: ‘If good arguments can be found to site the WF in my neighbourhood instead of in someone else's, I will accept such a facility’; ‘Because the WF has to be built somewhere, I don't object in advance to it being sited in my neighbourhood’; ‘The costs resulting from WFs should be borne by all of us’; and, ‘It's only common sense not to object in advance to the WF being built in your own neighbourhood’.

A single statement loads on the third factor (8.8% of variance explained) which seems to denote a ‘Not-In-Anybody's-Back-Yard’ stance: ‘As a matter of fact, I don't think it's fair to saddle another municipality with the WF’.

Finally, the fourth factor (6.4% of variance explained) also consists of a single statement, indicating a willingness to pay one's way out of the social dilemma: ‘I'm willing in some way to pay extra in order to contribute to the costs of building the WF in another municipality’.

2. As we demonstrate in the Data and Methods section, we use two manifest variables for constructing one latent variable of ‘fairness’, whereas Wolsink and Devilee (2009) incorporated those same manifest variables into their NIMBY latent variable (227‒228). This slightly different approach has no theoretical consequences, because it is evident from Hypothesis H2a, and it is employed solely for allowing us to study in greater detail the theorised relations between fairness, trust, NIMBY and opposing the WF.

3. The results of individual cases are not presented in detail herein due to space limitations, but they are available upon request. Analysing the two case studies separately returns, overall, very similar results. Qualitatively, the patterns of interdependencies between the various latent variables are highly similar between the aggregate data and either of the two case-study areas. The only differences, with reference to Model B, relates to (a) the correlations between institutional trust and unfairness for either case area, and (b) the correlation between unfairness and rejecting the WF for Lesvos, which were found to be not statistically significant, unlike the aggregate data. Yet in either case the NIMBY predictor was found strongly correlated with unfairness, risks and costs (in diminishing order). The second disparity is quantitative, i.e. the predictor variables’ total effects vary between the two cases. In particular, for Lakonia the largest total effects are due to unfairness followed by benefits and then by NIMBY and institutional trust. For Lesvos, costs have the largest effect followed by NIMBY and finally benefits and unfairness perceptions. Nevertheless, these differences in size should not obscure the fact that NIMBY is not the strongest predictor in either case study, while they are probably due to the particularities of the two projects’ characteristics and areas’ socio-political frameworks; other comparative research (e.g. Khan 2003; Warren et al. 2005; Wolsink 2007a; Toke, Breukers, and Wolsink 2008; Dimitropoulos and Kontoleon 2009; Pasqualetti 2011) has demonstrated that differences in the planning framework, perceived impacts, personal experiences, specific characteristics of individual landscapes, institutional factors etc. condition (and differentiate) locals’ stance towards a particular WF. Thus, one could plausibly argue that the costs predictor is much more influential for the Lesvos WF since this is a very much bigger project. On the other hand, the importance of the unfairness predictor for the Lakonian case may be due to the fact that the WF is to be built in spite of the local government's opposition. Clearly, these are tentative arguments and further research is needed to understand the reasons for these differences.