Understanding public support for smart meters: media attention, misperceptions, and knowledge

Abstract

Public opinion on critical issues, such as technology deployment aimed at reducing greenhouse gases or increasing energy efficiency, may be impacted through information disseminated through the media. It is therefore increasingly important to understand the role media plays in spreading misinformation to the public. Through this study, we add to the growing body of literature on misinformation by exploring the relationship between media attention, risk perceptions, knowledge of, and support for smart meter technology. Smart meter deployment has been hindered, in part, due to beliefs that the technology causes cancer or can lead to incorrect electricity billing. To examine the relationship between misinformation and support for smart meter technology, we analyzed a quota sample of 1,046 people living in the U.S. Results show that paying attention to smart meter stories is related to familiarity with false information about smart meters. Familiarity with false information is also associated with misperceiving risks regarding the technology (e.g. those who are aware of false information about health problems associated with smart meters are more concerned about health-related problems associated with this technology). Ultimately, reporting higher levels of concern about smart meters is associated with lower support for government and industry policies aimed at increasing smart meter installation. However, the relationship for misperceptions, which decreased support for smart meters, was only present among those with low knowledge levels of the technology. Results indicate that knowledge may be a protective factor in terms of mitigating the effects that misinformation could have on outcomes such as support for policies or technological developments.

KEYWORDS:

• Risk communication
• media attention

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Although our familiarity and concern variables use similar wording, these measures assess different concepts. Indeed, the correlation between health familiarity and concern was small (r = 0.08, p < .05). The correlation between cost familiarity and concern was also small (r = .13, p < .05). Results from factor analyses also suggest that familiarity and concern are two separate concepts.

2 In addition to the models reported in this manuscript, we also ran analyses where we utilized clustered robust standard errors to deal with potential variance in our measures across states. In essence, this model tried to deal with differences in our various measures between the states. Overall, the findings from our revised model found no substantial differences in terms of results. Therefore, we have kept the OLS regression analysis.

Additional information

Funding

The research project was funded through a grant from the Washington State University Energy Systems Innovation Center.