The Effects of Informational Feedback on the Energy Consumption of Online Services: Some Evidence for the European Union

Abstract

Information and Communication Technologies (ICT) have scarcely been considered in studies on green consumption. Likewise, little attention has been paid to the effects of informational feedback on household energy ICT-related consumption. This paper aims to fill these gaps in the literature. Using microdata from a representative sample of the European Union population, this paper analyzes, in a novel way, whether the provision of information about the energy consumed by online services would make internet users change to a greener ICT consumption behavior. To assess this issue, Heckman type selection models are estimated. The results show that people’s concerns about environmental problems, their environmental activism and self-perceived efficacy as consumers are directly related to the influence that information provision exerts. We also find that frequent internet users and those with better digital skills are more willing to change their online behavior if given information on energy consumption.

Willingness to share information for energy efficiency: exploring differences and drivers across the Nordic countries

“…Particularly salient among the significant findings of this study is citizens’ engagement in environmental behaviors which remained consistent throughout the Nordic pooled sample and individual Nordic countries, as well as the other 25 EU member countries—in line with other research further demonstrating that those who were environmentally responsible tended to be have more environmentally friendly values [ 60 , 62 , 66 ] further highlighting the influence and importance of congruency in behaviors [ 10 , 46 ]. It is, however, acknowledged that a limitation of the present study is that the Eurobarometer dataset (Wave 92.4) does not have questions on specific smart technologies, and it is hoped that future Eurobarometer waves and other studies conduct research employs additional survey items pertaining particular technologies and consumer behaviors associated.…”

“…It must be noted that a central tenet of the measure for smart grid deployment is that energy consumption information provided through the smart meter will enable end-users to make wiser and, in principle, “rational” decisions about their energy service demands [ 82 ], intended to transforming consumers from ‘passive participants to active users and optimizers of their extended energy possibilities’ [ 13 , 65 ]. Informational feedback has been used extensively as a tool to increase user knowledge and motivation [ 66 , 85 ]. However, among substantial barriers for smart meter technology utilization in the EU relates to privacy, data security, safety concerns and reluctance to share information [ 4 , 25 , 28 , 34 , 35 , 86 , 87 ].…”

“…For what purposes would you be willing to share some of your personal information securely?”, with Respondents’ answering “To improve energy efficiency”[ 64 ]. Thusly other relevant variables were specifically chosen and excluded from the available items of the recent Eurobarometer dataset to explain the outcome, guided by previous studies that employed corresponding variables in their models addressing willingness [ 17 , 65 , 66 ] relevant to smart technology, stated preferences and environmentally significant behavior of citizens [ 53 , 58 , 59 , 67 ].…”

“…This study employs indicators such as climate change concern, environmental attitudes, and behaviors: comprising five single-item variables and two composite indexes, with the model also including socio-demographic variables: gender, age, social class, education, having children, life satisfaction, marital status, political orientation, and community type [10,29,30,57,61,66]. In the order of importance for the purpose of this study, the Special Eurobarometer 501 Wave items information are elaborated as follows in Table 1, with the description, coding, and relevant research that the variables were adapted from.…”

“…For studies that have utilized variables from environmental surveys [18,35,68], tackling items such as the willingness to share personal information which assumes a binary or dichotomous value entail analyses with logistic regression or bivariate probit models [10,59,61,66]. This paper adopts an approach utilizing logistic regression similar to established research on environmental attitudes and behaviors with Eurobarometer surveys relevant to energy [10,44].…”