Abstract
The past decade has witnessed considerable research activity devoted to better understanding the process determining energy conservation behaviors. The factors affecting the rate of diffusion of sustainable technology and the effects of energy policy in encouraging such adoption have important implications for policy-making (Mcdougall, 1981; Rogers, 2003; IEA, 2016; Jeffe and Stavins, 1994). The benefits deriving from home energy efficiency improvement are also an important instrument to combat energy poverty (INSHIGHT E, 2015; Boardman, 2010; Urge-Vorsatz and Herrero, 2012). ¨ Energy efficient technologies represent a key driver for the reduction of energy demand (EC, 2012; IEA, 2016). This aspect is particularly relevant in the residential sector that represents 25% of the final energy consumption in EU (Eurostat, 2018). Research and development and robust global market growth have driven significant solar photovoltaic system price and performance improvement and several demand-side incentives have been introduced in the EU. However, even if energy efficient technologies costs have been reduced financial grants introduced, the adoption process has remained gradual. In the literature, potential explanations for understanding the energy-efficiency gap have included non-monetary costs explanation. Environmental problem awareness, technical and bureaucratic barriers may have also had a significant influence on the decision to purchase energy-efficient technologies (Jager, 2006; Rai, 2016). Addressing these barriers requires a better understanding of households decision-making process. Growing agreement over the critical importance of changing human behavior to make progress toward energy sustainability as been address also in the policy filed (World Bank, 2014). Analyze the purchase motives of adopters may disclose to what extent additional factors influence the decision to purchase energy efficient technologies and may contribute to the development of effective strategies in stimulating the diffusion of capital-intensive technologies. Exploiting the increased availability of large households’ surveys, a set of recent papers has provide some quantitative evidence of the main determinants of households energy consumption and the relative importance of sociodemographic and energy related behavioral characteristics (Barr et al. 2005; Sutterlin et al. 2011; Trotta et al. 2018; Martinsson et al. 2011; Han et al. 2013). Drawing insight from environmental psychology literature energy conservation behaviors are conceptually divided into consumption oriented behavior 2 (i.e. investment behavior) and habitual actions (i.e. curtailment behavior) (Stern, 1992). The general results highlight the importance of households energy related attitudes (i.e. specific type of individual behavior) in determining the diversity that exists across the population that helps identifying targeting policy interventions. Moreover, policies aim at reducing energy consumption do not affect only greenhouse gas emissions but also improve household life standards. Poor residential energy efficiency has been address as one of the principal causes for most of the adverse effect of energy poverty (Bouzarovski, 2011). Strategies to capture residential sector mitigation potential provide the ground for successfully aligning shorter-term social and longer-term environmental priorities (Boardman, 2010, Urge Vorsatz and Herrero, 2012). At the policy level a set measures linking energy poverty and energy efficiency are recognized (Directive 2012/27/EU, Directive 2009/72/EC). However, despite the potential synergies between climate change and fuel poverty shared policy goal have not been addressed. This paper seeks to contribute to the body of growing literature focusing on the design of policies to speed the diffusion of energy efficient technologies. First, we ask based on available data what are the main trends characterizing household energy saving behaviors. Second, selecting some European countries based on their energy poverty levels, we focus our analysis on countries heterogeneity. Third, we aim at measuring the direct effect of households’ characteristics and behaviors on technology adoption. Finally, from a policy perspective, we aim at identifies the links between energy efficiency policy measures and energy poverty. This paper starts from the analysis of data collected in 2014 from a consumer survey covering all EU member States, Iceland and Norway. We study the survey by selecting the questions regarding household energy demand.? We analyze the types of individuals engaged in energy conservation activities highlighting their energy poverty status. To perform this task we adopt a logit regression model selecting the covariates based on the existing literature. The paper is divided in 5 sections. Section 2 briefly surveys the available evidence and discussed the novelty of the paper. Section 3 describes the data and the methodology. Section 4 gives the econometrics results. Section 5 concludes