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Preamble

“Energy Efficiency (EE) consists one of the main pillars of efforts to mitigate climate change. Τhere is plethora of relevant policy instruments (energy labelling, audits etc) that support the penetration of EE technologies and practices, but different types of barriers affect negatively the achievement of targets set under scenarios. According to the Energy Efficiency Communication of July 2014, the EU is expected to miss the 20% energy savings target of year 2020 by 1%-2% (European Commission, 2014; 2012). The Dutch Government lowered its initial reduction target from 30% to 20% (Vringer K. et al., 2016). Also, Malta’s 2020 EE target was lowered in 2015 from 0.825 Mtoe to 0.726 Mtoe expressed in primary energy consumption (European Commission, 2015a).

The EE policies and measures due to barriers do not deliver the expected benefits associated with improvements in EE (such as energy savings, reductions in Greenhouse Gases, employment, poverty alleviation etc) (UNEP, 2014; IEA, 2014). Among these types of barriers, those related to end-users behaviour need to be incorporated also in forward looking energy efficiency modelling after being identified and analysed (McCollum L. David et al., 2016; EC, 2015; EEA, 2013).

Forward-looking models are used for medium-to-long-term scenario analyses, aiming to support relevant policy options; some of these models are designed to consider both technological, economical and socio-behavioral elements in developing their scenarios (McCollum L. David et al., under press; Knoblocha F., Mercure J.-F., 2016). Bridging the gap between these elements has historically been presented as a challenge (McCollum L. David et al., under press). Furthermore, demands of improving the design of models so as to become more ‘realistic’ by incorporating features observed in the real world are increasing (McCollum L. David et al., under press). One group of such features of the ‘real world’ relates to human behavior.

The demands are based on the following arguments (McCollum L. David et al., under press):

i) Models lacking behavioral realism are restricted in evaluating energy efficiency policies and other influences on end-user demand;
ii) Improving the behavioral realism of models consequently affects policy-relevant model analysis of EE as part of the climate change mitigation efforts.

However, current modeling of behavioral features in energy-economy and integrated assessment models is relatively limited (McCollum L. David et al., under press). Usually, models and particularly Integrated Assessment Models (IAMs) represent the behavior of consumers or energy end-users through economic relationships: energy demand as a function of price, technology investments to minimize levelized costs, etc (McCollum L. David et al., under press).

End-user behaviour is complex and rarely follows traditional economic theories of decision-making (McCollum L. David et al., under press; Frederiks R. et al., 2015; Knoblocha F., Mercure J.-F., 2016). End-users patterns of energy consumption are influenced by social-cultural-educational (status quo, social interactions etc), economic (risks of investment, financial incentives) and institutional factors (split incentives, hassle factor etc) that are characterized as barriers (Vringer K. et al., 2016; Frederiks R. et al., 2015; UNEP, 2014).

Efforts are focused in overcoming existing barriers and increasing the sophistication of energy and economic modelling (European Commission, 2015b; 2014). Key insights in the outcomes of such efforts can guide the effective design and implementation of end-user-focused strategies and public policy interventions to improve the level of EE interventions (by adopting technologies or practices) (Frederiks R. et al., 2015; UNEP, 2014).

This methodology transforms qualitative research outcomes related to barriers linked to end-users behavior, into quantitative ones allowing their incorporation in the form of numerical inputs in forward looking EE modelling.”

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