Mar 2018 – Briskee, Deliverables & reports

[D 2.2 Results of Survey]

Results of BRISKEE Survey

Deliverable 2.2: Results of Survey
Grenoble Ecole de Management (GEM): Xavier Gassmann, Yashar Bashirzadeh, Corinne Faure, Stacey Malek, Thomas Meissner, Joachim Schleich (Coordination). Revised version 2018-03-23

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As part of Work Package 2 of the BRISKEE project, representative online surveys were carried out in the residential sector in eight European Union (EU) member states:

These countries currently represent about three quarters of the EU 28 population, The objective of the survey was to:

This report provides an overview of the survey methodology and of the main descriptive results, thereby focussing on differences across countries. The full questionnaire (in English) is included in the Appendix. The survey data were only available as anonymized data to the researchers. Personal information about the respondents such as name, address, IP address, or any other personal identifiers were not included in the database.

The conceptual background for the survey is documented in BRISKEE Deliverable D.2.5 and in Schleich et al. (2016). Within BRISKEE, the findings of the survey will be used, among others, to conduct multivariate analysis of household energy efficient technology adoption behaviour. That is, information on stated technology uptake by households (notably on lighting, appliances, retrofit) will be used to create dependent variables in the multivariate analyses of
technology adoption. The variables reflecting preferences for time and risk, attitudinal variables, socio-demographic variables and structural variables such as dwelling information will enter the multivariate analyses of energy efficient technology adoption as covariates. The findings of these multivariate analyses will be reported in D2.3. Similarly, the survey provides information on factors underlying the implicit discount rate such as risk and time preferences, environmental preferences, social norms or barriers to energy efficiency (e.g. lack of capital). These are used as dependent variables in D2.4, which employs multivariate analysis to explore the relation between the factors underlying the implicit discount rate and attitudinal and socio-demographic variables.

Likewise, the survey results provide an input to the energy demand modelling in Work Package 3, for example unique data on the shares of various energy-using technologies in the member states of the EU. This is explained in detail in deliverables D3.2, D3.3 and D3.4. Work Package 3 investigates the impact of policies addressing decision-making on long term energy demand using the energy demand models FORECAST and Invert/EE-Lab. Furthermore, the results offer guidance for Work Package 5, where policy recommendations are derived.

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