Energy Efficiency Standards of Single-Family Houses : Factors in Homeowners ’ Decision-Making in Two Austrian Regions

The energy efficiency of residential buildings is a central issue in the widely discussed energy transition. This study investigates which factors influence homeowners ́ decisions regarding the energy efficiency standard of their houses. Homeowners who built or renovated their houses between 2008 and 2013 participated in a questionnaire survey in two Austrian “energy regions” within the federal states of Styria and Burgenland. In the majority (66%) of cases, homeowners chose the low-energy house standard B (≤ 50kWh/ma) for their building or renovation projects, followed by the conventional standard C (≤ 100kWh/ma) (21%). Only 13% realized ultra-low-energy, passive or plus-energy houses with a higher energy efficiency standard (A (≤ 25kWh/ma), A+ (≤ 15kWh/ma), or A++ (≤ 10kWh/ma)). Expert recommendations on energy standards showed the highest correlation with the selected standards, and on average, new building projects realized better energy efficiency standards than did renovations. Further variables that were significantly related to the realized standards included homeowners’ attitudes and knowledge about building energy efficiency standards and the age of the respondents. Although the homeowners who were surveyed were initially satisfied with the selected energy efficiency standard, many now indicate a preference to implement significantly higher energy efficiency standards than those achieved in their project. Further, they would recommend even significantly higher energy efficiency standards to friends than the standards preferred for their own house. These findings suggest that current preferences and communication in social networks promote higher future energy efficiency standards.


Introduction
A central issue in the widely discussed energy transition is the energy demand of residential buildings, particularly in countries in which space heating is necessary and few energy sources exist.One example is Austria, which imports approximately 65% of its energy (Europe´s Energy Portal, 2013) and where the number of heating degree days in the different regions ranges from 3080 to 6365, with the latter defined as the product of the number of days with heating demand (i.e., in Austria, the number of days with an outside temperature < 12°C) multiplied by the average difference between 20°C (agreeable room temperature) and the average outside temperature on these days (Krischan, 2013a,b;Paschotta, 2015).
The potential for savings in Austria's residential construction and renovation sectors is high because heating and hot water account for 87% of the total household energy demand (Bohunovsky, 2008).The European directive 2002/91/CE on the energy performance of buildings, which aims to foster energy efficiency and energy savings in buildings, has been implemented in Austria in the state and provincial (federal states) laws and bylaws.In addition, numerous subsidy schemes for the promotion of energy efficiency in single-family houses at the state, provincial and municipality level provide considerable financial support.Although the market potential of very energy efficient houses, such as passive or plus energy houses, is strong in Austria, the implementation rate remains low because homeowners rarely implement the most energy efficient technologies in building and renovation projects (Plate, Moser, & Elvin, 2010).
However, none of these existing studies has quantitatively examined the extent to which different factors, such as knowledge and attitudes toward energy efficiency, technology acceptance, economic aspects, contextual factors and involved actors influence homeowners' decisions on the energy efficiency standards of the building as reflected in energy labels (A ++ , A + , A, B, and C, as described below) of contemporary energy efficiency schemes.Recent changes in zoning laws, energy efficiency standards, subsidy systems and local activities (e.g., "energy regions") may have triggered specific developments that require an analysis of specific domains.To contribute to knowledge on this issue, we address the following research questions: 1) Which energy efficiency standards do homeowners in two Austrian energy regions choose for the construction or renovation of their single-family houses?2) When considering energy efficiency standards, what are the main factors that influence homeowners' decision-making during a building or renovation project? 3) How satisfied are homeowners with the energy standard that they selected in their building or renovation project, and how does this influence current preferences and intentions for recommendations of such standards to other persons in their social network?
This paper quantitatively analyzes the building and renovation projects of single-family houses in two regions of Austria.In Section 2, we provide a short introduction to our study regions, the procedure and method of the research and the analytical framework.In Section 3, we present the main results from the survey, and in Section 4, we discuss their implications.Section 5 draws conclusions regarding interventions to promote higher energy efficiency standards and makes suggestions for further research.

Study Regions
The study area encompasses two regions in Austria, namely the "Energieregion Weiz-Gleisdorf" in Styria and the "ökoEnergieland" in Burgenland (Figure 1).These regions were chosen because of their pioneering role as model "energy regions" in which we can observe how changes in the energy sector in the past two decades have promoted awareness toward energy efficiency in buildings and have fostered the diffusion of corresponding technologies.
Figure 1.Sopha et al., 2010) were found to be relevant regarding energy-related decisions.

Conceptual Framework and the Operationalization of Variables
Decision-making in the field of energy use and conservation is even more complex than often assumed, as is pro-environmental behavior in general.Although empirical evidence remains inconsistent, there is general agreement that "broad yet interrelated categories of variables may explain individual differences in household energy use" (Frederiks, Stenner, & Hobmann, 2015, p. 567).These variables include socio-demographic, psychological, contextual, and situational variables (Frederiks et al. 2015).To integrate this broad spectrum of variables into our examination of households' decision-making, we adopted an integrative approach combining Giddens' structuration theory (Giddens, 1984) with Triandis' theory of interpersonal behavior (Jackson, 2005;Triandis, 1977Triandis, , 1980;;Wilson & Dowlatabadi, 2007), and the integrative behavior model by Hansmann and Steimer (2015).The recently proposed integrative agent-centered (IAC) framework (Feola & Binder 2010a, b, c) provided a reasonable basis for this encompassing approach.The IAC is composed of reflexive feedback loops from decisions through a post-decisional evaluation of perceived consequences to revised decision preferences and future behavior.The IAC model thus covers a self-regulative development of the decision-making and behavior of agents (households, institutions) over time.This model was suitable for our analysis because we were interested not only in homeowners' decisions but also in measuring satisfaction with the implemented energy efficiency standards and the development of corresponding preferences and intentions for the future.We adapted the IAC framework as follows: Because we solely regarded investment-oriented measures, we excluded the concept of habits and routines.The building decisions of homeowners can be considered to be conscious decisions that are typically made only a few times in one's lifespan and thus cannot be considered habitual.We likewise omitted the measurement of affects and physiological arousal included in the IAC framework.However, attitudes have a strong affective component (like vs. dislike) and may thus correlate with affective, emotional processes.
The adapted IAC framework, as shown in Figure 3, considers (i) personal factors such as homeowners' attitudes, knowledge, technology acceptance, demographic variables and economic aspects including household income; (ii) project-specific factors that distinguish between new building projects and encompassing renovations; (iii) contextual factors such as political (e.g., legislation and subsidy schemes), economic (e.g., the price of a technology) and region-specific factors (e.g., subsidies); and (iv) social influence, exerted by communication with experts and within social networks.According to Giddens' structuration theory (1984), the building and renovation decisions and actions of households influence contextual factors and vice versa.External aspects as cognitively represented within the decision maker are connected to the actual external aspects through dotted lines representing a relation of probabilistic functionalism (Brunswik, 1955) in Figure 3. Arrows at the end of dashed lines represent influential feedback on the decision-maker (Homeowner i) and on other homeowners (Homeowners n), which result from the decision that has been made.The primary focus of the analysis is to determine which factors influence the homeowners of both regions in selecting the energy efficiency standard of their buildings.We considered the following dependent variables: (i) the actual energy efficiency standard of the house after completion of the renovation or building project (A++, A+, A, B or C); (ii) the level of satisfaction of the participants given the selected energy standard, the standard they would prefer now; and (iii) the energy standard that the homeowners would recommend to a friend.  1 Based on our sample, we considered only new building projects and larger renovations in which more than 25% of the surface of the building was renovated (Bundeskanzleramt, 2015).
In Table 4 we summarize the socio-demographic and economic characteristics of households from the sample.The age of the interviewees ranged from 30 to 55, and a clear majority (79%) of the participants were males.The subsamples from the two regions were similar in their gender distribution and with respect to the income and education levels of the participants.However, there was a significant difference between the two regions with respect to the age of the participants.Respondents from the ÖkoEnergieland (M = 43.7 years) were on average significantly older than those from the Energieregion Weiz-Gleisdorf (M = 39.4 years).

Analyses Performed
We first examined differences in the potential influential factors between homeowners who selected different energy efficiency standards.To effectively compare large subgroups in this regard, the following three levels of dependent variables were distinguished: 1 = homeowners with conventional houses (C), 2 = homeowners with low-energy houses (B) and 3 = homeowners with plus-energy houses (A ++ ), passive houses (A + ) or ultra-low-energy houses (A) For each potential influence factor, two tests were computed.The first series of tests straightforwardly compared the three groups with respect to each influence factor.For this purpose, Kruskal Wallis tests, or in cases of dichotomous factors, Chi-square tests were applied.In addition, the rank correlation between each factor and the selected energy efficiency standard (1, 2, 3) was computed to test for monotonous increases or decreases over the three ordered standards.
Thereafter, we conducted a multiple regression analysis, which uses potential influential factors to predict the exact energy efficiency level selected.To perform this multiple regression at a metric-dependent variable representing the energy efficiency standards, we substituted the five levels from C to A ++ by the numeric value of the upper bounds of these energy efficiency classes (A ++ ≤ 10kWh/m 2 a, A + ≤ 15kWh/m 2 a, A ≤ 25kWh/m 2 a, B ≤ 50kWh/m 2 a, C ≤ 100kWh/m 2 a).Because it is advisable not to use a large number of predictors in a multiple regression if the sample size is moderate, we only included those independent variables in the basic regression model that were significantly related with the grouping according to one or both of the previous bivariate analyses.Subsequently, we examined whether the inclusion of any single additional previously non-significant variable in the linear regression function would substantially increase the goodness of fit.
We further investigated the satisfaction of the homeowners with the implemented energy efficiency standards, the currently preferred energy efficiency standards, and energy efficiency standards recommended to a friend.All statistical analyses were performed using SPSS Version 21.

Attitude
Importance of energy efficiency of the house (Scale: 1 = not at all to 5 = very important) How important to you was the energy efficiency of your house in the planning phase of the project?
Technology acceptance Acceptance of passive houses (Scale: 1 = not at all true to 5 = absolutely true) The energy saved in a passive house compensates the initial investment.
A passive house contributes to environmental protection.
A passive house is a healthy home.
Acceptance of ventilation systems with heat recovery (Scale: 1 = not at all true to 5 = absolutely true) Index: Average rating for the items A passive house is a comfortable home.
Ventilation systems with heat recovery are still too prone to failure.a A ventilation system with heat recovery is good for the health of the residents.
General knowledge (about construction and renovation issues) Number of information channels used for information gathering about construction and renovation issues (Scale of items: 0 = no, 1 = yes) Index: Sum of items In the orientation phase of your building or renovation project, did you …?

Selected Energy Efficiency Standards and Potential Influential Factors
Only 13.2% of the buildings built or renovated during the period from 2005 until 2013 by the survey participants had an energy efficiency standard of A (including A + and A ++ ), 66.1% were low-energy houses (B), and 20.5% were of conventional standard (C).As Table 5 shows, there was no significant difference between the two study regions in this regard, but newly built houses turned out to be significantly more energy efficient than renovated buildings.For example, averaged over both regions, the conventional standard (C) was quite rare for new building projects (14.3%), whereas this standard still encompasses 41.4 % of renovated houses.Ultra-low energy standards were realized in 12.2% of the new buildings but only in 3.4% of the renovations, whereas plus-energy houses (A ++ ) and passive houses (A + ) were chosen only in 4.1% of new building projects and not at all in renovation projects (Table 2).
In addition to the project type (new building = 1), the attitude of the homeowners, their specific knowledge of energy efficiency standards, and the highest energy efficiency standard recommended by experts were positively related to the actual energy efficiency standards of the houses (Table 5).The age of the respondents was also positively correlated with the actual energy efficiency standards, which means that older participants have more energy efficient houses.The rank correlations of factors, which were found to be significantly related to the selected energy efficiency standards, are provided in Table 5.The highest energy efficiency standard recommended by experts correlated with r = .56substantially and highly significant (p < .001)with the selected energy efficiency standard.This correlation is based on the answers of 59% of homeowners who received a corresponding recommendation for their project from one or several experts.For 2.7% of them, the plus-energy house (A ++ ) was the highest recommended energy efficiency standard, followed by 13.3% for the passive house standard (A + ), 17.3% the ultra-low-energy house (A), 58.7% for the low-energy house (B) and 8% for a conventional house (C).Thus, a majority of experts recommended the moderate standard B. b For the categories, see Table 4.
A moderate but significant correlation of r = .25(p < .01)was found for the specific knowledge of the homeowners.This variable was defined as the highest energy efficiency standard homeowners informed themselves.In total, 67.7% of the homeowners informed themselves about one or several energy efficiency standards.The results on these energy efficiency standards was 19.8% for the plus-energy house (A ++ ), 45.3% for the passive house (A + ), 8.1% for the ultra-low-energy house (A), 23.3% for the low-energy house (B) and only 3.5% for the conventional house standard (C).
The correlation between the attitude of the homeowners and the selected energy efficiency standard was r = .22.The average value of M = 4.1 on the corresponding five-point rating scale (1 = not at all important, 5 = very important) reflects that the energy efficiency of the house was "important" to the homeowners.
The technology acceptance (M = 3.4) was considerably lower and not significantly related to the actual energy efficiency of the house.The latter was also true for the variable general knowledge, which captured how many different sources of information were consulted for the building or renovation project.Among these sources of information, consultations with family and friends (87%) were mentioned most frequently, followed by the internet (78%), visiting a construction fair (77%), reading construction guidebooks (53%), consultation at a subsidy office (53%) and consultation with an energy advisor (33%).Additional ratings for the importance of the different sources of information on five-point scales (1 = not at all important, 5 = very important) were given from those participants who used a specific source of information.These ratings were not considered in the general knowledge index.The rank order of these ratings was as follows: consultations with family and friends (M = 4.1) and the internet (M = 3.9) received the highest importance ratings, followed by consultation at a subsidy office and reading construction guidebooks (both M = 3.6), consultation with an energy advisor (M = 3.5), and visiting a construction fair (M = 3.3).
Whether homeowners received subsidies was also not significantly related to the selected energy efficiency standard.This finding may be explained by the rather low variance of this variable because a majority (78 %) of homeowners in our study obtained subsidies.However, the homeowners who received subsidies were also asked how important the subsidies were in the implementation of the project.A majority of the respondents judged the obtained subsidies as highly important (25%) or important (26.9%).The other categories were partially important (18.5%),not important (13%), and not at all important (16.7%).Homeowners were also asked whether they would have implemented a higher energy efficiency standard if a subsidy or a higher subsidy had been available.Approximately 34.3% answered "yes," 15.7% "perhaps," and 50% "no."

Multiple Regression Analysis for Predicting Energy Efficiency Standards
The factors that we assumed would influence homeowners'decisions on energy efficiency were examined via a multiple regression analysis.For the selected energy standard, a multiple regression was conducted using those variables as predictor variables, which differed between (or were correlated with) the three levels of energy efficiency standards of the houses according to the previous analyses shown in Table 5.Any missing values of single predictor variables for specific cases were estimated by the overall mean of the variable for this analysis.
The dependent variable was a metric value representing the selected energy efficiency standards (see Section 2.5).The prediction equation of the basic regression model was accordingly formulated as follows: This prediction model was highly significant (p < .001)with multiple R = 0.56, which means that R 2 = 31% of the variance in the dependent variable could be explained (adjusted R 2 = 0.28).The predictor variables highest expert recommendation (β = -0.35,p < .001)and project type (β = -0.24,p < .01)proved to be significant, whereas attitude (β = -0.16,p = .054),specific knowledge (β = -0.15,p = .086)and age (β = -0.14, p = .075)were slightly above the significance threshold (p < .1),but still not significant.This result means that the latter variables did not contribute significantly to the prediction of the selected energy efficiency standard, when expert recommendation and project type were considered, even though they were shown to be significantly related to the energy efficiency standard in the bivariate analysis (Table 5).None of the other variables included in Table 5 was found to be significant when included as additional predictors in the multiple regression, and none led to a noticeable increase in the adjusted R 2 (for all additionally included variables, adjusted R 2 ≤ 0.29).
The basic multiple regression model including the significant predictors and those with p < .1 thus resulted in the best prediction model.This prediction model is depicted in the left half of Figure 4, which refers to the integrative agent-centered framework of Figure 3.The model thus also illustrates how preferences are revised in connection with the evaluation of the project, thus leading to distinct recommendations to others (as shown in the right side of Figure 4) as investigated in the two subsequent sections.

Satisfa
The satisfa scales in fo all satisfie 0.54), follo = 4.2, SD ratings and also true fo

Most P
The ).The highest correlation was observed between the most preferred energy efficiency standard today and the one recommended to a friend (r = .58,p < .001).Homeowners' satisfaction with the selected energy efficiency standards was not significantly correlated with the most preferred and recommended energy efficiency standards.
Finally, we analyzed the bivariate rank correlations between the variables technology acceptance, social network, household income, education level, and use of subsidies, and the energy efficiency standard most preferred today and recommended to a friend.In our study, these variables were found to be unrelated to the homeowners' selection of energy efficiency standards (Table 5).However, previous studies and the problem-centered interviews of our pre-study indicate that these variables could play a role in preferences regarding energy efficiency measures.The following significant findings emerged (Figure 4): technology acceptance was significantly positively related to both the energy efficiency standard preferred today (r = .19,p < .05)and recommended to a friend (r = .38,p < .001);and the variable social network (r = .21,p < .05)and the household income (r = .19,p < .05)were both significantly correlated with the energy efficiency standard preferred today.

Discussion
As shown in the review of Frederiks et al. (2015) on factors influencing household energy usage, several general tendencies can be observed.However, because of inconsistencies between studies, it is necessary to conduct focused studies that reveal the complex interplay of relevant variables within specific groups and contexts.The primary focus of this study was to examine the factors influencing homeowners' decision-making about the energy efficiency standards of their buildings in two regions of Austria.We also analyzed homeowners' satisfaction with the implemented standards and the resulting current preferences and intentions for recommending energy efficiency standards to others.In the following section, we discuss our findings and integrate them with the results of previous studies to draw practical conclusions for facilitating and promoting higher energy efficiency standards of buildings for the study regions.The integration with previous findings shall also establish a basis for evaluating the transferability of our conclusions to other decision contexts and regions, and thus, to add to the general knowledge base on household decision-making on energy efficiency measures.
The household survey revealed that the majority of homeowners (66%) selected low-energy houses (standard B), followed by the conventional standard C (21%) and 13% total for a plus-energy house (A ++ ), passive house (A + ) or ultra-low-energy house (A).Based on our findings, the two most important factors in homeowners' decision-making were expert recommendations and the project-specific differentiation between new building projects and renovations.In addition, attitudes in relation to energy efficiency standards of buildings, specific knowledge of energy efficiency standards and the age of the respondents were significantly related to the selected energy efficiency standards.
The higher energy efficiency of new buildings relative to the energy efficiency of renovations is understandable because the implementation of new technologies can be planned from the beginning for new buildings but is constrained by the existing building substance in the case of renovations.A study by Stieß and Dunkelberg (2013) suggests that in Germany, renovation activities undertaken by homeowners result in only subtle improvements in energy efficiency and far less than what would be technically viable.
In a study on the adoption of energy efficiency measures in detached houses, Nair, Gustavsson, and Mahapatra (2010b) found that experts (companies, installers and energy advisers) were an important source of information for homeowners.Backhaus et al. (2011), examining the role of real estate agents and other actors in the building sector, found that practical recommendations from such experts were found helpful for homeowners who required additional information and advice.However, because the experts in our study mostly recommended energy efficiency standard B, the full potential of their recommendations for promoting more efficient energy standards was presumably not realized.The recommendations of our experts instead resemble the findings of Guy and Shove (2000), who examined the role of designers, building companies and other relevant actors and found that energy conservation is often not of primary importance as long as the criteria of existing building regulations are met.
As in our study, previous studies have also identified specific knowledge as crucial for the implementation of energy efficiency standards.Banfi et al. (2008) suggest that a lack of knowledge regarding the advantages of the efficiency measures prevents corresponding investments.Similarly, a lack of knowledge about technologies related to energy efficiency can represent a barrier to energy saving (Tuominen & Klobut, 2009).This lack has been identified as a barrier in a study by Nair et al. (2010b), in which half of the respondents did not know or knew little about energy efficiency measures.Backhaus et al. (2011) examined homeowners' knowledge about the energy performance certificate (EPC), concluding that policy-makers should provide more useful and trustworthy information.According to Adjei, Hamilton and Roys (2011), receiving information and talking with energy professionals can promote energy efficiency improvements.Similarly, Tambach Hasselaar, and Itard (2010) recommend policies for knowledge transfer among homeowners and experts and has found, identical to our findings, that interpersonal communications are an important source of information.In line with previous studies, the results of our survey suggest that there is still a need to improve both homeowners' and experts' knowledge to foster the most energy efficient building standards.
Our findings also support previous studies underscoring the importance of homeowner attitudes.Stern (2000) suggests that attitudes are a causal variable in pro-environmental behavior.Various studies show that pro-environmental attitudes foster pro-environmental behavior (Herring, Caird, & Roy, 2007;Jackson, 2005).Mahapatra and Gustavsson (2008) suggest that the diffusion of heating systems depends on people's attitudes.In a study by Herring et al. (2007), concerns about saving energy and the environment were identified as factors promoting the adoption of energy efficiency measures.
Contrary to our findings, previous studies have found technology acceptance to be a crucial factor for decisions related to energy efficiency (Tambach et al., 2010).In his ABC theory, Stern (2000) considers constraints provided by technology, available technology and the introduction of new technology to be limiting factors of environmentally relevant behavior.The acceptance of building technologies such as plus-energy and passive houses was found to be an indicator of intentions for their use in a previous study on the energy region Energieregion Weiz-Gleisdorf (Schaffer et al., 2012).Although our results confirmed that the acceptance of technologies related to energy efficiency is still moderate among homeowners, we did not observe a significant relationship between technology acceptance and the implemented energy efficiency standards.One reason for this finding may be the time gap between the former decisions and the assessment of technology assessment in the survey.Another explanation may be the scope of the items used to measure technology acceptance; their content was constrained to passive house technologies and ventilation systems for heat recovery (Table 3).Nevertheless, we found significant positive relationships between technology acceptance and current preferences for energy efficiency standards and with intentions for recommending energy efficiency standards to others.
Whereas age does often not show an influence on energy use and conservation (Frederiks et al. 2015), the age of respondents was also significantly related to the selected energy efficiency standards in our study, with older participants having the more energy efficient houses.This finding is also at odds with a study by Nair et al. (2010a) that revealed that better educated residents below the age of 55 years were more likely to adopt investment energy efficiency measures than older residents.
Two further aspects of our study that conflict with previous findings (see also Frederiks et al. 2015) are the lack of influence of homeowners' income and education level on energy efficiency decisions.A study by Rohracher and Ornetzeder (2001) suggested that residents of "ecological buildings" tend to be well-educated with high incomes and good access to information.Such homeowners can be described as innovators and early adopters (Rogers 1995;2003).Nair et al. (2010b) found that homeowners emphasized economic aspects such as investment costs and annual energy costs when implementing energy efficiency measures.Furthermore, a household survey on energy labels in buildings showed that on average, households with a higher income more often had an EPC (Adjei et al., 2010).Although our study confirmed a positive relationship between income and the revised preferences for energy efficiency standards today, we found no relationship with the implemented efficiency standard or influence of homeowners' education level on selected or currently preferred energy efficiency standards.Because energy efficiency has become a mainstream topic in the two Austrian energy regions in our study, it is possible that people from all socio-economic strata, not just well-educated homeowners, have considerable interest in this issue.
Today, homeowners in our sample prefer higher energy standards than the ones they previously implemented in their homes.This result suggests that there exists a corresponding trend in the two study regions towards implementing higher energy efficiency standards in buildings.This suggestion is also supported by the finding that homeowners recommend even higher efficiency standards to friends than the homeowners preferred for themselves.Based on social comparison theory (Festinger, 1954), changes in private opinions to those expressed in social interaction usually occur in the direction of norms supported by the majority, respectively towards the values supported in the cultural surrounding (Goethals & Zanna, 1979).Providing recommendations, and hence expressing opinions in favor of higher energy efficiency standards in buildings, aligns with the socio-cultural climate that has developed in the two energy regions.Because these regions adopted this role by naming themselves energy regions, renewable energy and energy efficiency are important local topics.

Conclusions
This paper examined the energy efficiency standards chosen by the homeowners of single-family houses in two study regions in Austria.The study specifically analyzed (i) which factors were important in the decision-making process, (ii) how satisfied the homeowners were concerning the implemented standards, and (iii) which standards the homeowners prefer now and would recommend to others.
The results suggest that experts' recommendations and homeowners' personal attitude and specific knowledge related to energy efficiency standards are key factors in decision-making.To promote efficient energy standards for residential buildings, policymakers should focus accordingly on experts involved in the building and renovation projects.Furthermore, information on energy standards, effects on well-being and costs should be made readily available to facilitate an increase in knowledge for people planning to construct a new house or renovate their existing home.Currently, the established managements of the Austrian energy regions have access to the local population through media, public events, local energy providers, energy cooperatives and schools.These outlets provide a good starting point to implement various measures to further raise the awareness concerning energy saving and energy efficiency and to promote energy efficient technologies and consequently improve their acceptance.Such activities must be continued and intensified to further the development towards sustainable development in the building sector.With regard to experts such as architects, builders, engineers, and firms involved in building and renovation projects, we propose continued and expanded training organized by the energy region's management.To foster the energy transition, interventions concerning the zoning law and subsidies for homeowners also appear to be promising.In the zoning law, for example, the maximum allowed heating demands for residential buildings are prescribed; a direct way to influence builders would therefore be to reduce the allowed upper limits more rapidly, as currently planned in favor of A ++ , A + and A houses.This requirement would thereby influence homeowners, builders and experts by encouraging them to focus on corresponding technologies.
i) visit a fair devoted to construction/renovation? ii) read construction guide books?iii) consult the internet?iv) consult your family, friends or neighbors?v) consult an energy advisor?vi) consult a subsidy office?Specific knowledge about energy efficiency standards Knowledge about energy efficiency standards (Scale of items: 0 = no, 1 = yes); Index: Highest EES with positive response (C = 1, B = 2, A = 3, A+ = 4, A++ = 5) About which energy efficiency standards for houses did you inform yourself?(C, B, A, A+, A++) Expert recommendations Recommendation of expert (architect, builder, engineer) about the energy efficiency standard for the house Index: Highest energy efficiency standard recommended by expert (C = 1, B = 2, A = 3, A+ = 4, A++ = 5) Did an expert involved in the planning of your house give you a recommendation about energy efficiency standards?(yes vs. no) Which EES did he/she recommend?(C, B, A, A+, A++) Social network Scale of items: 0 = no person, 1 = one person, 2 = two persons, 3 = three or four persons, 5 = more than five Index: Average value of items How many people do you personally know who ...?i) recently renovated their homes energy-wise?ii) recently built an energy efficient house?iii)

Table 2 .
3. Chosen energy standards in building and renovation projects 1 in the two study regions

Table 3 .
Description and measurement of possible factors (independent variables) influencing homeowners' decisions on energy efficiency standards Item was inversely poled for the statistical analyses, such that high ratings reflect a high acceptance of ventilation systems with heat recovery.

Table 5 .
Comparison of three energy efficiency standard groups with respect to influential independent variables * p < .05,** p < .01,*** p < .001;Column "total": Analysis of differences among the means of the three groups using Kruskal Wallis tests.Last column: Rank correlations testing linear associations a Chi-square tests, df = 2;