Next Article in Journal
Sustainable Development and Workers Ability: Considerations on the Education Index in the Human Development Index
Next Article in Special Issue
Drinking Water Supply in Rural Africa Based on a Mini-Grid Energy System—A Socio-Economic Case Study for Rural Development
Previous Article in Journal
Assessing the Impact of Entrepreneurial Education on Entrepreneurial Intentions among Romanian Doctoral Students and Postdoctoral Researchers
Previous Article in Special Issue
Modeling and Evaluating Beneficial Matches between Excess Renewable Power Generation and Non-Electric Heat Loads in Remote Alaska Microgrids
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Renewable Energy Acceptance by Households: Evidence from Lithuania

by
Dalia Štreimikienė
*,
Vidas Lekavičius
,
Gintare Stankūnienė
and
Aušra Pažėraitė
Lithuanian Energy Institute, Breslaujos 3, LT-44403 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(14), 8370; https://doi.org/10.3390/su14148370
Submission received: 2 June 2022 / Revised: 4 July 2022 / Accepted: 5 July 2022 / Published: 8 July 2022

Abstract

:
Although renewable energy adoption in the residential sector has increased significantly in the EU due to the governmental policies, aiming to reduce the barriers of renewable energy penetration, the full potential of renewable energy deployment in households is still not realized due to the behavioral and other barriers. One of the most important factors in the adoption of renewable energy technologies in households is the decision-making to implement renewables; therefore, the behavioral economics insights should be taken into account during the analysis of renewable energy acceptance by households. The paper provides a systematic literature review on renewable energy use in households by analyzing policies and measures, which could increase the use of renewable energy in households by overcoming the major barriers. The dynamics of renewable energy consumption in EU households was performed by applying Eurostat data, and the empirical case study was conducted in Lithuania to understand the main reasons of renewable energy acceptance by the household. Even though the use of renewable energy sources has increased significantly in the EU member states during the recent years, the study has found the following most common barriers that the traditional policies are unable to overcome: (1) high upfront cost and long pay-back period, (2) a lack of information and knowledge, (3) low priority of environmental concern, (4) resistance to change; human habits. The case study shows that the majority of Lithuanian households would like to use renewable energy technologies in their homes, but they encounter financial difficulties and lack of infrastructure. The policy recommendations were developed based on the results of the conducted study.

1. Introduction

Due to the high increase in energy prices linked to the Russian invasion of Ukraine and restrictions imposed on the Russian gas and oil imports, the increased use of renewables can be understood as the main way to reduce the burden of high fossil fuel prices and end EU dependency on the Russian oil and natural gas supply. At the same time, this will allow for the faster transition of the EU towards a carbon neutral society by 2050. Therefore, the increase in renewable energy use is currently one of the most important policy goals in the EU.
The residential sector was responsible for almost 30% of the final energy consumption in the EU in 2019 and has huge greenhouse gas (GHG) emission reduction potential, which has not been realized yet. The usage of renewable energy sources (RES) in households is one of the main climate change mitigation measures in this sector together, with energy use efficiency improvement.
In 2019, the European Green Deal (EGD) was initiated with a set of 50 actions for 5 years for all sectors of the economy. These actions would allow the achievement of the transition to low carbon energy and economy and climate neutral society in the EU by 2050. National Renewable Energy Action Plans were initiated for the EU member states for boosting renewables [1]. Few EU member states have decided to set ambitious targets in order to achieve 100% renewables scenarios by 2050 and ensure climate neutrality. However, the implementation of these targets and achieving a climate neutral society by 2050 might be problematic in the EU if GHG emission reduction potential in the residential sector remains unrealized. Due to the many independent actors that are responsible for the energy consumption and GHG emission reduction, the classical economic and regulatory policies are not enough in the residential sector. It is important to gain a better understanding of the processes that are driving acceptance of renewables in households. This deeper understanding must involve occupant behavior and lifestyle, the main drivers and barriers for the selection of renewable energy sources, in order to shape the proper policies for overcoming these barriers and ensuring faster penetration of renewables in households [2].
The use of renewable energy (RE) sources by the introduction of micro generation technologies at home are the basic climate change mitigation measures that are related to the energy consumption in households. In order to establish a decarbonized society, two conflicting conditions must be met, i.e., (1) cutting the need for energy and (2) creating flexibility in the demand for energy to respond to the fluctuations in the production of renewable electricity. However, these initiatives have not been effective in addressing some major barriers to combating climate change in the households [3].
There is a limited number of publications that have analyzed renewable energy acceptance in households and the main barriers to renewable energy adoption in the residential sector [4,5,6]. There are several studies that deal with renewable energy development in the EU households’ [7,8,9,10,11,12,13] transition from fossil fuels to renewables challenges [14,15] or with policies to promote renewables in EU households [16,17,18,19]; however, these studies do not provide an in-depth analysis of renewable energy acceptance by the households, including identification of the main barriers of renewable energy penetration in the households and development of new policy proposals.
The paper aims to overcome this gap and presents the analysis of renewable energy use in EU households, together with the empirical case study in the selected EU member states (Lithuania), for revealing the main barriers of renewable energy acceptance based on the representative survey of Lithuanian households. New policies and measures to promote renewable energy penetration in the residential sector will be developed based on the conducted research.

2. Literature Review

2.1. Barriers of Renewable Energy Penetration in Households

The use of energy is very important in the everyday life of all people. The technological development, energy consumption, and economic and social development are closely interlined [20]. The energy supply to households is necessary for meeting their basic social needs, and it is an important commodity in terms of household expenditures [21]. The scholars agree that in order to ensure the necessary quality of life, the thermal comfort of residential housing plays an important role [22,23]. However, in countries with cooler or hot climate households, in order to ensure thermal comfort in their homes, the consumer’s high quantities of energy resources, if this energy is based on fossil fuels, climate change and environmental issues become the major problem [24,25]. Therefore, by seeking to ensure the social needs of households and implementing climate change mitigation objectives, the governments need to support the use of renewables and energy saving measures in the residential sector.
Even though renewable energy sources are becoming more and more competitive, there are important barriers that are hampering their penetration in households. The lack of infrastructure for the wide penetration of renewables is a very significant problem in many countries; however, there are other problems, such as household’s attitudes and acceptance of renewable microgeneration technologies. Yeatts et al. [26] analyzed various issues linked to the social acceptance of renewables in households and proved that for the low carbon energy transition, better targeted policies and measures are necessary to overcome the social barriers of renewable energy penetration in households.
The study by Jacksohn et al. [27] analyzed the prophesy of decision to implement renewable energy technologies in households and found that households tend to make rational decisions towards their investments based on the weighting of costs and benefits. Scholars in their studies have confirmed that financial incentives are an important stimulus to switch to renewable energy sources. The studies that were conducted in Italy and Austria found that higher financial support for solar PV attracted younger and less educated households to invest in solar plans [28]. The study by Wasi and Carson [29] confirmed that the introduction of financial support schemes for heat pumps increased the probability of a household investing in these renewable energy technologies. Therefore, many studies have proved that economic support measures for renewables are appropriate measures to promote their development in the households; however, the financial incentives are not enough to navigate households in making decisions to implement renewables in their homes or buy distant solar or wind plants.
In addition, the impact of financial initiatives was found to be dependent on the household’s income, education, available access to the gas grid, energy consumption levels and prospects about the development of energy prices in the future. The case study conducted in Germany found that the acceptance of renewable energy technologies in households was prejudiced by the household energy consumption levels, and the spatial issues as socio-demographic variables, such as age, education, income, gender, etc., were discovered to be unimportant [30]. The scholars [25,31,32] analyzed the main drivers of acceptance of renewable residential heating systems and found [25] that socio-demographic and geographical features played a major role in households, including renovating their heating systems in already built houses in the wild. However, for selecting renewable heating systems in newly built houses, socio-demographic and spatial characteristics were found irrelevant for making such decisions. Even though some studies have found that men and well-educated people are keener to invest in renewables than older or less educated people [25], the studies conducted in other countries [33] revealed that the social acceptance of renewable energy technologies is linked to age, gender, education, income, and residential building type. The highest acceptance of renewables was shown by single men, aged 30–49 years old, with a secondary technical education, etc. The decision to invest in renewable energy technologies was driven by the expected long-term benefits due to the electricity bill savings. The highest barriers in Poland were indicated to be the lack of financial measures of households to invest in renewable energy sources. Moreover, for Polish prosumers, the financial issues, technical constraints, blurred regulations, grid connection problems, and lack of experience and knowledge were the key barriers to investing in renewables. Thus, the Polish case study found that better targeted policies and additional support in education, capacities buildings as well as technical and legal support are necessary in order to increase the number of potential prosumers [33].
The study [32] confirmed that the probability of households investing renewable energy sources is growing with increasing income, number of children in households, and higher environmental awareness.
The study conducted in Lithuania [34] has shown that employment status, education, and income influence willingness to pay for renewable energy sources in households. The self-employed households with a higher education level revealed the highest willingness to pay for the renewable energy technologies. Therefore, the study [34] proved that the awareness of renewable energy sources, education level, and income were the main drivers of households’ willingness to pay for the renewable energy technologies in Lithuanian households. In addition, the studies have shown that membership in environmental organizations, race, political views do not have a big impact on the renewable energy acceptance in households [35,36]. The study conducted in Croatia by Luttenberger [4] revealed that the social acceptance of renewable energy projects increases with the increase in knowledge about the benefits of renewables and formation of positive perceptions about renewables. The study analyzed such barriers of renewable energy penetration in Croatian households as high administrative barriers and complex procedures for installing renewable energy technologies in households, including infrastructure obstacles [4]. Nevertheless, these studies also emphasized the importance of financial initiatives for making decisions in favor of renewable energy technologies.
Based on the analysis of the conducted studies, it is possible to state that there is lack of know-how and understanding of renewable energy benefits for the household. The renewable energy sources provide many environmental and other benefits, including reduction in energy import dependency. Courses in universities should include disciplines such as low carbon energy transition benefits, including energy presumption benefits. The lack of such courses causes the lack of knowledge experienced by the trained professionals in the field of renewable energy technologies [20]. Therefore, based on the analysis of the studies, dealing with the main drivers and barriers of renewable energy technologies in households, it is possible to conclude that aside from the financial initiatives, information, awareness, and education levels play an important role in the acknowledgement of renewables in households. The information provision, training, and capacity building of households would allow for the reducing of social barriers of renewable energy acceptance and financial initiatives to support renewables that should be supplemented by better-targeted policies and measures, taking into account spatial and socioeconomic characteristics of households [37,38]. The policies and measures to promote renewables should be developed further by taking into account the identified barriers of renewable energy acceptance in households.

2.2. Policies to Overcome Barriers of Renewable Energy Technologies in Households

Scholars have agreed that governments play very important roles in boosting renewables in households [39,40,41,42]. Several years ago, the cost of energy produced from renewables was higher than from the fossil fuels, due to the not fully integrated external costs of energy generation from the fossil fuels and external benefits of energy generation from the renewables. For this purpose, the support of renewables was necessary to ensure their competitiveness and fast penetration in the markets. Although renewable energy capacities are more capital intensive, the shifting of renewables from conventional generation to renewables can provide many benefits that are linked to the reduction in energy import dependency and positive environmental impacts. In addition, the prices of renewable energy technologies were drastically decreasing due to the economies of scale.
The aim of policies and measures to promote renewables is to address the main economic, technical, social, organizational, etc. barriers that are hampering the penetration of renewables in households. The scholars have shown in their studies that renewable energy subsidies had a positive effect on the use of renewable energy sources and the feed-in tariffs are successful in the promotion of the adoption of solar energy [43,44,45,46]. Lekavičius et al. [47] claim that although funding is energy efficient, it mostly favors households with higher incomes because vulnerable households have limited investment opportunities. Therefore, the social policy initiatives are important in terms of policies and measures to promote renewables.
In Table 1, the policies and measures to promote renewables in households are provided, linking them to the main barriers to renewable energy adoption in households.
Adoption of renewable energy generation technologies is a dynamic and multidimensional process, which is driven and hampered by a number of variables. These variables need to be studied in developing policies and measures that enable the main drivers and overcome the major barriers of renewable energy penetration in households.
As several studies have proved that consumer intentions have a direct impact on the willingness to pay for renewables in households [36,59,60,61], the policies and measures are targeting “barriers” or conditions that prohibit RES adoption decisions in households [20,62]. The high initial RES cost is often the major barrier of renewable energy technology adoption in households, and they are the main target of policies and measures to promote RES in the residential sector [21,22]. Improved knowledge and understanding of renewable energy benefits also enhances public acceptance of renewables and faster deployment of renewable energy in households [23].
Various studies have indicated that the cost of switching to renewable energy in the residential sector remains an important factor, hampering the fast penetration of renewables in the residential sector [60,63,64]. Pereira et al. [45] concluded that low incomes and the risk of energy poverty are interrelated with the problems of renewable energy adoption in the households. Lau et al. [65] found that awareness is an important driver of RES technology adoption; however, the price has a greater impact on the adoption of solar photovoltaic in households.
Scholars have shown in their studies the correlation between awareness about renewable energy and willingness of households to adopt it [66,67,68]. Ulkhaq et al. [68] analyzed the consumer preferences of renewable energy sources in their homes. The results of this study demonstrated that there are the following important variables that are driving the adoption of renewable energy in households: education, status of employment, income, average cost per month for the electricity consumption, trust in benefits of renewable energy projects, influence of relatives, tax deductions for RES energy, and price of non-renewable energy sources.
Many studies have shown that the technical components for adoption and fast penetration of renewables in households are important as well [52]. Building-integrated renewable energy and smart digitalized home technologies with integrated energy management systems can considerably enlarge the penetration of renewable energy in residential buildings, as well as increase the willingness to pay for renewables in households [53,54,55].

3. Methods and Data

The systematic literature review [69] was carried out in order to consolidate the literature on renewable energy barriers and policies to promote renewables in households. The systematic search and study of the literature was carried out by applying the framework of Search, Appraisal, Synthesis, and Analysis (SALSA) [70]. The SALSA approach allows a potential subjectivity factor to be reduced and is known as one of the most appropriate methods for literature recognition, assessment, and systematization, which guarantees the precision and completeness of methodology [71,72]. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement as well guarantees the precision and completeness of the analysis [73]. Table 2 offers the basis for the systematic literature search and analysis that is applied in this research.
It is necessary to define the scope of the research before starting the search of the database and to identify the correct keywords that will be used during the search process. In the Web of Science (WoS) and SCOPUS databases, a literature search was carried out on a variety of topics, including “barriers of renewable energy in household ”+“ acceptance of renewable energy in households ”+“ policies and measures”. The results of the SCOPUS database completely duplicated the results of the WoS; thus, the SCOPUS was not considered in the subsequent analysis. The search for papers was performed in all categories of the WoS database in order to carry out the widest possible review of literature and include as many research papers as possible, relating to the subject of the search.
The papers collected during the search were evaluated, and the recommendations for the selection of papers were followed by the PRISMA statement. There are two inclusion criteria for the selection of articles, which are as follows: keywords are in the title, the section of keywords, or the paper; the abstract paper is published in a peer-reviewed scientific journal. Therefore, the exclusion requirements are as follows: proceedings of conferences, sources not related to the consumption of energy and households, duplicate articles in individual searches. Thus, 17 conference proceedings papers, 121 duplicate articles in individual searches, and 49 articles that are not related to the consumption of energy or households were excluded from the review of the material. In total, 176 papers were found after the search of all combinations, and 82 articles were found from the search combinations “policies and measures to increase renewable energy in households” and “barriers of renewable energy in household”; 122 of them met the inclusion criteria. A snowballing process was also introduced. Therefore, for the other papers, which did not participate in the search, the content review was carried out. Six papers were additionally found. The authors of the article searched in these academic databases during the year of publication to cover the complete spectrum of scientific papers. The first articles related to renewable energy in household started being published in 1999. The first articles on barriers to energy efficiency and renewable energy consumption in households have been published since 2011. Finally, 79 documents were selected for the analysis from 243 sources in total that were found.
The analysis of the statistical data on RES usage in the EU households was carried out by applying EUROSTAT data. The RES consumption indicators in households in 2019 were discussed and compared among the EU member states, including Lithuania, by identifying similarities and differences and their reasons.
The case study on renewable energy acceptance in Lithuania was developed by employing the results of the representative survey of Lithuanian households that was performed by Vilmorus at the end of 2020 (November–December) in the framework of households in the context of an energy transition project that was financed by the Research Council of Lithuania grant S-REP-20-3. The aim of this survey was to evaluate energy saving and renewable energy consumption situation in Lithuania. The number of respondents was N = 1008; Lithuanian residents, aged 18 and older, were approached face-to-face. The sampling method was probabilistic selection. The survey was carried out in 35 cities and 41 villages in all districts of Lithuania.

4. Analysis of Renewable Energy Use in EU Households

The use of RES for heating and cooling in the EU grew solidly from 12% in 2004 to 22% in 2019. In 2019, the renewables accounted for 20% of the EU final energy consumption. The distribution of the share of renewable energy consumption in the residential sector of the EU member states is provided in Figure 1.
As one can observe from Figure 1, in Lithuania, the share of RES consumption in the final energy consumption in households was 33% in 2019 and was higher than the EU-27 average (20%). The leading countries in terms of the highest share of RES in the final household’s energy consumption were Croatia (46.1%), Slovenia (43.4%), Latvia (43%), Romania (39.6%), Estonia (39.3%), Portugal (36.7), etc. These countries mainly use biomass in the residential sector.
Ireland (2.5%), Luxemburg (4.2%), the Netherlands (5.6%), Belgium (8.6%), Sweden (11%), etc. had the lowest share of RES in the final energy consumption in households, dominated by electricity and natural gas consumption.
The share of renewables in the residential sector for space heating in the EU member states in 2019 is presented in Figure 2.
The highest share of RES that was used for residential heating and cooling was in Portugal (80.7%), Croatia (64.4%), Bulgaria (59.9%), Slovenia (58.1%), Latvia (53.9%), Estonia (49.5%), and Malta (48.4%). The EU-27 share of RES in residential heating and cooling in 2019 was 27.9%, and in Lithuania, it was significantly higher or 44.5% in the same year. The main RES used for heating and cooling were biomass and heating pumps.
The countries with the lowest share of RES in residential heating are the same countries that had the lowest share of RES in the energy consumption in households, including Ireland (2.3%) Luxemburg (4.3%), the Netherlands (8.5%), Belgium (11.1%), Sweden (17.4%), etc. The residential space heating in these countries is dominated by natural gas.
The share of renewables in the residential sector for water heating in the EU member states in 2019 is presented in Figure 3.
As one can observe from Figure 3, the leading country in the usage of RES for residential water heating in 2019 was Malta (77.3%), followed by Greece (48.3%), Slovenia (39.2%), Romania (36.8%), etc. The main RES source for residential water heating is solar and biomass.
The EU-27 share of RES in residential water heating was 13.1%, and in Lithuania, it was higher, i.e., 19%. The EU MS with the lowest share of RES in residential water heating are the Netherlands (1.4%), Belgium (3.6%), France (4.2%), Hungary (4.6%), Ireland (5.7%), etc. These countries are mainly using natural gas or electricity for water heating instead of renewables.
The share of renewables in the residential sector for cooking in the EU member states in 2019 is presented in Figure 4.
The highest share of renewables in the residential sector for cooking in 2019 was in Latvia (30.9%), Portugal (30.6%), Estonia (25.2%), Greece (12.5%), Bulgaria (11.8%), etc. The main RES source for cooking is biomass.
The EU-27 average share of RES in residential cooking in 2019 was 5.7%, and in Lithuania, it was almost twice as high, i.e., 9.1%. The countries that had the lowest share of RES in residential cooking in 2019 were Malta, the Netherlands, France, Ireland, Denmark, Belgium, etc. Electricity and natural gas dominated the final energy consumption for cooking in these countries.

5. Lithuanian Case Study

The representative survey of Lithuanian households was performed by Vilmorus at the end of 2020 (November–December) in the framework of households in the context of the energy transition project financed by the Research Council of Lithuania grant S-REP-20-3. The aim of this survey was to evaluate energy saving and renewable energy consumption situation in Lithuania. The number of respondents was N = 1008; Lithuanian residents, aged 18 and older, were approached face-to-face. The sampling method was a probabilistic selection. The survey was conducted in 35 cities and 41 villages in all districts of Lithuania.
The main questions linked to the situation of renewable energy consumption in households in this survey were the following:
  • Do you agree to paying more for the electricity that is produced from renewable energy sources (RES)?
  • Do you use biodiesel or bioethanol in your car?
  • Do you use renewable energy in your home?
  • Do you want to use renewable energy in your home for heating, air conditioning, hot water or electricity production?
  • Do you have the opportunity to use renewable energy resources in your home?
  • Do you encounter financial difficulties in the installation of renewable microgeneration technologies at home?
  • Do you have an electric or hybrid vehicle?
  • Do you want to buy an electric or hybrid vehicle?
  • Do you encounter financial difficulties in purchasing an electric or hybrid vehicle?
  • Do you have information about the RES and other energy support measures for households?

The Results of the Survey

The results of survey revealed that 60% of the respondents do not agree to paying for renewable electricity. About 30% agree to paying less than 5% more for the electricity, and 8% of respondents agree to paying 11–25% more for the electricity, produced from RES. Only 0.3% agree to paying 11–25% more for the RES electricity.
Another important issue is the usage of RES in homes and the main barriers that are hampering the installation of renewable energy technologies in households. The survey showed that the majority of Lithuanian households that were surveyed (62.5%) use biodiesel or bioethanol in their cars. However, just 5.5% of the respondents use electric or hybrid vehicles. Nevertheless, 62% of the respondents would like to buy an electric or hybrid vehicle, but 74.9% of them encounter financial difficulties in purchasing electric or hybrid vehicles.
Moreover, the survey revealed that just 10% of respondents indicated that they use renewables in their homes. More than 60% of households would like to use RES in their homes for heating, air conditioning, hot water, or electricity generation, but only 16.6% have the opportunity to do this. The majority of households (60.5% of respondents) encounters financial difficulties in the installation of renewable microgeneration technologies in their homes.
Therefore, the financial barriers and lack of infrastructure are important barriers for households to use renewables. The information about the state support for renewables can help to overcome financial barriers; however, Lithuanian households do not have enough information about the available support measures for RES and other energy related innovations in households.
According to the results of the survey, more than 30% of respondents had information about the available support measures for renewables in households, 45.6% respondents have heard about possible support measures, but some information is missing. Only 16.5% of respondents are very well informed about the possible support measures and can even advise other households.

6. Discussion of Results

The systematic analysis provided that boosting renewables in households is among the most important modes to reduce greenhouse gas emissions in households and to achieve the low carbon energy transition and carbon neutral society by 2050.
Households are obviously among the main climate policy players. The policy makers are progressively considering energy efficiency improvements, such as the main strategy for decreasing fossil fuel consumption and GHG emissions [74,75,76,77,78]. A penalty and/or subsidy can have an impact on the energy markets through the positive or negative motivations created for the energy consumers. Knobloch et al. [79] have shown in their study that the combination of improved energy efficiency of residential buildings and installation of renewable energy technologies can aid in the decarbonization of the residential heating and cooling sector of the EU by 2050. However, policies are necessary. Knobloch et al. [79] have revealed in their study that decarbonization up to 84% can be achieved due to the carbon taxes; however, for more deeper decarbonization, up to 90%, the carbon taxes should be complemented by the subsidies to renewables and green procurement policies.
Some countries have developed and implemented various policy tools to promote renewables, ranging from financial incentives, various carbon taxes and emission trading schemes to consumption commitments [80,81]. The support measures for renewables are dedicated to direct subsidies and public involvement; however, the scholars stress that there are risks of overloading the economy with high energy costs; therefore, it is recommended to introduce a market-based mechanism for the promotion of renewable energy sources, such as green certificates or GHG emission trading [82]. Polzin et al. [83] emphasize that the GHG emission allowances have a bigger impact on the financial capacity of the institutional investor than the feed-in tariffs for renewables, because investors favor state interventions based on the market mechanisms.
Factors such as information and understanding the benefits of renewables have an important impact on the aptitude of households towards the implementation of renewables in their home, including having the necessary infrastructure in place, i.e., resources, etc. [84].
The studies have shown that the understanding of renewable energy benefits does not always convert into a willingness to pay for renewable energy technologies [85,86]. In this case, the conducted study confirmed this. Lithuanian households do not want to pay extra for renewable electricity, although most of them would like to install renewables in their homes or would like to drive electric or hybrid cars. However, the survey has shown that they do not have enough resources to install renewables in their homes. The lack of necessary infrastructure is an important barrier as well. This is in line with the empirical findings that were confirmed by the other studies that human behavior factors are more important than the economic stimulus [87,88,89,90].
Various studies have shown that the implementation of renewables in households has not been fully realized due to the energy efficiency paradox, including other important psychological and behavioral hurdles [91,92]. The main focus of policies to promote renewables in households is on the financial incentives and socio-economic variables, and the behaviors are not addressed by these policies, even though they are very significant variables affecting renewable energy adoption in the residential sector. The behavioral barriers are linked to the lack of awareness about the benefits of renewables, as well as the lack of knowledge or behavioral inconsistencies in information handling, lack of confidence, habits etc. [49,93]. In the policy hierarchy, the demand management measures should be highlighted instead of supply side measures [94,95]. Most scholars stress the importance to increase the acceptance of renewable energy technologies, providing comprehensive information and knowledge about the benefits of renewable energy and encouraging people to engage in renewable energy technologies across households throughout capacity building, education, and creation and establishment of acceptable social norms [38,77,78].
Some studies have found that the organizational barriers, information scarcity, and information retrieval costs are very important barriers to the adoption of renewables in the residential sector [28,63,67,88], which could be overcome by various modern financing models, such as the ESCO (Energy Service Company) model, where the energy supplier assumes responsibility for all the organizational and financial issues related to the installation of renewables in building and frees the households from all organizational worries and information retrieval costs [92]. The ESCO business model can be a good alternative to self-financing renewable energy projects, using the principle of on-bill financing, as all investments are made by the energy supplier, which recovers all the costs from the energy saved by the consumer within a certain period of time. At the end of the project, all implemented measures, including renewable energy technologies, become property to the user [50]. In addition, in the ESCO scheme, it is possible to use nudges where all residents of the multi-apartment building are automatically included in the renewable energy scheme and some additional action is required to opt for a multi-apartment renewable energy program. This would reduce the cognitive burden of decision-making, as applied smart choice measures would complement economic, financial, regulatory, and control measures and lead to better results in reducing climate change that is related to household energy consumption [3,53].
In many countries, all the necessary conditions are in place to legitimize geographically remote generating consumers and the sharing of renewable energy technologies between prosumers. This enables the prosumers to generate electricity at one geographical point and consume it at another geographical point, regardless of distance. However, the promotion of renewable energy in households requires boosts, such as education, information dissemination, capacity building, and community empowerment. Empowering communities and establishing energy citizenship would encourage people to become more involved in the local government processes, thus strengthening local democracy and citizenship and the culture of political participation, addressing climate change mitigation at the local level and using downstream models [21,67,85].

7. Conclusions and Policy Implications

Even though the share of RES in the residential final energy consumption was increasing steadily in the EU and Lithuania, the sharp increase in energy prices due to the Russian invasion in Ukraine has shown that the fossil fuel dependency on Russia should be dramatically reduced by increasing the use of renewables.
The case study preformed in Lithuanian households indicated that the majority of Lithuanian households that were surveyed (62.5%) use biodiesel or bioethanol in their cars; however, only 5.5% of the respondents use electric or hybrid vehicles. Moreover, 62% of the respondents would like to buy an electric or hybrid vehicle, but 74.9% of them encounter financial difficulties. Moreover, the survey of households that were conducted in Lithuania showed that just 10% of respondents use renewables in their homes, and the majority (60%) of them would like to use RES in their homes for heating, air conditioning, hot water or electricity generation, but just 16.6% have the opportunity or infrastructure to do this. The majority of households (60.5%) encounter financial difficulties for the installation of renewable microgeneration technologies in their homes.
Therefore, the financial barriers and the lack of infrastructure are important barriers for the household use of renewables. The information about the state support for renewables can help to overcome the financial barriers; however, Lithuanian households do not have enough information about the available support measures for RES and other energy related innovations in households. Moreover, the infrastructure of barriers needs to be addressed by the state support policies. The investment costs for the essential digitalization and digital infrastructure in residential buildings must be maintained low in order to support the flexibility-based business models in the first place. The energy management systems, smart metering, and smart control are necessary for the increase in RES usage in multi-flat buildings and to create smart buildings, smart districts, smart cities.
To this point, renewable energy promotion policies and measures have been developed mainly based on the rational decision-making approach. However, the economic stimulus is not enough for the implementation of renewables in households, as people’s decisions are systematically different from the rational choice theory. Therefore, it is necessary to focus on the removal of behavioral barriers by strengthening the financial support for renewables in households and to better shape the targeted policies and measures. It is necessary to stress that the advertisement or exhortation to increase the action of governments, local governments, or potential RES users will, firstly, force their reactions and, secondly, change their habits or previous experiences.
The use of nudges and boosts for the promotion of renewables in households is recommended. The lack of information and know-how is also an important barrier for the adoption of renewables in households, and the boosts can help a significant amount. The nudges would help households to save time and other resources related to the information rescue and reduce the cognitive burden, as these measures offer the “right” default solution for the households. By applying nudges and boosts, the households can be more involved in the renewable energy programs, forced to change to a better energy supplier, or switch to a renewable energy supplier.
The paper has limitations, as the case study on renewable energy usage was performed in one country. The comparative case studies on renewable energy acceptance conducted in several EU member states would allow for a deeper understanding of the main barriers of RES penetration in households and development of policies and measures to promote RES in households. The conducted study has value in national and regional contexts, as it provides important information for the policy makers in Lithuania and can be replicated in other EU member states, with similar RES and GHG emission reduction targets established by the European Commission.

Author Contributions

Conceptualization, D.Š.; Data curation, G.S.; Formal analysis, D.Š. and A.P.; Investigation, V.L. and G.S.; Project administration, A.P.; Software, V.L.; Validation, G.S.; Writing—original draft, D.Š.; Writing—review & editing, V.L. and A.P. All authors contributed to the manuscript equally. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. European Commission. National Renewable Energy Action Plans 2020|Energy (europa.eu). Available online: https://ec.europa.eu/energy/topics/renewable-energy/directive-targets-and-rules/national-renewable-energy-action-plans-2020_en (accessed on 10 July 2021).
  2. Shimoda, Y.; Yamaguchi, Y.; Iwafune, Y.; Hidaka, K.; Meier, A.; Yagita, Y.; Kawamoto, H.; Nishikiori, S. Energy demand science for a decarbonized society in the context of the residential sector. Renew. Sustain. Energy Rev. 2020, 132, 110051. [Google Scholar] [CrossRef]
  3. Streimikiene, D.; Balezentis, T.; Alebaite, I. Climate change mitigation in households between market failures and psychological barriers. Energies 2020, 13, 2797. [Google Scholar] [CrossRef]
  4. Luttenberger, L.R. The barriers to renewable energy use in Croatia. Renew. Sustain. Energy Rev. 2015, 49, 646–654. [Google Scholar] [CrossRef]
  5. Streimikiene, D.; Balezentis, T.; Alisauskaite-Seskiene, I.; Stankuniene, G.; Simanaviciene, Z.A. A review of willingness to pay studies for climate change mitigation in the energy sector. Energies 2019, 12, 1481. [Google Scholar] [CrossRef] [Green Version]
  6. Zhou, S.; Matisoff, D.C.; Kingsley, A.; Brown, M. Understanding Renewable Energy Policy Adoption and Evolution in Europe: The Impact of Coercion, Normative Emulation, Competition and Learning. Energy Res. Soc. Sci. 2019, 51, 2. [Google Scholar] [CrossRef]
  7. De Lauretis, S.; Ghersi, F.; Cayla, J.M. Energy consumption and activity patterns: An analysis extended to total time and energy use for French households. Appl. Energy 2017, 206, 634–648. [Google Scholar] [CrossRef] [Green Version]
  8. Piekut, M. Patterns of energy consumption in Polish one-person households. Energies 2020, 13, 5699. [Google Scholar] [CrossRef]
  9. Brodny, J.; Tutak, M. Analyzing similarities between the European Union countries in terms of the structure and volume of energy production from renewable energy sources. Energies 2020, 13, 913. [Google Scholar] [CrossRef] [Green Version]
  10. Simionescu, M.; Strielkowski, W.; Tvaronaviciene, M. Renewable energy in final energy consumption and income in the EU-28 ˙countries. Energies 2020, 13, 2280. [Google Scholar] [CrossRef]
  11. Kotsila, D.; Polychronidou, P. Determinants of household electricity consumption in Greece: A statistical analysis. J. Innov. Entrep. 2021, 10, 20. [Google Scholar] [CrossRef]
  12. Bak, I.; Spoz, A.; Zioło, M.; Dylewski, M. Dynamic Analysis of the Similarity of Objects in Research on the Use of Renewable Energy Resources in European Union Countries. Energies 2021, 14, 3952. [Google Scholar] [CrossRef]
  13. Pietrzak, M.B.; Iglinski, B.; Kujawski, W.; Iwanski, P. Energy transition in Poland—Assessment of the renewable energy sector. Energies 2021, 14, 2046. [Google Scholar] [CrossRef]
  14. Brauers, H.; Oei, P.Y. The political economy of coal in Poland: Drivers and barriers for a shift away from fossil fuels. Energy Policy 2020, 144, 111621. [Google Scholar] [CrossRef]
  15. Kochanek, E. The energy transition in the Visegrad group countries. Energies 2021, 14, 2212. [Google Scholar] [CrossRef]
  16. Mengova, E. What Determines Investment in Renewable Energy? J. Strateg. Innov. Sustain. 2020, 15, 22–38. [Google Scholar] [CrossRef]
  17. Tanil, G.; Jurek, P. Policies on renewable energy at the European and national level of governance: Assessing policy adaptation in the Czech Republic. Energy Rep. 2020, 6, 548–553. [Google Scholar] [CrossRef]
  18. Briguglio, M.; Formosa, G. When households go solar: Determinants of uptake of a Photovoltaic Scheme and policy insights. Energy Policy 2017, 108, 154–162. [Google Scholar] [CrossRef]
  19. Braito, M.; Flint, C.; Muhar, A.; Penker, M.; Vogel, S. Individual and collective socio-psychological patterns of photovoltaic investment under diverging policy regimes in Austria and Italy. Energy Policy 2017, 109, 141–153. [Google Scholar] [CrossRef]
  20. Risholt, B.; Time, B.; Hestnes, A.G. Sustainability assessment of nearly zero energy renovation of dwellings based on energy, economy and home quality indicators. Energy Build. 2013, 60, 217–224. [Google Scholar] [CrossRef] [Green Version]
  21. Hansla, A.; Gamble, A.; Juliusson, A.; Gärling, T. Psychological determinants of attitude towards and willingness to pay for green electricity. Energy Policy 2008, 36, 768–774. [Google Scholar] [CrossRef]
  22. Wolske, K.S.; Todd, A.; Rossol, M.; McCall, J.; Sigrin, B. Accelerating demand for residential solar photovoltaics: Can simple framing strategies increase consumer interest? Glob. Environ. Change 2018, 53, 68–77. [Google Scholar] [CrossRef]
  23. Liu, W.; Wang, C.; Mol, A.P.J. Rural public acceptance of renewable energy deployment: The case of Shandong in China. Appl. Energy 2013, 102, 1187–1196. [Google Scholar] [CrossRef]
  24. Trotta, G.; Spangenberg, J.; Lorek, S. Energy efficiency in the residential sector: Identification of promising policy instruments and private initiatives among selected European countries. Energy Effic. 2018, 11, 2111–2135. [Google Scholar] [CrossRef]
  25. Matar, W. Residential energy efficiency investment and behavioural response under different electricity pricing schemes: A physical-microeconomic approach. Int. J. Sustain. Energy 2021, 40, 1–21. [Google Scholar] [CrossRef]
  26. Yeatts, D.E.; Auden, D.; Cooksey, C.; Chen, C.F. A systematic review of strategies for overcoming the barriers to energy-efficient technologies in buildings. Energy Res. Soc. Sci. 2017, 32, 76–85. [Google Scholar] [CrossRef]
  27. Jacksohn, A.; Grösche, P.; Rehdanz, K.; Schröder, C. Drivers of renewable technology adoption in the household sector. Energy Econ. 2019, 81, 216–226. [Google Scholar] [CrossRef] [Green Version]
  28. Levesque, A.; Pietzcker, R.C.; Luderer, G. Halving energy demand from buildings: The impact of low consumption practices. Technol. Forecast. Soc. Change 2019, 146, 253–266. [Google Scholar] [CrossRef]
  29. Carson, R.T.; Louviere, J.J.; Wasi, N. A Cautionary Note on Designing Discrete Choice Experiments: A Comment on Lusk and Norwood’s “Effect of Experiment Design on Choice-Based Conjoint Valuation Estimates”. Am. J. Agric. Econ. 2009, 91, 1056–1063. [Google Scholar] [CrossRef]
  30. Weber, I.; Wolff, A. Energy efficiency retrofits in the residential sector—Analysing tenants’ cost burden in a German field study. Energy Policy 2018, 122, 680–688. [Google Scholar] [CrossRef]
  31. Poncin, S. Energy policies for Eco-friendly households in Luxembourg: A study based on the LuxHEI model. Environ. Model. Assess. 2021, 26, 37–61. [Google Scholar] [CrossRef]
  32. Teske, S.; Sawyer, S.; Schäfer, O. Energy [R]Evolution—A Sustainable World Energy Outlook 2015–100% Renewable Energy for All; Greenpeace International: Hamburg, Germany, 2015; 364 S; Available online: https://elib.dlr.de/98314/ (accessed on 20 May 2022).
  33. Samadi, S.; Gröne, M.C.; Schneidewind, U.; Luhmann, H.J.; Venjakob, J.; Best, B. Sufficiency in energy scenario studies: Taking the potential benefits of lifestyle changes into account. Technol. Forecast. Soc. Change 2017, 124, 126–134. [Google Scholar] [CrossRef] [Green Version]
  34. Štreimikiene, D.; Baležentis, T. Assessment of willingness to pay for renewables in Lithuanian households. Clean Technol. Environ. Policy 2015, 17, 515–531. [Google Scholar] [CrossRef]
  35. Cagno, E.; Worrell, E.; Trianni, A.; Pugliese, G. A novel approach for barriers to industrial energy efficiency. Renew. Sustain. Energy Rev. 2013, 19, 290–308. [Google Scholar] [CrossRef]
  36. Irfan, M.; Zhao, Z.Y.; Li, H.; Rehman, A. The influence of consumers’ intention factors on willingness to pay for renewable energy: A structural equation modeling approach. Environ. Sci. Pollut. Res. 2020, 27, 21747–21761. [Google Scholar] [CrossRef] [PubMed]
  37. Moglia, M.; Cook, S.; McGregor, J. A review of Agent-Based Modelling of technology diffusion with special reference to residential energy efficiency. Sustain. Cities Soc. 2017, 31, 173–182. [Google Scholar] [CrossRef]
  38. Klöckner, C.A.; Nayum, A. Specific barriers and drivers in different stages of decision-making about energy efficiency upgrades in private homes. Front. Psychol. 2016, 7, 1362. [Google Scholar] [CrossRef] [Green Version]
  39. Bode, S. On the Impact of Renewable Energy Support Schemes on Power Prices; Research Paper, No. 4–7; Hamburgisches WeltWirtschaftsInstitut (HWWI): Hamburg, Germany, 2006; Available online: http://hdl.handle.net/10419/48172 (accessed on 20 May 2022).
  40. Farrell, N.; Lyons, S. Who should pay for renewable energy? Comparing the household impacts of different policy mechanisms in Ireland. Energy Res. Soc. Sci. 2015, 7, 31–42. [Google Scholar] [CrossRef]
  41. Zander, K.K.; Simpson, G.; Mathew, S.; Nepal, R.; Garnett, S.T. Preferences for and potential impacts of financial incentives to install residential rooftop solar photovoltaic systems in Australia. J. Clean. Prod. 2019, 230, 328–338. [Google Scholar] [CrossRef]
  42. Lin, W.M.; Chang, K.C.; Chung, K.M. The impact of subsidy programs for solar thermal applications: A case study for a remote island. Energies 2019, 12, 3944. [Google Scholar] [CrossRef] [Green Version]
  43. Kalogirou, S.A. The energy subsidisation policies of Cyprus and their effect on renewable energy systems economics. Renew. Energy 2003, 28, 1711–1728. [Google Scholar] [CrossRef]
  44. Sensfuß, F.; Ragwitz, M.; Genoese, M. The merit-order effect: A detailed analysis of the price effect of renewable electricity generation on spot market prices in Germany. Energy Policy 2008, 36, 3086–3094. [Google Scholar] [CrossRef] [Green Version]
  45. Pereira, D.S.; Marques, A.C.; Fuinhas, J.A. Are renewables affecting income distribution and increasing the risk of household poverty? Energy 2019, 170, 791–803. [Google Scholar] [CrossRef]
  46. Castaneda, M.; Zapata, S.; Cherni, J.; Aristizabal, A.J.; Dyner, I. The long-term effects of cautious feed-in tariff reductions on photovoltaic generation in the UK residential sector. Renew. Energy 2020, 155, 1432–1443. [Google Scholar] [CrossRef]
  47. Lekavičius, V.; Bobinaite, V.; Galinis, A.; Pazeraite, A. Distributional impacts of investment subsidies for residential energy technologies. Renew. Sustain. Energy Rev. 2020, 130, 109961. [Google Scholar] [CrossRef]
  48. Alam, S.S.; Nik Hashim, N.H.; Rashid, M.; Omar, N.A.; Ahsan, N.; Ismail, M.D. Small-scale households renewable energy usage intention: Theoretical development and empirical settings. Renew. Energy 2014, 68, 255–263. [Google Scholar] [CrossRef]
  49. Bagaini, A.; Colelli, F.; Croci, E.; Molteni, T. Assessing the relevance of barriers to energy efficiency implementation in the building and transport sectors in eight European countries. Electr. J. 2020, 33, 106820. [Google Scholar] [CrossRef]
  50. Strielkowski, W.; Volkova, E.; Pushkareva, L.; Streimikiene, D. Innovative policies for energy efficiency and the use of renewables in households. Energies 2019, 12, 1392. [Google Scholar] [CrossRef] [Green Version]
  51. Fang, D.; Zhao, C.; Yu, Q. Government regulation of renewable energy generation and transmission in China’s electricity market. Renew. Sustain. Energy Rev. 2018, 93, 775–793. [Google Scholar] [CrossRef]
  52. Lu, J.; Ren, L.; Yao, S.; Rong, D.; Skare, M.; Streimikis, J. Renewable energy barriers and coping strategies: Evidence from the Baltic States. Sustain. Dev. 2020, 28, 352–367. [Google Scholar] [CrossRef]
  53. Jung, N.; Moula, M.E.; Fang, T.; Hamdy, M.; Lahdelma, R. Social acceptance of renewable energy technologies for buildings in the Helsinki Metropolitan Area of Finland. Renew. Energy 2016, 99, 813–824. [Google Scholar] [CrossRef]
  54. Song, J.; Song, S. A framework for analyzing city-wide impact of building-integrated renewable energy. Appl. Energy 2020, 276, 115489. [Google Scholar] [CrossRef]
  55. Krikser, T.; Profeta, A.; Grimm, S.; Huther, H. Willingness-to-Pay for district heating from renewables of private households in Germany. Sustainability 2020, 12, 4129. [Google Scholar] [CrossRef]
  56. Agnew, S.; Dargusch, P. Consumer preferences for household-level battery energy storage. Renew. Sustain. Energy Rev. 2017, 75, 609–617. [Google Scholar] [CrossRef]
  57. Schweer, D.; Sahl, J.C. The Digital Transformation of Industry—The Benefit for Germany. In The Drivers of Digital Transformation. Management for Professionals; Abolhassan, F., Ed.; Springer: Cham, Switzerland, 2017. [Google Scholar] [CrossRef]
  58. Scharl, S.; Praktiknjo, A. The Role of a Digital Industry 4.0 in a Renewable Energy System. Int. J. Energy Res. 2019, 43, 3904. [Google Scholar] [CrossRef]
  59. Scarpa, R.; Willis, K. Willingness-to-pay for renewable energy: Primary and discretionary choice of British households’ for micro-generation technologies. Energy Econ. 2010, 32, 129–136. [Google Scholar] [CrossRef]
  60. Bollino, C.A. The Willingness to pay for renewable energy sources: The case of Italy with socio-demographic determinants. Energy J. 2009, 30, 81–96. [Google Scholar] [CrossRef]
  61. Entele, B.R. Analysis of households’ willingness to pay for a renewable source of electricity service connection: Evidence from a double-bounded dichotomous choice survey in rural Ethiopia. Heliyon 2020, 6, e03332. [Google Scholar] [CrossRef]
  62. Beck, F.; Martinot, E. Renewable Energy Policies and Barriers. Encycl. Energy 2004, 365–383. [Google Scholar] [CrossRef]
  63. Allen, S.R.; Hammond, G.P.; Mcmanus, M.C. Prospects for and barriers to domestic micro-generation: A United Kingdom perspective. Appl. Energy 2008, 85, 528–544. [Google Scholar] [CrossRef]
  64. Wee, H.M.; Yang, W.H.; Chou, C.W.; Padilan, M.V. Renewable energy supply chains, performance, application barriers, and strategies for further development. Renew. Sustain. Energy Rev. 2012, 16, 5451–5465. [Google Scholar] [CrossRef]
  65. Lau, L.S.; Choong, Y.O.; Wei, C.Y.; Seow, A.N.; Choong, C.K.; Senadjki, A.; Ching, C.L. Investigating nonusers’ behavioural intention towards solar photovoltaic technology in Malaysia: The role of knowledge transmission and price value. Energy Policy 2020, 144, 111651. [Google Scholar] [CrossRef]
  66. Larsen, S.P.A.K.; Gram-Hanssen, K. When space heating becomes digitalized: Investigating competencies for controlling smart home technology in the energy-efficient home. Sustainability 2020, 12, 6031. [Google Scholar] [CrossRef]
  67. Allen, J.; Sheate, W.R.; Diaz-Chavez, R. Community-based renewable energy in the Lake District National Park—Local drivers, enablers, barriers and solutions. Local Environ. 2012, 17, 261–280. [Google Scholar] [CrossRef]
  68. Ulkhaq, M.M.; Widodo, A.K.; Yulianto, M.F.A.; Mustikasari, A.; Akshinta, P.Y. A logistic regression approach to model the willingness of consumers to adopt renewable energy sources. IOP Conf. Ser. Earth Environ. Sci. 2018, 127, 012007. [Google Scholar] [CrossRef]
  69. Tranfield, D.; Denyer, D.; Smart, P. Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. Br. J. Manag. 2003, 14, 207–222. [Google Scholar] [CrossRef]
  70. Lempel, R.; Moran, S. SALSA: The Stochastic Approach for Link-Structure Analysis. ACM Trans. Inf. Syst. 2001, 19, 131–160. [Google Scholar] [CrossRef]
  71. Fernández Del Amo, I.; Erkoyuncu, J.A.; Roy, R.; Palmarini, R.; Onoufriou, D. A systematic review of Augmented Reality content-related techniques for knowledge transfer in maintenance applications. Comput. Ind. 2018, 103, 47–71. [Google Scholar] [CrossRef]
  72. Grant, M.J.; Booth, A. A typology of reviews: An analysis of 14 review types and associated methodologies. Health Inf. Libr. J. 2009, 26, 91–108. [Google Scholar] [CrossRef]
  73. Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA Statement. Open Med. 2009, 3, 123–130. [Google Scholar] [CrossRef] [Green Version]
  74. Lester, T.W. Dedicating new real estate transfer taxes for energy efficiency: A revenue option for scaling up Green Retrofit Programs. Energy Policy 2013, 62, 809–820. [Google Scholar] [CrossRef]
  75. Astudillo, M.F.; Vaillancourt, K.; Pineau, P.O.; Amor, B. Can the household sector reduce global warming mitigation costs? sensitivity to key parameters in a TIMES techno-economic energy model. Appl. Energy 2017, 205, 486–498. [Google Scholar] [CrossRef]
  76. Palm, J. Household installation of solar panels—Motives and barriers in a 10-year perspective. Energy Policy 2018, 113, 1–8. [Google Scholar] [CrossRef] [Green Version]
  77. Dubois, G.; Sovacool, B.; Aall, C.; Nilsson, M.; Barbier, C.; Herrmann, A.; Bruyère, S.; Andersson, C.; Skold, B.; Nadaud, F.; et al. It starts at home? Climate policies targeting household consumption and behavioral decisions are key to low-carbon futures. Energy Res. Soc. Sci. 2019, 52, 144–158. [Google Scholar] [CrossRef]
  78. Hesselink, L.X.W.; Chappin, E.J.L. Adoption of energy efficient technologies by households—Barriers, policies and agent-based modelling studies. Renew. Sustain. Energy Rev. 2019, 99, 29–41. [Google Scholar] [CrossRef]
  79. Knobloch, F.; Pollitt, H.; Chewpreecha, U.; Daioglou, V.; Mercure, J.F. Simulating the deep decarbonisation of residential heating for limiting global warming to 1.5 °C. Energy Effic. 2019, 12, 521–550. [Google Scholar] [CrossRef] [Green Version]
  80. Connor, P.; Bürger, V.; Beurskens, L.; Ericsson, K.; Egger, C. Devising renewable heat policy: Overview of support options. Energy Policy 2013, 59, 3–16. [Google Scholar] [CrossRef]
  81. Ürge-Vorsatz, D.; Cabeza, L.F.; Serrano, S.; Barreneche, C.; Petrichenko, K. Heating and cooling energy trends and drivers in buildings. Renew. Sustain. Energy Rev. 2015, 41, 85–98. [Google Scholar] [CrossRef] [Green Version]
  82. Marques, A.C.; Fuinhas, J.A. Are public policies towards renewables successful? Evidence from European countries. Renew. Energy 2012, 44, 109–118. [Google Scholar] [CrossRef]
  83. Polzin, F.; Migendt, M.; Täube, F.A.; von Flotow, P. Public policy influence on renewable energy investments—A panel data study across OECD countries. Energy Policy 2015, 80, 98–111. [Google Scholar] [CrossRef] [Green Version]
  84. Adjakloe, Y.D.A.; Osei, S.A.; Boateng, E.N.K.; Agyapong, F.; Koranteng, C.; Baidoo, A.N.A. Household’s awareness and willingness to use renewable energy: A study of Cape Coast Metropolis, Ghana. Int. J. Sustain. Energy 2021, 40, 430–447. [Google Scholar] [CrossRef]
  85. Claudy, M.C.; Michelsen, A.; O’driscoll, A.; Mullen, M.R. Consumer awareness in the adoption of microgeneration technologies: An empirical investigation in the Republic of Ireland. Renew. Sustain. Energy Rev. 2010, 14, 2154–2160. [Google Scholar] [CrossRef]
  86. Walters, R.; Walsh, P.R. Examining the financial performance of micro-generation wind projects and the subsidy effect of feed-in tariffs for urban locations in the United Kingdom. Energy Policy 2011, 39, 5167–5181. [Google Scholar] [CrossRef]
  87. Wilson, C.; Dowlatabadi, H. Models of decision making and residential energy use. Annu. Rev. Environ. Resour. 2007, 32, 1. [Google Scholar] [CrossRef]
  88. Balcombe, P.; Rigby, D.; Azapagic, A. Motivations and barriers associated with adopting microgeneration energy technologies in the UK. Renew. Sustain. Energy Rev. 2013, 22, 655–666. [Google Scholar] [CrossRef]
  89. Ortiz, M.A.; Kurvers, S.R.; Bluyssen, P.M. A review of comfort, health, and energy use: Understanding daily energy use and wellbeing for the development of a new approach to study comfort. Energy Build. 2017, 152, 323–335. [Google Scholar] [CrossRef]
  90. Simpson, G. Looking beyond incentives: The role of champions in the social acceptance of residential solar energy in regional Australian communities. Local Environ. 2018, 23, 127–143. [Google Scholar] [CrossRef]
  91. Schleich, J. Energy efficient technology adoption in low-income households in the European Union—What is the evidence? Energy Policy 2019, 125, 196–206. [Google Scholar] [CrossRef]
  92. Streimikiene, D.; Lekavičius, V.; Baležentis, T.; Kyriakopoulos, G.L.; Abrhám, J. Climate change mitigation policies targeting households and addressing energy poverty in European Union. Energies 2020, 13, 3389. [Google Scholar] [CrossRef]
  93. Jia, J.J.; Xu, J.H.; Fan, Y.; Ji, Q. Willingness to accept energy-saving measures and adoption barriers in the residential sector: An empirical analysis in Beijing, China. Renew. Sustain. Energy Rev. 2018, 95, 56–73. [Google Scholar] [CrossRef]
  94. Browne, D.; O’Regan, B.; Moles, R. Use of ecological footprinting to explore alternative domestic energy and electricity policy scenarios in an Irish city-region. Energy Policy 2009, 37, 2205–2213. [Google Scholar] [CrossRef]
  95. Schleich, J.; Gassmann, X.; Faure, C.; Meissner, T. Making the implicit explicit: A look inside the implicit discount rate. Energy Policy 2016, 97, 321–331. [Google Scholar] [CrossRef]
Figure 1. The share of renewables and waste in the final energy consumption in the residential sector of the EU member states in 2019.
Figure 1. The share of renewables and waste in the final energy consumption in the residential sector of the EU member states in 2019.
Sustainability 14 08370 g001
Figure 2. The share of renewables and waste in the residential sector for space heating in the EU member states in 2019.
Figure 2. The share of renewables and waste in the residential sector for space heating in the EU member states in 2019.
Sustainability 14 08370 g002
Figure 3. The share of renewables and waste in the residential sector for water heating in the EU member states in 2019.
Figure 3. The share of renewables and waste in the residential sector for water heating in the EU member states in 2019.
Sustainability 14 08370 g003
Figure 4. The share of renewables in residential sector for cooking in the EU member states in 2019.
Figure 4. The share of renewables in residential sector for cooking in the EU member states in 2019.
Sustainability 14 08370 g004
Table 1. Policies and measures to promote renewables in households, linking them to the main barriers [48,49,50,51,52,53,54,55,56,57,58].
Table 1. Policies and measures to promote renewables in households, linking them to the main barriers [48,49,50,51,52,53,54,55,56,57,58].
PolicyMeasuresDescriptionBarriersReferences
Fiscal and financial policiesGovernmental subsidies
  • Small-scale renewable energy projects for households
  • Financial incentives (direct subsidies, performance-based subsidies, tax credits, or tax deductions, investment subsidies for residential energy technologies)
  • Lack of financial resources
  • Uncertain energy costs and long payback period
[48,49,50]
Financial fines from governments
  • Direct monetary penalties, fines, and alternative compliance payments
  • Lack of financial resources
  • Uncertain energy costs and long payback period
[49,50,51]
Feed-in tariffs
(FiTs)
  • Direct subsidies for electricity producers from renewables, such as fixed prices per kWh of electricity produced from solar, wind, biomass, etc.
  • Lack of financial resources
  • Uncertain energy costs and long payback period
[49,50,52]
Renewable energy tender
  • The winners of the tender are awarded a set price for the renewable energy for the length of the arrangement when they bid for a particular volume of electricity or heat from the renewables
  • Uncertainty of energy costs/benefits and payback period
[48,49]
Market-based incentives
  • GHG emission schemes
  • Green certificates schemes
  • Lack of trust in government policy
[48,49]
Social and political measuresGovernmental regulation
  • By introducing sanctions based on the established renewable energy portfolio norms
  • Renewable quota by establishing goals for the share of renewables in electricity
  • Environmental concern/low priority
[49,51]
Public education and awareness rising
  • Education, training, and information dissemination for awareness-raising about numerous renewable energy benefits, including environmental and energy import dependency
  • Lack of awareness and experience
  • Time constraints and low capability to use information
[49,50]
Technical information and awareness
  • Transmission of technical information about renewable energy technologies among the society members
  • Advertising the programs in local press proved
  • Introduction to smart home technologies that are necessary for renewable energy use at home
  • Lack of awareness and experience
  • Negative attitudes towards technologies
  • Environmental concern/low priority
[48,49,50]
Energy demand modeling
  • Effective energy management for grid balancing due to the supply of renewable energy to address fluctuations
  • Modelling of the impacts of subsidies and other economic incentives
  • Application of agent-based models for modelling residents’ behavior and evaluation of possible intervention impacts on the behavioral changes
  • Customs, habits, and related behavioral aspects
  • Resistance to change
  • Environmental concern/low priority
[49,50]
Technical and infrastructure measuresState financial support for necessary RES infrastructure
  • Smart controls and energy management systems are necessary to permit flexible interactions between domestic heating and electric vehicles and even more decentralized electricity generation
  • Limited infrastructure for the installation of renewables
[52,53,54,55]
State financial support research and development in energy storages
  • New energy storage technologies for the RES usage in households should be developed
  • Complexity of technologies
[52,53,54,55,56]
State financial support research and development for digitalization and software
  • Open interfaces and the capacity to control intelligent hardware (heaters, wall boxes, and battery storage) are being developed
  • Limited capacity in using smart digitalized technologies
[53,54,55,57,58]
Table 2. The search and analysis process of the systematic literature search.
Table 2. The search and analysis process of the systematic literature search.
StepsExplanation
SearchMain activities: keywords definitions, databases for search.
Scope of the research: barriers of renewable energy in households and policies to overcome them.
AppraisalMain activities: selection of papers through the PRISMA statement.
SynthesisMain activities: data withdrawal and categorization.
AnalysisMain activities: analysis of the data, comparison of results, and conclusions.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Štreimikienė, D.; Lekavičius, V.; Stankūnienė, G.; Pažėraitė, A. Renewable Energy Acceptance by Households: Evidence from Lithuania. Sustainability 2022, 14, 8370. https://doi.org/10.3390/su14148370

AMA Style

Štreimikienė D, Lekavičius V, Stankūnienė G, Pažėraitė A. Renewable Energy Acceptance by Households: Evidence from Lithuania. Sustainability. 2022; 14(14):8370. https://doi.org/10.3390/su14148370

Chicago/Turabian Style

Štreimikienė, Dalia, Vidas Lekavičius, Gintare Stankūnienė, and Aušra Pažėraitė. 2022. "Renewable Energy Acceptance by Households: Evidence from Lithuania" Sustainability 14, no. 14: 8370. https://doi.org/10.3390/su14148370

APA Style

Štreimikienė, D., Lekavičius, V., Stankūnienė, G., & Pažėraitė, A. (2022). Renewable Energy Acceptance by Households: Evidence from Lithuania. Sustainability, 14(14), 8370. https://doi.org/10.3390/su14148370

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop