Households and Sustainable Green Technologies: A Review
Simona Bigerna
, Carlo Andrea Bollino
, Silvia Micheli
and Paolo Polinori
Department of Economics, University of Perugia, via A. Pascoli 20, 06123, Perugia, Italy
Department of Economics and Business Science, Guglielmo Marconi University, Via Plinio, 44, 00193 Rome, Italy
Keywords: CO2 Emissions, Green Electricity, Buildings Energy Savings, Smart Meters, Alternative Fuel Vehicles.
Abstract: There is widespread consensus in the climate research community that households show different attitudes
toward the broad spectrum of technologies and policy instruments implemented to reduce CO2 emissions.
The aim of this paper is to investigate the monetary aspect of socio-economic acceptability of four
sustainable green technologies development: green electricity, energy savings in residential buildings, smart
meters and alternative fuel vehicles. We obtained information on willingness to pay, and/or willingness to
accept, for such technologies from a sample of 35 papers taken from the literature. We homogenize this
information computing an implicit price of a Kg of CO2 avoided, named PCO2. A qualitative analysis is
carried out to explain the households’ attitude to avoid CO2 in monetary terms. Results show that on
average PCO2 is positive. There are, however, some negative attitudes only in the case of alternative fuel
vehicles. In conclusion, empirical results show that households have a favorable attitude toward sustainable
green technologies, but further research is desirable to design new policies to make the future of the
sustainable society more plausible.
Two-thirds of global greenhouse-gases (GHG)
emissions and CO2 emissions are related to the
energy sector. GHG emissions have been growing
over time. Indeed, in 1990 they amounted to 20.6
gigatonnes, and it is expected that GHG emissions
will amount to 36.7 gigatonnes in 2040 (IEA, 2015).
For this reason, global policies are aimed at the
mitigation of climate change, through the
implementation of sustainable patterns of
consumption and production. Consumption and
production are the core of the world economy, but
the current models have a negative impact on the
environment. On the demand-side, households play
an important role through the adoption of
environmental friendly behavior. For the reduction
of CO2 emissions it is important to assess public
environmental awareness, namely willingness to
accept (WTA) and/or willingness to pay (WTP)
(Banfi et al., 2008; Achtnicht, 2012). Our paper fits
in the current debate on measures to combat climate
change (COP21, December 2015), with the aim of
reviewing and evaluating the socio-economic
acceptability of four main sustainable green
technologies (SGT). In particular, we recognize that
acceptability is strongly related to the socio-
economic barriers for SGT development. We
homogenize the heterogeneous information acquired
in the literature to compute the implicit price of a Kg
of CO2 avoided, here named PCO2. The paper is
organized as follows. Section 2 provides the
description of the SGT. Section 3 presents data and
methods. Section 4 presents results and discussion.
Section 5 draws conclusions.
For this research, we have considered the available
information related to the main four SGT: electricity
production from renewable energy sources, e.g.
green electricity (GE), energy savings in residential
buildings (ESB), smart meters (SM) and alternative
fuel vehicles (AFV), such as electric vehicles and
biofuels (the energy storage technology is not
considered). So far, there has been active research
on households’ attitude toward GE. Renewable
energy sources (RES) mitigate environmental
degradation, the depletion of the world’s
conventional energy sources and environmental
Bigerna, S., Bollino, C., Micheli, S. and Polinori, P.
Households and Sustainable Green Technologies: A Review.
In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2016), pages 378-383
ISBN: 978-989-758-184-7
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
issues such as GHG effect and the ozone hole.
However, on average, GE is characterized by higher
costs with respect to conventional fuels. Public
authorities support RES because their market price is
not yet competitive with existing technologies in the
electricity market. A number of studies have
explored the preference for and use of GE by
households (Bigerna and Polinori, 2014). Results
show that public interests in GE arise as efficient
technologies, but households perceive that costs of
renewable energy are still high (see among others
Stigka et al., 2014).
ESB, such as facades and ventilation, are an
opportunity to reduce energy consumption and CO2
emissions. Many buildings are facing
comprehensive renovations in terms of energy
savings measures and new residential buildings are
built as energy-optimal as possible. Studies on
households’ attitudes towards ESB show that
households with high income have a significant
WTP for such measures with respect to households
with a lower income (Banfi et al., 2008). Moreover,
one of the major barriers for people not to energy-
renovate their buildings seems to be lack of
knowledge and interest (Tommerup and Svendsen,
2006). This lack is related to a shortcoming of
transparent information about the benefits of ESB
(Pelenur and Cruickshank, 2012).
Smart grids technologies have created significant
opportunities for electric-grid modernization. These
technologies connect producers and consumers,
integrating behaviours and actions of all users
connected to it. For the households, the first step
towards the smart grid is the installation of a SM
(Krishnamurti et al., 2012). Most of the economic
literature studying households’ perceptions of SM
shows that households value smart metering and are
even willing to pay for it. In particular, the higher
the expected energy saving, the higher households’
WTP for SM is (Gangale et al., 2013). However,
barriers for the deployment of SM are represented
by households’ concerns about costs, security and
privacy (Bigerna et al., 2015).
Policies to reduce gasoline consumption
increasingly promote AFV as a means, among
others, to enhance energy security and reduce CO2
emissions (Graham-Rowe et al., 2012). Currently,
AFV represent a small market share of vehicles in
service. Despite these potential advantages,
significant barriers remain to the widespread
adoption of AFV. The literature shows that
households’ WTP for AFV increases with youth,
education and green life style (Ito et al., 2013).
Given that these technologies are, on average, quite
young, the related literature is investigating the
consumers’ potential WTP and WTA for SGT
through the stated preference methods. The main
objective of these studies is to identify people's
preferences towards such technologies.
An Internet literature survey has been developed to
collect information on households’ WTP for the
SGT. We have considered the combination of the
following keywords. GE: willingness to pay/accept,
green electricity, renewable electricity, sustainable
electricity. AFV: willingness to pay/accept, electric
vehicle and alternative fuel vehicle. ESB:
willingness to pay/accept, energy saving and
residential buildings. SM: willingness to pay/accept,
smart meters and smart metering. All the papers
have been analyzed to extrapolate information for
computing the PCO2. We model the households’
attitudes and perceptions towards SGT in monetary
terms. With this aim we survey the literature
collecting the elicited WTPs to attain some
environmental benefit, possibly modifying the
households’ lifestyle. We identify the environmental
benefit with the reduction of CO2 emissions.
Consequently, the monetary values of such WTPs
can be negative, if the perceived benefits are
outweighed by the perceived adjustment costs. In
this latter case, we refer to the concept of the WTA
that is the monetary compensation required for the
introduction of new SGT. We used monetary
information about the WTP or WTA for each SGT,
expressed in EUR per Kg of CO2 avoided per year.
First, considering the introduction of AFV, the
consumers’ PCO2 is computed using information on
the average life, fuel efficiency, and of the average
mileage of the new vehicles:
2 = ( )( )
where W is the WTP expressed as the nominal
capital expenditure, Y is the vehicles’ average life in
years, K is the average Km per vehicle, T is the
technical factor which represents the reduction of a
Kg of CO2 emission per Km of the AFV with
respect to conventional vehicles.
Second, considering ESB, the consumers’ PCO2
is calculated taking into account the WTP expressed
as the capital price (K) of dwelling for owners and,
alternatively, the rental price (R) for rented
apartments per month. The percentage premium the
respondents are willing to pay for a given retrofitting
measure is distinguished in PRh for homeowners
Households and Sustainable Green Technologies: A Review
and PRr for rented apartments. Then, the CO2
emissions (E) are computed multiplying the average
TOE per dwelling consumption (C) by the
conversion factor of TOE into tons of CO2:
= 2.331
In order to compute the PCO2, the reported
energy savings percentage (S) is considered for each
retrofitting measure and the number of years (N) for
the amortization of the retrofitting investment. Then,
the PCO2 in the case of homeowners is:
2 = [( ) ℎ]/( )
while, in the case of rented homes the PCO2 is:
2 = ( 12 )/( )
Thirdly, considering SM, the measure of PCO2
is constructed considering the consumers’ WTP for
the installation of the new device in their homes.
This payment can be a one-time capital expenditure
(D) or a monthly rent on the electricity bill (M) for
the usage of the device. All other variables are as
defined above. In the case of capital expenditure the
PCO2 is:
PCO2 = (D/N)/(E S)
and in the case of monthly rent the PCO2 is:
2 = ( 12)/( )
Fourthly, considering GE, the consumer
preferences are modeled as the households’ WTP for
a KWh generated with RES. We compute a measure
of PCO2, using an estimation of the CO2 emissions’
saving. Consequently, the PCO2 is:
2 = 
where W is the WTP for a percentage variation in
the GE share, H is the households’ electricity
average annual consumption, ΔG is the variation in
the share of GE and F is the specific CO2 emissions
factor for KWh produced. This latter is specific to
the electricity generation mix for each country in
each period. An example of PCO2 computation for
GE is now described. Bigerna and Polinori (2014)
estimated eight bimonthly values of households’
WTP for GE development that lie between 4.62 and
15.09 EUR. The annual average households’
consumption (H) in Italy was 2,793 kWh and the
RES target was 26%, implying an increase of 11%
(ΔG). Given that, at the time, the RES share in
Italian fuel generation mix was 15% the specific
CO2 emissions factor (F) was equal to 0.3985.
Applying equation (7) yields eight PCO2 values,
which range from 0.26 to 0.84 EUR/Kg taking also
into account the inflation adjustment of the data.
We reviewed a vast literature comprising 35 papers
published in the period 2000-2015 and we extracted
from each paper the primary information (220
observations) to compute PCO2 according to
equations (1) to (7) and as reported in Table 1.
We have computed the average values of PCO2
for the four technologies (Table 2), over time and for
continents (Table 3) underlining the existing
heterogeneity by the computation of the coefficient
of variation (C.V.). The overall average value of
PCO2 for the whole sample is positive, 0.065
EUR/Kg CO2. The implication of such computation
is that the sample of households analyzed shows a
positive amount to avoid emissions, irrespective of
the type of technology. Subsequently, we have
analyzed PCO2, distinguished by the four different
technologies. We obtain a negative value of PCO2
for AFV, -0.066 EUR/Kg of CO2. This implies that
the sample of households analyzed shows an
expectation of being subsidized to implement this
technology. Values are distributed in a decisive
skewed pattern, with large negative values (the
extreme is around -3 EUR/Kg CO2). We find
positive and significant values of PCO2 for the other
three technologies: 0.262 EUR/Kg for EBS, 0.418
EUR/Kg for GE and 0.134 EUR/Kg for SM. It is
important to note the extreme minimum value for
these last three technologies is positive. This implies
that the whole sample of households analyzed shows
a positive PCO2. The extreme maximum positive
value for the PCO2 for ESB is around 0.916
EUR/Kg CO2, a definitely plausible value. Most of
the studies on households WTP for the SGT have
been conducted in Europe, followed by Asia and
North America (Panels B, Table 3). Considering the
analysis of the households' attitude pre and post-
crisis (Panel A, Table 3), we find that the PCO2
values decrease overtime. This is the result of the
impact of the economic crisis similar to the findings
of Loureiro and Loomis (2010) and Metcalfe and
Baker (2012). However, this difference, between pre
and post crises studies, might not be robust due to
the different sample size. For this reason, it is not
possible to compare results over the years. Focusing
on the variability of the results, there exists a great
heterogeneity, especially if the authors have used
different methods (Table 1), yielding a coefficient of
variation range from 0.02 to 3.33. In particular, there
is great variability in studies using different
approaches e.g. stated and revealed preferences.
SMARTGREENS 2016 - 5th International Conference on Smart Cities and Green ICT Systems
Table 1: PCO2 descriptive statistics (EUR 2014, purchasing power parity) by study.
Author(s) Year Observations (Obs.) SGT Method
Mean S.D. C.V.
Banfi et al. 2008 16 EBS CE 0.240 0.294 1.225
Kwak et al. 2010 4 EBS CE 0.092 0.069 0.750
Farsi 2010 3 EBS CE 0.034 0.032 0.941
Kesternich 2010 1 EBS CE 0.079 -- --
Alberini et al. 2013 2 EBS CE 0.058 0.036 0.621
Achtnicht and Madlener 2014 1 EBS CE 0.817 --
Zalejska and Jonsson 2014 8 EBS CV-OE 0.479 0.084 0.175
Kaufmann et al. 2010 1 SM CE 0.480 -- --
Gerpott and Paukert 2013 2 SM CV-OE 0.253 0.322 1.273
Pepermans 2014 10 SM CE 0.168 0.030 0.179
Rihar et al. 2015 5 SM CV-DC 0.041 0.027 0.659
Ida et al. 2014 6 SM-AFV CA 0.057 0.023 0.404
Hidrue et al. 2011 4 AFV CE 0.023 0.036 1.565
Hackbarth and Madlener 2013 4 AFV CE 0.061 0.037 0.607
Hoen and Koetse 2014 6 AFV CE -0.032 0.023 0.719
Potoglou and Kanaroglou 2007 3 AFV CE 0.026 0.011 0.423
Mabit and Fosgerau 2011 2 AFV CE 0.013 0.002 0.154
Achtnicht 2012 32 AFV CE 0.019 0.046 2.421
Koetse and Hoen 2014 3 AFV CE -0.021 0.018 0.900
Axsen et al. 2009 5 AFV CE-RP -0.642 1.405 2.188
Helveston et al. 2015 28 AFV DC-CA -0.078 0.158 2.026
Bočkarjova et al. 2013 15 AFV CE-RP -0.123 0.409 3.325
Dimitropoulos 2014 9 AFV CE-RP -0.337 0.862 2.558
Dagsvik et al. 2002 24 AFV CE 0.025 0.014 0.560
Bigerna and Polinori 2012 1 GE BG 0.378 -- --
Bigerna and Polinori 2013 1 GE BG 0.465 -- --
Bigerna and Polinori 2014 8 GE MBDC 0.561 0.211 0.376
Kim et al. 2012 1 GE CV-DC 0.09 -- --
Grösche and Schröder 2011 2 GE CE 0.369 0.041 0.108
Zoric and Hrovatin 2012 2 GE CV_DC 0.373 0.013 0.035
Yoo and Kwak 2009 4 GE CV-DC 0.180 0.041 0.228
Ivanova 2005 2 GE CV-DC 0.539 0.120 0.223
Batley et al. 2000 1 GE CV 0.358 -- --
Batley et al. 2001 2 GE CV-DC 0.381 0.009 0.024
Bollino 2009 2 GE MBDC 0.519 0.431 0.830
CE, choice experiment; CA, conjoint analysis; CV, contingent valuation; OE, open ended; DC, dichotomous choice; BG, bidding
game; MBDC, multiple bounded dichotomous choice; RP, revealed preferences.
Table 2: PCO2 descriptive statistics by technology (EUR 2014, purchasing power parity).
Sample Obs. Mean S.D. [C.V.]
Overall 220 0.065 0.375 [5.769]
AFV 135 -0.066 0.385 [5.833]
EBS 35 0.262 0.268 [1.203]
SM 24 0.134 0.124 [0.925]
GE 26 0.418 0.208 [0.498]
Table 3: PCO2 descriptive statistics by period and Continent (EUR 2014, purchasing power parity).
Panel A: By period.
Years SGT Obs. Mean S.D. [C.V.]
2000 - 2007 (pre-crisis) AFV/GE 32 0.090 0.158 [1.756]
2008 - 2015 (post crisis) AFV/EBS/SM/GE 188 0.061 0.400 [6.557]
Panel B: By Continent (Oceania is omitted due the small sample size, # = 2)
Continent SGT Obs. Mean S.D. [C.V.]
Asia AFV/EBS/SM/GE 29 0.042 0.102 [2.429]
Europe AFV/EBS/SM/GE 163 0.104 0.336 [3.231]
North America AFV 26 -0.189 0.625 [3.307]
Households and Sustainable Green Technologies: A Review
Among several SGTs considered (Table 2) AFV
shows the greatest variability, possibly because of
the heterogeneity of the good under evaluation in
these primary studies. AVF varies according to type
of fuel, type of technology determining a great
variability in the original WTP estimated. Finally,
the geographical scale does not affect the results
variability; indeed, in the more populous continents,
which include main SGT, the coefficient of variation
are close to each other.
In the current debate on measures to combat climate
change, this paper provides a homogeneous measure
of CO2 reduction related to the development of four
major SGT. In line with the new COP21 scenario, an
implicit CO2 reduction price is computed using
useful information available from the prominent
economic and technical literature. The reviewed
papers indicate a relatively good stated acceptability
of the investigated SGT as a whole even if a great
heterogeneity exists. This great variability largely
depends on methods used to elicit the WTP and on
the difficulty to define properly the good under
evaluation in the primary studies. However, results
also suggest that households tend to be resistant and
less supportive to new technologies especially if
they are asked to bear high initial costs. In particular,
households expect to be supported in monetary
terms to deploy AFV.
The remaining technologies exhibit positive
PCO2 values. Our results show both spatial and
temporal heterogeneity in PCO2 values.
In conclusion this paper highlights that, despite
barriers, households’ are likely to adopt SGT to
make the future of the sustainable society closer.
Follow-up research will apply a quantitative method
to analyze information from the reviewed papers in a
deeper way in order to assess the robustness of our
Achtnicht, M., 2012. German car buyers' willingness to
pay to reduce CO2 emissions. Climatic Change. 113,
Achtnicht, M., Madlener, R., 2014. Factors influencing
German house owners’ preferences on energy retrofits.
Energy Policy. 68, 254–263.
Alberini, A., Banfi, S., Ramseier, C., 2013. Energy
efficiency investments in the home: Swiss
homeowners and expectations about future energy
prices. The Energy Journal. 34, 49-86.
Axsen, J., Mountain, D.C., Jaccard, M., 2009. Combining
stated and revealed choice research to simulate the
neighbor effect: The case of hybrid-electric vehicles.
Resource and Energy Economics. 31, 221–238.
Banfi, S., Farsi, M., Filippini, M., Jakob, M., 2008.
Willingness to pay for energy-saving measures in
residential buildings. Energy Economics. 30, 503-516.
Batley, S.L., Fleming, P.D., Urwin, O., 2000. Willingness
to pay for renewable energy: Implications for UK
green tariff offerings. Indoor and Built Environment.
9, 157–170.
Batley, S.L., Colbourne, D., Fleming, P.D., Urwin, O.,
2001. Citizen versus consumer: Challenges in the UK
green power market. Energy Policy. 29, 479–487.
Bigerna, S., Polinori, P., 2012. Households' willingness to
pay for renewable energy sources in Italy: a bidding
game approach, in: Uvalic, M., (Ed.), Electricity
markets and reforms in Europe. Franco Angeli,
Milano, pp. 61–84.
Bigerna, S., Polinori, P., 2013. A bidding game for Italian
households’ WTP for RES. Atlantic Economic
Journal. 41, 189–190.
Bigerna, S., Polinori, P., 2014. Italian consumers'
willingness to pay for renewable energy Sources.
Renewable and Sustainable Energy Reviews. 34, 110-
Bigerna, S., Bollino, C.A., Micheli, S., 2015. Overview of
socio-economic issues for smart grids development.
Proceedings of the 4th International Conference on
Smart Cities and Green ICT Systems.
SMARTGREENS-2015, 271-276.
Bočkarjova, M., Rietveld, P., Knockaert, J.S.A., 2013.
Adoption of electric vehicle in the Netherlands – A
stated choice experiment. Tinbergen Institute
Discussion Paper TI2013-100/VIII. http://papers.
Bollino, C.A., 2009. The willingness to pay for renewable
energy sources: The case of Italy with socio
demographic determinants. The Energy Journal. 30,
COP21 (Conference of Parties). http://www.cop21
Dagsvik, J.K., Wennemo, T., Wetterwald, D.G., Aaberge,
R., 2002. Potential demand for alternative fuel
vehicles. Transportation Research Part B:
Methodological. 36, 361–384.
Dimitropoulos, A., 2014. The influence of environmental
concerns on drivers’ preferences for electric cars.
Farsi, M., 2010. Risk aversion and willingness to pay for
energy efficient systems in rental apartments.
Policy. 38, 3078–3088.
Gangale, F., Mengolini, A., Onyeji, I., 2013. Consumer
engagement: An insight from smart grid projects in
Europe. Energy Policy. 60, 621-628.
Gerpott, T.J., Paukert, M., 2013. Determinants of
willingness to pay for smart meters: An empirical
SMARTGREENS 2016 - 5th International Conference on Smart Cities and Green ICT Systems
analysis of household customers in Germany. Energy
Policy. 61, 483–495.
Graham-Rowe, E., Gardner, B., Abraham, C., Skippon, S.,
Dittmar, H., Hutchins, R., Stannard, J., 2012.
Mainstream consumers driving plug-in battery-electric
and plug-in hybrid electric cars: A qualitative analysis
of responses and evaluations. Transportation Research
Part A: Policy and Practice. 46, 140-153.
Grösche, P., Schröder, C., 2011. Eliciting public support
for greening the electricity mix using random
parameter techniques. Energy Economics. 33, 363–
Hackbarth, A., Madlener, R., 2013. Consumer preferences
for alternative fuel vehicles: A discrete choice
analysis. Transportation Research Part D: Transport
and Environment. 25, 5–17.
Helveston, J.P., Liu, Y.M., Feit, E.M., Fuchs, E., Klampfl,
E., Michalek, J.J., 2015. Will subsidies drive electric
vehicle adoption? Measuring consumer preferences in
the U.S. and China. Transportation Research Part A:
Policy and Practice. 73, 96–112.
Hidrue, M.K., Parsons, G.R., Kempton, W., Gardner,
M.P., 2011. Willingness to pay for electric vehicles
and their attributes. Resource and Energy Economics.
33, 686-705.
Hoen, A., Koetse, M.J., 2014. A choice experiment on
alternative fuel vehicle preferences of private car
owners in The Netherlands. Transportation Research
Part A: Policy and Practice. 61, 199–215.
Ida, T., Murakami, K., Tanaka, M., 2014. A stated
preference analysis of smart meters, photovoltaic
generation, and electric vehicles in Japan: Implications
for penetration and GHG reduction. Energy Research
& Social Science. 2, 75–89.
IEA (International Energy Agency), 2015. World Energy
Outlook 2015, OECD/IEA.
Ito, N., Takeuchi, K., Managi, S., 2013. Willingness-to-
pay for infrastructure investments for alternative fuel
vehicles. Transportation Research Part D: Transport
and Environment. 18, 1–8.
Ivanova, G., 2005. Queensland consumers’ willingness to
pay for electricity from renewable energy sources. In
Proceedings of the Ecological Economics in Action
Conference, Massey University, Palmerston North,
New Zealand, 11–12 December 2005.
Kaufmann, S., Künzel, S.K., Loock, M., 2013. Customer
value of smart metering: Explorative evidence from a
choice-based conjoint study in Switzerland. Energy
Policy. 53, 229–239.
Kesternich, M., 2010. What Drives WTP for Energy
Efficiency when Moving? Evidence from a Germany-
wide Household Survey. Discussion Paper No. 11004.
Kim, J., Park, J., Kim, H., Heo, E., 2012. Assessment of
Korean customers’ willingness to pay with RPS.
Renewable and Sustainable Energy Reviews. 16, 695–
Koetse, MJ, Hoen, A., 2014. Preferences for alternative
fuel vehicles of company car drivers. Resource and
Energy Economics. 37, 279-301.
Krishnamurti, T., Schwartz, D., Davis, A., Fischhoff, B.,
Bruine de Bruin, W., Lave, L., Wang, J., 2012.
Preparing for smart grid technologies: A behavioral
decision research approach to understanding consumer
expectations about smart meters. Energy Policy. 41,
Kwak, S.-Y., Yoo, S.-H., Kwak, S.-J., 2010. Valuing
energy-saving measures in residential buildings: A
choice experiment study. Energy Policy. 38, 673–677.
Loureiro, M.L, Loomis, J.B., 2010. How sensitive are
environmental valuations to economic downturns?
Evidence fromthe 2009 recession. University of
Santiago de Compostela Working Paper. Available
Mabit, S.L., Fosgerau, M., 2011. Demand for alternative-
fuel vehicles when registration taxes are high.
Transportation Research Part D: Transport and
Environment. 16, 225–231.
Metcalfe, P.J, Baker, W., 2012. The sensitivity of
willingness to pay to an economic downturn. In:
Proceedings of the Envecon 2012: applied
environmental economics conference. London, UK:
Royal Society.
Pelenur, M.J., Cruickshank, H.J., 2012. Closing the
Energy Efficiency Gap: A study linking demographics
with barriers to adopting energy efficiency measures
in the home. Energy. 47, 348-357.
Pepermans, G., 2014. Valuing smart meters. Energy
Economics. 45, 280–294.
Potoglou, D., Kanaroglou, P., 2007. Household demand
and willingness to pay for clean vehicles.
Transportation Research Part D: Transport and
Environment. 12, 264–274.
Rihar, M., Hrovatin, N., Zoric, J., 2015. Household
valuation of smart-home functionalities in Slovenia.
Utilities Policy. 33, 42-53.
Stigka, E.K., Paravantis, J.A., Mihalakakou, G.K., 2014.
Social acceptance of renewable energy sources: A
review of contingent valuation applications.
Renewable and Sustainable Energy Reviews. 32, 100-
Tommerup, H., Svendsen, S., 2006. Energy savings in
Danish residential building stock. Energy and
Buildings. 38, 618-626.
Yoo, S.H., Kwak, S.Y., 2009. Willingness to pay for green
electricity in Korea: A contingent valuation study.
Energy Policy. 37, 5408–5416.
Zalejska-Jonsson, A., 2014. Stated WTP and rational
WTP: Willingness to pay for green apartments in
Sweden. Sustainable Cities and Society. 13, 46–56.
Zoric, J., Hrovatin, N., 2012. Household willingness to
pay for green electricity in Slovenia. Energy Policy.
47, 180–187.
Households and Sustainable Green Technologies: A Review