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.

1 INTRODUCTION

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.

2 SUSTAINABLE

TECHNOLOGIES

BACKGROUND

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

378

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

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.

3 DATA AND METHOD

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 = ( ⁄ )( ⁄ )

(1)

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

379

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

(2)

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]/( ⁄ )

(3)

while, in the case of rented homes the PCO2 is:

2 = ( ∙ 12 ∙ )/( ⁄ )

(4)

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)

(5)

and in the case of monthly rent the PCO2 is:

2 = ( ∙ 12)/( ∙ )

(6)

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 =  ∙  ∙  ∙ 

(7)

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.

4 RESULTS AND DISCUSSION

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.

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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

381

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.

5 CONCLUSIONS

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

findings.

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