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In ternationa l
Scholars
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African Journal of Environmental Economics and Management ISSN 2375-0707 Vol. 5 (3), pp. 302-315, March, 
2017. Available online at www.internationalscholarsjournals.org © International Scholars Journals 

 

Author(s) retain the copyright of this article. 
 
 
 

Full Length Research Paper 
 

Are investments in energy efficiency and renewable 
energy technologies complementary? An econometric 

analysis of households’ environmental investments 
 

George Abuchi Agwu 
 

Federal University, Ndufu-Alike Ikwo, Nigeria. 
E-mail: george78ng@yahoo.com. 

 
Accepted 14 November, 2016 

 

Economic incentives have received much attention in the literature of the two dimensions of environmental 
friendly energy consumption: energy efficiency and renewable energy investments. This study shows that the 
incentives, if not well targeted may be irrelevant because household investors in the different dimensions 
possess different characteristics that policies could target. The findings indicate that investments in renewable 
energy technologies decrease with age of household investors and not affected by household income 
constraint while investments in energy efficiency are not affected by age of household investors but by income 
constraint. Correlation coefficients among all the investment equations in the multivariate model are strongly 
positive suggesting complementary investment choices across dimensions. 
 
Keywords: Energy efficiency, renewable energy, multivariate probit, ordered probit. 
 
 
INTRODUCTION 
 
As scarcity of natural resources and environmental 
degradation get more national and international attention, 
calls for sustainable energy consumption get intensified. 
Most governments in the developed world employ 
incentives or information campaigns to promote energy 
savings and other eco-friendly behaviours. With respect 
to purchase of eco-friendly appliances, many empirical 
studies have assessed how measures such as eco- 
labelling, tax incentives, and subsidization policies impact 
on the governments objectives. However, most of these 
studies employ univariate methodologies that assume 
independent decisions with respect to the various 
dimensions of pro-environmental behaviours. 
Although industry and agriculture represent the bulk of 
energy demand, there exists substantial potential to 
reduce total energy demand in the residential sector. As 
mentioned above, a lot of policy efforts are presently 
concentrated at demand side management (DSM) 
policies, i.e., price and non-price policies designed to 
promote energy conservation in the residential sector. 

This opens up a number of choice for household energy 
consumption: In order to reduce the quantity of energy for 
a given energy service, a household can decide to invest 
in energy efficient technology that induces energy saving 
or decide to reduce energy waste by engaging in 
prudential energy use such as switching off standby 
model of appliances when not in use (Jansson et al., 
2009). By reducing its consumption of energy, the 
household contributes to reducing future greenhouse gas 
(GHG) emissions. Reducing greenhouse gas emission 
can also be achieved by investing in renewable energy 
technologies such as wind turbine, ground source heat 
pump or solar panel. The use of renewable energy 
technologies differs from the other forms of sustainable 
energy consumption because for a given level of 
renewable energy consumption, less greenhouse gases 
are emitted. 
The relationship among these choices of responsible 
energy use is an important policy consideration. For 
instance, energy efficiency technology investments and



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Agwu          303 
 
 
 
prudential energy use maybe driven by money savings, 
environmental consciousness or both. On the other hand, 
investment in renewable energy technology are most 
likely driven by environmental consciousness and other 
non-monetary concerns. If households are 
simultaneously environmentally conscious and income 
constrained, they could leverage on the synergy between 
these dimensions of pro-environmental choices as 
identified by Dato (2015) to satisfy their objectives. 
However, certain combinations of energy efficiency and 
renewable energy technologies may be more appealing 
to the households than others. For instance, households 
may favour energy efficiency-wind turbine combination or 
energy efficiency- solar panel combination due to for 
example existing policies or space requirement. Thus, the 
lumping together in two groups of all energy efficiency 
and renewable energy investments do not provide a clear 
path for policy mediation. Furthermore, prudential energy 
use is more of an attitude than a decision: such that its 
role is to predict energy efficiency, renewable energy 
investments or both rather than being an alternative 
choice as Dato (2015) suggests. 
 
 
LITERATURE REVIEW 
 
Empirical determinants of Pro-Environmental 
Decisions 
 
The traditional economic theory examines household’s 
behaviour on the basis of consumption as broadly defined in 
terms of households 'purchases and other decisions such as 
labour supply. The idea is that households’ decisions are 
described by utility function subject to budget constraint and 
other household’s characteristics which generates testable 
restrictions on household’s behaviour. The Household 
characteristics include disposable household income, age, 
gender, education, occupation, marital status, family size 
and number of children. Empirical studies of energy 
efficiency and renewable energy technologies adoption 
suggest that these characteristics are important decision 
variables: household size is hypothesized to predict 
adoption because budget constraints increase in household 
size given limited number of income earners within the 
household. On the other hand, large household increases 
the incidence and size of economics of scale of energy 
savings incident upon adoption of the appliance. So on the 
one hand, households size may tighten the constraint faced 
by households with respect to clean energy adoption. On the 
other hand, household’s size increases, the private benefits 
derivable through clean energy adoption. This is thus one 
critical testable hypothesis. Millock and Nauges (2010) found 
a positive and significant relationship between the 
household size and the adoption of water saving 
technologies. 

 
Socio-Economic Characteristics 
 
Household’s income represents largely the budget to be 

maximized and thus all things being equal, adoption of 
clean energy is expected to rise with income. However, 
on the basis of economic theory, the role played by 
income in driving households’ decisions is difficult to 
assess. On the one hand wealthier households should 
have greater financial ability to invest in energy-saving 
appliances. On the other hand, wealthier households 
could have less elastic demand for consumption in 
general compared to poorer households. Not surprising 
therefore, empirical studies have found varied results 
regarding household income. Whereas, Berk et al. (1993) 
shows that income has positive effect on water 
conservation, De Oliver (1999) shows that lower income 
households are more conservative of water than higher 
income households. Sütterlin et al. (2011) found that 
lower-income households were more likely to adopt 
energy saving habits. Martínez-Espiñeira and García-
Valiñas (2013) also found negative relationship between 
income levels and the adoption of water conservation 
behaviour. 
 

A higher education level is expected to be positively 
correlated with knowledge and awareness about issues 
relating to the environment. Thus, it is expected apriori 
that more educated households/respondents are more 
likely to undertake pro-environmental actions given their 
knowledge. The analysis of the OECD survey undertaken 
in eleven countries showed that the level of educational 
attainment increased pro environmental behaviours in all 
the five areas (transportation, water, energy, waste, and 
food) covered (OECD, 2011). This was confirmed 
empirically by Martínez-Espiñeira and García-Valiñas 
(2013). The roles played by marital status, age and 
occupation varies substantially depending on the dataset 
and technologies being studied (see Ameli and Brandt, 
2015) 

 
Social and Relational Factors 

 
The social system or norms just like the cost benefit 
analyses in the case of energy efficiency investment may 
affect the adoption of environmentally friendly 
behaviours. In addition to environmental concern, Zhai 
and Williams (2012) investigates the influence of social 
acceptance and shows in a specific case of photovoltaic 
(PV) adoption that social acceptance also affects the 
adoption of renewable energy. In a model grounded in 
diffusion of innovation and social capital theories, 
McMichael and Ship worth (2013) underscore social 
system as important factor in the diffusion of 
environmentally friendly energy technologies. In 
particular, McMichael and Shipworth (2013) emphasized 
that word-of-mouth and experience of innovators is far 
more potent than standard campaigns and advocacy in 
the diffusion of clean energy innovations among British 
communities. 
 

Those community members who are seen as opinion 
leaders might influence other members of the community



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304          Afr. J. Environ. Econ. Manage. 
 
 
 
towards a particular orientation with respect to the 
desirability of environmentally friendly energy 
technologies. Mills and schleich (2012) explored how 
opinion leadership contributes to the innovativeness of 
adopters and found that those classified as opinion 
leaders are more likely to be among the first 5% of 
adopters of given innovation. Nair et al. (2010) show that 
adoption is associated with income, education and age 
but suggest that opinion leaders influence non-adopters 
through personal communication channels that maybe 
fostered by policy. 

 
Moral Hazard and Spillovers 

 
Many other factors not classified above have been shown 
to matter for household’s consideration of adopting 
environmentally friendly energy technology. These 
factors may be related to tenure in primary 
accommodation, incentives structure and spillover 
effects. For instance, at what point during the 
households’ residency in the primary accommodation is it 
optimal to install energy efficiency or environmentally 
friendly energy technology? Most empirical studies find 
evidence of positive tenure effect (Davis, 2010;Murray 
and Mills, 2011; Martínez-Espiñeira and García-Valiñas 
2013). However, Tovar (2012) found negative effect of 
tenure on household’s adoption of energy efficiency eco-
innovation in the UK. The most common justification for 
positive tenure effect is that as households develop 
attachment to their current residence, they become more 
likely to install measures to make it compliant to 
sustainable consumption of energy. However, the 
question of when to install these measures depends also 
on the particular type of measure under consideration. 
While it might be optimal to install measures that have to 
do with the built environment such as cavity walls 
insulation at the beginning of residency when other 
renovations or constructions are going on, for others such 
as light bulbs anytime may be just fine for adoption. 
Given that Tovar, (2012) model was based on cavity 
walls insulation, loft insulation and boiler upgrade, this 
result is not at all surprising. 
Split incentives between landlords who install energy 
using equipment in rental accommodations and renters 
who pay the energy bills has been extensively studied as 
factors that may get in the way of households’ energy 
efficiency behaviour. Landlords may buy cheap inefficient 
appliances when it is the responsibility of the tenants to 
pay the energy bills. Davis (2010) empirically compared 
appliances ownership patterns between homeowners and 
renters and found that renters are less likely to have 
efficient appliances compared to homeowners. This is 
because of the lemon problem which arises from the fact 
that potential tenants cannot observe appropriately the 
quality of energy using equipment installed by the 
landlords (Akerlof, 1970). As a result of this, the landlords 
cannot possibly signal the quality of the equipment in 

their homes through higher rental fees (Gilligham et al., 
2012) 
On spillover effects, Nair et al. (2010) show that as 
households become aware of the benefits of adopting 
energy efficiency technologies through past investments, 
they become more likely to devote further outlay to 
energy efficiency. This thus implies that information 
acquisition, education or learning by doing is related to 
the unobservable driving the probability of households 
investing in more than one of the energy efficiency 
measures. Similarly, Brown (2014) show that adoption of 
energy conservation habits has positive spillover effects 
on related habits such as recycling and water 
conservation and is mediated by positive attitudes to the 
environment. A model of clean energy adoption should 
therefore be multi-disciplinary in nature including 
economic as well as other considerations that are not 
purely economic and consider possible relatedness 
among the different dimensions of environmental 
behaviour. Such models would estimate environmental 
behaviour more efficiently and identify policy relevant 
covariates. 
 

The Interaction between Energy Efficiency and 
Renewable Energy Investments 
 
A number of empirical studies has been conducted on 
how households’ behaviours affect adoption and 
investment in energy efficiency. This is because 
substantial opportunities exist within the residential sector 
to reduce energy consumption and mitigate climate 
change (Fouquet, 2010; Dietz et al., 2009; and 
Vandenbergh et al., 2008). There is a large evidence that 
economic factors are motivations for energy efficiency 
(Kempton and Neiman, 1986; and Steg, 2008) and can 
be helpful in designing appropriate taxes or subsidies 
mechanism to promote energy saving actions. For 
instance, energy efficient behaviour maybe brought about 
by the need to save household expenses or to reduce 
energy bills. Achieving this objective however is not so 
simple; the potential gain from reducing energy use can 
be hindered by some problems such as split incentives, 
uncertainty about the gain of energy efficiency, moral 
hazard problem that may prevent households from 
adopting or investing in an energy conservation system. 
Reducing energy use can also lead to reverse effects 
such as rebound effect or take-back effect (Chitnis et al., 
2014; Urban and Scasny, 2012). Problems such as 
rebound effects operate through household’s motivations. 
If households are motivated by economic concerns such 
as savings in energy expenditure, they are more likely to 
be subject to rebound effects than those motivated by 
environmental concerns. Studies such as Urban and 
Scasny (2012) suggest that the rebound effect can be 
solved by environmental campaigns geared at cultivating 
pro-environmental motivation. There is however, no 
empirical consensus on the impact of environmental 
concern on energy-saving behaviour. Earlier literature



4 

 

 
 
 
 
found that environmental concern does not have any 
effect on both energy consumption and energy- saving 
actions (Gilligham et al.,2012). Others, mostly recent 
studies tend to find significant effects of environmental 
concerns on energy-saving actions (Capstick et al., 2015; 
Barr et al., 2005; and Whitmarsh and O’Neill,2010). 
While energy efficiency investments correlates with the 
desire for money savings, empirical studies show that 
investment in renewable energy is driven by 
environmental attitudes. This follows from the theory of 
reasoned action which characterizes peoples’ actions as 
arising from their cognitive assessment of possible 
outcomes based on held attitudes. Most empirical studies 
relying on consumers’ stated preference tend to find 
strong roles for environmental attitudes in consumers’ 
willingness to adopt clean energy technologies (Ek and 
Söderholm, 2008; Zoric and Hrovatin, 2012; Liu et al., 
2013.). Although such hypothetical studies may deviate 
from observed behaviour, it clearly distinguishes 
renewable energy investment choice from energy 
efficiency investment (Cameron et el, 2002; Kotchen and 
Moore, 2007). Thus on apriori, those households with 
greater environmental consciousness are expected to 
engage more with renewable energy technologies than 
others. If they are equally conscious of money savings, 
then they are more likely to invest jointly in renewable 
and energy efficiency technologies. 
Different dimensions of pro-environmental behavior may 
be distinguished (Lynn,2014). Both economic and 
environmental motivations have different effects when 
dimensions of environmental friendliness are 
distinguished. As far as energy consumption is 
concerned, energy efficiency can be distinguished from 
the use of renewable energy. The use of renewable 
energy has been compared to private provision of public 
goods and driven by altruistic motivation (Welsch and 
Kuhling, 2010). Energy efficiency on the other hand 
mainly derives from economic motivation in form of 
reducing expenditure (Diaz-Rainy and Ashton, 2015). 
Energy efficiency behaviour can be further sub-divided 
into energy efficiency investment and curtailments 
(Jansson et al., 2009). The former involves the 
acquisition of new technologies, low-energy appliances 
(Top-rated energy-efficient appliances, low-energy 
lightbulbs, energy-efficient windows, etc.) that needs 
monetary investment. The latter refers to non-monetary 
investments that are behaviour changes such as turning 
off lights, cutting down on heating or on airconditioning 
and switching off standby mode of appliances. Monetary 
energy efficiency investments are less affected by pro-
environmental motivations than non-monetary behaviour 
changes (Guagnano et al., 1995). Both economic and 
environmental concerns may have significant effects on 
energy-saving actions which are the outcome of both 
monetary and non- monetary investments. Urban and 
Scasny (2012) investigates in a multi-country setting how 
environmental concern affects the adoption of monetary 

versus non-monetary investments in energy efficiency. 
They find a positive and significant effect of pro-
environmental motivation in both cases. 
Notwithstanding the distinguishing features of energy 
efficiency behaviour and the use of renewable energy, 
relevant synergies may exist between them. The different 
variables that affect the renewable energy adoption 
decision of household may have significant effects on 
energy efficiency investments as well. The fact that 
studies mostly focus on either renewable energy adoption 
or energy efficiency investment may explain empirical 
disparities in the effect of economic and environmental 
concerns. If the two decisions are interrelated, it cannot 
be estimated independently. In this case, univariate 
models that estimate separately the two decisions of 
renewable adoption and energy efficiency are potentially 
inefficient because there may exist unobserved 
characteristics that motivate joint occurrence of the two 
decisions. For instance, household that is pro-
environmental in their held attitude can find it necessary 
to additionally invest in renewable energy having already 
invested in energy efficiency. In this case, the household 
may rely on its environmental conscientiousness to 
combine the two investments. Similarly, households 
having invested in energy efficiency may have limited 
financial capacity to additionally invest in renewable 
energy. Therefore, by jointly analyzing the two possible 
decisions of the adoption of renewable energy and 
investment in energy efficiency that are taken by the 
household, one can capture the interrelation and the 
interaction between them. Such investigation has 
potential gain for policy designs as adoption of renewable 
energy and investments in energy efficiency are both 
important in the transition to green economy. 
 
 
Data and variables description 
 
The data for this study comes from 2011 OECD cross-
sectional survey which collects data from a sample of 
more than 12 000 respondents, approximately 1000 
households for each country: Australia (shorthand: AUS), 
Canada (shorthand: CAN), Chile (shorthand: CHL), 
France (shorthand: FRA), Israel (shorthand: ISR), Japan 
(shorthand: JPN), Korea (shorthand: KOR), the 
Netherlands (shorthand:NLD), Spain (shorthand:ESP), 
Sweden (shorthand:SWE) and Switzerland 
(shorthand:CHE). The sample was stratified to achieve 
representativeness. In each country stratification was 
done according to different parameters: age, gender, 
region and socio-economic groups. 
The target respondents were between 18 and 70 years of 
age and those who had influence on household 
purchasing decision and expenditures. Gender was 
approximately half male and female for all countries. 
Region was stratified and quotas created using three to 
five regions. For income stratification, households’ after

Agwu          305 

 



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306          Afr. J. Environ. Econ. Manage. 
 
 
 

Table 1. Summary statistics for the dependent   variables. 
 

Country Appl Bulb Windows Thrm Heat Solar Pump Wind 

   Mean      

AUSTRALIA 0.690 0.910 0.130 0.580 0.150 0.200 0.030 0.034 

CANADA 0.670 0.870 0.510 0.380 0.650 0.040 0.040 0.029 
CHILE 0.410 0.950 0.140 0.310 0.060 0.020 0.010 0.013 

FRANCE 0.740 0.860 0.590 0.450 0.440 0.070 0.050 0.015 
ISRAEL 0.590 0.840 0.130 0.200 0.110 0.670 0.030 0.020 

JAPAN 0.480 0.480 0.200 0.200 0.070 0.050 0.010 0.001 

KOREA 0.690 0.630 0.490 0.380 0.590 0.070 0.030 0.027 
NETHERLANDS 0.610 0.890 0.730 0.490 0.480 0.040 0.020 0.011 

SPAIN 0.740 0.910 0.540 0.210 0.470 0.060 0.020 0.016 

SWEDEN 0.620 0.870 0.390 0.290 0.340 0.040 0.160 0.019 
SWITZERLAND 0.620 0.800 0.490 0.370 0.430 0.050 0.070 0.008 
Total 0.620 0.820 0.380 0.340 0.330 0.110 0.040 0.017 

 
 
 
 
tax income quintiles were estimated for each country, 
then responses from the survey income question were 
used to fill the quotas. When quotas were filled, 
respondents with these characteristics were stopped from 
completing the questionnaire. More details on the 
questionnaire design, respondent targeting and quota 
sampling are provided in (Ameli and Brandt, 2015; 
OECD, 2011). 
The study uses as dependent variables different forms of 
households’ self – reported environmentally friendly 
technology adoption collected as part of OECD, 2011: 
Households were asked whether they undertake the 
following activities between 2001 and 2011: 
Bought appliances that received a top rating in terms of 
energy efficiency (Appl) Bought low energy light bulbs 
(Bulb). 
Installed energy efficient windows i.e double or triple 
glazing (Windows). 
Engaged in thermal insulation of walls or roof (Thrm) 
Installed heat thermostat (Heat) 
Installed solar panels for electricity or hot water (Solar) 
Installed ground source heat Pump (Pump) 
Installed wind turbine for electricity (Wind) 
The variables; Appl, Bulb, Windows, Thrm, Heat, Solar, 
Pump and Wind representing the various types of 
environmentally friendly energy investments and 
considered as binary outcomes are the dependent 
variables of the equations defining multivariate probit 
models. Table 1 summarizes the dependent variables by 
countries surveyed. The following sub-sections describe 
the main independent variables used in the study. 
 

Socio-economic characteristics 
 

Available Socio-economic variables in the household 
dataset include the respondent’s age (Age), gender 
(Male), size of household (HHsize), the number of years 
of education after high school (Education), annual net 
household income (Income) and employment status of 
households’ head created as dummy variable 

(Professional). There is a dummy variable that takes a 
value of 1 for households stating that they cannot cope 
with their current income (Nocope). Studies suggest that 
individuals with higher income and education tend to be 
more likely to adopt energy efficient technologies, while 
the influence of age and gender is less clear (Ameli and 
Brandt, 2015). Income might be a relevant variable 
considering that investment cost seems to be one of the 
major barriers to investing in energy efficiency. However, 
income would only be expected to influence investment 
decisions if lower-income households are credit-
constrained or if their ability to assess the profitability of 
investments is less well developed than for higher income 
households. Otherwise, lower- income households would 
be able to identify profitable investments like higher-
income households and they would be able to obtain a 
credit to invest. At the same time, a variable that captures 
the respondent’s education should capture any difference 
in the ability to assess the profitability of investments 
across households better than an income variable. Thus, 
if income and/or education is significant predictor of 
adoption, it shows evidence of irrational consumer 
decision and is one of the tested hypotheses of the 
current study. 
Approximately, the average annual household net income 
in the dataset is 37 868 USD with considerable 
differences in means across countries. Chilean 
Households declared the lowest average annual income 
(13 585 USD), while the highest was declared by 
households living in Switzerland (62 278 USD). 36% of 
household’s experience difficulty in coping with their 
salary and as expected, there are more than average 
number of households expressing this difficulty in Chile 
(43.6%), France (43.8%) and Israel (42.8%). Chile and 
Israel are the two main countries with a highest 
percentage of professionals as household heads, 36% 
and 31.8% respectively, while France is (11.5%). A 
number of respondents in the sample has at least 
university education as the average length of education



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Agwu          307 
 
 
 
after high school is 3.5years. 
 
Dwelling Characteristics 
 
Questions about characteristics of dwellings were asked, 
such as home-ownership versus rental (Owner), dwelling 
type (Detached House), number of years lived in the 
primary residence (Tenure) and whether households live 
in a rural or urban area (Rural). Controlling for home 
ownership provides evidence on the frequently cited 
owner-effect. In most countries, investment incentives for 
owners who rent out their apartments are weak, since in 
general it would be the tenant who benefits from any 
energy savings. At the same time, tenants may not be 
allowed to invest, but even if they are, their incentives to 
do so are probably weaker than those of owners who live 
in their home, as tenants are more likely to move before 
their investment pays off. Many empirical studies have 
confirmed the presence of an owner-effect (Tovar, 2012; 
Davies, 2010). 
The number of years already spent by households in their 
house may also matter. Some investments may be more 
likely to be made as a household moves in to a dwelling, 
since this is usually a good moment for home 
improvements. In other cases, the length of time that 
households have already spent in their home may be 
indicative of their attachment to it and hence a longer 
tenure may increase the likelihood to invest. The floor 
size (Floor_size) of household’s home, whether 
households live in a rural area and/or have a detached 
house may be indicators of space availability (Michelsen 
and Madlener 2012), as living in larger homes may be 
relevant for investing in energy technologies requiring 
more space. 
A good number of respondents (63%) in the dataset own 
their residence and more than half of the investigated 
households live in a detached house (53%). Highest 
rates of ownership are observed in Spain (79.8%), Korea 
(69.8%) and the Netherlands (68%), while relatively many 
households live in a detached house in Australia (82.6%), 
Chile (77%) and the Netherlands (74%). On average, 
households have lived for approximately 13 years in their 
primary residence, although average tenure is longer in 
Japan (18 years). 
 
Attitudes and behaviour towards the environment 
 
To reflect the role of social context, households’ beliefs, 
attitudes and behaviours regarding the environment, 
some dummy variables were created. These include a 
dummy variable for households that participate in a non-
governmental organisation (NGO) and another one for 
those that are specifically in an environmental NGO (Env 
NGO). There is a dummy variable for people who rated 
the environment as the most pressing concern 
(Envtopcon) and another one for those who instead rated 
the economy as the most pressing concern (Ecotopcon) 

Respondents were asked questions regarding their 
willingness to make sacrifices to protect the environment, 
their assessment of the need to do so and the role of 
technology in solving environmental problems. 
Depending on their answers to those questions 
households were grouped in three clusters i) those who 
are the environmentally motivated and are willing to make 
sacrifices in their lifestyle to solve environmental 
problems (Altruists), ii) those who are not willing to make 
much effort to solve environmental problems because 
they believe that the climate problems are often 
exaggerated (Sceptics), and iii) the third group believe in 
the reality of climate problem but that technological 
innovations are key to solving them (Green Growthers) 
(see OECD, 2011)45% of the respondents are classified 
as Altruists (Altruists) with variations across countries, 
65% of respondents are classified as “Altruists” in Israel 
and around 55% in France and Sweden. On the other 
hand, on average across countries 35% of respondents 
are sceptical (Skeptics) about the existence of 
environmental problems. Japan showed the highest level 
of scepticism (45%), while Chile showed the lowest level 
(18.8%). 
In most countries, membership in non-governmental 
organisation is quite popular; more than 50% of 
respondents are engaged in some non-governmental 
organisation (NGO). Only in Japan is this share much 
lower, just above 30%. On average across countries 
around 10% of respondents are engaged in an 
environmental NGO (Env_NGO). 
To investigate cost bias hypothesis, a dummy is 
constructed in line with Ameli and Brandt, (2015) to 
capture whether households give higher weight to 
investment costs than to future energy savings. In 
principle, individuals should be indifferent between lower 
investment costs and higher energy prices, since these 
factors have an equivalent impact on the net present 
value of energy saving investments, the sum of 
forecasted discounted energy savings minus the upfront 
investment cost. When the rating given to initial 
investment cost exceeds the rating for future energy 
prices by three points on the scale, respondents are 
considered as having a bias towards initial investment 
costs and the dummy variable takes a value of 1. 
 
Households Perception of Quantity and Costs of 
Energy Use 
 
To measure this perception, questions regarding 
households’ knowledge about their energy 
consumption/spending and method of payment of energy 
bills were used to create dummy variables. During the 
survey, respondents were asked to get hold of their 
energy bills before answering the survey, but only about 
55% were able to provide information about their energy 
spending. (Ebill_known) captures if household was able 
to provide this information or not) on average across



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308          Afr. J. Environ. Econ. Manage. 
 
 
 

Table 2. Summary statistics for the independent variables. 
 

Variable Obs Mean St. Deviation Min Max 

Socio-economic characteristics 

INCOME ($) 8, 765 37868.04 24681.14 2304.525 159160.6 

EDUCATION (years after high school) 8, 765 3.500492 3.152784 0 12 

AGE (years) 8, 765 42.01352 13.7693 18 69 

HOUSEHOLD SIZE 8, 765 3 1.416855 1 6 

MARRIED 8, 765 0.6361252  0 1 

MALE 8, 765 0.4913949  0 1 

NOCOPE 8, 765 0.3619079  0 1 
COST BIAS 8, 765 0.3631372  0 1 

SHARE OF NON-GRADUATE 8, 765 0.0960471 0.2182375 0 1 

SHARE OF GRADUATE 8, 765 0.2209009 0.2778962 0 1 

SHARE OF UNDER 18 8, 765 .1538248 0.2155072 0 0.8333333 

SHARE OF OVER_55 8, 765 0.1203204 0.2503451 0 1 

Beliefs, Attitudes and Behaviour 

BEHAVIOUR INDEX 8, 765 7.427157 1.864599 0 10 

ECOTOPCON |MATERIALISM 8, 765 0.352811  0 1 

ENVTOPCON 8, 765 0.1589084  0 1 

NGO 8, 765 0.517784  0 1 

ENV NGO 8, 765 0.1070316  0 1 

SKEPTICS 8, 765 0.3531388  0 1 

Dwelling Characteristics 

FLOOR SIZE (SQUARE MILES) 8, 765 109.8426 59.85085 32.13473 334.1077 

DETACHED HOUSE 8, 765 0.5360597  0 1 

OWNER 8, 765 0.6306343  0 1 

TENURE 8, 765 12.85756 13.67766 1 40 

RURAL 8, 765 0.3445337  0 1 

Perception Energy use knowledge 

ELECTRIC BILL KNOWN 8, 765 0.5518767  0 1 

QUANTITY OF ELECTRICITY KNOWN 8, 765 0.1902967  0 1 

PAY AS YOU USE 8, 765 0.9116538  0 1 

 Note: Standard Deviations are not calculated for dummy variables 
 

 
countries while (Kwatt_known) captures if households 
were able to provide energy use information by volume. 
Regarding prudence in energy use (curtailment), the 
“behaviour index” variable captures whether respondents 
perform certain energy conservation actions regularly, 
such as turning off the lights when leaving the room, 
cutting down on heating/airconditioning to limit energy 
consumption, running full loads when using the washing 
machines, washing clothes using cold rather than 
warm/hot water, switching off the standby mode of 
appliances and air dry laundry rather than using a clothes 
dryer. The behaviour index ranges from 0 to 10, where 
higher values indicate that households perform several of 
the sections regularly. The data suggest that households 
perform quite regularly energy conservation actions, as 
on average across countries the behaviour index takes 
values around 7. Lower values of the index are observed 

in Sweden (5.55) and Switzerland (6.82), while higher 
values are observed in Chile (8.32) and Spain (8.39). 
Table 2 summaries the dependent variables: 
 
EMPIRICAL METHODOLOGY AND RESULTS 
 
A Model of Joint Investment: Energy Efficiency and 
Renewable Energy Technologies 
 
In principle, households could invest jointly in energy 
efficiency and renewable energy technologies. Empirical 
modelling of such joint investment follows from Ekholm et 
al. (2010) and Dato (2014) which describes the consumer 
energy investment problem as utility maximization under 
budget constraint. Assuming that utility derived from 
energy consumption is separable from utility due to other 
forms of consumptions, households may set aside energy 



8 

 

Agwu          309 
 
 
 
budget specific for solving households’ energy 
consumption problems. The energy budget holds the 
income that a household devotes to energy service 
demand and any grants or other forms of financial 
support the household may receive for energy 
procurement. It could be assumed that the households 
would prefer to invest in energy efficiency in order to 
minimize energy expenditure and in renewable energy to 
satisfy private affection for the environment. The affection 
for the environment unlike materialistic consumption is 
intrinsically motivated and may receive less than optimum 
budgetary allocation from the household’s budget (Welsh 
and Kuhling, 2010). The energy budget constraint 
expresses the limited investment capacity of the 
household. 
Aside energy, other realms of consumption compete for 
households’ income. Furthermore, for households that 
have environmental affection, energy efficiency 
investments compete with renewable energy investment 
for the households’ energy budget. Therefore, there may 
be substitutability rather than complementarity between 
energy efficiency and renewable investments. However, 
apart from private utility from income and/or other forms 
of consumption, households may also derive private 
affective utility by virtue of pro- environmental energy 
consumption. In this case, investment in renewable 
energy complements energy efficiency investment. Two 
opposing forces therefore, drive households’ investments 
in environmentally friendly energy technologies. That is, 
the forces of income or other forms of consumption and 
the forces of environmental affection. Thus, under multi 
variant estimation framework, the sign of the correlation 
coefficients suggests the nature of relationship existing 
between the choice dimensions. 
 
Econometric Estimation 
 
To study household choices of the six pro-environmental 
investments, a corresponding number of binary equations 
were generated. Every type of pro-environmental 
investment corresponds to binary choice equation and 
households’ choices are modeled using correlations 
among the equations’ disturbances. If the correlation 
coefficients are significantly different from zero, then 
meaningful relationships are established among the 
various choices. A significant positive relationship 
suggests complementary choices while significant 
negative relationship suggests substitute choices. Since, 
MVP model is used in this case, the sign of the 
correlation coefficients depends on the data rather than 
specified apriori. The multivariate model specifies 
households’ choices of the different types of clean energy 
investments on some function of covariates, allowing for 
simultaneity of investment choices. As households could 
adopt more than one type of energy investment 
simultaneously, another advantage of this model is that 
patterns of household’s investments in as many 

investment types as possible is estimated empirically on 
the basis of the data. No restriction in this pattern is 
needed under the multivariate model. Relations among 
the different choices of households’ investments are 
modelled through correlation parameters that are 
assumed arbitrary and have to be estimated (Calia and 
Farrante, 2013). These correlations  tell  us  how  
different  types  of  clean  energy investments  are  
combined by households and if there are unobserved  
factors,  besides  those explicitly considered, that 
simultaneously affect different choices of investment. 
This reflects the traditional household’s utility 
maximization over a basket of goods under a budget 
constraint. 
The MVP is adopted similar to Calia and farrante (2013), 
specifying a joint multivariate normal distribution for the 
error terms.  Formally consideringMclean energy 
investments for each observation, there are M equations 
each describing a latent dependent variable to which 
there is a corresponding observed binary outcome.  The 
model is written with suppressed subscripts as follows;

 

𝛾𝑚
∗  =    𝛽𝑚

′ 𝑋𝑚   +   ℰ𝑚𝑚 = 1, . . . , 𝑀 

𝛾𝑚   =   1    𝑖𝑓𝛾𝑚
∗ > 0 𝑎𝑛𝑑 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 

𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 1 

Where Xm is a vector of P covariates for the m-the 

equation (m = 1, …, M ), β’
m is the corresponding vector 

of parameters and εm  is a vector of error terms 

corresponding to each equation of the model assumed to 
be jointly normally distributed with means of 0 and 

variance of 1 and correlation coefficients; 𝜌. 
The multivariate probit model considers six potentially 
related binary outcomes. The relatedness exists beyond 
conditioning on regressors and occurs through correlation 
of the errors that appear in the index-function model 
formulation of the binary outcomes. In general, the 
multivariate probit models consider two or more 
equations of binary outcomes and is the multinomial 
analogue of seemingly unrelated regression model with 
the dependent variables in form of binary outcomes. 
The observed outcomes are: 
 

𝑌𝑖 =   
1 𝑖𝑓𝑌𝑖

∗ > 0

0 𝑖𝑓𝑌𝑖
∗ ≤ 0

 𝑖 = 1, 2, 3 …𝑀𝑬𝒒𝒖𝒂𝒕𝒊𝒐𝒏𝟐 

 
The model collapses to univariate probit models for the 

separate dependent variables if 𝜀s are identically and 
independently distributed or if 𝜌equals zero. The model 
may be  estimated by means of simulated maximum 
likelihood (SML) using the Geweke-Hajivassiliou-Keane 
(GHK) simulator that results in unbiased estimates of the 
multivariate normal probabilities (see Cappellari and 
Jenkins2003). 
 
 

ESTIMATIONRESULTS 
 
Two sets of estimations were conducted: In the first



9 

 

310          Afr. J. Environ. Econ. Manage. 
 

 
 

Table 3. Ranking pro-environmental investments. 
 

Description Investment 
Rank 

Household invests in neither Energy efficiency nor renewable energy 0 
Household Invests in only one of either Energy efficiency or renewable energy 1 

Household invests in more than one of Energy efficiency and at most one renewable 2 

Household invests in more than one renewable energy technology only 3 

Household obtains a mixture of both dimensions involving more than one renewable 4 

 
 
 

Table 4.Technology specific estimates: energy efficiency. 
 

 WINDOWS APPL THRM 

LOG_INCOME -0.0093* 0.0081 0.0056 

EDUCATION 0.0010 -0.0071 0.0026 
MALE 0.0318 0.0359 0.0200 

MARRIED 0.1170** 0.2149*** 0.1260*** 

NOCOPE -0.0881** -0.0222 -0.0860** 
AGE 0.0045** -0.0027 -0.0004 

HHSIZE 0.0215 0.0010 0.0229 
SHARE AGE55 0.1059 0.1067 0.3289*** 
BEHAVIOUR_INDEX 0.0554*** 0.0789*** 0.0789*** 

NGO 0.1247*** 0.1945*** 0.1382*** 

ENV_NGO 0.0201 0.0851 0.0775 

ECOTOPCON -0.0224 0.0222 -0.0335 

ENVTOPCON 0.0263 0.0206 0.0196 

SKEPTICS -0.0551 -0.0485 0.1028*** 

LOG_FLOOR 0.2238*** 0.1695*** 0.2891*** 

OWNER 0.4042*** 0.2293*** 0.3903*** 

DHOUSE 0.0250 0.0630 0.2616*** 

TENURE -0.0029* 0.0021 -0.0060*** 

RURAL 0.0564 0.0117 0.1507*** 

PAY AS YOU USE 0.1402* 0.1532** 0.0652 

EBILL_KNOWN 0.0426 0.0640 0.0181 

KWATT_KNOWN 0.0034 0.1656*** 0.0276 
Sample Size 8,765 8,765 8,765 

Waldchi2 (22) = 3,832.48  [Six EquationModel]  
 

*p <0.05; **p <0.01; ***p <0.001. 

 
 
 
estimation, determinants of specific choices were 
estimated using MVP model (Tables 4 and 5). Along with 
this, cross correlation coefficients among the various 
equations were measured (Table 6). The objective of the 
second estimation is to identify the determinants of joint 
investments in the two dimensions of pro- environmental 
investments: Energy efficiency and renewable energy 
technologies. Investments were ranked according to how 
they theoretically affect the environment as shown in 
table 3; Ordered probit estimation procedure was used to 
identify correlates of being in the topmost category. 
 
Technology SpecificResults 
 
Tables 4 and 5 report the results. From table 4, 

determinants of investments in energy efficiency 
dimension are marital status (married), income constraint 
(nocope), energy use prudence (behavior index), social 
engagement (NGO), space availability (Floorsize), 
occupancy status (Onwer), length of stay in residence 
(tenure) and metering method (Pay as you use). In effect, 
the results confirm popular theoretical predictions: 
income constrained households are less likely to invest in 
sustainable energy use and households who are prudent 
in their energy use tend to seek more opportunities to 
enhance their energy conservation. There is the lemon 
problem since those who live in owned houses are more 
likely than renters to invest in energy efficiency. There is 
also space factor since investment in energy efficiency 
increases in floor size of the living home. Variation in



10 

 

Agwu          311 
 
 
 

Table 5.Technology specific estimates: renewable energy. 
 

 PUMP WIND SOLAR 

LOG_INCOME 0.0042 0.0012 0.0026 

EDUCATION 0.0228 0.0144 0.0214* 
MALE -0.0200 0.0709 0.1899 
MARRIED 0.0618 0.0679 -0.0456 
NOCOPE 0.0248 0.0386 -0.0799 

AGE -0.0113*** -0.0136*** -0.0076** 

HHSIZE 0.0516 0.0435 0.0732*** 

SHARE AGE55 0.3710 0.6198** 0.3703** 

BEHAVIOUR_INDEX 0.0342* 0.0442* 0.0614*** 
NGO 0.1241* 0.1094 0.2144*** 

ENV_NGO 0.1911** 0.3867*** 0.2999*** 

ECOTOPCON -0.0842 -0.0027 -0.0224 

ENVTOPCON -0.0993 0.1147 -0.0382 
SKEPTICS 0.2869** 0.2191** -0.0029 

LOG_FLOOR 0.1755** 0.1519* 0.1491*** 

OWNER 0.2088** 0.1253 0.3050*** 

DHOUSE 0.0616 -0.1325 0.1645** 

TENURE -0.0001 0.0031 -0.0014 
RURAL 0.0325 -0.2518* -0.1033* 

Pay_AS_YOU_USE -0.2442** -0.1359 0.0342 

EBILL_KNOWN -0.0354 -0.1742* -0.0452 

KWATT_KNOWN -0.0292 0.0547 0.2123*** 

Sample size 7,861 7,861 7,861 

Wald chi2(22) = 3,832.48  [Six Equation Model] 
 

*p <0.05; **p <0.01; ***p <0.001. 

 
 
 

Table 6. Cross correlation coefficients. 
 

EFFICIENCY × EFFICIENCY 

rho21 EFFICIENT WINDOWS ↔ EFFICIENT APPLIANCES 0.2157*** 

rho31 EFFICIENT WINDOWS ↔ THRM INSULATION 0.5833*** 

rho32 EFFICIENT APPLIANCES ↔THRM INSULATION 0.2682*** 
RENEWABLE × RENEWABLE 

rho54 WIND TURBINE  ↔ GROUND SOURCE HEAT PUMP 0.7882*** 
rho64 GROUND SOURCE HEAT PUMP ↔ SOLAR PANEL 0.5013*** 

rho65 WIND TURBINE ↔ SOLAR PANEL 0.5326*** 

EFFICIENCY × RENEWABLE 

rho43 THRM INSULATION ↔ GROUND SOURCE HEAT PUMP 0.3069*** 

rho51 EFFICIENT WINDOWS ↔ WIND TURBINE 0.3504*** 
rho52 EFFICIENT APPLIANCES ↔ WIND TURBINE 0.1951*** 

rho53 THRM INSULATION ↔ WIND TURBINE 0.3766*** 
rho61 EFFICIENT WINDOWS ↔ SOLAR PANEL 0.2639*** 

rho62 EFFICIENT APPLIANCES ↔ SOLAR PANEL 0.1956*** 
rho63 THRM INSULATION ↔ SOLAR PANEL 0.3489*** 

 

*p <0.05; **p <0.01; ***p <0.001. 

 
 
 
energy bill payment matters as households who their bills 
according to what the use as opposed to flat rate are 
more likely to invest in energy efficiency. The tenure 
effect aligns with the result of Tovar (2012). Households 
in this sample prefer to install energy efficiency measure 
earlier rather than later in their occupancy of the living 

home. Income of households, age of reference person, 
knowledge of volume of energy consumed at home, living 
in detached houses or in rural areas only matter for 
selected technologies within the dimension. However, 
general social orientation matters for energy efficiency 
technologies whereas environment specific social



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312          Afr. J. Environ. Econ. Manage. 
 
 
 
orientation (Env NGO) does not matter for any equation 
in the energy efficiency dimension. 
With respect to table 5 determinants of investment in 
renewable energy are Age, elders popular at home 
(share of age 55), energy use prudence (behaviour 
index), general and specific social orientation (NGO and 
Env_NGO). In general, similar factors predict investments 
in both dimensions. However, important differences 
recorded in respect to age and social orientation. 
Investors in renewable energy technologies are generally 
younger than investors in energy efficiency measures 
and tend to associate with both general and specific 
NGOs. There is further heterogeneity separating the 
energy efficiency and the renewable technologies 
adopters: adopters of the energy efficiency technologies 
are likely to express difficulty coping with their current 
income but this is not observed for renewable energy 
adopters. That is to say that income constraints are 
operative with respect to energy efficiency investment but 
not with respect to renewable energy investment. This 
may reflect the effect of subsidies on investments since 
renewable energy technologies are more likely to be 
subsidized than energy efficiency investment (OECD, 
2011). 
 
Relationship between choice across the dimensions 
 
The correlation coefficient of the six equations in table 6 
suggest positive relationships among all the investment 
choices. Tests of significance was conducted on the 
correlation matrix and all correlation coefficients were 
found to be statistically greater than zero. Although 
greater correlation is found within dimension, across 
dimensions the correlation is not positive nor negative. 
This suggest a complementary relationship between 
energy efficiency investments and renewable energy 
investments. Households seem to engage in energy 
efficiency to save money and in renewable energy to 
satisfy intrinsic environmental affection. 
Table 7 further explores the determinants of these 
relationships and identified education in terms of number 
of years of schooling after high school and in terms of the 
share of graduates in the home as contributory factors. 
There equally seem to be economies of scale among the 
factors: household size, share of children (Share under 
18) are positively associated with joint investment. On the 
other hand, tenure is negatively associated with joint 
investment. Owners of their living home are less likely to 
jointly invest in energy efficiency and renewable energy. 
Method of energy bill payment does not matter as well. 
 
 
CONCLUSION AND POLICY RECOMMENDATIONS 
 
This study sets out to identify the factors that determine 
households’ joint adoption of energy efficiency and 
renewable energy technologies. In the process, 

technology specific determinants were estimated and the 
results show that the two dimensions of pro-
environmental investment are determined by different 
sets of covariates. Income constraints affect investments 
in the energy efficiency dimension but not the renewable 
energy dimension. The study found evidence for split 
incentives between home owners and renters and 
negative tenure effect for both dimensions. Space 
availability in the living home matters generally for 
adoption of environmentally friendly energy behaviours. 
Payment of energy bills according to use matters only for 
energy efficiency while households’ association with 
environmental NGO only matters for renewable energy 
investment. 
A complementary relationship between households’ 
choices of investment in energy efficiency and renewable 
energy technologies is suggested by this work: all 
correlation coefficients of the multivariate energy 
investment model were positive and significant with 
highest coefficient observed within rather than across 
dimensions. Determinants of the joint investments was 
found to relate to education, economies of scale and 
social engagement in the form of association with non-
Governmental organizations. Owners are less likely than 
renters to jointly invest in both dimensions even though 
owners are more likely to invest in energy efficiency 
measures. Joint investments in both dimensions 
decrease in length of staying the living home. 
This study suggests that when it comes to promoting 
energy efficiency technologies, subsidization policies 
could be effective since it was found that income 
constraint plays significant role in households’ adoption of 
these set of technologies. Therefore, Subsidization 
policies targeted at energy efficiency technologies should 
be effective. However, such policies if targeted at 
renewable or low penetrated energy efficiency 
investments may not be effective as they will be taken up 
by consumers who are not subject to income constraint. 
Apart from economic incentives, since participation in 
NGO is positively associated with behaviour in all 
dimensions, raising awareness through informal channels 
could have effects on all the technologies irrespective of 
whether the technology is energy efficiency or renewable 
option. 
Finally, the correlation of errors in any pair of the 
equations, in particular between energy efficiency and 
renewable energy technologies are significant and 
positive. This suggests that investment in one 
environmentally friendly energy technology increases the 
likeliness of investing in another. Thus the interaction 
between households’ choices of environmentally friendly 
energy technologies should play a role in policy design. 
More effective policies should explore the knowledge that 
certain environmentally friendly energy investment can 
lead to heightened tendency to adopt other forms of 
environmentally friendly energy behaviour. Specifically, 
positive correlations between pairs of energy efficiency



12 

 

Agwu          313 
 
 
 

Table 7. Correlates of joint investment. 
 

VARIABLES Investment Rank 
LOG_INCOME 0.00510 
 (0.00367) 

EDUCATION 0.0152*** 

 (0.00587) 

MALE -0.0610** 

 (0.0256) 

MARRIED 0.00729 

 (0.0317) 

NOCOPE 0.0194 

 (0.0274) 

SHARE GRADUATE 0.178** 

 (0.0690) 

AGE -0.000658 

 (0.00144) 

HHSIZE 0.0287** 

 (0.0145) 

SHARE UNDER_18 -0.134* 

 (0.0724) 

SHARE AGE55 0.0168 

 (0.0727) 

BEHAVIOUR_INDEX 0.0475*** 

 (0.00750) 

NGO 0.149*** 

 (0.0272) 

ENV_NGO 0.0765* 

 (0.0427) 

LOG_FLOOR 0.0494 

 (0.0303) 

OWNER -0.408*** 

 (0.0305) 

DHOUSE -0.342 

 (0.00142) 
TENURE -0.00421*** 
 (0.00106) 
RURAL -0.0306 
 (0.0291) 

PAY AS YOU USE -0.0755 

 (0.0507) 

KWATT_KNOWN 0.0903*** 
 (0.0329) 

Constant cut1 -1.811*** 

 (0.165) 

Constant cut2 -0.202* 

 (0.164) 
Constant cut3 0.297* 

 (0.164) 

Observations 9,784 

Pseudo R2 0.1064 
Country Dummies Included 

 

Standard errors in parentheses 
*** p<0.01, ** p<0.05, * p<0.1 

 
 
 
and renewable technologies suggest complementarity of 
the two forms of environmental behavior and should be 
explored in policies design. 

Most previous models of environmental behaviour do not 
explore these correlations. Such models are likely 
inefficient and lacking in essential information. Future



13 

 

314          Afr. J. Environ. Econ. Manage. 
 

 
 
research should focus on incorporating the nature of 
relationships among different forms of environmental 
behaviour in the behavioural models. 
 
 
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APPENDIX 
 

Appendix A. Country effects on technologies  adoption. 
 

 WINDOWS APPL THRM PUMP WIND SOLAR 

AUS -1.2555*** 0.0089 0.2093** -0.1193 0.3789* 0.5789*** 

CAN 0.0069 0.0268 -0.1751* -0.1196 0.2101 -0.4474*** 

CHE -0.0360 -0.1863** -0.1821* 0.4561*** -0.2699 -0.1256 

CHL -1.0360*** -0.8287*** -0.3760*** -0.73811*** 0.0052 -0.8388*** 

ESP -0.0158 0.0214 -0.6808*** -0.255 -0.1287 -0.1511 

FRA 0.1669* 0.0657 -0.0607 0.1830 -0.0142 -0.1050 

ISR -1.0317*** -0.2225*** -0.5013*** -0.0788 -0.0154 1.9455*** 

JPN -0.8022*** -0.5244*** -0.6022*** -0.7463*** -1.0694** -0.3527** 

NLD 0.5705*** -0.2308*** 0.1299 -0.3566* -0.2786 -0.236* 

SWE -0.2244** -0.1248 -0.3278*** 1.0185*** 0.1639 -0.3421** 
 

*p <0.05; **p <0.01; ***p <0.001. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 


