Bio-gass paper.pdf

An evaluation of influencing factors and public attitudes for the adoption
of biogas system in rural communities to overcome energy crisis: A case
study of Pakistan
Bowen Luoa
, Arshad Ahmad Khan a
, Muhammad Abu Sufyan Ali a
, Jin Yu a,b,
⁎
a
College of Economics and Management, Northwest A&F University, Yangling 712100, Shaanxi, China
b
Institute for Six-sector Economy, Northwest A&F University, Yangling 712100, Shaanxi, China
H I G H L I G H T S
• Evaluation of households' willingness to
adopt biogas
• Biogas adoption and their associated
impacts on the society
• Potential of electricity production to
overcome the current energy crisis
• Public awareness and government at-
tention to promote biogas
G R A P H I C A L A B S T R A C T
a b s t r a c t
a r t i c l e i n f o
Article history:
Received 30 November 2020
Received in revised form 22 February 2021
Accepted 25 February 2021
Available online 3 March 2021
Editor: Huu Hao Ngo
Keywords:
Biogas
Livestock
Renewable energy resource
Probit model
Northwest of Pakistan
Energy is the backbone of a country's economy and development. The potential contribution of renewable energy
technology to energy stability, climate change mitigation and economic growth is immense. Biogas, is a renew-
able energy resource and enriched with methane, produced through the breakdown of organic matters (anaer-
obically). The large quantity of livestock has significant contributions in Pakistan's economy, and also having a
high potential for production of biogas. Therefore, this study was conducted in 6 southern districts of Khyber
Pakhtunkhwa province of Pakistan, to identify the expected willingness of households for adopting any biogas
system. A sum of 360 households (livestock-farmers) was selected for data collection by the procedure of
equal allocation. The identification of the influencing factors on the household's willingness to adopt any biogas
system was determined through probit analysis. It has been validated from the research outcomes of probit
analysis that the selected household's qualification, electricity shortfall on daily basis and its impact on children's
education and female work, awareness of the selected household's about the biogas utilization and its benefits as
well as the availability of space have demonstrated their significance and relationship with the household's
willingness to adopt biogas system. The overall model is statistically significant at 1% significance level and con-
firmed the impact of socioeconomic features as the prominent factors for the household's decision to adopt a bio-
gas system. This study suggests the public awareness, which has more significant impact on identifying the
household's adoption behavior. Similarly, adequate investments both at the private and public level, should be
encouraged for promoting biogas technology. At the same time, the government's fiscal policy should be subsi-
dized which will encourage the lower-income populations' participation in adopting and installing biogas plants.
© 2021 Elsevier B.V. All rights reserved.
Science of the Total Environment 778 (2021) 146208
⁎ Corresponding author at: College of Economics and Management, Northwest A&F University, Yangling 712100, Shaanxi, China.
E-mail addresses: lbw1995@nwafu.edu.cn (B. Luo), arshadkhan@nwafu.edu.cn (A.A. Khan), sufyanali@nwafu.edu.cn (M.A.S. Ali), yujin@nwsuaf.edu.cn (J. Yu).
https://doi.org/10.1016/j.scitotenv.2021.146208
0048-9697/© 2021 Elsevier B.V. All rights reserved.
Contents lists available at ScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv

1. Introduction
Since a few decades where climatic changes and their repercussion
for economic development has been under consideration and focused
by research scholars, governments and policy makers. An increasing in-
dication of interaction between climate changes and the untenable and
ineffective utilization of energy fuels is delivered by modern literature
(Awan and Khan, 2014). Besides as the world's population relies heavily
on fossil fuels for the consumption of energy, these fuels are declining
rapidly, leading to higher energy costs and augmented the emission of
greenhouse gases (Shahsavari and Akbari, 2018). Over the years, the
rapid demand for prevailing fuels and the rapid depletion of natural en-
ergy resources have drawn worldwide consideration to the search for
alternative energy sources.
Globally, the production of energy from renewable resources
can overwhelm the energy crisis in an eco-friendly and cost-
effective manner (Erdinc and Uzunoglu, 2012; Qari et al., 2017).
The various systems of renewable energy production such as
solar and wind energy, bio-mass energy, geo-thermal energy, ma-
rine energy as well as the technology of fuel cells have the capabil-
ity to disarrange the emission of greenhouse gases from fossil fuels
consumption and thus reduce climate changes (Suleman et al.,
2016). Similarly, the production of renewable energy can ade-
quately resolve the long-awaited issues regarding energy that are
faced by developing countries like Pakistan. To alleviate the
poverty in developing nations, they must have to overcome the
poverty of energy because the inadequate energy hinders a
nation's economic growth (Chakravarty and Tavoni, 2013;
González-Eguino, 2015).
According to the findings of Kebede et al. (2010), Odhiambo
(2009) and Tang et al. (2016) that consumption of per capita energy
is a prominent and a leading factor for economic development,
whereas growing consumption of per capita energy was referred as
country's economic growth by Lee and Chang (2008) and Sadorsky
(2009). Similarly, based on the reports of Jamil and Ahmad (2010)
and Shahbaz et al. (2012), that there is a significant association be-
tween Gross Domestic Product and energy consumption. It ensures
that high growth rate of Gross Domestic Product is guaranteed by
high consumption of energy.
The consumption of per capita energy guaranteed economic devel-
opment (Shah et al., 2020), but this consumption of energy in develop-
ing nations, including Pakistan, is depressing (Alam et al., 2007). Based
on the per capita energy consumption world wide's ranking, Pakistan
ranked on 165th in 2019. Fig. 1 represents the scenario of per capita en-
ergy consumption of top 3 and some other countries compared with
Pakistan. It portrayed the low, and discouraging position of Pakistan in
per capita energy consumption.
The biggest challenge to economic growth in Pakistan is in the
shape of an energy crisis, especially in rural vicinity where the
population is ensnared in the trap of massive poverty calamities
(Kumar, 2010; Nawaz and Alvi, 2018). Thus, reliable and reasonable
energy access has a prominent role in enhancing production,
encouraging economic development, health improvement, increas-
ing competitiveness as well as in reduction of poverty in a country
(Kaygusuz, 2012). The Pakistan Strategic Support Program intends
to conserve energy as a key commitment to achieve sustainable
and comprehensive development in the country. Recently, the
energy crisis is the most pressing problem around the globe
whereas Pakistan which is currently going through a great energy
crisis (Rauf et al., 2015). Therefore, ensuring sustainable economic
growth and the preservation of energy, the government of
Pakistan is taking into consideration all possible steps. Similarly,
based on the country's potential for renewable energy resources,
the government is trying to diversify its energy through the rapid
development of renewable energy resources (Rafique and Rehman,
2017; Zafar et al., 2018).
1.1. Renewable energy (biogas) potential in Pakistan
Pakistan is one of the world's more populated country and
population-wise ranked 5th globally, having more than 216.57 million
in 2019 (https://www.statista.com/statistics/262879/countries-with-
the-largest-population/, n.d) in which a major portion of the population
is residing in rural areas i.e., 63.09%. Whereas, most of these rural people
are attached to agricultural activities for their livelihood. Thus, the gov-
ernment is taking all possible measures through the implementation of
different development strategies to boost agricultural growth and en-
hance the living standard of the rural population in order to enhance
the overall economy (Liu et al., 2020; Shahbaz et al., 2013). Moreover,
the agriculture sector in Pakistan is also confronted with a severe threat
of energy crisis like other sectors, influencing the economic develop-
ment of the country, while based on the findings of murugan Nathan
and Wong (2012) that the role of energy in the development of agricul-
ture is like the provision of fuel.
Globally, about 40% of gross domestic product from agriculture (in
general) is delivered by livestock (in specific) by providing the employ-
ment opportunity to 1.3 (billion) population and generating livelihood
to the one billion poor people around the globe (Naqvi and Sejian,
2011; Newsroom, 2006). Livestock farming is also playing a pivotal
role and has a significant position in the agriculture sector of Pakistan.
It has many benefits for the farming community like milk, meat, cash in-
come and many more, especially for the small-holding farmers. Table 1
is showing the different types of livestock and their associated popula-
tion in millions. Previously, Amjid et al. (2011) stated that out of 172
Fig. 1. Consumption of per capita energy (kWh), country-wise ranking (2019).
Source: CIA World Factbook (CIA, n.d).
Table 1
Estimated livestock population (in million).
Type of livestock/species Livestock population
Cattle 49.6
Buffalo 41.2
Sheep 31.2
Goat 78.2
Camels 1.1
Horses 0.4
Asses 5.5
Mules 0.2
Source: Ministry of National Food Security & Research, Pakistan Economic Survey
(2019–20).
B. Luo, A.A. Khan, M.A.S. Ali et al. Science of the Total Environment 778 (2021) 146208
2

million animals, Pakistan is getting more than 652 million kg manures
only from cattle and buffalos on daily basis. Animal waste is usually uti-
lized for burning in rural areas for their domestic use, which becomes
the primary source of the emission of greenhouse gases. The proper
conservation of the manures could be utilized to generate biogas that
will meet the domestic needs of 112 million rural populations. The
above mentioned amount of manures has the potential to produce bio-
gas of 16.3 million m3
/day and bio-fertilizer of 21 million tons yearly.
Being an energy carrier, biogas has several potentials for both produc-
tion and consumption while it is mostly generated from a variety of
wastes (Olsson and Fallde, 2015). Crude biogas can be utilized for heat
and electricity generation while its little upgradation can make it appli-
cable for vehicle's fuel or could be incorporated into the natural gas res-
ervoir (Berglund, 2006). Thus, based on huge opportunities and
potentials, the use of biogas systems could be drawn in rural areas of
the country (Raheem et al., 2016; Sheikh, 2009).
In Pakistan, based on the benefits mentioned above and capabilities
associated with biogas, government initiated a program in the name of
the Biogas Support Program in year 2000. However, it has been revealed
by the findings of Amjid et al. (2011) that instalment of 1200 units of
biogas has already accomplished while in the next 5 years, the instal-
ment of more 10,000 biogas units are likely to be achieved that will
bring about 27% of the country's biogas capacity. In addition to animal
waste, street garbage, by-products of sugarcane, slaughterhouses, citrus
pulps, paper industries as well as from aquatic weeds, the exploration
and capabilities of biogas can be achieved in Pakistan (Tareen et al.,
2018; Zaigham and Nayyar, 2005). However, these entire sources are
not focused on this specific study.
Energy, which is a prime need of an individual for daily-life and also
playing a vital role in the improvement of a nation's economy. However,
Pakistan is full of natural resources and has the excellent potential for
energy production but still relying on foreign sources and has been
confronted with severe problems in the energy sector recently (Rauf
et al., 2015). The reports made by Asian Development Bank and IEA
(International Energy Agency) where they stated that Pakistan's energy
demand is expected to be increased by a 2.2% growth rate annually from
84.6 million tons in 2010 to 145.8 million tons in 2035. This growing
demand for energy will shift per capita energy's demand in Pakistan
from 0.49 tons in 2010 to 0.59 tons in 2035 (Ozturk, 2014).
Pakistan is facing severe problems of load-shedding or intermittent
cut-off or interruption in power supply which posing severe repercus-
sions for the country, specifically in the rural vicinities (Ali and Imtiaz,
2019). Lack of power supply leads to an increase in electricity prices,
which consequently divested the poor and rural populations of
Pakistan from getting proper and cheap refined fuel (Asif, 2012;
Sandilah and Yasin, 2011). The supply of electricity from the reserves
of natural gas and from national grid is not enough and cheap. It is
mostly out of the range of low-income rural people, whereas IEA (IEA
I, 2011) stated that almost 64 million Pakistani people are deprived of
electricity.
Additionally, the people with electricity are facing the problem of
load-shedding and become out of power from 12 to 18 h/day which
drastically influences the socioeconomic characteristics of their life. Fur-
thermore, to overcome this shortfall of power, the government of
Pakistan is importing fossil fuels of about 7 billion US dollars (Amjid
et al., 2011; Saghir et al., 2019). The energy crisis and its impacts on
the people as well as on the economic development are very crucial
for the government; that is why it attracts the attention of
policy-makers and regulatory authorities. Nevertheless, still, this prob-
lem remains and exists on the ground due to a lack of government inter-
est towards the generation of energy at a lower cost, financial
constraints and also lack of proper administration (Baloch et al., 2019;
Kessides, 2013). Under such conditions, the concept of a regionalized re-
newable energy system has been envisioned as a response to meet the
domestic energy requirements as well as in agricultural and industrial
sectors (Chaudhry et al., 2009; Ghafoor et al., 2016).
With the information mentioned above regarding biogas, it is evi-
dent that Pakistan has a greater capacity for biogas energy production.
The identification of factors that make contribution in individual's will-
ingness to adopt a biogas system in case of providing them any biogas
technology in future, in Khyber Pakhtunkhwa province of Pakistan is
attempted in the current study. There are several implications of empir-
ical identification of these factors, like they can assist in accelerating the
endorsement of any future intercession and serving as efficient compo-
nents in formulating inclusive energy policies at the provincial and na-
tional level.
The general goal of the present study is to increase the access of de-
moted rural populations to off-grid energy services in order to reinforce
Pakistan's energy-deficient economy. The key objectives of the current
research are 1). To evaluate the willingness of households in the project
area to adopt biogas systems. 2). To determine those factors that influ-
ence household willingness to adopt biogas systems. 3). The provision
of commendations for a viable policy with a focus on Pakistan's massive
energy transition.
2. Theoretical ideas underlying the study
Theoretically, the concept of the present study is established on
energy's choice theory. Usually, the individuals' fuel choice theory is
established on the energy ladder's model (Heltberg, 2003) and related
“fuel switching” concepts. Whereas, Masera et al. (2000) stated that
this model emphasizes income in defining energy choices. According
to the income of households, their energy choice experienced a linear
3 stage exchanging method. In which the 1st phase is characterized by
a heavy reliance on conventional bio-mass fuels. At the same time, the
2nd stage is referred to as the transition stage relating to the utilization
of traditional fuels. Whereas the utilization of modern/advanced energy
fuels such as Liquefied Petroleum Gas (LPG), natural gas as well as elec-
tric power is involved in the 3rd phase of this model. Mostly, the coun-
tries are unable and failed to provide clean energy to fulfil the demand
of their population. Therefore, they are trying to discover and focus on
the renewable energy resources to meet their demand (Afsharzade
et al., 2016).
Based on the findings of some researchers like (Andadari et al., 2014;
Hiemstra-Van der Horst and Hovorka, 2008; Masera et al., 2000) put
criticism on the simple nature of the model, which emphasizes wealth
and substitution. The exemptions to the general energy model are use-
ful to consider. Thus, the modern version regarding the energy choice
theory proposes that in addition to income, carriers of other factors
like socioeconomic and demo-graphic characteristics, institutional and
technological features as well as ecological features had a vital role in af-
fecting the energy choice of households (Hyde et al., 2000; Narain et al.,
2008; Van der Kroon et al., 2013). Therefore, given this theoretical back-
ground, the current study is an attempt for the identification of those
factors/aspects that can predict the households' willingness to adopt a
specific system of energy like biogas energy system in the study area
of Khyber Pakhtunkhwa province.
On the basis of the aforementioned literature, the energy shortage
and its increasing demand by the population of the developing nations
including Pakistan can be concluded. The reliance over traditional
sources of energy has become unstable due to their immediate con-
sumption, and ecological inadequacies. In order to fulfil the energy re-
quirements of these populations in an environmentally conducive
manner, recently, there has been an increase in exploring renewable
resources for energy production. However, based on some socio-
economic, technical and financial restrictions, Renewable Energy
Targets have not yet reached large-scale adequacy. Thus, prior to the de-
sign and implementation of any Renewable Energy Target plan, the
identification and study of these aspects are significantly important.
The current research aims to recognize those elements which affect
the households' expected willingness to adopt any specific biogas sys-
tem in the rural vicinities of Khyber Pakhtunkhwa province. Based on
3

the empirical field-work, the following hypotheses were examined
through the implementation of probit model. H0: There is no impact
of the explanatory variables on the willingness to adopt of households
for any biogas system (i.e., βp = 0). H1: The explanatory variables
might have an impact on the willingness-to-adopt of households for
any biogas system (i.e., βp ≠ 0).
3. Methodology
3.1. Description of study area and sample collection
This study was carried in the southern region of Khyber
Pakhtunkhwa (KPK) province, involving six districts, namely Kohat,
Hangu, Karak, Bannu, Lakki Marwat and Dera Ismail Khan (D.I.Khan)
as shown in Fig. 2. The focus and consideration of the current study
were on the livestock farmers, in which data was collected through dou-
ble stage purposive sampling procedure, in the first step of data collec-
tion, where 2 villages through the pre-determined criteria were
selected from every district, while the pre-determined criteria was the
number of farmers (livestock farmers) in that particular village as well
as having the problems of energy. A concise exploratory study about
the village was carried out prior to the selection of villages in order to
verify that there was sufficient number of livestock farmers in the vil-
lage. Hence, a total of 12 villages were nominated from the selected 6
districts of the study area, whereas, from each village, a sum of 30
households were finalized in the second phase. At the same time, the
purposive technique was applied for household selection. One buffalo
or two cows are the minimum requisite for a biogas plant (as per day
production of waste by one cow or two buffalo is needed to meet the re-
quirements of a small biogas (non-commercial) plant). Thus, the house-
holds meeting these criteria, were nominated as the sampled
households for this study. Finally, a sample of 360 livestock farmers
were selected from the already nominated 12 villages as shown in
Table 2. The data was collected through a semi-structured questionnaire
from the sampled respondents (households). The questionnaire em-
phasized mainly on the information on the socio-economic and demo-
graphic features of households, their composition, possession of land
and livestock, health-related problems (where emphasis were on the
children diseases, the problem of diarrhoea and respiration), their use
of energy, access to water, their opinion regarding any biogas system
as well as the use of any biogas system.
3.2. Model specification
The household's adoption behavior can be evaluated by applying
various econometric methods, which relies on type of dependent and
independent variables. We have a dichotomous endogenous variable
as well as a combination of categorical, and numerical exogenous vari-
ables in this current study. Therefore, the public decision regarding will-
ingness to adopt any biogas system was assessed by implementing
probit model. According to Greene (2004) and Sajaia (2008), probit
models using probit linked functions are usually assessed through stan-
dard Maximum Likelihood technique (ML). Whereas, Mittal and Mehar
(2016) and Sardianou and Genoudi (2013) stated that the use of probit
models had been implemented in many adoption behavior studies.
Mathematically, general linear-regression model is:
Yi ¼ β0 þ β1X1 þ β2X2 þ . .. þ βpXp þ εi ¼ Xβ þ ε ð1Þ
In Eq. (1), the dependent/endogenous variable is represented by Y,
coefficients of regression are represented by βp. The vector of
Fig. 2. Study area.
4

explanatory/independent variables are represented by Xp, and the error
term is illustrated by ε.
However, dependent/endogenous variable (Y) in Eq. (1) is in linear
form. Whereas dichotomous dependent/endogenous variable (Y) is
used as a number of social science problems in the exploration of non-
linear assessment, in which a linking function is presented in the econo-
metric model like a function connecting actual (Y) to the evaluated (Y).
Generally, this link function in any function F(Y) can be illustrated as:
F Y
ð Þ ¼ Y
⌢
¼ χβ þ ε ð2Þ
By reconsidering the dependent/endogenous variable, we will say:
Y ¼ ϕ χβ þ ε
ð Þ
ϕ−1
Y
ð Þ ¼ χβ þ ε
ð3Þ
whereas, the link function in our case F(Y) = Φ − 1(Y), referred to as
Probit link. Thus,
Y ¼ χβ þ ε ð4Þ
In our case, Y = 1 and Y = 0 representing that households' are will-
ing to adopt and not willing to adopt, respectively. Similarly, we also
have Xp which represents the vector of explanatory variables and hy-
pothesized causes of Y. Hence; the model can be illustrated as:
P Y ¼ 1=χ
ð Þ ¼ ϕ χ0
β
ð Þ ð5Þ
The description of this binary choice model is; Y = 1 for given func-
tion F(.). The assessment of parameters, i.e. βs, are done by Maximum
Likelihood. The probit model for a latent variable can be demonstrated
as:
Y ¼ χ0
β þ ε ð6Þ
In which ε ~ N(0,1). Then Y can be regarded as an indicator, if this la-
tent variable is positive:
Y ¼
1 if Y
0 i:e:−ε X0
0 otherwise:

; ð7Þ
3.3. Data analysis
The analysis of collected data was done through statistical software
SPSS 17 and STATA 16. The analysis of descriptive statistics and inferen-
tial statistics was accomplished through SPSS. The depiction of the
whole scenario regarding energy issues and the available alternative
energy options was described through descriptive analysis. The
Chi-Square test was also applied to determine the relationship between
various variables and a household's decision to adopt any biogas system.
At the same time, STATA 15.0 was implemented for regression analysis.
Similarly, the impact of various variables on the household's decision re-
garding the adoption of a biogas plant was assessed through probit anal-
ysis. The obtained results from these analyses are presented in the
following sections.
4. Results and discussion
4.1. Variables in the empirical model
The household's willingness to adopt decision for any biogas sys-
tem was modelled as dichotomous variable having two values i.e. 0
and 1, in which 1 demonstrates the willingness to adopt decision of
household's while 0 is showing the household's disagreement. The
household's willingness to adopt probability was based on the indi-
vidual/respondent, household and village level features. The follow-
ing sections provide a brief introduction to the explanatory variables
in the whole model as well as their predicted impact on the depen-
dent variables.
The individual's age is a significant variable in predicting a family's
adoption behavior. According to the findings of Liu et al. (2013),
Muneer (2003) and Sardianou and Genoudi (2013), that age is seen as
a proxy for literary experience and enhanced income. Therefore, signif-
icant expectations are predicted from age regarding a household's deci-
sion in the adoption of renewable energy interference. However, the
societal and cultural aspects as well as the nature of the study and keep-
ing them in mind, it is expected that individual's age will have negative
but significant implications in this specific model. It means that the
household's decision of adopting any biogas system(s) will be less
with respect to an increase in the respondent's age, and vice versa. As
the elder members of a family are more traditional and trying their
best to avoid risks as compared to young members, thus their percep-
tions will be less regarding the adoption of any biogas system (Kelebe
et al., 2017; Walekhwa et al., 2009). Hence, we can say that decrease
in the age of respondents will bring an increase in the adoption of
new biogas technology. In Table 3, mean age and standard deviation
are 48.73 and 17.32 respectively.
Another key element is education, which has been utilized by many
researchers (Uhunamure et al., 2019; Zeng et al., 2019) as a regressor in
various studies related to the adoption of biogas technologies. We used
education (qualification) as a categorical variable in this specific study
and expected that it would have a significant and positive impact on
the household's adoption behavior for any biogas technology. Another
significant explanatory variable i.e. respondent as a head of the house-
hold is expected to influence the dependent variable like accepting or
rejecting the adoption of the biogas system. It has been evident from
the literature that in the societies where males as the head of the family
are more dominant than females while adopting new technologies
(Mengistu et al., 2016; Uhunamure et al., 2019). We used it as a
dummy variable in this study. Out of the total sampled respondents,
87.3% of respondents were the heads of the household.
The income of the households is labelled as one of the significant ex-
planatory variable in adoption related studies as per the findings of
Yasmin and Grundmann (2019). It is anticipated that the households'
income has a positive impact on the outcome variable as the more afflu-
ent households' are likely to adopt it. In the absence of external funding
to install the biogas plant, it is expected that these households will tol-
erate the whole or some part of the initial cost for installing a biogas
plant. In developing nations, where this problem is enlisted as the
main constraint in which many households are willing for the adoption
of biogas system by installing biogas plant but failed because of afford-
ability issues (Mwirigi et al., 2014). Therefore, the probability of
installing a biogas system/plant and the income of households have a di-
rect relation i.e. an increase in the income of households will increase
the likelihood of installing a biogas plant. In the current study, where
Table 2
Sampling design for the study.
District Village name Number of households Total
Kohat Gumbat 30 (8.33) 60 (16.66)
Dhoda Shareef 30 (8.33)
Hangu Darsamander 30 (8.33) 60 (16.66)
Karbogha Shareef 30 (8.33)
Karak B.D.Shah 30 (8.33) 60 (16.66)
Sabar Abad 30 (8.33)
Bannu Shero 30 (8.33) 60 (16.66)
Nehar Ghara 30 (8.33)
Lakki Marwat Kot Kashmir 30 (8.33) 60 (16.66)
Tajori 30 (8.33)
D.I.Khan Daraban 30 (8.33) 60 (16.66)
Kulachi 30 (8.33)
Total (N) 360 (100) 360 (100)
5

the mean of households per month income and standard deviation
were 24,950 and 10,105 respectively.
According to Akram et al. (2017) and Sarker et al. (2020) that total
land-holding of a household is also a significant independent variable
and is anticipated that it has a vital role in the decision of households
for adopting biogas technology. However, the expectation of positive
and negative coefficients is presumed because the households having
more land-holding will not adopt due to their preferences towards an-
other clean energy system. However, the determination of actual sign
will come through empirical analysis, in the current study, where
mean and standard deviation for land-holding by the sampled house-
holds was 9.33 and 4.93, respectively, as illustrated in Table 3.
Similarly, based on the previous literature, i.e. Mottaleb (2019) and
Walekhwa et al. (2009), households' cost on energy fuels is a significant
explanatory variable and playing a key role and can bring changes in the
dependent variable. The household's cost on the use of energy was esti-
mated on a per month basis, whereas this specific explanatory variable
was taken in a quantitative form. This explanatory variable was ex-
pected to have a positive impact on the household's willingness to
adopt any biogas system, as the households with higher energy con-
sumption costs were anticipated to shift towards cheaper alternatives
like biogas. The observed mean and standard deviation for monthly
cost on energy fuels were 5650 PKR and 3148 respectively.
The period of daily load shedding could be another significant ex-
planatory variable to determine the household's adoption behavior.
The probability of adoption can increase in those areas that are facing
a severe shortfall of electric power as compared to the areas having rel-
atively less shortfall of electric-power (Uddin et al., 2016). According to
descriptive statistics, the average daily electricity shortage and its
standard deviation in the study area were 16.10 (hours) and 5.47 re-
spectively. The energy shortfall and its impact on the children's educa-
tion is also a significant independent variable that is capable of
bringing and explains the variations in dependent variable. This specific
variable was also expected to be positive and significant during the de-
cision of households regarding the adoption of any biogas system. As the
households whose children's education is influenced by electricity
shortage will switch towards alternative and cost-effective options for
energy carriers (Ahmad et al., 2014). The amount of livestock among
our sampled respondents is also a significant element in the determina-
tion of a household's decision to adopt a biogas system, as it is the source
of providing essential manure for the biogas (Li et al., 2016). Whereas, in
the current study, we utilized this number of livestock as a quantitative
variable. Based on the current outcomes, we obtained the mean and
standard deviation for the number of livestock owned by the sampled
households were 4.40 (animals) and 2.61 respectively. The aggregation
of the entire set of variables (utilized in this section) in a single dimen-
sion was formulated through factor analysis.
Consequently, after obtaining the new quantitative dimension vari-
able which was marked as awareness, was implemented as an explana-
tory variable in the model. The obtained mean and standard deviation
for this specific variable were 0.09 and 1.03 respectively. Similarly, the
findings of Nzimande (2004) and Srinivasan (2008) revealed that the
impact of electricity shortfall on the women drudgery/work and the oc-
currence of diseases associated with smoke are also important explana-
tory variables. While the studies of Parawira (2009) endorsed that the
household's location to the excessive amount of water and space are is
anticipated to have the potential of bringing variations in the dependent
variable. The requirements of excess water and space are necessary for
the establishment and proper operation of any biogas system; therefore,
the possibility of adopting any biogas system will increase as these fac-
tors become available (Kabir et al., 2013; Mwakaje, 2008). In the current
study, the implementation of these variables was formulated as dummy
variables, whereas the outcomes of these variables are presented in
Table 3.
4.2. Factor analysis
The inadequate information and lack of awareness about the bene-
fits associated with biogas is one of the prominent factor that hinder
the household's decision to adopt any biogas system (Mittal et al.,
2018; Muvhiiwa et al., 2017; Uhunamure et al., 2019). To that end, a
whole part of the questionnaire for data collection was provided to ad-
dress the respondents' understanding of the biogas system and its asso-
ciated advantages and disadvantages. The aggregation of the whole set
of variables utilized in this section was formulated by means of factor
analysis. The resulting variable which is identified as awareness was
attained in the quantitative form that was utilized as explanatory vari-
able in the analysis. The assessment of awareness about biogas technol-
ogy for which major acquired questions were related to general
information regarding biogas, its utilization, and socioeconomic as
well as ecological impacts. The provision of standardized value i.e.
(z-score) for awareness was established by factor analysis. The mean
(0.09) and standard deviation (1.03) values for awareness are pre-
sented in Table 3. The descriptive statistics about these individual
queries and their ratio of responses acquired from the selected house-
holds are illustrated in Table 4.
4.3. Estimated results from the Probit Model
Recently, the rapid depletion of non-renewable fossil energy re-
sources has induced an increasing tendency in renewable biofuels ener-
gies. Renewable energy sources like biogas are perceived to be clean
energy resource that reduce effects on the environment and are sustain-
able in terms of existing and future social and economic needs. The ap-
pearance of biogas technology is often appear in affluent households
Table 3
Descriptive statistics for the selected variables applied in the Probit Model.
Variable description Mean SD Min. Max.
Quantitative variables
Respondent's age 48.73 17.32 20 78
Total Land-holding (hectare) 9.33 4.93 2 38
Working members at the selected households 2.78 2.18 1 5
Per month income of the selected households
(PKR)a
24,950 10,105 3945 80,850
Monthly cost incurred on energy 5650 3148 730 13,720
Daily shortfall of electricity (hours) 16.10 5.47 3 15
Total quantity of livestock owned by selected
households
4.40 2.61 1 42
Monthly production of dung (total quantity in
mounds)
2071 1865 403 13,446
Awareness (factor analysis) 0.09 1.03 1.34 1.79
Qualitative variables Mean
Qualification
None 32.3
Primary 18.7
Middle 22
Secondary 19.6
Graduation 8.49
Occupation
None 12.8
Farmer 61.2
Service 11.4
Business 4.6
Labour 10.6
If the selected respondent is head of household 87.3
Electricity shortfall's effect on children's education (=1, if Yes) 71.6
Electricity shortfall's effect on female's drudgery/work (=1, if Yes) 58
Occurrence of smoke-related diseases in the households' in the last 5 years
(=1, if Yes)
78.2
Available excess space (=1, if Yes) 76.7
Available excess water (=1, if Yes) 57.3
N = 360
a
Income (1USD = 158 PKR).
6

with improved socio-demographic status and other resource capabili-
ties (Shallo et al., 2020). The determination of prominent factors that
are capable to affect the household's willingness to adopt decision for
a biogas system was accomplished through probit analysis. It has been
evident from the results that the qualification of the individuals of se-
lected households, daily shortfall of electricity, and its effect on female's
work and children's education, awareness factor and the availability of
excess space were found statistically significant. While respondent's
age, total land-holding by the selected households, and the working
members of the households, per month income and the monthly cost
incurred on energy from households and the availability of excess
water were found statistically non-significant. The overall outcomes of
the model through probit analysis are demonstrated in the following
Table 5.
In Table 5, the outcomes signified the association between the
respondent's qualification and the household's decision about adopting
the biogas technology in the study area. This explanatory variable was
applied in the categorical form in the current study. The respondent's
qualification was comprised of different levels i.e. primary, middle, sec-
ondary and graduation, in which all levels of education qualification
were found statistically significant at 1% and 5% level of significance.
These outcomes are in line with the findings of Mwirigi et al. (2009)
by concluding that education has a vital role in the household's willing-
ness to adopt decision of any biogas technology in Kenya. Simulta-
neously, a similar conclusion was drawn by Kabir et al. (2013) in
Bangladesh. Similarly, the studies of Walekhwa et al. (2009) and
Mwirigi et al. (2014) approved that the probability of adopting biogas
system is more in the households having a high level of education.
The research outcomes also validated the high significance (at 1% level
of significance) of daily shortfall of electricity and confirmed the positive
association with the household's decision of adopting biogas technol-
ogy. This implies that an increase in the daily shortfall of electricity
will increase the probability of adopting biogas technology by
households.
Moreover, the results revealed a significant outcome for electricity
shortfall's effect on children's education at 5% level of significance. How-
ever, it showed a negative correlation with the household's decision to
adopt biogas technology. It has been evident from the descriptive statis-
tics that 71.6% (Table 3) of the selected households reported the effects
of the daily electric shortfall on children's education that signifies the
household's awareness about power shortage and its associated reper-
cussions. This specific study revealed that awareness has not positive
impact on the decision of households regarding the adoption of biogas
technology, as indicated by the coefficient's sign. Similarly, a positive
and statistically significant (at 5% level of significance) result for the
effects of electricity shortfall of female's work approved its association
with the household's decision of adopting biogas system. The collection
of fuel-wood and water to meet the consumption of households is the
females' responsibility especially in rural areas. Whereas, the require-
ments of fuel-woods become increase during the high period of electric
shortage, therefore, the female's responsibility of collecting fuel-woods
increases and consuming more time. The research outcomes of
Surendra et al. (2011) showed that in many rural areas where many
people, especially children and women spent several hours in the col-
lection of fire-wood each day, in order to meet the daily household's re-
quirements. While the study of Karekezi et al. (2005) revealed that
females in Nepal specifically in hilly areas are spending almost 2.5 h
per day in the collection of fuel-woods. As a result, the households are
continuously seeking for the alternative energy sources at a compara-
tively lower cost. Therefore, in such a scenario, the probability of
adopting biogas system by the households is increasing.
Based on the findings of Baloch et al. (2019) and Ghafoor et al.
(2016) that the major disadvantage of utilizing fossil fuel is the ecolog-
ical risks related to its application. Globally, in order to minimize the
ecological risks, people are switching towards the adoption of renew-
able energy systems. The growing awareness of the people about the as-
sociated benefits of renewable energy has increased its adoption (Mittal
et al., 2018). The awareness level of the selected households was applied
in a quantitative form as an explanatory variable in the model. The re-
sults approved the importance of awareness having statistically signifi-
cant result at 1% level of significance, indicating positive relations with
Table 4
Descriptive statistics of the selected variables applied in Factor Analysis.
Description of the variables Yes No
1. Any information regarding biogas (=1, if Yes) 35 65
2. Any past experience of biogas technology (=1, if Yes) 30 70
3. Any information about the use of biogas for performing daily oper-
ations (=1, if Yes)
42 58
4. Any information about the benefits of the biogas system and its
impact on children's education (=1, if Yes)
59 41
impact on female's work (=1, if Yes)
48 52
impact on agriculture production (=1, if Yes)
38 62
7. Any information about the use of gas for cooking will produce lesser
smoke (=1, if Yes)
36 64
8. Any information about the use of gas for cooking will retain our
kitchen neat and clean (=1, if Yes)
30 70
9. Any information about the use of gas for cooking has benefits for
health (=1, if Yes)
37 63
10. Any information regarding the slurry provision by biogas plant (=
1, if Yes)
28 72
Table 5
Estimated results on the likelihood of biogas plant installation through Probit Model.
Description of the variables Coef. Std. Err. z
Respondent's age 0.0303 0.0291 1.04
Qualification
Primary (C) 0.9164⁎⁎ 0.4801 1.91
Middle (C) 0.4108⁎⁎ 0.2104 1.95
Secondary (C) 0.5907⁎⁎⁎ 0.2805 2.11
Graduation (C) 0.1674⁎⁎ 0.0910 1.84
Occupation
Farming (C) 0.4935 0.3109 1.59
Services (C) 0.1746 0.1407 1.24
Business (C) 0.2537 0.2112 1.20
Labour (C) 0.7053 0.8140 0.87
If the selected respondent is head of house hold
(C)
0.3794 0.0304 1.03
Total Land-holding (hectare) 0.0519 0.1184 0.43
Working members at the selected households 0.2469 0.3304 0.75
Per month income of the selected households
(PKR)
0.8152 0.5967 1.37
Monthly cost incurred on energy 0.6012 0.5741 1.05
Daily shortfall of electricity (hours) 0.4472⁎⁎⁎ 0.0937 4.77
Electricity shortfall's effect on children's
education (C)
−0.6818⁎⁎⁎ 0.3261 −2.09
Electricity shortfall's effect on female's
drudgery/work (C)
0.0946⁎⁎ 0.0516 1.83
Total quantity of livestock owned by selected
households
0.0954 0.1019 0.94
Monthly production of dung (total quantity in
mounds)
0.3713 0.2973 1.25
Awareness (factor analysis) 1.0517⁎⁎ 0.5375 1.95
Occurrence of smoke-related diseases in the
households' in the last 5 years (C)
0.1651 0.2104 0.78
Available excess space (C) 0.5937⁎⁎ 0.3088 1.92
Available excess water (C) 0.4108 0.3842 1.07
Constant −3.235673 0.978567 −1.26
Summary statistics
Number of obs 360
LR chi2
(23) 91.78⁎⁎⁎
Log-likelihood −57.756268
Pseudo R2
0.5346
⁎, ⁎⁎, ⁎⁎⁎ level of significance at 10%, 5% and 1%. (C) = Categorical representation of
variables.
7

the adoption of the biogas system. While these results are in line with
the findings of Muvhiiwa et al. (2017) and Shallo et al. (2020) who
stated that enhancement of awareness among households have a signif-
icant role in the adoption of renewable energy systems and attaining
the associated benefits.
This study has approved the importance of one another explanatory
variable, i.e. excessive space in nearby selected households. The results
signified the statistically significant outcome at 5% level of significance
for this explanatory variable which reflects the relationship with the
household's decision to adopt biogas system. The availability of excess
space has a prominent role in the installation of any biogas system.
Our results are in line with the research outcomes accomplished by
Akinbami et al. (2001) and Kabir et al. (2013).
Unlike our determined hypothesis, some of the selected indepen-
dent variables like respondent's age, occupation of the selected respon-
dents that includes farming, services business, and labour, whether the
selected respondents are the heads of the households, total land-
holding by households, per month income of the households and their
monthly cost incurred of energy, the total amount of livestock as well
as monthly production of dung produced by livestock, the occurrence
of smoke and its associated diseases and the amount available of excess
water have not revealed a significant association with household's deci-
sion of adopting biogas technology. Despite significance in adoption
studies from these explanatory variables, the fact is that the willingness
of under observation households about adopting biogas system is very
susceptible to the differences e.g., social and economic variations, con-
textual, and institutional factors.
However, the highly significant (at 1% level of significance) esti-
mated value of LR chi-square is 91.78 along-with log-likelihood
−57.756268 value, which revealed the statistical significance of the
overall model. This reflects that the enlisted socioeconomic (variables)
have validated the important impact on the selected household's deci-
sion regarding adopting biogas technology. Thus, it approved that the
hypothesis is true as the independent variables have an impact on the
decision of selected households regarding the adoption of biogas system
in the study area.
The households' decisions are different regarding adoption of any
technology depending on the social, economic, cultural, ecological and
technical factors. A number of studies had carried out and revealed
mixed results. For instance, the study conducted by Shallo et al.
(2020) reported that level of income and education, access to credit
and electronic media, and distance to fire-wood sources had shown pos-
itive and significant impact on the households' decision of adopting bio-
gas technology. Similarly, based on the study of Berhe et al. (2017) that
gender, size of cattle holding, mobility of livestock, working age, and ac-
cess to credit services and to electricity are the influential factors regard-
ing household's energy choice in Ethiopia. While, Uhunamure et al.
(2019) concluded that besides other factors like gender, age and educa-
tion of household head, cattle's quantity, income, and loan and subsi-
dies, the awareness factor had also revealed influence in the adoption
of biogas technology.
5. Concluding remarks, limitations and policy implications
The current study was designed to evaluate the selected households'
expected willingness regarding their adoption behavior of the biogas
system in the southern 6 districts of Khyber Pakhtunkhwa province of
Pakistan. Identifying the expected willingness of the households to
adopt a biogas system was accomplished through probit regression
analysis. The study revealed based on field visits that socioeconomic
features of the selected households have a prominent role in determin-
ing their adoption behavior of biogas technology in the specified rural
vicinity. The explanatory variables in the model like the qualification
of the selected households, electricity shortfall and consequently its im-
pact on the children's education and female's work, the awareness of
the selected household about the biogas utilization and its benefits as
well as the availability of excessive space have shown their significance
and relationship with the household's willingness to adopt biogas sys-
tem. While on the other hand, other significant explanatory variables
have not revealed their impact and significant relationship with the
adoption decision of households about biogas systems. Which involved
the respondent's age and occupation, total land-holding, total working
members and the per month income of the selected households as
well as their monthly cost incurred on energy, amount of livestock
owned by households and their monthly production of dung, the occur-
rence of diseases associated with smoke and the availability of excessive
amount of water. However, the overall model has been found statisti-
cally significant at 1% level of significance. Hence, it has been validated
on the current outcomes that socioeconomic features of respondents
have a significant relationship with a household's adoption behavior
of biogas system in the study area.
Moreover, in spite of potential contributions to the literature, this
study still has some limitations. The achievement of significant conclu-
sions where the quantitative evaluation needs large number of cases.
One of the study's limitations is that the number of households sur-
veyed was not large enough for quantitative analysis. Therefore, it is rec-
ommended that the current problems required greater attention and
should be studied over a broader scale. Similarly, the limitation is linked
to the possible biogas capacity, such as how this capacity can be utilized
to supply energy to different loads, i.e. residential and farm? While, for a
biogas production system with a grid interface in order to balance
demand-supply management, further analysis and simulation will be
performed.
Based on the current study, some important policy commendations
were made. The countrywide promotion of biogas in general and specif-
ically in the study area is required by applying comprehensive plans. On
the one hand, various campaign strategies like print and electronic
media should be followed to raise awareness of the population regard-
ing biogas technology. Public awareness is an important factor in iden-
tifying a household's adoption behavior, so increasing public awareness
will provide remarkable results. On the other hand, adequate invest-
ments both at the private and public level should be encouraged for pro-
moting biogas technology. Moreover, the government should plan their
financial policies in accordance to reduce the poverty and inequality by
providing subsidy on the installation of biogas plants. So that the partic-
ipation of the lower-income population is encouraged in the adoption
and installing of biogas plants.
CRediT authorship contribution statement
Bowen Luo: Conceptualization, Formal analysis, Investigation, Soft-
ware, Methodology, Writing – original draft. Arshad Ahmad Khan:
Writing – original draft, Data curation, Writing – review editing.
Muhammad Abu Sufyan Ali: Investigation, Software, Methodology,
Writing – review editing. Jin Yu: Data curation, Formal analysis,
Funding acquisition, Investigation, Software, Methodology, Project ad-
ministration, Supervision.
Declaration of competing interest
I would like to declare on behalf of my co-authors that the work de-
scribed is original research that has not been published previously, and
not under consideration for publication elsewhere, in whole or in part. I
confirmed that no conflict of interest exists in the submission of this
manuscript, and is approved by all authors for publication in your
journal.
Acknowledgement
This paper is supported by “Research on the Experimental Measure-
ment of Farmer's Individual Preference and Evolution of Land Transfer
Policy based on the Separation of Rural Land Ownership Rights, Contract
8

Rights and Management Rights”, the National Natural Science Founda-
tion of China(NSFC) 71874139. Sponsor and Host: Jin Yu.
This research work is supported by “Experimental Measurement on
Farmers' Risk Preferences and Interconnection between Public Policies
and Farmers—A Case of 1600 Rural Households From Shaanxi, Gansu,
Shandong and Henan Provinces in China”, the National Natural Science
Foundation of China(NSFC) 71573208. Sponsor and Host: Jin Yu.
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