This cook stoves, area covered by housing, topography

This
section gives an overview of the research that has been taken place in terms of
energy access, energy quality and energy demand in the rural households.

Limited
research has been done on energy demand for rural Nepal. These studies had laid
the foundation and determining key aspect in the energy demand pertaining to
rural Nepal.

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 Parajuli
(2011) in
his research illustrated the ‘energy
poverty’ situation of Nepal, in which he has referred, energy poverty as
the absence of access to convenient, reliable, efficient and modern energy
technologies to satisfy the basic needs that support human and economic
development. Further, Parajuli
(2011) in
his research listed some of the challenges encountered in Nepal during the  expansion of energy access, like, geographical
variations, poor transportability, fragmented settlements, illusive energy
development strategy and lack of sufficient resource and planning etc. Moreover,
Rijal,
Bansal et al. (1990) in
their research, have found that the relevancy of socio-economic variable for
estimating end-use energy requirements for different villages vary according to
the availability, price and choice of specific fuels for different end-use
activities. In this study they have, considered eight variables for projecting
end-use energy requirement, these are: standard population, household
expenditure, agriculture commodity, number of livestock, number of cook stoves,
area covered by housing, topography and forest accessibility condition of the
particular village (Rijal, Bansal et al. 1990).

Similarly(On the other hand), Bhattacharjee
and Reichard (2011)
reviewed different research works on the basis of socio-economic factors
affecting household energy consumption. They reviewed different socio-economic
factors independently and later, grouped them in four broader categories based
on their prevalence (Bhattacharjee and Reichard 2011). The broader
groups where demographics: where they found that the most significant determinant
of household energy consumption where: family size, age distribution, the
number of wage-earner in the household, the occupancy time in the house. They
also categorised Consumer Attitude and Economic variables on broad basis and
grouped mostly the following determinants like, income, knowledge, attitude,
education, positive experience, dwelling size, dwelling type and dwelling
characteristics, energy prices which had influenced the energy consumption. Additionally,
weather, dwelling microclimate, and atmosphere was grouped in one as climate,
which affected the energy consumption (Bhattacharjee and Reichard 2011).

 Belaid
and Abderrahmani (2013) in
their research investigated and analysed the causal relationship between
electricity consumption, oil prices and economic growth in Algeria. The
empirical results show a bi-directional short run relationship between
electricity consumption and real GDP for Algeria and a strong linear
bi-directional long-run causality between the two variables in long run.
Short-run causality explains electricity generation directly affects economic
activity (Belaid and Abderrahmani 2013).
Their finding lays emphasis on electricity consumption as a prerequisite of
achieving higher GDP growth for Algeria (Belaid and Abderrahmani 2013). Fu,
Allen et al. (2015) in
their research evaluated the relationship between the population trends, heat
waves and demand of Energy Consumption, change in the average electricity
price, average annual temperature and extreme weather conditions in big cities
like New York, Chicago and Los Angeles. They found that only in one city, Los
Angles, the energy demand was affected by the variation in the price of
electricity(Fu, Allen et al. 2015).

Louw,
Conradie et al. (2008) in
their studies examined the household energy consumption patterns in the light
of energy transition theories. Energy transition theory suggests that there is
a ‘ladder of the fuel preference’ from low-quality fuels to the more convenient
and versatile ‘modern fuels’. At the macro-level, a cross-sectional analysis of
energy consumption across low, middle and high income countries suggests that
this transitions “from traditional biomass fuels to fossil fuels and
electricity appears to be a basic feature of economic growth” (Louw, Conradie et al. 2008). Foley
(1995) has
said the demand for energy is derived from that for the services it provides or
makes possible. In terms of developing countries, at the most subsistence level
the only energy required is for cooking and keeping warm, which is mostly
supplied by biomass fuels. But as the economic circumstances begins to improve
and as they emerge from the subsistence existence, additional and more
diversified energy demand begins to surface, which can be satisfied by
commercial energy sources (Foley 1995).

 As the disposable incomes rises, the most
preferred facilities are better lighting, as the disposable income continues to
increase, demand emerges for electricity-using devices such as radios, radio
cassette player and small black and white TVs. Further up the income level,
families want for the electrical appliances increases. Similarly, communities
also exhibit a similar patterns of evolution in energy demand as the economic
development takes place (Foley 1995). The author
also stresses the importance of electricity and its effect on increasing the
living standard in the rural areas. >   The emergence of different and more effective,
means of meeting these evolving electricity demands is key feature in the rural
development process (Foley 1995).  Louw,
Conradie et al. (2008)  in their research find the positive
correlation between the income and electricity, and also looks at the
possibilities of displacement of other fuels by electricity. Through the
research it has been seen that; the shift in the fuel use due to electricity is
mostly in the wealthier homes. Likewise, electrified households tend to spend
more energy than un-electrified households, however the difference is low as
the electrified households tend to rely less on candles, kerosene and batteries(Louw, Conradie et al. 2008).

 Frederiks,
Stenner et al. (2015) from
their comprehensive review of numerous research have come up with two broad
categories of variables that are important for explaining variability in energy
consumption and conservation. They are socio-demographic factors and
psychological factors; the Figure 2 below gives an overview of the impacting
variables. The authors from their literature found that, the household energy
consumption and conservation are associated with the two broad variables
mentioned above, but the association were not always substantial,
straightforward or consistent (Frederiks, Stenner et al. 2015).
They further outlined the fact that, the variables were linked in complex ways
and their impact was heavily contingent upon those moderating factors. Adding
to that, the household energy use was not shaped in direct linear fashion by
few principal individual-level factors. Rather, there were multitude of
variables that together influence the nature, intensity and duration of
behaviour around energy consumption and conservation (Frederiks, Stenner et al. 2015).  Further, their review suggested that, socio-demographic
predictors like, household income, dwelling type/size, income ownership, family
size/consumption are strongly associated with household energy usage, but in
some case the effects were mixed. Whereas, the effect of psychological variables

 
 
Socio-Demographic Factors
·        
Age
·        
Gender
·        
Education and
literacy
·        
Employment status
·        
Socio-economic status and income
·        
Household characteristics
(e.g., size type, life cycle stage )
·        
 Dwelling
characteristics
(e.g., age size, condition, ownership)
·        
Geographical location
(e.g., urban/rural, climate zone)
Psychological Factors
·        
Knowledge/awareness
(e.g., perceived risk/threat)
·        
Values, beliefs and attitudes
·        
Motives, goals and intentions
·        
Personal norms
·        
Perceived responsibility and sense of moral
obligation
·        
Personality tendencies
(e.g. altruism, self-efficacy, perceived behavioral control, etc.)
·        
Group membership and normative social influence
·        
Other cognitive, affective and motivational
influences

Individual
Predicators

 
 
Contextual &
structural Factors
·        
Laws, regulations and policies
·        
Available technology
·        
Pricing (e.g., tariffs, rebates and subsidies)
·        
Built environment
(design and infrastructure)
·        
Information, mass media and advertisement
·        
Neighborhood factors
(e.g., community spirit, cohesion, etc.)
·        
Broader public norms and community expectations
·        
Socio-cultural traditions and customs
·        
Other social, cultural, economic, political and
legal influences in the environment

Situational
Predictors

Household Energy Behavior

Energy Consumption & Conservation

in the energy usage was far from consistent and
conclusive.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure
2 : Integrative
conceptualization of the various individual and situational factors
that may influence household energy consumption and conservation.
 Source:Frederiks,
Stenner et al. (2015)
 

 

 

 

 

 

 

Pokharel (2007) from
his research endorsed the close tie between economic growth and energy demand
and also stressed the fact that the understanding of broader impacts of energy
use can be of great help in designing better energy policies at the macro
level.  The author has proposed different
econometric models
after studying the influence and significance of various independent variables
with chosen dependent variables. The two broad econometric models are based on fuel
& consumption, further they are explained according to the fuel type and
energy consumption sector(Pokharel 2007). The two
scenarios were used to analyze the impact of energy in the economy and came up
with the conclusion that while formulating the plan for economic growth, they
must also outline the energy requirements. Also, to meet the energy targets the
indigenous energy sources must be promoted, along with increase in the supply
source, end-use efficiency and reduce energy use cost (Pokharel 2007).

Similarly,
Parajuli, Østergaard et al. (2014) in
their research, projected the energy consumption of Nepal using simple linear
logarithmic energy consumption models. Similar to Pokharel (2007), Parajuli, Østergaard et al. (2014)
analyzed the energy consumption using different scenarios on the basis of the
growth rates of economic indicators along with a the different variables like
private consumptions, population, transport vehicle numbers, prices of fossil
fuels. They found that, energy consumption in all the sectors and for all fuel
types were not statistically correlated with every economic parameter tested in
the assessment. Furthermore, they concluded that total primary energy consumption
and electricity consumption were significantly dependent to the total GDP and
population growth (Parajuli, Østergaard et al. 2014). They also
estimated the energy required in 2030, with business as usual scenario will be
3.47 times that of 2009, likewise with medium growth scenario it will rise to
5.71 times, whereas with high growth scenario the energy need will increase by
10 folds to that of 2009 (Parajuli, Østergaard et al. 2014).

 In another studies, the author examined the
residential electricity demand in South Africa, using income and price of
electricity as the independent variables for the electricity consumption (Ziramba 2008) and found that
the income was the main determinant of electricity demand, whereas price pf
electricity was insignificant (Ziramba 2008).

York
(2007) in
his suggested that the expected decline of population growth in Europe will
help curtail expansion in energy consumption. Further York
(2007),
found that the relationship between population size and energy consumption was
highly elastic and the change in age structure influences energy consumption.
However, Liddle
(2014)
found that, age as the variable for energy consumption was insignificant or the
impact was very low. In case of family size and energy consumption, there
exists a complex and nonlinear relationship. Likewise, in different study Liu,
Spaargaren et al. (2013)
found that household size was one of the highly significant variable for energy
consumption in case of rural China. Similarly, another author has concluded
that the old people display different consumption patterns than that of young
people, the portion of increase in old people will affect the overall energy
consumption (Kronenberg 2009).

 Räty
and Carlsson-Kanyama (2010) in
their studies has analyzed the role of gender in terms of energy consumption.
They found that men are more likely to use more energy as compared to women. Similarly,
Permana,
Aziz et al. (2015) in
their attempt to understand gender and residential energy consumption, came up
with argument that, when women solely makes decision regarding energy
expenditure and control then the total energy consumption tented to be lowest.
However, in developed economies like Denmark, the finding showed that energy
use was perceived differently by men and women as a consequence of different
daily activities and therefore gender affected the energy use. Also author
stressed on the fact that, gender was significant factor in regards to energy
use (Tjørring 2016). Moreover,
findings reveal that there is need of women involvement in energy conservation
and the necessity of mainstreaming gender into the policies on energy demand
and efficiency (Permana, Aziz et al. 2015).

 Ben
Abdelkarim, Ben Youssef et al. (2014) in
their research found that, economic growth, energy consumption and education
are interlinked and considers education as one of the driver of energy
consumption using economic growth as an instrument. Gylfason
(2001)
ponders education as the prerequisite for the rapid economic development.
Similarly, Kanagawa
and Nakata (2008) in
their study, considered education as one of the most essential component of
poverty reduction.

The
UN report concluded that, lack of electricity at primary and secondary schools
created considerable obstacles toward escaping poverty, and correlated with
many factors that contributed directly towards it (UNDESA 2014). However,
education not only affected energy consumption through economic growth but also
affected it through the purchasing behavior of the consumers, technology advancements,
adaptation, and fuel substitution. Education coupled with awareness level of
informed individuals within a society helps disseminate information about fuel
efficiency, technological developments but at the same time, due to improved
education levels particularly in developing countries, more energy intensive
goods and services were being demanded and supplied (Ben Abdelkarim, Ben Youssef et al. 2014).

Another
authors, put forward the argument that, energy consumption increases with
education in developing countries whereas energy consumption decreases with
education in the developed nations (Inglesi-Lotz and Morales 2017).
Further, they also formulated that, there exists non-linearity between energy
consumption and education.

 In different study, Tewathia
(2014)
found that the stock of appliance in the households contributes the most in
terms of  energy consumption in case of
Delhi. Chen
(2017) came
up with the findings that, residential energy consumption is the resultant of
electrical appliance used in the households which is also highly correlated to
the GDP. Similarly, one of the important variables in socio-economy is
employment, Chen
(2017)
found that employment had also positive impact on per capita electricity
consumption. Similarly, there exist bi-directional causality between
electricity consumption and economic growth, as increase in electricity
consumption directly affects economic growth and that economic growth also
simulates further electricity consumption (Yoo 2005).