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The impact of targeted subsidies plan on electricity consumption, sale, receivables collection and operating cash flow: Evidence from the agricultural and rural sectors of Iran

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Purpose The purpose of this paper is to investigate the implementation of the targeted subsidies plan in the rural and agricultural sectors of Iran and its impact on the government’s sales income, operating cash flow (OCF) and receivables collection ratio. Design/methodology/approach Using the panel data approach, the authors examine their hypotheses on a sample of six provinces of Iran, including Khorasan Razavi, Khorasan Jonoubi, Kerman, Semnan, Kermanshah and Kurdistan, during 2009-2013. Findings The findings indicate that the implementation of the targeted subsidies plan leads to increased actual electricity sales in the rural sector. Further, while the coefficient on OCF in the estimated model suggests a significant and positive relationship between the OCF and the implementation of the targeted subsidies plan, the coefficient on receivables collection ratio demonstrates a significant but negative association. Contrary to the government’s primary expectations, the results do not provide any support for the reduction of electricity consumption. Originality/value The current study is apparently the first study which conducted on the subject under study.
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International Journal of Social Economics
The impact of targeted subsidies plan on electricity consumption, sale,
receivables collection and operating cash flow: Evidence from the agricultural
and rural sectors of Iran
Mahdi Salehi Hamdollah Sojasi Qeidari Ahmad Asgari
Article information:
To cite this document:
Mahdi Salehi Hamdollah Sojasi Qeidari Ahmad Asgari , (2017)," The impact of targeted subsidies
plan on electricity consumption, sale, receivables collection and operating cash flow Evidence from
the agricultural and rural sectors of Iran ", International Journal of Social Economics, Vol. 44 Iss 4 pp.
505 - 520
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http://dx.doi.org/10.1108/IJSE-08-2015-0207
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The impact of targeted subsidies
plan on electricity consumption,
sale, receivables collection and
operating cash flow
Evidence from the agricultural and rural
sectors of Iran
Mahdi Salehi
Department of Accounting, Ferdowsi University of Mashhad, Mashhad, Iran
Hamdollah Sojasi Qeidari
Department of Geography, Faculty of Letters and Humanities,
Ferdowsi University of Mashhad, Mashhad, Iran, and
Ahmad Asgari
Ferdowsi University of Mashhad, Mashhad, Iran
Abstract
Purpose The purpose of this paper is to investigate the implementation of the targeted subsidies plan in
the rural and agricultural sectors of Iran and its impact on the governments sales income, operating cash flow
(OCF) and receivables collection ratio.
Design/methodology/approach Using the panel data approach, the authors examine their hypotheses
on a sample of six provinces of Iran, including Khorasan Razavi, Khorasan Jonoubi, Kerman, Semnan,
Kermanshah and Kurdistan, during 2009-2013.
Findings The findings indicate that the implementation of the targeted subsidies plan leads to increased
actual electricity sales in the rural sector. Further, while the coefficient on OCF in the estimated model
suggests a significant and positive relationship between the OCF and the implementation of the targeted
subsidies plan, the coefficient on receivables collection ratio demonstrates a significant but negative
association. Contrary to the governments primary expectations, the results do not provide any support for
the reduction of electricity consumption.
Originality/value The current study is apparently the first study which conducted on the subject under study.
Keywords Consumption, Operating cash flow, Panel data approach, Receivables collection ratio,
Sale, Targeted subsidies
Paper type Research paper
1. Introduction
The incremental tendency of todays communities toward using electronic devices in all
aspects of life has led to an upward trend in electricity consumption. However, despite the
great efforts globally to reduce the level of electricity consumption, there is still a
substantial increase in the consumption and demand for the electrical energy. On the other
hand, the electrical energy is regarded as the cornerstone of development of different
countries as well as competing companies. This is mainly because of its various
applications in different fields such as industry, transportation, agriculture and services.
Therefore, it is vital for a given country to consume the electrical energy more efficiently
and effectively if her economic growth is of interest. More specifically, the production and
consumption of electrical energy have a considerable bearing on the economic growth and
development of all countries in the world. In this regard, the government of some
developing countries including Iran has paid cash production subsidies with the aim of
International Journal of Social
Economics
Vol. 44 No. 4, 2017
pp. 505-520
© Emerald Publishing Limited
0306-8293
DOI 10.1108/IJSE-08-2015-0207
Received 11 August 2015
Revised 3 December 2015
Accepted 22 January 2016
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0306-8293.htm
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providing financial support for domestic manufacturers. This policy, however, has been
accompanied by unrealistic reduction in energy prices. Low energy prices along with its
potential advantages for production have posed a major problem of incremental energy
consumption, particularly the electrical energy (Armen and Zare, 2005). In this case,
rural areas are among the most energy-consuming sectors as compared to other sectors of
an economy. Moreover, the sheer abundance of this energy is consumed in agriculture
and rural production. Considering the large proportion of rural residents in Iran (about
31 percent of the total population), it is necessary for the country to manage the electricity
consumption in this sector. Statistics show that rural electricity subscribers comprise
1 percent of total subscribers and consume 15 percent of total electricity supply (Ministry
of Energy of Iran, 2012). In addition, from 2009 to 2012, the number of rural electricity
subscribers were 202,000, 258,000, 285,000 and 302,000, respectively. The proportions of
rural electricity consumption are also 12.7, 13.1, 16.3 and 16.3 percent of the countrystotal
consumption over the same period. The rural electricity consumption is primarily
composed of residential lighting, household electrical appliances as well as the electricity
consumed by agricultural wells. In this regard, one of the most important government
policies has been the provision of electricity for agricultural wells in recent years.
Prior literature argues that the elimination of the electricity subsidy is a procedure for
controlling the incremental growth of its consumption in production and industry as it
optimizes the consumption patterns in line with the countrys economic condition (Boqiang
and Zhujun, 2010). Accordingly, the Iranian government ratified the Targeted Subsidies Act
in 2010 to manage and reduce the energy consumption. Ministry of Energy of Iran (2010) also
reports that the per capita electricity consumption in the household sector of Iran (2,900 kWh)
is over three times the world average (900 kWh). Prior to the implementation of
targeted subsidies plan, the production cost of electricity energy per kWh including the
cost of transformation, transmission and distribution in the rural and urban areas (excluding
the fuel costs) amounted to 680 rials, most of which was paid by the subscribers to the Irans
Ministry of Energy (i.e. 430 rials). This sum of money is currently paid to both the
Organization of Targeted Subsidies (166 rials) and the IransMinistryofEnergyinthe
post-implementation era (264 rials which is about one-third of the total cost). The world
average electricity cost falls within the range of 11-17 cents (equal to 3,300-5,100 rials).
This suggests that the cost of electricity energy per kWh in Iran, considering the US dollar
exchange rate, is about one-third a cent. That is, the cheapest electricity energy of the world is
currently produced in Iran (Ministry of Energy of Iran, 2013).
Although the implementation of the targeted energy subsidies plan has a direct bearing
on the social, environmental and economic aspects of the country, the present paper
attempts to scrutinize the implementation of this plan from an economic or financial
viewpoint, primarily due to the fact that the electricity consumption was influenced
significantly by the price variable in agricultural and rural sectors. Overall, the present
study aims to address the hitherto unexplored question of whether the implementation of
targeted subsidies significantly affects the level of electricity consumption, the
governments revenues from electricity sales, the governments receivables collection ratio
and finally its operating cash flows (OCFs) in the rural and agricultural sectors of Iran.
2. Theoretical framework and hypotheses development
A subsidy is one of the most crucial protective tools commonly extended from government
to support both the consumers and producers financially. Specifically, the government
extends subsidies as a form of economic and/or market intervention tool. Economists use
various concepts to define a subsidy (Fetini and Robert, 1999). According to the 2006 report
of Organization for Economic Cooperation and Development, there is no globally agreed,
precise and clear definition of subsidy. There are, however, three major definitions proposed
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by different organizations and in the following academic fields (United Nations
Environment Program Division of Technology, Industry and Economics, 2002):
(1) Accounting, defined by the European system of accounts: a subsidy is the
governments grant to the producers with the aim of affecting the level of
production, price and/or the award of those involved in the production processes
(Mulas-Granados et al., 2008).
(2) Trade, defined by the World Trade Organization (WTO): based on the WTO
definition, a subsidy is recognized when (Annand et al., 2001; WTO, 2001):
there is a direct payment (e.g. grants, loans and capital investment) and/or a
hidden and direct transfer of money and debts;
there are pending governments revenues (e.g. financial incentives including tax
credits);
the government performs different acts for supplying or purchasing goods and
services instead of investing in the countrys infrastructures;
there is any price or income support; and
there is any advantage gained as a result of subsidies.
The OECD (2009) definition of subsidy: a subsidy is a reflection of the governments action
to provide the consumers and producers with advantages in the form of income increasing
and even decreasing their aid.
Overall, a subsidy is a form of governments aid or support allowing the consumers to
purchase goods and services at a price lower than the market value and also increasing the
producersincome (or decreasing the production costs) as compared to the governments
direct interference (Schwartz and Clements, 1999; United nation Environment Program
Division of Technology, 2002). Indeed, the government takes the role of the producer who
sells its goods and services at a price which does not cover the production costs and/or
compensate the loss incurred by the private sector producer (Bacon et al., 2010). On the other
hand, a subsidy is considered as the governments tool to interfere in the economic system
and achieve three major goals, namely, the optimal allocation of resources, economic
stability and equitable income distribution (Pitt, 1985). In this regard, inflating the prices is
an appropriate way of reducing the subsidies. Some economists argue that the development
and occurrence of economic boom in a given country is dependent on price liberalization.
The price liberalization is typically defined as a situation in which the goods and services
are priced ordinarily and considered as an informative and contributing factor in preventing
consumers from incurring unnecessary costs. This policy also provides production
incentives for the producers and facilitates the optimal allocation of resources (Institute for
Planning Studies and Agricultural Economy, 2007). A subsidy can be concurrently allocated
to a set of goods, services, productions and/or resources in different sectors of an economy.
In this case, the energy subsidy is regarded as the most common subsidy in the world.
The analysis of decisions and policies made on energy resources as prerequisites for
economic growth and development is a matter of considerable importance. Specifically, the
energy resources are the fundamental resources in the economy as the economic growth is
directly associated with the amount of energy consumed in a given country (Töpfer, 2003).
According to the 2011 report of the British Petroleum, despite the financial crisis of 2010, the
total energy consumption in the world has witnessed a 5.6 percent increase which has been the
highest energy consumption since 1973 (in all forms of energy and also in all economic sectors).
In this case, China is the worlds largest consumer with 20.3 percent energy consumption rate.
Moreover, the worlds energy consumption is outpacing the economic growth, leading to CO2
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emissions into the atmosphere caused by burning more fossil fuels. The report also indicates
that the energy consumption in Iran has risen by 1 percent from 2009 to 2010 and the Iranian
people consume 2.1 percent of the total energy in the world (British Petroleum, 2011).
2.1 The concept of energy subsidy
The issue of energy subsidy is regarded as one of the most controversial economic, social,
environmental and political issues in the energy sector. The amount and distribution methods
of subsidies among various sectors and consumers are noteworthy (Lin and Jiang, 2011).
The energy subsidy is the difference between energy cost and its present value recorded as
indirect and hidden subsidiesin national accounts. International Energy Agency (IEA)
(2010) defines energy subsidy as the governments action on the reduction of energy
production costs in the energy sector of an economy. In other words, this definition implies the
increase in price for energy producers, but, by contrast, the decline in energy price for
the consumers. In general, free markets have always been energy inefficient, depending on the
tools used. Moreover, free markets do not take account of social and environmental
cost-benefit with respect to energy operations. Accordingly, the governments intervene in
energy markets in order to fulfill their economic, social and/or environmental goals and come
up with an ideal solution for problems caused in the energy markets. Social observance of the
poor and deprived segments of the population is also regarded as other reason justifying the
implementation of targeted energy subsidies. Subsidizing typical energies or technologies,
governments can provide some incentives for investors to channel their funds into industrial
production and subsequently increase the capacity of production and develop new
technologies (Nasimi, 2003). Therefore, the supply of electricity energy at prices lower than the
return on investment is considered as energy subsidy even if it is not reflected in the
governments budget. Based on the preceding discussion, one of the most noticeable aspects
of energy subsidies is the economic aspect, implying the changes in the method and type of
energy subsidies, their price and the level of energy consumption.
2.2 The economic effects of energy subsidy
Subsidies bring about some complicated changes in the economy through affecting the cost
and price of goods and services. In addition, the advantages and disadvantages of subsidies
are not easily quantifiable and consequently have led to some contradictory propositions
among experts and researchers. Pindyck (1979), for instance, uses the total cost function to
illustrate this issue and conducts his analysis based on the proportion of elasticity of
production cost to energy cost. The authors argue that if capital and work are considered as
the replacements of energy, an increase in energy price will be accompanied by an increase
in both factors. Furthermore, the increase in production costs stemming from higher energy
prices not only affects a change in the allocation of production factors, but also gives rise to
both capital and work-relative contributions to the production process (Pindyck, 1979).
The IEA has estimated that the present value of impaired economic growth resulting from
the implementation of energy subsidies in eight countries, namely, China, Iran, Russia,
Kazakhstan, India, Indonesia, South Africa and Venezuela amounts to 257 billion dollars
each year (United Nations Environment Program Division of Technology, 2002). Recent
analysis of the implementation of targeted subsidies plans in different countries conducted
by Cody et al. (2004) indicates that high-income countries have better administrative
capacities and therefore generate more profit for the poor (Cody et al., 2004). However,
statistics indicate excessive energy consumption and inefficient use of electricity energy in
Iran prior to the implementation of the targeted energy subsidies, primarily due to low
electricity prices. Furthermore, there were some disparities between the rich and the poor
segments of the society concerning the usage of subsidies. Specifically, the deprived
segment of the population with limited access to electrical equipment was less subsidized by
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the government as compared to the rich. This phenomenon was also more accentuated in the
agricultural and rural sectors (i.e. the sectors with higher concentration of the poor).
Therefore, the untargeted payment of subsidies brought about some negative impacts on
the national economy and also led to the government budget deficit. However, imbuing with
international bodies such as the World Bank and the International Monetary Fund, the issue
of targeting subsidies, which has been high on the agenda since the First Development Plan
of Iran (1988), was finally implemented in the 2010. The implementation of the targeted
subsidies, particularly in the electricity sector with considerable portion of farmers and the
rural residents, produces several economic effects such as the reduction in electricity
consumption, actualizing the prices and the increase in government revenues.
2.3 Targeting subsidies
Theestablishmentsofsocialandeconomicjustice along with the environment protection
are the primary goals of development, particularly sustainable development. This is mainly
because of the social and economic gaps affecting human societies negatively (AtKisson and
Hatcher, 2001). The evolution of unemployment, rural emigrations, depression and vulnerability
are typical examples of such negative effects. Accordingly, throughout the history, governments
have been seeking to bridge development gaps and enhance the life quality of rural residents
as the poorest segment of a given society. Indeed, the implementation of poverty elimination
plans appears to be absolutely necessary. Accordingly, there have been some economic plans
with the aim of designing targeted payment systems in recent years, particularly in the
subsidies section (Besley and Kanbur, 1993). The advantage of targeted subsidies system is that
high-income strata are excluded from the governments support. Some instances of these
targeted plans have been previously implemented in Bangladesh, Tunisia, Morocco (Alderman
and Lindert, 1998), Egypt (Adams, 2000) and India (Dutta and Ramaswami, 2004). Various types
of targeted subsidies can be extended from the government in the rural sectors, including the
electricity energy subsidies. The improvement of consumption patterns is the outcome of the
decline in the level of subsidies along with the subsequent increase in energy prices. In this
approach, the liberalization of energy prices will trigger off the improvement of consumption
patterns by both the households and the producers, and consequently push them into
consuming the electricity energy more efficiently (see Figure 1).
As it is evident from Figure 1, the direct payment of subsidies leads to an increase
in the individual or households income and the consequent escalation in electricity
consumption. On the other hand, the energy prices increase in line with the subsidies and
subsequent to the implementation of the targeted subsidies plan, leading to the reduction of
energy consumption. More specifically, the implementation of targeted subsidies plan
direct payment
of subsides
Income of the
individuals
energy
consumption
productivity in
energy consumption
energy price
+
+
+
+
+
Source: Ghaderi et al. (2005)
Figure 1.
The conceptual model
of targeted subsidies
and its impact on
energy consumption
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incentivizes the consumers to use the energy more efficiently and consequently reduce their
consumption. Considering the alarming rate of energy consumption in Iran as compared to
the world average, the above-cited conceptual model seeks to encourage the consumers to
reduce energy consumption (Ghaderi et al., 2005).
2.4 Targeting subsidies in Iran
Targeting subsidies has been one of the fundamental economic issues in Iran in the last
decades. The past Iranian governments have always sought for targeting subsidies for a
variety of purposes including:
(1) preventing the development of poverty and social crises;
(2) providing assistance for the vulnerable strata;
(3) providing assistance for the production of some goods and services;
(4) establishing economic stability and controlling prices;
(5) attempting to establish justice even with the cost of losing economic efficiency;
(6) providing assistance for the improvement in income distribution;
(7) developing economic infrastructures;
(8) providing assistance for bankrupt industries or establishing new industries; and
(9) developing research and development function and creating new science of
protecting environment (Hosseini and Maleki, 2005).
However, not only the government has not been successful in fulfilling the above-mentioned
goals by the implementation of targeted subsidies plan, but also there have been severe
damages to the economic, social and environmental conditions in the long term as follows:
the governments policy on keeping the prices of basic goods down has led to the
reduction of economic growth as well as more economic dependence;
the existence of vacant production capacities in various factories and plants;
growing environmental concerns caused by high energy consumption and
inexpensive fossil fuels; and
the necessity of targeting and eliminating some subsidies as a prerequisite for joining
the WTO (the Iranian Organization of Targeted Subsidies, 2013).
Although there have been consistent and contradictory statements among Iranian politicians
and economists concerning the implementation of targeted subsidies plan, the first phase of
this plan is currently being implemented in the country. The proponents of the plan argue that
the targeted subsidies have been followed by a chorus of advantages such as the improvement
in consumption pattern, the reduction in energy consumption, increased production efficiency
and equitable income distribution. Furthermore, they contend that this planhas also given rise
to the level of household income, along with the increase in energy prices, in the rural sectors
of the economy and this extra income, if appropriately planned, can be utilized to develop the
employment and investment opportunities. On the contrary, the opponents of the plan point
out its weaknesses as: inflationary pressures in the economy, the reduction of export,
production depression, increased liquidity, decline in domestic producerscompetitive
capability, increased unemployment rate and negative social consequences (Soleimani and
Nazari, 2009). Accordingly, it seems reasonable to determine the level of goal fulfillment
during the implementation of the first phase in order to estimate the long-term economic
viability of the plans next phases. A closer look at the implementation of targeted subsidies in
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the electricity sector during 2000-2009 shows that a significant proportion of energy subsidies
has been allocated to the electricity sector. Table I exhibits the value and proportion of
electricity subsidies as compared to total subsidies.
The downright allocation of subsidies as shown in the above table has led to the growing
rate of the countrys energy consumption. Statistics show that the intensity of electricity
consumption in Iran in the 2008 is 1.09 kWh/USD which significantly outnumbers the world
average of 0.46 kWh/USD. On the other hand, based on the law of demand, the price of a
product is negatively associated with its demand. That is, as the price of a product increases, the
quantity demanded falls, and vice versa. The demand function is generally presented as follows:
Qx¼abrx
where ais the intercept, indicating the level of demand regardless of the price and bis the
inverse of the function slope, the negative sign represents the downward law of demand.
The impact of price change on the reduction of demand subsequent to the
implementation of targeted subsidies still merits further investigation.
2.5 The conceptual model of the study
On the basis of preceding discussions, the present paper aims to examine the effects of the
implementation of the targeted subsidies in the rural sector. For this purpose, we test a set of
variables including electricity consumption, sale, OCF and receivable collection ratio before
and subsequent to the implementation of targeted subsidies. Since the targeted subsidies
plan has been implemented for 2.5 years by now, we choose our study time period in the
range of two years before and 2.5 years after the implementation of the subsidies plan.
Our variable of interest is the implementation of targeted subsidies plan defined as a
dummy variable taking the value of 1 for the period prior to the plan and 0 for the
post-implementation period. Furthermore, sales income, operating cash inflows, electricity
consumption and receivables collection ratio are used as the dependent variables. We also
use the actual electricity price or levy as an intervening variable affecting the level of
consumption, electricity sales income, OCF and receivables collection. Since the actual
electricity price is required for study time periods, we utilize the index of actual electricity
price measured by the consumer price index (CPI) (see Figure 2).
According to the conceptual model shown in the above figure, we define our variables
as follows:
Sale. A sale is typically defined as the exchange of valuable commodities, stock, money
and/or services as the price of a good or service. In this paper, sale is defined as the
Electricity subsidy
Year Value Proportion
2000 32,828.5 25.7
2001 37,015 32.35
2002 34,712.8 28.79
2003 33,473.6 26.47
2004 37,355.2 21.62
2005 90,828.1 22.61
2006 75,484.3 14.2
2007 86,738.5 12.8
2008 118,379.4 17.7
2009 115,775.5 19.95
Source: The Irans Ministry of Energy Annual Balance Sheet
Table I.
The value and
proportion of
electricity subsidies as
compared to total
subsidies
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governments sale income gained from electricity provision for the consumers as an
essential good. We calculate the variable as the amount of electricity consumption
(measured in kWh) multiplied by the electricity price.
OCF. The International Accounting Standards No.7 defines cash and cash equivalent as
cash on hand and demand deposits. The present paper calculates the OCF as the amount of
cash paid by electricity subscribers to related electricity providers.
Receivable collection ratio. This ratio represents a firms effectiveness in extending credit
sales and collecting debts on that credit. We calculate this ratio by dividing the OCF during
a given period by the sale value during the same period:
RCR ¼OCF
Sale income
Consumption. Consumption is considered as an important concept in economy. It has also
been a controversial issue in other fields of study and science. In this paper, we investigate
the electricity consumption measured in kWh in rural and agricultural sectors of the
countrys economy.
Targeted subsidies. A plan affecting a change in the process of subsidies payment.
Specifically, the previously allocated subsidies are gradually eliminated by this plan and the
resulting money is paid directly to the public.
Altogether, we present our hypotheses in the null form as follows:
H1. There is a significant and positive relationship between the implementation of
targeted subsidies plan and the governments revenues earned from electricity sale
to rural subscribers.
H2. The implementation of targeted subsidies plan in the rural sector of Iran is
significantly and positively associated with OCF.
H3. The implementation of targeted subsidies plan in the rural sector of Iran is
significantly and negatively associated with receivable collection ratio.
H4. The implementation of targeted subsidies plan in the rural sector of Iran is
significantly and negatively associated with the level of electricity consumption.
3. Research background
A large body of research has examined the relationship between the elimination of subsidies
and some macroeconomic variables such as economic growth and inflation. However, to the
Targeted subsides
The earning resulted
by electricity sale
Operating cash flow
Electricity
consumption
Collecting
receivables
Price
(tariff)
Figure 2.
The conceptual model
of the study
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best of our knowledge, to date, little evidence (both in the domestic and international setting)
has been provided regarding the issue addressed in the present study. What follows is a
succinct review of prior foreign and domestic literature.
Eltony et al. (1993) estimate the electricity demand function on a sample of countries listed
on the Gulf Cooperation Council (i.e. Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and
United Arab Emirates) during 1975-1989 and indicate the electricity demand as a function of
actual per capita income, actual gas price and per capita consumption of household electricity.
Using ordinary least squares (OLS), the authors conclude that the electricity demand is not
significantly associated with its price in both short- and long-term periods and subsequent to
the payment of the government subsidies. Having been supported by the World Bank,
Hope and Singh (1995) analyze the effect of increased oil and electricity prices on
macroeconomic variables in China, Malaysia, Ghana, Colombia, Indonesia and Turkey in the
1980s. The authorsfindings indicate that in spite of the increase in energy prices, industrial
production volume has been higher than the periods prior to reforms implementation for all
sample countries except for Turkey. They also examine the impact of increased price of
domestic energy on inflation and argue that the CPI is not significantly high. Using the
cross-sectional data on 1,500 Swedish households in 1997, Anderson and Damsgaard (2002)
estimate the demand for household electricity. Their findings suggest that the number of
electrical appliances, electricity price, household income and finally the type of heating system
significantly affect the amount of electricity consumption. Jensen and Tarr (2003) examine the
relationship between exchange rate and energy pricing reform in Iran. They develop a
multisector computable general-equilibrium model with ten rural and ten urban households to
analyze the various reforms, jointly and separately. Reflecting the large initial distortions,
the authorsfindings suggest that the combined reforms could generate large welfare gains
equal to 50 percent of aggregate consumer income. Moreover, the results show that
well-intentioned policies of commodity subsidies for the poor can have perverse effects.
Even non-targeted direct income payments to all households (not just the poor) would
enormously and progressively increase the income of the poor compared to the status quo.
Holland et al. (2007) attempt to predict the effect of increased energy prices on the US
agricultural economy. The results demonstrate that an increase in energy price would lead to
increased cost growth and the loss of competitive advantage in sectors having been highly
energy dependent. Their research suggests that the growth of energy price can be
compensated by an improvement in the efficiency of technology and replacement with more
efficient technology in energy. Lin and Jiang (2011) analyze the energy subsidies reform by
using the general-equilibrium model in China. The results indicate that the reduction in
subsidies has direct bearing on the demand for energy and negatively affects the
macroeconomic variables. Azarbaijani et al. (2011) examine the impact of implementation of
targeted electricity subsidies plan on the performance of the Iranian factories/plants
and indicate that the factories are the largest consumers of electricity in Iran and the
subsequent rise in the energy price has led to the increase in the cost of goods manufactured.
Hosseini-Yekani (2011) investigates the effects of targeted subsidies on the microeconomic
variables in the agriculture sector of Iran and suggests that the distribution of cash subsidies
has led to an increase in the income of ordinary people and the consequent rise in the demand
for agricultural and food products in both urban and rural areas. Nevertheless, the authors
demonstrate that there has been a fall in the demand for industrial products as a result of the
increase in electricity price and the subsequent rise in the cost of industrial products.
Maghsoudi and Tohidy Ardahaey (2012) conduct their study on the implementation of targeted
subsidies plan in Iran and argue that the inappropriate method of distributing subsidies has
caused social and economic inequality. For instance, the bulk of government subsidies are
consumed by the rich. Further, the cost of energy is much cheaper than that of world average.
In a recent domestic study, Pourhossein et al. (2014) evaluate the possible transition in Irans
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current and complete electricity market subsequent to the elimination of electricity subsidies by
using a system dynamic framework and generating scenarios. The authors evaluate two
scenarios based on the impact of eliminating electricity subsidies on electricity demand in the
short and long term and then evaluate the complete electricity market.
4. Research methodology
4.1 Regression models
The present paper conducts the following regression estimation models to examine the
impact of targeted subsidies plan on the dependent variables as follows:
Yit ¼aþb1X1it þb2X2it þUit
where Y
it
is the dependent variable; β
1
the coefficient on actual electricity levy; β
2
the
coefficient on targeted subsidies; αthe constant; X
1it
the actual levy on electricity for
province iin time t; and X
2it
the targeted subsidies for province iin time t.
Therefore, we present the following regression models to test our hypotheses:
SALEit ¼aþb1LEVYit þb2HADAFit þUit (1)
CASHit ¼aþb1LEVYit þb2HADAFit þUit (2)
RATEit ¼aþb1LEVYit þb2HADAFit þUit (3)
CONSit ¼aþb1LEVYit þb2HADAFit þUit (4)
where SALE
it
is the governments electricity sale revenues earned from rural subscribers;
CASH
it
the operating cash flow; RATE
it
the receivable collection ratio; and CONS
it
the level of electricity consumption; αthe constant; β
1
the coefficient on actual electricity
levy; β
2
the coefficient on targeted subsidies; LEVY
it
the actual levy on electricity for
province iandintimet;HADAF
it
the targeted subsidies for province iand in time t;and
U
it
the error term.
4.2 Specification test (diagnostic) in panel data models
We conduct the Hausman specification test for each regression models to choose the
appropriate model between fixed effects and random effect models. The null and alternative
hypotheses of this test are presented as follows:
H0. The preference of the random effects model
H1. The preference of the fixed effects model
The results of this test are indicated in Table II. The obtained p-value for the first model
(p: 0.7396) implies the appropriateness of random effects model as it is more than the
Regression model χ
2
statistic F-statistic p-value Test result
1 0.603 2 0.7396 Preference of random effects model
2 0.165 2 0.9207 Preference of random effects model
Note: The results of the Hausman test for electricity sale and operating cash flow in the agriculture and rural
sectors
Table II.
The results of the
Hausman test
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significance level of 0.05. Likewise, the results of the Hasuman test for the second model
implies the preference of random effects model ( p: 0.9207W0.05). On the basis of the results
obtained from the Hasuman test, it is appropriate to estimate our regression models by
using random effects model as the test provides supporting evidence for H0 concerning
both the electricity sale and operational cash flow variables.
Table III exhibits the results of the Hausman test for the regression models (3) and (4).
As it is evident, the results of the Hasuman test provide support for H0 (po0.001),
suggesting the preference of the fixed effects model for estimating the primary regression
model to analyze the impact of targeted energy subsidies on the receivables collection ratio
and electricity consumption in the rural and agricultural sectors.
4.3 Estimation results
After conducting the Hasuman specification test and estimating the final regression models,
we examine our hypotheses in this section. As the estimation results for the first regression
model suggest (shown in Table IV), the coefficient on targeted subsidies is significant at 0.05
of significance level (Co0.001; po0.001). Therefore, our findings provide the supporting
evidence for H1, suggesting the significant and positive relationship between the
implementation of targeted subsidies plan and the governments sale revenues earned from
rural subscribers. As the estimation results imply, the implementation of targeted electricity
subsidies has led to increased electricity price and the consequent growing trend of
electricity sale income in the agriculture and rural areas. Accordingly, it can be inferred that
the implementation of targeted energy subsidies and the subsequent increase in electricity
prices have contributed to the fulfillment of governments primary goal with respect to
increased revenues from rural electricity consumption. To put it simply, the government
had been intending to channel the additional electricity income from rural and agricultural
areas into mechanization and rural-related industries from the very beginning.
As the results shown in Table V imply, the coefficient on targeted subsidies plan is
again significant at the margin of error of 0.05 (Co0.001; po0.001) and consequently our
findings support H2. Indeed, it can be concluded that the implementation of targeted
subsidies has contributed to the liberalization and rise of electricity price in the agriculture
and rural areas. Therefore, the increase in electricity price, per se, has led to higher
Regression model Test F-statistic Statistics p-value
3 Cross-sectional Ftest 7.214 7.863 o0.001
Cross-sectional χ
2
test 751.270 o0.001
4 Cross-sectional Ftest 7.214 36.040 o0.001
Cross-sectional χ
2
test 7174.453 o0.001
Note: The results of the Hausman test for receivables collection ratio and electricity consumption in the rural
and agriculture sectors
Table III.
The Hausman test for
receivables collection
ratio and electricity
consumption
Variable t-statistics Coefficient p-value
CONSTANT 1.2765 o0.001 0.2031
LEVY 24,306,805 24,306,805 o0.001
HADAF 1.8 +E+09 o0.001 o0.001
R
2
¼0.5837 D-W ¼0.9688 Fo0.001
Note: The estimation results of the impact of targeted subsidies plan on electricity sale income in the
agriculture and rural sectors
Table IV.
The results of the
impact of targeted
subsidies plan on
electricity sale income
in the agriculture and
rural sectors
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operating cash inflows in these sectors. Given the unchanged level of electricity
consumption, it can be inferred that the increased levies on electricity is the contributing
factor in increasing operating cash inflows in line with the governments primary goal of
implementing targeted subsidies.
Our findings provide supporting evidence for H3 as the coefficient on targeted subsidies
plan is significant (C: 21.2716; po0.001). Therefore, we argue that the implementation of the
targeted subsidies plan is significantly and negatively associated with operating cash
inflows. Since the great majority of Irans deprived population resides in rural and
agriculture areas, it is arguable that a considerable proportion of governments electricity
receivables belong to agriculture and rural residents. This is primarily due to the residents
inability to pay their electricity consumption debts. Indeed, the implementation of targeted
energy subsidies and the subsequent liberalization and rise of electricity cost has led to
ordinary peoples inability to pay electricity costs and consequently has given rise to the
number of the governments debtors (Table VI).
The results shown in Table VII suggest that the coefficient on targeted subsidies plan
is insignificant at 0.05 of significance level (C: 1.9078; p0.0578). Therefore, our results do
not support H4, implying that the targeted subsidies plan is not significantly and
negatively associated with the level of electricity consumption in the rural and
agricultural sectors of the countrys economy. As mentioned previously, the Iranian
government has attempted to prevent the limitless increase in the level of electricity
consumption in rural areas by implementing the targeted subsidies plan. The supply of
Variable t-statistics Coefficient p-value
CONSTANT 14.5835 87.8032 o0.001
LEVY 2.8799 01.1008 0.0044
HADAF 3.055 21.2716 0.0025
R
2
¼0.1849 D-W ¼1.7611 Fo0.001
Note: The estimation results of the impact of targeted subsidies plan on receivables collection ratio in
agriculture and rural sectors
Table VI.
The results of the
impact of targeted
subsidies plan on
receivables collection
ratio in agriculture
and rural sectors
Variable t-statistics Coefficient p-value
CONSTANT 69,848,576 11.4974 o0.001
LEVY 102333 2.8974 0.0042
HADAF 13,403,638 1.9078 0.0578
R
2
¼0.6396 D-W ¼1.6466 Fo0.001
Note: The estimation results of targeted subsidies impact on the level of electricity consumption in the
agriculture and rural sectors
Table VII.
The results of
targeted subsidies
impact on the level
of electricity
consumption in
the agriculture
and rural sector
Variable t-statistics Coefficient p-value
CONSTANT 2.4812 o0.001 0.0138
LEVY 3.7165 8,146,263 0.0003
HADAF 7.8782 o0.001 o0.001
R
2
¼0.4199 D-W ¼1.2640 Fo0.001
Note: The estimation results of the impact of targeted subsidies plan on operating cash inflow in the
agriculture and rural sectors
Table V.
The results of the
impact of targeted
subsidies plan on
operating cash inflow
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cheap electricity to farmers and rural residents and the subsequent rise in the level of
electricity consumed prior to the implementation of targeted subsidies were the reasons
behind the government policy. In addition, there were no sign of dramatic changes in the
level of agricultural and rural productions and the efficiency of the electricity consumed
was, therefore, extremely poor. Accordingly, the government had been attempting to
eliminate unnecessary electricity consumption in agriculture and rural areas through the
implementation of targeted subsidies. The results obtained from the sample data analysis
in the present paper, however, indicate that the increase in the electricity price by
implementing the targeted subsidies has not been consistent with the governments
primary goal concerning the reduction of electricity consumption of rural residents.
The reduction of monitoring impact on the electricity consumption in agriculture and rural
sectors as a result of non-periodic change in electricity levies by the government,
particularly in the second and third years of targeted subsidies plan, is considered as the
potential reason contributing to the above-cited discussion.
Altogether, our findings suggest that the government has increased its revenues from
electricity sale in the rural areas and also her operating cash inflows by imposing the actual
levy on the electricity consumption. However, this phenomenon, per se, has led to increased
electricity price in the rural and agricultural sectors of the country, indicating the sharp fall
in the governments receivable collections. Furthermore, although imposing actual
levy on the electricity consumption, in the short term, has led to decreased levels of
electricity consumption in rural areas, the results are indicative of higher levels of electricity
consumption in the long term, suggesting the non-incremental increase in actual levy on
electricity consumption by the government.
5. Concluding remarks
The aim of this study is to examine that the impact of targeted subsidies plan on the
electricity sale income, OCF, receivables collection ratio and also the level of electricity
consumption in the rural and agricultural sectors of Irans economy. Consistent with our
primary expectation, our findings suggest that the implementation of targeted
subsidies plan is significantly and positively associated with the governments sale
revenues earned from rural electricity subscribers. In other words, the increased
levy on rural electricity has given rise to the level of electricity sales income. On the
basis of the Iranian Targeted Subsidies Act of 2010, it is required that the government uses
the extra revenues earned from rural electricity consumption for providing assistance for
the countrys industries and making infrastructural reforms in the electricity industry.
Based on our results, the implementation of targeted subsidies plan and the subsequent
increase in levies on electricity along with the price liberalization have led to the realization
of the governments predicted revenues from electricity consumption in the rural and
agricultural areas. It is also noteworthy that the unchanged levies on electricity
consumption in the subsequent years of the implementation of the targeted subsidies plan is
the primary reason of the decreased level of the governments revenues from electricity
consumption in comparison with the first year of the study time period. Indeed, the law
required that the levy on electricity consumption be increased in a stepwise manner to reach
the actual cost in the subsequent years of the plan. Consistent with Manzoor et al. (2012),
we argue that the increased levies on electricity consumption give a rise to the governments
revenues in the short and long term. In other words, if the government seeks to increase
its revenues from electricity consumption, it sounds reasonable to increase the electricity
price, but at the cost of losing the demand for electricity.
Our findings also provide supporting evidence with respect to the positive relationship
between the targeted subsidies plan and the OCFs. This finding can be attributed to
the electricity energies considered as essential goods and services, particularly for the
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rural residents. Moreover, the government has imposed some strict rules regarding
subscribers who pay their electricity cost afterspecificdelayssuchascuttingoffthe
electricity or levying fines. In contradiction to our expectations, our research does not
provide consistent insight into the reduction of electricity consumption in the rural areas
as a result of targeted subsidies plan. Altogether, it can be concluded that the
implementation of targeted subsidies plan has not been effective enough since the
governments primary goals such as the reduction of electricity consumption in the rural
sectors have not been achieved.
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Corresponding author
Mahdi Salehi can be contacted at: mehdi.salehi@um.ac.ir
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... Historic energy demand [17,27,30,[61][62][63]75,[84][85][86]91,96,118,131,133,134,138,140,143,146,[148][149][150]152,154,[156][157][158][159][160]168,181,189,192,202,230,231,234,239,240,242,243,255,262,263,268,270,273,277,280,282,285,289,292,327,329,331,337,338,340,344,356,357,363,364,367,371,395,397,398,400,401,404,404,407,[438][439][440]442,446,447,465,468] Weather data [27,[61][62][63]75,84,91,96,118,138,[140][141][142]146,[148][149][150]154,157,159,160,183,239,242,255,289,292,329,338,340,363,367,394,397,404,407,[438][439][440]442,446,447,449,465,468] Calendar data [27,[61][62][63]118,138,140,146,150,154,157,159,160,183,230,239,280,292,331,340,400,438,440,446,447,449,468] Demographic or economic data [75,85,133,141,148,157,158,189,234,255,262,270,277,285,292,298,327,330,331,338,351,395,439,440,446,447,465,468] Technical system data [61,63,75,85,96,144,152,158,159,327,329,330,337,338,344,358,394,395,398,400,449,458,468] Usage or behavioral data [63,86,96,142,255,327,330,395,400,401] Energy prices [63,96,148,280,331,439,458] TSA/ ARCH Historic energy demand [17,29,30,39,47,48,59,60,79,81,95,104,105,109,113,120,131,134,136,143,155,156,162,167,[178][179][180]182,202,227,231,240,241,243,[245][246][247][249][250][251]253,256,259,261,[263][264][265][271][272][273]276,281,283,288,290,294,296,297,324,333,339,342,343,346,356,363,367,369,371,373,403,407,414,418,430,443,479] Weather data [39,95,136,143,180,202,243,253,259,261,263,264,290,339,343,356,363,367,407,430] Calendar data [39,48,95,109,113,120,136,162,202,227,231,241,243,245,263,290,342,343,414] Demographic or economic data [29,81,178,240,241,246,273,356,443] Technical system data n/a Usage or behavioral data [339] Energy prices [178,241,246] Stochastic Historic energy demand [20,27,29,30,33,34,40,44,45,64,66,68,79,93,98,133,139,167,176,184,202,203,228,238,243,246,257,276,294,324,414,418] Weather data [27,40,52,68,93,98,139,176,202,203,238,243,334,347] Calendar data [27,44,52,93,98,121,122,184,202,203,228,238,243,347,414] Demographic or economic data [29,34,40,66,93,122,133,246,334,347] Technical system data [20,52,66,68,93,121,334,347] Usage or behavioral data [40,44,52,66,98,121,122,334] Energy prices [98,246] Fuzzy Historic energy demand [16,18,[47][48][49]51,59,69,70,75,[112][113][114]118,133,135,163,164,192,251,252,270,272,284,285,327,336,349,426,453,470] Weather data [69,70,75,112,114,118,151,163,164,349,470] Calendar data [48,70,[112][113][114]118,164,284,349,470] Demographic or economic data [49,75,133,270,285,327,336,475] Technical system data [327,409,453] Usage or behavioral data [151,327] Energy prices [49] Table A7. Techniques and input data used per article (4/4). ...
... Historic energy demand [17,27,30,[61][62][63]75,[84][85][86]91,96,118,131,133,134,138,140,143,146,[148][149][150]152,154,[156][157][158][159][160]168,181,189,192,202,230,231,234,239,240,242,243,255,262,263,268,270,273,277,280,282,285,289,292,327,329,331,337,338,340,344,356,357,363,364,367,371,395,397,398,400,401,404,404,407,[438][439][440]442,446,447,465,468] Weather data [27,[61][62][63]75,84,91,96,118,138,[140][141][142]146,[148][149][150]154,157,159,160,183,239,242,255,289,292,329,338,340,363,367,394,397,404,407,[438][439][440]442,446,447,449,465,468] Calendar data [27,[61][62][63]118,138,140,146,150,154,157,159,160,183,230,239,280,292,331,340,400,438,440,446,447,449,468] Demographic or economic data [75,85,133,141,148,157,158,189,234,255,262,270,277,285,292,298,327,330,331,338,351,395,439,440,446,447,465,468] Technical system data [61,63,75,85,96,144,152,158,159,327,329,330,337,338,344,358,394,395,398,400,449,458,468] Usage or behavioral data [63,86,96,142,255,327,330,395,400,401] Energy prices [63,96,148,280,331,439,458] TSA/ ARCH Historic energy demand [17,29,30,39,47,48,59,60,79,81,95,104,105,109,113,120,131,134,136,143,155,156,162,167,[178][179][180]182,202,227,231,240,241,243,[245][246][247][249][250][251]253,256,259,261,[263][264][265][271][272][273]276,281,283,288,290,294,296,297,324,333,339,342,343,346,356,363,367,369,371,373,403,407,414,418,430,443,479] Weather data [39,95,136,143,180,202,243,253,259,261,263,264,290,339,343,356,363,367,407,430] Calendar data [39,48,95,109,113,120,136,162,202,227,231,241,243,245,263,290,342,343,414] Demographic or economic data [29,81,178,240,241,246,273,356,443] Technical system data n/a Usage or behavioral data [339] Energy prices [178,241,246] Stochastic Historic energy demand [20,27,29,30,33,34,40,44,45,64,66,68,79,93,98,133,139,167,176,184,202,203,228,238,243,246,257,276,294,324,414,418] Weather data [27,40,52,68,93,98,139,176,202,203,238,243,334,347] Calendar data [27,44,52,93,98,121,122,184,202,203,228,238,243,347,414] Demographic or economic data [29,34,40,66,93,122,133,246,334,347] Technical system data [20,52,66,68,93,121,334,347] Usage or behavioral data [40,44,52,66,98,121,122,334] Energy prices [98,246] Fuzzy Historic energy demand [16,18,[47][48][49]51,59,69,70,75,[112][113][114]118,133,135,163,164,192,251,252,270,272,284,285,327,336,349,426,453,470] Weather data [69,70,75,112,114,118,151,163,164,349,470] Calendar data [48,70,[112][113][114]118,164,284,349,470] Demographic or economic data [49,75,133,270,285,327,336,475] Technical system data [327,409,453] Usage or behavioral data [151,327] Energy prices [49] Table A7. Techniques and input data used per article (4/4). ...
... Historic energy demand [17,27,30,[61][62][63]75,[84][85][86]91,96,118,131,133,134,138,140,143,146,[148][149][150]152,154,[156][157][158][159][160]168,181,189,192,202,230,231,234,239,240,242,243,255,262,263,268,270,273,277,280,282,285,289,292,327,329,331,337,338,340,344,356,357,363,364,367,371,395,397,398,400,401,404,404,407,[438][439][440]442,446,447,465,468] Weather data [27,[61][62][63]75,84,91,96,118,138,[140][141][142]146,[148][149][150]154,157,159,160,183,239,242,255,289,292,329,338,340,363,367,394,397,404,407,[438][439][440]442,446,447,449,465,468] Calendar data [27,[61][62][63]118,138,140,146,150,154,157,159,160,183,230,239,280,292,331,340,400,438,440,446,447,449,468] Demographic or economic data [75,85,133,141,148,157,158,189,234,255,262,270,277,285,292,298,327,330,331,338,351,395,439,440,446,447,465,468] Technical system data [61,63,75,85,96,144,152,158,159,327,329,330,337,338,344,358,394,395,398,400,449,458,468] Usage or behavioral data [63,86,96,142,255,327,330,395,400,401] Energy prices [63,96,148,280,331,439,458] TSA/ ARCH Historic energy demand [17,29,30,39,47,48,59,60,79,81,95,104,105,109,113,120,131,134,136,143,155,156,162,167,[178][179][180]182,202,227,231,240,241,243,[245][246][247][249][250][251]253,256,259,261,[263][264][265][271][272][273]276,281,283,288,290,294,296,297,324,333,339,342,343,346,356,363,367,369,371,373,403,407,414,418,430,443,479] Weather data [39,95,136,143,180,202,243,253,259,261,263,264,290,339,343,356,363,367,407,430] Calendar data [39,48,95,109,113,120,136,162,202,227,231,241,243,245,263,290,342,343,414] Demographic or economic data [29,81,178,240,241,246,273,356,443] Technical system data n/a Usage or behavioral data [339] Energy prices [178,241,246] Stochastic Historic energy demand [20,27,29,30,33,34,40,44,45,64,66,68,79,93,98,133,139,167,176,184,202,203,228,238,243,246,257,276,294,324,414,418] Weather data [27,40,52,68,93,98,139,176,202,203,238,243,334,347] Calendar data [27,44,52,93,98,121,122,184,202,203,228,238,243,347,414] Demographic or economic data [29,34,40,66,93,122,133,246,334,347] Technical system data [20,52,66,68,93,121,334,347] Usage or behavioral data [40,44,52,66,98,121,122,334] Energy prices [98,246] Fuzzy Historic energy demand [16,18,[47][48][49]51,59,69,70,75,[112][113][114]118,133,135,163,164,192,251,252,270,272,284,285,327,336,349,426,453,470] Weather data [69,70,75,112,114,118,151,163,164,349,470] Calendar data [48,70,[112][113][114]118,164,284,349,470] Demographic or economic data [49,75,133,270,285,327,336,475] Technical system data [327,409,453] Usage or behavioral data [151,327] Energy prices [49] Table A7. Techniques and input data used per article (4/4). ...
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In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.
... Energy-consumption subsidies are an effective tool for changing energy-consumption patterns [81]. ...
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... It is also consistent with the development direction of the international power grid, meets the requirements of economic and social development and is an important measure to speed up the transformation of urban and rural power grids and expand domestic demand. It will lay the foundation for measuring marketing, constructing meter reading fee standard and information system engineering of electric power enterprises, make the decision of the electric power enterprise more immediate and reasonable and get the effective technical support, which will play a very strong role in promoting the leap-forward development of electric power enterprises [1][2][3] . With the continuous promotion of power marketing, it is not only power consumption information collection system can help electric power enterprise to get more data to maintain power consumption order, but also a lot of technology is also effectively used, such as computer hardware and software, digital communication and so on. ...
... Thus, subsidizing energy can result in lower prices for essential products; Doing so can protect both low-income families and control inflation (Abdelrahim, 2014;Moor 2001;Koplow, 2004;Fattouh, & El-Katiri, 2012;Salehi, Qeidari, and Asgari, 2017). The effect of energy subsidy on production is summarized in Figure 1. ...
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the foreign direct investment of a country by using the data collected in Egypt. The model investigating this relation will also highlight the main determinants that derive FDI in the Egyptian economy. The results provided are crucial for public policy decision making with the continuous effort to attract FDI and decrease energy subsidy.
... Thus, subsidizing energy can result in lower prices for essential products; Doing so can protect both low-income families and control inflation (Abdelrahim, 2014;Moor 2001;Koplow, 2004;Fattouh, & El-Katiri, 2012;Salehi, Qeidari, and Asgari, 2017). The effect of energy subsidy on production is summarized in Figure 1. ...
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... Thus, subsidizing energy can result in lower prices for essential products; Doing so can protect both low-income families and control inflation (Abdelrahim, 2014;Moor 2001;Koplow, 2004;Fattouh, & El-Katiri, 2012;Salehi, Qeidari, and Asgari, 2017). The effect of energy subsidy on production is summarized in Figure 1. ...
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For a transitional economy such as China, some energy subsidies are reasonable, and sometimes even necessary for achieving social goals. However, with rising energy prices and environmental concerns, we see conflicts emerging between energy subsidies, energy demand/supply fundamentals and climate change considerations. Energy subsidies have important implications for sustainable development through their effects on energy use, efficiency and the choice of fuel source. This paper applies the price-gap approach to estimate China's energy subsidies. Results indicate that China's energy subsidies amounted to CNY 356.73 billion in 2007, equivalent to 1.43% of GDP. Subsidies for oil products consumption are the largest, followed by subsidies for the electricity and coal sectors. Furthermore, a CGE model is used to analyze the economic impacts of energy subsidy reforms. Our findings show that removing energy subsidies will result in a significant fall in energy demand and emissions, but will have negative impacts on macroeconomic variables. We conclude that offsetting policies could be adopted such that certain shares of these subsidies are reallocated to support other sustainable development measures, which could lead to reducing energy intensity and favoring the environment.