Un estudio educativo sobre el efecto del consumo de energía en la
productividad del capital en el sector agrícola por el
método ARDL (estudio de caso de Irán)
An educational study on the effect of energy consumption on capital productivity in
the agricultural sector by the ARDL method (an Iran case study)
Maryam Keshavarziyan
1a
, Azadeh Dabbaghi
2
Research Institute of Petroleum Industry (RIPI), Iran
12
Orcid ID: https://orcid.org/0000-0003-0745-2782
1
Orcid ID: https://orcid.org/0000-0001-9476-0067
2
Recibido: 15 de abril de 2020 Aceptado: 18 de octubre de 2020
Resumen
El presente documento investiga los efectos del consumo de energía en la productividad
del capital utilizando la producción de travesaños en el sector agrícola de Irán, mediante el
método econométrico apropiado, que es el retraso distribuido autorregresivo (ARDL).
Asimismo, el método de esta investigación es descriptivo y utiliza métodos relacionados
con los objetivos. Los hallazgos muestran que la relación de consumo de energía per cápita
en la relación a largo plazo es -3,8, lo que significa que la productividad del capital
disminuye alrededor del 4% para un aumento del uno por ciento en el consumo de energía
per cápita. En este estudio, los factores que afectan la productividad energética se han
estudiado en el sector agrícola durante 1990-2016 en Irán. En este sentido, la productividad
energética se calculó primero utilizando un indicador de productividad parcial. Los
resultados revelaron que la productividad energética en el sector agrícola aumentó en
promedio 1,1% por año durante el período de estudio. El promedio de la fuerza laboral por
unidad de capital y el capital humano promedio por unidad no afecta la productividad del
capital, por lo que el consumo promedio de energía por unidad de capital afecta la
productividad del capital.
Palabras clave: Consumo de energía, productividad del capital, agricultura, economía
Abstract
This paper investigates the effects of energy consumption on capital productivity using
transom production in the agricultural sector in Iran through an appropriate econometric
a
Correspondencia al autor
Email: keshavarziyan@aftermail.ir
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Apuntes Universitarios, 2021: 11(1), enero-marzo
ISSN: 2304-0335 DOI: https://doi.org/10.17162/au.v11i1.600
apuntesuniversitarios.upeu.edu.pe
method, and autoregressive distributed lag (ARDL). The method of this research is
descriptive and using methods related to the objectives. The findings show that the per
capita energy consumption ratio in the long-term relationship is -3.8, it means that the
capital productivity decrease about 4% for one percent increase in per capita energy
consumption. In this study, the factors affecting energy productivity have been studied in
the agricultural sector during 1990-2016 in Iran. In this regard, energy productivity was
first calculated using a partial productivity indicator. The results revealed that energy
productivity in the agricultural sector increased by average 1.1% per year during the study
period. The average of labor force per unit of capital and the average human capital per
unit of capital is Affectless on the capital productivity, so the average energy consumption
per unit of capital effect on capital productivity.
Keywords: Energy consumption, capital productivity, agriculture, economics
Introduction
Agriculture as an economic section has been played a vital role in employment in
the world. Human life is very much influenced by the products of this sector. So the
relationship between energy consumption and its impact on capital has an important effect
on the improvement of the agricultural sector (Ahmadim, 2013). According to the
increasing importance of food supply in countries and the vital role of the agricultural
sector in this field and the more important role of this sector in production, exports and
employment, as well as the development of the agricultural sector as a prerequisite and
essential need for economic development founded the special importance (Aghayi, and
Rezagholozadeh, 2015).
In the agricultural sector energy has been considered as one of the most important
inputs in order to production. It also can be considered as a bridge to move from traditional
agriculture to industrial agriculture (Khalilian and Teymouri, 2016). Energy carriers were
initially widely used in industry, but with the advancement of technology as well as the
production of equipment to provide services in different sectors, including agriculture, they
were able to show their role as a factor in the production of goods and services (Akbari,
2013). The aim of this study was to investigate the effect of the energy consumption on
capital productivity by ARDL technique in agriculture sectors among selected provinces
of the country.
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Theoretical foundations
Concepts and basics of productivity
Productivity is an economical and management concept that is defined as: the
amount of goods or services produced in comparison of each unit of energy or labor
expended without reducing quality. In other words, productivity is the maximum possible
profit by using and optimally using labor, power, talent and skill of labor force, land,
machinery, money, equipment, time, place, etc. in order to improve the society welfare.
Productivity refers to the work that has carried rather than the work done (Arman, 2015).
These attempts can be named obtaining maximum productivity. In deeper concepts,
productivity is equated with efficiency, in addition to effectiveness; it implies acting
rationally, because just doing the right things is not enough. Rather, it is needed to carry
the right thing logically and as expected, and productivity is the hybrid impact of these two
factors (Soheili, 2017).
On the other hand, we are talking about the relationship between the system inputs
(system / organization) and its outputs (products) in the productivity discussion. So, a
change in the value of each of these can influence productivity. Among the different factors
affecting productivity, two groups play the vital role: internal factors (hardware factors
including: product, machinery, etc. and software factors including specialized people,
organization, etc.) and external factors (structural factors, including: economic changes,
etc. resources including: human resources... and factors associated to productivity,
including: performance and productivity of governmental agencies, etc.) (Azam, 2015).
Productivity indicators
Productivity in the general concept refers to the output/ data ratio. In other words,
productivity refers to the mean production per unit of total inputs, so that if the mean output
per unit of inputs increased, it means increased productivity and vice versa means reduced
productivity. In general, productivity indicators are assigned into two categories: partial
productivity indicators and total productivity indicators. In partial productivity indicators
showed the relationship between the output and input, so the relationship between the
output and the whole input in the productivity indicators of all factors of production is
examined (Krueger, 2014).
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Partial productivity indicators of production factors
These indicators are obtained by dividing the value added by a certain amount of
input, and in order to exclude inflation, it needed to use value added at a fixed price in the
base year (Krueger, 2014).
Productivity indicators of total production factors (TFP)
The productivity indicators of total factors (labor and capital together) show the
output/ input ratio. In fact, it shows the mean production per unit of total production factors.
This indicator shows the results of changes in labor productivity and capital (Krueger,
2014).
Factors affecting the productivity of total production factors
It is considered that the productivity growth of all production factors is equal to the
Average weight of labor and capital productivity growth of, as a result the factors such as
increase the quality level of labor and capital, better allocation of resources and optimal
use of available resources and facilities helps to increase the productivity of all factors of
production (Krueger, 2014). According to Asian Productivity Organization (APO) report,
training and education of the labor force, productivity and knowledge management, foreign
investment are the most important factors accelerating the productivity growth of
production factors.
Capital productivity indicator
The concept of capital productivity indicator is that each dollar of capital has
created several dollars of added value. This indicator is obtained by dividing the value
added by the capital inventory value (at a fixed price). Usually in calculating this indicator,
first the value added and the capital inventory value are converted to the fixed prices of the
base year, and after dividing the added value into the capital inventory, the capital
productivity is obtained (Krueger, 2014).
Research background
Naji et al (2014) in a study examined the efficiency and productivity of energy in
different economic sectors and also estimate the input and price elasticity of energy in
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agriculture, industry and transportation by the method of multi-stage Least Squares method
over time 1970-1990. In their paper, they investigate some indicators of energy
consumption including the energy intensity indicator. The results estimate the upward trend
of the energy intensity indicator in all sectors during the period under review and the
downward trend in energy productivity, which is the inverse of the energy intensity
indicator.
Khalilian and Rahmani (2016) have examined the factors affecting labor
productivity in Iran's agricultural sector. In this study, macroeconomic statistics for the
years 1974-2008 have been used. The results show that labor productivity is upward in this
sector. Nofarasati (2018) have studied the factors affecting the growth rate of labor
productivity in the agricultural sector of Iran during 2000-2020. The study findings showed
that the main reason for the increase in labor productivity growth rate can be associated to
the relative increase in productivity of total production factors and the effect of total
Substitution.
Akbari (2013) in a study entitled "investigation of productivity growth in the
agricultural sector of Iran" have measured the productivity growth of total factors of
production in the agricultural sector of Iran during 1966-2000. The results of measuring
productivity growth and output growth showed that the mean growth of productivity and
output growth in the agricultural sector during the study period is 2.6 and 4.8, respectively.
Aghayi, and Rezagholozadeh (2015), examined the long-term and short-term
relationship between energy consumption and value-added growth in various economic
sectors using the multivariate panel error correction model (PECM) and the Cointegration
test and panel causality. The results show that the increase (decrease) in energy
consumption in different sections of the country leads to an increase (decrease) in the
growth of value added in them. Azam, et al. (2015), examined the causal relationship
between energy consumption and economic growth in five member countries of the
ASEAN group such as Indonesian , Malaysia, Thailand, Singapore and the Philippines.
The results of the study showed that there is a significant and long-term relationship
between energy consumption and economic growth among these countries.
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Research area
The statistical population consisted of this study including selected provinces in the
entire agricultural sector of Iran. Provinces with high share of wheat production have been
selected in the study. The scope of this research is the available information published for
25 years during 1990-2016 in the country. Time series data used is from Agricultural Bank
databases and Statistics Center and Central Bank of Iran. Value-added statistics from Iran's
national accounts have been collected at current and constant prices in 1997, published by
the Central Bank of the Islamic Republic of Iran. The Ministry of Energy is statistics
reference of price and the amount of energy consumed, including diesel oil s and
electricity. The statistics of labor force working in the agricultural sector are collected
from the EAO website.
Methodology
Definition of Translog production Function
The Translog production Function first proposed by Christensen (Jorgenson & Lau,
in 1972), proposed all the features of a Translog production Function. Another feature of
this function is that it allows elasticity of substitution and elasticity of production changes
depending on the level of consumption of inputs. In addition, the first derivative of this
function has no limit in terms of sign. In other words, Translog Function shows all three
production areas, and the final Production in it is increasing decreasing or negative
(Hatirilig, 2005) . In the Translog production Function, the coefficients of the interaction of
the variables are also estimated in addition to the parameters of the main variables. The
following form of this function for the production input N is as follows: (Krueger, 2014).
 







Autoregressive Distributed Lags Method (ARDL)
Although the use of traditional methods in econometrics is based on the assumption
of stability of variables for experimental studies, however, the researches show that many
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time series are unstable, which leads to spurious regression and, finally the confidence in
the coefficients will be lost. Therefore, , it is necessary to use methods based on the Co-
integration theory in estimating functions when using time series that pay attention to the
issue of stability and Co-integration investigates the long-term relationship between
variables without any preconditions about the Accumulation degree of variables. This
method gives unbiased estimates of long-term coefficients e. Unlike other common
techniques in co-integration analysis method, such as the Engle Granger method, there is
no need first know the degree of co-integration e of the variables under study. Also, ARDL
method is able to simultaneously estimate the long-term and short-term coefficients of the
model and determine the direction of causality between the variables of the model.
A simple ARDL pattern is shown below:





Where
is the constant value,
is the dependent variable, and L is the lag
operator.
Is the non-random variables such as intercept, trend variable, dummy variable
or exogenous variables, or fixed i lag. P is the number of lags used for the dependent
variable,
is the number of lags for the independent variables. There are two steps to
estimate the ARDL model. In the first step, the number of ARDL lag is determined based
on one of the Akaike, Schwartz- Bayesian, and Hannan-Quinncriteria , and in the second
step the selected pattern is estimated by normal least squares method. Eviews 6 and
Microfit 4.1 econometric software have been used to estimate the model (Krueger, 2014).
Introducing the model of factors affecting capital productivity
The variables of capital productivity, labor, human capital and energy consumption
are used to investigate the effect of energy consumption on capital productivity in the
agricultural sector. The following equation has a linear form and is a type of translog
production function.
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Ln(Q/K)= β0+β
1
ln(L/K)+α
2
ln(H/K)+ α
3
ln(E/K) + α
4
ln(L/K)ln(H/K) + α
5
ln(L/K)ln(E/K)
+
α
6
ln(H/K)ln(E/K)+1/2 α
7
(ln(L/K))
2
+1/2 α
8
(ln(H/K))
2
+1/2 α
9
(ln(E/K))
2
Where in
•Q / K capital productivity or average production per unit of capital consumed
•L / K average labor force per unit of capital
•H / K is average human capital per unit of capital
•E / K is average energy consumption per unit of capital
Capital productivity
Productivity shows the activity efficiency; on the other hand, it evaluates the role
of productivity in obtaining the desired goal. Productivity is divided into two parts based
on definition Effectiveness and efficiency. Effectiveness determines how much of the
planned activity has been accomplished and the planned results have been achieved. In
efficiency, the relationship between the result and the used resources is examined. In other
words, productivity is the simultaneous realization of efficiency and effectiveness. In this
paper, capital productivity is obtained by dividing the added value into capital in the
agricultural sector. As can be seen in the chart below, the productivity of capital has
generally reduced during 1990- 2015 years (Krueger, 2014).
Figure 1
Average production trend per unit of capital
(Source: Iran Statistics Center report)
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Estimation steps
:
Investigating the static stability of variables
The use of traditional and conventional econometric methods in estimating
coefficients using serial data is based on the assumption that model variables are stable. If
the time series variables used in estimating the coefficient are unstable, there may be no
relationship or concept between the variables, so that the researcher reach inaccurate
inferences about the relationship between the variables. The presence of unstable variables
in the model in any case can cause the normal t and F tests to be invalid and the resulting
regression to be a pseudo regression. In a time, series variable, if its mean, variance, and
covariance are independent of the time, that variable is Stationary is, or more accurately,
the covariance Stationary. The following generalized Dickey-Fuller method is used to
investigate Stationary in a time series variable.
In this study, the absolute value of the generalized Dickey–Fuller statistic is smaller
than the critical values for all the variables under study, so the H
0
hypothesis that is the unit
root of these variables is at a high level of Confidence is confirmed. Then, the generalized
Dickey-Fuller test is repeated for the first difference of the variables to determine the
degree of accumulation of the desired variables. The results of the generalized Dickey-
Fuller show that some variables become Stationary in first difference, in other words, they
are Stationary from degree one degree I (1), but there are some other variables that are
Stationary from degree zero f I (0) (table1).
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Table 1
The results of model reliability test
Variable
Mania test
Q
I(0)
E
I(1)
E
2
I(1)
H
I(1)
H
2
I(1)
HE
I(0)
L
I(0)
LE
I(0)
LH
I(0)
LL
I(0)
Source: Author’s calculations
Results
Model estimation
Now the ARDL model used to estimate the desired function. In this model, there is
no precondition to investigate the long-term relationship between the variables for the
degree of cointegration. Also, this method carried out long-term and short-term economic
analysis. The Hannan-Quinn indicator is used to select the optimal lag in the above model.
(Figure2)The optimal lag is selected based on the mentioned criteria (2, 0, 0.0, 0, 0, 0, 0,
0, 0, and 0). (Table 2).
Table 2
Optimal interruption selection
Specification
Adj.R-
sq
HQ
BIC
Alc
LogL
Model
ARDL(2,0,0,0,0,0,0
0.944812
-
3.625056
-
3.156234
-
3.794347
58.532163
1
ARDL(1,0,0,0,0,0,0
0.920052
-
3.263766
-
2.831009
-
3.420035
53.040426
2
Source: Author’s calculations
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Figure 2
Optimal interruption selection
Source: Author’s calculations
According to considering the maximum of two lags, after estimating of the dynamic
linear model of capital productivity in the agricultural sector, the following results were
observed. The results of estimating the dynamic model by ARDL method show that the
coefficient of average labor force per unit of capital variable and average human capital
per unit of capital is statistically insignificant, which show the no significant effect of these
two variables to capital productivity.. The coefficient of average energy consumption
variable for each unit of capital is statistically significant. In other words, this variable has
a significant effect on capital productivity.
Also, the LE and HE coefficients were not statistically significant, so these two
variables did not affect capital productivity. So the LH coefficient is statistically
significant. In other words, this variable has a significant effect on capital productivity. The
coefficients of LL, EE and HH are all significant, so they all affect the productivity of
capital. Lagged amounts of capital productivity also have a significant effect on capital
productivity.
R
2
shows the adjusted coefficient of determination that is 97% and it reveals that
97% of the changes in capital productivity are explained by the explanatory variables
presented in the model. The statistical value of F is 33.81, it indicates that the regression is
correct at a probability level of more than 99 % (Table 3).
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Table 3
Results of estimating dynamic model by ARDL method
Prob
T-statistic
Std.Error
Coefficient
Variable
0.0366
-2.379056
0.203563
-0.484288
Q(-1)
0.0281
-2.526623
0.241607
-0.610449
Q(-2)
0.2963
-1.096424
2.814284
-3.085649
L
0.7529
-0.322855
3.533146
-1.140694
H
0.8665
-1.882427
2.914398
-5.486141
E
0.8123
-0.243243
0.530792
-0.129111
LE
0.0109
-3.055780
2.648063
-8.091899
LH
0.4563
-0.772095
0.837078
-0.646304
HE
0.0443
-2.269752
0.915390
-2.077708
LL
0.0279
-2.646001
0.354895
-5-0.584173
EE
0.0923
-1.840735
2.508030
-4.616619
HH
0.1573
-1.517673
0.011621
-0.017638
T
0.4791
-0.732565
6.300198
-4.615302
C
2.495417
Mean dependent var
0.973606
R-squared
0.132763
S.D dependent var
0.944812
Adjusted R-squared
-3.794347
Akaike info ceiterion
0.031189
Sam Squared resid
-3.156234
Schwarz ceiterion
58.53216
Log likelihood
-3.625056
Hannan-Quinncriter
33.81323
F-statistic
2.486188
0.000001
Prob(F-Statistic)
Source: Author’s calculations
The Bundes test is used to test the existence of a long-term relationship between
the explanatory and dependent variables of the model after estimating the dynamic
equation. As it can be observed, the F statistic has a long-term relationship in the model
(Table 4).
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Table 4
Bundes test
K
Value
Test Static
10
3.188178
F-static
Critical Value Bounds
11Bound
10 Bound
Significance
2.77
1.76
10%
3.04
1.98
5%
3.28
2.18
2.5%
3.61
2.41
1%
Source: Author’s calculations
The long-term relationship of capital productivity is estimated in the agricultural
sector after confirming the assumption that there is a long-term relationship between the
variables of the model at 5% and 10% confidence levels. In the table below, it is observed
that the results obtained based on estimating the model and its long-term and short-term
coefficients. In this regard, the average energy consumption variable has a significant effect
on capital productivity in the long term and the average labor and human capital has no
significant effect on capital productivity.
The LE and HE coefficients were not statistically significant, so the two variables
did not affect long-term capital productivity. So the LH coefficient is statistically
significant. In other words, this variable has a significant effect on capital productivity in
the long term. The coefficients of LL, EE and HH are all significant, so they all affect the
productivity of capital in the long term.
Table 5
Long-term relationship coefficients
Prob
t-statictic
Std.Error
coefficient
Variable
0.2698
-1.156873
1.431151
-1.655661
L
0.6744
-0.430626
1.851442
-0.797280
H
0.0222
-2.624301
1.454581
-3.817260
E
0.5138
-0.672782
0.266061
-0.179001
LE
0.0025
-3.815699
1.052677
-4.016697
LH
0.2160
-1.306000
0.414874
-0.541826
HE
0.0124
-2.939528
0.380519
-1.118546
LL
0.O286
-2.487308
0.169314
0.421137
EE
0.1220
-1.663983
1.139462
-1.896045
HH
0.1563
-1.512448
3.145964
-4.758107
C
Source: Author’s calculation
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Table 6
Short-term relationship coefficients
Co integrating Form
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(Q(-1))
D(L)
D(H)
D(E)
D(LE)
D(LH)
D(HE)
D(LL)
D(EE)
D(HH)
D(T)
ContEq(-1)
0.616905
-3.053056
0.542679
-5.169843
0.053472
-8.201781
0.207100
-1.715561
-0.636673
-5.765753
-0.009997
-2.044971
0.214208
1.367696
1.918207
1.977435
0.390613
1.824075
0.576217
0.716881
0.258118
1.568054
0.009603
0.311364
2.879931
-2.232263
0.282909
-2.614419
0.136892
-4.496406
0.359414
-2.393088
-2.466600
-3.677012
-1.041017
-6.567784
0.0150
0.0473
0.7825
0.0241
0.8936
0.0009
0.7261
0.0357
0.0313
0.0036
0.3202
0.0000
Cointeq=Q-(-1.4730
*
L -0.5446
*
H -2.6190
*
E -0.0616
*
LE -3.8630
*
LH-0.3085
*
HE
-0.9919
*
LL -0.2789
*
EE -2.2039
*
HH -0.0084
*
T -2.2033)
Source: Author’s calculations
The error correction model was also used to investigate the short-term relationships
between energy productivity and other studied variables. The results obtained are as above
table. As can be seen, the error correction coefficient is significant ge and its value is
negative. The coefficient value is 61%. This means that 61% of the variable deviations of
capital productivity from its long-term values will disappear after one period. In other
words, the results of a policy to be fully adjusted need two years.
Discussion
In this study, the factors affecting energy productivity have been studied in the
agricultural sector during 1990-2016 in Iran. In this regard, energy productivity was first
calculated using a partial productivity indicator. The results revealed that energy
productivity in the agricultural sector increased by average 1.1% per year during the study
period. The average of labor force per unit of capital and the average human capital per
unit of capital is Affectless on the capital productivity, so the average energy consumption
per unit of capital effect on capital productivity.
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The study of total energy consumption in the Iran shows that during the period
1990-2012, the final energy consumption has almost doubled and a half, reaching from
403.7 million barrels of crude oil to 99.7 (Arman, 2015). In 2016, the agricultural sector
accounted for 3.8% of total energy consumption. Increasing the share of gas is one of the
phenomena that has been significantly observed in the last two decades (Soheili, 2017).
Petroleum products in 1990 constitute about 0.93% of the total energy consumption of the
agricultural sector, which in 2016 this share has decreased to 0.65%. This article has both
positive and negative aspects. With the increase in the share of electricity and gas in the
total energy consumption of the agricultural sector, the share of petroleum products has
decreased and reached 0.65% (Soheili, 2017). On the one hand, it should be noted that with
the increase in the share of electricity, the amount of environmental pollution, in the
emission of greenhouse gases in the agricultural sector has not decreased, because the
source of power generation and electricity are the same fossil fuels (Khalilian and
Teymouri, 2016). On the other hand, we do not see the replacement of new energy sources
with fossil fuels in the agricultural sector during this period, and therefore environmental
costs remain.
This amount of energy consumption is predicted until 2025. For this purpose, first,
based on the available information on the value added of the agricultural sector in the
period 1990-2012, the amount of this variable for the period 1990-2016 has been predicted.
Then, by obtaining the average intensity of energy consumption for the period 1990-2016,
the obtained figure has been used as a basis. In the reference scenario, it is assumed that
the past trend of energy consumption will spread to the future (Soheili, 2017).
The results of this research are in line with the following research. Khalilian and
Rahmani (2016) have examined the factors affecting labor productivity in Iran's
agricultural sector. In this study, macroeconomic statistics for the years 1974-2008 have
been used. The results show that labor productivity is upward in this sector. Nofarasati
(2018) have studied the factors affecting the growth rate of labor productivity in the
agricultural sector of Iran during 2000-2020. The study findings showed that the main
reason for the increase in labor productivity growth rate can be associated to the relative
increase in productivity of total production factors and the effect of total Substitution.
Akbari (2013) have measured the productivity growth of total factors of production in the
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agricultural sector of Iran during 1966-2000. The results of measuring productivity growth
and output growth showed that the mean growth of productivity and output growth in the
agricultural sector during the study period is 2.6 and 4.8, respectively.
The results show that the consumption of fossil fuels has increased sharply and
consequently social costs have also increased. Due to the reduction in the intensity of
energy consumption, the amount of energy consumption has decreased compared to
previous periods and its benefits are found in saving fossil energy consumption and
reducing environmental costs.
Conclusion
According to the study results, it can be said that In Iran, due to the potentials of
using solar energy, wind energy, geothermal energy, biomass energy, biogas, etc., there
are huge possibilities to replace fossil fuels, the realization of which requires careful
scientific and long-term planning. In order to optimize energy consumption and reduce its
intensity in the agricultural sector, various strategies should be considered, including
saving the use of fossil fuels and using new energies in this sector. Given the current trend
of using fossil fuels and their non-renewable properties, as well as the negative effects of
consuming such energies on human health and the environment, the need to use new and
renewable energy in the agricultural sector seems inevitable. The use of renewable energy
is one of the effective ways to create a sustainable agriculture.
Therefore, in order to achieve a sustainable agriculture in Iran, the implementation
of energy consumption management policies along with production management, along
with regular planning to optimize the consumption of fossil fuels, reduce their current
consumption and use of renewable energy seems necessary.
References
Aghayi, M., Rezagholozadeh, M. (2015). Energy Consumption and Value-Added Growth
in Different Sectors of Iran's Economy: Cointegration Approach and Panel Error
Correction. Journal of Economic and Regional Development, 22(9), 31-67. DOI:
Revista de Investigación Apuntes Universitarios
2021: 11(1),514 - 531
ISSN 2312-4253(impresa)
ISSN 2078-4015(en línea)
529
https://doi.org/10.22067/erd.v22i9.30393
Ahmadim, Sh. (2013). The effect of technological progress and performance changes on
agricultural productivity growth. China economic review, 10(34), 157-174.
http://eco.iaufb.ac.ir/article_585186.html
Akbari, N., Mehdi, R. (2013). A study of productivity growth of total production factors in
the agricultural sector and its opinion in the fourth development plan, Agriculture
development, 50, 59-77. http://aead.agri-peri.ac.ir/article_58884.html
Arman, A., Ruhollah, Z. (2015). A study of the relationship between granular causality
between energy consumption and economic growth in Iran during 1985-2005.
Iranian Economic Research Quarterly, 7(24), 143-117.
https://www.sid.ir/Fa/Journal/ViewPaper.aspx?ID=93554
Azam, M., Khan, A. Q., Bakhtyar, B., & Emirullah, C. (2015). The Causal Relationship
between Energy Consumption and Economic Growth in the ASEAN-5 Countries.
Energy Reviews, 47, 732-745. http://ecoj.sbu.ac.ir/article_57453.html
Hatirilig S. A., Ozkan, B., Fert, K. (2005). An econometric analysis of energy input –
output in Turkish agriculture. Renewable and sustainable Energy Reviews, 9, 608-
623. http://ecj.iauctb.ac.ir/article_520791.html
Khalilian, S., and Teymouri, A. (2018). Calculation and analysis of productivity in
agricultural sectors. Journal of Economics and Agriculture, 2(4), 93-113.
https://www.sid.ir/fa/journal/ViewPaper.aspx?id=115296
Khalilian, Sadegh., Rahmani, F. (2016). A study of factors affecting labor productivity in
iran's agricultural sector. Journal of Agricultural Economics and Development,
22(1), 23-40. https://www.sid.ir/fa/Journal/ViewPaper.aspx?id=91025
Krueger, A. O., and Tancer, B. (2014). Growth of Factor productivity in Turkish
Manufacturing Industries. journal of Development Economics, 11(3), 307-315.
https://doi.org/10.1016/0304-3878(82)90009-8
Naji Meydani, A., Mahdi Adeli, M., Arabshahi Deloyi, M. (2015). Investigating the
Relationship between Industrialization and Energy Efficiency in the Industrial
Sector in Iran. Journal of Economic Policy, 7(13), 27-56.
http://ep.yazd.ac.ir/article_552_89f6314fefae7b3221ef770f56c3b48d.pdf
Nofarasti, M. (2018). Unified and collective roots in econometrics. Tehran Rasa Cultural
Revista de Investigación Apuntes Universitarios
2021: 11(1),514 - 531
ISSN 2312-4253(impresa)
ISSN 2078-4015(en línea)
530
Services Institute publisher.
Soheili, K. (2017). The impact of improving production technology in the agricultural
sector on the long-term energy demand in this sector using the MEDEE-S economic
technical model. Quarterly Journal of Agricultural Economics and Development,
15(60), 70-45.https://www.sid.ir/fa/journal/ViewPaper.aspx?id=80698
Revista de Investigación Apuntes Universitarios
2021: 11(1),514 - 531
ISSN 2312-4253(impresa)
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