جستجوی مقالات مرتبط با کلیدواژه « Energy Consumption » در نشریات گروه « اقتصاد کشاورزی »

Although the economic growth of Iran is not completely reliant on agricultural production but has been one of the most prominent drivers of the economy of Iran. Without removing the obstacles to development in this sector, it cannot be expected that other economic sectors of the country will achieve development. Since the development of the agricultural sector inevitably leads to the massive consumption of energy, this study aims to investigate the effects of oil derivatives consumption (OILC), electricity consumption (ELC), gas consumption (GASC), CO2 emissions, and agricultural exports (AGexp) on agricultural economic growth (AEG) in Iran.

In this regard, the time-series data from 1980 to 2018 was used to analyze the short-run and long-run relationships among studied variables using Autoregressive distributed lag (ARDL) modeling and different tests associated with it.

The ARDL model results indicate that GASC and OILC positively affect AEG in the long run, while CO2 has a negative effect in the short run. Additionally, AGEX and ELC do not significantly affect the dependent variable. Further results show that AGexp, OILC, and GASC increase AGE by 0.115%, 0.499%, and 0.278%, respectively, in the long run, while CO2 and ELC do not significantly affect AGE. Suggestion: Implementing policies such as Customization of production according to global standards, creating efficient and appropriate administrative structures for export affairs, and investing export earnings in the agricultural sector can be of interest to policymakers. On the other hand, considering that the most influential factor preventing energy consumption in the agricultural sector is the reduction of the cultivated area, applying policies that simultaneously reduce the cultivated area and increase productivity can achieve environmental goals and agricultural development to help.

Energy is one of the most important inputs of agricultural production, the consumption of which is directly related to increasing demand for various products in this sector. In this regard, due to the implementation of various phases of targeted subsidies law, energy consumption pattern has been changed in this sector, so the present study intends to show the effect of increasing the final demand of selected agricultural products on direct and indirect uses. In order to achieve the objectives of this research, the latest supply and consumption tables (2016) were used. Therefore, first the supply and consumption tables were integrated, then the effect of increasing demand for cereals, livestock and other agricultural products on the consumption of liquefied petroleum gas, diesel, gasoline, electricity, refined and distributed gas and other products were investigated. The results showed that with the increase in demand for agricultural products, diesel is still the most widely used energy carrier directly. But directly and indirectly, liquefied petroleum gas has the highest consumption coefficients among energy carriers for the agricultural sector. Therefore, based on the analysis of the results, it is suggested that appropriate measures be taken according to the type of product, energy consumption ratios, energy resources, as well as various subsidy policies. Also pay sufficient attention to the role of energy carriers such as liquefied gases that are used indirectly.

 In today's world, energy plays a prominent role in various economic and political fields. A role that has plagued many countries with a monopoly economy and its effects, and has left others with costly problems caused by rising energy prices, and in general, even international relations and disputes has made an impact. Accordingly, communities are looking for ways to reduce energy consumption without harming their economic growth. Due to the shortage energy resources and its role in economic growth, its optimal use is an important goal in the economic development of each country. Therefore, it is necessary to know the factors affecting changes in energy consumption, which requires the decomposing of energy consumption into different factors affecting changes in energy consumption. In the present study, the factors affecting energy consumption are analyzed using statistics and information of industry and agriculture sectors during the period 1397-1387 to three factors: structural effect, activity effect and energy intensity effect. The relationship between energy consumption and GDP growth in these two sectors has been studied to determine which factor has the greatest role in energy consumption changes in industry and agriculture and to suggest appropriate policy solutions.

 Decomposing Analysis is one of the most widely used approaches in analyzes related to energy consumption and CO2 emissions. It is concluded that one is structural analysis (SDA) and the other is index analysis (IDA). These two analysis techniques are different and each has advantages and disadvantages. The SDA technique is more complex and requires a lot of data. On the other hand, the data-output table is prepared every few years and annual data are not available. Instead, more information and findings are obtained using the SDA technique. The IDA technique is simple and does not require much data. It can be used with big data and does not need the data of any particular department or product. For this reason, the SDA technique has been used more. This study is used the Tapio decoupling index to explain the decoupling status. Decoupling index is the best technique to describe the dependence of economic growth on energy consumption and it is used to discover the relationship between energy consumption and economic growth. In the present study, using the Logarithmic Mean Division (LMDI) index, to analyze the factors affecting energy consumption in industry and agriculture and then using the decoupling index to examine the relationship between energy consumption and GDP growth in these two sections are discussed.

The results of the decomposing  of energy consumption from GDP in the industrial sector showed that in most of the studies on the effect of energy intensity in agricultural sector, the effect of production had the greatest role in the decompose of energy consumption from GDP.

This study examined the decoupling of energy consumption from GDP of the industrial and agricultural sectors and the factors affecting energy consumption for each sector in Iran. The results of decomposition of energy consumption from GDP in the industrial sector showed that in most of the studied years, the effect of energy intensity and in the agricultural sector, the production effect had the greatest role in decomposing energy consumption from GDP. In 2010 and 2016, the production effect had a negative effect on energy consumption and increased energy consumption. In 1394, the index of decoupling of energy consumption from the growth of the agricultural sector was the best and most ideal possible, strong decoupling, and this indicates that the growth rate of the agricultural sector was higher than the growth rate of energy consumption. This year, the effect of energy intensity has had a positive effect on reducing energy consumption. The composite index has not experienced much fluctuation. Different modes of decoupling index and factors affecting energy consumption in industry and agriculture can be used as evidence for countries efforts to strongly decoupling and save energy in industry and agriculture. Advances in technology and innovation can reduce the growth dependence of sectors on energy consumption. Also, attention should be paid to optimizing the economic structure to create economic stability and reach the ideal state of the decoupling index.

Carbon dioxide emission is one of important challenges facing humanity, which has attracted the attention of many researchers and policymakers in recent years. The purpose of this study is investigating the factors influencing carbon dioxide emission. For achieving the purpose of paper spatial econometrics model and panel data of MENA countries are used over the period of 2000-2015. The results indicate that carbon dioxide emission in the MENA region countries have significant effect on surrounding countries. Also, the variables of energy consumption (0.01), economic globalization (0.134), industrialization (0.405) and urbanization (0.042) have positive and significant effect on carbon dioxide emission in the MENA region. The interaction among energy consumption and globalization (0.361), energy consumption and, industrialization (0.234) and urbanization (0.085) have a positive and significant effect on carbon dioxide emission. According to the results of this study, it is necessary that policymakers should not only focus on the national economic benefits but also consider the national and regional environmental benefits in policy making.

 Today, the food-energy nexus is a vital issue. Energy in the food production chain is an essential feature of agricultural development and a critical factor in achieving food security. Energy use in the agricultural sector has increased to respond to the growing demand of the population, as well the limited supply of cultivated lands, and the desire for high standards of living. Therefore, the agricultural sector is heavily dependent on energy that affects agricultural prices. Agricultural price fluctuations are one of the most critical challenges for policymakers. The rapid rise in food prices has a significant negative impact on social welfare, especially the poor in developing countries, which is an issue that is more critical in developing countries than in developed countries. According to the Food and Agriculture Organization (FAO) report in 2018, the food world price index increased from 89.6 to 229.9 during the period from 2002 to 2011. Our literature review shows a distinct lack of research on modeling and analyzing the linkage between agricultural input price shock, especially energy and agricultural commodity prices in Iran.

The Markov Switching model is a popular non-linear time-series model that involves multiple equations and can characterize the time-series behaviors in different regimes. This model is suitable for describing correlated data that exhibit distinct dynamic patterns during different periods. So, considering the sensitivity of food security and the impact of agricultural input, the main objective of this paper is to develop an econometric model to gain reliable insight into the impact of energy consumption on agricultural inflation, using the Markov Switching approach. To estimate this equation, we will run a MS-AR model, some preliminary tests, such as unit root test and stability test, are employed to ensure the reliability of MS-AR estimation results.

Due to use of time series data, it is necessary to check the stationary status of variables. We performed a common non-linear unit root test (Kapetanios, Shin and Shell (KSS), Zivot and Andrews, Lee and Strazicich). These results reveal that we can significantly reject the null hypothesis of unit root for API, PPI, FPI, and EC, implying that all four variables considered in this study are stationary with structural breaks at levels. The Markov-Switching model has the various types that each of these is a particular component of the regime-dependent equation. Therefore, to choose the best type, the Akaike information criterion was used, and the model with the minimum value was selected as the optimal one. After model estimation and selection, the LR test indicated that the hypothesis of linearity could be rejected in favor of a Markov switching model. According to this model, the period of the Markov switching model estimation is classified into two regimes. Approximately, all the estimated coefficients of the MSIAH (2) - AR (5) model are found to be significant at the conventional level.

The estimation results are consistent with theoretical foundations illustrating the importance of input prices and energy consumption on agricultural commodity prices. As with most experimental studies reviewed, this study has also shown energy consumption has a negative impact on agricultural commodity prices. In other words, it can be contended that during the study period, agricultural input prices have been influential factors on agricultural commodity prices. The findings revealed that the low inflation rate and high inflation rate regimes are stable and that only extreme events can switch regimes. The results of the MS model showed that the effect of input prices on agricultural inflation is different in regimes. In the case of energy, the impact of energy consumption on agricultural commodity prices in the high inflation rate regime is less than the low inflation rate regime because the elimination of energy subsidies policy has been applied in the second regime (high inflation rate). Thus, the results indicate the asymmetric impact of energy consumption shocks on agricultural commodity prices. The effect of agricultural input prices on agricultural commodity prices indicates that Iranian agriculture is significantly affected by changes in input prices. In this study, changes in input prices were caused by various shocks, such as the elimination of energy subsidies and drought. Therefore, it can be concluded that the elimination of energy subsidies and drought were, directly and indirectly, able to affect agricultural inflations through the price of inputs. In conclusion, planners and policymakers must pay attention to this asymmetry in agricultural commodity prices volatility to increase the price stability in agriculture as much as possible by appropriate policy tools.

The concept of low-carbon economy postulates the consumption of less natural resources and causing less environmental pollution, while gaining more economic efficiency. According to the concept of lowcarbon economy, low carbon agriculture is a specific model of agricultural production operations with both the lowest greenhouse gas emissions and maximum economic benefits having which has three characteristics including lower energy consumption, lower greenhouse gas emissions and lower pollution. Therefore, this research studies the approaches to low-carbon agriculture in Iran

In this research, energy consumption and consequently economic growth, which is expected to play a role in carbon emissions, other macroeconomic variables such as trade openness and financial development have been used. The estimated model of the research is linear-logarithmic based on Shahzad et al (2017). For this purpose, the ARDL and ECM patterns and the time series data of 1989-2014 have been used in current study. The data related to carbon dioxide emissions and energy consumption have been collected from the energy balance sheet of the Ministry of Energy, The data related to financial development have been collected from World Bank, Value Added of agriculture section Growth Ratio and Trade Openness data have been gathered from Central Bank of the Islamic Republic of Iran. Eviews9 software has been used to analyze the results.

Statistically significant impact of energy consumption logarithm and energy consumption logarithm square on carbon dioxide emissions at the level of 1% in the long run has been revealed by the results. The positive amount of energy consumption and negative amount of the square of energy consumption indicates a U-shaped inverted relationship between energy consumption and carbon dioxide emissions. The energy consumption threshold in the agricultural sector is 46.98 million barrels of crude oil, while the actual maximum energy consumption is 50.26 million barrels of crude oil. So the agricultural sector's performance is now above the mentioned level, then it is expected to reduce carbon emissions by technological improvements while increasing energy consumption. The coefficient of financial development variable in the long run is -0.014169. The financial development efficiency index is considered as national development variable which means each one percent of increase in bank credits allocated to the private sector will reduce about 1.02 ton of carbon dioxide. The coefficient of trade openness variable is 0.010443 in long run. Whereas the trade openness index is considered as the ratio of the total value of exports and imports to gross domestic product, so every one percent increase in the volume of exchanges to gross domestic product leads to a 1.01 ton increase in carbon dioxide and pollution which confirms the hypothesis. The growth rate of value added of agriculture section in the long run does not have any effect on carbon dioxide emissions. In the short run, the coefficient of trade openness is 0.00581. In other words, one percent increase in the ratio of international trade to GDP will increase about one ton of carbon dioxide emissions. The growth rate of value added of agriculture section, financial development, and the first lag of financial development in short run have no effect on carbon dioxide emissions.

The results indicated a long-term U-shaped inverted relationship between carbon emissions and energy consumption in this sector. The maximum energy consumption threshold was also equivalent to 46.98 billion barrels of crude oil. At present, performance of the sector is on downward, and carbon emissions are expected to gradually decrease by the technological improvement as energy consumption increases. Higher level of the energy consumption than the threshold level indicates that technology effect dominates the scale and composition effects. The results shows that the growth rate of value added of agriculture section in long and short run did not affect carbon emissions. Moreover, in the long run, financial development has negative effect on carbon emissions while in the short run financial development has no effect on carbon emissions. But the effect of trade openness index on carbon emissions in the long and short run is positive. According to the results of the study, increasing the volume of credits to the private sector will help reduce carbon emissions. It is also proposed to change the pattern of trade considering the environmental advantages and the use of green energy programs to reduce carbon emissions.

Increasing energy consumption in agriculture sector, causes emission of greenhouse gases that destroy environment and incur many costs on the society which is called destruction cost. Increasing the price of energy carriers, is one way to decrease the emission of air pollutants and destruction cost of them. Since this policy also has a economic effects, So the aim of this paper is to investigate this effects on the agriculture sector of Iran using a CGE model. Database for the CGE model, is social accounting matrix of the year (2004) for Iran. Results show that after shock, consumption of energy carriers, production level, domestic demand, employment and export falls and price of agriculture activities and import of them raises. On the other hand, emissions of air pollutants decreases and therefor destruction cost of Other Agriculture activity and Cultivation and Horticulture activity decreases about 251.9 and 748.5 billion rials (fix price of the year 2000). In total, destruction cost of agriculture sector falls from about 5% to almost 3.2% of the value added of agriculture sector.

Reducing pollution and achieving economic benefits with cleaner environment is one of the most important issues in recent decades. In this research, a macro approach was applied to assess the economic factors affecting environmental pollution in Iran during the years 1971-2013. The results indicated that the effect of variable energy consumption was at high levels of statistical significance, so that by 10 percent increase per capita energy consumption is expected that per capita emissions increased in the long-term and short-term by about 8 and 6 percent respectively. Also by 10 percent increase in urban population growth is expected that per capita emissions reduced in the long-term and short-term about1.2 and 0.86 percent respectively. Trade liberalization had negative impact on the emissions. Thus Iran can not be considered naturally immune against environmental concerns and we propose that at levels of per capita income of over $ 2,300 (second concave curve), that pollution increases more quickly so that politicians should look for more pollution control methods.

Reducing pollution and achieving economic benefits with cleaner environment is one of the most important issues in recent decades. In this research, a macro approach was applied to assess the economic factors affecting environmental pollution in Iran during the years 1971-2013. The results indicated that the effect of variable energy consumption was at high levels of statistical significance, so that by 10 percent increase per capita energy consumption is expected that per capita emissions increased in the long-term and short-term by about 8 and 6 percent respectively. Also by 10 percent increase in urban population growth is expected that per capita emissions reduced in the long-term and short-term about1.2 and 0.86 percent respectively. Trade liberalization had negative impact on the emissions. Thus Iran can not be considered naturally immune against environmental concerns and we propose that at levels of per capita income of over $ 2,300 (second concave curve), that pollution increases more quickly so that politicians should look for more pollution control methods.

During recent decades, the importance of environmental issues, all countries are trying to plan properly and using appropriate methods, not only to achieve their economic goals, but also to minimize the environmental damage caused by economic growth. Accomplish this without the knowledge of the relationship between economic activity and environmental pollution and the interplay between them is not possible and the problem for developing countries such as Iran, which is still in the early stages of economic development, has become more important. In this paper, short-run and long-run relationship between energy consumption, carbon dioxide emissions (as an indicator of environmental pollution), using data from 1974-2011 of Iran were studied. To investigate the relationship between the variables, the econometric regression methods Distributed Lag (ARDL) were used. The results showed that carbon dioxide emission relative to environmental degradation and its value in the long-run elastic 2.27. According to the results of variable energy consumption and carbon dioxide emissions significant positive and a long-term relationship with environmental degradation. In this case, the increase of energy consumption and carbon dioxide emissions further, environmental degradation is more; the comparison results show that short-run and long-term relationships Energy consumption in the short term greater impact on environmental degradation and destruction of carbon dioxide emissions in the long-run have a greater impact on the environment. The results show that the imbalance short term after two terms are moderated.

The issue of subsidies and the effects on costs and followed by consumption pattern of inputs، must be noted to the optimization of inputs energy consumption. The purpose of this study is optimization of energy use in agricultural sector in Kashmar district using information of 2009-2010. The model used is multi-objective programming with interval parameter. First، energy input of products and consumption share of energy inputs in production was estimated and then optimization of energy consumption in the agricultural sector was considered with regard to the objectives and constraints and optimal cropping pattern was presented with regard to different levels of available inputs. Finally، regard to the energy equivalent inputs، optimal value of energy consumption for each input was calculated in different levels of risk. The results showed that there is a less difference between actual and optimal energy consumption of inputs in risk free conditions rather than conditions that risk values is 1. 65 and 1. 95. In the model with risk conditions، the difference between actual and optimal energy consumption levels was increased. Water at all levels has the most difference in actual and optimal values and nitrate fertilizer showed minimal differences. Finally، use of products with less water needs، and determining the amount of input energy of different irrigation methods and also optimal use of the other inputs are suggested.

In this paper، the effect of energy consumption on non-oil economic growth has been investigated on three economic sectors of Iran (agriculture، industry and services) from 1991 to 2010. Basic model of study has been selected according to the augmented fluctuation of neoclassical economic growth framwork. Empirical analysis has been achieved on the basis of panel unit root tests، cointegration panel tests and estimation of panel long-run cointegration relations with FMOLS and DOLS. The results show that there is a long-run cointegration relationship between energy consumption and non-oil GDP. Elasticity of non-oil GDP to energy consumption، physical gross capital and employed manpower is according to the theories and previous empirical studies. Comparison of this elasticity with both estimators of cointegration panel long-run relations shows that results are very close to each other. Also by using estimation of fixed effects model، elasticity of non-oil GDP to energy consumption in agricultural، industry and services sectors are 0. 34، 1. 15 and 0. 39، respectively.

Unlike other studies in which the autours have focused on testing the existence of an environmental Kuznets Curve، in this study، causality relationship between the energy consumption (oil equivalent)، carbon emissions، gross domestic production، total labor force and fixed capital formation is investigated during 1960 to 2007 in Iran. Following an examination of stationary tests for all the variables، Vector Autoregressive (VAR) is employed to analyze the relationships existing among all the variables. Results revealed that Gross Domestic Production accompanied by energy consumption led to carbon emission. In addition، carbon emissions in Iran، as compared with other countries respond to shocks in energy consumption relatively quickly. Results of Variance Decomposition Analysis indicate that energy consumption has a greater share، in variance residual prediction of carbon emission، as compared with that of GDP. So، it is concluded that carbon emission could be controlled with decrease in energy consumption، through either price policy or fuel quota in Iran.

Unlike other studies in which the autours have focused on testing the existence of an environmental Kuznets Curve، in this study، causality relationship between the energy consumption (oil equivalent)، carbon emissions، gross domestic production، total labor force and fixed capital formation is investigated during 1960 to 2007 in Iran. Following an examination of stationary tests for all the variables، Vector Autoregressive (VAR) is employed to analyze the relationships existing among all the variables. Results revealed that Gross Domestic Production accompanied by energy consumption led to carbon emission. In addition، carbon emissions in Iran، as compared with other countries respond to shocks in energy consumption relatively quickly. Results of Variance Decomposition Analysis indicate that energy consumption has a greater share، in variance residual prediction of carbon emission، as compared with that of GDP. So، it is concluded that carbon emission could be controlled with decrease in energy consumption، through either price policy or fuel quota in Iran.

The main object of this study is to predict electricity consumption of agricultural sector in Iran. To get the objective, time series method of Auto-Regressive Moving Average (ARMA) and artificial neural networks (ANN) were used. Annual data for period of 1967 to 2008 was used. The Mean Absolute Percent Error (MAPE), Root of Mean of Squared Error (RMSE) and Mean Absolute Error (MAE) criteria were used for comparing the ability of different forecasting methods. As the result showed Feed Forward artificial neural network with back proportion algorithm can predict electricity consumption with MAPE equal to 1.02%, while the corresponding value for time series model obtained 1.13 percent. Other criteria also revealed the same result, so, ANN is expected to predict electricity consumption more precise than ARMA model. Therefore, energy ministry may use ANN in future predictions.