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Showing 4 results for Agricultural Sector
Volume 0, Issue 0 (1-2024)
Abstract
Oil is one of the significant factors in promoting economic activities. Oil provides a considerable part of the government's revenue sources. The price of oil has always been fluctuating over the years for various reasons including political, social, and economic developments in countries. Since the price of oil affects different sectors of economy including the agricultural sector through the government budget and the revenue sources of the government, this study investigated the effect of positive and negative fluctuations in OPEC oil prices on the value added of the agricultural sector during 1990-2019. The new GAS method was used for estimating the OPEC oil price fluctuations and the NARDL method was used for estimating the long-term relationships between positive and negative OPEC oil price fluctuations on the value added of the agricultural sector. In addition to OPEC oil price fluctuations, other independent variables such as the consumer price index, employment in the agricultural sector, and the degree of trade openness were examined in the model. Based on the obtained results, the positive and negative fluctuations of OPEC oil prices in the long term had a negative effect on the value added of the agricultural sector. Furthermore, the degree of trade openness had a positive effect on the value added of the agricultural sector in a long term. results indicate that oil income is one of the most important issues that decline value added of agriculture sector and government should control this negative relation to develop agriculture as a vital sector of economic.
Mahmoud Ahmadpour Borazjani, Mahmood Sabouhi Sabouni,
Volume 15, Issue 2 (6-2015)
Volume 15, Issue 2 (6-2015)
Abstract
In order to study the effects of subsidies targeted plan on Iran agricultural sector, a regional agricultural sector model is made. For this purpose, Iran is divided into 9 equal radar regions agro-climatologically. Then agricultural sector is modeled within 14 commodity groups and 23 production activities using positive mathematical programming (PMP) method and applying generalized maximum entropy (GME) approach. The results indicate that making subsidies targeted by increasing prices of inputs (irrigation water, fertility, pesticide and machinery), escalating energy prices for poultry and dairy farms and raising transportation cost, leads to decrease total surplus of agricultural sector, decrease in many crop and livestock activity levels in different regions, increase in prices, decrease in consumption and exports and increase in imports of some agricultural products. According to the results, if subsidies targeted policy is mixed with support payments to producers, dependent on amount of support payment, the social surplus of agricultural sector may decrease or increase. A support payment equal to 25 percent of production costs can compensate losses of producers due to targeted subsidies, transmit loss of social welfare to zero and decrease volatility of economic variables such as activity levels, price and quantity of consumption of commodity groups.
Esmaeil Pishbahar, Zahra Rasoli Beirami,
Volume 15, Issue 3 (11-2015)
Volume 15, Issue 3 (11-2015)
Abstract
Fisher-Seater's approach is applied to the Iran's economic data 2008 to test the long-Run neutrality and super neutrality of money during 1988-2008. Our results support the neutrality of M2 w real GDP and real agricultural output. For nominal agricultural output neutrality of M2 is strongly rejected. The result of neutrality for the nominal GDP varies depending on the unit root test. The results also showed that the super neutrality of M2 with respect to real GDP is confirmed.
Mr Ramin Amani, Mr Zanko Ghorbani, Dr Zana Mozaffari,
Volume 24, Issue 3 (9-2024)
Volume 24, Issue 3 (9-2024)
Abstract
Introduction
Climate change can occur once in a thousand years. However, recent abrupt and severe climate shifts have emerged as a significant concern within societies and a substantial environmental challenge. Escalating temperature, polar ice melting, global sea level rise, and shifting weather patterns, all stem from climate change. One of the pathways towards achieving sustainable development involves the advancement of the agricultural sector, a vital economic segment. Progress in almost all sectors of economy, even the industry sector is closely correlated to growth in agriculture. Looking at the experiences of leading countries in agricultural production, the utilization of capital equipment across various agricultural activities has proven to enhance the productivity of factors like land, labor, and management. This, in turn, results in decreased production costs, increased investment returns, surplus domestic supply, and expanded agricultural product exports. The world confronts a climate change crisis due to widespread and damaging human pursuits aimed at resource acquisition. The repercussions of climate change, including rising sea levels, global warming, floods, droughts, and landslides, pose substantial threats to human existence. Among economic sectors, agriculture holds a particularly critical role in ensuring the sustenance of human populations. Yet, climate change places the agricultural sector under a severe risk, jeopardizing its capacity to provide food for humanity. Hence, the principal objective of this current study is to explore the impact of two climatic variables i.e. climate change risk and climate change performance index, on the added value of the agricultural sector across 54 member countries of the climate change performance index. These countries are categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries). The study period spans from 2010 to 2020, and the quantile regression method is employed on panel data to conduct the analysis.
Methodology
The general definition of quantile regression states that if the linear regression model is assumed as the following equation, we have:
y i = ˊ x i β τ + u τi . 0< τ <1
Quant τ ( y i | x i )= x i β τ
Quant τ ( u τi | x i )=0
In the quantile regression structure, the effect of observable features on the conditional distribution is estimated through the process of minimizing the absolute value of the error element. To estimate the model coefficients, the absolute value of the errors with appropriate weighting is used:
As mentioned, quantile regression is resistant to outliers. However, this method is not intended to recognize the heterogeneity of a country. In this research, the quantile panel regression method with fixed effects is used, which makes it possible to estimate the effects of conditional heterogeneous covariance of inflation rate stimuli, thus controlling invisible individual heterogeneities. A suitable method has been suggested by Koner (2004) for solving such problems. He considers invisible fixed effects as parameters that are jointly estimated with the effects of auxiliary variables for different quantiles. The unique feature of this method is that it introduces a penalty term in the minimization to address the computational problem of a set of parameters; The parameters are calculated as follows:
min ( α . β ) Σ k =1 K Σ t =1 T Σ i =1 N w k ρ τk y it - α i - x it T β τ k + λ Σ i N α i
In equation (6), i represents the number of countries (N), T represents the index for the number of observations of each country, K represents the quantile index, x is the matrix of explanatory variables, and ρτk is the quantile loss function. Also, Wk represents the relative weight for the kth quantile. λ is an adjustment parameter that reduces individual effects to zero to improve the performance of β estimates. If λ tends to zero, the penalty term is eliminated and a conventional fixed effects estimator is obtained. Whereas if λ tends to infinity, an estimate of the model is obtained without fixed effects. In this research, λ = 1 (Damette and Delacote, 2012).
Results and Discussion
The results of this research show that both climate change performance index variables and climate change risk have a positive and significant effect on the added value of the agricultural sector in all three functional groups and all deciles. This means that an increase in climate change performance and an increase in climate change risk, both of which represent the improvement of climate conditions in a country, which have a positive effect on the added value of the agricultural sector.
Conclusion
Nowadays, climate change crisis has become one of the most critical challenges facing humanity in the present century. Scientists and researchers attribute the main cause of climate change to destructive human activities aimed at obtaining more resources to meet their needs and desires. Global warming, rising sea and ocean levels, landslides, floods, and droughts are just some of the consequences related to the climate change crisis.
Within this context, agricultural sector, as one of the most important economic sectors for providing human food, is under the influence of climate change and could seriously endanger the future of humanity due to food resource scarcity. The main objective of this research is to examine the impact of two climate variables, namely climate change risk and climate change performance index, on the value added by the agricultural sector in 54 countries categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries) during the period from 2010 to 2020, using a multiple regression method on tabular data.
The results of this study indicate that both climate change performance index and climate change risk have a positive and significant impact on the value added by the agricultural sector in all three performance groups and throughout all decades. This means that increasing climate change performance and climate change risk, both of which signify improved climate conditions in a country, positively affect the value added by the agricultural sector.Introduction
Climate change can occur once in a thousand years. However, recent abrupt and severe climate shifts have emerged as a significant concern within societies and a substantial environmental challenge. Escalating temperature, polar ice melting, global sea level rise, and shifting weather patterns, all stem from climate change. One of the pathways towards achieving sustainable development involves the advancement of the agricultural sector, a vital economic segment. Progress in almost all sectors of economy, even the industry sector is closely correlated to growth in agriculture. Looking at the experiences of leading countries in agricultural production, the utilization of capital equipment across various agricultural activities has proven to enhance the productivity of factors like land, labor, and management. This, in turn, results in decreased production costs, increased investment returns, surplus domestic supply, and expanded agricultural product exports. The world confronts a climate change crisis due to widespread and damaging human pursuits aimed at resource acquisition. The repercussions of climate change, including rising sea levels, global warming, floods, droughts, and landslides, pose substantial threats to human existence. Among economic sectors, agriculture holds a particularly critical role in ensuring the sustenance of human populations. Yet, climate change places the agricultural sector under a severe risk, jeopardizing its capacity to provide food for humanity. Hence, the principal objective of this current study is to explore the impact of two climatic variables i.e. climate change risk and climate change performance index, on the added value of the agricultural sector across 54 member countries of the climate change performance index. These countries are categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries). The study period spans from 2010 to 2020, and the quantile regression method is employed on panel data to conduct the analysis.
Methodology
The general definition of quantile regression states that if the linear regression model is assumed as the following equation, we have:
y i = ˊ x i β τ + u τi . 0< τ <1
Quant τ ( y i | x i )= x i β τ
Quant τ ( u τi | x i )=0
In the quantile regression structure, the effect of observable features on the conditional distribution is estimated through the process of minimizing the absolute value of the error element. To estimate the model coefficients, the absolute value of the errors with appropriate weighting is used:
As mentioned, quantile regression is resistant to outliers. However, this method is not intended to recognize the heterogeneity of a country. In this research, the quantile panel regression method with fixed effects is used, which makes it possible to estimate the effects of conditional heterogeneous covariance of inflation rate stimuli, thus controlling invisible individual heterogeneities. A suitable method has been suggested by Koner (2004) for solving such problems. He considers invisible fixed effects as parameters that are jointly estimated with the effects of auxiliary variables for different quantiles. The unique feature of this method is that it introduces a penalty term in the minimization to address the computational problem of a set of parameters; The parameters are calculated as follows:
min ( α . β ) Σ k =1 K Σ t =1 T Σ i =1 N w k ρ τk y it - α i - x it T β τ k + λ Σ i N α i
In equation (6), i represents the number of countries (N), T represents the index for the number of observations of each country, K represents the quantile index, x is the matrix of explanatory variables, and ρτk is the quantile loss function. Also, Wk represents the relative weight for the kth quantile. λ is an adjustment parameter that reduces individual effects to zero to improve the performance of β estimates. If λ tends to zero, the penalty term is eliminated and a conventional fixed effects estimator is obtained. Whereas if λ tends to infinity, an estimate of the model is obtained without fixed effects. In this research, λ = 1 (Damette and Delacote, 2012).
Results and Discussion
The results of this research show that both climate change performance index variables and climate change risk have a positive and significant effect on the added value of the agricultural sector in all three functional groups and all deciles. This means that an increase in climate change performance and an increase in climate change risk, both of which represent the improvement of climate conditions in a country, which have a positive effect on the added value of the agricultural sector.
Conclusion
Nowadays, climate change crisis has become one of the most critical challenges facing humanity in the present century. Scientists and researchers attribute the main cause of climate change to destructive human activities aimed at obtaining more resources to meet their needs and desires. Global warming, rising sea and ocean levels, landslides, floods, and droughts are just some of the consequences related to the climate change crisis.
Within this context, agricultural sector, as one of the most important economic sectors for providing human food, is under the influence of climate change and could seriously endanger the future of humanity due to food resource scarcity. The main objective of this research is to examine the impact of two climate variables, namely climate change risk and climate change performance index, on the value added by the agricultural sector in 54 countries categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries) during the period from 2010 to 2020, using a multiple regression method on tabular data.
The results of this study indicate that both climate change performance index and climate change risk have a positive and significant impact on the value added by the agricultural sector in all three performance groups and throughout all decades. This means that increasing climate change performance and climate change risk, both of which signify improved climate conditions in a country, positively affect the value added by the agricultural sector.
Climate change can occur once in a thousand years. However, recent abrupt and severe climate shifts have emerged as a significant concern within societies and a substantial environmental challenge. Escalating temperature, polar ice melting, global sea level rise, and shifting weather patterns, all stem from climate change. One of the pathways towards achieving sustainable development involves the advancement of the agricultural sector, a vital economic segment. Progress in almost all sectors of economy, even the industry sector is closely correlated to growth in agriculture. Looking at the experiences of leading countries in agricultural production, the utilization of capital equipment across various agricultural activities has proven to enhance the productivity of factors like land, labor, and management. This, in turn, results in decreased production costs, increased investment returns, surplus domestic supply, and expanded agricultural product exports. The world confronts a climate change crisis due to widespread and damaging human pursuits aimed at resource acquisition. The repercussions of climate change, including rising sea levels, global warming, floods, droughts, and landslides, pose substantial threats to human existence. Among economic sectors, agriculture holds a particularly critical role in ensuring the sustenance of human populations. Yet, climate change places the agricultural sector under a severe risk, jeopardizing its capacity to provide food for humanity. Hence, the principal objective of this current study is to explore the impact of two climatic variables i.e. climate change risk and climate change performance index, on the added value of the agricultural sector across 54 member countries of the climate change performance index. These countries are categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries). The study period spans from 2010 to 2020, and the quantile regression method is employed on panel data to conduct the analysis.
Methodology
The general definition of quantile regression states that if the linear regression model is assumed as the following equation, we have:
In the quantile regression structure, the effect of observable features on the conditional distribution is estimated through the process of minimizing the absolute value of the error element. To estimate the model coefficients, the absolute value of the errors with appropriate weighting is used:
As mentioned, quantile regression is resistant to outliers. However, this method is not intended to recognize the heterogeneity of a country. In this research, the quantile panel regression method with fixed effects is used, which makes it possible to estimate the effects of conditional heterogeneous covariance of inflation rate stimuli, thus controlling invisible individual heterogeneities. A suitable method has been suggested by Koner (2004) for solving such problems. He considers invisible fixed effects as parameters that are jointly estimated with the effects of auxiliary variables for different quantiles. The unique feature of this method is that it introduces a penalty term in the minimization to address the computational problem of a set of parameters; The parameters are calculated as follows:
In equation (6), i represents the number of countries (N), T represents the index for the number of observations of each country, K represents the quantile index, x is the matrix of explanatory variables, and ρτk is the quantile loss function. Also, Wk represents the relative weight for the kth quantile. λ is an adjustment parameter that reduces individual effects to zero to improve the performance of β estimates. If λ tends to zero, the penalty term is eliminated and a conventional fixed effects estimator is obtained. Whereas if λ tends to infinity, an estimate of the model is obtained without fixed effects. In this research, λ = 1 (Damette and Delacote, 2012).
Results and Discussion
The results of this research show that both climate change performance index variables and climate change risk have a positive and significant effect on the added value of the agricultural sector in all three functional groups and all deciles. This means that an increase in climate change performance and an increase in climate change risk, both of which represent the improvement of climate conditions in a country, which have a positive effect on the added value of the agricultural sector.
Conclusion
Nowadays, climate change crisis has become one of the most critical challenges facing humanity in the present century. Scientists and researchers attribute the main cause of climate change to destructive human activities aimed at obtaining more resources to meet their needs and desires. Global warming, rising sea and ocean levels, landslides, floods, and droughts are just some of the consequences related to the climate change crisis.
Within this context, agricultural sector, as one of the most important economic sectors for providing human food, is under the influence of climate change and could seriously endanger the future of humanity due to food resource scarcity. The main objective of this research is to examine the impact of two climate variables, namely climate change risk and climate change performance index, on the value added by the agricultural sector in 54 countries categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries) during the period from 2010 to 2020, using a multiple regression method on tabular data.
The results of this study indicate that both climate change performance index and climate change risk have a positive and significant impact on the value added by the agricultural sector in all three performance groups and throughout all decades. This means that increasing climate change performance and climate change risk, both of which signify improved climate conditions in a country, positively affect the value added by the agricultural sector.Introduction
Climate change can occur once in a thousand years. However, recent abrupt and severe climate shifts have emerged as a significant concern within societies and a substantial environmental challenge. Escalating temperature, polar ice melting, global sea level rise, and shifting weather patterns, all stem from climate change. One of the pathways towards achieving sustainable development involves the advancement of the agricultural sector, a vital economic segment. Progress in almost all sectors of economy, even the industry sector is closely correlated to growth in agriculture. Looking at the experiences of leading countries in agricultural production, the utilization of capital equipment across various agricultural activities has proven to enhance the productivity of factors like land, labor, and management. This, in turn, results in decreased production costs, increased investment returns, surplus domestic supply, and expanded agricultural product exports. The world confronts a climate change crisis due to widespread and damaging human pursuits aimed at resource acquisition. The repercussions of climate change, including rising sea levels, global warming, floods, droughts, and landslides, pose substantial threats to human existence. Among economic sectors, agriculture holds a particularly critical role in ensuring the sustenance of human populations. Yet, climate change places the agricultural sector under a severe risk, jeopardizing its capacity to provide food for humanity. Hence, the principal objective of this current study is to explore the impact of two climatic variables i.e. climate change risk and climate change performance index, on the added value of the agricultural sector across 54 member countries of the climate change performance index. These countries are categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries). The study period spans from 2010 to 2020, and the quantile regression method is employed on panel data to conduct the analysis.
Methodology
The general definition of quantile regression states that if the linear regression model is assumed as the following equation, we have:
In the quantile regression structure, the effect of observable features on the conditional distribution is estimated through the process of minimizing the absolute value of the error element. To estimate the model coefficients, the absolute value of the errors with appropriate weighting is used:
As mentioned, quantile regression is resistant to outliers. However, this method is not intended to recognize the heterogeneity of a country. In this research, the quantile panel regression method with fixed effects is used, which makes it possible to estimate the effects of conditional heterogeneous covariance of inflation rate stimuli, thus controlling invisible individual heterogeneities. A suitable method has been suggested by Koner (2004) for solving such problems. He considers invisible fixed effects as parameters that are jointly estimated with the effects of auxiliary variables for different quantiles. The unique feature of this method is that it introduces a penalty term in the minimization to address the computational problem of a set of parameters; The parameters are calculated as follows:
In equation (6), i represents the number of countries (N), T represents the index for the number of observations of each country, K represents the quantile index, x is the matrix of explanatory variables, and ρτk is the quantile loss function. Also, Wk represents the relative weight for the kth quantile. λ is an adjustment parameter that reduces individual effects to zero to improve the performance of β estimates. If λ tends to zero, the penalty term is eliminated and a conventional fixed effects estimator is obtained. Whereas if λ tends to infinity, an estimate of the model is obtained without fixed effects. In this research, λ = 1 (Damette and Delacote, 2012).
Results and Discussion
The results of this research show that both climate change performance index variables and climate change risk have a positive and significant effect on the added value of the agricultural sector in all three functional groups and all deciles. This means that an increase in climate change performance and an increase in climate change risk, both of which represent the improvement of climate conditions in a country, which have a positive effect on the added value of the agricultural sector.
Conclusion
Nowadays, climate change crisis has become one of the most critical challenges facing humanity in the present century. Scientists and researchers attribute the main cause of climate change to destructive human activities aimed at obtaining more resources to meet their needs and desires. Global warming, rising sea and ocean levels, landslides, floods, and droughts are just some of the consequences related to the climate change crisis.
Within this context, agricultural sector, as one of the most important economic sectors for providing human food, is under the influence of climate change and could seriously endanger the future of humanity due to food resource scarcity. The main objective of this research is to examine the impact of two climate variables, namely climate change risk and climate change performance index, on the value added by the agricultural sector in 54 countries categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries) during the period from 2010 to 2020, using a multiple regression method on tabular data.
The results of this study indicate that both climate change performance index and climate change risk have a positive and significant impact on the value added by the agricultural sector in all three performance groups and throughout all decades. This means that increasing climate change performance and climate change risk, both of which signify improved climate conditions in a country, positively affect the value added by the agricultural sector.
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