Investigation of relationship between the modern irrigation expansion and machinery development with pollutant emissions in Iran's agricultural sector: A case study of CO2

Over the past few decades, advances in technology have led to the increased use of energy and emissions of greenhouse gases. In this regard, researching the economic dimension of greenhouse gas emissions and their environmental effects, particularly in the present condition that their volume is increasing, has gained a lot of attention. This is due to the fact that the industrialization of modern societies has led to the use of fossil fuels such as coal, oil, and gas in production and services. Combustion of fossil fuels causes an increase in greenhouse gas emissions into the atmosphere of which carbon dioxide (CO2) is one of the most important. About 60% of greenhouse gas emissions in Iran is caused by the agricultural sector (Energy Balance, 2012) which is regarded as one of the main sources of such emissions. In the process of the production of agricultural products, greenhouse gases will be emitted as a result of inappropriate application of land and fertilizer, livestock, and the use of fossil energy (Behboodi et al., 2010) .So, in this study, the relationship between technological progress (land under modern irrigation and agricultural machinery), energy price and carbon dioxide emissions in Iranʼ s agricultural sector has been investigated.
For this purpose, a vector autoregressive model (VAR) and time series data during the period 1970-2012 were used .The reason for this choice of model, namely VAR, in this study is that it determines the long-running relationship between the variables and also has the ability to explain the short-run relationship between them. At first, the stationary degree of the variables was examined (Abrishami, 1996).
The results showed that, although not all variables are stationary at all levels, their first order differencing was stationary. After ensuring there is a convergence between variables, the results of model estimation are presented in two categories: the long-run relationship and the short-run relationship. The results showed that the energy price variable with a lag has a negative effect on CO2 emissions in the agricultural sector. With an increase in the energy price in agricultural sector energy demand, energy consumption will be reduced which leads, in turn, to reduced CO2 emissions. The machinery variable with a lag has a positive effect on carbon dioxide emissions in the agricultural sector. By assuming the same technology, increasing the number of such machinery results in more energy being consumed; increasing energy consumption leads to increased emissions of CO2. The land under modern irrigation variable with a lag has a negative effect on carbon dioxide emissions in the agricultural sector, with the short-run results confirming the long-run results. Share variables in the prediction error variance decomposition of carbon dioxide in the agricultural sector were also examined using the analysis of variance decomposition for a 10-year period. Results showed that energy price and land under modern irrigation have the most and the least effect on this variable both in the short- and long-run.
Therefore, one method to reduce carbon dioxide emissions without reducing of production is to improve energy intensity in the agricultural sector. If energy efficiency increases, it can reduce carbon dioxide emissions in this sector without reducing of production. Also, by employing multi-purpose machines it is possible to use less machinery and thus less use of energy. Modern technologies such as pressurized irrigation that reduce energy consumption and CO2 emissions are therefore recommended.