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## The effect of higher fuel price on pollutants emission in Iran | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Caspian Journal of Environmental Sciences | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

مقاله 1، دوره 16، شماره 1، بهار 2018، صفحه 1-11
اصل مقاله (477 K)
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نوع مقاله: Research Paper | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

شناسه دیجیتال (DOI): 10.22124/cjes.2018.2777 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

نویسندگان | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

S.N Mousavi^{1}؛ Z Mozaffari^{2}؛ M.K Motamed^{3}
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^{1}Islamic Azad University of Marvdasht | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

^{2}University of Tabriz | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

^{3}University of Guilan | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

چکیده | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

A key aspect of sustainable development in a country is how energy, environment and economic sectors interact. Greenhouse gas emissions and their impacts are among important environmental issues that have been in focus. The increase in the concentration of these gases in atmosphere to levels above the natural level results in global warming, depletion of the Earth’s protective layer against harmful solar radiation, and threatening whole natural life. The present study aimed at examining the factors affecting CO_{2} emission in Iran in 1981-2015. The studied variables included per capita CO_{2} emission, fuel price, per capita production, and per capita energy consumption. The relationship was examined by auto-regressive distributed lag (ARDL) model. It was found that CO_{2} emission is related to actual price of fuel indirectly and to per capita production and per capita energy consumption directly. According to the findings, 1% higher price of fuel would decrease CO_{2} emission by 0.14%, while 1% higher per capita production would increase it by 0.59%. Given the effectiveness of subsidy reform policy and the increased price of fuel on the alleviation of greenhouse gas emissions by road transport sector, it is advisable to gradually increase fuel price until it reaches FOB price in the Persian Gulf. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

کلیدواژهها | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

CO2 Emission؛ Environment؛ Fuel؛ Kuznets Curve؛ ARDL | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

اصل مقاله | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

(Received: July 24. 2017 Accepted: Jan. 09. 2018)
A key aspect of sustainable development in a country is how energy, environment and economic sectors interact. Greenhouse gas emissions and their impacts are among important environmental issues that have been in focus. The increase in the concentration of these gases in atmosphere to levels above the natural level results in global warming, depletion of the Earth’s protective layer against harmful solar radiation, and threatening whole natural life. The present study aimed at examining the factors affecting CO
When the activities of one consumer or enterprise are directly influenced by the environment of another consumer or enterprise, it is said that there is an externality (Varian 1992). The enterprises produce goods and services by exploiting economical resources like raw materials and energy. This process returns a part of the used inputs to the environment as wastage and residues. These wastages which are mostly as CO, CO Atmosphere, especially CO CO The relationship between economic growth and environment quality has been subjected to a lot of studies, of those the environmental Kuznets curve (EKC) can be mentioned as the most famous one. The concept of the curve has been derived from Kuznets (1995) idea about the presence of an ∩-shape relationship between per capita income and income distribution inequality. It was first introduced in the 1990s when Grossman & Krueger (1991) studied the potential environmental impacts of North American Free Trade Agreement (NAFTA) and also a report by Shafik & Bandyopadhyay (1992) was published on global development in 1992 (Behbudi The foundation of EKC is reflected in Beckerman’s idea about the effect of economic growth on the quality loss of environment (Kaika & Zervas 2013). Beckerman (1992) holds that although environment quality may be lost during the early stages of the economic growth, finally the best and maybe the only way to attain clean environment in most countries is their prosperity. The environmental Kuznets curve (EKC) shows a positive relationship between income and the loss of environment quality. The situation gets worse with economic growth. The trend of environment degradation is accelerating at first, but the slope of the curve gradually decreases and finally, economic growth mitigates the environment degradation as technology develops. According to Roca The comparison between gas consumption and the induced pollution well shows that Iran produces more pollution that what is expected from its gas consumption, whereas most neighboring countries and even European nations produce less pollution in spite of their higher gas consumption. Energy consumption rate is another factor influencing CO With the commencement of industrial evolution in 1830 and the increasing growth of human knowledge, human life has transformed drastically. The demand for energy and the consumption of various fossil fuels including coals, oil, and natural gas has considerably increased the level of such materials as CO Fukui & Miyoshi (2017) used ordinary least squares method to study the effect of fuel tax on fuel price and CO In a study on prediction of the effect of alternative transport fuels on CO In a study on the relationships among energy consumption, air pollution, and economic growth in Nepal using ARDL method, Bastola & Sapkota (2015) found a long-run unidirectional relationship between energy consumption and CO Gurtu Kohler (2013) examined the impact of energy consumption, income, and foreign trade on carbon emission in South Africa and found a long-run relationship among foreign trade, per capita energy consumption, and carbon emission. According to this study, energy consumption is directly related to carbon emission, but higher foreign trade results in lower carbon emission. Hatzigeorgiou Virley (1993) focused on the impact of fuel price increase on CO In a study on the influence of fuel price changes on greenhouse gas emissions from road transport sector of Iran from 1991-2013 using RLS method, Delangizan Behbudi In an analysis of CO Pollution coefficient and energy intensity were not so effective on CO
After a review of CO (1) Where, Data for per capita energy consumption and per capita CO
The model was estimated and the long and short-run relations between dependent variable and descriptive variables were studied by auto-regressive distributed lag (ARDL) using Microfit statistics package. ARDL has two steps. At the first step, we test the presence of a long-run relationship between the studied variables. Given the number of observations, the maximum number of lags is considered and given the tendency of the Schwartz-Bayesian criterion to brief specification. It shows better results in less than 100 observations. Immediately after dynamic (short-run) equation estimation, the test should be run on the presence/absence of a long-run relationship. Now, to make sure that the resulting long-run relationship is not false, we test the following hypothesis. The null hypothesis assumes the lack of auto regression or a long-run relationship, because the condition for the tendency of a short-run dynamic relationship towards a long-run equilibrium is that the sum of coefficients be smaller than 1. To test the condition, 1 should be subtracted from the sum of lagged coefficients of dependent variable and the result should be divided by the sum of standard deviations of these coefficients. The second phase of the analysis is the use of ARDL options in estimating the long-run relationships and statistical inference of their values. Noteworthy, it is appropriate to start this phase only once ensuring that the long-run relationships between the variables are not false (Tashkini 2005). After estimation of ARDL model, the following hypothesis is tested: (2)
(3) Or the relationship is long run because the condition for the tendency of a short-run dynamic relationship towards a long-run equilibrium is that the sum of coefficients be smaller than one. To test the condition by the method developed by Banerjee, Dolado, and Master in 1992, one should be subtracted from the sum of lagged coefficients of dependent variable and the result should be divided by the sum of standard deviations of these coefficients in which the test statistic will be derived from t-statistic (Nowferesti 2008). (4) The presence/absence of a long-run relationship between the model variables can be understood by comparing the quantity of calculated t-statistic and the critical value given by Banerjee, Dolado & Master (1992) at a certain confidence level. The present study used t-statistic test developed by Banerjee, Dolado and Master to test the long-run relationship (Tashkini 2005). At the second phase, the long-run coefficients are estimated and analyzed ant their values are inferred. The optimum number of lags for each descriptive variable can be determined by Akaike Information Criterion, Schwarz Bayesian Criterion, Hannan-Quinn Criterion, or adjusted-coefficient of determination (Nowferesti 2008). This method has more advantages relative to the similar methods, so it is used widely (Tashkini 2005). The most important advantage of ARDL the ability of ARDL to examine the relationships between variables, regardless of their stationary or non-stationary. Also in this method, in addition to the ability of calculating long-term relationships between variables, it is possible to calculate the dynamic and short-term relationships. A short while though quickly adjust imbalances in each period, to achieve long-run equilibrium is also calculated. While the speed of balancing the short-term imbalance of each period also is calculable for achieving balance.
Before model estimation, the stationary of variables in CO
According to the results in Table 2, it can be concluded that all variables become stationary after one- differentiating, so, all variables areI (1). As mentioned previously, analysis by ARDL method is based on interpretation of three equations: dynamic, long-run and error correction. Table 3 shows results for the estimation of dynamic equations. The optimum lag can be selected by Akaike, Schawrz Bayesian, or Hannan-Quinn criteria or adjusted-coefficient of determination. The following Table 3 shows results of the dynamic equation estimation. Several diagnostic tests were carried out to ensure the model is an appropriate model, such as the test for serial correlation (LM test), heteroscedasticity (ARCH test) and normality (JB (N)). The statistics reported shows that there are no problems associated with serial correlation, normality or heteroscedasticity. After estimating dynamic equation, a test should be run to ensure the presence of a long-run relationship in regression. In this test, the lagged coefficient of the dependent variable is subtracted from 1 and is divided by its standard deviation. Then, the calculated t-statistic (in absolute value) is compared with the values in Banerjee, Dolado and Mestre’s Table. If the calculated t-statistic is greater than the critical absolute value, it can be concluded that the null hypothesis of the absence of cointegration between the model variables is rejected and it is said that there is a long-run relationship.
Since in models estimated for consumption and capital expenditure, the dependent variable has emerged just with one lag on the right side, the t-statistic for CO
(5)
The absolute values of both statistics are greater than the equivalent absolute value in Banerjee, Dolado and Mestre’s Table (-3.50). Thus, it can be concluded that the null hypothesis about the lack of cointegration among model variables is rejected at the 90% confidence level and so, there is a long-run equilibrium relationship among the variables in carbon emission model. Table 4 shows the results of the estimation of this model. Results presented in Table 4 reflect a negative relationship of actual price of fuel with CO The statistic is plotted against time.
As shown in the graph, the test statistic is located inside the straight lines, showing the stability of the coefficients at the 5% significance level. In other words, the null hypothesis about the stability of the coefficients cannot be rejected at the 95% confidence level.
The present research studied the factors underpinning CO
per capita production and energy consumption was evaluated on per capita CO (2015), and Fukui & Miyoshi (2017). These findings align with those of Bonney & Jaber (2011), who note: ‘‘Perhaps the continued increase in energy prices may result in deglobalisation of markets, if not decentralization. In other words, a move towards smaller self-sustainable markets would be a strategic solution to consider.’’
Sustainable development depends on concern for environment and its conservation for the next generations. The increasing rate of greenhouse gas emissions, especially CO Today, air pollution has gained so much importance in most countries and, especially talking, in large cities in Iran that the governments are forced to take the issue serious and develop short- and long-term plans and policies. One of the recent policies has been the elimination of energy subsidy and gradual increase in fossil fuel prices in a hope of the reform in consumption patterns and movements towards energy efficiency. So, it can be expected to see gradual alleviation of air pollution and greenhouse gas production. Obviously, studies on factors affecting greenhouse gas emissions can help policy-makers make sound decision to overcome environment degradation and reduce the related costs. According to the results, carbon emission is related to actual price of fuel indirectly and to per capita production and energy consumption directly. Also, 1% increase in per capita energy consumption will increase CO Higher fuel price would hinder unnecessary commutes and traffic and would facilitate commutes in larger cities which would alleviate pollution on the one hand and would save commute time and fuel consumption on the other hand resulting in higher efficiency of active people. From an economical point of view, pollution decrease can enhance work efficiency resulting in higher labor efficiency. Equipping vehicles with bi-fuel system does not reduce fuel consumption, but it changes the type of fuel in use. In light of the increase in global price of natural gas, this transformation will not be economical. Although Iran enjoying huge resources of natural gas as an advantage, these resources will eventually start to deplete in future and also, its global price is a function of unpredictable variables.
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مراجع | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abbaspour, M 2007, Energy, environment, and sustainable development. (S Khadivari, Ed.) Tehran, Iran: Sharif University Press, Elmi Publishing Institute (In Persian).
Amadeh, H, Ghafari, A & Farajzadeh, Z 2015, Analysis of environmental and welfare effects of energy subsidy reform (application of computable general equilibrium model). Journal of Iranian Energy Economics, 4: 33-62 (In Persian).
Andrews, J, Jelley, N & Jelley, NA 2013, Energy science: principles, technologies, and impacts. Oxford University Press.
Barghi Osgooyi, M 2008, The impact of trade liberalization on the greenhouse gases (CO _{2}) emissions in EKC. Journal of Economic Research (Tahghighat-e-Eghtesadi), 43: 1-22 (In Persian).
Bastola, U & Sapkota, P 2015, Relationships among energy consumption, pollution emission, and economic growth in Nepal. Energy, 80: 254-262.
Beckerman, W 1992, Economic growth and the envrionment: Whose growth? Whose environment? World Development, 20: 481-496.
Behbudi, D, Fallahi, F & Barghi, E 2010, The economical and social factors affecting CO _{2} emission in Iran. Journal of Economic Research (Tahghighat-e-Eghtesadi), 45: 1-17 (In Persian).
Bonney, M & Jaber, MY 2011, Environmentally responsible inventory models: Non-classical models for a non-classical era. International Journal of Production Economics, 133: 43-53.
Delangizan, S, Khanzadi, A & Heidarian, M 2015, Studying the effects of fuel price changes on greenhouse gas emissions in the road transportation sector of Iran: Approach of robust least squares. Quarterly Journal of Quantitative Economics, 11: 47-77 (In Persian).
Fukui, H & Miyoshi, C 2017, The impact of aviation fuel tax on fuel consumption and carbon emissions: The case of the US airline industry. Transportation Research Part D: Transport and Environment, 50: 234-253.
Grossman, GM & Krueger, AB 1991, Environmental impacts of a North American free trade agreement (No. w3914). National Bureau of Economic Research.
Gurtu, A, Jaber, M & Searcy, C 2015, Impact of fuel price and emissions on inventory policies. Applied Mathematical Modelling, 39: 1202-1216.
Hatzigeorgiou, E, Polatidis, H & Haralambopoulos, D 2011, CO2 emissions, GDP and energy intensity: A multivariate cointegration and causality analysis for Greece, 1977-2007. Applied Energy, 88: 1377-1385.
Kaika, D & Zervas, E 2013, The environmental Kuznets curve (EKC) theory - Part A: Concept, causes and the CO _{2} emissions case. Energy Policy, 62: 1392-1402.
Kohler, M 2013, CO _{2} emissions, energy consumption, income and foreign trade: A South African perspective. Energy Policy, 63: 1042-1050.
Lotfalipour, M & Ashena, M 2010, A study on factors affecting the variations of CO _{2} emission in Iran's economics. Quarterly Energy Economics Review, 7: 121-145 (In Persian).
Maghelal, P 2011, Investigating the relationships among rising fuel prices, increased transit ridership, and CO _{2} emissions. Transportation Research Part D: Transport and Environment, 16: 232-235.
Manzoor, D & Rezaee, H 2014, Power plant fuel price reform, emissions pollution and greenhouse: A system dynamics approach. Journal of Iranian Energy Economics, 3: 199-215 (In Persian).
Nocera, S & Cavallaro, F 2016, The competitiveness of alternative transport fuels for CO _{2} emissions. Transport Policy, 50: 1-14.
Nowferesti, M 2008, Unit root and autoregression in econometrics. Iran: Rasa Institute. (In Persian).
Roca, J, Padilla, E, Farré, M & Galletto, V 2001, Economic growth and atmospheric pollution in Spain: discussing the environmental Kuznets curve hypothesis. Ecological Economics, 39: 85-99.
Shafik, N & Bandyopadhyay, S 1992, Economic growth and environmental quality: time-series and cross-country evidence (Vo. 904). World Bank Publications.
Shahbaz, M, Solarin, SA, Mahmood, H & Arouri, M 2013, Does financial development reduce CO _{2} emissions in Malaysian economy? A time series analysis. Economic Modelling, 35, 145-152.
Tashkini, A 2005, Practical econometrics by Microfit package. Tehran, Iran: Dibagaran Institute (In Persian).
Varian, H 1992, Microeconomic analysis. (R Hosseini, Trans.) Tehran, Iran: Nashreney.
Vaseghi, E & Esmaeili, A 2010, Investigation of the determinant of CO _{2} emission in Iran (Using environmental Kuznets curve). Journal of Environmental Studies, 35: 99-110 (In Persian).
Virley, S 1993, The effect of fuel price increases on road transport CO
_{2} emissions. Transport Policy, 1: 43-48.
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