The impacts of fuel price policies on air pollution: case study of Tehran

This study aims to investigate the impacts of fuel price policies on the concentration of air pollutants in Tehran city. Autoregressive distributed lag (ARDL) estimation models were used to investigate the impacts of gasoline and diesel prices along with the weather and economic variables on the following traffic-related pollutants: carbon monoxide (CO), nitrogen dioxide (NO2), and particular matter 10 micrometers or less (PM10). In the short term, a 1% increase in gasoline prices leads to a 0.02 and 0.012% decrease in the concentration of CO and PM10, respectively. In addition, in the short term, a 1% increase in diesel prices leads to a 0.008, 0.02, and 0.015 % decrease in the concentration of CO, PM10, and NO2, respectively. Results demonstrate that a 1% increase in gasoline prices leads to a 0.011 and 0.02 % increase in NO2 concentration in the short term and long term, respectively. Fuel prices had a greater impact on air pollutant concentration in the long term than in the short term. In the long term, a 1% increase in diesel prices leads to a 0.011, 0.024, and 0.029 % decrease in the concentration of CO, NO2, and PM10, respectively. Although fuel price increases lead to a significant reduction in PM10 and CO concentrations, other factors related to weather conditions (wind speed, temperature, and rainfall) as well as economic activities have a greater impact on air pollution. Therefore, other policies such as improving fuel quality and technology along with other economic policies can be more effective.


Introduction
Air pollution is a serious threat to public health and the environment, and it is estimated that millions of pollutants enter the environment each year (Piraino et al. 2006). Nowadays, many major cities in the world are facing environmental problems, especially due to air pollution, which puts the health of people at risk. Air pollution often has exceeded the World Health Organization (WHO) standards in these cities (Mohan and Kandya 2007;Scorer 2019). Today, almost half of the world's population lives in cities with population congestion and a variety of environmental problems (Baklanov et al. 2018).
Tehran is one of the eight most polluted cities in the world in terms of air pollution. Although it takes up only 1.2 % of the country's total area, it accounts for 20% of the country's total population, 40% of the industry, and 85% of its total expertise. The main causes of Tehran's air pollution are motor vehicles and cars (more than 80 %). Also, non-standard vehicles, the use of private cars instead of the public transportation system, low quality of fuels, and growth of migration have resulted in an increasing trend of air pollution (Atash 2007;Hosseini and Shahbazi 2016). According to the statistics and reports of the Iranian Air Quality Control Organization (IAQCO), most of Tehran's air pollutants are carbon monoxide (CO), nitrogen dioxide (NO 2 ), and particular matter 10 micrometers or less (PM 10 ), respectively (Heger and Sarraf 2018). Among the different sectors in Iran, transportation plays the most important role in emitting these pollutants, so the Iran transportation sector annually produces 807245 (64 %), 8312710 (99 %), and 270761 (80 %) tons of NOX, CO, and PM2.5 pollutants, respectively (Hosseini and Shahbazi 2016).
On the other hand, Iranian governments are always seeking to alleviate environmental pollution problems and the Responsible Editor: Philippe Garrigues negative effects of human activities on the environment by adopting different policies and programs. However, reducing air pollution due to traffic is very difficult because policies that aim to reduce vehicle usage are met with resistance from the powerful car lobby. Also, the contribution of many people to the creation of air pollution due to vehicle usage is another difficulty in reducing air pollution (Douglas et al. 2011).
However, the Iranian government has implemented various short-and long-term policies and programs and dedicated huge amounts of budgeting to control air pollution in Tehran (Atash 2007).
Old vehicle retirement programs, the expansion of subway lines, and the implementation of traffic restrictions in the city center are examples of strategies that have been implemented to control air pollution in Iran's major cities. Meanwhile, the implementation of fuel pricing policies to reduce air pollution and control fuel consumption in Tehran, due to its low running costs, has always been a priority among various plans (Hajihoseinlou and Karooni 2007;World Bank 2018).
Accordingly, a change in fuel prices could contribute to a change in the demand for fuel/fuel demand and consequently the emissions from vehicles. For example, taking public transport instead of private vehicles, substituting vehicles with higher rates of fuel consumption with vehicles with lower rates of fuel consumption, and decreasing the distance driving are some responses which tend to decrease air pollutant concentration (Delsalle 2002).
Hence, few studies have examined the effects of fuel price policies on air pollution. Two studies, one conducted by Ninpanit and Barnett and one by Knibbs, declare that increases in fuel prices have played an important role in improving air quality and reducing emissions (Barnett and Knibbs 2014;Ninpanit 2019).
The elimination of energy subsidies and the gradual rise in fossil fuel prices are some fuel pricing policies that have been implemented in recent years and could improve fuel consumption patterns and some pollutants like CO, NO 2 , and PM 10 emitted from the transport section. Therefore, as fuel prices rise, it can be expected that air pollution and emissions will decrease. This study aims to investigate the effects of gasoline and diesel fuel price policies on air pollution concentration (CO, NO 2 , and PM 10 ).

Data
Tehran is the capital of Iran and is located in the north of the country. This city is the most populous city in Iran (the eighth most polluted city in the world) and had a permanent resident population of 8.73 million people in 2017 (Statistical Center of Iran 2020) (Hensel and Gharleghi 2012). Figure 1 shows the location of Iran, Tehran, and the monitoring stations in the 22 districts of Tehran.
Monthly concentration of CO, NO 2 , and PM 10 was collected from the Air Quality and Control Company (AQCC) from March 21, 2009, to December 21, 2019. Figure 2a-c show the trends of monthly air pollutant changes during this study period.
The real prices for gasoline and diesel in Iran between January 2009 and December 2019 were used in this study (see Fig. 2d). These data were collected from the National Iranian Oil Product Distribution Company (NIOPDC). This study used real fuel price data; consequently, fuel price data were expressed as a consumer price index based on the constant price of 2016.
Also, this study gathered monthly averages of weather data (rainfall, wind speed, and temperature) across 10 weather stations from the Tehran meteorological organization. The Gross Domestic Production (GDP) per person in Tehran city was used to measure income per person. These variables gathered at constant 2016 prices from the Central Bank of the Islamic Republic of Iran.

Model framework
Over time, it becomes increasingly important to examine the air pollution status and its related factors. Besides human activities, changes in the atmospheric status such as wind, rainfall, and temperature have an important role on the air pollution status.
In many cases, the effect of explanatory variables is associated with significant lags and researchers seek to examine the long-term effects of explanatory variables along with the short-term effects. To estimate and analyze long-term and short-term relationships between the variables under study, the autoregressive distributed lag (ARDL) method introduced by Pesaran et al. in 2001was used (Pesaran et al. 2001. In these models, the dependent variable (e.g., Y t ) is affected by its lags (Y t-j ), other independent variables (e.g., X t ), and lags of independent variables (X t-j ). These models can be used as tools to avoid the spurious regression problem (16). The general framework of the ARDL (p, q) model is as follows: where p and q are the optimum lag for the dependent variable and independent variables respectively. β j and γ j are the model parameter.
At first, this method estimates the short-term relationship between variables. Then the model examines the long-term results by including the trend of variables in the long term and the relationship between these trends. In other words, considering the long-term trend of variables in estimates is  the reason for the difference between short-term and longterm estimates.
Meanwhile, the following model was used to examine the impacts of fuel prices, atmospheric variables, and other control variables on air pollutant concentration: where m denotes month. A is a dependent variable and denotes air pollutant concentration. In this study, we estimate three models separately and PM10, CO, and NO2 pollutants are dependent variables in each model. P is a vector for the monthly average of fuel prices per liter. In addition, X is a vector of control variables. In this model, the logarithm (Ln) of variables such as average monthly wind speed, total rainfall, and temperature was considered control variables. Also, a public holiday dummy denotes as a dummy variable (Dum). A linear time trend is considered a control variable. This study considered GDP per capita as another control variable. ɛ is an error term. Reasons for controlling these variables are described below.
Wind speed, temperature, and rainfall are considered due to their impacts on air pollutant concentration. There is evidence showing the influence of wind speed and rainfall on air pollutant concentration. As the wind speed and rainfall increase, the concentration of pollutants reduces (Bhaskar and Mehta 2010;Creamean et al. 2013). Also, an increase in the number of holidays and weekends in each month would lead to a reduction in fuel demand followed by the reduction of air pollution emission. This study considers the number of holidays as a control variable. Commonly, people use fewer personal vehicles to get their children to school or to work on holidays.
GDP per capita is used as a proxy for measuring the changes in household welfare and economic growth. Based on environmental Kuznets curve (EKC), air pollution is almost an inevitable part of economic growth. According to this curve, further growth first leads to adverse environmental conditions in an ascending path; then after a peak, the downward trend begins and further economic growth continues towards improving the environment (Brajer et al. 2011).
With reference to the fact that the population and quality of fuels change gradually over time, a time trend is considered a control variable for measuring factors in this model. Unfortunately, fuel quality in Iran has been decreasing over time due to international economic sanctions; meanwhile, the population is growing gradually. Therefore, the concentration of pollutants is expected to increase. In addition, this model uses a dummy variable to measure increasing fuel price shocks that occurred in five time periods (2009/04, 2010/12, 2014/04, 2015/03, and 2019/10) by the government. All variables entered in logarithm form in the model. Table 1 provides a list of variables, abbreviations, and units used throughout the model framework.

Monthly estimates
The first step in analysis of longitudinal studies is the examination of the stationary of variables by augmented Dickey-Fuller test. According to the results in Table 2, all variables become stationary after one differentiating (I(1)) except for PM 10 . PM 10 were stationary at level (I (0)). Therefore, it can be concluded that the relationship between the time series is sufficient, so the results of the regression are to be true. Since variables in this study are a combination of different stationary (I (0) and I (1)), the use of the autoregressive distributed lag (ARDL) method is preferred to other methods. Table 2 represents the results of the stationary test.
The findings on dynamic models (short term) show that changes in gasoline fuel price have a greater impact on CO concentration than other pollutants. According to the results of the short term, a 1% change in gasoline fuel prices leads to 0.02, 0.012, and 0.011% change in CO, PM 10 , and NO 2 concentrations, respectively. Also, a 1% increase in diesel fuel prices leads to a 0.008, 0.02, and 0.015 % decrease in CO, PM 10 , and NO 2 concentrations, respectively. The findings indicated that diesel price had a greater impact on NO 2 and PM 10 concentrations and gasoline fuel price had a greater impact on CO concentration than diesel fuel.
The findings on weather variables revealed that rainfall had no significant impact on CO and NO 2 concentrations. However, rainfall had a significant negative impact on PM 10 concentration. The higher temperature was also associated with less air pollution. Wind blow had a significant effect on the concentration of all pollutants, and this relationship was inverse. That is 1% increase in wind speed resulted in a 0.11, 0.09, and 0.44 % decrease in the concentration of CO, NO 2 , and PM 10 , respectively.
The findings revealed that the time trend had positive impacts on CO and NO 2 concentrations in a significant manner but no significant effect on PM 10 concentration was observed. In other words, an increase in the population and change in fuel quality led to an increase in concentrations of CO and NO 2 .
The findings also indicated that GDP had positive impacts on air pollutant concentration, but not a significant effect.
In regards to the holidays, the findings indicated that an increase in the number of holidays and weekends in each month had a negative impact on the concentrations of CO, NO 2 , and PM 10 ; so a 1% increase in the number of holidays in each month led to 0.11, 0.09, and 0.07 % decrease in CO, NO 2 , and PM 10 concentrations, respectively. However, this effect was not significant for PM 10 concentration.
In this study, the coefficient of determination (R 2 ) in all models was near 1.00 (R 2 = 0.80), which indicates that the models had a relatively perfect explanatory power. Also, the Durbin-Watson statistics in all three models were near two, which means no autocorrelation problem was present in the models.
This study implemented several diagnostic tests to ensure model appropriateness, such as (1) test for the correctly specified model (Ramsey's RESET test), (2) serial correlation (LM test), (3) heteroscedasticity (ARCH test), and (4) normality (Jarque-Bera (N)). The diagnostic test results show that there is no problem associated with the correctly specified model, serial correlation, normality, or heteroscedasticity. Table 3 shows the results of the dynamic equation estimation for three pollutants in three columns along with several diagnostic tests to ensure the accuracy of estimation models.
Furthermore, this study conveyed a cumulative sum control chart (CUSUM) test to investigate the stability of the model's coefficients. In this test, the confidence interval is two straight lines that show a 95 % confidence level. If the test statistics were located between these two lines, and then the null hypothesis would not be rejected (H0: stability of parameters). The findings of this test indicated that all estimated parameters were stable at a 5 % significant level. Based on Figure 3, values for the CUSUM tests in all models were located between these two lines, which indicate the stability of the parameters in all models. Also, the bound test was used to investigate the existence of cointegration and the long-term relationship between variables. In this method, two critical bounds are presented, upper bound for time series I(1) and lower bound for series I(0). In this test, if the F statistic is greater than the upper bound value, the null hypothesis (lack of cointegration) is rejected; and if the F statistic is less than the lower bound value, the null hypothesis is confirmed. Moreover, if the F statistic locates (1, 0, 0, 2, 0, 2, 0, 0, 0, 0) Optimum lag (1, 0, 0, 1, 0, 2, 0, 0, 0, 0) Optimum lag (1, 2, 0, 0, 1, 0, 0, 0, 0, 0)

Coefficient t value (p) C o e f f i c i e n t t value (p) C o e f f i c i e n t t value (p)
Ln  Table 4 shows the results of the bound test for all models. The long-term results of the models show a 1% increase in gasoline fuel price leads to a 0.027 and 0.016 % decrease in the concentrations of CO and PM 10 , respectively, and a 0.02 % increase in NO 2 concentration. However, the impact on PM 10 was not significant in the long term. Furthermore, a 1% increase in diesel fuel price leads to a 0.011, 0.024, and 0.029 % decrease in concentrations of CO, NO 2 , and PM 10 , respectively. Therefore, in the long term, fuel price changes had a greater impact on the concentrations of pollutants compared to the short term. In the short term, however, changes in gasoline fuel prices had a greater impact on the concentration of pollutants, than diesel fuel prices.
Other variables such as GDP had a positive impact on the concentration of all air pollutants in a significant manner. So 1% increase in GDP causes a 2.96, 2.96, and 0.88 % increase in CO, NO2, and PM 10 concentrations, respectively. Consequently, these two variables had greater impacts in the long term compared to the short term.
For other coefficients related to weather variables, the findings revealed that rainfall, wind blow, and temperature had no significant impacts on pollutant concentration except for PM 10 . That is one % increase in rainfall and temperature leads to a 0.01 % decrease in PM 10 concentration in the long term. The stated coefficients were greater in the long term than in the short term.
The findings also indicated that the time trend had maximum impacts on all pollutant concentrations in the long term. This means the increase in the population and decrease in fuel quality in the long term lead to a 1.78, 1.93, and 0.05 % increase in CO, NO 2 , and PM 10 concentrations, respectively. Table 5 shows results of the long term for models with three different dependent variables (i.e., CO, NO 2 , and PM 10 ).

Sub-period analysis gasoline-compressed natural gas (CNG) substitution
Over the past decade, the Iranian government has paid a lot of attention to the production of CNG vehicles in order to reduce gasoline consumption and air pollution. As part of such a plan, gasoline-fueled vehicles have been converted to CNG and the number of CNG stations has been increased around the nation, especially in Tehran city in 2013. However, the CNG-fueled vehicles emit less CO and more NO 2 than gasoline-powered vehicles. In order to increase the validity of the results, the models were studied in two different periods-during 01.2009-03.2013 (period 1) and 04. 2013-12.2019 (period 2) ( Table 6).
Results show that an increase in gasoline price leads to an increase in NO 2 concentration in recent years and the second period of study as well. Based on substitution between gasoline and CNG, an increase in gasoline fuel price leads to increases in CNG consumption and consequently increases NO 2 emission.

Discussion
Tehran, as a megacity and capital of Iran, has been dealing with air pollution problems for years, problems largely originated from high traffic density, and gases emitted from all kinds of vehicles. This study focused on the effect of fuel prices and weather circumstantial on the concentration of CO, NO 2 , and PM 10 as main pollutants that are emitted from fossil fuel consumption in transport sectors.
The findings of the present study show that the trend of CO and PM 10 concentrations in Tehran was diminishing over the investigated period, but NO 2 had an increasing trend. Expanding the culture of using the subway and public transportation, and promoting vehicles with a Euro 2 standard or higher, has played a key role in the downward trend of CO and PM 10 concentrations (Sanie et al. 2017). Also, replacing the consumption of vehicles' fuel from gasoline to CNG plays an important role on the increasing trend of NO 2 concentration (Krittayakasem et al. 2011;Yilmaz and Gumus 2017).
In a review study done in developing and developed countries, Baldasano et al. showed that the trend of PM 10 and CO concentration levels was declining (Baldasano et al. 2003). A study conducted in Korea showed that CO and PM10 concentrations had an increasing trend and NO 2 had no clear and steady trend (Kim et al. 2018).
Overall, our results show that fuel prices have negative impacts on CO and PM 10 concentrations for both the short and long term. The increase in gasoline and diesel prices leads a decrease in CO and PM 10 concentrations. An increase in the gasoline and diesel price, as the main price policies to control air pollution, reduces fuel consumption. Since high consumption of fuel in the transport section of the country is one of the main contributors to air pollutant emission, reducing the consumption of these carriers results in the reduction of pollutant emission and consequently concentration of pollutants. In contrast to other pollutants, gasoline price had a positive impact on NO 2 concentration in the long and short term. Since gasoline and CNG are substitutes for each other, increasing the price of gasoline leads to a decrease in gasoline consumption and an increase in substitute fuel (CNG). In such circumstances, increasing government investments in using clean fuels such as CNG and an increase in the number of CNGfueled vehicles results in easy substitute of gasoline with CNG and increase demand for CNG due to rising gasoline prices.
Since the CNG has a higher NO 2 emission rate than other fuels, the increase in gasoline prices was associated with increased emission and concentration of NO 2 . These results are consistent with the results of the Ninpanit study (Ninpanit 2019).
In a study conducted in New Zealand, Shaw et al. showed a 1 % increase in gasoline price was associated with a 0.32 % (−1.21 to 0.58) reduction in NO 2 concentration in the short term (Shaw et al. 2018). Although, in another study that was conducted in Brisbane, results showed that changes in petrol prices had no impact on air pollution (Barnett and Knibbs 2014), in a study conducted by Heger et al. in Cairo, results showed that 30 and 80 % increase in fuel price leads to the overall decline in traffic, and consequently 1.6 and 2.3 % decrease in PM 10 concentration (Heger et al. 2019).
Also, results show that changes in gasoline price had a greater impact on CO concentration compared to NO 2 and PM 10 concentrations. Considering that, the volume of CO pollutant emitted by gasoline-powered vehicles is higher than NO 2 and PM 10 , an increase in gasoline prices will result in a greater reduction in CO concentration compared to other pollutants. These results are consistent with the findings of other studies (Delangizan et al. 2015;Ninpanit 2019).
On the other hand, almost 80 % of the PM 10 emission created in the transportation sector comes from dieselpowered vehicles (Ministry of Energy 2018). Therefore, increasing the diesel fuel price and reducing its consumption proportionally cause a further reduction in emissions and pollution of PM 10 . A study conducted by Ninpanit showed that a one % increase in gasoline price leads to a 0.36 % decrease in CO concentration, while a 1 % increase in gasoline price leads to 0.23 and 0.25 % change in NO 2 and PM 10 concentrations, respectively (Ninpanit 2019).
The comparison of the findings on short-and long-term models revealed that an increase in fuel prices had greater impacts for diminishing the concentration of the pollutant in the long term compared to the short term. These findings were consistent with the finding of Mousavi et al. and Ninpanit (Mousavi et al. 2018;Ninpanit 2019). In a time series study conducted by Mousavi et al., results showed that a 1 % increase in fuel price leads to a 0.06 and 0.14 % decrease in CO concentration in the short and long term, respectively (Mousavi et al. 2018). Researchers believe the introduction of optimum fuel technologies in the transport sector, improvement of the public transportation system, and expanding the usage of hybrid or low-power vehicles in the long term lead to a significant reduction in fuel consumption. In addition, rising fuel prices lead to a reduction in emissions of pollutants caused by transporting vehicles. In the long term, people are also more capable of changing their pattern of fuel consumption, which results in more people's capability to respond to fuel price policies (Manzour and Haghighi 2012;Taheri et al. 2017).
Comparison of estimated fuel price coefficients with the other coefficients revealed that the fuel policies had a lower impact on air pollutant concentration. In other words, weather status and other variables such as GDP had greater impacts on pollutant concentrations. Increased economic growth and GDP per capita due to increased energy and fuel consumption cause more pollutants. On the other hand, due to the low productivity and efficiency of industries, increasing GDP and economic growth lead to greater increases in fuel consumption and consequently greater pollutant emission. Hence, policymakers should try to enhance the utilization efficiency of inputs, such as fuels, to maintain production and finally reduce the growth of emission pollutants (Biswas et al. 2012;Shapiro and Walker 2015). Also, many studies have revealed the role of meteorological conditions on air pollution (Guo et al. 2019;Yang et al. 2020). Results of a study conducted by Ma et al. in the Yangtze River Delta region in China showed that meteorological conditions mainly influence dayto-day variations in pollutant concentrations. PM 2.5 concentration was inversely related to wind speed, while concentrations of other pollutants were negatively correlated with relative humidity and positively correlated with temperature (Ma et al. 2019). In conditions such as increased relative humidity of the air accompanied by the phenomenon of rainfall, the washing operation can reduce air pollutant concentrations. Also, the findings of the present study reveal that the rising number of holidays and weekends had a significant effect on decreasing the concentrations of pollutants. This fact is based on fundamental changes in vehicular usage of individuals on weekends and holidays. Most people do not use personal vehicles on weekends or holidays because of the shutdown of schools and offices. In two studies conducted by Bahreini et al. and Harley et al., results showed that emissions from fuel consumption could be reduced by 60-80% on the weekends due to reductions in road traffic (Bahreini et al. 2012;Harley et al. 2005).
This study has limitations. A lack of access to air pollution data before 2009 is considered one of the study limitations. So, the study period was limited to after 2009. Also, data in air pollution monitoring stations were incomplete. However, we still managed to gather air pollution data from 19 out of 22 monitoring stations in Tehran. Also, sulfur dioxide (SO 2 ) was another most pollutant emitted from the transportation sector in Tehran. However, since many monitoring stations did not record this pollutant, it was not possible to study the effect of price policies on the concentration of SO 2 in this study.

Conclusion
The results of this study show that the pollutant concentrations in Tehran have been decreasing in general; however, this trend has been increasing for some pollutants or in some districts in this city. According to the extracted results from study models, increase diesel and gasoline price policy reduces the concentration of CO and PM 10 that are emitted from the transport sector in the long and short term. Also, increase gasoline price policy increases the concentration of NO 2 that is emitted from the transport sector in the long and short term. However, considering the impact of other factors, the increase in gasoline and diesel prices had a low impact on Tehran's air pollution concentration. In other words, weather and economic conditions, as well as improved technology, have a greater impact on the concentration of pollutants than fuel prices. Therefore, focus on policies such as improved technology and enhancing the quality of fuel can be more effective.
Also, given the different impacts of gasoline and diesel prices, an increase in the price of energy carriers requires paying attention to their relative prices. Therefore, unbalanced and dissimilar increases in prices can lead to fuel substitution. This will lead to an increase in alternative and cheaper fuel consumption and ultimately an increase in related pollutants.