فصلنامه اقتصاد مقداری
سال بیستم شماره 1 (پیاپی 76، بهار 1402)

Due to the water shortage crisis, water resources and optimal consumption of these resources are of great significance. The effects of water scarcity on all countries are a global threat, and the issue of water has been considered regional and international cooperation. "water economy" refers to the set of water resources and consumer sectors that are related through physical capital (equipment, infrastructure) and social capital (institutions, norms and laws) ( Zemel & Tsur2018). The transfer and redistribution of water is considered as reallocation of water. According to the United Nations World Water Development Report, three-quarters of jobs around the world depend on water, so water scarcity and lack of access to water may hamper economic growth in the coming years. Water is an important factor in the development of employment opportunities. According to Kuznets' study, Structural change includes a change in direction from agriculture to non-agricultural activities and then from industry to services. One of the most important factors in the movement of inputs between sectors is total factor productivity therefore, in this research, it has been determined to what extent the positive shock of productivity can be effective on the process of structural change. The article answer two questions. First, will there be structural changes in Iran despite the positive productivity shock? And second, is there a potential for water transfer between Iran and neighboring countries with the positive productivity shock? 

In this study, calculable general equilibrium models have been used. The data required to simulate the scenarios proposed in this research is taken from the ninth version of GTAP. This version includes the world economy with 140 countries or regions and 57 economic sectors. It is not possible to present all relationships and equations in one article. In addition to the GTAP database, IMPACT data has also been used. The IMPACT model is a hybrid model of water simulation and agricultural partial balance. According to the objectives of the research, the aggregation model has been changed and instead of 140 regions or countries, in one aggregation, Iran and other regions and in other aggregations Iran and  neighboring countries (Pakistan, Turkey, Russia, Kazakhstan, UAE, Armenia, Azerbaijan, Bahrain, Kuwait, Oman, Qatar, Saudi Arabia) are considered. 57 parts of the model have been changed to 13 parts and 5 production factors to 8 production factors, these changes are as follows. The sectors include: 1. Agriculture (rice, wheat, oilseeds) 2. Other crops (cereals, fruits, vegetables...) 3. husbandry 4. Forestry 5. Fisheries 6. Coal 7. Oil 8. Gas 9. Industry 10. Petrochemical 11. Electricity 12. Water 13. Services. Factors of production include: water, land, rainfed land, pasture land, skilled labor, unskilled labor, capital and natural resources. One of the most important factors affecting the demand input is productivity, so it has been tried to evaluate the effect of changing this variable in a computable general equilibrium model structure. Productivity shock is considered 0.6% according to the trend of changes in productivity and taking into account Iran's economic conditions, including sanctions and the international crisis such as Corona. Two scenarios are considered in this article. The first scenario of productivity shock for Iran and the second scenario of productivity shock for Iran and neighboring countries.

First scenario: Productivity shock in Iran
The positive productivity shock 0.6% has no effect on the demand for agriculture and other crops. But on the other three agricultural sectors, the impact on input demand is positive. Oil and gas sectors reduce demand for water with productivity shock. The water demand of the industrial sector is more than all other sub-sectors. That is, with a productivity shock the industrial sector can demand more water, and considering its share of value added, it can be concluded that the transfer of water from agriculture to industry can be justified. The results showed that the productivity shock reduces the average growth of skilled and non-skilled laborers in economic sectors, increasing the capital demand on average. Therefore, the defined shock provides a basis for structural changes in Iran.The productivity shock has increased production in most sectors, but notic that the shock in the sectors that have increased water demand has also increased production. But the important point is that the production growth of the industrial sector is much higher than the increase in the demand of this sector for water, which shows the importance of water in the industrial sector. The average comparison of production growth in agriculture and production growth in the industry and services sector is a confirmation of the transfer of water from the agriculture sector to the industry sector.
Second scenario: Productivity shock in Iran and neighboring countries
The effect of the productivity shock in the neighboring countries shows that the structural changes in these countries have a slower process than in Iran.
The results show that even though the economic sectors of bordering countries react to water demand, considering the size of the countries and the contribution of value added of the sectors in the GDP, the justification of water transfer is defensible. This issue can justify the longrun of moving water from neighboring countries to Iran. That is, if there is regional convergence that leads to an increase in productivity, it can provide the potential of transferring water from neighboring countries to Iran. However, to justify economic convergence, it is necessary to use other methods, including gravity models. But we must accept that economic convergence increases productivity. Therefore, the conditions of convergence between Iran and the bordering countries can provide the potential of moving water between these countries

In this research, the effect of the positive productivity on structural changes has been investigated. The results showed that the productivity shock reduces the average growth of skilled and non-skilled laborers in economic sectors, increasing the capital demand on average. Therefore, the defined shock provides a basis for structural changes in Iran. Also, the positive shock of productivity can affect the demand for water in the economic sector due to its effect on allocating resources in the economic sector. The results of the 0.6% productivity simulation model for Iran and the rest of the world showed that in the economic sector, structural changes occur through changes in demand for labor and capital. This process occurs if economic growth is shaped by an unbalanced and unstable pattern. On the other hand, a model with a production efficiency of 0.6 percent has been simulated for Iran and neighboring countries . This simulation showed that due to various effects of this impulse on water demand in different economic sectors, there is the potential for water transfer between Iran and neighboring countries. Therefore, one of the appropriate strategies to achieve the optimal use of water is to investigate the possibility of transferring water between geographical borders. The specific policy suggestions of the research are that, firstly, because the productivity index plays a decisive role in the structural changes, therefore any effective step that can improve productivity has also managed structural changes. Secondly, moving towards water-centered economic convergence between Iran and bordering countries can help in the optimal allocation of water resources.

Since increasing stress in financial markets is important for analysis and forecasting of economic activities and can be reflected in many variables of the financial market, recognizing the main sources of financial stress and its effects on various economic activities and sectors is one of the most important areas in the financial discussions. Considering the importance of this issue, in the present research, the financial stress index in Iran will be calculated, and then the short-term and long-term relationship between financial stress and stock returns of top industries in the Tehran Stock Exchange by using the panel method in the form of a multivariate model will be evaluated during the period of 1384-1398 using daily data. The analysis of this relationship in the long and short term will be investigated using Co integration and Panel Error Correction Model (PECM) and dynamic ordinary least squares (DOLS) methods will be used to investigate the long-term dynamic relationship between model variables. It should be noted that in order to more accurately analyze, in addition to investigation the effects of total financial stress index FSI (which is a combined index of financial stress in the capital, currency and money markets) on the stock returns of the studied industries, the financial stress index of each markets (money, capital and exchange market) has been entered into the model separately and we have investigated the effect of financial stress index in each of  studied financial markets on the stock returns.

In this research, in order to investigate the effect of financial stress, oil price and other independent variables on the stock returns of the studied industries, in the form of a multivariate panel model and the analysis of long-term and short-term coefficients, using Pedroni panel data method (Pedroni 1999&2004) and The panel error correction model (PECM) is used, and for this purpose the following is considered:                                                                                    
    (1)
 
SR: stock returns of the top industries in the Tehran Stock Exchange. This variable is calculated using the following: (Maditinos., & Theriou, 2011)                                                                         (2)                                                                           
 Ti,t  is the stock price index of industry i in period t.
FSI: Financial Stress Index
INF: inflation rate
INT: Interest rate
RER: real exchange rate
                                                                           (3)
       ER: Nominal exchange rate
       CPI*: Foreign CPI
       CPI: Domestic CPI
OIL: oil price 

The results show that in all four estimated models, the effect of the financial stress index on the stock returns of industries is negative and statistically significant. The estimated coefficients for the oil price variable are positive in all four models, which are statistically significant in all models.The estimated coefficients for the inflation rate variable in all models have a negative sign and are statistically significant.The estimation results indicate the positive effect of the interest rate on the stock returns of the studied industries.Based on the obtained results, the exchange rate in all models will have a positive effect on the stock returns.

The results indicate that in all four estimated models, the effect of financial stress index on industry stock returns is negative and statistically significant. In other words, financial stress in the studied markets, including the capital market, money market and foreign exchange market has a negative impact on the stock returns of industries and decrease stock returns of these industries. Also, the research findings show that in all estimated models, oil prices, exchange rates and interest rates have a positive effect on stock returns of the studied industries in Iran. In addition, the findings show that the estimated coefficients for the inflation rate variable are negative in all models and are statistically significant.

Economic growth, effective factors and how to promote it have always been one of the most challenging issues in the economics and the most important goals of economic systems. Economic stability has been regarded as conspicuous among the factors affecting the growth. So, instability will change the outlook and make the results of economic activities unpredictable. Uncertainty is one of the concepts related to economic instability that has a long history in the economic literature. Considering the wide dimensions of the impact of uncertainty on the country's economy, addressing the impact of key economic variables on the issue of uncertainty is of great importance. Since Keynes introduced consumption as a function of income, various forms of income have become an integral part of the set of factors affecting consumption in all economic schools. The income needed to finance consumption also requires high and continuous economic growth. Therefore, uncertainty is also expected to affect consumption behavior.

Given the importance of this issue, this study was to investigate the direct and interactive effect of uncertainty on the consumers’ behavior. In this regard, the maximum available data on consumption expenditures (As an alternative of consumption), revenues, and money supply of OPEC countries over the period 2006–2018 was addressed. The economic uncertainty index was also calculated by MII method. All variables were considered in logarithmic and per capita form.  Also, according to the nature of the model and the data used, the panel threshold Regression (PTR) model was used. In this model, the direct effect of uncertainty were measured by introducing the uncertainty index in the equation, and the interactive effect were measured by studying the role of the uncertainty index in the explanatory variables’ effect. Using this model required the presence of a threshold and a nonlinear relationship between the variables. Accordingly, the threshold effect test was used.

The results of threshold effect test showed that one threshold value for the uncertainty variable was confirmed. The results indicated that the uncertainty variable, as expected, has a negative effect on the logarithm of per capita consumption expenditure. The need for precautionary savings and uncertainty about the sufficiency of income in financing consumer needs due to increased uncertainty reduces current consumption expenditures. The variable of per capita money (as representative of individual wealth) in both regims had a positive effect on the logarithm of per capita consumption expenditure. The magnitude of the coefficients indicated that at high levels of uncertainty, this effect was reduced. The logarithm of per capita income also in both regims had a positive effect on the logarithm of per capita consumption expenditure. However, at high levels of uncertainty, this effect was reduced.

According to the results, it can be said that that uncertainty had a negative significant effect on per capita consumption expenditures in the countries and period under study. The variables of per capita income and wealth had a positive significant effect on consumption expenditures. In contrast, there is a positive and significant correlation between the variables of per capita income and wealth with current consumption expenditures. However, the effect of these variables was asymmetric in different uncertainty regimes. So that at high levels of uncertainty, this effect was reduced. Therefore, it can be said that uncertainty will also indirectly affect consumption expenditures by affecting income and wealth. Given the results of the study and the widespread dimensions of uncertainty, the study's authors advise policymakers to be committed to the announced policies and to exercise caution in implementing discretionary policies. It is also suggested that the study of the future consequences of uncertainty using futuristic methods (Futures studies) be given more attention by other researchers.

Markowitz model of asset portfolio optimization has some deficits, one of the most important of which is the normality assumption of stock market returns. Normality of returns has been rejected in numerous studies and has been shown that mean is not a good maximization objective anymore. Mean of returns is quite sensitive to outliers. There are three different ways of facing this problem in the literature. The first approach is generally to abandon the modern portfolio theory and turn to using meta-heuristic algorithms in portfolio optimization. The second approach still considers the modern portfolio theory important and valuable and uses it with adjustments. Some studies under the ultra-modern portfolio theory have focused on the inappropriateness of variance and use other measures of risk. Some others studies focus on drastic changes in the optimal portfolio as a result of changes in input values and use robust statistics. The third approach, on the other hand, tries to avoid the mentioned problems simply by using different parameters in optimization instead of average and variance. This paper based on the third approach seeks to use the median instead of the mean in stock portfolio optimization. The purpose is to compare the performance of mean and median in optimization. Moreover, the variance is not enough to control the risk because of heavy tails of return distribution and, thus, this paper incorporates various risk measures into models to test which one performs better beside median as an alternative to mean-variance models. 

Five median maximization models are presented with different risk measures of mean absolute deviations (MAD), value at risk (VaR), average value at risk (CVaR), and maximum loss (ML). Models are solved using GAMS software package and daily real data of twenty stocks from Tehran Stock Exchange from the beginning of 2016 to the end of 2019. For this purpose, the models are first employed for portfolio optimization in a certain period of 50 days. After computation, then the optimal weights are used for the next period of 50 days. This procedure is then repeated for the next 100 days to the end of 2019. Finally, the average and the distribution of returns of the optimal portfolio obtained from different models are compared with three other models: the mean optimization method without any control for risk, the mean optimization method subject to value at risk (VaR) constraint, and the portfolio with equal weights (EqW).

Findings show that the median has a better performance in portfolio optimization. The model of median maximization gains higher returns in seventy percent of cases and a higher return on average. This means that a higher value of portfolio would be obtained using median in optimization. As a second conclusion, it was also shown that the median optimization method results in a portfolio with higher degree of diversification. The result remains true adding various risk measures to the model showing that median optimization instead of mean obtains a more diversified portfolio. In comparison among different median optimization models, it was also shown that CVaR and MAD risk measures controls the risk better than VaR and Maximum loss and obtains even further diversification.

The results generally show that portfolio optimization models based on the mean instead of the median and subject to risk measures of the average value at risk (VaR) and the mean absolute deviations (MAD) have a better performance in return maximization, control of risk and portfolio diversification.

Especially since the end of the Cold War, economic sanctions have been increasingly used as an alternative to military action to exert economic pressure on countries for a broad range of objectives. Empirical evidence suggests that sanctions affect the economy of the target countries in various ways. These studies use different approaches examine the adverse effects of sanctions on economic variables such as GDP, production costs, capital formation, trade, government expenditures, exchange rate, private expenditures, and household welfare. Following the 1979 Islamic Revolution, Iran became subject to extensive economic sanctions, which have intensified in recent years. Given the dependence of Iranian economy on oil revenues, the oil sector has been one of the main targets of these sanctions. Banking sanctions have also been used as an attempt to prevent the transfer of oil revenues to Iran as part of “smart sanctions” against the country. Sanctions imposed on Iran’s oil exports can intensify fluctuations in macroeconomic variables by reducing foreign exchange earnings and restricting access to capital and intermediate goods, and impact household welfare by affecting relative prices as well as household income and consumption expenditure. Therefore, the present article examines the impact of oil export sanction on the welfare of households in Iran using Equivalent Variation (EV) criterion. Due to the capabilities of Computable General Equilibrium (CGE) models compared to single-equation models, a recursive dynamic CGE model has been used based on the Social Accounting Matrix (SAM) data of 2016. In order to study and compare welfare changes in different groups of urban and rural households, 70 percent reduction in oil exports has been simulated for the entire period under study. 

In this study, PEP-1-t, a recursive dynamic CGE model developed by Decaluwe et al. (2013), has been used to investigate the welfare effects of reduction in oil exports. This model is the result of the joint effort between the Partnership for Economic Policy (PEP) and the International Food Policy Research Institute (IFPRI) in the context of the African Growth and Development Policy Modeling Consortium (AGRODEP) project. PEP-1-t is a recursive dynamic model with a multi-sector, single-country framework containing a broad set of tax instruments and several categories of workers and capital. It is compatible with social accounting matrices that have the accounts required for the main categories and also has standard assumptions. In this model, labor, capital, and intermediate inputs are used in the production process. At the highest level of production, a Leontief production function is considered which combines intermediate inputs and added value to determine the level of activity. Labor and capital are also combined in a production function with Constant Elasticity of Substitution (CES). Each activity produces one or more commodities. The product is then converted into an export commodity and a market commodity using a Constant Elasticity of Transformation (CET) function. Consumers buy composite goods, which are either imported or produced domestically. The composite of imports and domestic production is determined by the Armington function, which is a CES function. The country in question is a “small country” that receives global export and import prices at a fixed level. By establishing factor market equilibrium, commodity market equilibrium, savings-investment equilibrium, domestic market equilibrium, and export market equilibrium, equilibrium is established in the whole system. This model has a set of simultaneous equations, many of which are nonlinear. In the present article, the impact of oil export sanction has been simulated following Farzanegan et al. (2015). Moreover, to study consumer behavior and calculate welfare changes based on the EV criterion, the Linear Expenditure System (LES) and the Stone-Geary utility function have been used, and EV criterion calculation codes have been incorporated into the model. Since the structure of the SAM as the database of this article should be consistent with the general equilibrium model used, a SAM has been developed for the year 2016. In addition to SAM, a number of behavioral parameters adopted from other studies have been used to solve the model. GAMS software has been used for calibration and simulation. 

Regarding the impact of oil export sanction on some macroeconomic variables, the results show that a decrease in oil exports leads to an increase in exchange rate and non-oil exports, as well as a decrease in domestic absorption, total imports and exports, and GDP. Regarding the impact of reduced oil exports on these macroeconomic variables, the results are consistent with Gharibnavaz and Waschik (2017), Farzanegan et al. (2015), Mohammadikhabbazan et al. (2015), Haqiqi and Bahalou (2013), and Haqiqi and Bahador (2015) who also used a CGE modeling approach. In addition, other studies such as Nakhli et al. (2020), Keshavarz Haddad et al. (2020), Kiumarthi et al. (2019), Tuzova and Qayum (2016), Marzban and Ostadzad (2015) and Siddig (2011) have shown the adverse effects of economic sanctions on key macroeconomic variables. As oil exports decrease, the income and consumption expenditures of urban and rural households decline over this period. The decrease in income and consumption expenditures of different groups of urban and rural households has an increasing trend. Also, the upper deciles of urban and rural households experience more decline in their income and expenditures. In addition, the income and expenditures of rural households are more affected than urban households. Calculating the EV as an indicator of household welfare in the studied period shows that the welfare of urban and rural households declines in all groups and the upper deciles experience more decline in welfare. Also, the decline in welfare of all rural household groups during the period is always more than urban households. In addition, with the continuation of the oil export sanction, the gap between the welfare decreasing of rural and urban households declines. Regarding the adverse welfare effects of oil export sanction, similar results have been reported in Farzanegan et al. (2015) and Mohammadikhabbazan et al. (2015) using a static CGE modeling approach, while more limited effects have been reported in Gharibnavaz and Waschik (2017) using the same approach. Marzban and Ostadzad (2015) have also shown the tangible effect of oil sanctions on social welfare by developing a generalized stochastic growth model. Cheptea and Gaigné (2020) and Siddig (2011) have also shown the negative effects of economic sanctions on the welfare of households in the target country. Eventually, sensitivity analysis to the key elasticities of the model demonstrates that by changing the initial elasticities individually and in combination, no significant change is observed, therefore, it confirms that the main results are robust. 

This article investigates the impact of oil export sanction on the welfare of urban and rural households in Iran. To this end, PEP-1-t, which is a recursive dynamic CGE model developed by Decaluwe et al. (2013), has been used based on the SAM data of 2016. Also, the EV criterion has been used to examine welfare changes in different groups of urban and rural households. Between 2012 and 2019, Iran’s crude oil exports have dropped by about 70 percent. Therefore, 70 percent reduction in oil exports has been applied to all periods in the model to simulate the sanction scenario. The results reveal that the reduction in oil exports leads to an increase in exchange rate and non-oil exports, as well as a decrease in domestic absorption, total imports and exports, and GDP. Our results show that with a reduction in oil exports, the income, consumption expenditures, and welfare of urban and rural households decrease, and this downward trend continues over the studied period. Moreover, in both urban and rural households, upper deciles experience more reduction in income, consumption expenditures, and welfare. In addition, the shock of reduced oil exports affects the income, consumption expenditures, and welfare of rural households more strongly than urban households. The results also indicate that although the welfare loss of all rural household groups is always higher than urban households during this period, the welfare gap between rural and urban households decreases as oil export sanction continues. Sensitivity analysis to the key elasticities confirms the robustness of the main results.

One of the requirements of economic growth is the development of the transport sector and its related services. But the transportation development, in addition to the positive economic effects, has an undeniable impact on the pollutants emission. Now the question is “what should be done to reduce carbon dioxide emissions in the transport sector so that economic development is not Disrupted”. Decoupling carbon dioxide emissions from economic growth in the transport sector is the key to providing a practical solution to achieve low-carbon economic development.
The decoupling concept refers to conditions in which economic growth  increases but environmental Stress decreases over the same period. The Organization for Economic Co-operation and Development (OECD) was the first organization  which developed decoupling concept (OECD, 2000). Then Vehmas et al. (2003) described different aspects of decoupling and Tapio (2005) presented a elasticity-based decoupling model. Subsequently, the decoupling of carbon dioxide emissions from economic growth Was considered by many researchers (e.g., Loo & Banister, 2016; Zhang, 2018; Riti et al., 2017; Wu et al., 2018; Wang et al., 2018). But in this field no study has been done in Iran so far. Therefore, this study is going to investigate this issue in Iran to contribute to identifying appropriate measures to achieve decarbonization in the transport sector. 

Many decomposition techniques are used to decompose the CO2 emissions changes into various driving factors, such as Structural Decomposition Analysis (SDA) and Index Decomposition Analysis (IDA). However,  nowadays  decoupling analysis  is a novel concept that requires less data and calculation, and provides more details on the relationship between economic growth and carbon emissions. There are three approaches to do decoupling analysis: OECD, Vehmas et al. (2003) and Tapio (2005). The OECD approach has several disadvantages, such as inaccurate measurement and unclear criteria that caused to develop this method by Vihmas et al. (2003) and finally Tapio( 2005) by modifying the classification of Vehmas et al. (2003) improved their method. To doing so, Tapio (2005) at first changed the six possible linking and de-linking states introduced by Vehmas et al. (2003) to eight possible logic states; Second, he introduced the concept of negative decoupling, and third, he defined specific intervals for the environmental stress elasticity of economic growth, and  he specified each decoupling and linking states more accurate. Accordingly, in this paper, we use the Tapio approach to investigate the decoupling  of environmental stress from economic growth (Tapio, 2005). A summary of this approach is given in Table (1).
Table 1. Degrees of the coupling and decoupling process based on Tapio(2005) approach
Source: Researcher Conclusion Based on Tapio(2005) Classification
Degrees of the linking and decoupling
[0-0.8]
*
*
Weak decoupling
[0-0.8]
*
*
Weak negative decoupling
*
*
Strong decoupling
*
*
Strong negative decoupling
*
*
Expansive negative decoupling
*
*
Recessive decoupling
[0.8-1.2]
* 
*
Expansive coupling
[0.8-1.2]
*
*
Recessive coupling
The Research data have been extracted from the Statistical Center of Iran and the Iran energy balance sheets during the period 2000-2018. The period is seleted  so that it covers the  ten years before and after the implementation of the targeted subsidies law, because changes in energy prices in this period can affect the activities of the transport sector, energy consumption and carbon dioxide emissions. The variables of this research include the value added of the sub-sectors of the transport (road, rail, air and sea transport), non–oil real GDP , the total volume of freight and passenger in the transport sector and its sub-sectors and the amount of carbon dioxide emissions of transport sector and its sub-sectors.

The Decoupling results of freight and passenger transport from economic growth and carbon dioxide emissions are given in Tables (2) and (3), respectively. In these tables the symbols TG, TP, GDP and CO2 are used to represent freight transport, passenger transport, GDP and carbon dioxide emissions, respectively.
The decoupling results of passenger and freight transport from economic growth in Table (2) show that the increase in energy prices in the early years of targeted subsidies law implementation (2010-2014) has led to a strong decoupling of freight and passenger transport growth from economic growth, this means that despite the positive economic growth, the growth of transport has been negative. But the affectability level of passenger transport has been significantly greater than freight transport. After that, the decoupling state in the freight transport sector has changed to a expansive negative decoupling, while in the passenger transport sector, there is still a strong decoupling.
Table 2. Decoupling of transport volume growth from economic growth
Source:

Decoupling of passenger transport volume growth from economic growth
State Decoupling
( )
( )
Period
Weak decoupling
0.07
29.64
2.04
2000-2004
Expansive coupling
0.92
25.74
23.68
2005-2009
Strong decoupling
-6.66
2.28
-15.17
2010-2014
Strong decoupling
-0.73
13.97
-10.16
2015-2018
Decoupling of freight transport volume growth from economic growth
State Decoupling
( )
( )
Period
Expansive negative decoupling
1.43
29.64
42.37
2000-2004
Expansive coupling
0.88
25.74
22.61
2005-2009
Strong decoupling
-3.10
2.28
-7.06
2010-2014
Expansive negative decoupling
1.51
13.97
21.13
2015-2018 
Table 3. Decoupling of transport volume growth from co2 emission
Source:

Decoupling of passenger transport volume growth from CO2 emission
State Decoupling
( )
( )
Period
Expansive negative decoupling
13.34
27.25
2.04
2000-2004
Expansive coupling
1.15
27.29
23.68
2005-2009
Strong negative decoupling
-0.80
12.19
-15.17
2010-2014
Strong negative decoupling
-0.39
3.92
-10.16
2015-2018
Decoupling of freight transport volume growth from CO2 emission
State Decoupling
( )
( )
Period
Weak decoupling
0.64
27.25
42.37
2000-2004
Expansive negative decoupling
1.21
27.29
22.61
2005-2009
Strong negative decoupling
-1.73
12.19
-7.06
2010-2014
Weak decoupling
0.19
3.92
21.13
2015-2018
Based on Table (3) which shows  the decoupling state of freight and passenger transport from carbon dioxide emissions, we  can say  that the increase in the energy price in the period 2010-2014 has led to
Significant  reductions in passenger and freight transport, But ,although  the carbon dioxide emission growth has decreased compared to the previous period, it is still positive. Therefore, the carbon dioxide emission growth  has occurred along with the reduction of passenger and freight transport, reveals the need for complementary policies implementation. In  2015-2018 period, although the freight transport sector is still affected by the implementation of this law, but this effect is not observed in the passenger transport sector.

Based on the results, Although the reform of energy prices in 2010, is caused to change the relationship between economic growth and traffic volume from an expansive coupling (economic growth with increase in traffic volume) to a Strong decoupling (economic growth with decrease in traffic volume), but due to the lack of a Strong decoupling between traffic volume and carbon emissions, energy price reform has not reduced carbon emissions. Therefore, low carbon development in the transport sector does not achieved only by energy prices reform and requires the implementation of appropriate energy efficiency policies and using related technologies to reducing carbon emissions. Based on the findings of this study, the following suggestions are recommended to define appropriate carbon reduction strategies for the transport sector, First, the energy prices of the transport sector should be corrected. Second, ban traffic of high-carbon vehicles while promoting new technologies and energy sources proportionate with modern transportation. Third, improving transport infrastructure, especially road transport, should be on the agenda. Fourth, the share of non-road transport (rail, air and sea), especially rail transport in the freight transport sector, should be substantially strengthened so that a large volume of goods can be moved with less energy consumption and less pollution.