International Journal of Economic Perspectives, 2015

International Journal of Economic Perspectives, 2015, Volume 9, Issue 4, x-x.

The Relationship between China’s Airway Freight in terms of Carbon-Dioxide Emission and Export Volume Salih KALAYCI Institute of Social Sciences, Beykent University, Ayazaga, Maslak Istanbul, Turkey, 34433. Email: [email protected].

Cihat KOKSAL* Department of International Trade, Istanbul Commerce University, Sutluce Mahallesi, Imrahor Caddesi, No: 90, Beyoglu 34445, Istanbul, Turkey, E-mail: [email protected].

ABSTRACT China is one of the leading economies in the world in terms of both exports and production, requiring a sophisticated logistical system. Given that transportation causes carbon-dioxide (CO2) emissions, this paper analyzes the impact of China’s Airway Freight industry on CO2 emissions through econometric models such as linear regression and the Johansen Co-integration tests, with the ultimate goal of understanding the extent of the freight industry’s influence on carbon dioxide emissions. The effect of airway freight on CO2 emissions has been especially studied between the years 1980 and 2011. Jel Classification: F18, R40, Q53 Keywords: CO2 Emission, Airway Freight Transportation, Export Volume, Johansen Cointegration Test *Corresponding author. 1. INTRODUCTION International trade has been sustained for ages through a barter economy, even before tangible money existed in the monetary arena. The political, economic, and social significance of international trade has soared in the past centuries, notably due to the presence of industrialization, globalization, and increasingly sophisticated transportation methods including air travel, highways, maritime activity, and railroads. The transportation sector plays a crucial role in contemporary life as it sustains economic growth and high standards of living. Transportation technology thus became popular when we take into account the many preexisting papers in the literature. These papers are more concentrated on effective and efficient management of energy and energy sector and the relationship between transportation sector and volume of trade (Ghandoor, Jaber, Al-Hinti and Abdallat, 2013). For many reasons, China seemed in the past to not have been a major trading country in the international arena, being instead largely self-sufficient. In the entirety of 2005, China’s net trade volume never exceeded 60% of Japan’s total trade performance. More recently, however, the country’s role in international trade has been rapidly increasing. The massive rate of expansion in China’s trade over the decade 1995–2005 demonstrates that they have already passed over Japan’s net import, which has been characterized as having a lower growth trend (Schandl & West, 2012). With the process of globalization, the world’s energy consumption has increased from 4.676 million tons in 1973 to 8.429 million tons in 2008, increasing the share of the transportation sector in the world energy market from 23.1% to 27.3%. This trend can be explained by the fact that both household income and the amount of vehicles on the road is increasing every day. The global transportation sector contributed to carbon dioxide emissions by 6.6 billion tones in 2008: 22.5% of the world’s entire CO2 emissions output that year (Ghandoor, 2013).

International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

International Journal of Economic Perspectives, 2015, Volume 9, Issue 4, x-x. 2. LITERATURE REVIEW International air transport causes greenhouse gas emissions. In an effort to combat this trend, the Kyoto Protocol was established in order to limit the amount of CO2 released into the atmosphere. In order to deal with this problem, many countries have come together to discuss solutions to this global danger that may produce disastrous outcomes like global warming, pollution, and climate change. The Emission Trading System is an incentive program put in place by the Kyoto Protocol in order to decrease carbon emissions and to help countries and companies buy and sell caps from each other. Greenhouse gas emissions are being reduced in the majority of European countries, as the E.U. has striven to lower emissions in accordance with the Kyoto Protocol. Interestingly, many current publications point out that Europe’s decreasing emissions may be due to the relocation of manufacturing to other regions, like China. If consumption is taken into account, carbon dioxide emission in industrialized nations becomes significantly higher and may have never actually been reduced at all. Significantly, emissions caused by transportation are taken out in consumption-based calculations. As all trade contains transport, usually by cargo vessel but also by plane, this factor adds significantly to the total emissions growth, which was initially compensated through manufacturing shifts. To restate, one of the main results of increasing transportation is increasing CO2 emissions, as seen in the example of exports from China (Andersen, Gössling, Simonsen, Walnum, Peeters & Neiberger, 2010). Pan, Phillips and Chen (2008) demonstrate that China contributed more than 30% of the world’s CO2 emissions in 2006 with around 1,660 million tons. This means that China was behind 30% of global emissions in 2006. Because of the country’s dependency on fossil fuels for electricity and steel manufacturing, China has been, until recently, unwilling to determine company goals for limiting emissions. Today, China continues to use fossil fuels and release carbon dioxide into the atmosphere, albeit at a slightly lower rate than before. Civil and freight transportation are distinguished in many academic papers. The types of transport modes have been expanded by way of Gross Domestic Product through largely “carbon intensive modes” such as airlines, cars, and trucks. These new modes account for much of the emissions nowadays. Especially considering its travelling capabilities, the car possesses a high rate of CO2 emission. Fluctuation in the use of gasoline per passenger over distance traveled decreased carbon dioxide emissions in the U.S. between 1973 and the early 1990s. As a result of this fluctuating gasoline use, the number of drivers stabilized during this period. For other countries, the use of gasoline did not fluctuate in this period as much, frequently raising emissions. The key components of this change are load effects, car power, size and the impact on the traffic (Schipper & MarieLilliu, 1999). Japan is one of the most successful countries in terms of internalizing the effective and efficient energy management. As a result of its management policies, Japan has distinguished itself among the major countries in its handling of the transportation sector. Especially after the 1970s, an increased ownership of vehicles triggered an increased use of gasoline. The low level of traffic prevented the high fuel economy; however, after the 2000s, the trend reversed due to rising energy consumption. The transportation system of Japan diverged from other countries in terms of structure, format, and operation compared with the U.S. and the E.U. countries. In the beginning, Japan’s transport system consisted mostly of railways due to the convenience of short distance implementation. In more recent decades, the use of railways has been reduced dramatically in favor of other modes of transportation such as roadways and maritime travel. The Japanese government has taken precautions to limit the effects of cars by introducing high tax rates and heavy highway wages in an effort to encourage low carbon emission vehicles (Lipscy and Schipper, 2013). Ghandoor (2013) focused more on the relationship between energy and the transportation sector, working out quantitative methods and stating that, specifically, Japan is one of the most successful countries in terms of managing energy resources efficiently and effectively by implementing production economics. He found empirical results regarding the relationship between the transportation sector and CO2 emissions. Smith, Watkiss, Tweddle, McKinnon, Browne & Hunt (2005) elaborated that airway cargo had the highest CO2 emissions per ton and is the quickest expanding transportation sector. Because of the airway freight sector’s expansion, the level of carbon dioxide emissions has been rising across the earth. Academicians have bluntly demonstrated this trend through empirical evidence. At the present time, freight transportation (including roadway, railway, maritime trade, and aviation) to the United States accounts for nearly 470 million metric tons of CO2 every year, or around 8.3% of the United States’ fuel consumption and International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

International Journal of Economic Perspectives, 2015, Volume 9, Issue 4, x-x. nearly 7.8% of total CO2 emissions. The situation is the same for other countries like Canada regarding greenhouse gases, whereby 9% of total emissions are taken up by aviation freight transportation alone (Steenhof, Woudsma & Sparling, 2006). The U.S. government spends nearly 6% of its GDP (Gross Domestic Product) on airway transportation yearly and the freight transportation system has been expanding significantly day by day. Unremarkably, the role of commercial goods and services occupies around 22% of total U.S. GDP in 2005. It was 10% in 1970 and 12% in 1990 as good production expanded to global markets. For instance, the quantity of the United States’ output of goods moved through multiple modes increasing 12.2% between 1993 and 2002 whereas the value of these products jumped 68%. These results are consistent with previous findings which demonstrated a rising energy use in U.S. air freight since the early 1970s. Such trends are also found in the E.U. countries (Winebrake, Corbett, Falzarano, Hawker, Korfmacher, Ketha & Zilora, 2008). Howitt, Carruthers, Smith & Rodger (2011) calculates the amount of total fuel burnt from New Zealand’s air freight trade volume. The calculation takes into account both exports and imports to provide a holistic evaluation of CO2 emissions in 2007. The result could also be applied for other countries or cities. The paper collected data about the aviation freight fuel, the air craft movements, the air civil transportation’s loadings, total passengers, and aviation freight; the resultant findings about carbon dioxide emissions were found to be 0.82 kg. CO2 per ton and 0.69 kg CO2 per ton for short haul and long haul flights, respectively. The whole quantity of fuel burned for the international aviation transport of New Zealand’s trade volume was found to be 0.21 Mt and 0.17 Mt, in relation to total CO2 emissions of 0.67 Mt and 0.53 Mt. To improve airway freight transportation emissions, new methods of transporting were implemented in two huge European ports: the Port of Rotterdam in Holland and the Port of Gdansk in Poland. The previous method, depending upon the optimization of freight costs, indicates that the mode share of freight is the domestic and local freight systems, whereas the other procedure, balanced with carbon dioxide emissions, demonstrates that the mode share is altered into an intermodal freight transportation, which is up to a hub and network. On the other hand, according to changing requests and capacities of freight transportation, five scenarios are tested to examine the influence of mode and route alteration on the trade-off. The results of the findings indicate that the trade-off is significantly affected by the requests and capacities of transportation (Kim, Janic & Van Wee, 2009). The correlation among variables including carbon dioxide emissions and air freight transportation takes into account intermodal networks as well as freight networks. In fact, CO2 restrictions in transportation markets will need to be realized very soon; a modal shift in air freight logistics will be needed to decrease CO2 emissions within the appropriate cost and time restrictions. The mechanism of optimization will need to be implemented as the nucleus of the main decision component for illuminating the correlation. To improve airway freight transportation emissions, new methods of transporting were applied in two huge European ports: the Port of Rotterdam in Holland and the Port of Gdansk in Poland. The previous method, depending upon the optimization of freight costs, indicates that the mode share of freight is the domestic and local freight systems, whereas the other procedure, balanced with carbon dioxide emissions, demonstrates that the mode share is altered into an intermodal freight transportation, which is up to a hub and network. On the other hand, according to changing requests and capacities of freight transportation, five scenarios are tested to examine the influence of mode and route alteration on the trade-off. The results of the findings indicate that the trade-off is significantly affected by the requests and capacities of transportation (Kim, Janic & Van Wee, 2009). Yamaguchi (2008) finds the empirical results of airfare and transportation are more consequentially correlated with export volume than with distance; the flexibility of exports ad valorem in terms of airfare is − 0.571. On the other hand, market aggregation and cost per unit also have a crucial positive influence on airfare. The airway transportation sector through the Open Sky Agreements has lowered the cost per unit. However, excessive market aggregation has not had a positive dynamic impact on market aggregation and the cost per unit’s index. 3. METHODOLOGY AND DATA ANALYSIS The construction of this research paper is as follows: first of all, it will investigate the impact of Airway Freight Transportation on both China’s CO2 emissions and Export Volume. Secondly, this paper will carry out an ADF test in order to convert the data from non-stationary I (0) to stationary I (1) to apply both the Johansen cointegration test and the Granger causality analysis. Thirdly, this paper will map the long-run relationship between the aforementioned variables and the concluded results to provide an area open to future research. The data was collected from the World Bank’s official website. A logarithm was applied to the data series and the data size is International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

International Journal of Economic Perspectives, 2015, Volume 9, Issue 4, x-x. more than n >30 in order to make it parametrically testable. The main questions are: Does China’s Airway Freight Transportation have a significant impact on CO2 Emissions? Does China’s Export Volume have a significant impact on Airway Freight Transportation? The linear regression analysis was applied to answer the research questions. The E-views software program was used to carry out the econometric models such as the Johansen co-integration test, linear regression and the Granger causality test. At Table 1, AR(1) was applied to the model in order to prevent autocorrelation and heteroscedasticity, thus distributing residuals randomly. The Airway Freight Transportation was selected as the independent variable to comprehend the effect on CO2 emissions. Finally, the score of the Durbin-Watson test turned out to be 1.89, which is close to the value 2. When the score of the Durbin-Watson test is near 2, the risk of autocorrelation is downgraded. Table 1: The Effects of China’s Airway Freight Transportation on CO2 Emission LN(Airway Freight Transportation)t = ß0 + ß1LN(CO2 Emission) t + ut Dependent Variable: CO2, Sample 1981 – 2011, Included observations 31 after adjustments Variable Coefficient Std. Error t-Statistics Prob. AIR_TRNS 0.115376 0.049716 2.320727 0.0278 C 0.914351 0.416595 2.194820 0.0366 AR(1) 0.777892 0.110462 7.042197 0.0000 R-Squared

0.793879

Mean dependent var 1.817962

Adjusted R-Squared 0.779157

S.D. dependent var

0.162833

Durbin Watson stat

Inverted AR Roots

1.893021

0.78

According to the results of Table 2, there is remarkable impact of air transportation on the export volume, which was found at the value of 0.0000. The same procedure was applied correspondingly to Table 1. AR(1) was implemented to the model to prevent autocorrelation and heteroskedasticity at Table 2, thus distributing residuals randomly. Table 2: The Effects of China’s Airway Freight Transportation on Export Volume LN(Airway Freight Transportation)t = ß0 + ß1LN(Export Volume) t + ut Dependent Variable: EXP_VOLUME, Sample 1981 – 2011, Included observations 31 after adjustments Variable Coefficient Std. Error t-Statistics Prob. AIR_TRNS 0.118867 0.003527 33.70474 0.0000 C 1.127528 0.032167 35.05244 0.0000 AR(1) 0.836128 0.022492 37.17497 0.0000 R-Squared

0.799745

Mean dependent var 2.008844

Adjusted R-Squared

0.799734

S.D. dependent var

0.189722

Durbin Watson stat 1.694718

Inverted AR Roots

0.84

Table 3: Johansen Cointegration Test Hypothesized No. of CE(s)

Eigenvalue

Trace Statistic

None * At most 1 * At most 2 *

0.590291 0.515100 0.146470

51.46038 25.58345 4.592862

0.05 Critical Value 29.79707 15.49471 3.841466

Prob.** 0.0000 0.0011 0.0321

According to the Augmented Dickey Fuller Test results at Table 4 and Table 5, the data are not stationary. The E-Views program code was applied to convert them from non-stationary to stationary. genr air_trns1=air_trnsair_trns(-1), genr CO21=CO2-CO2(-1) ve genr exp_volume1=exp_volume-exp_volume(-1). After converting the data from non-stationary I (0) to stationary I(1), the variables have been put in the Johansen cointegration test and the Granger causality test. The tests show that there is long-term relationship between Export Volume, Air International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

International Journal of Economic Perspectives, 2015, Volume 9, Issue 4, x-x. Transportation, and CO2. According to Table 3, there is a long-term relationship between the variables of Air Transportation, CO2, and Export Volume. Table 4: ADF Test Results before Converting I (0) to I (1) Null Hypothesis: AIR_TRNS has a unit root Lag Length: 4 (Automatic - based on SIC, maxlag=7) Exogenous: Constant Augmented Dickey-Fuller test statistic Test critical values:

1% level 5% level 10% level

t-Statistic

Prob.*

-1.786697

0.3787

-3.699871 -2.976263 -2.627420

Null Hypothesis: CO2 has a unit root Lag Length: 0 (Automatic - based on SIC, maxlag=7) Exogenous: Constant Augmented Dickey-Fuller test statistic Test critical values:


t-Statistic

Prob.*

-1.027495

0.7308

-3.661661 -2.960411 -2.619160

Null Hypothesis: EXP_VOLUME has a unit root Lag Length: 4 (Automatic - based on SIC, maxlag=7) Exogenous: Constant Augmented Dickey-Fuller test statistic Test critical values:


t-Statistic

Prob.*

-2.844463

0.0655

-3.699871 -2.976263 -2.627420

Table 5: ADF Test Results after Converting I (0) to I (1) Null Hypothesis: AIR_TRNS1 has a unit root Lag Length: 3 (Automatic - based on SIC, maxlag=7) Exogenous: Constant Augmented Dickey-Fuller test statistic Test critical values:


t-Statistic

Prob.*

-4.027499

0.0046

-3.699871 -2.976263 -2.627420

Null Hypothesis: CO21 has a unit root Lag Length: 1 (Automatic - based on SIC, maxlag=7) Exogenous: Constant Augmented Dickey-Fuller test statistic Test critical values:

1% level 5% level

t-Statistic

Prob.*

-5.341677

0.0001

-3.679322 -2.967767


International Journal of Economic Perspectives, 2015, Volume 9, Issue 4, x-x. 10% level

-2.622989

Null Hypothesis: EXP_VOLUME1 has a unit root Lag Length: 3 (Automatic - based on SIC, maxlag=7) Exogenous: Constant Augmented Dickey-Fuller test statistic Test critical values:

t-Statistic

Prob.*

-3.945829

0.0492


-3.699871 -2.976263 -2.627420

Figure 1. The Inverse Root of AR/MA Polynomials Test. Inverse Roots of AR/MA Polynomial(s) 1.5

1.0

AR roots

0.5

0.0

-0.5

-1.0

-1.5 -1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Regarding the inverse root of the AR/MA polynomials test in Figure 1, the inverse root fell within the circle, which supports the linear regression analysis in terms of the impact of air transportation on both CO2 and export volume between the periods of 1980-2011. In addition, all the features of the root mean fell within the circles at the inverse roots of the AR Characteristic Polynomial test, which supports the idea that the VAR model is stationary at Figure 2. Therefore, both the Variance Decomposition and the Impulse Response analysis can be done. According to the results of Figure 3 and Table 6, the impact of air transportation is found to increase carbon dioxide emissions more than the impact of export volume. Figure 2. VAR Model Inverse Roots of AR Characteristic Polynomial 1.5

1.0

0.5

0.0

-0.5

-1.0

-1.5 -1.5

-1.0

-0.5

0.0

0.5

1.0

1.5


International Journal of Economic Perspectives, 2015, Volume 9, Issue 4, x-x. Figure 3. Impulse Response Analysis

Table 6: Variance Decomposition of AIR_TRNS1 Period

S.E.

EXP_VOLUME

CO2

AIR_TRNS1

1 2 3 4 5 6 7 8 9 10

0.085990 0.086141 0.092546 0.092684 0.093627 0.093716 0.093844 0.093870 0.093891 0.093901

2.271915 2.303772 2.034730 2.277785 2.353753 2.434859 2.447871 2.494374 2.499676 2.506727

97.72808 97.47049 95.69859 95.44954 95.41820 95.24406 95.18406 95.13877 95.12871 95.10883

0.000000 0.225733 2.266676 2.272670 2.228044 2.321083 2.368064 2.366857 2.371617 2.384440

According to the Granger causality analysis at Table 7, 8, 9, and 10, when the lag number is 1, 2, 3, or 4, then China’s air transportation is the Granger cause of the export volume, which means that China’s air transportation increases the export volume. For this reason, the export volume is selected as the dependent variable and air transportation is selected as the independent variable. Table 7: Granger Causality Test at Lag 1 Sample: 1980 2011 Null Hypothesis:

Obs

F-Statistic

Prob.

EXP_VOLUME1 does not Granger Cause AIR_TRNS1 AIR_TRNS1 does not Granger Cause EXP_VOLUME1

30

4.18839 9.74749

0.0498 0.0043

Null Hypothesis:

Obs

F-Statistic

Prob.


29

3.93273 7.51638

0.0333 0.0029

Table 8: Granger Causality Test at Lag 2 Sample: 1980 2011


International Journal of Economic Perspectives, 2015, Volume 9, Issue 4, x-x. Table 9: Granger Causality Test at Lag 3 Sample: 1980 2011 Null Hypothesis:

Obs

F-Statistic

Prob.


28

2.31316 3.31052

0.1053 0.0399

Null Hypothesis:

Obs

F-Statistic

Prob.


27

2.24477 4.08450

0.1047 0.0158

Table 10: Granger Causality Test at Lag 4 Sample: 1980 2011

4. CONCLUSION Kågeson (2001) insistently accentuated the effect of the transportation sector on CO2 emissions and how to reduce its impact and damage. Furthermore, there is much research about the relationship between the transportation sector and CO2 emissions. According to Ghandoor (2013), total CO2 emissions in the world amounted to approximately 21 billion tons in 1990 and rose to 29.4 billion tons in 2008. The transportation sector contributed around 6.6 billion tons to total carbon dioxide emissions in 2008 an amount which made up 22.5% of the world’s total CO2 emissions that year. In addition, Smith, Watkiss, Tweddle, McKinnon, Browne & Hunt (2005) elaborated that airway cargo has the highest CO2 emissions per ton, and Andersen, Gössling, Simonsen, Walnum, Peeters & Neiberger (2010) demonstrated that transportation indeed causes CO2 emissions, such as the example of exports from China. Ghandoor, Jaber, Al-Hinti & Abdallat (2013) and Kim, Janic & Van Wee (2009) found similar results on the subject of the relationship between airway transportation and export volume, affirming the results of this paper. On the other hand, Kalayci & Yazici (2015) indicated that the U.S.’s export volume has a crucial effect on air transportation within the period of 1980 to 2012. The results of this paper’s research are consistent with their findings. The Granger causality analysis of their paper clearly demonstrates the two-way relationship between export volume and air transportation at lag 1 and lag 2 at Table 7 and Table 8. The two main research questions are proved empirically by implementing econometric models. The recommendation of this paper is that production facilities ought to be established in the nearest regions of target countries, so that logistic costs, CO2 emissions, and energy consumption would be reduced automatically, and the total world trade volume and FDI rates would significantly increase between the countries. This study examined civil air transportation in relation to both export volume and GDP. The implication of these findings indicates that the impact of factors on air transportation is very contingent on other dependent variables. These variables could be considered as independent variables for future academic studies. REFERENCES Andersen, O., Gössling, S., Simonsen, M., Walnum, H. J., Peeters, P., & Neiberger, C. (2010), CO2 Emissions from the Transport of China's Exported Goods. Energy Policy, 38(10), 5790-5798. Ghandoor, A. (2013), An Approach to Energy Savings and Improved Environmental Impact through Restructuring Jordan’s Transport Sector. Renewable & Sustainable Energy Reviews, 18, 31-42.


International Journal of Economic Perspectives, 2015, Volume 9, Issue 4, x-x. Ghandoor, A., Jaber, J., Al-Hinti, I. & Abdallat, Y. (2013), Statistical Assessment and Analyses of the Determinants of Transportation Sector Gasoline Demand in Jordan. Transportation Research Part A-Policy and Practice, 50, 129-138. Howitt, O. J., Carruthers, M. A., Smith, I. J., & Rodger, C. J. (2011), Carbon Dioxide Emissions from International Air Freight. Atmospheric Environment, 45(39), 7036-7045. Kågeson, P. (2001). The impact of CO2 emissions trading on the European Transport Sector. Vinnova report VR 2001: 17. Nature Associates, Stock-Holm. Kalayci, S., & Yazici, S. (2015), The Impact of Export Volume and GDP on USA’s Civil Aviation in between 1980-2012. International Journal of Economics and Finance, 8(1), 229-235. Kim, N., Janic, M., & Van Wee, B. (2009), Trade-off between Carbon Dioxide Emissions and Logistics Costs based on Multiobjective Optimization. Transportation Research Record: Journal of the Transportation Research Board, (2139), 107-116. Lipscy, P.Y., & Schipper, L. (2013), Energy Efficiency in the Japanese Transport Sector. Elsevier Journal of Energy Policy, 56, 248-258. Pan, J., Phillips, J., & Chen, Y. (2008), China's Balance of Emissions Embodied in Trade: Approaches to Measurement and Allocating International Responsibility. Oxford Review of Economic Policy, 24(2), 354-376. Schandl, H., & West, J. (2012), Material Flows and Material Productivity in China, Australia, and Japan. Journal of Industrial Ecology, 16(3), 352-364. Schipper, L. J., & Marie-Lilliu, C. (1999), Carbon-Dioxide Emissions from Transport in IEA Countries: Recent Lessons and Long-Term Challenges. KFB meddelande, (1999: 11). Smith, A., Watkiss, P., Tweddle, G., McKinnon, A., Browne, M., Hunt, A. & Cross, S. (2005), The Validity of Food Miles as an Indicator of Sustainable Development-Final Report. REPORT ED50254. Steenhof, P., Woudsma, C., & Sparling, E. (2006), Greenhouse Gas Emissions and the Surface Transport of Freight in Canada. Transportation Research Part D: Transport and Environment, 11(5), 369-376. Winebrake, J. J., Corbett, J. J., Falzarano, A., Hawker, J. S., Korfmacher, K., Ketha, S., & Zilora, S. (2008), Assessing Energy, Environmental and Economic Tradeoffs in Intermodal Freight Transportation. Journal of the Air & Waste Management Association, 58(8), 1004-1013. Yamaguchi, K. (2008), International Trade and Air Cargo: Analysis of US Export and Air Transport Policy. Transportation Research Part E: Logistics and Transportation Review, 44(4), 653-663.


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