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Evaluation of carbon emission efficiency in China's airlines
Journal of Cleaner Production 243 (2020) 118500
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Evaluation of carbon emission efficiency in China's airlines Zilong Wang a, * , Xiaodi Xu b, Yongfeng Zhu a, Tian Gan a a b
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China School of Public Administration, Nanjing Normal University, Nanjing, 210023, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 March 2019 Received in revised form 31 August 2019 Accepted 18 September 2019 Available online 18 September 2019
Aviation CO2 emissions are increasing along with traffic growth, and expected technological improvements are not enough to reduce this continuous growth. In response to the rapid growth in air transport CO2 emissions, International Civil Aviation Organization has proposed the “Carbon Neutral Growth from 202000 strategy to reduce aviation carbon emissions. Using 13 Chinese airlines data from 2009 to 2013, this study investigates the static and dynamic efficiencies of carbon emissions through the global slack based measure (GSBM) model and the Global Malmquist-Luenberger productivity index (GML). The results indicate that, first, the static efficiency of carbon emissions in airlines showed an inverted Ushaped trend during the inspection period. Second, the average values of dynamic carbon emissions efficiency of China's airlines in 2009e2013 are 1.098, 1.157, 0.995, and 0.950 respectively. Third, the reasons for the changes in the dynamic efficiency of each airline are different. Monitoring the carbon emissions efficiency of airlines may help the government to develop targeted carbon-reduction policies. © 2019 Elsevier Ltd. All rights reserved.
Handling editor; Sandro Nizetic Keywords: Airlines Carbon emission efficiency GSBM model GML model China
1. Introduction In recent years, the CO2 emission from flights is a raising serious concern (Cui, 2019). The global flow of people and goods has contributed to the rapid development of airlines, which has led to a significant increase in airline CO2 emissions (Chen et al., 2017). It is worth noting that the aviation industry is one of the few industries in which energy consumption has grown by more than 6% over the past decade (Cui and Li, 2015b). Moreover, Boeing forecasts that by 2025, the CO2 emissions from the global aviation industry could increase to 1.23e1.49 billion tons (Boeing, 2012). If CO2 emissions are not effectively controlled, the irreversible risks resulting from climate change may continue to increase in the future (Lin and Zhu, 2019). As the world's second-largest air transport market, China's air transportation has an important contribution to economic growth. The operating income of China's airlines increased from 388.98 billion in 2012 to 533.38 billion in 2017 (see Fig. 1). However, with the rapid development of air transportation, the contradiction between the carbon emission of airlines and the environment is becoming more and more prominent. For example, the total CO2 emissions of China's airlines showed an increasing trend from 2012
* Corresponding author. E-mail address: [email protected] (Z. Wang). https://doi.org/10.1016/j.jclepro.2019.118500 0959-6526/© 2019 Elsevier Ltd. All rights reserved.
to 2017 (see Fig. 2), which highlights the urgency of managing and controlling CO2 emissions from China's aviation industry. To investigate the carbon efficiency of air transportation, scholars have carried out a great deal of empirical exploration (Liu et al., 2017). The static efficiency is the evaluation of carbon emissions at a certain point in time, reflecting the utilization efficiency of input resources by decision-making units (DMUs) (Li et al., 2019). In contrast, the dynamic efficiency measures the change in carbon emission efficiency over a certain time, essentially reflecting the changes in static efficiency (Liu et al., 2017). To the best of our knowledge, no study to date has combined static efficiency with dynamic efficiency to explore the carbon emission efficiency of China's air transportation. Given this research gap, this study will utilize the panel data of China's 13 airlines to investigate the carbon emission efficiency of China's air transportation from a micro perspective. The remainder of this study is arranged as follows. Section 2 provides a literature review. Section 3 provides methods. Section 4 shows data. Section 5 discusses carbon emissions efficiency. The conclusions and policy implications are drawn in Section 6.
2. Literature review and this study's contributions The carbon emission efficiency of air transportation has attracted extensive attention from researchers both domestically and
2
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55.0
14.0
53.0 Operating income
49.0
Growth rate
10.0
47.0 45.0
8.0
43.0
6.0
41.0 39.0
4.0
37.0 35.0
Growth rate (%)
Operating income (Billion)
12.0
51.0
2012
2013
2014
2015
2016
2017
2.0
Fig. 1. Operating income of China's airlines from 2012 to 2017. Data source: Statistical Bulletin of Civil Aviation Industry Development 2012e2017.
82.9
85.0
(million tons)
80.0
carbon emission
75.2
75.0 70.0
66.5
68.8
65.0 60.0
59.9 56.3
55.0 50.0
2012
2013
2014
2015
2016
2017
Fig. 2. Total CO2 emissions from China's airlines in 2012e2017. Data source: Statistics on Civil Aviation and National Civil Aviation Flight Operation Efficiency Report.
internationally. Researchers mainly focus on the implementation of emission reduction targets for airlines and the influencing factors of emission reduction effectiveness for airlines. For example, Jovanovic and Vracarevic (2016) combined the goal of global climate stabilization (70% reduction in carbon emissions) with the ICAO's predictions of commercial aviation growth to explore the possibility of achieving goals of climate stabilization. They believed that the goal of reducing carbon emissions can only be achieved through more stringent market-driven tools and emission reduction technology. Based on carbon emissions and noise from aircraft, Teoh and Khoo (2016) discussed environmental issues and believed that the resolution of environmental issues requires the joint efforts of all parties. Using structural equation modeling, Singh et al. (2017) investigated the potential of greenhouse gas emission reduction in air transport. They founded that the reduction in greenhouse gas emissions is mainly affected by aircraft technology, aviation operations, aviation infrastructure, socio-economic, policy measures, alternative fuel, and fuel characteristics. Singh (2016) used the structural equation model to study the potential of carbon emission reduction of the airline and concluded that aircraft design technology, aviation infrastructure and socio-economic policies are the main factors for reducing greenhouse gas emissions. (Lin and Zhu, 2019) used the panel data of China Airlines from 2007 to 2013 for empirical analysis, proposing an improved generalized production decomposition analysis (PDA). Their research found that income changes can lead to an increase in carbon emissions, and that energy intensity plays a leading role in reducing emissions from most airlines. Comparing Asian airlines and European airlines, Arjomandi et al. (2018) found that improvement in the environmental performance of the European
airlines could be an outcome of the European Emission Trading Scheme (ETS). Using the predicted data by BP neural network, Cui (2019) investigated the total CO2 emission reduction of 28 airlines, and found that for these 28 airlines, the maximum expandable Total Revenue and Greenhouse gases emission reduction can be achieved simultaneously. Investigating 15 varied Spanish airlines, Lazaro et al. (2019) found that differences in the operating model and route configurations can lead to big differences in terms of impact of biofuels introduction on the total airline costs. It is worth noting that most of the carbon emissions research on airlines focuses on the amount and the intensity of emissions. However, there are few studies investigating the efficiency of carbon emissions from the micro-perspective of airlines. Moreover, some scholars have thrown more attentions to study the static efficiency of air transport CO2 emissions, ignoring the dynamic changes in carbon emission efficiency. As a result, the contributions of this study have the following three aspects. First, this study uses the data envelopment analysis (DEA) model to evaluate carbon emission efficiency in China's airlines, which may enhance the theoretical research related to air transport from the micro-perspective of airlines. Second, the GSBM model modified by Boostrap and the GML model are used to measure the static and dynamic efficiencies of carbon emissions, respectively, which may develop a theoretical analysis framework for quantitative research-related issues in the future. Third, exploring the relationship between operating income and carbon emissions may be helpful for airlines to achieve the goal of lowcarbon development. 3. Methodology 3.1. Model for calculation of carbon emissions of airlines The sources of carbon emissions of the airlines are divided into two parts e fuel combustion and net purchase of electricity and heat. The sources of fuel combustion include aircraft, airline boilers and transportation vehicles. The majority of carbon emissions in airlines are derived from the fuel combustion of aircraft. Considering the availability of data, this paper only calculates carbon emissions from aviation kerosene combustion (IPCC, 2006). The calculation formula is as follows:
CO2 ¼ AD COF ¼ FC NCV 106 *ðCC OF 44=12Þ (1) 3.2. GSBM model considering undesired outputs The basic idea of the DEA model is to get as much output as possible with as little input as possible. However, it is inevitable that there will be undesired outputs in the actual production process such as pollutants. Tone (2001) first proposed the SBM model, which is a non-radial and non-angled data envelopment method based on slack variables. The SBM model integrates the original input-oriented and output-oriented models. On this basis, (Fu et al., 2012) combined the SBM model with the directional distance function to improve the calculation accuracy. The SBM model directional distance function takes into account both the slack variable and the undesired output, so it is used to measure the carbon emission efficiency of airlines in this paper. The dataset containing inputs, expected outputs, and undesired outputs is built. Assume that each airline is a DMU, and there are M inputs N x ¼ ðx1 ; x2 ; :::; xm Þ2RM þ , N outputs y ¼ ðy1 ; y2 ; :::; yn Þ2Rþ , J undesirable outputs, b ¼ ðb1 ;b2 ;:::;bj Þ2RJþ . The production possibility set
Z. Wang et al. / Journal of Cleaner Production 243 (2020) 118500
bias in the case of small samples. Simar and Wilson (1998) first introduced Bootstrap method into DEA analysis model, and proposed Bootstrap-DEA method, which effectively solved above shortcomings. The specific steps of the Bootstrap-DEA method are as follows:
is defined by formula (2):
P t xt ¼
8 ! I X > > t t > lti ytin ytin ; n ¼ 1; :::; N; > y ;b > > > > i¼1 > > > > I > X > > > lti xtim xtim ; m ¼ 1; :::; M; > > < i¼1
(1) The SBM model is used to measure the carbon emission efficiency of airlines, which obtains the original efficiency value of the sample q ¼ ðq1 ; q2 ; :::; qI Þ. (2) A simple bootstrap sample qb ¼ ðqb1 ; qb2 ; :::; qbI Þ of size I is extracted from q using a repetitive sampling method with a return. (3) The sample qb is smoothed to obtain a smoothed sample qb ¼ ðqb1 ; qb2 ; :::; qbI Þ . (4) The sample input and output data are adjusted using qb and q , as follows:
(2)
> I X > > > > lti btij ¼ btij ; j ¼ 1; :::; J; > > > > i¼1 > > > > I > X > > > lti ¼ 1; lti 30; i ¼ 1; :::; I > : i¼1
here: the explanation of each notation in the formula (2) is shown P in Table 2. If Ii¼1 lti ¼ 1 and lti 0 , it means that the scale return is variable; if lti 0 , it means that the scale return is unchanged. The SBM directional distance function model is constructed by formula (3):
0
1
B C B C C !t B t t t x y bC B s v Bxi0 ; yi0 ; bi0 ; g ; g ; g C ¼ max B C @ A
xbi ¼ ðqi =qbi Þ,xi ybi ¼ ðqi =qbi Þ,yi
(5)
0 ! b J M N 1 X sxm 1 @X syn X sj þ þ M m¼1 g xm N þ J n¼1 g yn j¼1 g b j 2
8 I > X > > > lti xtim þ sxm ¼ xti0 m ; m ¼ 1; :::; M; > > > > i¼1 > > > > > > > > > I >X > > > lti ytin syn ¼ yti0 n ; n ¼ 1; :::; N; > > > > i¼1 > > > > > < I s:t: X > lti btij þ sbj ¼ bti0 j ; j ¼ 1; :::; J; > > > > i¼1 > > > > > > > >X > I > > > lti ¼ 1; lti 0; i ¼ 1; :::; I; > > > > > i¼1 > > > > > > > y > : sxm 0; sn 0; sbj 0
(3)
!t when s v ðxti0 ; yti0 ; bti0 ; g x ; g y ; g b Þ ¼ 0, it means that the DMU is effi!t cient; when s v ðxti0 ; yti0 ; bti0 ; g x ; g y ; g b Þ >0, it means that the DMU is inefficient. The carbon emission efficiency of airlines can be calculated from the directional distance function, and the formula is as follows:
CE ¼
3
1 !t 1 þ s v xti0 ; yti0 ; bti0 ; g x ; gy ; g b
(4)
(5) Using the adjusted input and output data to measure the efficiency value again, the efficiency score of the bootstrap sample is obtained b q b ¼ ðbq b1 ; bq b2 ; :::; bq bI Þ. (6) Repeat steps (2)e(5) B times (such as B ¼ 1000) to obtain the error value, efficiency correction value and estimation interval for each efficiency value.
bbiasðqi Þ ¼ B1
B X
b q b qi ;
(6)
b¼1
when the DEA model evaluates the efficiency of multi-input and multi-output, there are two shortcomings: (1) neglecting the influence of random factors such as statistical errors and missing variables; (2) unable to solve the problem of efficiency evaluation
q*i ¼ qi bbiasðqi Þ
(7)
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Z. Wang et al. / Journal of Cleaner Production 243 (2020) 118500
b*a=2 þ qi q0i qi þ a*a=2
(8)
where bbiasðqi Þ represents the error value; q*i represents the efficiency correction value; formula (8) represents the confidence interval. 3.3. GML model The production process of the airline is a long-term and continuous process. In this process, the level of production technology in the airline is dynamically changing, which indicates that the static efficiency of carbon emissions is not suitable and that the dynamic efficiency of carbon emissions needs to be considered. The dynamic efficiency of carbon emissions is usually investigated usn-Esteve et al., 2019; Wang ing the ML productivity index (Beltra et al., 2019; Zhao and Lin, 2019). The ML productivity index calculated by the geometric mean method is not cyclical or cumulative, which can only be used for short-term changes in efficiency. The ML productivity index is not suitable for measuring long-term changes in efficiency, which may lead to situations where linear programming has no solution. Oh (2010) proposed the GML model to measure the change in efficiency. This method constructs a common production frontier to avoid linear programming without solutions. The global production possibility set is defined by formula (9):
P G ðxÞ ¼
8 T X I X > > t t > lti ytin ytin ; n ¼ 1; :::; N; > y ;b > > > > t¼1 i¼1 > > > > T X I > X > > > lti xtim xtim ; m ¼ 1; :::; M; > > < t¼1 i¼1
> T X I X > > > > lti btij ¼ btij ; > > > > t¼1 i¼1 > > > > I > X > > > lti ¼ 1; lti 0; > :
(9) j ¼ 1; :::; J; i ¼ 1; :::; I
i¼1
Under the global production possibility set, the GML model of DMU from period t to period tþ1 and its decomposition are expressed as:
GMLt;tþ1
!G 1 þ S v xt ; yt ; bt ; g ty ; g tb ¼ !G tþ1 1 þ S v xtþ1 ; ytþ1 ; btþ1 ; g tþ1 y ; g b
GMLt;tþ1 ¼ GMLEC t;tþ1 ,GMLTC t;tþ1
GMLEC t;tþ1
GMLTC t;tþ1
!t 1 þ S v xt ; yt ; bt ; g ty ; g tb ¼ !tþ1 tþ1 tþ1 tþ1 tþ1 x ; y ; b ; g y ; gtþ1 1þ Sv b
(10)
(11)
(12)
here: GML is decomposed into technical efficiency changes (GMLEC) and technological progress changes (GMLTC). If GMLEC t;tþ1 >1, which indicates that compared with the t period, DMU is closer to the productive frontier in the tþ1 period; if GMLTC t;tþ1 >1, it indicates that DMU has technological progress in the tþ1 period; if GMLt;tþ1 >1, it indicates that the carbon emission rate shows an increasing trend. This paper uses the GML productivity index to measure the dynamic efficiency of carbon emissions in airlines and to further analyze the specific reasons for efficiency changes. 4. Data analysis Based on the existing research and combined with the availability of the data, this paper selects the fleet size, flight shifts and flight hours as input; the operating income and total transportation turnover are used as the desirable output; the carbon dioxide emissions is used as undesired output (Cui and Li, 2015a; Xiao et al., 2017; Zhang et al., 2017a). Due to the difficulty in obtaining data on capital and labor, following the research of (Cui and Li, 2015a), the capital variable is replaced by the fleet size; flight shifts and flight hours can reflect the investment in the production process to a certain extent, which are selected as input variables. In terms of desirable outputs, the total turnover of transportation covers passenger traffic, passenger turnover, cargo turnover, etc. The total transportation turnover and operating income are used as the desirable output. The specific input-output variables are defined as follows: (1) fleet size: the number of aircraft owned by airlines, including passenger and cargo aircraft; (2) flight shifts: the sum of aircraft's flights; (3) flight hours: the sum of the aircraft's ground taxi time and air flight time; (4) operating income: the monetary income obtained by airline providing air transport services; (5) total transportation turnover: the total distance traveled by airlines for passengers, goods, and mails; (6) Carbon dioxide emissions: the total amount of carbon emissions generated by airlines during the production process, and the aviation kerosene consumption is used to estimate the carbon dioxide emissions. In addition, 13 airlines are used as research samples in this study (see Table 1). Due to the change of statistical caliber in the statistical yearbook, the fuel consumption of each airline has not been announced since 2014. Therefore, the research period of this paper is from 2009 to 2013. The data are from the Statistical Data on Civil Aviation of China, China Transportation Yearbook, and the annual reports of China Southern Airlines, Air China, China Eastern Airlines and Hainan Airlines (Department, 2010e2014; Transportation, 20102014). The data of Tianjin Airlines in 2009 is missing. Descriptive statistics for each input-output variable are shown in Table 2. 5. Results 5.1. Static efficiency of carbon emissions in airlines According to formulas (2)e(4), the GSBM model considering undesired outputs is used to measure the carbon emission efficiency of 13 airlines, which is considered to be the original value. At
h i.h i !G !t 1 þ S v xt ; yt ; bt ; gty ; gtb 1 þ S v xt ; yt ; bt ; g ty ; g tb ¼h i.h i !G !tþ1 tþ1 tþ1 tþ1 tþ1 tþ1 1 þ S v xtþ1 ; ytþ1 ; btþ1 ; g tþ1 1þ Sv x ; y ; b ; g y ; gtþ1 y ; g b b
(13)
Z. Wang et al. / Journal of Cleaner Production 243 (2020) 118500
5
Table 1 Description of the research sample. Airlines
Abbreviation
Type
China Southern Airlines Co., Ltd Air China Co., Ltd China Eastern Airlines Co., Ltd Hainan Airlines Co., Ltd Sichuan Airlines Co., Ltd Shandong Airlines Co., Ltd China Xinhua Airlines Co., Ltd Lucky Air Co., Ltd Shenzhen Airlines Co., Ltd Air Chang'an Co., Ltd Tianjin Airlines Co., Ltd Spring Airlines Co., Ltd Juneyao Airlines Co., Ltd
China Southern Airlines Air China China Eastern Airlines Hainan Airlines Sichuan Airlines Shandong Airlines Xinhua Airlines Lucky Air Shenzhen Airlines Air Chang'an Tianjin Airlines Spring Airlines Juneyao Airlines
Central airline Central airline Central airline Local airline Local airline Local airline Local airline Local airline Local airline Local airline Local airline Private & joint airline Private & joint airline
Sources: Cao et al. (2015).
Table 2 Descriptive statistics of input and output variables. Indicators
Indicators
Unit
Min
Max
Mean
S.D.
Input
Fleet size Flight shifts Flight hour Operating income Transportation turnover Carbon Emission
Item times hour million yuan 104 t/km tonne
7 11879 25679 1413 20244 196227
482 565821 1352687 101483 1399109 16491056
115 153635 343669 25747 318612 3623445
130 157117 375461 33194 383423 4596154
Desirable output Undesirable output
the same time, the Bootstrap method is used for the simulated sampling. After 1000 iterations, the corrected carbon emission efficiency value is obtained, which is considered to be the correction value. The specific measurement results are shown in Table 3: According to Table 3, the annual average carbon emissions efficiency of airlines during the inspection period is 0.736, 0.753, 0.826, 0.820 and 0.784, respectively. This result indicates that the carbon emission efficiency of Chinese airlines generally shows a fluctuant rising tendency, which is consistent with the conclusion drawn by Liu and Zhou (Liu et al., 2017). Moreover, the carbon emission efficiency of airlines from 2009 to 2013 shows an inverted U-shaped trend. The average efficiency first showed a relatively increase, reached its peak in 2011 and then was followed by a slow decline. It can be seen from Table 3 that it is impossible to sort these airlines when the original values of carbon emission efficiency are 1. After the correction by the Bootstrap method, the adjusted values of the carbon emission efficiency are different from each other, making it possible to compare all airlines. The carbon emission efficiency of 13 airlines is further compared by sorting the original values and the adjusted values, as shown in Table 4. It can be seen from Table 4 that there are differences in the development trends of carbon emission efficiency rankings in each airline. Specifically, (1) During the inspection period, the ranking of China Southern Airlines shows a gradual upward trend, rising from the eighth place in 2009 to the first place in 2013. (2) Air China's ranking has always remained in the top three, indicating that Air China has consistently performed outstandingly and can achieve economies of scale and green development. (3) China Eastern Airlines has always been at the bottom of the rankings during the inspection period. (4) Hainan Airlines' ranking still remains in the top five, indicating that Hainan Airlines can effectively control carbon emissions. (5) Sichuan Airlines ranks at the middle and lower reaches during the inspection period. It can be seen that there is a certain improvement in the later period. (6) Although the ranking of Shandong Airlines during the inspection period has changed slightly, it is still at the final level. (7) Spring Airlines is closer to the productive frontier, indicating that it has the highest
carbon emission efficiency among 13 companies. (8) The ranking of Xinhua Airlines has always been at the middle level and doesn't fluctuate much. (9) The carbon emission efficiency of Juneyao Airlines gradually drops from the fourth place to the eighth place. According to Table 3, it is mainly affected by two aspects: (i) its own efficiency value decreases slightly; (ii) the efficiency value of other airlines keeps improving. (10) Lucky Air's efficiency rankings are gradually declining. (11) The efficiency of Shenzhen Airlines is ranked in the middle and lower reaches, ranking sixth in 2011 and tenth in 2010. (12) Air Changan ranked in the top four during the inspection period. Air Changan can also generate as much revenue as possible with limited investment while effectively controlling carbon emissions. (13) Tianjin Airlines is always at the bottom during the inspection period. 5.2. Dynamic efficiency of carbon emissions in airlines According to formulas (10)e(13), the GML model is used to measure the dynamic efficiency of carbon emissions of 13 airlines from 2009 to 2013, as shown in Table 5. It can be seen from Table 5 that the average values of dynamic carbon emissions efficiency in these 13 airlines are respectively 1.098, 1.157, 0.995 and 0.950, indicating that the overall carbon emission efficiency is not continuously rising. This is possibly because airlines are affected by competition from high-speed rail (Fu et al., 2012; Zhang et al., 2017b). The competitive advantage for High Speed Rail over Air is primarily a function of the elapsed time for the total journey (Bukovac and Douglas, 2019). The average values of GML and GMLEC is less than 1 after 2011. The average value of GMLTC is less than 1 only in 2012e2013. Therefore, it can be considered that the decline in the dynamic efficiency of carbon emissions in the latter two stages (i.e., 2011e2012 and 2012e2013) is caused by changes in technical efficiency and technological progress. In addition, central Airlines (i.e., China Southern Airlines, Air China, and China Eastern Airlines) have relatively higher carbon emissions efficiency than local airlines (i.e., Hainan Airlines, Sichuan Airlines, Shandong Airlines, etc). This is because central
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Z. Wang et al. / Journal of Cleaner Production 243 (2020) 118500
Table 3 The carbon emission efficiency value of airlines and its Bootstrap correction value. Airlines
Year efficiency value
bias error
original value
correction value
confidence interval
Year efficiency value
lower limit
upper limit
Airlines
original value
correction value
bias error
confidence interval lower limit
upper limit
China Southern Airlines
2009 2010 2011 2012 2013
0.617 0.715 0.854 1.000 1.000
0.616 0.793 1.024 1.059 1.063
0.001 0.078 0.171 0.059 0.063
0.565 0.675 0.932 1.006 1.006
0.715 1.027 1.105 1.155 1.160
Xinhua Airlines 2009 2010 2011 2012 2013
0.686 0.782 0.898 0.866 0.836
0.702 0.780 0.913 0.866 0.826
0.015 0.002 0.016 0.000 0.010
0.667 0.746 0.854 0.821 0.787
0.779 0.831 1.009 0.926 0.877
Air China
2009 2010 2011 2012 2013
0.832 0.939 1.000 1.000 1.000
0.845 1.017 1.046 1.055 1.060
0.013 0.078 0.046 0.055 0.060
0.764 0.918 1.005 1.008 1.008
1.026 1.122 1.163 1.172 1.168
Juneyao Airlines
2009 2010 2011 2012 2013
0.701 0.817 0.837 0.735 0.739
0.761 0.827 0.835 0.728 0.738
0.060 0.010 0.002 0.007 0.002
0.709 0.781 0.788 0.696 0.706
1.009 0.925 0.916 0.778 0.780
China Eastern Airlines
2009 2010 2011 2012 2013
0.546 0.672 0.689 0.624 0.618
0.543 0.682 0.698 0.627 0.621
0.003 0.010 0.009 0.003 0.003
0.504 0.633 0.638 0.576 0.564
0.613 0.830 0.866 0.738 0.738
Lucky Air
2009 2010 2011 2012 2013
0.634 0.660 0.699 0.677 0.602
0.701 0.708 0.742 0.706 0.617
0.067 0.049 0.043 0.029 0.015
0.647 0.666 0.688 0.654 0.582
0.883 0.840 0.864 0.809 0.683
Hainan Airlines
2009 2010 2011 2012 2013
0.739 1.000 1.000 1.000 1.000
0.732 1.047 1.027 1.026 1.015
0.007 0.047 0.027 0.026 0.015
0.697 1.007 1.006 1.007 0.964
0.789 1.089 1.060 1.060 1.053
Shenzhen Airlines
2009 2010 2011 2012 2013
0.613 0.650 1.000 0.945 0.685
0.608 0.647 0.867 0.853 0.697
0.005 0.003 0.133 0.092 0.012
0.581 0.612 0.757 0.740 0.655
0.654 0.695 1.025 1.023 0.775
Sichuan Airlines
2009 2010 2011 2012 2013
0.590 0.617 0.768 0.771 0.745
0.585 0.611 0.764 0.768 0.743
0.005 0.006 0.004 0.003 0.002
0.557 0.580 0.727 0.731 0.707
0.622 0.654 0.822 0.826 0.800
Air Changan
2009 2010 2011 2012 2013
1.000 0.738 1.000 1.000 0.930
1.186 0.842 1.025 1.106 1.034
0.186 0.104 0.025 0.106 0.104
1.007 0.743 0.858 1.011 0.937
1.505 1.059 1.139 1.289 1.187
Shandong Airlines
2009 2010 2011 2012 2013
0.541 0.605 0.816 0.733 0.594
0.543 0.598 0.674 0.651 0.586
0.002 0.007 0.142 0.082 0.009
0.514 0.570 0.639 0.620 0.562
0.589 0.647 0.755 0.720 0.627
Tianjin Airlines 2009 2010 2011 2012 2013
e 0.245 0.274 0.304 0.327
e 0.257 0.270 0.297 0.324
e 0.012 0.004 0.007 0.003
e 0.241 0.253 0.276 0.309
e 0.296 0.302 0.328 0.350
Spring Airlines
2009 2010 2011 2012 2013
1.000 1.000 0.922 1.000 1.000
1.010 0.985 0.849 0.912 0.876
0.010 0.015 0.073 0.088 0.124
0.917 0.910 0.799 0.809 0.779
1.059 1.032 1.004 1.027 1.020
Means
0.708 0.726 0.827 0.820 0.775
0.736 0.753 0.826 0.820 0.784
Table 4 Ranking of carbon emission efficiency in airlines. Year
2009
Airlines
OR
PCR
OR
2010 PCR
OR
2011 PCR
OR
2012 PCR
OR
2013 PCR
China Southern Airlines Air China China Eastern Airlines Hainan Airlines Sichuan Airlines Shandong Airlines Spring Airlines Xinhua Airlines Juneyao Airlines Lucky Air Shenzhen Airlines Air Changan Tianjin Airlines
8 3 11 4 10 12 1 6 5 7 9 1 e
8 3 11 5 10 12 2 6 4 7 9 1 e
7 3 8 1 11 12 1 5 4 9 10 6 13
6 2 9 1 11 12 3 7 5 8 10 4 13
7 1 12 1 10 9 5 6 8 11 1 1 13
4 1 11 2 9 12 7 5 8 10 6 3 13
1 1 12 1 8 10 1 7 9 11 6 1 13
2 3 12 4 8 11 5 6 9 10 7 1 13
1 1 10 1 7 12 1 6 8 11 9 5 13
1 2 10 4 7 12 5 6 8 11 9 3 13
Note: “OR” indicates the original ranking; “PCR” indicates the Post-correction ranking.
airlines generally have the most advanced technology, the largest operating scale, and best management system (Cao et al., 2015; Liu et al., 2017). According to Table 5, this study takes 1 as the critical value to further analyzes the specific reasons for the dynamic changes in airline carbon emission efficiency. The results are shown in Table 6.
2009 2010 2011 2012 2013
According to Table 6, 13 airlines are categorized into two groups. The first category of airlines includes China Southern Airlines, Air China, Hainan Airlines, and Tianjin Airlines. The annual GML index of these airlines is greater than 1 during the inspection period, indicating that these airlines are more efficient in carbon emissions. The second category is the remaining airlines, which can be further divided according to the GML index. First, the decline in the GML index is due to GMLEC, such as China Eastern Airlines, Shandong Airlines, and Juneyao Airlines. The GMLEC values of these airlines are less than 1 in the corresponding time period, indicating that the main reason for the decrease in carbon emissions efficiency of these airlines is the change in technical efficiency. Second, the decline in the GML index is due to GMLTC, such as Sichuan Airlines, Spring Airlines, Shenzhen Airlines, and Air Changan. The GMLTC values of these airlines are less than 1 in the corresponding time period, indicating that the main reason for the decrease in efficiency of these airlines is the change in technological progress. Third, the decline in the GML index is caused by GMLEC and GMLTC, such as Xinhua Airlines and Lucky Air. 6. Conclusions and policy implications Taking Chese 13 airlines as an example, the SBM-Global model and the GML productivity index are used to discuss the trends of carbon emission efficiency from 2009 to 2013. We find that the
Z. Wang et al. / Journal of Cleaner Production 243 (2020) 118500
7
Table 5 Dynamic efficiency and decomposition of carbon emissions in airlines. Airlines
indicators
①
②
③
④
Airlines
indicators
①
②
③
④
China Southern Airlines
GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC
1.159 1.359 0.853 1.130 1.000 1.130 1.230 1.107 1.112 1.354 1.000 1.354 1.046 0.862 1.213 1.120 1.000 1.120 1.000 1.000 1.000
1.194 1.000 1.194 1.065 1.000 1.065 1.024 0.959 1.068 1.000 1.000 1.000 1.245 1.252 0.994 1.348 0.843 1.600 0.922 1.000 0.922
1.171 1.000 1.171 1.000 1.000 1.000 0.906 0.913 0.992 1.000 1.000 1.000 1.003 0.986 1.018 0.899 0.939 0.958 1.084 1.000 1.084
1.000 1.000 1.000 1.000 1.000 1.000 0.991 0.983 1.008 1.000 1.000 1.000 0.966 0.999 0.967 0.810 0.799 1.015 1.000 1.000 1.000
Xinhua Airlines
GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC
1.139 1.058 1.077 1.165 1.320 0.883 1.040 1.488 0.699 1.059 0.662 1.601 0.738 1.000 0.738 e e e 1.098 1.071 1.065
1.148 1.221 0.940 1.025 1.000 1.025 1.060 0.824 1.285 1.539 1.511 1.018 1.355 1.000 1.355 1.119 0.983 1.139 1.157 1.046 1.123
0.965 0.893 1.081 0.878 0.748 1.173 0.969 0.822 1.180 0.945 1.000 0.945 1.000 1.000 1.000 1.110 0.978 1.135 0.995 0.945 1.057
0.965 1.001 0.964 1.006 1.049 0.959 0.889 0.964 0.922 0.725 1.000 0.725 0.930 1.000 0.930 1.074 1.155 0.930 0.950 0.996 0.955
Air China
China Eastern Airlines
Hainan Airlines
Sichuan Airlines
Shandong Airlines
Spring Airlines
Juneyao Airlines
Lucky Air
Shenzhen Airlines
Air Changan
Tianjin Airlines
Means
Note: ①, ②, ③, and ④ represent time periods 2009e2010, 2010e2011, 2011e2012, and 2012e2013, respectively. Table 6 Results of dynamic efficiency decomposition of carbon emissions from airlines. Airlines
GML<1
GMLEC1
GMLTC1
Main cause
China Southern Airlines Air China China Eastern Airlines Hainan Airlines Sichuan Airlines Shandong Airlines Spring Airlines Xinhua Airlines Juneyao Airlines Lucky Air Shenzhen Airlines Air Changan Tianjin Airlines
0 0 2 0 1 2 1 2 1 2 2 2 0
4 4 1 4 1 1 4 3 3 1 3 4 1
3 4 3 4 2 3 3 2 2 2 2 2 2
e e GMLEC e GMLTC GMLEC GMLTC GMLEC、GMLTC GMLEC GMLEC、GMLTC GMLTC GMLTC e
carbon emissions efficiency through different data sources. Second, it is very difficult to establish a dynamic and universally applicable carbon emission efficiency evaluation index system for airlines, which can be studied in the future. Finally, according to the results of empirical analysis, this study puts forward policy implications to improve the carbon emission efficiency of airlines. Whether these policy implications are effective in practice needs to be further tested.
Appendix A: Description of the notations.
Notations Explanation
static efficiency of carbon emissions in China's airlines shows an inverted U-shaped during the period 2009e2013. The dynamic efficiency of carbon emissions varied from airline to airline through the entire period. Employing the empirical analysis in Section 5, we draw three policy implications for improving carbon emission efficiency and achieving cleaner production are as follows: First, our study reveals that the main reason for the decline in carbon emission efficiency of China Eastern Airlines, Shandong Airlines, and Juneyao Airlines is the change in technical efficiency. These airlines can improve their carbon emission efficiency through focusing on technical efficiency. Second, our study confirms that the main reason for the decline in carbon emission efficiency of Sichuan Airlines, Spring Airlines, Shenzhen Airlines, and Air Changan is the change in technological progress. These airlines can improve technological progress to reduce carbon emissions, such as introducing new technologies. Third, our study also reveals that the main reason for the decline in carbon emission efficiency of Xinhua Airlines and Lucky Air is the change in technical efficiency and technological progress. These airlines need to improve carbon emissions efficiency in terms of both technical efficiency and technological progress. While our study provides important theoretical and managerial implications, we still recognize some limitations. First, due to the changes in the statistical caliber of statistical yearbook, the data used in the measurement of carbon emission efficiency in this study are only up to 2013. Later researchers can analyze long-term trends in
CO2 AD COF FC NCV CC OF 44/12 P t ðxt Þ yt bt
lti ytin
Carbon emission Amount of burned aviation kerosene Coefficient of carbon emissions per unit of activity level Consumption of aviation kerosene Low calorific value of aviation kerosene
44100 kJ/ kg Carbon content per unit calorific value of aviation kerosene 19.5tC/TJ Carbon oxidation rate of aviation kerosene 100% Ratio of the molecular weight of carbon dioxide to carbon Production possibility set of the t period Desirable output in period t Undesirable output in period t Weight of the i-th decision unit in the t period
xti0
Observation of the nth desirable output of the i-th decision unit during the t period Observation of the Mth input of the i-th decision unit during the t period Observation of the Jth undesirable output of the i-th decision unit during the t period Inputs
yti0
Desirable outputs
bti0 gx gy gb sxm
Undesired outputs
xtim btij
syn sbj
Values
Direction vectors of input reduction Direction vectors of desirable output increase Direction vectors of undesired output compression Slack vectors of input Slack vectors of desired output Slack vectors of undesired output
Note: Parameter values for NCV, CC, and OF are from relevant literature (IPCC, 2006). kJ/kg means kilojoule/kilogram; tC/TJ means tonne/terajoule.
8
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Funding This research is supported by National Social Science Fund Key Project (grant number 18AGL028); Jiangsu Provincial Social Science Fund Series (grant number 17ZTB005); Jiangsu Provincial Social Science Key Project (grant number 2018SJZDI070); and Jiangsu Provincial Social Science Key Project (grant number 2018SJZDI064). References Arjomandi, A., Dakpo, K.H., Seufert, J.H., 2018. Have Asian airlines caught up with European Airlines? A by-production efficiency analysis. Transp. Res. A Policy Pract. 116, 389e403. n-Esteve, M., Gime nez, V., Picazo-Tadeo, A.J., 2019. Environmental producBeltra tivity in the European Union: a global Luenberger-metafrontier approach. Sci. Total Environ. 692, 136e146. Boeing, 2012. Current Market Outlook Boeing. Seattle, USA. Bukovac, S., Douglas, I., 2019. The potential impact of High Speed Rail development on Australian aviation. J. Air Transp. Manag. 78, 164e174. Cao, Q., Lv, J., Zhang, J., 2015. Productivity efficiency analysis of the airlines in China after deregulation. J. Air Transp. Manag. 42, 135e140. Chen, Z., Wanke, P., Antunes, J.J.M., Zhang, N., 2017. Chinese airline efficiency under CO2 emissions and flight delays: a stochastic network DEA model. Energy Econ. 68, 89e108. Cui, Q., 2019. Investigating the airlines emission reduction through carbon trading under CNG2020 strategy via a Network Weak Disposability DEA. Energy 180, 763e771. Cui, Q., Li, Y., 2015a. An empirical study on the influencing factors of transportation carbon efficiency: evidences from fifteen countries. Appl. Energy 141, 209e217. Cui, Q., Li, Y., 2015b. Evaluating energy efficiency for airlines: an application of VFBDEA. J. Air Transp. Manag. 44e45, 34e41. Fu, X., Zhang, A., Lei, Z., 2012. Will China's airline industry survive the entry of highspeed rail? Res. Transp. Econ. 35, 13e25. IPCC, 2006. IPCC Guidelines for National Greenhouse Gas Inventories. Global Environmental Strategy Institute, Japan. Jovanovic, M., Vracarevic, B., 2016. Challenges Ahead: mitigating air transport carbon emissions. Pol. J. Environ. Stud. 25, 1975e1984. Lazaro, A.L., Perez-Campuzano, D., Benito, A., Alonso, G., 2019. Analyzing carbon neutral growth and biofuel economic impact for 2017-2025: a case study based on Spanish carriers. J. Aerosp. Eng. 233, 1943e1959. Li, Y., Chiu, Y.H., Lu, L.C., Chiu, C.R., 2019. Evaluation of energy efficiency and air pollutant emissions in Chinese provinces. Energy Effic. 12 (4), 963e977. Lin, B., Zhu, J., 2019. The role of renewable energy technological innovation on climate change: empirical evidence from China. Sci. Total Environ. 659, 1505e1512. Liu, X., Zhou, D., Zhou, P., Wang, Q., 2017. Dynamic carbon emission performance of Chinese airlines: a global Malmquist index analysis. J. Air Transp. Manag. 65, 99e109. Oh, D.H., 2010. A metafrontier approach for measuring an environmentally sensitive
productivity growth index. Energy Econ. 32, 146e157. Simar, L., Wilson, P.W., 1998. Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models. Manag. Sci. 44, 49e61. Singh, V., 2016. Perceptions of emission reduction potential in air transport: a structural equation modeling approach. Environ. Syst. Decis. 36, 1e27. Singh, V., Chauhan, S.C., Tejyan, S., 2017. Greenhouse gas emission reduction potentials in air transport: a structural equation modelling-based multi-group analysis. Technol. Anal. Strateg. Manag. 29, 442e461. Teoh, L.E., Khoo, H.L., 2016. Green air transport system: an overview of issues, strategies and challenges. KSCE J. Civ. Eng. 20, 1040e1052. Tone, K., 2001. A slacks-based measure of efficiency in data envelopment analysis. Eur. J. Oper. Res. 130, 498e509. Transportation, China, 2010-2014. China Transportation Yearbook. China Communications Press, Beijing. Wang, Y., Duan, F., Ma, X., He, L., 2019. Carbon emissions efficiency in China: Key facts from regional and industrial sector. J. Clean. Prod. 206, 850e869. Xiao, L., Zhou, D., Peng, Z., Wang, Q., 2017. What drives CO2 emissions from China's civil aviation? An exploration using a new generalized PDA method. Transp. Res. Part A Policy Practice 99, 30e45. Zhang, J., Fang, H., Fang, S., 2017a. Environmental efficiency of China's airlines under environmental constrains. J. Transp. Syst. Eng. Inf. Technol. 17, 243e248. Zhang, Q., Yang, H., Wang, Q., 2017b. Impact of high-speed rail on China's Big Three airlines. Transp. Res. A Policy Pract. 98, 77e85. Zhao, H., Lin, B., 2019. Assessing the energy productivity of China's textile industry under carbon emission constraints. J. Clean. Prod. 228, 197e207.
Dr Zilong Wang is a Professor at the Nanjing University of Aeronautics and Astronautics, from where he has also received his Ph.D. He previously did his post doctorate at the Peking University. His research interest focuses on industrial economics and management. He has received several funding from important Chinese research institutions as the National Natural Science Foundation of China and Aeronautics Science Foundation. He has published more than 60 articles and Scholarly Monographs.
Dr Xiaodi Xu is an associate Professor at the Nanjing Normal University. She obtained her Doctor of Management from the Nanjing University of Aeronautics and Astronautics. She has received a research funding from important Chinese research institutions as the National Natural Science Foundation of China. Her research interest focuses on Knowledge management.
Yongfeng Zhu is a Ph.D candidate in College of Economics and Management, Nanjing University of Aeronautics and Astronautics, China. She is interested in the regional innovation and ecological economy.
Tian Gan is a graduate student in College of Economics and Management, Nanjing University of Aeronautics and Astronautics, China. She is interested in the energy environment.
Contents lists available at ScienceDirect
Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Evaluation of carbon emission efficiency in China's airlines Zilong Wang a, * , Xiaodi Xu b, Yongfeng Zhu a, Tian Gan a a b
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China School of Public Administration, Nanjing Normal University, Nanjing, 210023, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 March 2019 Received in revised form 31 August 2019 Accepted 18 September 2019 Available online 18 September 2019
Aviation CO2 emissions are increasing along with traffic growth, and expected technological improvements are not enough to reduce this continuous growth. In response to the rapid growth in air transport CO2 emissions, International Civil Aviation Organization has proposed the “Carbon Neutral Growth from 202000 strategy to reduce aviation carbon emissions. Using 13 Chinese airlines data from 2009 to 2013, this study investigates the static and dynamic efficiencies of carbon emissions through the global slack based measure (GSBM) model and the Global Malmquist-Luenberger productivity index (GML). The results indicate that, first, the static efficiency of carbon emissions in airlines showed an inverted Ushaped trend during the inspection period. Second, the average values of dynamic carbon emissions efficiency of China's airlines in 2009e2013 are 1.098, 1.157, 0.995, and 0.950 respectively. Third, the reasons for the changes in the dynamic efficiency of each airline are different. Monitoring the carbon emissions efficiency of airlines may help the government to develop targeted carbon-reduction policies. © 2019 Elsevier Ltd. All rights reserved.
Handling editor; Sandro Nizetic Keywords: Airlines Carbon emission efficiency GSBM model GML model China
1. Introduction In recent years, the CO2 emission from flights is a raising serious concern (Cui, 2019). The global flow of people and goods has contributed to the rapid development of airlines, which has led to a significant increase in airline CO2 emissions (Chen et al., 2017). It is worth noting that the aviation industry is one of the few industries in which energy consumption has grown by more than 6% over the past decade (Cui and Li, 2015b). Moreover, Boeing forecasts that by 2025, the CO2 emissions from the global aviation industry could increase to 1.23e1.49 billion tons (Boeing, 2012). If CO2 emissions are not effectively controlled, the irreversible risks resulting from climate change may continue to increase in the future (Lin and Zhu, 2019). As the world's second-largest air transport market, China's air transportation has an important contribution to economic growth. The operating income of China's airlines increased from 388.98 billion in 2012 to 533.38 billion in 2017 (see Fig. 1). However, with the rapid development of air transportation, the contradiction between the carbon emission of airlines and the environment is becoming more and more prominent. For example, the total CO2 emissions of China's airlines showed an increasing trend from 2012
* Corresponding author. E-mail address: [email protected] (Z. Wang). https://doi.org/10.1016/j.jclepro.2019.118500 0959-6526/© 2019 Elsevier Ltd. All rights reserved.
to 2017 (see Fig. 2), which highlights the urgency of managing and controlling CO2 emissions from China's aviation industry. To investigate the carbon efficiency of air transportation, scholars have carried out a great deal of empirical exploration (Liu et al., 2017). The static efficiency is the evaluation of carbon emissions at a certain point in time, reflecting the utilization efficiency of input resources by decision-making units (DMUs) (Li et al., 2019). In contrast, the dynamic efficiency measures the change in carbon emission efficiency over a certain time, essentially reflecting the changes in static efficiency (Liu et al., 2017). To the best of our knowledge, no study to date has combined static efficiency with dynamic efficiency to explore the carbon emission efficiency of China's air transportation. Given this research gap, this study will utilize the panel data of China's 13 airlines to investigate the carbon emission efficiency of China's air transportation from a micro perspective. The remainder of this study is arranged as follows. Section 2 provides a literature review. Section 3 provides methods. Section 4 shows data. Section 5 discusses carbon emissions efficiency. The conclusions and policy implications are drawn in Section 6.
2. Literature review and this study's contributions The carbon emission efficiency of air transportation has attracted extensive attention from researchers both domestically and
2
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55.0
14.0
53.0 Operating income
49.0
Growth rate
10.0
47.0 45.0
8.0
43.0
6.0
41.0 39.0
4.0
37.0 35.0
Growth rate (%)
Operating income (Billion)
12.0
51.0
2012
2013
2014
2015
2016
2017
2.0
Fig. 1. Operating income of China's airlines from 2012 to 2017. Data source: Statistical Bulletin of Civil Aviation Industry Development 2012e2017.
82.9
85.0
(million tons)
80.0
carbon emission
75.2
75.0 70.0
66.5
68.8
65.0 60.0
59.9 56.3
55.0 50.0
2012
2013
2014
2015
2016
2017
Fig. 2. Total CO2 emissions from China's airlines in 2012e2017. Data source: Statistics on Civil Aviation and National Civil Aviation Flight Operation Efficiency Report.
internationally. Researchers mainly focus on the implementation of emission reduction targets for airlines and the influencing factors of emission reduction effectiveness for airlines. For example, Jovanovic and Vracarevic (2016) combined the goal of global climate stabilization (70% reduction in carbon emissions) with the ICAO's predictions of commercial aviation growth to explore the possibility of achieving goals of climate stabilization. They believed that the goal of reducing carbon emissions can only be achieved through more stringent market-driven tools and emission reduction technology. Based on carbon emissions and noise from aircraft, Teoh and Khoo (2016) discussed environmental issues and believed that the resolution of environmental issues requires the joint efforts of all parties. Using structural equation modeling, Singh et al. (2017) investigated the potential of greenhouse gas emission reduction in air transport. They founded that the reduction in greenhouse gas emissions is mainly affected by aircraft technology, aviation operations, aviation infrastructure, socio-economic, policy measures, alternative fuel, and fuel characteristics. Singh (2016) used the structural equation model to study the potential of carbon emission reduction of the airline and concluded that aircraft design technology, aviation infrastructure and socio-economic policies are the main factors for reducing greenhouse gas emissions. (Lin and Zhu, 2019) used the panel data of China Airlines from 2007 to 2013 for empirical analysis, proposing an improved generalized production decomposition analysis (PDA). Their research found that income changes can lead to an increase in carbon emissions, and that energy intensity plays a leading role in reducing emissions from most airlines. Comparing Asian airlines and European airlines, Arjomandi et al. (2018) found that improvement in the environmental performance of the European
airlines could be an outcome of the European Emission Trading Scheme (ETS). Using the predicted data by BP neural network, Cui (2019) investigated the total CO2 emission reduction of 28 airlines, and found that for these 28 airlines, the maximum expandable Total Revenue and Greenhouse gases emission reduction can be achieved simultaneously. Investigating 15 varied Spanish airlines, Lazaro et al. (2019) found that differences in the operating model and route configurations can lead to big differences in terms of impact of biofuels introduction on the total airline costs. It is worth noting that most of the carbon emissions research on airlines focuses on the amount and the intensity of emissions. However, there are few studies investigating the efficiency of carbon emissions from the micro-perspective of airlines. Moreover, some scholars have thrown more attentions to study the static efficiency of air transport CO2 emissions, ignoring the dynamic changes in carbon emission efficiency. As a result, the contributions of this study have the following three aspects. First, this study uses the data envelopment analysis (DEA) model to evaluate carbon emission efficiency in China's airlines, which may enhance the theoretical research related to air transport from the micro-perspective of airlines. Second, the GSBM model modified by Boostrap and the GML model are used to measure the static and dynamic efficiencies of carbon emissions, respectively, which may develop a theoretical analysis framework for quantitative research-related issues in the future. Third, exploring the relationship between operating income and carbon emissions may be helpful for airlines to achieve the goal of lowcarbon development. 3. Methodology 3.1. Model for calculation of carbon emissions of airlines The sources of carbon emissions of the airlines are divided into two parts e fuel combustion and net purchase of electricity and heat. The sources of fuel combustion include aircraft, airline boilers and transportation vehicles. The majority of carbon emissions in airlines are derived from the fuel combustion of aircraft. Considering the availability of data, this paper only calculates carbon emissions from aviation kerosene combustion (IPCC, 2006). The calculation formula is as follows:
CO2 ¼ AD COF ¼ FC NCV 106 *ðCC OF 44=12Þ (1) 3.2. GSBM model considering undesired outputs The basic idea of the DEA model is to get as much output as possible with as little input as possible. However, it is inevitable that there will be undesired outputs in the actual production process such as pollutants. Tone (2001) first proposed the SBM model, which is a non-radial and non-angled data envelopment method based on slack variables. The SBM model integrates the original input-oriented and output-oriented models. On this basis, (Fu et al., 2012) combined the SBM model with the directional distance function to improve the calculation accuracy. The SBM model directional distance function takes into account both the slack variable and the undesired output, so it is used to measure the carbon emission efficiency of airlines in this paper. The dataset containing inputs, expected outputs, and undesired outputs is built. Assume that each airline is a DMU, and there are M inputs N x ¼ ðx1 ; x2 ; :::; xm Þ2RM þ , N outputs y ¼ ðy1 ; y2 ; :::; yn Þ2Rþ , J undesirable outputs, b ¼ ðb1 ;b2 ;:::;bj Þ2RJþ . The production possibility set
Z. Wang et al. / Journal of Cleaner Production 243 (2020) 118500
bias in the case of small samples. Simar and Wilson (1998) first introduced Bootstrap method into DEA analysis model, and proposed Bootstrap-DEA method, which effectively solved above shortcomings. The specific steps of the Bootstrap-DEA method are as follows:
is defined by formula (2):
P t xt ¼
8 ! I X > > t t > lti ytin ytin ; n ¼ 1; :::; N; > y ;b > > > > i¼1 > > > > I > X > > > lti xtim xtim ; m ¼ 1; :::; M; > > < i¼1
(1) The SBM model is used to measure the carbon emission efficiency of airlines, which obtains the original efficiency value of the sample q ¼ ðq1 ; q2 ; :::; qI Þ. (2) A simple bootstrap sample qb ¼ ðqb1 ; qb2 ; :::; qbI Þ of size I is extracted from q using a repetitive sampling method with a return. (3) The sample qb is smoothed to obtain a smoothed sample qb ¼ ðqb1 ; qb2 ; :::; qbI Þ . (4) The sample input and output data are adjusted using qb and q , as follows:
(2)
> I X > > > > lti btij ¼ btij ; j ¼ 1; :::; J; > > > > i¼1 > > > > I > X > > > lti ¼ 1; lti 30; i ¼ 1; :::; I > : i¼1
here: the explanation of each notation in the formula (2) is shown P in Table 2. If Ii¼1 lti ¼ 1 and lti 0 , it means that the scale return is variable; if lti 0 , it means that the scale return is unchanged. The SBM directional distance function model is constructed by formula (3):
0
1
B C B C C !t B t t t x y bC B s v Bxi0 ; yi0 ; bi0 ; g ; g ; g C ¼ max B C @ A
xbi ¼ ðqi =qbi Þ,xi ybi ¼ ðqi =qbi Þ,yi
(5)
0 ! b J M N 1 X sxm 1 @X syn X sj þ þ M m¼1 g xm N þ J n¼1 g yn j¼1 g b j 2
8 I > X > > > lti xtim þ sxm ¼ xti0 m ; m ¼ 1; :::; M; > > > > i¼1 > > > > > > > > > I >X > > > lti ytin syn ¼ yti0 n ; n ¼ 1; :::; N; > > > > i¼1 > > > > > < I s:t: X > lti btij þ sbj ¼ bti0 j ; j ¼ 1; :::; J; > > > > i¼1 > > > > > > > >X > I > > > lti ¼ 1; lti 0; i ¼ 1; :::; I; > > > > > i¼1 > > > > > > > y > : sxm 0; sn 0; sbj 0
(3)
!t when s v ðxti0 ; yti0 ; bti0 ; g x ; g y ; g b Þ ¼ 0, it means that the DMU is effi!t cient; when s v ðxti0 ; yti0 ; bti0 ; g x ; g y ; g b Þ >0, it means that the DMU is inefficient. The carbon emission efficiency of airlines can be calculated from the directional distance function, and the formula is as follows:
CE ¼
3
1 !t 1 þ s v xti0 ; yti0 ; bti0 ; g x ; gy ; g b
(4)
(5) Using the adjusted input and output data to measure the efficiency value again, the efficiency score of the bootstrap sample is obtained b q b ¼ ðbq b1 ; bq b2 ; :::; bq bI Þ. (6) Repeat steps (2)e(5) B times (such as B ¼ 1000) to obtain the error value, efficiency correction value and estimation interval for each efficiency value.
bbiasðqi Þ ¼ B1
B X
b q b qi ;
(6)
b¼1
when the DEA model evaluates the efficiency of multi-input and multi-output, there are two shortcomings: (1) neglecting the influence of random factors such as statistical errors and missing variables; (2) unable to solve the problem of efficiency evaluation
q*i ¼ qi bbiasðqi Þ
(7)
4
Z. Wang et al. / Journal of Cleaner Production 243 (2020) 118500
b*a=2 þ qi q0i qi þ a*a=2
(8)
where bbiasðqi Þ represents the error value; q*i represents the efficiency correction value; formula (8) represents the confidence interval. 3.3. GML model The production process of the airline is a long-term and continuous process. In this process, the level of production technology in the airline is dynamically changing, which indicates that the static efficiency of carbon emissions is not suitable and that the dynamic efficiency of carbon emissions needs to be considered. The dynamic efficiency of carbon emissions is usually investigated usn-Esteve et al., 2019; Wang ing the ML productivity index (Beltra et al., 2019; Zhao and Lin, 2019). The ML productivity index calculated by the geometric mean method is not cyclical or cumulative, which can only be used for short-term changes in efficiency. The ML productivity index is not suitable for measuring long-term changes in efficiency, which may lead to situations where linear programming has no solution. Oh (2010) proposed the GML model to measure the change in efficiency. This method constructs a common production frontier to avoid linear programming without solutions. The global production possibility set is defined by formula (9):
P G ðxÞ ¼
8 T X I X > > t t > lti ytin ytin ; n ¼ 1; :::; N; > y ;b > > > > t¼1 i¼1 > > > > T X I > X > > > lti xtim xtim ; m ¼ 1; :::; M; > > < t¼1 i¼1
> T X I X > > > > lti btij ¼ btij ; > > > > t¼1 i¼1 > > > > I > X > > > lti ¼ 1; lti 0; > :
(9) j ¼ 1; :::; J; i ¼ 1; :::; I
i¼1
Under the global production possibility set, the GML model of DMU from period t to period tþ1 and its decomposition are expressed as:
GMLt;tþ1
!G 1 þ S v xt ; yt ; bt ; g ty ; g tb ¼ !G tþ1 1 þ S v xtþ1 ; ytþ1 ; btþ1 ; g tþ1 y ; g b
GMLt;tþ1 ¼ GMLEC t;tþ1 ,GMLTC t;tþ1
GMLEC t;tþ1
GMLTC t;tþ1
!t 1 þ S v xt ; yt ; bt ; g ty ; g tb ¼ !tþ1 tþ1 tþ1 tþ1 tþ1 x ; y ; b ; g y ; gtþ1 1þ Sv b
(10)
(11)
(12)
here: GML is decomposed into technical efficiency changes (GMLEC) and technological progress changes (GMLTC). If GMLEC t;tþ1 >1, which indicates that compared with the t period, DMU is closer to the productive frontier in the tþ1 period; if GMLTC t;tþ1 >1, it indicates that DMU has technological progress in the tþ1 period; if GMLt;tþ1 >1, it indicates that the carbon emission rate shows an increasing trend. This paper uses the GML productivity index to measure the dynamic efficiency of carbon emissions in airlines and to further analyze the specific reasons for efficiency changes. 4. Data analysis Based on the existing research and combined with the availability of the data, this paper selects the fleet size, flight shifts and flight hours as input; the operating income and total transportation turnover are used as the desirable output; the carbon dioxide emissions is used as undesired output (Cui and Li, 2015a; Xiao et al., 2017; Zhang et al., 2017a). Due to the difficulty in obtaining data on capital and labor, following the research of (Cui and Li, 2015a), the capital variable is replaced by the fleet size; flight shifts and flight hours can reflect the investment in the production process to a certain extent, which are selected as input variables. In terms of desirable outputs, the total turnover of transportation covers passenger traffic, passenger turnover, cargo turnover, etc. The total transportation turnover and operating income are used as the desirable output. The specific input-output variables are defined as follows: (1) fleet size: the number of aircraft owned by airlines, including passenger and cargo aircraft; (2) flight shifts: the sum of aircraft's flights; (3) flight hours: the sum of the aircraft's ground taxi time and air flight time; (4) operating income: the monetary income obtained by airline providing air transport services; (5) total transportation turnover: the total distance traveled by airlines for passengers, goods, and mails; (6) Carbon dioxide emissions: the total amount of carbon emissions generated by airlines during the production process, and the aviation kerosene consumption is used to estimate the carbon dioxide emissions. In addition, 13 airlines are used as research samples in this study (see Table 1). Due to the change of statistical caliber in the statistical yearbook, the fuel consumption of each airline has not been announced since 2014. Therefore, the research period of this paper is from 2009 to 2013. The data are from the Statistical Data on Civil Aviation of China, China Transportation Yearbook, and the annual reports of China Southern Airlines, Air China, China Eastern Airlines and Hainan Airlines (Department, 2010e2014; Transportation, 20102014). The data of Tianjin Airlines in 2009 is missing. Descriptive statistics for each input-output variable are shown in Table 2. 5. Results 5.1. Static efficiency of carbon emissions in airlines According to formulas (2)e(4), the GSBM model considering undesired outputs is used to measure the carbon emission efficiency of 13 airlines, which is considered to be the original value. At
h i.h i !G !t 1 þ S v xt ; yt ; bt ; gty ; gtb 1 þ S v xt ; yt ; bt ; g ty ; g tb ¼h i.h i !G !tþ1 tþ1 tþ1 tþ1 tþ1 tþ1 1 þ S v xtþ1 ; ytþ1 ; btþ1 ; g tþ1 1þ Sv x ; y ; b ; g y ; gtþ1 y ; g b b
(13)
Z. Wang et al. / Journal of Cleaner Production 243 (2020) 118500
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Table 1 Description of the research sample. Airlines
Abbreviation
Type
China Southern Airlines Co., Ltd Air China Co., Ltd China Eastern Airlines Co., Ltd Hainan Airlines Co., Ltd Sichuan Airlines Co., Ltd Shandong Airlines Co., Ltd China Xinhua Airlines Co., Ltd Lucky Air Co., Ltd Shenzhen Airlines Co., Ltd Air Chang'an Co., Ltd Tianjin Airlines Co., Ltd Spring Airlines Co., Ltd Juneyao Airlines Co., Ltd
China Southern Airlines Air China China Eastern Airlines Hainan Airlines Sichuan Airlines Shandong Airlines Xinhua Airlines Lucky Air Shenzhen Airlines Air Chang'an Tianjin Airlines Spring Airlines Juneyao Airlines
Central airline Central airline Central airline Local airline Local airline Local airline Local airline Local airline Local airline Local airline Local airline Private & joint airline Private & joint airline
Sources: Cao et al. (2015).
Table 2 Descriptive statistics of input and output variables. Indicators
Indicators
Unit
Min
Max
Mean
S.D.
Input
Fleet size Flight shifts Flight hour Operating income Transportation turnover Carbon Emission
Item times hour million yuan 104 t/km tonne
7 11879 25679 1413 20244 196227
482 565821 1352687 101483 1399109 16491056
115 153635 343669 25747 318612 3623445
130 157117 375461 33194 383423 4596154
Desirable output Undesirable output
the same time, the Bootstrap method is used for the simulated sampling. After 1000 iterations, the corrected carbon emission efficiency value is obtained, which is considered to be the correction value. The specific measurement results are shown in Table 3: According to Table 3, the annual average carbon emissions efficiency of airlines during the inspection period is 0.736, 0.753, 0.826, 0.820 and 0.784, respectively. This result indicates that the carbon emission efficiency of Chinese airlines generally shows a fluctuant rising tendency, which is consistent with the conclusion drawn by Liu and Zhou (Liu et al., 2017). Moreover, the carbon emission efficiency of airlines from 2009 to 2013 shows an inverted U-shaped trend. The average efficiency first showed a relatively increase, reached its peak in 2011 and then was followed by a slow decline. It can be seen from Table 3 that it is impossible to sort these airlines when the original values of carbon emission efficiency are 1. After the correction by the Bootstrap method, the adjusted values of the carbon emission efficiency are different from each other, making it possible to compare all airlines. The carbon emission efficiency of 13 airlines is further compared by sorting the original values and the adjusted values, as shown in Table 4. It can be seen from Table 4 that there are differences in the development trends of carbon emission efficiency rankings in each airline. Specifically, (1) During the inspection period, the ranking of China Southern Airlines shows a gradual upward trend, rising from the eighth place in 2009 to the first place in 2013. (2) Air China's ranking has always remained in the top three, indicating that Air China has consistently performed outstandingly and can achieve economies of scale and green development. (3) China Eastern Airlines has always been at the bottom of the rankings during the inspection period. (4) Hainan Airlines' ranking still remains in the top five, indicating that Hainan Airlines can effectively control carbon emissions. (5) Sichuan Airlines ranks at the middle and lower reaches during the inspection period. It can be seen that there is a certain improvement in the later period. (6) Although the ranking of Shandong Airlines during the inspection period has changed slightly, it is still at the final level. (7) Spring Airlines is closer to the productive frontier, indicating that it has the highest
carbon emission efficiency among 13 companies. (8) The ranking of Xinhua Airlines has always been at the middle level and doesn't fluctuate much. (9) The carbon emission efficiency of Juneyao Airlines gradually drops from the fourth place to the eighth place. According to Table 3, it is mainly affected by two aspects: (i) its own efficiency value decreases slightly; (ii) the efficiency value of other airlines keeps improving. (10) Lucky Air's efficiency rankings are gradually declining. (11) The efficiency of Shenzhen Airlines is ranked in the middle and lower reaches, ranking sixth in 2011 and tenth in 2010. (12) Air Changan ranked in the top four during the inspection period. Air Changan can also generate as much revenue as possible with limited investment while effectively controlling carbon emissions. (13) Tianjin Airlines is always at the bottom during the inspection period. 5.2. Dynamic efficiency of carbon emissions in airlines According to formulas (10)e(13), the GML model is used to measure the dynamic efficiency of carbon emissions of 13 airlines from 2009 to 2013, as shown in Table 5. It can be seen from Table 5 that the average values of dynamic carbon emissions efficiency in these 13 airlines are respectively 1.098, 1.157, 0.995 and 0.950, indicating that the overall carbon emission efficiency is not continuously rising. This is possibly because airlines are affected by competition from high-speed rail (Fu et al., 2012; Zhang et al., 2017b). The competitive advantage for High Speed Rail over Air is primarily a function of the elapsed time for the total journey (Bukovac and Douglas, 2019). The average values of GML and GMLEC is less than 1 after 2011. The average value of GMLTC is less than 1 only in 2012e2013. Therefore, it can be considered that the decline in the dynamic efficiency of carbon emissions in the latter two stages (i.e., 2011e2012 and 2012e2013) is caused by changes in technical efficiency and technological progress. In addition, central Airlines (i.e., China Southern Airlines, Air China, and China Eastern Airlines) have relatively higher carbon emissions efficiency than local airlines (i.e., Hainan Airlines, Sichuan Airlines, Shandong Airlines, etc). This is because central
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Z. Wang et al. / Journal of Cleaner Production 243 (2020) 118500
Table 3 The carbon emission efficiency value of airlines and its Bootstrap correction value. Airlines
Year efficiency value
bias error
original value
correction value
confidence interval
Year efficiency value
lower limit
upper limit
Airlines
original value
correction value
bias error
confidence interval lower limit
upper limit
China Southern Airlines
2009 2010 2011 2012 2013
0.617 0.715 0.854 1.000 1.000
0.616 0.793 1.024 1.059 1.063
0.001 0.078 0.171 0.059 0.063
0.565 0.675 0.932 1.006 1.006
0.715 1.027 1.105 1.155 1.160
Xinhua Airlines 2009 2010 2011 2012 2013
0.686 0.782 0.898 0.866 0.836
0.702 0.780 0.913 0.866 0.826
0.015 0.002 0.016 0.000 0.010
0.667 0.746 0.854 0.821 0.787
0.779 0.831 1.009 0.926 0.877
Air China
2009 2010 2011 2012 2013
0.832 0.939 1.000 1.000 1.000
0.845 1.017 1.046 1.055 1.060
0.013 0.078 0.046 0.055 0.060
0.764 0.918 1.005 1.008 1.008
1.026 1.122 1.163 1.172 1.168
Juneyao Airlines
2009 2010 2011 2012 2013
0.701 0.817 0.837 0.735 0.739
0.761 0.827 0.835 0.728 0.738
0.060 0.010 0.002 0.007 0.002
0.709 0.781 0.788 0.696 0.706
1.009 0.925 0.916 0.778 0.780
China Eastern Airlines
2009 2010 2011 2012 2013
0.546 0.672 0.689 0.624 0.618
0.543 0.682 0.698 0.627 0.621
0.003 0.010 0.009 0.003 0.003
0.504 0.633 0.638 0.576 0.564
0.613 0.830 0.866 0.738 0.738
Lucky Air
2009 2010 2011 2012 2013
0.634 0.660 0.699 0.677 0.602
0.701 0.708 0.742 0.706 0.617
0.067 0.049 0.043 0.029 0.015
0.647 0.666 0.688 0.654 0.582
0.883 0.840 0.864 0.809 0.683
Hainan Airlines
2009 2010 2011 2012 2013
0.739 1.000 1.000 1.000 1.000
0.732 1.047 1.027 1.026 1.015
0.007 0.047 0.027 0.026 0.015
0.697 1.007 1.006 1.007 0.964
0.789 1.089 1.060 1.060 1.053
Shenzhen Airlines
2009 2010 2011 2012 2013
0.613 0.650 1.000 0.945 0.685
0.608 0.647 0.867 0.853 0.697
0.005 0.003 0.133 0.092 0.012
0.581 0.612 0.757 0.740 0.655
0.654 0.695 1.025 1.023 0.775
Sichuan Airlines
2009 2010 2011 2012 2013
0.590 0.617 0.768 0.771 0.745
0.585 0.611 0.764 0.768 0.743
0.005 0.006 0.004 0.003 0.002
0.557 0.580 0.727 0.731 0.707
0.622 0.654 0.822 0.826 0.800
Air Changan
2009 2010 2011 2012 2013
1.000 0.738 1.000 1.000 0.930
1.186 0.842 1.025 1.106 1.034
0.186 0.104 0.025 0.106 0.104
1.007 0.743 0.858 1.011 0.937
1.505 1.059 1.139 1.289 1.187
Shandong Airlines
2009 2010 2011 2012 2013
0.541 0.605 0.816 0.733 0.594
0.543 0.598 0.674 0.651 0.586
0.002 0.007 0.142 0.082 0.009
0.514 0.570 0.639 0.620 0.562
0.589 0.647 0.755 0.720 0.627
Tianjin Airlines 2009 2010 2011 2012 2013
e 0.245 0.274 0.304 0.327
e 0.257 0.270 0.297 0.324
e 0.012 0.004 0.007 0.003
e 0.241 0.253 0.276 0.309
e 0.296 0.302 0.328 0.350
Spring Airlines
2009 2010 2011 2012 2013
1.000 1.000 0.922 1.000 1.000
1.010 0.985 0.849 0.912 0.876
0.010 0.015 0.073 0.088 0.124
0.917 0.910 0.799 0.809 0.779
1.059 1.032 1.004 1.027 1.020
Means
0.708 0.726 0.827 0.820 0.775
0.736 0.753 0.826 0.820 0.784
Table 4 Ranking of carbon emission efficiency in airlines. Year
2009
Airlines
OR
PCR
OR
2010 PCR
OR
2011 PCR
OR
2012 PCR
OR
2013 PCR
China Southern Airlines Air China China Eastern Airlines Hainan Airlines Sichuan Airlines Shandong Airlines Spring Airlines Xinhua Airlines Juneyao Airlines Lucky Air Shenzhen Airlines Air Changan Tianjin Airlines
8 3 11 4 10 12 1 6 5 7 9 1 e
8 3 11 5 10 12 2 6 4 7 9 1 e
7 3 8 1 11 12 1 5 4 9 10 6 13
6 2 9 1 11 12 3 7 5 8 10 4 13
7 1 12 1 10 9 5 6 8 11 1 1 13
4 1 11 2 9 12 7 5 8 10 6 3 13
1 1 12 1 8 10 1 7 9 11 6 1 13
2 3 12 4 8 11 5 6 9 10 7 1 13
1 1 10 1 7 12 1 6 8 11 9 5 13
1 2 10 4 7 12 5 6 8 11 9 3 13
Note: “OR” indicates the original ranking; “PCR” indicates the Post-correction ranking.
airlines generally have the most advanced technology, the largest operating scale, and best management system (Cao et al., 2015; Liu et al., 2017). According to Table 5, this study takes 1 as the critical value to further analyzes the specific reasons for the dynamic changes in airline carbon emission efficiency. The results are shown in Table 6.
2009 2010 2011 2012 2013
According to Table 6, 13 airlines are categorized into two groups. The first category of airlines includes China Southern Airlines, Air China, Hainan Airlines, and Tianjin Airlines. The annual GML index of these airlines is greater than 1 during the inspection period, indicating that these airlines are more efficient in carbon emissions. The second category is the remaining airlines, which can be further divided according to the GML index. First, the decline in the GML index is due to GMLEC, such as China Eastern Airlines, Shandong Airlines, and Juneyao Airlines. The GMLEC values of these airlines are less than 1 in the corresponding time period, indicating that the main reason for the decrease in carbon emissions efficiency of these airlines is the change in technical efficiency. Second, the decline in the GML index is due to GMLTC, such as Sichuan Airlines, Spring Airlines, Shenzhen Airlines, and Air Changan. The GMLTC values of these airlines are less than 1 in the corresponding time period, indicating that the main reason for the decrease in efficiency of these airlines is the change in technological progress. Third, the decline in the GML index is caused by GMLEC and GMLTC, such as Xinhua Airlines and Lucky Air. 6. Conclusions and policy implications Taking Chese 13 airlines as an example, the SBM-Global model and the GML productivity index are used to discuss the trends of carbon emission efficiency from 2009 to 2013. We find that the
Z. Wang et al. / Journal of Cleaner Production 243 (2020) 118500
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Table 5 Dynamic efficiency and decomposition of carbon emissions in airlines. Airlines
indicators
①
②
③
④
Airlines
indicators
①
②
③
④
China Southern Airlines
GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC
1.159 1.359 0.853 1.130 1.000 1.130 1.230 1.107 1.112 1.354 1.000 1.354 1.046 0.862 1.213 1.120 1.000 1.120 1.000 1.000 1.000
1.194 1.000 1.194 1.065 1.000 1.065 1.024 0.959 1.068 1.000 1.000 1.000 1.245 1.252 0.994 1.348 0.843 1.600 0.922 1.000 0.922
1.171 1.000 1.171 1.000 1.000 1.000 0.906 0.913 0.992 1.000 1.000 1.000 1.003 0.986 1.018 0.899 0.939 0.958 1.084 1.000 1.084
1.000 1.000 1.000 1.000 1.000 1.000 0.991 0.983 1.008 1.000 1.000 1.000 0.966 0.999 0.967 0.810 0.799 1.015 1.000 1.000 1.000
Xinhua Airlines
GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC GML GMLEC GMLTC
1.139 1.058 1.077 1.165 1.320 0.883 1.040 1.488 0.699 1.059 0.662 1.601 0.738 1.000 0.738 e e e 1.098 1.071 1.065
1.148 1.221 0.940 1.025 1.000 1.025 1.060 0.824 1.285 1.539 1.511 1.018 1.355 1.000 1.355 1.119 0.983 1.139 1.157 1.046 1.123
0.965 0.893 1.081 0.878 0.748 1.173 0.969 0.822 1.180 0.945 1.000 0.945 1.000 1.000 1.000 1.110 0.978 1.135 0.995 0.945 1.057
0.965 1.001 0.964 1.006 1.049 0.959 0.889 0.964 0.922 0.725 1.000 0.725 0.930 1.000 0.930 1.074 1.155 0.930 0.950 0.996 0.955
Air China
China Eastern Airlines
Hainan Airlines
Sichuan Airlines
Shandong Airlines
Spring Airlines
Juneyao Airlines
Lucky Air
Shenzhen Airlines
Air Changan
Tianjin Airlines
Means
Note: ①, ②, ③, and ④ represent time periods 2009e2010, 2010e2011, 2011e2012, and 2012e2013, respectively. Table 6 Results of dynamic efficiency decomposition of carbon emissions from airlines. Airlines
GML<1
GMLEC1
GMLTC1
Main cause
China Southern Airlines Air China China Eastern Airlines Hainan Airlines Sichuan Airlines Shandong Airlines Spring Airlines Xinhua Airlines Juneyao Airlines Lucky Air Shenzhen Airlines Air Changan Tianjin Airlines
0 0 2 0 1 2 1 2 1 2 2 2 0
4 4 1 4 1 1 4 3 3 1 3 4 1
3 4 3 4 2 3 3 2 2 2 2 2 2
e e GMLEC e GMLTC GMLEC GMLTC GMLEC、GMLTC GMLEC GMLEC、GMLTC GMLTC GMLTC e
carbon emissions efficiency through different data sources. Second, it is very difficult to establish a dynamic and universally applicable carbon emission efficiency evaluation index system for airlines, which can be studied in the future. Finally, according to the results of empirical analysis, this study puts forward policy implications to improve the carbon emission efficiency of airlines. Whether these policy implications are effective in practice needs to be further tested.
Appendix A: Description of the notations.
Notations Explanation
static efficiency of carbon emissions in China's airlines shows an inverted U-shaped during the period 2009e2013. The dynamic efficiency of carbon emissions varied from airline to airline through the entire period. Employing the empirical analysis in Section 5, we draw three policy implications for improving carbon emission efficiency and achieving cleaner production are as follows: First, our study reveals that the main reason for the decline in carbon emission efficiency of China Eastern Airlines, Shandong Airlines, and Juneyao Airlines is the change in technical efficiency. These airlines can improve their carbon emission efficiency through focusing on technical efficiency. Second, our study confirms that the main reason for the decline in carbon emission efficiency of Sichuan Airlines, Spring Airlines, Shenzhen Airlines, and Air Changan is the change in technological progress. These airlines can improve technological progress to reduce carbon emissions, such as introducing new technologies. Third, our study also reveals that the main reason for the decline in carbon emission efficiency of Xinhua Airlines and Lucky Air is the change in technical efficiency and technological progress. These airlines need to improve carbon emissions efficiency in terms of both technical efficiency and technological progress. While our study provides important theoretical and managerial implications, we still recognize some limitations. First, due to the changes in the statistical caliber of statistical yearbook, the data used in the measurement of carbon emission efficiency in this study are only up to 2013. Later researchers can analyze long-term trends in
CO2 AD COF FC NCV CC OF 44/12 P t ðxt Þ yt bt
lti ytin
Carbon emission Amount of burned aviation kerosene Coefficient of carbon emissions per unit of activity level Consumption of aviation kerosene Low calorific value of aviation kerosene
44100 kJ/ kg Carbon content per unit calorific value of aviation kerosene 19.5tC/TJ Carbon oxidation rate of aviation kerosene 100% Ratio of the molecular weight of carbon dioxide to carbon Production possibility set of the t period Desirable output in period t Undesirable output in period t Weight of the i-th decision unit in the t period
xti0
Observation of the nth desirable output of the i-th decision unit during the t period Observation of the Mth input of the i-th decision unit during the t period Observation of the Jth undesirable output of the i-th decision unit during the t period Inputs
yti0
Desirable outputs
bti0 gx gy gb sxm
Undesired outputs
xtim btij
syn sbj
Values
Direction vectors of input reduction Direction vectors of desirable output increase Direction vectors of undesired output compression Slack vectors of input Slack vectors of desired output Slack vectors of undesired output
Note: Parameter values for NCV, CC, and OF are from relevant literature (IPCC, 2006). kJ/kg means kilojoule/kilogram; tC/TJ means tonne/terajoule.
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Z. Wang et al. / Journal of Cleaner Production 243 (2020) 118500
Funding This research is supported by National Social Science Fund Key Project (grant number 18AGL028); Jiangsu Provincial Social Science Fund Series (grant number 17ZTB005); Jiangsu Provincial Social Science Key Project (grant number 2018SJZDI070); and Jiangsu Provincial Social Science Key Project (grant number 2018SJZDI064). References Arjomandi, A., Dakpo, K.H., Seufert, J.H., 2018. Have Asian airlines caught up with European Airlines? A by-production efficiency analysis. Transp. Res. A Policy Pract. 116, 389e403. n-Esteve, M., Gime nez, V., Picazo-Tadeo, A.J., 2019. Environmental producBeltra tivity in the European Union: a global Luenberger-metafrontier approach. Sci. Total Environ. 692, 136e146. Boeing, 2012. Current Market Outlook Boeing. Seattle, USA. Bukovac, S., Douglas, I., 2019. The potential impact of High Speed Rail development on Australian aviation. J. Air Transp. Manag. 78, 164e174. Cao, Q., Lv, J., Zhang, J., 2015. Productivity efficiency analysis of the airlines in China after deregulation. J. Air Transp. Manag. 42, 135e140. Chen, Z., Wanke, P., Antunes, J.J.M., Zhang, N., 2017. Chinese airline efficiency under CO2 emissions and flight delays: a stochastic network DEA model. Energy Econ. 68, 89e108. Cui, Q., 2019. Investigating the airlines emission reduction through carbon trading under CNG2020 strategy via a Network Weak Disposability DEA. Energy 180, 763e771. Cui, Q., Li, Y., 2015a. An empirical study on the influencing factors of transportation carbon efficiency: evidences from fifteen countries. Appl. Energy 141, 209e217. Cui, Q., Li, Y., 2015b. Evaluating energy efficiency for airlines: an application of VFBDEA. J. Air Transp. Manag. 44e45, 34e41. Fu, X., Zhang, A., Lei, Z., 2012. Will China's airline industry survive the entry of highspeed rail? Res. Transp. Econ. 35, 13e25. IPCC, 2006. IPCC Guidelines for National Greenhouse Gas Inventories. Global Environmental Strategy Institute, Japan. Jovanovic, M., Vracarevic, B., 2016. Challenges Ahead: mitigating air transport carbon emissions. Pol. J. Environ. Stud. 25, 1975e1984. Lazaro, A.L., Perez-Campuzano, D., Benito, A., Alonso, G., 2019. Analyzing carbon neutral growth and biofuel economic impact for 2017-2025: a case study based on Spanish carriers. J. Aerosp. Eng. 233, 1943e1959. Li, Y., Chiu, Y.H., Lu, L.C., Chiu, C.R., 2019. Evaluation of energy efficiency and air pollutant emissions in Chinese provinces. Energy Effic. 12 (4), 963e977. Lin, B., Zhu, J., 2019. The role of renewable energy technological innovation on climate change: empirical evidence from China. Sci. Total Environ. 659, 1505e1512. Liu, X., Zhou, D., Zhou, P., Wang, Q., 2017. Dynamic carbon emission performance of Chinese airlines: a global Malmquist index analysis. J. Air Transp. Manag. 65, 99e109. Oh, D.H., 2010. A metafrontier approach for measuring an environmentally sensitive
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Dr Zilong Wang is a Professor at the Nanjing University of Aeronautics and Astronautics, from where he has also received his Ph.D. He previously did his post doctorate at the Peking University. His research interest focuses on industrial economics and management. He has received several funding from important Chinese research institutions as the National Natural Science Foundation of China and Aeronautics Science Foundation. He has published more than 60 articles and Scholarly Monographs.
Dr Xiaodi Xu is an associate Professor at the Nanjing Normal University. She obtained her Doctor of Management from the Nanjing University of Aeronautics and Astronautics. She has received a research funding from important Chinese research institutions as the National Natural Science Foundation of China. Her research interest focuses on Knowledge management.
Yongfeng Zhu is a Ph.D candidate in College of Economics and Management, Nanjing University of Aeronautics and Astronautics, China. She is interested in the regional innovation and ecological economy.
Tian Gan is a graduate student in College of Economics and Management, Nanjing University of Aeronautics and Astronautics, China. She is interested in the energy environment.