What drives CO2 emissions from China’s civil aviation? An exploration using a new generalized PDA method

Transportation Research Part A 99 (2017) 30–45

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Transportation Research Part A journal homepage: www.elsevier.com/locate/tra

What drives CO2 emissions from China’s civil aviation? An exploration using a new generalized PDA method Xiao Liu, Dequn Zhou, Peng Zhou, Qunwei Wang ⇑ College of Economics and Management, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Nanjing 211106, China Research Centre for Soft Energy Science, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Nanjing 211106, China

a r t i c l e

i n f o

Article history: Received 4 November 2016 Received in revised form 24 January 2017 Accepted 6 March 2017

Keywords: Carbon dioxide Driving factors Airlines PDA

a b s t r a c t Understanding the main drivers contributing to the increased CO2 emitted by airlines can inform carbon-reduction policies for the civil aviation sector. Production decomposition analysis (PDA) is a theoretical tool widely used to investigate the factors influencing changes in CO2 emissions. Instead of the standard constant returns to scale assumption, the study proposes a new generalized PDA method that considers the influence of changes in scale efficiency. The study used a panel data set for China’s airlines during the period of 2007–2013 to conduct an empirical analysis and generate meaningful results. First, it was found that changes in Revenue Ton Kilometers is the largest factor contributing to increased civil aviation CO2 emissions. Second, changes in potential energy intensity play a dominant role in decreasing CO2 emissions for most airlines. Third, changes in production technology effects exert a relatively small influence on CO2 emissions, and the effect of scale efficiency change positively contributes to curbing CO2 emissions. Based on the PDA analysis, we propose policy implications related to civil aviation of China. Ó 2017 Elsevier Ltd. All rights reserved.

1. Introduction Climate change motivates the reduction of Greenhouse Gas (GHG) emissions across all sectors. Global airlines consume more than 5 million barrels of oil per day (Grote et al., 2014), resulting in significant CO2 emissions by aircraft. Aviation CO2 emissions currently contribute relatively little to climate change, comprising only approximately 3% of global emissions (Karen and Richard, 2010; Owen et al., 2010). However, as marginal fuel efficiency improvements decrease and the demand for air transportation increase, global air traffic has doubled in size every 15 years since 1977. The International Civil Aviation Organization (ICAO, 2013) estimates that total transport volumes in 2020 will be 2.5–3 times the 2010 level. This suggests that the aviation sector will contribute more to climate change in the future. Based on forecasts from Boeing (2012), CO2 emissions from global aviation are expected to increase to 1.23–1.49 billion tons in 2025. Given this, aviation could become a progressively greater CO2 emissions source (Bows and Anderson, 2007). Meanwhile, reforms in China have created great opportunities for the development of China’s civil aviation. China’s civil aviation revenue has increased from 0.37 billion yuan in 1980 to 366.38 billion yuan in 2013, representing an average annual growth rate of 23.2%. In the same period, the sector’s Revenue Ton Kilometers (RTK), reflecting the total air transportation turnover volume, has increased from 1271.02 million RTKs to 67172.31 million RTKs, at an average annual growth rate of ⇑ Corresponding author at: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Nanjing 211106, China. E-mail address: [email protected] (Q. Wang). http://dx.doi.org/10.1016/j.tra.2017.03.002 0965-8564/Ó 2017 Elsevier Ltd. All rights reserved.

X. Liu et al. / Transportation Research Part A 99 (2017) 30–45

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15.2% (SDCAC, 2014). For several years, China has been ranked second in the world for RTK (Zhou et al., 2016). The civil aviation industry has stimulated China’s economic growth, representing 1.03% of the country’s Gross Domestic Product (GDP) in 2013 (SDCAC, 2014). This growth in civil aviation has, however, led to increased CO2 emissions and significant environmental and climate concerns. CO2 emitted from China’s civil aviation reached 62.94 million tons in 2013, accounting for almost 1% of national CO2 emissions (ICAO, 2013). In 2014, the Commercial Aircraft Corporation of China projects that the Revenue Passenger Kilometers (RPK) metric will increase by 4.8% a year across the total aviation industry for the next 20 years. This means the total passenger transport demand will be 2.6 times the current level. Because of the large and continuing growth in the scale of the air transport system, the resulting CO2 emitted by airline is expected to significantly increase in China. Focusing on the building a more ‘‘green” civil aviation sector requires adhering to basic principles of energy conservation and environmental protection. For the 12th Five-Year Plan period (2011–2015), the Civil Aviation Administration of China set a target that the average CO2 emissions per unit RTK be reduced by 3% compared to the 11th Five-Year Plan period (2006– 2010). If these targets are met, by 2020, CO2 emissions from civil aviation should be 22% lower than in 2005. Reaching these goals requires policies that focus on reducing civil aviation CO2 emissions. However, scientifically-based and effective reduction policies are only possible if we understand the mechanisms driving the CO2 emitted by airlines. As such, this study developed a new generalized production theoretical decomposition analysis (PDA) method, under the variant returns to scale (VRS) framework, to analyze trends in China’s airlines CO2 emission changes and their underlying factors. This paper is organized as follows. Section 2 reviews the relevant literature, focusing on the research method and its application in the aviation sector. Section 3 details the decomposition of civil aviation CO2 emissions. Section 4 discusses the results. Section 5 concludes the study, and summarizes policy recommendations based on the analysis.

2. Literature review Many studies have found that GHG concentrations in the Earth’s atmosphere have been increasing due to human activities (Tyteca, 1996; Oreskes, 2004; Wang, 2007; Andreoni and Galmarini, 2012).The civil aviation sector is becoming a progressively important CO2 emissions source. Previous literature has focused on three aspects of civil aviation carbon emissions. The first set of studies has focused on carbon accounting in both the cruising phase (Williams et al., 2002) and the landing and take-off (LTO) phase (Masiol and Harrison, 2014). For example, He (2011) used a fuel-based top-down method to estimate CO2 emissions from aircraft in China’s civil aviation sector between 1960 and 2009. Fan et al. (2010) inventoried several pollutants within China’s aviation industry in 2010, including CO, NOx, CO2 and SO2, during both the cruising and LTO stages. These studies have generally used top-down methods (based on the fuel consumption) due to data availability. However, calculation boundaries and data sources differed, yielding slightly different results. The second set of studies has evaluated the impact of carbon emissions on the environment. Airline technologies rely on fossil fuel combustion, which emit combustion products primarily at cruise altitudes. These emissions affect the environment differently than emissions from fossil-fuel combustion at the earth’s surface. In addition, aviation operations cause changes in cloudiness by forming contrail and contrail cirrus clouds (ICAO, 2013). Lee et al. (2009) found that total aviation-driven radiative forcing (excluding induced cirrus) in 2005 was 3.5% of total anthropogenic forcing. The third set of studies measure carbon emissions in the changing civil aviation industry, including examining changes in technology (King et al., 2010) and sustainable alternative fuels (Loo and Li, 2012). Zhou et al. (2016) developed scenarios for CO2 emissions from China’s civil aviation industry through 2030. The results show that technological improvements in fuel intensity and the adoption of biomass-based fuel will likely fail to achieve the ambitious targets for this industry without a disruptive technological breakthrough. Research on civil aviation carbon emissions has been conducted over many years, resulting in fruitful insights. However, few studies have used influencing factor analysis (IFA). Research on the factors driving civil aviation carbon emission changes can inform new carbon emission reduction policies and help develop a low-carbon economy (Long et al., 2015). Existing studies using IFA suggest that economic growth and technological progress are the two main factors affecting civil aviation CO2 emissions. Examples of this work include papers by Sgouridis et al. (2011), and Andreoni and Galmarini (2012). Sgouridis et al. (2011) used an econometric method to examine five generic factors for reducing civil aviation emissions. The study found that four policies could significantly impact capacity and emissions: technological and operations efficiency improvements; biofuel use; moderate carbon pricing; and transitions to short-haul demand. Combined, these policies could achieve a 140% increase in capacity by 2024 compared to 2004, while only increasing carbon emissions by 20% compared to 2004. Andreoni and Galmarini (2012) investigated the main factors influencing CO2 emissions from European aviation activities for the period 2001–2008. The economic growth effect contributed the most to increased CO2 emissions in 27 European countries. The studies above demonstrate that IFA can be successfully used to study civil aviation CO2 emissions. An important branch of IFA involves decomposing CO2 emissions growth into possible influencing factors. Decomposition analysis is conducted by using an identical equation to decompose the changes of CO2 emission into several pre-defined factors (Ang, 2004). Structural decomposition analysis (SDA) and index decomposition analysis (IDA) are the two main approaches for decomposing changes in CO2 emissions; both have been successfully used in previous studies to quantify impact factors (Ang and Zhang, 2000; Wang et al., 2005; Zhang et al., 2009; Su and Ang, 2012; Wang et al., 2016a, 2016b).

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SDA uses data from an input-output (IO) table and analyzes how production categories directly or indirectly effect CO2 emissions. Typical SDA studies provide details about structural factors, such as the technical effect (Leontief effect), and can shape socio-economic drivers from both production and final demand perspectives(Cansino et al., 2016;Wang et al., 2013; Butnar and Llop, 2011). SDA mainly uses an additive form (Su and Ang, 2012). Researchers have recently improved the multiplicative SDA method. For example, Su and Ang (2014, 2015) introduced attribution analysis into multiplicative SDA, and analyzed aggregate carbon intensity changes at the country level. Compared to SDA, IDA is a more widely accepted decomposition tool for informing national environmental policy, because it can be adopted more flexibly and requires less data (Ang and Zhang, 2000). Popular IDA methods include two indices: the Laspeyres index and the Divisia index. The Laspeyres index measures the percentage change in some aspect of a group of items over time, using weights based on values from defined base years (Hatzigeorgiou et al., 2008; Andreoni and Galmarini, 2012). In contrast, the Divisia index is a weighted sum of logarithmic growth rates, where the weights are the components’ shares in total value, in the form of a line integral (Ang, 2004; Ang and Liu, 2007; Dong et al., 2013). In addition to SDA and IDA approaches, several studies have applied an emerging decomposition technique that uses the distance function estimated through data envelopment analysis (DEA) (Pasurka, 2006; Wang, 2007). These methods separately assess technical efficiency effects, technological change effects, and the effect of production technology. This is done by combining decomposition analysis with DEA. Zhou and Ang (2008) named this decomposition technique PDA. PDA combines the characteristics of IDA and environmental production technology, embedding a nonparametric distance function into the decomposition formula. This allows a contribution analysis based on potential efficiency and intensity factors. PDA has several advantages compared to SDA and IDA (Li, 2010; Wang et al., 2015). For example, in terms of data requirements, PDA uses panel aggregate data instead of sector data; panel aggregate data are easier to collect. In addition, PDA separately assesses technical efficiency effects and technological effects. Finally, as suggested in a survey by Ang and Zhang (2000), PDA satisfies three key criteria tests: time-reversal, factor-reversal, and zero-value robust. In contrast, only 3 of the ten SDA/IDA decomposition methods investigated by the survey passed all tests. Existing PDA studies mainly focus on applied research (Zhou and Ang, 2008; Zhang et al., 2012) and model modification (Lin and Du, 2014; Wang et al., 2015). For example, in applied research, Zhou and Ang (2008) decomposed CO2 emissions in world regions and OECD countries. Li (2010) constructed a PDA approach to assess the contribution of seven factors to CO2 emissions, using the Shephard input distance function. Zhang et al. (2012) proposed an alternative PDA decomposition model, and applied it to empirically analyze 20 developed countries. Kim and Kim (2012) analyzed worldwide CO2 emissions by concentrating on production technologies. This provided more details about the influence of both production technical efficiency and technological change on CO2 emissions. Lin and Du (2014) introduced the logarithmic mean Divisia index method into a production decomposition analysis framework to combine different contributing factors. Wang et al. (2015) investigated the challenge associated with the infeasibility of DEA liner programming, by modifying the PDA approach and applying it to analyze the driving factors of CO2 emissions in China. All these studies uphold the original characterization of PDA, and assume that production technology exhibits constant returns to scale (CRS). In reality, VRS and other cases are likely to be seen (Zhou et al., 2008). When undesirable outputs exist, it is unrealistic to simply impose additional constraints, as with traditional DEA models, because that may violate the basis of environmental DEA technology. The new generalized PDA method proposed in this study may address this problem. This study’s proposed PDA can be applied to study the CO2 emissions of Chinese civil aviation. Civil aviation operation systems include source inputs, operating processes, and final outputs. This defines CO2 emissions from airlines as an input-output production process (Sgouridis et al., 2011; Cui and Li, 2015; Zhou et al., 2016). Further, the PDA method allows the separate assessment of technical efficiency and technological change, shaped by production technology. As such, the PDA method can analyze two important aspects of civil aviation production processes. First, it addresses how well each airline innovates new, advanced, energy saving, and emission reduction technology. Second, it addresses how efficiently each airline uses relevant technologies and information. PDA data requirements also make the method attractive for studying airline CO2 emissions, compared to other approaches (Zhou and Ang, 2008). This is because the indicators of civil aviation sector data and indicators are incomplete, and involve synchronously expanding samples across time and section dimensions. This leads to the lower statistical quality of the data. In addition, China’s rapid expansion of civil aviation suggests that its real production process is most likely consistent with VRS. The method in this study proposes that production technology exhibits VRS, allowing the further decomposition and analysis of scale efficiency change effects of China’s civil aviation CO2 emissions.

3. Methodology and data 3.1. Inputs and outputs In general, efficiency refers to technical efficiency; it is defined as the capacity to optimally use existing resources. In other words, it is the capacity to realize maximum outputs when inputs are fixed, or the capacity to require few inputs when the outputs are fixed.

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X. Liu et al. / Transportation Research Part A 99 (2017) 30–45 Table 1 Inputs and outputs in selected airline efficiency studies. Paper

Research object

Input variable

Output variable

Good et al. (1993)

The performance of the 8 largest European airlines and the 8 largest American airlines 1976–1986 The technical efficiencies of 11 U.S. airlines 1970–1990 An evaluation of European airlines’ operational performance Technical efficiency of mainstream airlines and low-cost carriers The energy efficiency of 11 airlines from 2008 to 2012

Labor, Materials, Flight equipment

Revenue (passenger service, cargo service and incidental service)

Flight capital, Labor, Energy, Materials Labor, Operational cost, number of planes Kilometers flown, Labor, Total assets Labor, Capital, Energy

Revenue ton miles

Alam and Sickles (1998) Barros et al. (2013) Lee and Worthington (2014) Cui et al. (2016)

Scottia and Voltab (2015) Cui and Li (2015)

Cao et al. (2015)

An empirical assessment of the CO2 sensitive productivity of European airlines 2000–2010 The change trend and influencing factors of civil aviation safety efficiency The productivity efficiency of the airlines in China after deregulation

Scheduled available seats, Available freight ton km Labor, Capital, R&D investments, Safety software and staffs investments Labor, Fuel, Number of aircrafts

Revenue passenger km, Earnings before interest and taxes Available ton km Revenue ton km, Revenue passenger km, Total business income and CO2 emissions Revenue passenger km, Scheduled total freight ton km, CO2 emissions Revenue ton km, Revenue passenger km, Total business income and CO2 emissions Total flight, Revenue ton km of passengers and freight

This study considered the actual operating and production process associated with civil aviation CO2 emissions, as well as insights from the previous studies listed in Table 1.The study modeled fixed asset investment (K) and the amount of labor (L) as the inputs; RTK (Y) as the desirable output; and CO2 emissions (C) as the undesirable output. The study assumes that the fixed asset investment, the aggregate labor, the aggregate CO2 emissions, and the aggregate RTK of a certain entity, i.e. entity i, varies from K 0i ; L0i ; Y 0i ; C 0i in the period 0 to K Ti ; LTi ; Y Ti ; C Ti in time period T. Together, these inputs impact civil aviation CO2 emissions. 3.2. A new generalized PDA method Undesirable outputs are always accompanied by desirable outputs in China’s civil aviation production processes. For each time period, the production technology is described as the following set:

S ¼ fðK; L; Y; CÞ : ðK; LÞ can produce Y and Cg Assume there are m ¼ 1; 2; . . . ; M entities. For entity m, the observed dates are K m ; Lm ; Y m ; C m . Based on this, the piecewise linear production technology S is formulated as:

S ¼ fðK; L; Y; CÞ :

M X zi K i 6 K m i¼1 M X zi L i 6 L m i¼1 M X zi Y i P Y m

ð1Þ

i¼1 M X zi C i ¼ C m i¼1

zi P 0; i ¼ 1; 2; . . . ; Mg Some past studies (Färe et al., 1994; Zhou and Ang, 2008; Kim and Kim, 2012), refer to S as the environmental DEA technology exhibiting CRS and zi as an intensity variable. As mentioned in the literature review, VRS is more likely in real production processes (Färe and Grosskopf, 2004; Zhou et al., 2008), therefore, this study constructs environmental DEA technology with VRS as a key factor. Färe and Grosskopf (2003), Kuosmanen (2005), and Zhou and Ang (2008) used the nonparametric activity analysis frame to research this question; their work outlined possible approaches to solve the problem. A tradeoff between the traditional DEA forms and the output set concept is needed to further identify the impact of scale efficiency on CO2 emissions. Based on Zhou et al. (2008), the closed and bounded set Sis modified to satisfy the following two properties:

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(i) Outputs are weakly disposable, i.e., if ðK; L; Y; CÞ 2 S and 0 < h 6 1, then ðK; L; hY; hCÞ 2 S. (ii) Desirable and undesirable outputs are null-joint, i.e., if ðK; L; Y; CÞ 2 S and C ! 0, then Y ! 0. Here, h is the correction coefficient. The first modified assumption states that outputs are weakly disposable. The second modified assumption states that desirable outputs must be infinitesimal, if undesirable outputs are also infinitesimal. There is no material difference between the traditional assumption and the modified assumption, except that the production possibility set of the modified assumption excludes the point (0, 0, 0, 0) (Zhou et al., 2008). As such, the modified production possibility set can still be treated as the traditional environmental production possibility set. Therefore, the piecewise linear polluting technology of VRS environmental DEA Sv rs is formulated as1:

Sv rs ¼ fðK; L; Y; CÞ :

M X zi K i 6 K i¼1 M X

zi Li 6 L

i¼1 M X

zi Y i P aY

ð2Þ

i¼1 M X

zi C i ¼ aC

i¼1 M X

zi ¼ 1

i¼1

a P 1; zi P 0; i ¼ 1; 2; . . . ; Mg Now, assume that the aggregate CO2 emissions of a certain airline, i.e. the ith airline, vary from C 0i in time period 0 to C Ti in time period T. This change is expressed in the following multiplicative form:

Di ¼

C Ti C 0i

¼

C Ti =ETi C 0i =E0i

! 

ETi =Y Ti E0i =Y 0i

! 

Y Ti

! ð3Þ

Y 0i

Using the production technology, and further considering the arbitrary nature of the choice of reference technology, the change in the aggregate CO2 emissions for entity i can be decomposed as follows2:

Di ¼ ¼

C Ti C 0i C Ti =ETi

!

C 0i =E0i 1=2



ETi =ðY Ti ½DTYcrs ðK Ti ; LTi ; Y Ti ; C Ti ÞD0Ycrs ðK Ti ; LTi ; Y Ti ; C Ti Þ

1=2

E0i =ðY 0i ½D0Ycrs ðK 0i ; L0i ; Y 0i ; C 0i ÞDTYcrs ðK 0i ; L0i ; Y 0i ; C 0i Þ " #1=2 D0Ycrs ðK Ti ; LTi ; Y Ti ; C Ti Þ DTYcrs ðK Ti ; LTi ; Y Ti ; C Ti Þ   D0Ycrs ðK 0i ; L0i ; Y 0i ; C 0i Þ DTYcrs ðK 0i ; L0i ; Y 0i ; C 0i Þ ! Y Ti  Y 0i

Þ

! ð4Þ

Þ

If there are no inefficiencies associated with the observations, all the distance functions equal unity, and Eq. (4) collapses   C T =ET to Eq. (3). On the right-hand side of Eq. (4), the first component Ci0 =Ei0 accounts for the CO2 emission factor change (CFCHi). i

i

Because of the single energy consumption structure in the civil aviation sector, the CFCHi is generally assumed to be constant over time (Liu et al., 2007; Zhang et al., 2009; Wang et al., 2016b). The other determinant factors are: 1 According to Färe and Grosskopf (2004) and Zhou et al. (2008), the VRS environmental DEA technology may be obtained by multiplying the right-hand side of undesirable outputs constraints by an adjusting parameter (a) not less than 1. 2 To avoid the arbitrary nature of choosing one of the two reference technologies, Eq. (4) was handled using the geometric mean method.

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 (i) Intensity effect: based on Zhou and Ang (2008), the second component

ETi =ðY Ti ½DTYcrs ðK Ti ;LTi ;Y Ti ;C Ti ÞD0Ycrs ðK Ti ;LTi ;Y Ti ;C Ti Þ

1=2



Þ

1=2 E0i =ðY 0i ½D0Ycrs ðK 0i ;L0i ;Y 0i ;C 0i ÞDTYcrs ðK 0i ;L0i ;Y 0i ;C 0i Þ Þ

is inter-

3

preted as the potential energy intensity change (PEICHi) ;



(ii) Production technology effect: essentially, the third component

D0Ycrs ðK Ti ;LTi ;Y Ti ;C Ti Þ D0Ycrs ðK 0i ;L0i ;Y 0i ;C 0i Þ

DT

1=2

ðK T ;LT ;Y T ;C T Þ

i i i i  DYcrs T ðK 0 ;L0 ;Y 0 ;C 0 Þ Ycrs

i

i

i

is a Malmquist index

i

number, measuring the change of output production performance (OPPCHi);   YT (iii) RTK effect: the fourth component Y i0 accounts for the change of Revenue Ton Kilometers transportation (RTKCHi). i

Generally, the third component in Eq. (4) (OPPCHi) is a Malmquist productivity index. Based on Färe et al. (1994), it is further decomposed as:

" #1=2 D0Ycrs ðK Ti ; LTi ; Y Ti ; C Ti Þ DTYcrs ðK Ti ; LTi ; Y Ti ; C Ti Þ  D0Ycrs ðK 0i ; L0i ; Y 0i ; C 0i Þ DTYcrs ðK 0i ; L0i ; Y 0i ; C 0i Þ ! 0" #1=2 1 DTYv rs ðK Ti ; LTi ; Y Ti ; C Ti Þ D0Ycrs ðK Ti ; LTi ; Y Ti ; C Ti Þ D0Ycrs ðK 0i ; L0i ; Y 0i ; C 0i Þ @ A  ¼  DTYcrs ðK Ti ; LTi ; Y Ti ; C Ti Þ DTYcrs ðK 0i ; L0i ; Y 0i ; C 0i Þ D0Yv rs ðK 0i ; L0i ; Y 0i ; C 0i Þ , ! D0Yv rs ðK 0i ; L0i ; Y 0i ; C 0i Þ DTYv rs ðK Ti ; LTi ; Y Ti ; C Ti Þ  DTYcrs ðK Ti ; LTi ; Y Ti ; C Ti Þ D0Ycrs ðK 0i ; L0i ; Y 0i ; C 0i Þ

OPPCHi ¼

ð5Þ

On the right-hand side of Eq. (5), the first term is the effect of pure technical efficiency change (PTECHi); the second term is the effect of technological progress change (TPCHi); and the third term is the effect of scale efficiency change (SECHi).4 The fourth component in Eq. (4) (RTKCHi) can be further decomposed as:

RTKCHi ¼

Y Ti Y 0i

¼

TV Ti

TDTi

TV i

TD0i

 0

ð6Þ

On the right-hand side of Eq. (6), the first term is the effect of transport volume change (TVCHi); the second term is the effect of transport distance change (TDCHi). The intrinsic factors triggering the ith airline CO2 emission change can be decomposed as:

Di ¼ C Ti =C 0i ¼ PEICHi  PTECHi  TPCHi  SECHi  TVCHi  TDCHi (DsYcrs ðK ti ; Lti ; Y ti ; C ti Þ

ð7Þ

(DsYv rs ðK ti ; Lti ; Y ti ; C ti Þ)

The distance functions and can be estimated using the production technology in a certain time period s; t 2 f0; Tg. Using the definitions of these distance functions and the DEA technology, this study derived these factors by solving the following DEA type model:

½DsYv rs ðK ti ; Lti ; Y ti ; C ti Þ

1

¼ max g

s:t:

M X zi K si 6 bK ti i¼1 M X zi Lsi 6 bLti i¼1 M X zi Y si P gY ti

ð8Þ

i¼1 M X zi C si ¼ C ti i¼1 M X zi ¼ b i¼1

zi P 0; i ¼ 1; . . . ; M

3 Energy intensity was denoted as E/Y in this paper, where E means energy consumption, Y means the RTK production. Potential energy intensity is a hypothetical variable, and refers to the energy intensity based on output production (Zhou and Ang, 2008). In other words, the potential energy intensity is a kind of adjusted energy intensity.In the real production process, inefficiency in output production will result in the observed Y being smaller than that when there is no inefficiency. The value of Y will be changed if the output efficiency is improved from period 0 to period t. This results in the calculation of the potential energy intensity (E/Y). 4 Specifically, ‘‘pure technical efficiency” represents the relative distance between the sample point and the envelope line. This assumes that the production process exhibits VRS. Meanwhile, ‘‘technical efficiency” represents the relative distance between the sample point and the envelope line, assuming CRS. Based on these, scale efficiency can be expressed as the ratio of technical efficiency and pure technical efficiency.

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Table 2 Statistical descriptions of input and output variables.

Capital (K) Labor (L) RTK (Y) CO2 (C)

Plane 104 people Million ton-kilometers 104 tons

Sample size

Mean

Std. D.

Max

Min

12  7 12  7 12  7 12  7

100.07 1.75 2736.57 314.99

140.05 2.59 3863.14 456.60

500.00 9.39 133991.09 1647.46

2.00 0.016 2.54 0.45

Table 3 Sample airlines of China in the study. Type

Airline

Nature

ICAO code

Central airlines

Air China China Southern Airlines China Eastern Airlines

State-owned (listed) State-owned (listed) State-owned (listed)

CCA CSN CES

Local airlines

China Postal Airlines Hebei Airlines Hainan Airlines Sichuan Airlines

State-owned State-owned State-owned (listed) State-owned

CYZ HBH CHH CSC

Private & joint airlines

Spring Airlines Okay Airways China Express Airlines Juneyao Airlines Donghai Airlines

Non-state-owned (listed) Non-state-owned Non-state-owned Non-state-owned (listed) Non-state-owned

CQH OKA HXA DKH EPA

P The constraint M i¼1 zi ¼ b allows the production process to exhibit VRS (for details see Zhou et al. (2008)); however, the efficiency value under CRS conditions is also needed to further identify scale efficiency. This value is generated using the following DEA type model: 1

½DsYcrs ðK ti ; Lti ; Y ti ; C ti Þ

¼ max g

s:t:

M X zi K si 6 bK ti i¼1 M X zi Lsi 6 bLti i¼1 M X zi Y si P gY ti

ð9Þ

i¼1 M X zi C si ¼ C ti i¼1

zi P 0; i ¼ 1; . . . ; M For each influencing factor, if each component’s value is equal to one, there is no change of CO2 emissions. If the value is greater than one, the factor increases CO2 emissions. If the value is less than one, the factor contributes to a decrease in CO2 emissions. 3.3. Sample and data The study included 12 airlines in China from 2007 to 2013: Air China (CCA), China Southern Airlines (CSN), China Eastern Airlines (CES), China Postal Airlines (CYZ), Hebei Airlines (HBH), Hainan Airlines (CHH), Sichuan Airlines (CSC), Spring Airlines (CQH), Okay Airways (OKA), China Express Airlines (HXA), Juneyao Airlines (DKH), and Donghai Airlines (EPA). In this paper, labor (number of employed people) and fixed asset investments (total number of aircrafts) are treated as inputs.5 The RTK and CO2 emissions (proportional to the fuel used by a factor of approximately 3.15, in 104t) are treated as desirable and undesirable outputs, respectively. The data of input-output associated with the studied airlines were collected from the Statistical Data on Civil Aviation of China (2008–2014) and each airline’s annual report (2007–2013). Table 2 provides a statistical description of the input and output variables. 5 The fixed asset investments in civil aviation include the airport construction investment, air traffic management construction investments, and other variables (ICAO, 2013). However, for any specific airline company, the aircraft is the most important fixed asset. Therefore, the number of aircraft is a good representation of the fixed capital investment of each airline company (Cao et al., 2015).

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X. Liu et al. / Transportation Research Part A 99 (2017) 30–45

RTK

CO2

90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

2007

2008

2009

2010

2011

2012

2013

Fig. 1. RTK and CO2 emissions of the 12 airlines, as a percentage of the total industry.

Table 4 Cumulative decomposition results for each airline from 2007 to 2013. Airline

CCA CSN CES CYZ HBH CHH CSC CQH OKA HXA DKH EPA Mean

CT/C0

1.3249 1.7407 1.5149 1.4683 50.001 2.3042 2.9341 5.0689 2.8284 2.9113 7.6565 4.4442 2.6705

Intensity effect

Production technology effect

PEICH

PTECH

TPCH

SECH

TVCH

RTK effect TDCH

0.7475 0.8379 0.9008 1.2716 – 0.8630 1.0558 0.9174 0.8393 0.7260 0.8045 0.5856 0.8522

1.1079 1.0466 1.0093 0.9583 0.6967 1.0312 0.9324 1.0000 1.0900 1.2184 1.0631 1.0000 1.0389

0.9896 0.9961 0.9681 1.0440 – 1.1320 1.0340 1.0932 1.0933 1.0904 1.0800 1.0230 1.0461

1.0000 1.0000 1.0000 0.8864 1.2104 1.0000 1.0000 1.0000 0.9590 0.9218 1.0048 1.2507 0.9986

1.4556 1.5390 1.5026 1.2714 116.99 2.0515 2.3101 4.4309 3.3725 2.8804 7.0885 4.7237 2.5468

1.0528 1.2355 1.0622 1.0242 0.6169 1.2091 1.2360 1.1942 0.9152 1.1848 1.2045 1.2281 1.1356

Note: HBH is excluded in the calculation of geometric mean because of the infeasible linear programming (LP) problemsaand airline-specific values (corporate reorganization). a Zhou et al. (2010) and Wang et al. (2015) indicated that the LP infeasibility is caused by the weak disposability assumption. Färe et al. (2007) suggested that when there are too many infeasible LP problems, a ‘‘windows” approach to establish the production frontier may reduce this problem.

These 12 airlines were chosen for two reasons. First, empirical applications are more successful when there are more decision-making units(DMUs) (in this case, the airlines) than the products, and when the DMU number is at least two times larger than the sum of the number of inputs and outputs (12 > 8). DMU selection also depends on input and output data availability. Differences in statistical quality and indicators make data searches on each airline difficult, particularly when synchronously expanding samples from time and section dimensions. Second, as Table 3 shows, the 12 selected airlines represent China’s civil aviation sector, including: Central airlines (3), Local airlines (4), and Private and joint airlines (5). Of the 12 total, 7 airlines are state-owned and 5 are non-state-owned. In addition, China has 6 listed airline companies; they are all in the sample. Studying the factors that affect CO2 emissions from the 12 sampled airlines effectively represents China’s civil aviation CO2 emissions overall. Fig. 1 provides RTK and CO2 data, and demonstrates that the 12 airlines represent the total civil aviation industry. RTK levels for the sampled airlines account for more than 60% of the total, and CO2 emissions for the sample account for almost 80% of the total. 4. Results and discussion 4.1. Overview of CO2 change and its decomposition Several factors driving CO2 emissions across the 12 airlines were calculated using Eqs. (5) and (6). These included: PEICH (the potential energy intensity change), PTECH (the pure technical efficiency change), TPCH (technological progress change), SECH (scale efficiency change), TVCH (transport volume change), and TDCH (transport distance change). The decomposition analysis was conducted for each 2-year period between 2007 and 2013. This resulted in six 2-year periods for each airline.

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X. Liu et al. / Transportation Research Part A 99 (2017) 30–45

Fig. 2. The envelope diagram of driving factors in (a) Central airlines, (b) Local airlines, (c) Private and joint airlines, and (d) the geometric mean of whole scale.

Detailed results obtained from the decomposition are presented in the appendix. Table 4 presents the cumulative decomposition results for each airline. Despite variations, each airline experienced an overall increase in aggregate CO2 emissions. Table 4 shows that PEICH and SECH helped decrease civil aviation CO2 emissions; PEICH was the dominant factor. Driving factors that increased civil aviation CO2 emissions from 2007 to 2013 included PTECH, TPCH,TVCH, and TDCH; TVCH was the critical factor driving emissions growth.   C T =ET The first component Ci0 =Ei0 , on the right-hand side of Eq. (4), is the CO2 emission factor. It is assumed to be constant i

i

because of the particular type of energy consumption over time. Adjusting the energy structure adds another layer to the carbon emission reductions. Biomass feedstock and industrial waste carbon emissions ultimately return to their source material, neutralizing the CO2 emissions from alternative fuels. Significant studies have shown how using biofuels and synthetic fuels in place of common jet fuels provide environmental benefits associated with reducing CO2 emissions (Ma and Stern, 2008; Cai et al., 2011). Fig. 2 shows the changes in aggregated CO2 emissions and the decomposition across the 12 airlines and three different airline types: central, local, and private and joint. Carbon emissions associated with the central airlines (CCA, CSN and CES) become significantly higher than those associated with the other airlines. The CO2 emissions from the central airlines increased 53.07% from 26.52 Mt in 2007, to 40.61 Mt in 2013. Among these, CSN generated the most CO2 emissions, with strong growth throughout the study period. During the same timeframe, the local airlines and the private and joint airlines saw emissions triple, from 2.97 Mt to 9.02 Mt. This indicates that small and medium-size airlines experienced high-speed growth; this was particularly true for the private and joint airlines EPA, DKH and CQH. 4.2. Decomposition analysis 4.2.1. Intensity effect PEICH measures the impact of energy intensity change on CO2 emissions, without considering the inefficiency of the RTKproduction technology. In other words, the potential energy intensity is an adjusted energy intensity, and evaluates the input-output characteristics of an energy system. In this study, that involves capturing energy consumption per unit of RTK, reflecting the overall efficiency of energy and RTK activity. Table 4 shows that PEICH played a dominant role in decreasing CO2 emissions for most airlines during the study period. Airline EPA showed the best PEICH performance, followed by CCA and HXA. The cumulative value of PEICH ranged from 0.5856 to 1.2716, with a geometric mean of 0.8522. The geometric cumulative effect of PEICH was a decrease of 18.63%.This

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X. Liu et al. / Transportation Research Part A 99 (2017) 30–45

60% 40% 20% 0% -20% -40% -60% -80%

2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 2012-2013 CCA

CSN

CES

CYZ

HBH

CHH

CQH

OKA

HXA

DKH

EPA

Mean

CSC

Fig. 3. Contributions of the potential energy intensity effect influencing CO2 emissions for 12 airlines from 2007 to 2013.

1.26 1.24

Unit: Kg/t.km

1.22 1.20 1.18 1.16 1.14 1.12 1.10

2007

2008

2009

2010

2011

2012

2013

Fig. 4. Energy intensity for 12 airlines from 2007 to 2013.

40% 30% 20% 10% 0% -10% -20% -30%

2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 2012-2013 CCA

CSN

CES

CYZ

HBH

CHH

CQH

OKA

HXA

DKH

EPA

Mean

CSC

Fig. 5. Contributions of production technology effect on CO2 emissions for 12 airlines from 2007 to 2013.

finding aligns with other studies that have decomposed CO2 intensity or emissions; the decline of energy intensity is a significant force driving decreases in CO2 intensity or emissions (Zhou and Ang, 2008; Lin and Du, 2014; Wang et al., 2015). Fig. 3 shows the potential energy intensity effect influencing CO2 emissions from the 12 airlines from 2007 to 2013.PEICH had a decreasing effect on airline carbon emissions, particularly after 2011. This downward trend is mainly due to changes in energy intensity. Fig. 4 shows a continuous decrease of energy intensity between 2007and 2011, with a slight increase again after 2011. The decrease may be due to airline adoption of best practices in fuel consumption technology and high labor productivity; extensive energy-saving technologies and management advances accompanied these practices. The sharp energy intensity decrease led to a larger PEICH effect in 2009–2010 than during other years.

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Mean EPA

DKH

CCA 1.3 1.2 1.1 1 0.9 0.8 0.7 0.6

CSN CES PTECH

CYZ

TPCH SECH

HBH

HXA OKA

CHH CQH

CSC

Fig. 6. Contributions of the cumulative production technology effect, and its influence on the 12 airlines’ CO2 emissions from 2007 to 2013.

4.2.2. Production technology effect PTECH, TPCH and SECH effects are factors associated with production technology effects. All are derived by comparing DMUs on the best-practice production frontier, using the DEA framework (Kim and Kim, 2012; Wang et al., 2015). Therefore, the impacts of these contributing factors depend on differences in production technology, which vary by airline. Fig. 5 shows that production technology effect change had a relatively small influence on CO2 emissions. The contribution of the production technology effect in decreasing airline carbon emissions increased slightly in significance after 2011. Table 4 indicates that from 2007 to 2013, the geometric cumulative effect of both PTECH and TPCH played positive roles in increasing CO2 emissions; these results are consistent with the study of Kim and Kim (2012). SECH had a relatively small influence on carbon emissions reduction. TPCH reflects how well each airline innovates new and advanced RTK technology. The cumulative effect of TPCH in Fig. 6 shows mixed results, but generally indicates that technical progress has increased CO2 emissions. This, in turn, suggests that China’s civil aviation innovations related to RTK production technology did not significantly reduce CO2 emissions during the study period. Only three airlines experienced CO2 emission reductions due to the positive contribution of TPCH: CCA (0.9896), CSN (0.9961), and CES (0.9681). These three airlines are the central airlines, with higher advanced technology innovation capabilities than the other airlines. PTECH is the degree of changes for a given DMU when it pursues the optimal production boundary from the base year to the target year. As with TPCH, 9 of the 12 airlines registered a PTECH value greater than unity. PTECH positively contributed to CO2 emission reductions for three local airlines: CYZ (0.9583), HBH (0.6967), and CSC (0.9324). This indicates these airlines experienced an improvement in RTK production efficiency. HBH showed the best performance, with a PTECH value of 0.6967. This indicates that HBH reduced its CO2 emissions by approximately 30.3% from 2007 to 2013. SECH is another factor representing the production technology effect. Fig. 6 shows that scale efficiency change did significantly impact the airlines’ CO2 emissions. From 2007 to 2013, the cumulative effect of SECH positively curbed CO2 emissions. Table 4 shows that the geometric mean value score was 0.9986, yielding a decrease in CO2 emissions of 0.14% per year. Airlines CCA, CSN, CHH, CSC, CQH and CES did not experience any changes in scale efficiency (efficiency values equal to 1), and their CO2 emissions did not change (Fig. 6). SECH had different effects on CO2 emission changes experienced by other airlines during the study period. CYZ showed the best performance, with a SECH value of 0.8864 and an a 11.36% reduction of CO2

3.0 2.8 2.6 2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0

CCA

CSN

CES

CYZ

HBH

CHH

CSC

CQH

OKA

HXA

DKH

EPA

2007

2008

2009

2010

2011

2012

Fig. 7. The scale efficiency of 12 airlines from 2007 to 2013.

2013

X. Liu et al. / Transportation Research Part A 99 (2017) 30–45

41

Fig. 8. RTK and civil aviation’s CO2 emissions in China over the years 1987–2013.

TDCH

TVCH

100% 80% 60% 40% 20% 0% -20%

CCA CSN CES CYZ HBH CHH CSC CQH OKA HXA DKH EPA Fig. 9. Degree to which TVCH and TDCH contribute to the RTKCH.

from 2007 to 2013. EPA had the lowest performance, with a SECH value of 1.2507; it experienced an 25.07% increase in CO2 from 2007 to 2013. Scale efficiency is an important index measuring industry market performance; the measure helps airlines study their economies of scale, and make decisions accordingly. For example, airlines may review whether they are in the technically optimal productive scale (TOPS), and if not, decide whether they should expand or shrink. Fig. 7shows that half of the airlines were in the TOPS during the study period: CCA, CSN, CES, CHH, CSC, and CQH. The airlines that didn’t achieve scale efficiencies fluctuated in a well-balanced distribution, from 1.0035 to 2.7964. Most airlines (particularly HXA, OKA, and HBH) experienced increases in scale efficiencies from 2008 to 2010 (Fig. 7). In contrast, half the airlines (the least efficient overall) experienced their worst scale efficiency in 2010. This is mainly due to the world’s financial crisis at the same time, which significantly affected the airlines’ scale efficiencies. During this period, the RTK, RPK, and business income of almost all airlines declined sharply, reducingCO2 emissions. Scale efficiency in civil aviation can be divided several types: energy-saving scale efficiency, capital-saving scale efficiency, and labor-saving scale efficiency. Energy-saving (capital-saving, labor-saving) refers to the ratio of improvement in RTK to energy (capital, labor), when the ratios of other inputs to RTK remain the same. Scale efficiency also occurs when the ratios of output to all inputs increase. The energy-saving form of scale efficiency reduces CO2 emissions per unit RTK; the scale efficiency of capital-saving and the labor-saving do not. The empirical results show that energy-saving is generally the key form of scale efficiency; China’s civil aviation should focus on this variable more than outputs. 4.2.3. RTK effect RTK was the primary driving factor increasing CO2 emissions, with the largest effect across most airlines. Rapid RTK growth was a key contributor to the increased emission values for small and medium-sized airlines, including CHH, CSC, CQH and DKH. Due to a corporate reorganization, HBH experienced a significant change in RTK production, resulting in a significant increase in CO2 emissions from 2007 to 2013. In contrast, for airline CYZ, the RTK contribution to increased CO2 emissions declined from 19.9% in 2007 to 25.6% in 2013. Fig. 8 shows a strong positive correlation between CO2 emis-

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sions and RTK, suggesting that RTK growth must be reduced to curb carbon emissions. However, airlines will need to continue RTK growth, making it unlikely that China’s RTK growth rate will significantly decrease. For civil aviation, relying on controlling RTK growth to reduce emissions is insufficient; other emission reduction related activities are also required. RTKCH can be generally decomposed into transport volume change (TVCH) and transport distance change (TDCH). Fig. 9 shows that TVCH played a dominant role in increasing CO2 emission; TDCH had a weaker effect. From 2008 to 2009, TDCH even helped decrease CO2 emissions (0.9817). This outcome highlights possible policy actions. For example, with continuous RTK growth, air route distribution can be optimized to deepen and increase the air load per unit distance. This would minimize the impact of RTK on increasing CO2 emissions.

5. Conclusions China’s air transport system is large and still growing; significant increases in the CO2 emitted by airlines are expected. Policy-makers must understand the mechanisms driving the CO2 emitted by airlines to develop scientific and technically effective CO2 reduction policies. As such, this study developed a new generalized PDA method under the VRS framework, to delineate six factors contributing to changes in civil aviation CO2 emissions for 12 airlines from 2007 to 2013.This study resulted in three main conclusions during the studied period. First, Revenue Ton Kilometers change (RTKCH) was the primary driving factor increasing CO2 emissions, and had the largest effect across most airlines. Further, the transport distance change (TDCH) could be a key breakthrough to minimize the impact of RTK on increasing CO2 emissions. Second, potential energy intensity change (PEICH) played a dominant role in decreasing CO2 emissions for most airlines. Private and joint airlines showed the best PEICH performance, followed by central and local airlines. This is consistent with the finding that private and joint airlines experienced better overall efficiencies in energy and RTK activity. Third, scale efficiency change (SECH) had a cumulative effect in positively contributing to curbing airlines’ CO2 emissions. Overall, scale efficiencies in China’s civil aviation sector are seen in energy-saving approaches. However, China’s civil aviation sector development processes remain too focused on output; scale efficiency is expected to be further transformed in the future. The conclusions mentioned above highlight specific policy options. First, the government should seek new ways to balance RTK growth and CO2 emission reductions. This could include incentives to build and use rail or buses for short-haul trips, and to substitute business air travel with teleconferencing. Even with these changes, China’s civil aviation will continue to grow and develop in the future, likely increasing CO2emissions. Airlines with large turnover (CCA, CSN, and CES), should explore alternatives to the current RTK growth pattern. For example, airlines can optimize air route distribution by minimizing network diameter and maximizing passenger flow on each edge. This would improve network fitness and minimize the RTK effect on increasing CO2 emissions. Next, potential energy intensity is the most important factor driving CO2 emission reductions. As such, airlines should continue to modernize aircraft and engine technology, and improve flight operational efficiency by optimizing such factors as fuel consumption (such as controlling cruise speed and cruise level) and ground operations (such minimize queuing). These improvements would significantly reduce CO2 emissions. This would involve investments in labor, education, and money in R&D, particularly by the central and local airlines that can innovate advanced technology better than other airlines. Finally, production technologies significantly impact civil aviation CO2 emissions; as such, improving these technologies should be taken seriously given their potential to improve output production performance. For example, RTK production efficiency can be improved by changing existing civil aviation operations, such as providing training in environmentally-friendly piloting techniques. The efficiencies gained while also reducing airline CO2 emissions represents a win-win solution, particularly for private and joint airline owners. In addition, energy-saving is a key example of scale efficiency; China’s civil aviation should focus on this variable, more than outputs. Further research could improve and extend this study, by studying more airlines, extending the time series over a longer time period, including more relevant inputs and outputs, and improving data quality. Further, while this research studied Chinese airline companies, the method could be applied to airline companies in other countries as well. That extended research could use the same inputs, outputs, and influencing factors as proposed in this paper. It would be interesting to compare differences in study results.

Acknowledgements Authors are grateful to the financial support from the National Natural Science Foundation of China (Nos. 71203151, 71573186), the Fundamental Research Funds for the Central Universities (No. KYZZ16-0146), Jiangsu Innovation Program for Graduate Education, and Jiangsu Qing Lan project.

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Appendix A. CO2. emissions change and its components for China airlines from 2007 to 2013 Airline

CT/C0

PEICH

PTECH

TPCH

SECH

RTKCH

TVCH

TDCH

CCA 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013

0.9873 1.0521 1.1375 1.0346 1.0307 1.0515

0.9733 0.9456 0.9505 0.9436 0.9669 0.9366

1.0157 1.0025 1.0003 1.0360 1.0033 1.0465

0.9979 0.9258 1.0255 0.9937 1.0136 0.9874

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

1.0008 1.0819 1.1667 1.0651 1.0482 1.0864

0.9827 1.1430 1.1553 1.0433 1.0221 1.0519

1.0184 0.9466 1.0099 1.0209 1.0256 1.0328

CSN 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013

0.9905 1.0959 1.1881 1.0777 1.1328 1.1056

0.9680 1.0192 0.7985 0.9620 1.0306 1.0728

1.0185 0.9656 1.0913 1.0261 0.9719 0.9778

0.9979 1.0281 0.9255 0.9837 0.9936 0.9872

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

1.0067 1.0855 1.3295 1.0988 1.1158 1.0674

1.0420 1.1101 1.1694 1.0290 1.0613 1.0418

0.9661 0.9778 1.1369 1.0679 1.0514 1.0246

CES 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013

0.9450 1.1267 1.1265 1.0534 1.1004 1.0896

1.0470 0.9303 0.9434 0.9547 1.0313 0.9957

0.9794 1.0107 1.0040 1.0299 0.9716 1.0149

0.9979 0.8258 1.0154 0.9937 1.0136 0.9678

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

0.9235 1.1681 1.1598 1.0781 1.0836 1.0920

0.9473 1.2471 1.0820 1.0478 1.0601 1.0583

0.9749 0.9367 1.0719 1.0289 1.0222 1.0318

CYZ 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013

1.1435 1.0340 1.1615 1.1800 1.0053 0.9013

0.9103 0.8428 0.9129 1.1106 1.1167 1.4640

1.0814 1.1222 1.0339 0.9612 0.9522 0.8345

0.9979 1.0258 1.0255 0.9937 1.0136 0.9874

0.9716 0.9459 0.9880 0.9927 0.9812 1.0022

1.1990 1.1260 1.2168 1.1188 0.9520 0.7443

1.1622 1.2260 1.1813 1.1369 0.9705 0.6846

1.0316 0.9184 1.0300 0.9841 0.9809 1.0873

HBH 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013

10.2000 0.7168 1.4772 2.4156 0.9404 2.0380

– 1.0620 0.8588 0.5926 1.1204 0.9774

1.0000 1.0000 1.0000 0.8146 0.9803 0.8724

– 1.0258 1.0255 0.9937 1.0136 0.9874

1.1142 0.9431 1.0536 1.6069 0.9499 1.1748

11.3415 0.6934 1.5959 3.1421 0.8875 2.0625

17.7648 0.7342 1.8124 2.4791 0.9187 2.1730

0.6384 0.9444 0.8806 1.2674 0.9660 0.9491

CHH 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013

1.0826 1.3971 1.0316 1.1459 1.0872 1.1854

1.0110 0.9817 0.8794 0.9030 1.1014 0.9942

0.9966 0.9840 1.0399 1.0590 0.9401 1.0157

0.9979 1.0258 1.0255 0.9937 1.0136 0.9874

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

1.0767 1.4101 1.1002 1.2059 1.0359 1.1889

1.0092 1.3329 0.9843 1.1828 1.0902 1.2016

1.0668 1.0579 1.1177 1.0196 0.9502 0.9894

CSC 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013

1.0585 1.3232 1.2333 1.2109 1.1925 1.1763

1.0151 0.9667 0.9866 0.9961 1.0729 1.0204

0.9945 0.9916 0.9818 1.0084 0.9525 1.0026

0.9979 1.0258 1.0255 0.9937 1.0136 0.9874

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

1.0504 1.3459 1.2416 1.2134 1.1512 1.1645

1.0101 1.3211 1.1623 1.1821 1.1035 1.1418

1.0399 1.0187 1.0683 1.0265 1.0432 1.0199

(continued on next page)

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Appendix A. (continued)

Airline

CT/C0

PEICH

PTECH

TPCH

SECH

RTKCH

TVCH

TDCH

CQH 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013

1.2296 1.5137 1.3563 1.2671 1.3374 1.1849

1.0039 0.9501 0.9513 1.0128 0.9733 1.0257

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

0.9979 1.0258 1.0255 0.9937 1.0136 0.9874

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

1.2271 1.5527 1.3908 1.2591 1.3556 1.1700

1.2555 1.4491 1.3711 1.2133 1.2705 1.1523

0.9774 1.0715 1.0143 1.0378 1.0669 1.0153

OKA 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013

1.2533 0.9131 0.9387 2.0131 1.2449 1.0506

0.9602 0.9471 0.8578 1.0233 0.9738 1.0797

1.0429 1.0670 1.1061 0.9249 0.9924 0.9648

0.9979 1.0258 1.0255 0.9937 1.0136 0.9874

0.9801 0.9387 0.9519 1.0760 1.0076 1.0100

1.2783 0.9381 1.0135 1.9907 1.2617 1.0109

1.3739 0.8865 1.0258 1.9991 1.2483 1.0817

0.9305 1.0582 0.9880 0.9958 1.0108 0.9346

HXA 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013

1.1551 1.0096 1.2542 1.1761 1.3156 1.2864

1.0999 0.9619 0.8484 0.8930 0.9003 1.0060

1.0189 1.3185 1.7895 1.0000 1.0000 0.5068

0.9979 1.0258 1.0255 0.9937 1.0136 0.9874

0.9348 0.7560 0.5915 1.0634 1.0395 1.9950

1.0980 1.0322 1.3614 1.2428 1.3861 1.2841

0.9981 1.1318 1.1476 1.1040 1.4796 1.3602

1.1001 0.9120 1.1862 1.1256 0.9368 0.9441

DKH 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013

1.4627 1.5848 1.4678 1.2521 1.2701 1.4150

0.9080 0.8797 0.9129 0.9639 1.0766 1.0631

1.0602 1.0409 1.0145 1.0253 0.9506 0.9743

0.9979 1.0258 1.0255 0.9937 1.0136 0.9874

0.9919 0.9987 1.0059 0.9999 1.0003 1.0082

1.5351 1.6899 1.5361 1.2754 1.2241 1.3725

1.4813 1.6949 1.4696 1.2099 1.2191 1.3025

1.0363 0.9971 1.0452 1.0541 1.0041 1.0537

EPA 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013

1.8430 1.6472 1.2984 1.1741 0.9787 0.9812

0.5983 1.0198 0.9226 1.0443 1.0607 0.9392

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

0.9979 1.0258 1.0255 0.9937 1.0136 0.9874

1.2913 0.9673 1.0150 0.9854 0.9579 1.0451

2.3750 1.6344 1.3513 1.1497 0.9502 1.0125

1.8533 1.7364 1.2976 1.1851 0.9747 0.9793

1.2814 0.9412 1.0414 0.9701 0.9749 1.0339

Mean 2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013

1.9459 1.2012 1.2226 1.3334 1.1363 1.2055

0.9435 0.9589 0.9019 0.9500 1.0354 1.0479

1.0173 1.0419 1.0884 0.9904 0.9762 0.9342

0.9979 1.0258 1.0255 0.9937 1.0136 0.9874

1.0237 0.9625 0.9672 1.0604 0.9947 1.1029

2.0927 1.2298 1.2886 1.4033 1.1210 1.1880

2.5734 1.2511 1.2382 1.3177 1.1182 1.1858

1.0052 0.9817 1.0492 1.0499 1.0028 1.0097

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