Evaluating airline energy efficiency: An integrated approach with Network Epsilon-based Measure and Network Slacks-based Measure
Accepted Manuscript Evaluating airline energy efficiency: An integrated approach with Network Epsilonbased Measure and Network Slacks-based Measure Xi...
Accepted Manuscript Evaluating airline energy efficiency: An integrated approach with Network Epsilonbased Measure and Network Slacks-based Measure Xin Xu, Qiang Cui PII:
S0360-5442(17)30107-X
DOI:
10.1016/j.energy.2017.01.100
Reference:
EGY 10235
To appear in:
Energy
Received Date: 27 July 2016 Revised Date:
27 December 2016
Accepted Date: 19 January 2017
Please cite this article as: Xu X, Cui Q, Evaluating airline energy efficiency: An integrated approach with Network Epsilon-based Measure and Network Slacks-based Measure, Energy (2017), doi: 10.1016/ j.energy.2017.01.100. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Evaluating airline energy efficiency: An integrated approach with Network Epsilon-based Measure and Network Slacks-based Measure
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Xin Xua, Qiang Cuib* a. Transportation Management College, Dalian Maritime University, Dalian 116026, China b. School of Economics and Management, Southeast University, Nanjing 211189, China
Abstract This paper focuses on evaluating airline energy efficiency, which is firstly divided into four stages: Operations Stage, Fleet Maintenance Stage, Services Stage and Sales Stage. The new four-stage network structure
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of airline energy efficiency is a modification of existing models. A new approach, integrated with Network Epsilon-based Measure and Network Slacks-based Measure, is applied to assess the overall energy efficiency and divisional efficiency of 19 international airlines from 2008 to 2014. The influencing factors of airline energy efficiency are analyzed through the regression analysis. The results indicate the followings: 1. The integrated
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model can identify the benchmarking airlines in the overall system and stages. 2. Most airlines’ energy efficiencies keep steady during the period, except for some sharply fluctuations. The efficiency decreases mainly centralized in the year 2008 to 2011, affected by the financial crisis in the USA. 3. The average age of fleet is positively correlated with the overall energy efficiency, and each divisional efficiency has different significant influencing factors.
Key words Airline energy efficiency; Network Epsilon-based Measure; Network Slacks-based Measure;
1. Introduction
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Influencing factors
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The data of the World Bank [1] shows that the world’s Gross Domestic Product has increased from 63.09 trillion dollars in 2008 to 78.09 trillion dollars in 2014, meanwhile, the word’ Gross National Expenditures have accordingly grown from 62.79 trillion dollars in 2008 to 77.41 trillion dollars in 2014. With the fast development of the word’s economy and improvement of people’s living standard, the demand for air transportation keeps increasing. According to the industry statistics of International Air Transport Association (IATA) [2], from 2008 to 2014, the passenger traffic and freight traffic for system-wide global commercial airlines have increased by 735 million passengers and 6.4 million tonnes. Because time and efficiency are considered to be more and more valuable, the air transportation, with fast speed, low damage rate and high safety, becomes more and more indispensable in the transportation system [3]. From the statistical data of World Tourism Organization [4], in 2004, only 43% of international passengers selected air transport as their primary mode of travel, while in 2013, slightly over half of travelers (53%) reached their destination by airlines. The aviation industry has emerged in a large number of airlines to satisfy the needs of market. According to the data of IATA [5], there were 312 airlines on the IATA Operational Safety Audit registry in 2008, while in 2015, there data became 405. Followed by Li et al. [6], the changing situation of airline operation from 2008 to 2014 is shown in Figure 1. Figure 2 shows the changing situation of aviation kerosene. The data comes from the annual reviews of IATA. It can be seen from Figure 1 that the increasing number of airlines brings the increase of
Revenue Passenger Kilometers and Total Revenue, however, the net profit and Flight Tonne kilometers are fluctuating. The increasing Revenue Passenger Kilometers and fluctuating net profit implies that airlines have low efficiency in controlling operating costs. In the terms of cost efficiency or generally productive efficiency, as noted by Fried et al. [7], is the relative difference between the organization’s observed inputs/outputs and the optimal production plan from the organization’s standpoint. Among operating costs of airlines in IATA Annual Review 2015 [2], fuel costs constitutes the largest part, accounting for 29% in 2014. The aviation kerosene, as the main energy of the airline industry, plays an important role in airline operation. As shown in Figure 2, the aviation kerosene price is volatile during this period. Under the changing market of aviation kerosene, the energy utilization problem of the airline industry has drawn great public attention. The definition of energy efficiency provided in the EU Directive 2006/32/EC states that energy efficiency is a ratio between an output performance, service, goods or energy, and an input of energy. In Blomberg et al. [8], energy efficiency is defined to reflect whether energy has been used efficiently. The evaluation of energy efficiency can identify the advantages and disadvantages of airlines’ performance, which is significant for airlines’ development. Thus, it is meaningful to measure airline energy efficiency during 2008-2014 by applying an effective method.
In the respect of airline efficiency, the literature is vast and encompasses multiple methodologies, ranging from cost function measures, to frontier-production function methods, factor productivity measures, and the most widely employed Data Envelopment Analysis (DEA) approach. For cost function measures, Caves et al. [9] formulated a general model of airline costs. Oum and Yu [10] estimated a translog variable cost function. In Inglada et al. [11], the stochastic frontiers for cost function and production function were estimated to access the economic and technical efficiency of international air transport companies. Cost function measure is a kind of parametric approach, requiring the specific definition of cost function. As regards to another parametric approach, frontier-production function method, Coelli et al. [12] measured airline efficiency with stochastic frontier production functions. Assaf [13] used a Bayesian random stochastic frontier model to reflect the technical efficiency of U.S. airlines. In Assaf and Josiassen [14], a Bayesian distance frontier model subject to regularity constraints was estimated to measure airline efficiency. The idea of frontier-production function method is to build the functional relationship between inputs and outputs. When it came to factor productivity measures, productivity growth and total factor productivity are widely focused. Ehrlich et al. [15] developed a model of endogenous growth to access the productivity growth and cost decline on 23 international airlines. Windle [16] measured the total factor productivity and unit costs of 14 US and 27 non-US airlines in 1983; Oum et al. [17] computed the gross total factor productivity (TFP) of the airlines to reflect the airline efficiency. The DEA approach is also a frontier-based measure. Compared with the frontier analysis, the DEA model does not require a specific functional form for the production frontier and it is easy to extend to other forms of models. Partly because of this, the DEA model and its modifications have been extensively used to evaluate airline efficiency. Sengupta [18] considered a dynamic efficiency model in the framework of a DEA model. Capobianco and Fernandes [19] used the DEA approach to analyze the efficiency of airline industry's financial performance. The DEA model was also applied in Hong and Zhang [20] to compute the efficiency of airlines and air cargo/passenger divisions for major airlines in the world.
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In Wu and Liao [21], an integrated DEA-BSC model was proposed to evaluate the operational efficiency of airlines. Cui et al. [22] analyzed the impacts of the EU ETS emission limits on airline performance, which were calculated based on the historical emission data of 2004-2006. Cui et al. [23] proposed a Virtual Frontier Dynamic Slacks Based Measure to calculate the energy efficiencies of 21 airlines from 2008 to 2012. However, the previous studies ignore the internal process of the airline operational system, and the performance of an airline is assumed to be a function of a set of chosen inputs and outputs. For most airlines, different divisions cooperate together to ensure the overall operations. Take the example of Air China, according to its institutional framework presented on the official website, Air China has several divisions such as operation control center, flying corps, centralized purchasing department, comprehensive security, air defense detachment, ground services department, cabin service department and the sales department, etc. The roles of the operation control center flying corps and centralized purchasing department are to guarantee the operations of the airlines. These operational divisions need to fully use the resources, such as fuel, manpower, etc to provide the passenger and cargo service capacity, which can be reflected by Available Seat Kilometers (ASK) and Available Tonne Kilometers (ATK). The comprehensive security shall ensure the maintenance of aircrafts and the supply of certain number of effective aircrafts for service, which is the fleet size of airlines. The air defense detachment, ground services department and cabin service department are set for service. The service divisions have to satisfy the travel demand of passengers and freights from origin to destination through using the aircrafts, seats and freights loads provided by the operations divisions and the aircraft maintenance division. The outputs of the service divisions are the passenger traffic and freight traffic, which can be reflected by Revenue Passenger Kilometers (RPK) and Revenue Tonne Kilometers (RTK). The sales department is set to sell airline’s services as much as possible to produce revenues. To increase efficiency, each division shall fully use the inputs to produce as much as outputs. Hence, when exploring overall airline efficiency with DEA method, the internal process and divisional efficiency should also be considered in the DEA model. For energy efficiency, DEA method is also widely applied in various industries and regions. For example, Ramanathan [24] studied the energy efficiencies of transport modes in India based on DEA approach, while in the same industry, Cui and Li [25] evaluated energy efficiency of China’s transportation sector with an application of three-stage virtual frontier DEA. For manufacturing industry, in Azadeh et al. [26], an integrated DEA PCA numerical taxonomy method was used to assess energy efficiency and consumption optimization in energy intensive manufacturing sectors, and in Mukherjee [27], energy use efficiency in the U.S. manufacturing sector was measured based on DEA method. For national industry sector, Liu and Wang [28] applied an adjusted network DEA to evaluate energy efficiency of China's industry sector, and in Zhang et al. [29], Energy efficiency in Swedish industry was measured with a firm-level DEA approach. Energy efficiency was also analyzed in chemical industry based on fuzzy DEA cross-model in Han et al. [30]. Besides, the region of hotel was also considered in Onüt and Soner [31], where the energy efficiency of Antalya Region hotels in Turkey was assessed with DEA model. The inputs of these studies can be summarized as the resources consumed by the corresponding industry, and the outputs are the revenues or other products. Therefore, when applying DEA for industrial energy efficiency evaluation, most researches only choose the initial energy input and final economic output data as the variables, whereas the data on energy
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conversion related to the internal activities in each industry department are usually neglected. Most industries have its own operational process, the traditional “black box” DEA model may lack discriminating power [32], and consequently cannot provide process-specific guidance to separately improve industrial energy efficiency of different stages. To provide a more specific evaluation of airline energy efficiency, the inner structure of airline industry shall be considered. In recent years, a significant body of work has been directed at problem settings where the Decision Making Unit is characterized by a multistage process, and the operation of an airline takes this form. Table 1 shows the literatures on airline efficiency evaluation based on two-stage network DEA. It can be seen that the inputs are various resources used by the airlines, while the intermediate measures can be summarized as the airlines’ service capacity, and the outputs are generally revenues that airlines gained.
Airline efficiency was firstly divided into three stages in Mallikarjun [40], which were Operations Stage, Services Stage and Sales Stage. The input of the Operations Stage is defined as Operating Expenses. The intermediate measure between the Operations Stage and the Services Stage is Available Seat Miles. Besides, the extraneous inputs of the Services Stage are Fleet Size and Destinations. The intermediate measure between the Services Stage and the Sales Stage is Revenue Passenger Miles, while the output of the Sales Stage is the Operating Revenue. Li et al. [6], Li et al. [41] and Cui et al. [42] redefined the variables wherein Number of Employees and Aviation Kerosene were chosen as the inputs of the Operations Stage, Available Seat Kilometers and Available Tonne Kilometers were intermediate measures of the Operations Stage and the Services Stage, Fleet Size was the only extraneous input of the Services Stage, Revenue Passenger Kilometers and Revenue Tonne Kilometers were intermediate measures of the Services Stage and the Sales Stage, Sales Costs was the extraneous input of the Sales Stage and Total Business Income was the output. The previous studies lay a good foundation for this paper to explore the internal structure of airline energy efficiency. However, the performance of the aircraft maintenance has not been considered. Aircraft maintenance plays an important role in improving the continuity of flight schedule that all flights can deliver optimum performance. Well-planned and managed aircraft maintenance is effective to fulfill daily requirement of operation, service and commercial process by achieving aircraft availability, cabin functionality, cabin interior appearance and contribution on technical delay. The high performance of aircraft can also save the consumption of aviation kerosene. Thus, this paper extends the three-stage network structure in Li et al. [6] to a four-stage one by adding the Fleet Maintenance Stage to evaluate airline energy efficiency. In this paper, airline energy efficiency is divided into four stages: Operations Stage, Fleet Maintenance Stage, Services Stage and Sales Stage. The inputs and outputs for each stage are selected according to the previous papers. Then, the integrated approach with Network Epsilon-based Measure (NEBM) and Network Slacks-based Measure model (NSBM) is applied to assess the overall energy efficiency and divisional efficiency of 19 international airlines. The proposed model modifies the NEBM model by applying the NSBM model instead of Network Charnes-Cooper-Rhodes model (NCCR) in the one input stages, so the merits of NSBM model can be combined into the NEBM model. Based on the efficiency scores, the influencing factors are analyzed. Finally, some conclusions are drawn from the results. The remainder of this paper is organized as follows: Section 2 illustrates the method. Section
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2. Method
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Yl
X l
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Traditional DEA, introduced by Charnes et al. [43], is an approach for identifying best performance among peer DMUs in the presence of multiple inputs and outputs. Suppose the data set is (Y , X ) . Y and X represents the l × n matrix of outputs and the l × m matrix of inputs, respectively. Y = M , X = M . l , n, m denotes the number of decision making units, outputs and inputs, respectively.
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The ratio of weighted outputs to weighted inputs is used to measure the relative efficiency of each DMU, which is defined as uY i / vX i , where u , v are the weight vectors of outputs and inputs. After Charnes-Cooper transformation [44], the linear fractional programming for each DMU0 is as follows: max uY0
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vX 0 = 1 s.t. uY − vX ≤ 1 u ≥ 0, v ≥ 0
(1)
The above model is also called the CCR (Charnes-Cooper-Rhodes) model, for which the LP dual problem is
max θ
λ X ≤ θX 0 λ Y ≥ Y0 λ ≥ 0
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s.t.
(2)
The CCR measure is based on proportional reduction of inputs. In order to deal directly with the input excesses and the output shortfalls of the DMU concerned, the Slacks-based Measure (SBM) model is proposed by Tone [45]. The basic non-oriented SBM model under Variable Returns to Scale (VRS) is
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max ρ = 1 −
1 m
m
si−
∑x i =1
i0
1+
1 n
n
sr+
∑y r =1
r0
X 0 = λX + s − + Y = λY − s s.t. 0 k e λ = 1 λ ≥ 0,s − ≥ 0,s + ≥ 0
(3)
where s − , s + represents the input excess and output shortfall, and λ is the weight. To deal with the intermediate products between two divisions, the SBM model is extended to
evaluate the system and divisional efficiency of a network structure, which is the Network SBM proposed in Tone [46]. The overall efficiency is formulated as follows:
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λ ,s
∑ k =1 K
∑
1 Wk (1 + n k k =1
,s
mk
∑
1 mk
i =1 nk
∑ r =1
sik − ) xik0 srk + ) y rk0
X = λ X + s k k k k+ Y0 = λ Y − s s.t. λh Z ( k ,h ) = λk Z ( k ,h ) eλk = 1 λk ≥ 0 ,s k − ≥ 0 ,s k + ≥ 0 k 0
Z ( k ,h )
k
k−
k
(4)
denotes the intermediate products between Division k
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ρ = k min k− k+
Wk (1 −
and Division h . mk , nk
Division k and K is the number of division. The divisional efficiency of Division k is 1−
1 1+ nk
mk
k− i k i0
∑ sx i =1 nk
∑ r =1
srk + y rk0
(5)
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ρk =
1 mk
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represents the number of inputs and outputs of Division k , respectively. Wk is the weight of
In DEA, there are two measures of efficiency with different characteristics: radial and non-radial. CCR and SBM models can be considered as representatives of radial and non-radial measures of efficiency, respectively. For instance, in the input-oriented case, the CCR model deals mainly with proportionate reduction of input resources. In contrast, the non-radial models aim at obtaining maximum reduction of inputs. The main shortcoming of the CCR model is the neglect of non-radial slacks in evaluating the efficiency score, while the projected DMU in SBM model may lose the
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proportionality in the original inputs, because the slacks are not necessarily proportional to the inputs. In order to combine the merits and overcome the shortcomings of radial and non-radial models, a composite model called Epsilon-based Measure (EBM) is proposed in Tone [47], which has both radial and non-radial features in a unified framework. The basic input-oriented EBM model under VRS is
where wi− is the weight of input i and it satisfies
∑w
− i
= 1 ( wi ≥ 0∀i ) . ε x is a key parameter that
i =1
combines the radial θ and the non-radial slacks terms. Parameters ε x and wi− (i = 1,L, m ) must be calculated prior to the efficiency measurements. The EBM model is transformed into the NEBM model in Tavana [48] to evaluate the performance of a supply chain with network structure. The overall efficiency is determined by the following NEBM model:
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γ ∗ = min− θ ,λ , s
∑W (θ k
k
− ε xk
k =1
mk
∑ i =1
wik − sik − ) xik0
θ k X 0k = λk X k + s k − k k k Y 0 ≤ λ Y h ( k ,h ) = λk Z ( k ,h ) λ Z s.t. k eλ = 1 λk ≥ 0 s k − ≥ 0
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(7)
where W k represents the weight of division k , and k is the number of divisions.
As defined by Tone [46], when there is only one input, the EBM model is transformed into the CCR model. The CCR model is based on the assumption that inputs or outputs follow proportional changes. However, in practice, not all inputs or outputs behave in the proportional way. For example, if
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we employ labor, materials and capital as inputs, some of them are substitutional and do not change proportionally [45]. The CCR model cannot cope with such cases properly. Compared with CCR model, the SBM model has the following advantages [45]:
1. The SBM model deals directly with the input shortfall or output excess while CCR model
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neglects the slacks.
2. In contrast to the ratio maximization of the CCR model, the dual of the SBM model can be interpreted as profit maximization, which makes the SBM model understandable in the economic interpretation.
3. Because the slacks reflect the inefficiency of the DMU concerned, the SBM model is meaningful to measure the depth of inefficiency.
Considering the above merits of SBM, this paper modifies the NEBM model by applying NSBM expressed as follows:
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in divisions with only one input. The integrated approach with NEBM and NSBM model can be further
min(1, θ k ) −
γ ∗ = min−
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θ ,λ , s
max(1, γ k ) +
1
∑
(ε xk
K
k =1
mk
1
∑
k =1
nk
∑
wik − sik − ) xik0
nk
urk − srk + ) y rk0
i =1
(ε yk
K
mk
∑ r =1
θ k X = λ X + s k k k k+ γ k Y0 = λ Y − s h ( k ,h ) s.t. λ Z = λk Z ( k ,h ) eλk = 1 λk ≥ 0, s k − ≥ 0, s k + ≥ 0 k 0
k
k
k−
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If mk = 1 , then ε xk = 1 , θ k = 1 , and if n k = 1 , then ε yk = 1 , γ k = 1 . Note that the above model is non-oriented under VRS, the steps to solve the integrated NEBM and
NSBM model are depicted in Appendix A.
In contrast to the NSBM and NEBM, the objective function of the integrated model computes the slacks at the system level. It represents the ratio of the average total input reduction to the average total output expansion [49]. In this model, the one input case in NEBM is particularly focused by applying the NSBM model instead of NCCR model, where the efficiency model in Section 3.3 illustrates this case in detail. The integrated model takes full account of slacks and
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ACCEPTED MANUSCRIPT effectively observes the inefficiency. For each division with more than one input or output, there is a corresponding θk or γ k . In order to distinguish the efficiencies better, in the input side, the minimum one among 1 and θ k instead of 1 is chosen as the minuend, while the maximum one among 1 and γ k is chosen in the output level to lower the overall efficiency level.
3. Empirical study
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In this section, the empirical study of 19 airlines is performed by applying the proposed model. The four-stage network structure of airline energy efficiency is constructed, and then the data of the chosen 19 airlines is analyzed. Section 3.3 shows the detailed efficiency model, and the results are discussed in section 3.4. 3.1 The network structure
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Based on the previous literature review and analysis, the network structure of airline energy efficiency is constructed. The three-stage structure of Li et al. [6] is extended to a four-stage network by adding the Fleet Maintenance Stage, which reflects the efficiency of daily fleet maintenance and is parallel to the Operations Stage. Different from Li et al. [6], Fleet Size in this network is treated as the intermediate measure between the Fleet Maintenance Stage and the Service Stage instead of the extraneous input of the Service Stage. In addition, the Number of Destination is considered as an input of the Service Stage to make the inputs complete. The network structure is shown in Figure 3, in which airline energy efficiency is divided into Operations Stage, Fleet Maintenance Stage, Services Stage and Sales Stage.
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According to Figure 3, the inputs and outputs of each stage can be summarized as follows: Operations Stage: Inputs 1 = Number of Employees (NE) and Aviation Kerosene (AK). Outputs 1 = Available Seat Kilometers (ASK) and Available Tonne Kilometers (ATK). Fleet Maintenance Stage: Input 2=Maintenance Costs (MC). Output 2=Fleet Size (FS). Services Stage: Inputs 3= Available Seat Kilometers (ASK), Available Tonne Kilometers (ATK), Fleet Size (FS) and Number of Destination (ND). Output 3 = Revenue Passenger Kilometers (RPK) and Revenue Tonne Kilometers (RTK). Sales Stage: Inputs 4 = Revenue Passenger Kilometers (RPK), Revenue Tonne Kilometers (RTK) and Sales Costs (SC). Output 4 = Total Business Income (TBI). Intermediate products: Link (Operations Stage to Services Stage): Available Seat Kilometers (ASK) and Available Tonne Kilometers (ATK). Link (Fleet Maintenance Stage to Services Stage): Fleet Size (FS). Link (Services Stage to Sales Stage): Revenue Passenger Kilometers (RPK) and Revenue Tonne Kilometers (RTK). Maintenance costs refer to the expenses on the technical repair and daily maintenance of
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In this paper, an empirical study on energy efficiency of some main airlines is performed with the data from 2008 to 2014. The efficiencies during the seven-year period are reliable to reflect the airlines’ trends of development. Since 2008, the financial crisis in the US has deeply affected the global airline industry. To minimize the effect of the financial crisis, many airlines anchor their hopes on improving energy efficiency. It is meaningful to study the energy efficiency of some major airlines during this period. To better inspect the operational performance of airline industry before and after the financial crisis, the changes of several representative operational indicators during 2004 to 2014 are shown in Figure 4. According to Figure 4, the revenues changes of global commercial airlines before 2009 were fluctuated but positive. However, it became -16.5% in 2009. In 2010, there was a sharp recovery to 18.4% and then the growth slowed down in the next few years. It can be seen from Figure 4 that the changing trends of passenger growth, cargo growth and the world economic growth almost followed the same pattern. In summary, in line with the world economy, before 2009, the revenue, RPK, RTK of the global airline industry kept increased, but the obvious decline happened in 2009, and the sharp increase occurred in 2010. After that, the growth of these indicators slowed down. Hence, it can be concluded that the influence of financial crisis in the US on airline industry has a certain delay and the change from 2008 to 2009 can be a reference to discuss the impacts of financial crisis in the US. It is reasonable to choose the year from 2008 to 2014 as the research period. Furthermore, there is another reason to choose the period of 2008-2014, which is the limitation of the data. Some airlines' data before 2008 cannot be found in public sources, such as the ATK of Hainan Airlines and Delta Air Lines, the ATK, RTK, ASK and RPK of Air France-KLM. The empirical data is obtained from 19 airlines: China Eastern Airlines, China Southern Airlines, Korean Air, Qantas Airways, Air France-KLM, Lufthansa Airlines, Scandinavian Airlines, Delta Air Lines, Air China, Hainan Airlines, Emirates Airline, Air Canada, Cathay Pacific Airways, Singapore Airlines, All Nippon Airways, Eva Air, Turkish Airlines, Thai Airways, Garuda Indonesia. For comparison, all of the 19 airlines are traditional full service companies. According to the World Air Transport Statistics published by IATA [50], in 2014, Delta Air Lines, China Southern Airlines, China Eastern Airlines, Lufthansa Airlines and Air China are on the list of the world’s top 10 airlines on passenger traffic. Meanwhile, for Revenue Passenger Kilometers, the world’s top 10 airlines contain Delta Air Lines, Emirates Airline, China Southern Airlines, Lufthansa Airlines and Air China. According to the data published by IATA [51], in 2014, the total RPK of the global airline industry was approximately 6190 billion, while the total RPK of the targeted 19 airlines was approximately 2374 billion, accounting for 38.35% of the industry. Excluding the low-cost airlines, the RPK of these 19 airlines takes large percentage of the world’s airlines. Therefore, we select these airlines as the samples to analyze airline energy efficiency. The data on Number of Employees, Fleet Maintenance Costs, Available Tonne Kilometers,
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Available Seat Kilometers, Fleet Size, Revenue Tonne Kilometers, Revenue Passenger Kilometers, Sales Costs, and Total Business Income are collected from annual reports of airlines. The data on Aviation Kerosene are derived from the sustainability, environment and corporate social responsibility reports of the 19 companies. In the process of efficiency measurement, this study mainly involves the direct inputs and outputs related to the efficiency network. Other influencing factors, such as inflation and average age of fleet, are considered in Section 4. Table 2 shows the descriptive statistics of inputs, outputs and intermediate products. The Pearson correlation coefficients [52, 53] between the inputs and the outputs are provided in Table 3. As shown in Table 3, most coefficients are positive and over 81% of them are higher than 0.56. The coefficients of ATK and NE, RTK and FS and RTK and ND are below 0.5, but with reference to the variables selection of existing papers [6, 40], we still choose these factors. As illustrated in Li et al.
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[6], Number of Employees (NE), Aviation Kerosene (AK) and Sales Costs (SC) to replace Operating Expenses in Mallikarjun [40], which makes the content of expenses more specific. In Mallikarjun [40], fleet size and destinations are capital assets and are beyond the control of airline operational managers. An airline that has more fleet size and destinations tends to convert a given number of ASM into a
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larger number of RPM than a peer airline with less fleet size and destinations. This is because the additional FS and DS provide more options to the airline's operational mangers. Thus, the model in this paper also includes the two variables to ensure that the resulting efficiency scores are comparable across different airlines.
3.3 The efficiency model
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According to the integrated NEBM and NSBM model in Section 2, the non-oriented efficiency model under VRS is presented as follows. The objective function computes the slacks at the system
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level, indicating that the weights of the four stages are equal to the mean value.
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s0MC s ND s SC + + 0 + 0 MC0 ND0 SC0
∑
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∑
∑ ∑ ∑ ∑
∑
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(9)
In the objective function of overall energy efficiency, the network structure is considered as a
whole system to observe its total inputs excesses and outputs shortfall. For airline j ( j = 1,L, l ) , NE j is the Number of Employee, AK j is the Aviation Kerosene, MC j is the fleet Maintenance Costs, ASK j is the Available Seat Kilometers, ATK j is the Available Tonne Kilometers, FS j is the Fleet Size, ND j is the Number of Destination, SC j is the Sales Costs, RPK j is the Revenue Passenger Kilometers, RTK j is the Revenue Tonne Kilometers and TBI j is
the Total Business Income. All of these variables are non-negative. The efficiency of the Operations Stage is
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wNE s0NE wAK s0AK + ) NE0 AK0
(10)
The efficiency of the Fleet Maintenance Stage is
ρ2 = 1 −
s0MC MC0
(11)
ρ3 = 1 −
s0ND ND0
(12)
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The efficiency of the Services Stage is
The efficiency of the Sales Stage is s0SC SC0 ρ4 = s TBI 1+ 0 TBI 0 1−
SC
(13)
3.4 The results
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Table 5 shows the overall energy efficiencies of the 19 airlines.
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As shown in Table 5, among the 19 airlines, Garuda Indonesia has the highest average energy efficiency with the score of 0.811. The highest overall energy efficiency of Garuda Indonesia depends on its relatively high efficiencies in all of the four stages, especially in the Operations Stage and the Fleet Maintenance Stage. Except for Garuda Indonesia, the average overall energy efficiency of Lufthansa Airlines ranks second in the 19 airlines, with its high efficiency in the Sales Stage. In contrast, Cathay Pacific Airways is the airline with the lowest ranking, caused by the low efficiency in the Sales Stage. In order to inspect the divisional efficiencies better, the average stage efficiency scores and rankings are summarized in Table 6. Based on Table 6, the divisional efficiencies can be analyzed. The operational descriptions of airlines in this paper refer to the airlines’ annual reports.
In the Operations Stage, it can be seen from Table 6 that the efficiency of Garuda Indonesia reaches 1 during the seven years persistently. Its average ATK and ASK per aviation unit of kerosene are approximately 4,887.70 and 34,592.78, respectively, both ranking fifth among the 19 airlines. The average ATK and ASK per employee are approximately 0.76 million and 5.38 million, ranking fifth and forth, respectively. These four relatively high ratios interact together, resulting in the high operational efficiency of Garuda Indonesia. According to Garuda Indonesia’s Annual Reports [54], the efficient operation process lies in the fuel efficiency and employee efficiency. Garuda Indonesia has developed and implemented a Computerized Fuel Conservation Program to save the fuel consumption and monitor the use easily. The Company also constantly strives to ensure that costs are controlled by using fuel efficiently in all flight operations through effective and efficient use of alternative aircraft and evaluation of current contracts. In order to increase the employee productivity, Garuda Indonesia is always committed to continuously improving quality
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of personnel through development programs according to the needs and business strategy. During 2014, a total of 36,290 participants have attended the training. Compared with the number of employees at approximately 8,500 people, it indicates that each employee has the opportunity to attend training 2-5 times each year on average. In the newly added stage, the Fleet Maintenance Stage, Table 6 shows that the average efficiency of Garuda Indonesia ranks first among the 19 airlines. For each aircraft of Garuda Indonesia, it has relatively lower average maintenance costs, which is approximately 15.64 thousand dollars, while the corresponding point of Qantas Airways is approximately 51.06 thousand dollars, resulting in the lowest Fleet Maintenance efficiency of Qantas Airways to a certain extent. For Cathay Pacific Airways, the average maintenance costs per aircraft is approximately 73 million dollars, which is the highest among the 19 airlines. According to Garuda Indonesia’s Annual Reports [54], on one hand, Garuda Indonesia pays much more attention on airline maintenance. For example, it has established Plane Maintenance Center that focuses on the maintenance of aircrafts and facilities. The company also has maintenance subsidiary PT Garuda Maintenance Facility Aero Asia (GMFAA), which is established to perform and support the policy and program of the government in national economy and development in general, especially in maintenance of aircraft industry, creation and maintenance of supporting facilities. With the specialized operation of the maintenance subsidiary, Garuda Indonesia is able to effectively reduce maintenance cost and fuel cost. On the other hand, Garuda Indonesia received new aircrafts with the number of 22 in 2012, 36 in 2013 and 27 in 2014, accounting for approximately 20% of total number of aircrafts each year. The introduction of new aircrafts could lower the average age of feet and reduce the maintenance cost. Moreover, the improvement in the airline’s maintenance efficiency has also been stimulated by the government’s economic policy package that allows for the exemption of value-added tax (VAT) for imports of aircraft equipment and spare parts as it can reduce the operational and maintenance cost of an aircraft. In the Services Stage, Eva Air has an average efficiency of 0.989, ranking first among the 19 airlines, while the second one, Garuda Indonesia, is not far behind with the efficiency of 0.985. The high service efficiency of Eva Air relies on its relatively high cargo load factor the ratio of RTK and ATK) and RTK per aircraft. The two points are approximately 0.79 and 74.20, ranking second and fifth, respectively. The corresponding data points of the lowest rated airline, Qantas Airways, are 0.63 and 11.10, ranking 15th and 16th, respectively. According to Eva Air’s Annual Reports [55], Eva Air adopts the operating strategy that regards both of passenger traffic and cargo traffic as equally important. For Eva Air, air freight forwarder is one of its sub–industries. The business scope includes import and export of cargo, transport of cargo as well as customs declaration. It also provides charter transportation services for international exhibits. Eva Air’s full attention on cargo transport helps the company achieve a better performance in the service quality and efficiency. Moreover, in 2014, Eva Air’s global freight customer satisfaction survey showed the service of sales personnel achieved the highest satisfaction, which indirectly increased the service efficiency. In the Sales Stage, Lufthansa Airlines ranks first with the average efficiency of 1. The high performance on sales leads Lufthansa to the second ranking in the overall efficiency. The Total Business Income per sales cost and the Total Business Income per RPK of Lufthansa Airlines are approximately 36.54 and 19.83, ranking second and third among the 19 airlines, respectively. With regards to Cathay Pacific Airways, its Total Business Income per sales cost is approximately 7.77,
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Overall M it =
Overall E it , i = 1,2, L ,19, t = 2,3,4,5,6,7. Overall E it −1
represents the overall energy efficiency.
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Overall E
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ranks 19th among the 19 airlines, resulting in the low efficiencies of both the Sales Stage and overall system. As mentioned in the Annual Reports of Lufthansa Airlines [56], it has considered the constant improvement of efficiency as one of seven fields of action in its strategic program. Furthermore, it has implemented a SCORE Program that concentrated on cutting costs and improving efficiency. In order to increase the sales efficiency, Lufthansa Airlines has taken several measures to restructure and centralize the sales organization, such as practicing strict cost management by means of ongoing process optimization and the increasing standardization of its highly decentralized network, modernizing and optimizing the sales production and administration processes and developing a new remuneration model with a higher degree of performance-based remuneration for sales personnel. The streamlined sales organization has significant influence on the efficiency enhancement. To examine the yearly efficiency changes during 2008–2014, the overall energy efficiency change index is defined based on Li et al. [6], which is similar to the Malmquist index [57]. The overall energy efficiency change index Overall M it of airline i is: (14)
The stage efficiency change index Stage M its of stage s is defined as Stage M its =
Stage Eits , s = 1,2,3,4. Stage M its Eit −1
(15)
Stage E its represents the efficiency of stage s .
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Table 7 shows the overall energy efficiency change indices of the 19 airlines from 2009 to 2014. From Table 7, it can be seen that some airlines’ energy efficiencies present sharp increases and highly decreases in some years.
To analyze the reasons of these dramatic changing trends, this paper mainly focuses on airlines with yearly indices less than 0.6 and more than 3. The results are shown in Tables 8 and 9.
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It can be seen from Table 7 that most of the efficiency changes are steady, except for some sharply fluctuations. Due to the financial crisis in the USA, the efficiency decreases mainly centralized in the year 2008 to 2011, as shown in Table 8. At the same period, the energy efficiencies of airlines in Table 9 have sharply increased, caused by the lower efficiency in the previous year and the better performance in the current year.
4. Analysis of influencing factors To further explore the main concerns for airlines aiming at energy efficiency improvement, this study performs Regression Analysis to identify the influencing factors of the airline energy efficiency. According to the existing papers [57-61], six indicators are chosen to analyze the influencing factors of airline energy efficiency, as shown in Table 10.
The results of the regression analysis are shown in Table 11.
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5. Conclusions
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As shown in Table 11, only Average age of fleet has a significant impact on the overall energy efficiency, while other five factors' influence is not significant. Average age of fleet is positively correlated with the overall energy efficiency, and it has a positive and significant impact on Sales efficiency. The influence on Sales efficiency leads to the positive impacts on the overall efficiency. This is an unintended and understandable result, but it can be illustrated by the realities of some airlines, such as Delta Air Lines. In 2014, Delta Air Lines’ average age of fleet is 16.8 years and ranks 1st, while its corresponding average Sales efficiency is 0.694 and ranks 8th among the 19 airlines. The large average age of fleet is probably resulted from the mergence between Delta Air Lines and Northwest Airlines in 2008 [62]. However, the mergence expands the air routes and strengths its brand, which may result in more total business income, so the Sales efficiency is relatively high. For Operations Stage, only Per capita GDP has a positive and significant impact on Operational efficiency. This is an expected result in that Per capita GDP means high economic level and high employee and material efficiency, then Per capita GDP can have a positive role in the process of translating employee and kerosene into Available Seat Kilometers and Available Tonne Kilometers. Except Average age of fleet, Inflation rate also have a positive and significant influence on Sales efficiency. High inflation rate means high ticket price and high total business income, so inflation rate has a positive relationship with Sales efficiency. It is a unintended result that Average age of fleet is positively related with the overall energy efficiency, so we try to delete Average age of fleet and apply the other five influencing factors to run the regression analysis again. The results are shown in Table 12.
From Table 12, we can find that none of the five influencing factors has a significant impact on the overall energy efficiency, and the impacts on the four stage efficiencies are same as those of Table 11. This shows that the results in Table 11 are robust and the positive impact of Average age of fleet on the overall energy efficiency is resulted from its influence on Sales efficiency.
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The topic of airline energy efficiency is studied in this paper. The energy efficiency process is divided into four stages: Operations Stage, Fleet Maintenance Stage, Services Stage and Sales Stage. Number of Employees and Aviation Kerosene are the inputs of the Operations Stage to produce ASK and ATK. Fleet Maintenance Costs is the input of the Fleet Maintenance Stage to generate Fleet Size. The outputs of the previous two stages and Number of Destination are inputs of the Services Stage to produce RTK and RPK. The RTK, RPK and Sales Costs are the inputs of the Sales Stage to generate Total Business Income. A comprehensive and integrated approach, the integrated approach with Network EBM and Network SBM is proposed and applied to assess the energy efficiencies of 19 airlines from 2008 to 2014. Based on the efficiency scores, the influencing factors of the energy efficiency are analyzed. Overall, the contribution of this paper to the literature is embodied in two aspects. First, a new four-stage operating framework of airline energy efficiency is proposed. Compared with existing studies, the inputs are selected specifically to make the structure of energy efficiency more complete. The concept in this paper enriches the theory and method of airlines management research and supplies a new viewpoint for evaluating the performance of airlines. Second, an integrated approach with Network EBM and Network SBM is proposed, which can modify the
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ACCEPTED MANUSCRIPT one input case in the NEBM model by applying the NSBM model. In future research, the study will focus on evaluating airline efficiencies by adding the time dimension. A dynamic Network DEA model will be studied to measure the efficiency changes in a certain period.
Acknowledgements
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We are grateful to the anonymous reviewers and the editors for their constructive comments which improved this paper significantly. This research is funded by National Nature Science Foundation of China: (Nos.71403034 and 71521002), Nature Science Foundation of Liaoning Province (No.201601841), China Postdoctoral Science Foundation (No. 2016M590050) and National Key R&D Program (2016YFA0602603).
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Appendix A
The steps to solve the integrated NEBM and NSBM model are shown as follows:
Step 1: Form the diversity matrixes of division k . Taking the example from the inputs side, the diversity matrix of division k are formed by
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[ ]
k D k = D pq
using the values of X as follows: Define
m k × mk
, p , q = 1, L m k ,
where
represents the
k D pq
input projection vector dispersion ratio of X pk = ( x pk1,L, x pdk ) compared with X qk = ( xqk1 ,L, xqdk ) . is calculated as follows:
k D pq
l
∑c
X pk
c kj = ln
j =1
{ }
{ }
k = max c kj , cmin = min c kj .
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c
c kj
∑l, j =1
k max
,
X qk
l
ck =
−ck
k k l (cmax − cmin )
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k D pq = D( X pk , X qk ) =
k j
Following the above process, the diversity matrix of the outputs can be formed.
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Step 2: Form the affinity matrixes of division k . Define S k = [S pqk ]mk ×mk , p, q = 1,L, mk
as the affinity matrix of division k , where s kpq
represents the degree of the affinity of input projection vector X pk to X qk .
k S pq
is calculated as
follows:
k k . S pq = 1 − 2 × D pq
The affinity matrix of the outputs can be formed by the same approach. Step 3: Calculate ε xk and wik − from the affinity matrixes. After S k is obtained, its largest eigenvalue ρ xk and the associated eigenvector W xk = ( w1kx , L , w mk k x )
can be solved. The values of ε xk and wik − are calculated by the following
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mk − ρ xk ( if mk > 1), mk − 1
ε xk = 0 (if mk = 1), wik − =
wixk mk
∑w
.
k ix
ε yk and urk − can also be calculated through the above steps.
Step 4: Solve the integrated approach with NEBM and NSBM.
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i =1
After εxk , wik − , ε yk and urk− are determined, the integrated NEBM and NSBM model can be
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solved.
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[54]
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; 2009-2015. Eva
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[55]
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; 2009-2015.
[56]
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; 2009-2015.
[57] Cui Q., Kuang H. B., Wu C. Y., Li Y., 2014. The changing trend and influencing factors of energy efficiency: The case of nine countries. Energy, 64(1),1026-1034. [58] Low, J.M.W., Lee, B.K., 2014. Effects of internal resources on airline competitiveness. J. Air Transp. Manage. 36 (36), 23-32. [59] Dožić, S., Kalić, M., 2015. Three-stage airline fleet planning model. J. Air Transp. Manage. 46, 30-39. [60] Cui, Q., Li, Y., 2015. Evaluating energy efficiency for airlines: an application of VFB-DEA. J. Air Transp. Manage. 44, 34–41. [61] Delbari, S.A., Ng, S.I., Aziz, Y.A., Ho, J.A., 2016. An investigation of key competitiveness indicators and
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ACCEPTED MANUSCRIPT drivers of full-service airlines using Delphi and AHP techniques. J. Air Transp. Manage. 52, 23-34.
Figure 1 Changing operating situation during 2008-2014
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(Source: IATA Annual Review 2009-2015)
Figure 2 Changing situation of aviation kerosene during 2008-2014 (Source: IATA Annual Review 2009-2015)
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ACCEPTED MANUSCRIPT Number of Employee
ASK RPK
Operations
Aviation Kerosene
Total Business Income
ATK
Services Fleet Size
RTK
Number of Destination
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Fleet Maintenance
Maintenance Costs
Sales
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Sales Costs
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Figure 3 The airline energy efficiency network
Figure 4 Changing operating situation during 2004-2014
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(Source: IATA Airline Industry Economic Performance)
Table 1 Literature on airline efficiency evaluation based on two-stage network DEA Stages
Intermediate
Inputs
measures
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Papers Chiou
Cost
and
efficiency,
Chen
Service
[33]
effectiveness
Fuel cost, Personnel cost, Aircraft cost
Number of flights, Seat-miles
Outputs
Passenger-miles, Embarkation passengers
Cost per Available Seat Mile, Salaries per
Zhu
Two-stage
Available Seat Mile, Wages per Available Seat
Load factor, Fleet
Revenue
[34]
process
Mile, Benefits per Available Seat Mile, Fuel
size
Passenger Miles
Production
Number of employees, Fuel consumption,
Lu et
efficiency,
Total number of seats, Cost of flight
al. [35]
Marketing
equipment, Maintenance expenses, Cost of
efficiency
equipment and property
expense per Available Seat Mile
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Available Seat Miles, Available Tonne Miles
Revenue Passenger Miles, Non-Passenger
ACCEPTED MANUSCRIPT Revenue Lozano
process, Sales
Passenger
Available Tonne
Kilometers,
Kilometers, Selling
Revenue Tonne
costs
Kilometers
Technical
Tavass oli et al. [37]
salaries, Other operating costs
process
ez [36]
Kilometers,
Fuel cost, Non-current assets, Wages and
efficiency,
Number of passenger planes, Number of
Passenger-plane-k
Passenger-km,
Service
employees, Number of cargo planes
m, Cargo-plane-km
Ton-km
Capital, Labor, Materials
RTK/Load Factor
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Gutiérr
Production
Revenue
effectiveness
Duygu
Intermediate
n et al. [38]
efficiency, Final
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and
Available Seat
efficiency operations efficiency,
and Li
carbon
[39]
abatement
Salaries, Wages and benefits, Fuel expenses,
Estimated carbon
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Total assets, Abatement expense
efficiency
dioxide
Revenue ton kilometers
RPK, RTK, Carbon dioxide
Table 2 Descriptive statistics of the inputs, outputs and intermediate products Variables
Mean
Std. dev.
Min
Max
38,953.87
30,966.53
4,486.00
119,084.00
427.75
260.89
66.67
1,142.21
Maintenance Costs (10 dollars)
7.19
5.46
0.44
23.96
Number of Destination
165.82
91.32
51.00
469.00
7.26
4.51
0.65
18.28
138.75
102.40
16.08
419.18
130,575.19
85,197.98
24,672.00
385,710.59
Available Tonne Kilometers (10 )
13,688.90
11,171.76
504.00
50,844.00
Fleet Size
273.74
202.41
53.00
772.00
Revenue Passenger Kilometers
103,767.69
72,775.06
17,677.00
370,806.86
9,286.03
8,524.33
357.33
36,131.93
The inputs 4
Aviation Kerosene(10 tons) 8
Sales Costs
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The outputs
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Number of Employee
Total business income (108 dollars)
Intermediate products
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Available Seat Kilometers (106)
6
6
(10 person-kilometers)
Revenue Tonne Kilometers 6
(10 ton-kilometers)
Table 3 Input–output correlations ASK
ATK
NE
0.828
0.444
AK
0.961
0.653
MC
FS
RPK
0.782
22
RTK
TBI
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0.993
0.738
ATK
0.72
0.949
FS
0.845
0.493
ND
0.73
0.42 0.911
RTK
0.563
SC
0.667
Note: All correlation coefficients are statistically significant at the 1% level. Table 4 The values of ε x , w NE and w AK
w NE
2008
0.4765
0.5
2009
0.5598
0.5
2010
0.6192
0.5
2011
0.2495
0.5
2012
0.2474
0.5
2013
0.3435
0.5
2014
0.3285
w AK
0.5 0.5
SC
εx
0.5
0.5
0.50 0.5
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Year
RI PT
RPK
0.5
0.5
Table 5 Overall energy efficiencies of 19 airlines 2008
2009
2010
2011
2012
2013
2014
Average
China Eastern
0.454
0.434
0.227
0.219
0.201
0.436
0.538
0.359
China Southern
0.137
0.057
0.439
0.201
0.445
0.793
0.305
0.339
Korean Air
0.259
0.340
0.280
0.787
0.629
0.787
0.759
0.549
Qantas
0.021
0.403
0.240
0.325
0.340
0.403
0.310
0.292
Air France-KLM
0.921
0.950
0.520
0.468
0.455
0.447
0.400
0.594
Lufthansa
0.891
0.469
0.939
0.932
0.671
0.785
0.966
0.808
Scandinavian
0.441
0.757
0.924
0.354
0.377
0.380
0.397
0.519
Delta
1.000
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Airlines
0.189
0.226
0.250
0.337
1.000
0.458
0.053
0.120
0.244
0.163
0.283
0.329
0.185
1.000
0.307
0.362
0.453
0.314
0.358
0.369
0.452
0.038
0.149
0.035
0.142
0.145
0.323
0.377
0.173
Air Canada
0.045
0.076
0.104
0.263
0.260
0.402
0.447
0.228
Cathay Pacific
0.005
0.198
0.068
0.065
0.075
0.245
0.546
0.172
Singapore
0.237
0.188
0.450
0.580
0.558
0.624
0.656
0.470
All Nippon
0.243
0.448
0.400
0.813
0.499
0.602
0.623
0.518
Eva Air
0.233
0.194
0.259
0.389
0.463
0.701
1.000
0.463
Turkish
0.214
0.134
0.313
0.409
0.332
0.376
0.467
0.321
Thai
0.146
0.062
0.067
0.660
0.734
0.525
0.552
0.392
Indonesia
1.000
1.000
1.000
0.347
1.000
1.000
0.330
0.811
Hainan
AC C
Emirates
EP
0.206
0.106
Air China
Table 6 Average efficiencies and relative rankings of the four stages Airlines
E1
R1
E2
R2
23
E3
R3
E4
R4
ACCEPTED MANUSCRIPT China Eastern
0.822
8
0.389
10
0.674
10
0.430
18
China Southern
0.753
10
0.300
15
0.628
15
0.449
16
Korean Air
0.725
12
0.379
11
0.801
7
0.922
2
Qantas
0.609
14
0.117
19
0.477
19
0.792
5
Air France-KLM
0.773
9
0.407
9
0.835
5
0.866
3
Lufthansa
0.940
4
0.533
5
0.806
6
1.000
1
Scandinavian
0.958
3
0.498
6
0.696
9
0.679
11
13
0.486
7
0.670
0.540
18
0.307
14
0.488
12
0.694
8
18
0.430
17
Hainan
0.938
5
0.599
3
0.777
8
0.539
12
Emirates
0.458
19
0.440
8
0.670
13
0.463
15
Air Canada
0.545
17
0.213
18
0.525
16
0.689
9
Cathay Pacific
0.579
16
0.227
17
0.672
11
0.421
19
Singapore
0.732
11
0.285
16
0.972
3
0.715
7
All Nippon
0.836
6
0.315
13
0.654
14
0.686
10
Eva Air
0.974
2
0.560
4
0.989
1
0.518
13
Turkish
0.835
7
0.615
2
0.510
17
0.465
14
RI PT
0.687
Air China
M AN U
SC
Delta
Thai
0.584
15
0.368
12
0.921
4
0.749
6
Indonesia
1.000
1
0.963
1
0.985
2
0.815
4
* E i ( i = 1,2,3,4 ) represents the efficiency of the i th stage. Ri ( i = 1,2,3, 4 ) represents the ranking of the i th stage.
Table 7 Overall energy efficiency change indices 2009
China Eastern
0.96
China Southern
2011
2012
2013
2014
0.52
0.96
0.92
2.18
1.23
0.42
7.68
0.46
2.21
1.78
0.38
Korean Air
1.31
0.82
2.81
0.80
1.25
0.96
Qantas
18.86
0.60
1.35
1.04
1.19
0.77
Air France-KLM
1.03
0.55
0.90
0.97
0.98
0.89
0.53
2.00
0.99
0.72
1.17
1.23
1.72
1.22
0.38
1.06
1.01
1.05
0.21
0.92
1.20
1.10
1.35
2.97
Scandinavian
AC C
Delta
EP
Lufthansa
2010
TE D
Airlines
Air China
0.50
2.27
2.04
0.67
1.74
1.16
Hainan
0.31
1.18
1.25
0.69
1.14
1.03
Emirates
3.86
0.24
4.01
1.03
2.22
1.17
Air Canada
1.71
1.36
2.54
0.99
1.55
1.11
Cathay Pacific
40.89
0.35
0.95
1.16
3.26
2.22
Singapore
0.79
2.39
1.29
0.96
1.12
1.05
All Nippon
1.85
0.89
2.03
0.61
1.21
1.03
Eva Air
0.83
1.34
1.50
1.19
1.52
1.43
Turkish
0.63
2.34
1.31
0.81
1.13
1.24
Thai
0.42
1.08
9.88
1.11
0.71
1.05
Indonesia
1.00
1.00
0.35
2.88
1.00
0.33
24
ACCEPTED MANUSCRIPT Table 8 Analysis on airlines with highly declining overall energy efficiency The Airlines
synchronously
Year
declined stage
The reasons
efficiencies 2009-2010
1,2,3,4
increased AK, MC, ND, SC
China Southern
2008-2009
1,2,4
increased AK, NE and SC, declined TBI
2010-2011
1,2,3
increased AK, MC and ND
2013-2014
1,3,4
increased AK, NE, FS and SC
Air France-KLM
2009-2010
1,2,3,4
increased AK, SC
Lufthansa
2008-2009
1,2,3
increased AK, NE, MC and ND, declined RTK
Scandinavian
2010-2011
2,3,4
increased MC, ASK,ATK and SC, declined FS and RTK
Delta
2008-2009
1,2,3,4
increased AK, NE, MC, ND and SC, declined TBI
Air China
2008-2009
2,3,4
increased MC, ND, declined TBI
Hainan
2008-2009
1,2,3,4
increased AK, MC, ND and SC
Emirates
2009-2010
1,2,3,4
increased AK, NE, FS, increased MC more
Cathay Pacific
2009-2010
2,3,4
increased MC, ND and SC
Thai
2008-2009
1,2,4
M AN U
SC
RI PT
China Eastern
declined AK and NE, declined ASK and ATK more,
sharply declined TBI
Indonesia
2010-2011
3,4
2013-2014
2,3,4
increased ND and SC
increased MC, ND and SC
Table 9 Analysis on airlines with sharply increasing overall energy efficiency
Airlines
Year
TE D
The synchronously increased stage
The reasons
efficiencies
2009-2010
Qantas
2008-2009
Emirates
2008-2009
highly increased ASK, ATK, FS, RTK, RPK and TBI
1,2,3
increased FS, declined AK, NE and ND
1,3
increased ASK, ATK , FS, increased RPK and RTK more
2010-2011
1,2,4
highly increased ASK, ATK, FS and TBI
2008-2009
1,2,3
increased FS, declined AK, MC and ND
2012-2013
1,2,3
declined AK, NE and MC, increased FS and RPK
1,2,3,4
increased ASK, ATK and TBI
AC C
Cathay Pacific
1,2,3,4
EP
China Southern
Thai
2010-2011
Table 10 Influencing factors of airline energy efficiency
Influencing factors
Data Sources
Per capita GDP($)
The World Bank (www.worldbank.org.cn)
Average age of fleet(year)
The annual yearbook of the airlines
Number of destination
The annual yearbook of the airlines
Average haul(km)
The annual yearbook of the airlines
Inflation rate
The World Bank (www.worldbank.org.cn)
Consumer confidence index(CCI)
Trading Economics (www.tradingeconomics.com)
Table 11 The results of Regression Analysis
25
ACCEPTED MANUSCRIPT Overall energy efficiency
Operational efficiency
Fleet maintenance efficiency
Coefficient
t-Statistic
Prob.
Per capita GDP
5.25E-06
1.201
0.233
Average age of fleet
6.12E-02
2.108
0.038
Number of destination
-1.14E-04
-0.155
0.877
Average haul
-7.44E-05
-0.457
0.649
Inflation rate
6.24E-05
0.004
0.997
CCI
3.02E-03
0.693
0.490
Per capita GDP
5.80E-06
1.876
0.064
Average age of fleet
-1.33E-02
-0.649
0.518
Number of destination
-1.41E-04
-0.271
0.787
Average haul
1.96E-04
1.702
0.092
Inflation rate
-9.94E-03
-0.833
0.407
CCI
5.43E-03
1.759
0.082
2.59E-06
0.695
0.489
2.55E-02
1.028
0.306
4.60E-04
0.733
0.465
-2.71E-05
-0.195
0.846
Inflation rate
-9.16E-03
-0.637
0.526
CCI
2.36E-03
0.634
0.528
Per capita GDP Average age of fleet Number of destination
Per capita GDP
3.24E-06
0.986
0.326
Average age of fleet
1.32E-02
0.603
0.548
Number of destination
-8.34E-04
-1.506
0.135
Average haul
1.80E-05
0.146
0.884
Inflation rate
-1.18E-02
-0.933
0.353
CCI
2.21E-03
0.674
0.502
Per capita GDP
-2.74E-06
-0.672
0.503
Average age of fleet
8.62E-02
3.179
0.002
Number of destination
5.47E-04
0.795
0.429
Average haul
-2.05E-04
-1.349
0.180
TE D
Service efficiency
M AN U
Average haul
AC C
EP
Sales efficiency
RI PT
Influencing factors
SC
Efficiencies
Inflation rate
3.06E-02
1.941
0.055
CCI
-3.86E-03
-0.948
0.345
Coefficient
t-Statistic
Prob.
Table 12 The results after average age of fleet is deleted
Efficiencies
Influencing factors
Overall energy efficiency
Per capita GDP
3.13E-06
0.723
0.471
Number of destination
-1.92E-04
-0.257
0.798
Average haul
-4.90E-05
-0.297
0.767
Inflation rate
9.01E-04
0.053
0.958
CCI
5.31E-03
1.236
0.219
Per capita GDP
6.26E-06
2.087
0.039
Number of destination
-1.24E-04
-0.239
0.812
Average haul
1.91E-04
1.663
0.099
Inflation rate
-1.01E-02
-0.851
0.397
CCI
4.93E-03
1.654
0.101
Operational efficiency
26
ACCEPTED MANUSCRIPT
Sales efficiency
1.71E-06
0.471
0.639
Number of destination
4.28E-04
0.682
0.497
Average haul
-1.66E-05
-0.120
0.905
Inflation rate
-8.81E-03
-0.613
0.541
CCI
3.31E-03
0.918
0.361
Per capita GDP
2.79E-06
0.873
0.385
Number of destination
-8.51E-04
-1.544
0.126
Average haul
2.34E-05
0.192
0.848
Inflation rate
-1.17E-02
-0.922
0.359
CCI
2.70E-03
0.853
0.396
Per capita GDP
-5.73E-06
-1.382
0.170
Number of destination
4.36E-04
0.609
0.544
Average haul
-1.69E-04
-1.070
0.287
3.18E-02
1.932
0.056
-6.46E-04
-0.157
0.876
RI PT
Service efficiency
Per capita GDP
SC
Fleet maintenance efficiency
Inflation rate
AC C
EP
TE D
M AN U
CCI
27
ACCEPTED MANUSCRIPT 1. An integrated approach with Network Epsilon-based Measure and Network Slacks-based Measure is developed. 2. 19 airlines’ energy efficiencies are evaluated.
AC C
EP
TE D
M AN U
SC
RI PT
3. Garuda Indonesia has the highest overall energy efficiency.
