International Journal of Transportation Science and Technology xxx (xxxx) xxx
Contents lists available at ScienceDirect
International Journal of Transportation
Science and Technology
journal homepage: www.elsevier.com/locate/ijtst
Airline efficiency and environmental impacts – Data
envelopment analysis
Arun Saini, Dothang Truong ⇑, Jing Yu Pan
Embry-Riddle Aeronautical University, United States
a r t i c l e
i n f o
Article history:
Received 20 July 2021
Received in revised form 9 November 2021
Accepted 19 February 2022
Available online xxxx
Keywords:
Airline efficiency
Data envelopment analysis
Environmental impacts
Airline performance
a b s t r a c t
Airline efficiency has been a research interest for decades. While early airline efficiency
research focused primarily on revenue generation and profitability, growing airline social
responsibility is driving greater investment into understanding and improving the environmental impact on airline efficiency. This study developed a two-phase, two-stage model
using a data envelopment analysis (DEA) approach to simultaneously evaluate airline operations for available seat mile (ASM) generation, revenue passenger mile (RPM) generation,
carbon dioxide emissions abatement, and revenue generation on a sample of thirteen airlines. Efficiency evaluation was performed for the years between 2013 and 2015, between
U.S. and non-U.S. carriers, and between full-service carriers (FSCs) and low-cost carriers
(LCCs). Results indicated more accurate measurement of airlines’ overall efficiency using
the proposed DEA model, which included operational and cost factors as input variables
and environmental impact as both the input and output variables in the model. Service
and environmental factors were found to be significant in determining airline efficiency, with
environmental abatement affecting the overall efficiency of airline performance both inside
and outside the U.S. when emission reduction effort was properly accounted for. The findings
provided theoretical and managerial implications in the assessment of airline efficiency with
a special emphasis on incorporating environmental impact in the overall evaluation.
Ó 2022 Tongji University and Tongji University Press. Publishing Services by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/
licenses/by-nc-nd/4.0/).
1. Introduction
Airline efficiency describes the airline’s ability to maximize its performance while minimizing resource consumption
(Forsyth et al., 1986). Numerous studies have defined and measured airline efficiency, focusing on operational efficiency
and consumption of assets to produce revenue (Sengupta, 1999), the effect of marketing and passenger services on airline
efficiency (Scheraga, 2004), the impact of route configuration strategy and related costs on airline efficiency (Caves et al.,
1984), and unionization as a possible factor in airline efficiency (Greer’s, 2009). While studies have typically focused on
the impact of operational and cost factors such as labor, average load factor, and fleet optimization on airline efficiency,
recent studies have considered broader factors such as socioeconomic and environmental factors. The environmental impact
of aviation has received particular attention given the projected increase in aviation CO2 emission, from approximately 3.5 %
of the Global Greenhouse Emissions in the 1990 s to 15%-40% by 2025 (Gössling & Peeters, 2007; Intergovernmental Panel on
Peer review under responsibility of Tongji University and Tongji University Press.
⇑ Corresponding author.
E-mail address: truongd@erau.edu (D. Truong).
https://doi.org/10.1016/j.ijtst.2022.02.005
2046-0430/Ó 2022 Tongji University and Tongji University Press. Publishing Services by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: A. Saini, D. Truong and Jing Yu Pan, Airline efficiency and environmental impacts – Data envelopment analysis,
International Journal of Transportation Science and Technology, https://doi.org/10.1016/j.ijtst.2022.02.005
A. Saini, D. Truong and Jing Yu Pan
International Journal of Transportation Science and Technology xxx (xxxx) xxx
Climate Change, 1999). This has generated considerable research interest in the environmental aspect of airline operations
and its impact on airline efficiency (Cui & Li, 2016; Chen et al., 2017; Cui & Li, 2018, 2019, 2020; Li & Cui, 2021; Wang et al.,
2020; Cui, 2019, 2020, 2021; Cui & Yu, 2021; Kim & Son, 2021; Kaya & Aydin, 2021; Xu et al., 2021).
As airline efficiency analysis has started to incorporate both operational and social considerations, methods for measuring
efficiency have evolved to cope with the increasing complexity of analysis. Researchers have moved away from traditional
regression analysis to utilize methods such as data envelopment analysis (DEA) for airline efficiency (Mallikarjun, 2015). DEA
makes use of multiple inputs and outputs to evaluate decision-making units (DMUs) and their productive efficiencies
(Sengupta, 1999). This technique starts with defining a benchmark (production frontier) to provide a hypothesized optimal
performance level for DMUs to compare their efficiencies in relation to the consumption of inputs and production of outputs.
A major advantage of DEA is the ability to perform efficiency analysis without the need for cost information, which makes it
particularly useful for the research of efficiency in the aviation industry where cost-related data is often not available due to
data sensitivity (Merkert and Hensher, 2011; Sengupta, 1999). Early DEA research of airline efficiency focused on operations,
often involving converting inputs (e.g., materials, labor, and capital) into outputs (e.g., revenue) (Sengupta, 1999). Recent
DEA studies incorporated non-operational factors (e.g., environmental factors) for a more accurate analysis of airline efficiency. This often involves the use of single-stage DEA models (Wang et al., 2020; Xu et al., 2021; Kim & Son, 2021; Cui &
Li, 2020; Li & Cui, 2021; Cui & Yu, 2021; Cui, 2021) or multi-stage DEA models (Cui & Li, 2016; Chen et al., 2017; Cui &
Li, 2018, 2019; Shirazi & Mohammadi, 2019; Cui, 2019, 2020) with multiple input and output variables identified to uncover
relationships that may have been hidden for other methodologies.
Despite the increasing application of DEA in investigating airline efficiency, some gaps still exist in the literature, calling
for more research to deepen the understanding of various types of impact factors and their combined effect on airline efficiency. Specifically, there is a need to further explore the role of environmental factors in the study of airline efficiency. Prior
DEA studies of airline efficiency typically focused on factors that affect aircraft operating costs (Chen et al., 2017; Wang et al.,
2020; Xu et al., 2021; Kim & Son, 2021). In these studies, environmental impact was often treated as an output in the form of
pollution and particulate or acoustic emissions. There is a need to define environmental factors as both input and output
variables in the DEA model and structure the decision-making units in a way that considers environmental impact or
abatement expenses in the same total efficiency calculation. Incorporating environmental factors at multiple steps of the
decision-making process allows for better integration of operational and non-operational factors in the DEA analysis, with
environmental consideration playing a more significant role in model development, as both input and output variables, to
achieve the accurate analysis of airline efficiency. Few studies used environmental variables as one of the primary inputs,
such as the works by Cui and Li, (2016, 2018, 2019); Cui (2019), and Cui & Yu (2021). However, in those studies, environmental impact, measured by carbon oxide emissions created from aviation kerosene consumption, was used as an input
in either in stage 1 or in stage 2 in those models but not both. Additionally, they focused mainly on airlines operating outside
of the U.S., mostly in Asia and Europe. Due to the dynamism and high air travel volumes in the U.S. market, it is imperative to
understand further how efficiently airlines have been operating in this market over time.
This study developed a two-phase, two-stage multiplicative DEA model to evaluate and differentiate the efficiency of
selected airlines. In addition to operational factors, it considers environmental impact as both input and output variables
instead of treating it solely as an output or an input. The inclusive, multi-stage analytical process aims to answer two questions, including (1) Is environmental impact a viable input and output variable in addition to traditional operating and revenue generating factors in modeling airline efficiency, and, (2) What are the relative differences among airlines in terms of
achieving optimal efficiency benchmark, when all facets of airline efficiency - operational constraints, environmental
impacts, and revenue generating effectiveness – are taken into consideration? By defining environmental factors as both
input and output variables, environmental impact can be analyzed in both consumption and production stages, which allows
for new insights into airline efficiency. Furthermore, the study focuses mainly on the U.S.market, which has been inadequately examined in the current airline efficiency literature. We evaluate the efficiency of both domestic and international
airlines operating in the U.S. market and compare the efficiency scores in multiple ways: overall comparison of all airlines,
comparison between U.S.-based and non-U.S.-based airlines, and comparison between Full Service Carriers (FSCs) and Low
Cost Carriers (LCCs). For each analysis, we provide the efficiency for each phase, each stage, and overall efficiency. For the
airline industry, the model can be used to evaluate and compare airline operations in different scenarios while taking into
consideration of environmental impact, which helps improve the overall efficiency of airlines.
The remainder of the paper is structured as follows. Section 2 reviews the relevant literature and proposes the theoretical
framework for the study. The methodology for data collection and analysis is explained in Section 3, followed by result presentation in Section 4. Section 5 interprets the study results, and Section 6 provides conclusions to the study with a summary
of practical implications, study limitations, and future research directions.
2. Literature review
2.1. Environment impact in aviation
Air transportation consumes large amounts of resources. It is, therefore, essential that valuable resources be utilized
effectively to support the growing demand for air transport. One way to achieve sustainable growth is to fly more efficiently
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while minimizing the environmental footprint through technological, operational, and infrastructural improvement. In
recent years, airlines have directed considerable investment to fulfill the corporate and social environmental responsibility
(CSER) objectives. The decision to pursue the CSER goals has been largely driven by market-based factors, including CO2
emission allowances and landing fees for heavily polluting aircraft. In addition, maintaining constructive relationships with
customers expecting airlines to demonstrate corporate responsibility is another motivating factor for airlines to consider
environmental impacts (Lynes & Andrachuk, 2008).
Several studies attempted to quantify the environmental impacts on aviation, based on which emission reduction measures can be established. Lu and Morrell (2006) developed a social cost estimation method to calculate the noise social costs
by incorporating the population density of the communities located near the airport. The study utilized a summation equation combining emissions produced at each phase of flight operation to quantify the generation of noise pollution from
engine operations. It was determined that the noise impact was most significant during taxi, take-off, and landing (TT&L)
phases of a flight (Lu & Morrell, 2006). Accordingly, recommendations were made to use newer, more efficient aircraft to
reduce emissions. Indeed, fleet optimization has become a new focus in the research of environmental impact on the airline
industry. Rosskopf et al. (2014) developed a fleet planning optimization model containing cost data, airline network information, business financial capability, and nitrogen oxide emissions (NOx), taking into consideration factors such as aircraft
types and emissions in different flight segments. The model was then used to maximize fleet asset value over a multiple year
time framework, with different emission goals and fuel costs set at varying levels for comparison. While the results indicated
the necessity to replace aged, less efficient aircraft with newer, more efficient ones to achieve the operational and environmental goals, they also pointed out the significant costs associated with reducing aircraft noise emissions, which can put
further pressure on airline financial performance (Rosskopf et al., 2014).
The large costs associated with fleet modernization have prompted airlines to seek alternative means to fulfill their
environmental responsibility and improve efficiency (Delta, 2016). Lufthansa Airlines, for example, disclosed the airline’s
year-on-year comparison of fuel dumping in reducing its environmental footprint (Lufthansa, 2016). Recognizing that inflight catering generates 70% of all non-hazardous waste, Air France-KLM improved the design in catering trolleys to allow
for efficient separation of the different types of cabin waste (Air France-KLM, 2016). Airlines also prioritize fuel efficiency
while offsetting aircraft emissions. This was achieved by KLM where now 70% of its pre-conditioned air supply carts were
powered by electric instead of fossil fuel (Air France-KLM, 2016). Other ways of minimizing environmental impact include
environmental friendly packages for catering, weight-saving strategies in the cabin (e.g., removing magazines), and the use
of electric flight bags (EFB) in replacement of large printed mandatory pilot manuals (Air France-KLM, 2016; Lufthansa,
2016).
2.2. Data envelopment analysis (DEA) and related studies of airline efficiency
2.2.1. DEA
While airline efficiency has become a research interest since the 1900 s, the definition and measurement of airline efficiency have not been extensively covered until recent decades (Marti et al., 2015). As the industry continues to evolve, the
measure of efficiency has become complex to consider not only operational and cost characteristics but also socioeconomic
factors, with the environmental impact being a predominant one. Data envelopment analysis (DEA) has increasingly been
recognized as a suitable method for evaluating airline efficiency.
DEA uses a nonparametric approach to evaluate multiple decision-making units (DMUs), often involving multiple inputs
and outputs. This analysis method does not require numerical values to be assigned to input and output variables. Rather, the
research can define the units of measure for inputs and outputs regardless of their actual market values. The relationships
between input and output variables would then be analyzed using linear programming models. The analysis starts with the
development of an optimal DMU, based on which the relative efficiency of multiple DMUs is compared to each other and to
the optimal DMUs. DEA has been used in evaluating operational and business performance in various domains (Zhu, 2014),
including air transportation.
Essential to DEA is the identification of best practices of peer DMUs based on multiple input and output variables. In
many cases, there can be intermediate measures in the process of converting the inputs of a firm into outputs. When this
situation presents itself, multi-stage DEA, representing tiered decision-making efforts of a firm, becomes a suitable method
for efficiency analysis. In a multi-stage DEA, mathematical formulas are used to simultaneously optimize all stages of the
model by using the outputs of the earlier stage as the inputs of the subsequent stage. During this process, the multi-stage
DEA model generates a combined set of decisions (e.g., variable values) to represent aggregated, best practices of DMUs.
Multi-stage DEA has been widely used to assess performance efficiency in areas such as information technology (Chen &
Zhu, 2004), insurance (Kao & Hwang, 2008), and regional sustainability (Halkos et al., 2015). These studies generally
showed an improved understanding of efficiency. Kao and Hwang (2008), for example, modified the traditional DEA model
by dividing the decision process into two stages in measuring the product efficiency of non-life insurance companies in
Taiwan. In their model, the outputs of the first stage were used as the inputs of the second stage of analysis. Taking
the series of relationships into account, the study gained deeper insights into the efficiencies of the insurance companies
and demonstrated that the overall efficiency was the product of the efficiencies of the two sub-processes (Kao & Hwang,
2008).
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2.2.2. DEA studies of airline efficiency
Many studies have used DEA to measure airline efficiency, often comparing airlines in different geographical markets
within a given timeframe. DEA was found to be a preferred method in these studies, compared to other methods such as
regression-based analysis given its ability to generate a more comprehensive evaluation of the efficiency of individual DMUs
against predetermined benchmarks (Good et al., 1995). An inclusive and flexible modeling method, DEA allows for the addition of tertiary variables to gain deeper insights into airline operational efficiency. The aforementioned flexibility was evident in Scheraga (2004) which explored measures to balance investment between the goals of productive efficiency and
customer-driven improvement. Similarly, an input-oriented DEA was utilized to calculate efficiency scores for 38 airlines
across the world, considering both operational and environmental variables, which provided a holistic view of the airline
efficiency (Oum et al., 2005). Several studies evaluated airline efficiency with a focus on environmental impacts using
DEA models (Wang et al., 2020; Xu et al., 2021; Kim & Son, 2021; Cui & Li, 2020; Li & Cui, 2021; Cui & Yu, 2021; Cui,
2021). Those studies mainly used a single-stage DEA model to compare efficiency across airlines in the group.
To facilitate complex analyses and increase the validity of a DEA model, researchers have employed multi-stage analytical
technique in which DEA is conducted in consecutive sequences of individual stages. Each stage deploys its own mathematical equations to assess efficiency. Merkert and Hensher (2011) developed a two-stage DEA model, evaluating technical,
allocative, and cost efficiencies of 58 airlines from 2007 to 2009. An important component in their model development
was defining the interim values that connected the different stages. For example, the outputs of the first stage were interim
values, which served as inputs in the subsequent research stage. The results of the multiple stages were then combined to
represent the decision made by a DMU. The study produced mixed findings, with some (e.g., improved efficiencies due to
larger market exposure) agreeing with the expected trends while others (e.g., greater cost efficiencies due to longer stage
length) contradicted the literature (Merkert & Hensher, 2011). Zhu (2011), following a similar concept, developed a twostage DEA model to assess the efficiency of 21 airlines operating in the U.S. In the first stage, the focus was airlines’ operational efficiency. Several input variables such as fuel costs, salaries and wages, and operating costs were used to examine if
optimal load factor and fleet size can be achieved using these inputs. Airline efficiency was then used as the inputs in the
second stage of analysis, with the purpose of evaluating the airlines’ revenue generation. In this model, airline efficiency
became an interim variable that connects the two stages of DEA analysis. Results indicated different levels of airline performance in each stage, with no airlines achieving optimal efficiency for both stages (Zhu, 2011). Clearly, compared to the single
stage DEA model, the two-stage DEA model can provide a more accurate assessment of airline performance and efficiency.
Several studies have added stage(s) to DEA analyses of airline efficiency (Mallikarijun, 2015; Li et al., 2015). Mallikarjun
(2015) developed a three-stage DEA model to estimate the overall efficiency of the airline, with the stages being labeled cost
efficiency, service effectiveness, and sales, respectively. While the first stage of DEA analysis was very similar to that in Zhu
(2011), the evaluation of revenue generation (stage two in Zhu (2011)) was further segmented into two stages – generation
of passenger miles (service effectiveness) and revenue recognition (sales). It can be argued that the three-stage model developed by Mallikarjun (2015), compared to the two-stage model of Zhu (2011), was more representative of the real world airline operations because, instead of transforming the cost inputs directly into revenue, a three-stage analysis considered steps
of airlines to allocate resources and assessed if various decision makings would affect revenue maximization. Inspired by the
study of Mallikarjun (2015), Li et al. (2015) developed multi-stage models to examine airline efficiency, with added measures
to further improve the accuracy of efficiency evaluation.
Recently, more studies used multi-stage DEA models in evaluating airline efficiency. Shirazi & Mohammadi (2019) developed a robust multi-stage DEA to evaluate the efficiency of 14 Iranian airlines with undesirable output. Using the environmental impact as an output, Chen et al. (2017) used a two-stage DEA model to evaluate Chinese airlines’ efficiency for flight
delays and CO2 emission. More notably, a series of studies by Cui and Li (2016, 2018, 2019) and Cui (2019, 2020) used twostage or three-stage DEA models to evaluate the airline efficiency using environmental impact, measured by Aviation Kerosene, as single input. More specifically, in those models, environmental impact was used as input either in the first stage
or second stage, but not both.
The literature review in Section 2 highlighted the research gap in airline efficiency. While existing studies examined the
relationship between operational factors and airline efficiency, more research is needed to integrate these and other impact
factors to improve the evaluation of airline efficiency. Specifically, the relationship between environmental factors and airline efficiency merits further investigation. Environmental factors should be incorporated in the multiple stages of model
development so their effect on airline efficiency can be more clearly identified. From the methodology perspective, while
some studies used multi-stage DEA model to gain new insights into airline efficiency, most studies have used environmental
factors either as an input or output variable, but not both. Additionally, those studies focused mainly on airlines operating
outside the U.S., mostly in Asia and Europe. Some studies by Cui and Li (2021) and Cui (2019, 2020) did include several U.S.
airlines, such as United and American Airlines, but only capture their international operations. Due to the dynamism and
high air travel volumes in the U.S. market, it is imperative to understand further how efficiently airlines have been operating
in this market over time, especially how airlines compete in various market segments, such as between U.S.-based and nonU.S.-based airlines, and between FSCs and LCCs. Finally, although they used multi-stage DEA models, the results were only
presented for the overall model, and the efficiency comparison at each stage was lacking.
To fill the research gap, this study developed a high-fidelity model incorporating operational efficiency, revenue generation effectiveness, and environmental impact abatement to gain a holistic understanding of airline efficiency. Environmental factors were used as both input and output in the model. We focused on airlines operating in the U.S. market, including
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U.S.-based and non-U.S.-based airlines, FCSs, and LCCs. The efficiency comparison was conducted overall and within
each segment. Furthermore, the results were presented at each stage in each phase over time in addition to the total
efficiency.
3. Methodology
This quantitative study employed a two-phase, two-stage DEA approach to evaluate airlines in terms of their cost efficiency, carbon abatement efficiency, and operating efficiency. Section 3 describes population and sample, sources of data,
and model development.
3.1. Population and sample
The DMUs in this study were airlines. The population included airlines that (1) operated in the U.S. between 2013 and
2015 and reported their operations to the Department of Transportation, (2) provided publicly accessible data (specifically
Corporate Sustainability and Responsibility Reports) with specifications of expenditures of airlines to meet their respective
CSER goals and, (3) carried a minimum of 5,000,000 passengers in 2015. Thirteen airlines, FSCs and LCCs, were selected as
the sample for this study. The sample was selected intentionally to reflect the diversity of airline operations, containing U.
S. airlines and international flag carriers that provide service from and to the U.S. Boussofiane et al. (1991) recommended
the number of DMUS should be the multiple of the number of inputs and the number of outputs for the discriminatory
power to exist in the model. Since our model has two phases and two stages, it is necessary to determine the needed sample size for each stage at each phase and the overall model. As presented in Fig. 1, Phase 1 Stage 1 has one input and two
inputs, indicating the needed sample size is 1x2 or two DMUs. Similarly, Phase 1 Stage 2 needs a sample size of 3x2 or 6
DMUs, Phase 2 Stage 1 needs a sample size of 3x2 or 6 DMUs, and Phase 2 Stage 2 needs a sample size of 2x1 or 2 DMUs.
It is trickier to determine the sample size for the overall model due to multiple stages and phases. In this case, we use the
combined number of inputs and outputs in two phases for the calculation. More specifically, there are a total of four
inputs (one in Phase 1 and three in Phase 2) and three outputs (two in Phase 1 and one in Phase 2). Thus, the sample
size for the overall model is 4x3 or 12 DMUs. The study sample includes 13 airlines, thus, meeting those sample size
requirements airlines. They are Air Canada, Alaska Airlines, Air France-KLM, All Nippon Airways, American Airlines, British
Airways, Delta Air Lines, Emirates, Japan Airlines, JetBlue Airways, Lufthansa German Airlines, Southwest Airlines, and United Airlines. Nonetheless, it is worth noting that the purpose of the DEA method is to benchmark a group of DMUs, in
order to assess and explore the individual efficiencies; the purpose is not meant to serve as a regression analysis, so test
power is not the concern (Zhu, 2014). What is important is to include the airlines that actually compete in the same market and have sufficient data for the efficiency frontier determination. Zhu (2014) recommends that a DEA analysis that is
pursuing higher levels of discrimination should consider the weighting utilized to help narrow the requirements associated with the optimal efficiency frontier.
Fig. 1. Environmental operating efficiency measurement model.
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3.2. Sources of data
Two types of data – airline performance data and emission data - were used in the DEA analyses. For the U.S. carriers,
financial data was obtained from quarterly financial data collected by the Bureau of Transportation Statistics (BTS) under
Title 14 Part 41 requirements, which is available for public access through TranStats (BTS, 2017). For international carriers,
financial data were extracted from public disclosures on their websites. Operating data for both the U.S. and international
carriers were obtained from TranStats (2017). Airline specific emission data such as CO2 emission were obtained from airlines’ CSER reports or other annual reports. Data aggregation was performed to allow inputs to reflect summary data for performance analysis for each year and for any given time period.
3.3. Model development
The model development in this study was based on two established theoretical models in the literature – the three-stage
airline efficiency model proposed by Mallikarjun (2015) and the two-stage model developed by Kao and Hwang (2008), with
modifications to better incorporate environmental impact into efficiency analysis. As reviewed in Section 2, the three stages
of the model developed by Mallikarjun (2015) were designed to (1) convert labor and material resources into airline capacity
such as available seat miles (ASMs), which was the first intermediate output representing supply of product, (2) transform
the ASMs into revenue passenger miles (RPMs), which was the second intermediate output indicating service demand of airlines, and (3) transform the intermediate service into total recognized revenue. The three-stage structure of the DEA model,
while including multiple variables and forward–backward recursive iteration to add useful layers to data analysis, created
more calculation and complexity due to the number of the stages required. Ideally, the DEA model in the present study
can utilize an easy-to-implement, two-stage DEA model while retaining the comprehensive representation of the airline
business of the three-stage model proposed by Mallikarjun (2015). This study thus combined the three-stage model of
Mallikarjun (2015) with the two-stage model of Kao and Hwang (2008) to propose a new theoretical framework that can
be easily utilized to accommodate large datasets.
In this study, a two-phase, two-stage DEA model was developed to evaluate the efficiency of each phase and combine the
product of the two phases to generate the total airline efficiency, with the environmental operating efficiency been incorporated in both phases. Fig. 1 illustrates the two-phase, two-stage model for this study. In this model, the subprocesses were
performed in series based on the interrelationship between phases and stages, and the cross product of the two subprocess
efficiencies is calculated to generate total efficiency.
One major characteristic of the proposed model is the duplication of the second stage of Phase one and the first stage of
Phase two. This model construction helps keep the philosophical construct of the airline business presented in Mallikarjun
(2015) and at the same time enabling the use of DEA approach to model the conceptual relationships between various input
and output variables. The two phases evaluate capacity generation and revenue recognition, respectively. The model construction emphasizes environmental effect both as inputs and outputs. In both phrases, environmental abatement is incorporated to reflect its impact on airline efficiency. The remainder of Section 3.3 explains the multiplicative relational twophase, two-stage model design and corresponding mathematical formula. The mathematical formula was based on Kao
and Hwang (2008), adding environmental variables to evaluate airline efficiency.
3.3.1. Phase One: Capacity generation
Phase one, containing two stages, focused on the capacity generation and transformation of capacity to meet airline
demand. The first stage consumed operating expenses such as materials and labor resources invested for airline operations
to produce the intermediate output of capacity in the form of ASMs. The second stage evaluated two types of efficiencies service efficiency and environmental efficiency. The assessment of service efficiency contained an evaluation of the process
of consuming ASMs generated in Stage One to produce the intermediate output of RPKs, which represents the service
demand of airlines. The service efficiency evaluation, together with the evaluation of transforming resources to produce
ASMs, helped analyze the cost efficiency of an airline. The second stage of Phase One also evaluated environmentalrelated variables such as CO2 emissions and their effect on airline efficiency. In addition, Stage Two also consumed abatement expenses, which referred to airline’s financial expenditure to reduce the environmental impact of its operations.
The abatement-related intermediate output of Stage Two is actual CO2 emissions, which reflected the net carbon impact
on the environment. This value assessed the effectiveness of abatement. It reflected the reduced environmental impact,
which was calculated by subtracting the value of abatement from the estimated total carbon emissions.
Phase One used a VSR EA model to decrease input levels while simultaneously increasing the outputs. In this phase, the
objective function drove to either maximize the efficiency of the first stage for airline k, or minimize the approximate inverse
efficiency of the second stage. There were two constraints in this phrase to ensure the optimal production frontier airline is
increasing in efficiency through the iterations. The first constraint indicated no increase (only decrease is allowed) in the
consumption of operating expense inputs for successive iterations. The second constraint stated that an optimal airline
was increasing airline capacity generation for each successive iteration. The mathematical equation (Formula 1) for Phrase
One is presented below.
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A. Saini, D. Truong and Jing Yu Pan
Formula 1:
Ek ¼ max
s
X
ur Y rk
r¼1
s.t.
m
X
v i X ik ¼ 1
i¼1
s
X
m
X
ur Y rk r¼1
q
X
wp Z pj p¼1
s
X
v i X ij 0; j ¼ 1; ; n
i¼1
m
X
v i X ij 0; j ¼ 1; ; n
i¼1
ur Y rj r¼1
q
X
wp Z pj 0; j ¼ 1; ; n
p¼1
ur ; v i ; wp e; r ¼ 1; ; s; i ¼ l; ; m; p ¼ 1; ; q
where:
E1j: Phase 1 efficiency of airline j
XiOE: Operating expenses input for every iteration i for airline j
YrRPM: Revenue passenger mile output for every iteration r for airline j
YrCO2: Actual CO2 output for every iteration r for airline j.
ZpASM: Available seat mile intermediate output for every iteration p for airline j.
ZpECO2: Estimated CO2 intermediate output for every iteration p for airline j.
ur, vi, wp: All equal 0.5 for equivalence in weighting across input and output variables for both stages of the phase
3.3.2. Phase two: revenue generation
Phase Two focused on revenue generation, containing two stages to provide an efficient measurement of airline revenue
generation. By incorporating the intermediate outputs from the previous phrase, Phrase Two assessed the airline efficiency
in marketing the RPMs and transforming the intermediate service into revenue. The first stage of Phase Two replicated the
second stage of Phase One with the purpose of assessing (1) transformation of ASMs to RPKs and (2) effectiveness of airline
carbon dioxide emissions abatement. In the second stage, the DMU marketed the RPKs and converted the intermediate service into total recognized revenue. The model showed that in this stage, airlines consumed the CO2 output from the abatement segment of the first stage. Thus, the revenue generation was also influenced by airlines’ efforts to minimize operational
impact on the environment.
In terms of formula development, Phase Two used a two-stage VRS DEA model to decrease input levels while simultaneously increasing the outputs. The mathematical formula for Phase Two (Formula 2) is illustrated below:
Formula 2:
E1j ¼ max
s
X
ur fðY rRPM ÞðY rCO2 Þg
r¼1
s.t.
m
X
v i fðX iOE Þg ¼ 1
i¼1
s
X
ur fðY rRPM ÞðY rCO2 Þg m
X
r¼1
q
X
wp
m
X
Z pASM Z pECO2 j v i fðX iOE Þgj 0; j ¼ 1; ; n
p¼1
s
X
r¼1
v i fðX iOE Þgj 0; j ¼ 1; ; n
i¼1
i¼1
ur fðY rRPM ÞðY rCO2 Þgj q
X
wp
Z pASM Z pECO2 j 0; j ¼ 1; ; n
p¼1
ur ; v i ; wp e; r ¼ 1; ; s; i ¼ l; ; m; p ¼ 1; ; q
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International Journal of Transportation Science and Technology xxx (xxxx) xxx
where:
E2j: Phase 2 efficiency of airline j
XiASM: Available seat miles input for every iteration i for airline j
XiECO2: Estimated CO2 input for every iteration i for airline j
YrOR: Operating revenue output for every iteration r for airline j
ZpRPM: Revenue passenger mile intermediate output for iteration p, for airline j.
ZpCO2: Actual CO2 intermediate output for iteration p, for airline j.
ur, vi, wp: All equal 0.5 for equivalence in weighting across input and output variables for both stages of the phase
The multiplicative efficiency method was utilized to determine the total efficiency of each airline. The total efficiency is
defined as the cross-product of the efficiencies generated in the two phases using the formula presented below (Formula 3).
Formula 3:
2
Ek ¼ E1k Ek
Table 1 summarizes the DMU input and output variables in the DEA model. In this study, three types of efficiency analysis
were conducted. First, the analysis was performed on all airlines (13 airlines) to examine and compare their efficiencies for
the individual annual operations of 2013, 2014, 2015, and then also the aggregate operations from 2013 through 2015. Second, a comparative analysis of efficiency was conducted for U.S. and non-US airlines for their aggregate operations from 2013
to 2015. Finally, the efficiency difference between FSCs and LCCs was examined for the 2013 to 2015 aggregate operations.
4. Results
4.1. Descriptive statistics
Two-phase, two-stage DEA models were developed for analyzing and comparing the efficiency of 13 airlines (DMUs) with
respect to revenue generation and environmental impacts for a three-year period of time (2013–2015). Eight operating and
environmental variables were used as input and output variables. As shown in Table 2, the airlines differed extensively in
terms of these variables given their various scales and operations, with the standard deviation typically 51%-60% the value
of the means for all variables except Abatement Expense and Net Income. The wide coverage of these variables ensured the
representativeness of the study sample. Noticeably, due to British Airways not reporting environmental performance in
2015, there are two missing data (A.E. and CO2) in Table 2. Accordingly, B.A. was not included in the analyses related to
the year 2015.
4.2. Data envelopment analysis results
Both phase-based efficiency scores and total operational efficiency scores (i.e., cross-product) of the DMUs were calculated and compared. In total, eight DEA models were constructed to evaluate the efficiencies of the 13 airlines based on time
of operation (2013, 2014, 2015, and 2013–2015), U.S. and non-US airlines, and FSCs and LCCs. The model analysis focused on
three aspects. First, airline performance efficiency was assessed for all stages of the model. When an airline makes the best
use of the resources (being 100% efficient or achieves a unity score), it is considered operating on the efficient production
frontier. For each airline, a score indicating efficiency was obtained for each stage. For any given phase, only when an airline
possessed a unity score in both stages of a phase can they be considered operationally efficient for that phase. To achieve
efficient performance for the entire model, an airline must demonstrate a unity efficiency score in the four stages encompassing both phases.
Second, multiple sets of the values of efficient product frontier were evaluated in this study. These values can be obtained
from the number of variables and stages included in the modeling process. These production frontiers provided different
‘‘closest benchmark” points for different airlines in this study. At each stage, different benchmark references may be
provided for the inefficient airlines’ operations to be compared to.
Table 1
Summary of DMU Input & Output Variables.
Variable
Stage
Type
Definition
O.E.
ASM
ECO2
A.E.
RPM
CO2
NINC
OR
1
1/2
1/2
2
2/3
2/3
3
3
Input
Output/Input
Output/Input
Input
Output/Input
Output/Input
Output
Output
Total Operating Costs
Available Seat Miles
Estimated CO2 Emissions
Abatement Expense
Revenue Passenger Miles
Actual CO2 Emissions
Net Income, Profit, or Loss
Total Operating Revenues
8
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A. Saini, D. Truong and Jing Yu Pan
Table 2
Descriptive statistics – All Airlines.
Variable (units)
N
Minimum
Maximum
Mean
SD
OpExpenses ($1000 s)
ASM (1000000 s seat-mi.)
ECO2 (metrics tons CO2)
AE ($)
RPM (1000000 s pax–mi.)
CO2 (metrics tons CO2)
NetIncome ($1000 s)
OpRevenues ($1000 s)
39
39
39
38
39
38
39
39
4,293,788
16,033
2,292,719
0
12,883
4,337,568
(2,637,620)
5,150,814
42,751,965
220,437
31,522,487
21,324,498
188,375
42,300,000
10,549,234
43,349,652
19,758,671
119,237
17,050,836
1,464,402
97,201
20,656,127
1,158,784
22,343,522
11,056,135
69,531
9,942,884
4,795,230
58,682
12,204,412
2,180,254
12,037,311
Note. N = Available data points; SD = Standard Deviation; OpExpenses = Total Operating Expenses; ASM = Available Seat Miles; ECO2 = Estimated CO2
Emissions; AE = Abatement Expenses; RPM = Revenue Passenger Miles; CO2 = Net CO2 Emissions; NetIncome = Net Income; OpRevenues = Passenger-based
Operating Revenues.
Finally, airline total efficiency scores were assessed. Caution should be exercised when analyzing the efficiency scores at
full-model level. Because the phase and total scores were products of stage scores, poor performance in one stage may mask
the strong performance in other stages, leading to misinterpretation of relative distance to the efficient frontier.
4.2.1. Year-based analysis
The DEA models, which cover both airline operations and environmental abatement, were estimated to evaluate all airlines for each individual year of 2013, 2014, 2015, and for the three-year period of 2013–2015. Appendices 1–4(Table A1-A4)
show the stage-based analysis of airline efficiency in 2013. Appendices 5–8 (Table 3, 4, 5, and 6) summarize the airline efficiency scores for operational efficiency results in 2013, 2014, 2015, and the aggregate three-year period.
In 2013, all but three airlines achieved unity score efficiency (efficiency coefficient = 1) in the first stage of Phase One. All
FSCs utilized Emirates as a benchmark except for Delta Air Lines and United Airlines, along with two non-FSCs – Alaska Airlines and JetBlue – who defined their own efficient frontier scores. Except for these four airlines, all other airlines used JetBlue in conjunction with Emirates to define the efficient production frontier. The second stage of Phase One assessed
efficiency for two different firm areas of focus – ASM-RPM conversion and carbon dioxide abatement. This stage required
airlines to improve their performance in both aspects in order to approach benchmark-setting performance. Four airlines
obtained a unity efficiency score, each defining its own production frontier, with the exception of Air France–KLM (which
used Alaska Airlines and Delta Air Lines to define its benchmark). All remaining airlines either used a combination of Air
Canada and Alaska Airlines or Delta Air Lines to define the closest point on the efficient frontier. The first stage of Phase
Two further differentiated which airlines were operating at high efficiency scores following ASM conversion. Results indicated that the airlines with multi-airline benchmarks for every airline in the stage had the largest proportion (weighting)
of the improvement defined by the performance of Air Canada or Alaska Airlines, with Delta Air Lines supplying the other
defining benchmark. In the second stage of Phase Two, four airlines obtained a unity efficiency score, but only Air Canada
defined its own point on the efficient production frontier. All Nippon Airways and Japan Airlines used Air Canada and Alaska
Airlines as benchmarks for efficient production improvements while Lufthansa used Alaska Airlines and Delta Air Lines for
potential improvements to efficient production. Table 3 presents that overall, no airline’s performance in 2013 achieved efficient operation in all stages of the model. Air Canada and Alaska Airlines both demonstrated efficient performance in three of
the four model stages; however, Air France-KLM held the highest total efficiency score. Delta Air Lines and United Airlines
were the only two airlines besides Air France–KLM with total efficiency scores significantly over 50%.
In 2014, several FSCs failed in performing efficiently in the first stage of Phase One, indicating some FSCs were not successful in converting input resources to ASMs. Air France-KLM and JetBlue defined the efficient frontier for most of the airlines in this stage. The second stage of Phase One identified airlines that were not operating efficiently, with different
improvements needed for each airline group. The inefficient airlines included those who operated efficiently with either
ASM-RPM conversion or emission abatement and needed improvement with the other (e.g., Air Canada, Lufthansa, and
SWA), and airlines that needed improvement in both areas (e.g., All Nippon and British Airlines). In the second stage of Phase
Two, five airlines – Air Canada, Alaska, Delta, British Airways, and JetBlue achieved efficient performance, with Air Canada
and Delta the only two airlines to define their optimal efficient frontier operations using their own individual performance.
In the last stage of Phase Two, Air Canada, Delta, and Lufthansa each defined its own efficient frontier positions, with the
remaining efficient airlines in this stage (5 airlines) following either the individual or combination of these three airlines.
Overall, in 2014, no airline demonstrated efficient performance through the entire model (all four phases). Delta Air Lines
obtained the highest score in total efficiency and demonstrated efficient performance in three of the four stages. While
Alaska and Lufthansa also demonstrated efficient performance in three of the stages, neither demonstrated one of the top
three total efficiency scores for this year. United Airlines and Air France–KLM possessed the second and third highest total
efficiencies, respectively. Though no single airline demonstrated efficiency throughout the 2014 single–year model, Alaska
Airlines and Delta Air Lines both stood out as performers who defined their own efficient frontier positions and defined
the closest efficient frontier positions for other airlines in most stages.
For 2015, the first stage in Phase One indicated that American and Lufthansa were the only two inefficient airlines. Air
France-KLM, Alaska, Delta, Emirates, and JetBlue each individually defined its own efficient frontier, against which the
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International Journal of Transportation Science and Technology xxx (xxxx) xxx
remaining efficient airlines compared their performance and identified areas for improvement. In the second stage of Phase
One, four airlines – Alaska, American, Delta, and JetBlue achieved operating efficiency and each defined its own efficient production frontier. Air Canada defined performance improvement opportunities for itself and four other airlines, although it did
not demonstrate efficient performance, indicating Air Canada may have approached efficient frontier by improving carbon
emissions abatement. While the first stage in Phase Two collaborated to the observations made in the previous stage, the second stage in Phase Two identified six efficiently performing airlines -Air Canada, Delta, Lufthansa, All Nippon, Japan Airlines,
and United, with each of the first three airlines defining its own efficient frontier. The remaining airlines were inefficient in
this stage and associated with either Air Canada or Lufthansa benchmark factor in defining the efficiency of their performance.
Overall, in this year’s model, Delta scored the highest overall total efficiency ranking, demonstrating efficient production performance in all stages of the model. Alaska and JetBlue demonstrated efficiency in three out of the four model stages, with
Alaska presenting itself as the primary airline defining the efficient product frontier with respect to carbon dioxide abatement.
United Airlines and Air France-KLM demonstrated the second and third highest total efficient frontier positions.
For the three-year analysis, the first stage of Phase One identified five efficient airlines, with Alaska and JetBlue the only
two airlines each identifying its own efficient frontier. In the second stage of Phase One, six airlines performed efficiently,
with Alaska, Delta, and America each defining its own efficient production frontier. The inefficient airlines identified in this
stage had their operating positions defined by Alaska and Delta, except for Air Canada, which had its improvement opportunities defined by American and itself. Air Canada’s emission abatement is strong, but it needed improvement in ASM-RPM
conversion. The first stage of Phase Two identified four efficient airlines, among which only Air Canada and Delta individually
defined their own efficient frontiers. This represented a shift from individual-year models in which Alaska often defined production frontier efficiency in this stage, especially in terms of emission abatement. In the three-year model, Air Canada and
Delta demonstrated stronger emissions abatement in this stage while surpassing Alaska in ASM-RPM conversion. The second
stage in Phase Two identified six efficient airlines, with Air Canada, Delta, Lufthansa, and Southwest individually defined
their efficiency frontiers. All the inefficient airlines either followed the benchmark defined by Lufthansa or a combination
of Air Canada and Lufthansa performance. Overall, the three-year model saw no airline demonstrated efficient performance
in all stages of the analytical model. Delta Air Lines obtained the highest total efficiency score (over 98%). Air France–KLM
and Southwest Airlines were the only other airlines with total efficiency scores over 70%. Air Canada and Alaska Airlines were
the only carriers besides the benchmark (i.e. Delta Air Lines) to demonstrate efficient operations in three of four of the stages.
A summary of the total efficiencies for year-related analysis is presented in Table 7.
Fig. 2. Airline annual efficiency performance.
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A. Saini, D. Truong and Jing Yu Pan
Fig. 2 graphically presents the annual total efficiency scores for each airline over the three years of the study period.
From 2013 to 2014, Delta Air Lines and United Air Lines showed discernible improvements in annual efficiency, while Air
France–KLM and Southwest Airlines showed reductions in total efficiency relative to the sample. From 2014 to 2015, Delta
Air Lines and United Airlines continue to improve, though with less improvement relative to the 2013–2014 change. Southwest Airlines made a significant improvement, surpassing its 2013 efficiency score. Both Emirates and Lufthansa Airlines
demonstrated reductions in total efficiency from 2014 to 2015, after making marginal improvements from 2013 to 2014.
4.2.2. U.S. Versus Non-U.S. Airlines
The study sample was divided into U.S. and non-U.S. airlines, which were tested for the entire three-year study period.
The efficiency results are shown in Appendix 9 (Table 8). For the U.S. airline model, Alaska, JetBlue, and Southwest demonstrated efficient performance in the first stage of Phase One, with Alaska and JetBlue each defining its efficient frontier position. The three FSCs – American, Delta, and United – were found to be inefficient. In the second stage of Phase One and the
first stage of Phase Two, Delta and Alaska were the only efficient airlines defining their efficient frontier positions, both
demonstrating efficiency with respect to ASM-RPM conversion and carbon dioxide emissions abatement. In the second stage
of Phase Two, Dela and Southwest demonstrated efficient performance. The results of this stage aligned with the results
observed in the previous models in that Alaska and JetBlue generated far less revenue due to the limited size of their operations compared to the other airlines in this stage of the model. Overall, Alaska Airlines and Delta Air Lines both demonstrated efficient operations in three stages, with Delta Air Lines presenting the highest total efficiency for the U.S.–carrier
group. Alaska Airlines defined the optimal performance for this model with respect to carbon dioxide emissions abatement.
For the non-U.S. airline model, the first stage in Phase One identified all airlines to be efficient except for Lufthansa, with Air
France-KLM, Emirates, and Japan Airlines, each defining its efficient frontier. Air France-KLM and Emirates were the only airlines performing efficiently in the second stage of Phase One. Noticeably, Air Canada performed inefficiently in this stage but
becoming efficient in the first stage of Phase Two, despite the similar stage construction of the two stages. This indicated that Air
Canada’s performance of ASM and RPM generation lagged that of other airlines in the sample (Phase One Stage Two), but it was
able to define efficient frontier with respect to emission abatement to improve its efficiency (Phase Two Stage One). Four airlines performed efficiently in the second stage of Phase Two, with Air Canada and Lufthansa each defining its own position on
the efficient frontier. Emirates showed inefficient performance in emissions abatement and thus relied on revenue generation
and RPM maximization to improve to the efficient frontier. Overall, Air Canada was the only carrier to demonstrate efficient
operations in three stages, while Air France–KLM presented the highest total efficiency for the non-U.S.–carrier group. Air
Canada defined optimal performance for this model with respect to carbon dioxide emissions abatement.
4.2.3. Fscs versus LCCs
The study sample was divided into two groups based on their business models – FSCs or LCCs. The efficiency results
(2013–2015) of the two groups are presented in Appendix 10 (Table 9). The first stage in Phase One identified five airlines
to be efficient, with Air France-KLM, Emirates, and Japan Airlines, each defining its own position on the efficient frontier. All
the U.S-based FSCs demonstrated inefficiency. Opposite results were obtained in the second stage of Phase One, where American, Delta, and United were the only carriers demonstrating efficient performance, with American and Delta each defining
its own position on the efficient frontier. Air Canada, again, was efficient in emissions abatement performance but not ASMRPM conversion, which dragged down its overall performance inefficiency in this stage. In the first stage of Phase Two, Air
Canada and Delta performed efficiently, defining a position on the efficient frontier for emission abatement (Air Canada) and
ASM-RPM conversion (Delta). Five airlines performed efficiently in the second stage of Phase Two, with Air Canada, Delta,
and Lufthansa each defining its efficient frontier. The low efficiency score of Emirates in this stage, again, indicated the airline’s low efficiency in emissions abatement performance. Overall for the full–service carrier group, Air Canada and Delta Air
Lines both demonstrated efficient operations in three out of the four stages, with Air Canada defined the efficient production
frontier relative to emission abatement. Delta has the highest total efficiency scores but it, together with the other two U.S.
FSCs, demonstrated inefficiency in the first stage of Phase one. This noticeable pattern may suggest that there may be a
higher cost structure associated with those carriers competing in both large domestic and international markets. The inefficiency in the first stage (performing at 98%) can be specific to the U.S.–market. If so, Delta Air Lines would be considered the
most efficiently producing FSC relative to the model.
For the three airlines in the L.C. C/P2P sample, Alaska is the only efficient airline in the first stage of Phase One. The next
stage identified all airlines to be efficient in ASM-RPM conversion and emission abatement. The first stage in Phase Two again
identified Alaska as the only airline demonstrating the greatest efficiency in emissions abatement and ASM-RPM conversion
in the context of revenue generation. The last stage of the model duplicated the results of the second stage in Phase One. The
full efficiency results in these two stages were likely due to the small number of DMUs and the exclusion of competitors of
LCCs (e.g., regional airlines) in the sample. For similar reasons, the efficiency performance of some airlines may be underestimated, as can be seen in the case of SWA. In this analysis, only Alaska Airlines were efficient throughout all stages, defining
the efficient production frontier with respect to emissions abatement and revenue generation.
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5. Discussion
This study constructed two-phase, two-stage DEA models to evaluate airline efficiency, considering actual carbon dioxide
emissions produced by the airlines as part of the efficiency measure. The model development integrated existing models in
the literature (Kao & Hwang, 2008; Li et al., 2015; Mallikarjun, 2015) to provide a flexible, easy-to-used framework for efficiency assessment in the airline industry. While both service and environment variables were found to be important in
determining airline efficiency, environmental abatement affected the overall efficiency of airline performance both inside
and outside the U.S. when emission reduction effort was properly accounted for. Alaska Airlines were the only airline that
demonstrated efficient performance in the environmental abatement component of each phase of the analytical models for
all time-related analysis, while Delta performed efficiently in this regard for the year of 2015 and three-year analysis. None
of the non-U.S. airlines, however, achieved all-stage efficiency with respect to environmental abatement.
For the individual-year and three-year analysis in 2013, some FSCs such as Lufthansa, All Nippon Airways, British Airways,
and Japan Airlines did not perform efficiently, especially in the service component of the model (which is in agreement with
the literature indicating similar inefficient statistics for this year). LCCs/P2P airlines demonstrated efficiency except for the
second stage of Phase Two. Both Alaska and JetBlue showed low performance relative to the sample in this stage, suggesting
that LCCs/P2P airlines underperformed FSCs in revenue generation. For this reason, several FSCs, including Delta, U.A., Air
Canada, and Alaska (it operates a service closer to FSCs) achieved higher total efficiency in the two phases in 2013. In addition to Alaska, Air Canada is another efficient airline in carbon dioxide abatement, and it was able to generate a high level of
revenue. The division between efficient and inefficient airlines was more noticeable in 2014. Only Alaska achieved efficiency
in the two stages in Phase One, with Delta, Air France-KLM, and JetBlue achieved efficiency in one of the two stages. These
four airlines showed a stronger position in passenger traffic compared to 2013, and at the same time maintaining efficient
performance in carbon emission abatement. Only Delta and Air Canada performed efficiently in the revenue generating
phase. Similar to 2013, the performance of LCCs/P2P airlines fell sharply in the second stage of Phase Two, reinforcing the
observation that these airlines, due to their niche market operation, cannot compete with large carriers in total revenue generation (irrespective of profitability). The results of 2015 were consistent with that of the previous years. American Airlines
and Lufthansa appeared to struggle in Phase One. As both airlines have more employees and complex fleet composition, they
are facing higher costs which can negatively affect their efficiency performance. The three-year model further revealed differences in airline performance with respect to RPM generation and emissions abatement. Worth-mentioning is Emirates,
which demonstrated efficient performance in environmental emission that was not observed in two of the three yearlymodel analysis. The flights of Emirates consist mostly of long-haul, international flights, which may lend themselves to fuel
and emissions generation efficiency per passenger mile. Costs, however, remained to be a threat to Emirates’ efficiency, especially in ASM-RPM conversion. The three-year model analysis once again demonstrated that L.C. C/P2P airlines did not generate enough revenue in the last stage of the model, as none of the L.C. C/P2P airlines achieved an efficient score in the second
stage of Phase Two; similar to what was observed in the year–specific analysis.
The efficiency analysis of U.S.-based and non-U.S.-based airlines, using segregated samples, allowed for certain airlines’
performance to stand out among their peers. Alaska and Delta were the efficient airlines in the U.S. group. Delta achieved
less efficiency in Phase One compared to Phase Two, indicating some difficulty in transforming inputs into ASMs. Alaska
achieved efficiency except in the revenue generation stage of Phase Two, which, again, reinforced the earlier observation that
smaller LCC and P2P carriers are incapable of competing with larger airlines in the last stage of the model. While Alaska
demonstrated that it is the top U.S. airline with respect to emissions abatement, a comprehensive evaluation considering
emissions abatement, RPM generation from ASMs, and revenue generation indicated that Delta outperformed Alaska Airlines
in this analysis. Three airlines were efficient in the non-U.S. airline analysis in different model phrases. Air France-KLM and
Emirates demonstrated efficient performance in Phase One (converting inputs into ASM, ASM-RPM conversion, and emission
abatement). This agreed with the strategy of the two airlines as both combined short- and long-haul operations, focused on
improving load factors, especially for their long-haul flights to cover costs, and were relatively early in investing in fuel-burn
and emissions reduction initiatives. Phase Two saw several FSCs performed efficiently in revenue generation, but only Air
Canada demonstrated efficient performance through a combination of revenue generation and emission abatement.
Further analysis was conducted to examine the efficiency of airlines utilizing either FSC or L.C. C/P2P business model.
Phase One for the FSC group showed that non-U.S. FSCs were more effective in transforming input resources into ASMs while
the U.S. FSCs were stronger in RPM generation from the ASM supply. The operational characteristics of these airlines may
help explain this phenomenon. The international FSCs in this study (e.g., Emirates, Air Canada, and Japanese Airlines) typically focus on long-haul, international flights due to limited domestic markets. The U.S. FSCs, on the contrary, generate their
revenue more equally from domestic and international operations. As such, the total operational efficiency of non-U.S. FSCs
was more reflective of its long-haul operations, while the U.S. FSCs’ operational efficiency reflected a more even split
between long-haul international and short-haul/regional operations. In Phase Two, Air Canada and Delta demonstrated efficient performance in both stages, with Air Canada leading in emissions abatement and Delta in revenue generation. For the
LCC /P2P model analysis, Alaska performed efficiently in all stages in the two phases, which agreed with the results of yearspecific analyses. However, the small sample size of LCCs and P2P airlines may limit the conclusion it can make.
Most airlines in the sample performed optimally only in some of the four stages (ASM generation, ASM-RPM conversion,
emission abatements, and revenue generation). Deeper insights can be obtained through model comparison in terms of these
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A. Saini, D. Truong and Jing Yu Pan
airlines’ strengths and weaknesses in achieving efficiency. Overall, this study found Alaska Airlines to be the most efficient
airline that successfully executed the business strategies within the market space it defined. It performed efficiently in all
stages except revenue generation and also set the benchmark for the entire model of the FSC-LCC analysis. The lower revenue
generation efficiency scores, as previously explained, is related to the airline’s focus on the regional market and the use of
single-aisle aircraft, which make it difficult to match revenue generation with FSCs that enjoy wider market coverage and
aircraft types. Delta was the most efficient FSC in this study, achieving the highest overall efficiency rating with consistent
efficient performance in Phase Two in all DEA models. However, while several airlines performed efficiently in the first stage
of Phase One (transforming inputs into ASM), Delta was not efficient in this stage, which may be attributed to its use of huband-spoke system. As hub-and-spoke configuration allows flight scheduling with partially filled flights from smaller airports
into the hub, the ASM generation can be negatively affected. Some airlines performed efficiently in most of the model stages,
which can be considered relatively efficient airlines. One example is Air Canada which performed efficiently in emission
abatement and revenue generation but struggled with ASM-RPM conversion. Several airlines were considered inefficient
due to their low efficiency scores in most model stages. Lufthansa was overall inefficient as it only demonstrated efficiency
in revenue generation but lagged its peers in all other stages. High costs associated with aging fleet and operations may be
the reason for its overall inefficiency. Another inefficient airline was Air France-KLM, which showed efficiency mostly in
transforming inputs into ASM but performed inefficiently in other stages. The merger between Air France and KLM, which
required fleet reconstruction and organizational change, may be responsible for the decreased overall efficiency of the airline. United Airline’s execution of FSC business model and environmental abatement strategies lagged behind other airlines
(FSCs) in this study; overall, it consistently underperformed benchmarks set by other FSCs. Noticeably, several airlines such
as Emirates, Lufthansa, and the two Japanese airlines struggled in achieving efficiency in ASM-RPM and emission abatement.
Aging fleets and long-haul, international focus (with oversized aircraft for the demand) were among the suspected responsible factors for their poor performance in these areas, especially emission abatement.
6. Managerial contributions
The findings of this study provided major contextual forces that airline managers need to consider when making decisions with respect to evaluation and improvement of the overall airline efficiency. The evaluation of airline efficiency has
become increasingly complex in the highly competitive market, as airlines may simultaneously outperform and underperform relative to their counterparts in multiple factor categories. The two-stage, two-phase DEA model developed in this
study can be particularly useful in providing a holistic view for airline managers to prioritize strategies for future investments, evaluate how the investments are changing the airline’s total efficiency relative to its peers, and recognize areas
for improvement. As shown in this study, some airlines such as Delta and Air Canada were relatively efficient with respect
to both operations and emission abatement, thus requiring only minor adjustments of their business strategies to improve
overall efficiency. Delta, for example, should review its fleet composition and look for an opportunity to improve ASM generation. Another group of airlines, including Air France-KLM, All Nippon, Japan Airlines, and Emirates, struggled in achieving
environmental efficiency in addition to some operational issues and thus would need more adjustment in their business
strategies. The recommendation for these airlines would be to evaluate their emissions abatement programs, cost structure,
and fleet strategy (aircraft-to-route matching) to improve overall efficiency. Some airlines such as Lufthansa and United
faced greater challenges in meeting efficiency goals and would therefore need greater adjustment in their business strategies
to improve efficiency. Lufthansa can improve overall efficiency through developing cost-saving and fleet-network strategies
(i.e., replacing aging and/or underutilized large aircraft). For United, the recommendation would be to review all aspects of
its operations for opportunities to gain greater ASM creation, ASM-RPM conversion, and emission abatement. Fleet renewal
and fleet-network matching strategies can be useful for United to achieve greater efficiency. From a regulatory perspective,
the models developed in this study can be a useful tool for policy makers to evaluate the efficiency of airlines, especially
regarding environmental abatement, and to formulate a strategy to better serve the interest of the airline industry.
7. Conclusion
This study constructed a two-phase, two-stage DEA model and used a linear programming approach to assess the relative
efficiencies of 13 airlines from 2013 to 2015, considering operational and financial performance indicators of the airlines, and
environmental impact abatement success measured as a function of the carbon dioxide emissions produced by the airline
operations. The results indicated that the proposed model and method could successfully evaluate airline efficiency and provide useful insights into their strengths and weaknesses in achieving efficiency when multiple inputs and outputs are
considered.
This study contributed to theoretical knowledgebase in several ways. First, it is the first study to develop a measurement
model that incorporated carbon dioxide emission abatement and high-fidelity assessment, in which ASM creation, RPM
generation, and revenue realization were separately assessed as part of an airline’s business operations. Previous DEA studies
typically made environmental impact output of the total airline operations. In this study, environmental impact was treated
as both input and output variables in two stages of the model. This design allowed environmental considerations to be part
of the firm’s decision-making’s process prior to the final outputs and revenue generation, which can provide a deeper under13
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International Journal of Transportation Science and Technology xxx (xxxx) xxx
standing of the role of environmental factors in airline efficiency. Second, the two-phase, two-stage DEA model provided a
plausible alternative to the three-stage DEA model developed by Mallikarjun (2015). It reduced the complexities associated
with the forward/backward recursion required in a three-stage DEA while still maintaining the fidelity of the original
approach to airline efficiency. The validity of the model was supported, as the results from the models were consistent with
that in the literature as well as airlines’ disclosure during the study period (Cui & Li, 2016). Finally, by including different
types of airlines in the sample, this study provided insights into the effect of airlines business model and route/network
on airline efficiency. The study also reinforced the perspective that DEA results are more reliable when a greater number
of DMUs are used to represent each business model.
This study has some limitations. First, it is assumed that the weighting between any two consecutive stages in the DEA
model was the same. Efficiencies between these stages, as a result, were treated as the same. This can represent an oversimplified operational environment for the airline industry. Second, this study only included carbon dioxide emissions created
from direct operating activities (e.g., aircraft fuel consumption). Other types of aviation emission (e.g., from electric power
facilities supporting airline operations) were not considered. Thirdly, due to the model constraints, only selected variables
were used as inputs and outputs in this study. Finally, due to the number of airlines operating in the U.S. market with sufficient data, the number of DMUs did not meet the sample size requirements in several cases, including the comparison
between U.S. based and non-U.S. based airlines for the overall model (total efficiency) and comparison between FSCs and
LCCs in Phase 1 Stage 2, Phase 2 Stage 1, and overall model. Accordingly, those comparisons need further exploration in
the future using more airlines from other countries, if available. Nonetheless, as Zhu (2014) pointed out, the purpose of
DEA is to benchmark a group of DMUs and assess individual efficiencies; therefore, our results still provide useful findings
in comparing those airlines in separate groups. Additionally, airlines included in the DEA model must represent actual competitors in the market, and there must be sufficient data to determine the efficiency frontier. Thus, caution must be taken to
avoid adding outside airlines to the group since it would lead to invalid results.
Future researchers can extend the current study in several areas. First, the two-phase, two-stage DEA model can implement a disproportionate weighting between the two stages as part of the optimization routine. This could be especially useful when studying a sample of airlines adopting similar business philosophies. The second future research direction is related
to the variable selection that best reflects operational success. While this study used revenue generation in the final stage of
the multi-phrased model, future research can use net profit as the output of the final stage, which can provide deeper
insights into how emission abatement activities would impact on the overall airline operational efficiency. Finally, different
types of emissions can be incorporated to better reflect the environmental impact of air transportation. Future research can
extend the model by including additional forms of aircraft emissions, such as nitrous oxides.
Table A1
2013 Single Year VRS Model – Phase 1, Stage 1 Results.
Airline
Stage 1 Efficiency
1st Benchmark
1st Airline Benchmark
2nd Benchmark
2nd Airline Benchmark
Air Canada
Air France – KLM
Alaska Airlines
All Nippon Airways
American Airlines
British Airways
Delta Air Lines
Emirates
Japan Airlines
JetBlue Airways
Lufthansa Airlines
Southwest Airlines
United Airlines
1.00000
0.96758
1.00000
1.00000
0.84710
1.00000
1.00000
1.00000
1.00000
1.00000
0.52609
1.00000
1.00000
0.379
0.992
1.000
0.607
0.889
0.776
1.000
1.000
0.378
1.000
1.000
0.652
1.000
Emirates
Emirates
Alaska Airlines
Emirates
Emirates
Emirates
Delta Air Lines
Emirates
Emirates
JetBlue
Emirates
Emirates
United Air Lines
0.621
0.008
JetBlue
JetBlue
0.393
0.111
0.224
JetBlue
JetBlue
JetBlue
0.622
JetBlue
0.348
JetBlue
14
International Journal of Transportation Science and Technology xxx (xxxx) xxx
A. Saini, D. Truong and Jing Yu Pan
Table A2
2013 Single Year VRS Model – Phase 1, Stage 2 Results.
Airline
Stage 2
Efficiency
1st Benchmark
1st Airline
Benchmark
2nd Benchmark
2nd Airline
Benchmark
3rd Benchmark
3rd Airline
Benchmark
Air Canada
Air France – KLM
Alaska Airlines
All Nippon Airways
American Airlines
British Airways
Delta Air Lines
Emirates
Japan Airlines
JetBlue Airways
Lufthansa Airlines
Southwest Airlines
United Airlines
0.37664
1.00000
1.00000
0.42117
1.00000
0.68627
1.00000
0.93740
0.47938
0.97762
0.91257
0.85297
1.00000
0.462
0.212
1.000
0.024
1.000
0.080
1.000
0.206
0.122
0.928
0.206
0.457
1.000
Air Canada
Alaska Airlines
Alaska Airlines
Air Canada
American Airlines
Air Canada
Delta Air Lines
Alaska Airlines
Air Canada
Alaska Airlines
Alaska Airlines
Alaska Airlines
United Air Lines
0.538
0.788
American Airlines
Delta Air Lines
0.464
Alaska Airlines
0.512
Delta Air Lines
0.281
Alaska Airlines
0.639
Delta Air Lines
0.794
0.523
0.072
0.794
0.543
Delta Air Lines
Alaska Airlines
Delta Air Lines
Delta Air Lines
Delta Air Lines
0.355
Delta Air Lines
3rd Benchmark
3rd Airline
Benchmark
0.067
0.082
Delta Air Lines
Delta Air Lines
0.022
0.125
Delta Air Lines
Delta Air Lines
Table A3
2013 Single Year VRS Model – Phase 2, Stage 1 Results.
Airline
Stage 1 Efficiency
1st Benchmark
1st Airline
Benchmark
Air Canada
Air France – KLM
Alaska Airlines
All Nippon Airways
American Airlines
British Airways
Delta Air Lines
Emirates
Japan Airlines
JetBlue Airways
Lufthansa Airlines
Southwest Airlines
United Airlines
1.00000
0.90797
1.00000
0.52991
0.99421
1.00000
0.75218
0.91227
0.39998
1.00000
0.94162
1.00000
0.72121
1.000
0.291
0.809
0.765
0.291
0.598
0.291
0.291
0.938
0.645
0.291
0.527
0.291
Air Canada
Alaska Airlines
Air Canada
Air Canada
Alaska Airlines
Alaska Airlines
Alaska Airlines
Alaska Airlines
Air Canada
Air Canada
Alaska Airlines
Alaska Airlines
Alaska Airlines
2nd Benchmark
2nd Airline
Benchmark
0.709
0.124
0.152
0.709
0.402
0.709
0.709
0.040
0.231
0.709
0.473
0.709
Delta Air Lines
Alaska Airlines
Alaska Airlines
Delta Air Lines
Delta Air Lines
Delta Air Lines
Delta Air Lines
Alaska Airlines
Alaska Airlines
Delta Air Lines
Delta Air Lines
Delta Air Lines
Table A4
2013 Single Year VRS Model – Phase 2, Stage 2 Results.
Airline
Stage 2 Efficiency
1st Benchmark
1st Airline Benchmark
2nd Benchmark
2nd Airline Benchmark
Air Canada
Air France – KLM
Alaska Airlines
All Nippon Airways
American Airlines
British Airways
Delta Air Lines
Emirates
Japan Airlines
JetBlue Airways
Lufthansa Airlines
Southwest Airlines
United Airlines
1.00000
0.84990
0.33143
1.00000
0.59424
0.62725
0.87240
0.53433
1.00000
0.29617
1.00000
0.51761
0.88322
1.000
0.291
0.809
0.765
0.291
0.598
0.291
0.291
0.938
0.645
0.291
0.527
0.291
Air Canada
Alaska Airlines
Air Canada
Air Canada
Alaska Airlines
Alaska Airlines
Alaska Airlines
Alaska Airlines
Air Canada
Air Canada
Alaska Airlines
Alaska Airlines
Alaska Airlines
0.709
0.124
0.152
0.709
0.402
0.709
0.709
0.040
0.231
0.709
0.473
0.709
Delta Air Lines
Alaska Airlines
Alaska Airlines
Delta Air Lines
Delta Air Lines
Delta Air Lines
Delta Air Lines
Alaska Airlines
Alaska Airlines
Delta Air Lines
Delta Air Lines
Delta Air Lines
15
A. Saini, D. Truong and Jing Yu Pan
International Journal of Transportation Science and Technology xxx (xxxx) xxx
Table 3
2013 Operating efficiency results.
Airline
1st Phase
1st Stage
1st Phase
2nd Stage
2nd Phase
1st Stage
2nd Phase
2nd stage
Total
Efficiency
Air Canada
Air France – KLM
Alaska Airlines
All Nippon Airways
American Airlines
British Airways
Delta Air Lines
Emirates
Japan Airlines
JetBlue Airways
Lufthansa Airlines
Southwest Airlines
United Airlines
1.00000
0.96758
1.00000
1.00000
0.84710
1.00000
1.00000
1.00000
1.00000
1.00000
0.52609
1.00000
1.00000
0.37664
1.00000
1.00000
0.42117
1.00000
0.68627
1.00000
0.93740
0.47938
0.97762
0.91257
0.85297
1.00000
1.00000
0.90797
1.00000
0.52991
0.99421
1.00000
0.75218
0.91227
0.39998
1.00000
0.94162
1.00000
0.72121
1.00000
0.84990
0.33143
1.00000
0.59424
0.62725
0.87240
0.53433
1.00000
0.29617
1.00000
0.51761
0.88321
0.37664
0.74666
0.33143
0.22318
0.50046
0.43046
0.65620
0.45694
0.19174
0.28954
0.45206
0.44151
0.63699
Airline
1st Phase
1st Stage
1st Phase
2nd Stage
2nd Phase
1st Stage
2nd Phase
2nd stage
Total
Efficiency
Air Canada
Air France – KLM
Alaska Airlines
All Nippon Airways
American Airlines
British Airways
Delta Air Lines
Emirates
Japan Airlines
JetBlue Airways
Lufthansa Airlines
Southwest Airlines
United Airlines
1.00000
1.00000
1.00000
1.00000
0.76157
1.00000
0.95294
0.88910
1.00000
1.00000
0.61330
1.00000
0.94792
0.38800
0.99190
1.00000
0.45880
1.00000
0.69287
1.00000
1.00000
0.51641
0.98210
0.85003
0.47410
1.00000
1.00000
0.72334
1.00000
0.48085
0.99045
1.00000
1.00000
0.84993
0.33361
1.00000
0.93753
0.87228
0.80145
1.00000
1.00000
0.36244
1.00000
0.70825
0.65765
1.00000
0.63898
0.97822
0.33803
1.00000
1.00000
1.00000
0.38800
0.71748
0.36244
0.22061
0.53423
0.45566
0.95294
0.48285
0.16853
0.33198
0.48875
0.41355
0.75971
Airline
1st Phase
1st Stage
1st Phase
2nd Stage
2nd Phase
1st Stage
2nd Phase
2nd stage
Total
Efficiency
Air Canada
Air France – KLM
Alaska Airlines
All Nippon Airways
American Airlines
Delta Air Lines
Emirates
Japan Airlines
JetBlue Airways
Lufthansa Airlines
Southwest Airlines
United Airlines
1.00000
1.00000
1.00000
1.00000
0.73109
1.00000
1.00000
1.00000
1.00000
0.60252
1.00000
1.00000
0.49755
0.99678
1.00000
0.42168
1.00000
1.00000
0.89504
0.49273
1.00000
0.77293
0.96481
0.97621
1.00000
0.93221
1.00000
0.63568
0.79999
1.00000
0.79046
0.45512
1.00000
0.94216
1.00000
0.82812
1.00000
0.81323
0.41158
1.00000
0.92818
1.00000
0.65101
1.00000
0.40008
1.00000
0.57415
1.00000
0.49756
0.75566
0.41158
0.26805
0.54286
1.00000
0.46059
0.22425
0.40008
0.43877
0.55395
0.80842
Table 4
2014 Operating Efficiency Results.
Table 5
2015 Operating Efficiency Results.
Note. British Airways is omitted from analysis due to lack of environmental data.
16
International Journal of Transportation Science and Technology xxx (xxxx) xxx
A. Saini, D. Truong and Jing Yu Pan
Table 6
Total Efficiency Results – 3 Year Study Period (2013–2015).
Airline
1st Phase
1st Stage
1st Phase
2nd Stage
2nd Phase
1st Stage
2nd Phase
2nd stage
Total
Efficiency
Air Canada
Air France – KLM
Alaska Airlines
All Nippon Airways
American Airlines
British Airways
Delta Air Lines
Emirates
Japan Airlines
JetBlue Airways
Lufthansa Airlines
Southwest Airlines
United Airlines
1.00000
0.99233
1.00000
1.00000
0.79069
1.00000
0.98210
0.91302
1.00000
1.00000
0.57807
1.00000
0.95086
0.42263
1.00000
1.00000
0.43871
1.00000
0.69325
1.00000
1.00000
0.49746
0.98628
0.84035
0.76289
1.00000
1.00000
0.92276
1.00000
0.54746
0.91702
1.00000
1.00000
0.84028
0.38924
1.00000
0.94035
0.95937
0.72635
1.00000
0.83220
0.36631
1.00000
0.73383
0.64631
1.00000
0.60418
1.00000
0.34205
1.00000
1.00000
0.97626
0.42264
0.76203
0.36631
0.24018
0.53209
0.44805
0.98210
0.46352
0.19363
0.33736
0.45681
0.73190
0.67426
Note. British Airways data includes flight capacity (seat miles) and revenue generation from 2015, but no environmental data.
Table 7
Total efficiency summary – Time-Related Analysis.
Airline
2013
2014
2015
3-Year Analysis
3-Year Average
Air Canada
Air France – KLM
Alaska Airlines
All Nippon Airways
American Airlines
British Airways
Delta Air Lines
Emirates
Japan Airlines
JetBlue Airways
Lufthansa Airlines
Southwest Airlines
United Airlines
0.37664
0.74666
0.33143
0.22318
0.50046
0.43046
0.65620
0.45694
0.19174
0.28954
0.45206
0.44151
0.63699
0.388
0.71748
0.36244
0.22061
0.53423
0.45566
0.95294
0.48285
0.16853
0.33198
0.48875
0.41355
0.75971
0.49756
0.75566
0.41158
0.26805
0.54286
N/A
1.00000
0.46059
0.22425
0.40008
0.43877
0.55395
0.80842
0.42264
0.76203
0.36631
0.24018
0.53209
0.44805
0.98210
0.46352
0.19363
0.33736
0.45681
0.73190
0.67426
0.42073
0.73993
0.36848
0.23728
0.52585
0.44306
0.86971
0.46679
0.19484
0.34053
0.45986
0.46967
0.73504
Note. British Airways data includes flight capacity (seat miles) and revenue generation from 2015, but no environmental data.
Table 8
Total Efficiency Results – U.S.–based and Non-U.S. -based Carriers (2013–2015).
Airline (U.S.)
1st Phase
1st Stage
1st Phase
2nd Stage
2nd Phase
1st Stage
2nd Phase
2nd stage
Total
Efficiency
Alaska Airlines
American Airlines
Delta Air Lines
JetBlue Airways
Southwest Airlines
United Airlines
Airlines (non-U.S.)
Air Canada
Air France – KLM
All Nippon Airways
British Airways
Emirates
Japan Airlines
Lufthansa Airlines
1.00000
0.74644
0.81072
1.00000
1.00000
0.79160
1.00000
0.86405
1.00000
0.72353
0.53671
0.92979
1.00000
0.88940
1.00000
0.96313
0.92038
0.85647
0.37194
0.85779
1.00000
0.34852
1.00000
0.95622
0.84299
0.83942
0.95268
0.75880
0.86427
0.88352
1.00000
1.00000
1.00000
1.00000
1.00000
1.00000
0.52171
0.72414
1.00000
0.53671
0.76049
1.00000
0.86406
0.92978
1.00000
0.93014
0.55155
1.00000
0.84700
0.39001
0.94787
1.00000
0.83220
1.00000
0.65139
0.60418
1.00000
1.00000
0.72415
0.77406
0.29602
0.49537
0.51174
0.33699
0.45979
Note. British Airways data includes flight capacity (seat miles) and revenue generation from 2015, but no environmental data.
17
A. Saini, D. Truong and Jing Yu Pan
International Journal of Transportation Science and Technology xxx (xxxx) xxx
Table 9
Total Efficiency Results – FSCs and LCCs (2013–2015).
Airline (FSCs)
1st Phase
1st Stage
1st Phase
2nd Stage
2nd Phase
1st Stage
2nd Phase
2nd stage
Total
Efficiency
Air Canada
Air France – KLM
All Nippon Airways
American Airlines
British Airways
Delta Air Lines
Emirates
Japan Airlines
Lufthansa Airlines
United Airlines
Airline (LCCs)
Alaska Airlines
JetBlue Airways
Southwest Airlines
1.00000
1.00000
1.00000
0.79069
1.00000
0.98210
1.00000
1.00000
0.57807
0.95089
0.58662
0.99371
0.53358
1.00000
0.76049
1.00000
0.92094
0.85998
0.84093
1.00000
1.00000
0.92437
0.54951
0.91863
1.00000
1.00000
0.84175
0.38963
0.94200
0.72762
1.00000
0.83220
1.00000
0.73383
0.64866
1.00000
0.60418
1.00000
1.00000
0.97626
0.58662
0.76443
0.29321
0.53302
0.49330
0.98210
0.46836
0.33507
0.45792
0.67547
1.00000
0.83594
0.26806
1.00000
1.00000
1.00000
1.00000
0.71975
0.30502
1.00000
1.00000
1.00000
1.00000
0.60166
0.08177
Note. British Airways data includes flight capacity (seat miles) and revenue generation from 2015, but no environmental data.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have
appeared to influence the work reported in this paper.
Appendixes
Appendix A1-A10
References
France-KLM, Air, 2016. Corporate social responsibility report 2015 Retrieved from http://csrreport2015.airfranceklm.com/web/app.php/en/en/pdf.
Boussofiane, A., Dyson, R.G., Thanassoulis, E., 1991. Applied Data Envelopment Analysis. Eur. J. Oper. Res. 52 (1), 1–15.
Caves, D.W., Christensen, L.R., Tretheway, M.W., 1984. Economies of density versus economies of scale: Why trunk and local service airline costs differ. Rand
J. Econ. 15 (4), 471–489.
Chen, Y., Zhu, J., 2004. Measuring information technology’s indirect impact on firm performance. Inform. Technol. Manage. J. 5 (1–2), 9–2256.
Chen, Z., Wanke, P., Antunes, J.J.M., Zhang, N., 2017. Chinese airline efficiency under CO2 emissions and flight delays: A stochastic network DEA model.
Energy Econ. 68, 89–108. https://doi.org/10.1016/j.eneco.2017.09.015.
Cui, Q., 2019. Investigating the airlines emission reduction through carbon trading under CNG2020 strategy via a network weak disposability DEA. Energy
(Oxford) 180, 763–771. https://doi.org/10.1016/j.energy.2019.05.159.
Cui, Q., 2020. Airline energy efficiency measures using a network range-adjusted measure with unified natural and managerial disposability. Energ. Effi. 13
(6), 1195–1211. https://doi.org/10.1007/s12053-020-09868-2.
Cui, Q., 2021. A data-based comparison of the five undesirable output disposability approaches in airline environmental efficiency. Socio-Econ. Plann. Sci.
74,. https://doi.org/10.1016/j.seps.2020.100931 100931.
Cui, Q., Li, Y., 2016. Airline energy efficiency measures considering carbon abatement: A new strategic framework. Transport. Res. Part D: Transport Environ.
49, 246–258. https://doi.org/10.1016/j.trd.2016.10.003.
Cui, Q., Li, Y., 2018. Airline environmental efficiency measures considering materials balance principles: An application of a network range-adjusted
measure with weak-G disposability. J. Environ. Plann. Manage. 61 (13), 2298–2318. https://doi.org/10.1080/09640568.2017.1393401.
Cui, Q., Li, Y., 2019. Investigating the impacts of the EU ETS emission rights on airline environmental efficiency via a network environmental SBM model. J.
Environ. Plann. Manage. 62 (8), 1465–1488. https://doi.org/10.1080/09640568.2018.1511417.
Cui, Q., Li, Y., 2020. A cross efficiency distinguishing method to explore the cooperation degree in dynamic airline environmental efficiency. Transp. Policy
99, 31–43. https://doi.org/10.1016/j.tranpol.2020.08.010.
Cui, Q., Yu, L., 2021. Airline environmental efficiency comparison through two non-separable inputs disposability range adjusted measure models. J. Cleaner
Prod. 320,. https://doi.org/10.1016/j.jclepro.2021.128844 128844.
Delta. (2016). 2015 corporate responsibility report. Retrieved from http://www.delta.com/content/dam/delta-www/about-delta/corporate-responsibility/
2015DeltaAirLines_CorporateResponsibilityReport.pdf
Forsyth, P.J., Hill, R.D., Trengove, C.D., 1986. Measuring airline efficiency. Fiscal Studies 7 (1), 61–81.
Good, D.H., Röller, L., Sickles, R.C., 1995. Airline efficiency differences between Europe and the U.S.: Implications for the pace of E.C. integration and domestic
regulation. Eur. J. Oper. Res. 80 (3), 508–518. https://doi.org/10.1016/0377-2217(94)00134-X.
Gössling, S., Peeters, P., 2007. ’It does not harm the environment!’ An analysis of industry discourses on tourism, air travel and the environment. J. Sustain.
Tourism 15 (4), 402–417. https://doi.org/10.2167/jost672.0.
Greer, M., 2009. Is it the labor unions’ fault? Dissecting the causes of the impaired technical efficiencies of the legacy carriers in the United States. Transp.
Res. Part A 43 (9), 779–789. https://doi.org/10.1016/j.tra.2009.07.007.
Halkos, G.E., Tzeremes, N.G., Kourtzidis, S.A., 2015. Regional sustainability efficiency index in Europe: An additive two-stage DEA approach. Oper. Res. Int.
Journal 15 (1), 1–23. https://doi.org/10.1007/s12351-015-0170-4.
Kao, C., Hwang, S., 2008. Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. Eur. J.
Oper. Res. 185 (1), 418–429. https://doi.org/10.1016/j.ejor.2006.11.041.
Kaya Aydin, Gizem, Aydin, Umut, 2021. Is there any convergence in the CO2 emission efficiency of airlines? Environ. Sci. Pollut. Res. Int. 29 (12), 17811–
17820.
Kim, H., Son, J., 2021. Analyzing the environmental efficiency of global airlines by continent for sustainability. Sustainability (Basel, Switzerland) 13 (3), 1–
16. https://doi.org/10.3390/su13031571.
18
International Journal of Transportation Science and Technology xxx (xxxx) xxx
A. Saini, D. Truong and Jing Yu Pan
Li, Y., Cui, Q., 2021. Analyzing the role of competition and cooperation in airline environmental efficiency through two dynamic environmental crossefficiency models. Internat. J. Sustain. Transport. 15 (11), 850–864. https://doi.org/10.1080/15568318.2020.1821415.
Li, Y., Wang, Y., Cui, Q., 2015. Evaluating airline efficiency: An application of virtual frontier network SBM. Transport. Res. Part E: Log. Transport. Rev. 81, 1–
17. https://doi.org/10.1016/j.tre.2015.06.006.
Lu, C., Morrell, P., 2006. Determination and applications of environmental costs at different sized airports – aircraft noise and engine emissions.
Transportation 33, 45–61.
Lufthansa. (2016). Balance. Retrieved from https://www.lufthansagroup.com/fileadmin/downloads/en/responsibility/LH-sustainability-report-2016.pdf
Lynes, J.K., Andrachuk, M., 2008. Motivations for corporate social and environmental responsibility: A case study of Scandinavian airlines. J. Internat.
Manage. 14 (4), 377–390. https://doi.org/10.1016/j.intman.2007.09.004.
Mallikarjun, S., 2015. Efficiency of U.S. airlines: A strategic operating model. J. Air Transp. Manage. 43, 46–56. https://doi.org/10.1016/
j.jairtraman.2014.12.004.
Marti, L., Puertas, R., Calafat, C., 2015. Efficiency of airlines: Hub and spoke versus point-to-point. J. Econ. Stud. 42 (1), 157–166.
Merkert, R., Hensher, D.A., 2011. The impact of strategic management and fleet planning on airline efficiency – A random effects tobit model based on DEA
efficiency scores. Transp. Res. Part A 45 (7), 686–695. https://doi.org/10.1016/j.tra.2011.04.015.
Oum, T.H., Fu, X., Yu, C., 2005. New evidences on airline efficiency and yields: A comparative analysis of major North American air carriers and its
implications. Transp. Policy 12 (2), 153–164. https://doi.org/10.1016/j.tranpol.2005.01.002.
Rosskopf, M., Lehner, S., Gollnick, V., 2014. Economic-environmental trade-offs in long-term airline fleet planning. J. Air Trans. Manage. 34, 109. https://doi.
org/10.1016/j.jairtraman.2013.08.004.
Scheraga, C.A., 2004. The relationship between operational efficiency and customer service: A global study of thirty-eight large international airlines.
Transport. J. 43 (3), 48–58.
Sengupta, J.K., 1999. A dynamic efficiency model using data envelopment analysis. Int. J. Prod. Econ. 62 (3), 209–218. https://doi.org/10.1016/S0925-5273
(98)00244-8.
Shirazi, Fateme, Mohammadi, Emran, 2019. Evaluating efficiency of airlines: A new robust DEA approach with undesirable output. Res. Transport. Business
Manage. 33, 100467.
Wang, Z., Xu, X., Zhu, Y., Gan, T., 2020. Evaluation of carbon emission efficiency in china’s airlines. J. Cleaner Prod. 243,. https://doi.org/10.1016/j.
jclepro.2019.118500 118500.
Xu, Y., Park, Y.S., Park, J.D., Cho, W., 2021. Evaluating the environmental efficiency of the U.S. airline industry using a directional distance function DEA
approach. J. Manage. Anal. 8 (1), 1–18. https://doi.org/10.1080/23270012.2020.1832925.
Zhu, Joe, 2011. Airlines performance via two-stage network DEA approach. J. Centrum Cathedra 4 (2), 260–269.
Zhu, J. (2014). Quantitative models for performance evaluation and benchmarking: Data envelopment analysis with spreadsheets (3rd ed., International Series in
Operations Research & Management Science). Cham: Springer.
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