Evaluating environmental efficiency of U.S airline industry with. flight delays using a directional distance function DEA

Transcription

1 Evaluating environmental efficiency of U.S airline industry with flight delays using a directional distance function DEA Yuan Xu 1, Yong Shin Park 1, Wonjoo Cho 2 and Joseph Szmerekovsky 1 1 Department of Transportation and Logistics, College of Business, North Dakota State University, Fargo, ND 58102, USA 2 Research Fellow, Korea Rural Economic Institute Introduction Air transportation plays an important role in the U.S. In 2017, U.S domestic airlines served million passengers and 15.1 billion pounds of freight (Bureau of Transportation Statistics, 2018). Airlines, like the other fossil fuel-based transport, release air pollutants (i.e. carbon dioxide, nitrogen oxides), even though at a relatively small portion. According to Fukui and Miyoshi (2017), U.S domestic airlines were responsible for about 2.3% of total carbon emissions in the U.S in However, with the rapid growth of air travel in recent years, there is a dramatic increase in total pollution attributable to aviation. In order to mitigate the potential risks driven by environmental issues, many airlines have committed to the set of three industry-wide greenhouse gas (GHG) reduction goals established by IATA: (1) a 1.5% per year increase in fuel efficiency until 2020; (2) carbon-neutral growth from 2020 and (3) a 50% reduction in carbon emissions by 2050 compared to Therefore, it is important to construct a model to evaluate the environmental performance of the U.S airlines over years. Data envelopment analysis (DEA), a well-established nonparametric performance measurement technique, has been widely adapted to evaluate the productivity and efficiency of airlines. A lot papers have applied standard DEA as a methodology to evaluate airline efficiency (Ouellette et al., 2008; Bhadra, 2009;

2 Wang et al.,2011; Cui & Li, 2015). Standard DEA models use a radial objective function and can be estimated using either an input oriented assumption or an output oriented assumption to allow the analyst to either reduce the inputs or expand the outputs (Mallikarjun, 2015). To analyze the effects of non-radial slacks of input and output quantities in efficiency, many recent studies focus on non-radial DEA models(chang et al., 2014; Tavassoli et al., 2014; Wanke & Barros, 2016; Zhang et al., 2017). Tavassoli et al. (2014) applied a slacks-based measure (SBM) network DEA model to analyze both the technical efficiency and service effectiveness of 11 airlines in Iran. Zhang et al. (2017) adopted a SBM-DEA model to evaluate and compare the energy efficiency of Chinese and US airlines during In order to understand the internal structure or operation process, some researchers employ the two-stage (Lu et al., 2012; Lozano & Gutiérrez, 2014; Tavassoli et al., 2014) or threestage (Mallikarjun, 2015; Cui & Li, 2017; Li & Cui, 2017) network DEA models. For instance, Lu et al. (2012) proposed a two-stage network DEA model to evaluate the production and marketing efficiency of 30 US airlines. Li and Cui (2017) utilized a three-stage network DEA model to analyze the impacts of CNG2020 strategy (Carbon Neutral Growth) on environmental efficiencies of 29 international airlines. The directional distance function (DDF) DEA, first introduced by Chambers et al. (1996), serves as a powerful measure of technical efficiency reflecting the potential for output expansion and input contraction simultaneously. It has been widely used in various fields of study (Chen et al., 2013; Sahoo et al., 2014; Kuosmanen & Johnson, 2017; Zhang & Chen, 2017). For example, Chen et al. (2013) developed an integer-valued DDF DEA model to evaluate mood and its impact on performance. DDF DEA model was also used by Fan et al. (2014) to assess the technical efficiency of 20 Chinese airports from 2006 to In this research, both desirable and undesirable output (i.e. flight delays) were considered in the production framework. Ray (2008) proposed a procedure using a DDF model to deal with the infeasibility problem often encountered in computing super-efficiency. A dataset on 28 international airlines during the year 1990 was utilized to justify that the proposed

3 procedure can generally lead to a unique and easily interpreted result. Intrigued by these studies, this paper will implement a DDF DEA model to evaluate the environmental efficiency of U.S airline industry. Most studies analyzing efficiency of airlines only take into account desirable outputs. In recent years, considering environmental factors (i.e. carbon emission) when making benchmarking analysis in airline efficiency evaluation has become a new topic. For example, Cui and Li (2017) used a three-stage DEA model to assess environmental efficiency of 29 global airlines during by considering GHG emission as an undesirable output. Chang et al. (2014) employed a SBM-DEA model to examine the economic and environmental efficiency of 27 global airlines in 2010 by considering carbon emission. In this study, we will take GHG emission as an undesirable output to estimate the environmental efficiency. Apart from GHG emission, we will also consider flight delay as an undesirable output when evaluating environmental efficiency of U.S airlines. Flight delay is a widespread problem in the U.S. The Bureau of Transportation Statistics shows that national flight delay rates were higher than 10% in recent years (Bureau of Transportation Statistics, 2018). Lots of flight delays put a significant strain on the US air travel system and cost airlines several billion dollars in additional expense each year. The operational status of US airlines can be more precisely reflected by taking flight delays into account. Several studies working on airports efficiency evaluation took flight delays into consideration (Fan et al., 2014; Sebastián et al., 2011, 2013). To our knowledge, we are the first to analyze the environmental efficiency of U.S airlines by considering GHG emission and flight delays as undesirable outputs. This paper develops a directional distance function DEA model to evaluate the environmental efficiency of 12 U.S airlines from 2013 to 2016 with consideration of desirable and undesirable outputs (i.e. flight delays). Input variables considered are employment, operating expense and fuel consumption. Regarding the outputs, revenue passenger-mile are considered as desirable, while flight delays and GHG emissions are taken as undesirable outputs.

4 Methodology Let L, S, B, P, C and D represent employment, operating expense, fuel consumption, GHG emission, revenue passenger mile, and flight delay, respectively. Then the production possibility set can be described as: We shall use directional distance function (DDF), developed by Chambers et al. (1996) to measure the environmental efficiency of U.S. airline. Following Chambers et al. (1996) and Färe & Grosskopf (2003), we define the following DDF to measure U.S. airline s environmental efficiency. In this paper, we followed Pathomsiri, Haghani, Dresner, & Windle (2008) to select g = (g P, g C, g D ) as the direction, which can expand desirable output and reduce undesirable output with the given inputs. Suppose that there are j = 1, 2,...,n airlines and for airline j the vector of inputs, desirable, and undesirable outputs is (Lj, Sj, Bj, Pj, Cj, Dj). The following DEA model can be applied to derive DDF environmental efficiency score. The subscript 0 refers to the airline under evaluation, and λ j (j = 1, 2,,n) refers to the intensity levels at which the airlines conduct production activities.

5 Data The data on the three inputs, i.e. employment, operating expense, and fuel consumption, and three outputs, i.e. GHG emission, revenue passenger mile, and flight delay for U.S. twelve airline from 2013 to 2016 are collected and complied from Bureau of Transportation Statistics. Table 1 shows the descriptive statistics of the six variables. In DEA application, the data sample size should be considerably greater than the number of input and output measures to increase its discriminating power (Pathomsiri et al., 2008). A quantitative rule of thumb is to have the sample size n max {m*s, 2*(m+s)}, where m and s are the numbers of input and output variables, respectively. The sample size of this study meet the rule of thumb requirement. Table 1. Descriptive statistics of inputs and output, Variable Unit Mean Max Min Std.dev Employment Number 29,481 97,405 2,352 33,456 Operating Expense Thousand 11,406,678 39,741, ,619 13,568,026 Fuel Consumption Gallons 2,489,082 9,674,860 2,534 3,054,415 GHG Emission Metric tons 20,390, ,947,953 9,148 56,646,837 Revenue Passengermile Mile 64,844, ,832,426 9,150,619 68,724,970 Flight Delay Number 69, ,600 2,731 65,039 Results and discussion We first employ Eq. (3) to compute the efficiency scores of twelve airlines in U.S. from 2013 to To avoid the annual effect, we estimate separate frontier for each year. The optimal value β* of Eq. (3) is actually an inefficiency score, so a higher β* score means a lower level of environmental efficiency (EE). If an airline has zero β* score, its desirable and undesirable outputs cannot be expanded and contracted simultaneously. Otherwise, it means that the desirable outputs can be expanded and

6 undesirable output can be reduced by β * multiplying by their original values. To make the DDF EE scores be comparable with those derived from the traditional DEA models, we here use (1 - β *)/(1 + β *) to denote the EE of airline j as desirable outputs become (1 + β *) times and undesirable output become (1 - β *) times of the original values. Table 2 shows the DDF results obtained (Fan et al., 2014). It can be seen that Alaska and Sky West Airlines showed a increase in their EE levels, while Hawaiian was experiencing a fluctuating trend in EE. American airline s EE decreased in 2014 and increased again during 2015 to In particular, there are ten airlines which had a high EE level during the entire sample period, i.e. Delta, ExpressJet, Frontier, JetBlue, Southwest, Spirit, United, and Virgin America. U.S. airline experienced a pleasant shift from inefficiency to efficiency. Meanwhile, we found that the most inefficient airline was Hawaiian, with an average score of Alaska, American, and SkyWest Airlines are other three airlines with poor performance. As a whole, we found that the average EE scores showed an slow increasing trend over time. Furthermore, we use the input-oriented CCR model proposed by Charnes, Cooper, & Rhodes (1978) to compute U.S. airline EE scores without considering undesirable outputs. It should be noted that Eq.(3) without considering flight delays will result in an EE score of 1 which is same as the EE score from input-oriented CCR model. The results are also presented in Table 2. There is an obvious decrease in the EE scores and number of environmentally efficient airlines. Comparing with DDF EE scores, only two airlines including ExpressJet and Spirit were found to be environmentally efficient. In CCR model, Hawaiian airline also was found to be the poorest performance airline followed by Southwest and SkyWest Airlines. Examining the yearly average score, we observed the same rising trend as the DDF results. There are two reasons to explain why DDF-DEA model derive higher EE score. The first is from methodological difference. When there is an additional variable in the model in linear programming analysis, the EE score will become higher (Martini, Manello, & Scotti, 2013). The second reason is that when we

7 consider flight delay in the model, the airlines producing low GHG emission and revenue passenger mile may also have lower levels of delayed flight, which make these airlines be closer to the best frontier. It can also be found that when considering flight delays majority of airlines experienced significant change in their EE scores (See Table 3). For example, SouthWest with rank 11, United with rank 8, JetBlue with rank 7, Frontier with rank 6, and delta with rank 3 when removing delay, while when taking delay into account, they ranked 1. This finding indicates that when delay is involved, these airlines are not operating at so bad situation. While the other four airlines, i.e. ExpressJet, Spirit, American, SkyWest Airlines, and Hawaiian, were experiencing similar change, implying that these airlines had pleasant desirable input-output efficiency, and they are doing great job in managing flight delays. Table 2. Environmental efficiency score of twelve airlines in U.S. from CCR and DDF-DEA models Average Average Airline CCR DDF CCR DDF CCR DDF CCR DDF CCR DDF Alaska American Delta ExpressJet Frontier Hawaiian JetBlue SkyWest Airlines Southwest Spirit United Virgin America Average # of environmentally efficient airline

8 Table 3. Environmental efficiency rank of twelve airlines in U.S. from CCR and DDF-DEA models. Airlines CCR_Rank DDF_Rank ExpressJet 1 1 Spirit 1 1 Delta 3 1 Virgin America 4 1 Alaska 5 9 Frontier 6 1 JetBlue 7 1 United 8 1 American 9 10 SkyWest Airlines Southwest 11 1 Hawaiian Conclusion In the airline operations, passenger revenue and GHG emission will inevitably be associated with flight delays. By considering flight delays as an undesirable output in airline operation, this paper assesses the EE of 12 U.S airline company for the period It is expected that our research can provide a relatively fair perspective for environmental performance benchmarking of U.S. airline industry. When considering flight delays, some airline such as Alaska, American, SkyWest, Hawaiian showed different result as compared to not considering flight delays. As a policy implication, those airlines with low EE score should pay more attention to the congestion management by reducing fuel consumption while improving revenue passenger mile. Further research may be carried out in the following directions. Given data availability, it is worthwhile to consider more variables such as noise, baggage claims, terminal area, cargo throughput of each airlines. Ownership such as privatization and commercialization of airline can be also considered to see the impact of this factor on EE.

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