Will airline efficiency be affected by “Carbon Neutral Growth from 2020” strategy? Evidences from 29 international airlines

Journal of Cleaner Production 164 (2017) 1289e1300

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Will airline efficiency be affected by “Carbon Neutral Growth from 2020” strategy? Evidences from 29 international airlines Qiang Cui a, *, Ye Li b a b

School of Economics and Management, Southeast University, Nanjing 211189, China School of Business Administration, Nanjing University of Finance and Economics, Nanjing 210023, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 6 December 2016 Received in revised form 27 April 2017 Accepted 8 July 2017 Available online 10 July 2017

In this paper, the impact of the “Carbon Neutral Growth from 2020” (CNG2020) strategy on airline efficiency is analyzed based on the predicted data of 29 international airlines during 2021e2023. The data is predicted through neural network. Following the principle of CNG2020 strategy, we calculate the emission limit for each airline. Two models, Network Range Adjusted Measure with natural disposability and Network Range Adjusted Measure with managerial disposability, are proposed to discuss the efficiency change when the CNG2020 strategy is considered. The following findings are gotten: 1. Under natural disposability, the indices related with undesirable output have a larger role in deciding benchmarking airline. 2. For most of the 29 airlines, CNG2020 strategy has little influence on their efficiency. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Network Range Adjusted Measure Natural disposability and managerial disposability Airline efficiency CNG2020 strategy Undesirable output

1. Introduction In recent years, the carbon dioxide emissions of airline industry have caused great attentions. According to the statistical data of the International Air Transport Association, in 2014, air transport was responsible for about 2% of man-made carbon emissions annually (CNG2020, 2017). Although this proportion is relatively small, the industry recognizes that it must work even harder on behalf of the environment to achieve long-term sustainability. Furthermore, the International Civil Aviation Organization (ICAO) predicts that in the absence of mitigation measures, driven by a sevenfold increase in air traffic, total greenhouse gas (GHG) emissions associated with aviation will be 400e600% higher in 2050 than in 2010 (CNG2020, 2017). European Union (EU) enacted the 2008/101/EC decree in November 2008, in which international airline business was brought into the European Union Emission Trading System (EU ETS). From January 1, 2012, each international flight taking-off and landing in European Union would be given an emission permit (EU ETS, 2016). This policy causes great controversy all over the world and it has not become a global action framework. On October 6, 2016, the 39th conference of International Civil

* Corresponding author. E-mail address: [email protected] (Q. Cui). http://dx.doi.org/10.1016/j.jclepro.2017.07.059 0959-6526/© 2017 Elsevier Ltd. All rights reserved.

Aviation Organization in Montreal adopted a resolution in which the member states of ICAO must work together to achieve aviation carbon neutral growth from 2020, the resolution was called “Carbon Neutral Growth from 2020” and could be labeled as “CNG2020” strategy for short. CNG2020 strategy is the first global market mechanism on emission reduction for a special industry, whose core is to build a series of market-based measures, such as levies, emissions trading systems, and carbon offsetting (CNG2020, 2017). According to the principle of CNG2020 strategy (CNG2020, 2017), there are three phases for CNG2020: pilot phase (from 2021 through 2023), first phase (from 2024 through 2026) and second phase (from 2027 through 2035). The member states voluntarily decide to participate in the strategy in pilot phase and first phase, but they are mandatorily to participate in second phase, except some exempted states. The emission baseline is the emission volume of international aviation in 2019 and 2020. According to the analyses of ICAO, the estimated quantity to be offset to achieve the carbon neutral growth from 2020 would be of the order of 142e174 million tons of CO2 in 2025; and 443 to 596 million tons of CO2 in 2035, with these ranges being determined by the definitions of nine scenarios for CO2 trends assessment from the most optimistic scenario to the less optimistic one (CNG2020, 2017). Therefore, the CNG2020 strategy has direct impacts on global airlines.

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Although International Civil Aviation Organization has analyzed the impacts of CNG2020 strategy on the total aviation industry, no papers have focused on CNG2020 strategy’s impacts on a single airline, especially on airline efficiency. Airline efficiency is defined to reflect the relationship between the inputs and outputs. As introduced in Li et al. (2015), for airlines, the efficiencies of divisions or stages are important to explore the development of airline efficiency. In this paper, we focus on discussing the influence of CNG 2020 strategy on airline efficiency with the model under natural disposability and managerial disposability. The key questions to be answered include the following: How to predict the emission volume for a single airline in the years after 2020? How to analyze the impacts of the CNG2020 strategy on airline efficiency? How to measure airline efficiency considering the stages? By targeting these questions, in this paper, the influence of CNG2020 strategy on the efficiencies of global airlines will be discussed. The remainder of this paper is organized as: Section 2 proposes the literature review. Section 3 introduces the methodology. Section 4 is the case study. Section 5 summarizes the conclusions. 2. Literature review Airline efficiency has been a popular topic since Morrell and Taneja (1979). Many papers have applied Data Envelopment Analysis (DEA) as the basic model to evaluate airline efficiency. Schefczyk (1993) used standard DEA model to measure 15 international airlines during 1989e1992 and found that high operational performance was a key factor of high profitability. Alam and Sickles (1998) built DEA and free disposal hull (FDH) to measure 11 US airlines from 1970 to 1990, and the results showed that annual rates of return under DEA and FDH were 17.39% and 17.68%. Fethi et al. (2000) used DEA and Tobit Analysis to evaluate 17 European airlines from 1991 to 1995 and concluded that the state ownership did not provide an impediment for being efficient. Capobianco and Fernandes (2004) employed standard DEA to analyze the efficiency of 53 international airlines, and the results showed that large airline companies used capital efficiently to generate return with a low level of fixed assets. Bhadra (2009) applied standard DEA on 13 US airlines and found that efficiency tended to be affected by block hours, reducing them would increase efficiency. Wang et al. (2011) used standard DEA to analyze 30 airlines in 2006 and found that performance of carriers was not just related to the number of committees and non-executive directors, but also affected by the external factors. Cui and Li (2015a) proposed a Virtual Frontier Benevolent DEA Cross Efficiency Model to evaluate the efficiency of 11 international airlines during 2008e2012, and thought that capital efficiency was an important factor in driving energy efficiency. Cui and Li (2015b) applied standard DEA model to calculate the civil aviation safety efficiency of 10 Chinese airlines from 2008 to 2012. However, the standard DEA models in these papers have not considered the effects of non-radial slacks in the efficiency, but the slacks are important in providing more policy-making references for airline managers. In recent years, many non-radial DEA models are taken as the basic method to assess airline efficiency. Chang et al. (2014) applied Slacks-Based Measure model to analyze 27 international airlines in 2010 and concluded that fuel consumption and revenue structure were major causes of inefficient airlines. Cui et al. (2016a) analyzed the impacts of the EU ETS emission limits on airline performance, which were calculated based on the historical emission data of 2004e2006. Cui et al. (2016b) proposed a Virtual Frontier Dynamic Slacks Based Measure to calculate the energy efficiencies of 21

airlines from 2008 to 2012. Li et al. (2016a) proposed a Virtual Frontier Dynamic Range Adjusted Measure (RAM) to calculate the energy efficiencies of 22 airlines from 2008 to 2012. However, above models treat the production system as a black box when measuring efficiency, ignoring its internal structure. As introduced in the Introduction, for airlines, divisional efficiency is important when exploring the development of airline efficiency. Hence, in recent years, many DEA models with network structure have been applied to measure airline efficiency. Lozano
rrez (2014) employed a two-stage Slacks-Based Meaand Gutie sure model to measure the efficiency of 16 European airlines, and the results showed that Network DEA approach had more discriminative power than the single-process DEA. Li et al. (2015) utilized a virtual frontier network Slacks-Based Measure to analyze 22 international airlines during 2008e2012, and found that most airlines’ efficiency increased from 2008 to 2009. The stages, the inputs and the outputs of the network airline efficiency papers are shown in Table 1. These previous studies in Table 1 lay a suitable foundation for this paper in exploring the internal structure of airline performance, which can reflected by the division of the stages and the inputs and outputs for each stage. In the previous papers, airline efficiency was divided into two stages and Mallikarjun (2015) firstly proposed a three stage structure, in which airline production system was divided into three stages: Operations stage, Services stage and Sales stage. Li et al. (2016b) and Cui et al. (2016c) adopted the basic three-stage structure and defined greenhouse gases as an undesirable output of Services stage. Because the topic in this paper is to analyze the impacts of the CNG2020 strategy on airline efficiency and greenhouse gas is an important factor, greenhouse gases are set as the undesirable output of Services stage too. In this paper, airline efficiency is divided into three stages: Operations, Services and Sales. The inputs and outputs for each stage are selected according to the previous papers. Network Range Adjusted Measure with natural disposability and Network Range Adjusted Measure with managerial disposability are built to analyze the impacts of the CNG2020 strategy on airline efficiency based on the empirical data of 29 international airlines. Finally, some conclusions are drawn from the research results. 3. Methodology Data Envelopment Analysis (Charnes et al., 1978) is a nonparametric method to evaluate the relative efficiencies of decisionmaking units (DMUs) with multi-inputs and multi-outputs. Different with some parametric methods such as Stochastic Frontier Analysis, DEA models don’t need to pre-establish the particular form of production frontier and can get the results only according to the value of inputs and outputs. Therefore, DEA models have been widely applied in evaluating efficiency in many fields, such as energy and CO2 emissions efficiency (Iftikhar et al., 2016), environmental management efficiency (Xie et al., 2016), environmental efficiency analysis of regional industry (Chen and Jia, 2017), regional natural resource allocation and utilization efficiency (Zhu et al., 2017) and industrial eco-efficiency (Zhang et al., 2017). When undesirable outputs are considered, many disposability methods have been proposed, such as weak disposability in F€ are et al. (2007), strong disposability in Hailu and Veeman (2001), by-production model in Murty et al. (2012), natural disposability and managerial disposability in Sueyoshi and Goto (2012) and weak G-disposability in Hampf and Rødseth (2015). As stated in Hoang and Coelli (2011) and Hampf and Rødseth (2015), the weak disposability can become compatible with material balance

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Table 1 Details of the literature with network structure. Papers

Stages

Inputs and outputs of 1st stage

Inputs and outputs of 2nd Inputs and stage outputs of 3rd stage

Inputs: number of flights, seat-miles; Outputs: passenger-miles, embarkation passengers; Zhu (2011) Two stages Inputs: cost per ASM, salaries per ASM, wages per ASM, benefits per ASM, fuel Inputs: load factor, fleet size expense per ASM; Outputs: RPM Outputs: load factor, fleet size; Gramani Two phases: operational Inputs: Aircraft fuel, wages, salaries and benefits, cost per ASM; Inputs: the inverse of (2012) performance, financial Outputs: RPK efficiency in phase 1; performance Outputs: flight revenue, flight income Inputs: number of employees, fuel consumption, total number of seats, cost of Inputs: ASM, ATM; Lu et al. Two stages: production flight equipment, maintenance expenses, cost of equipment and property; Outputs: RPM, Non(2012) efficiency, marketing Passenger Revenue Outputs: ASM, ATM; efficiency Inputs: ASK, ATK, selling Two stages: production Inputs: fuel cost, non-current assets, wages and salaries, other operating Lozano and
rrez costs; process, sales process costs; Gutie Outputs: RPK, RTK Outputs: ASK, ATK; (2014) Inputs: Number of passenger planes, number of employees, number of cargo Inputs: passenger-planeTavassoli et al. Two stages: technical km, cargo-plane-km; planes; (2014) efficiency, service Outputs: passenger-km, Outputs: passenger-plane-km, cargo-plane-km; effectiveness ton-km; Mallikarjun Three stages: operations stage, Inputs: operating expenses; Inputs: ASK, fleet size, (2015) services stage, sales stage Outputs: ASK; destinations; Outputs: RPK; Chiou and Chen (2006)

Two stages: cost efficiency, service effectiveness

Inputs: fuel cost, personnel cost, aircraft cost; Outputs: number of flights, seat-miles;

Li et al. (2015) Three stages: operations stage, Inputs: number of employees, aviation kerosene; services stage, sales stage Outputs: ATK, ASK;

Li et al. (2016b)

Three stages: operations stage, Inputs: number of employees, aviation kerosene; services stage, sales stage Outputs: ATK, ASK;

Cui and Li (2016)

Two stages: operations stage, Inputs: salaries, wages and benefits, fuel expenses, total assets; carbon abatement stage Outputs: RPK, RTK and estimated carbon dioxide;

e

e

e

e

e

e

Inputs: RPM; Outputs: Operating Revenue Inputs: ATK, ASK, fleet size; Inputs: RTK, Outputs: RTK, RPK RPK, sales costs; Outputs: total business income Inputs: ATK,ASK, fleet size; Inputs: RTK, RPK, sales Outputs: RTK, RPK, costs; greenhouse gases Outputs: total business income Inputs: estimated carbon e dioxide, abatement expense; Outputs: carbon dioxide;

Notes: ASM: Available Seat Miles, RPM: Revenue Passenger Miles, ATM: Available Tonne Miles, ASK: Available Seat Kilometers, ATK: Available Tonne Kilometers, RPK: Revenue Passenger Kilometers, RTK: Revenue Tonne Kilometers.

principles under the presence of end-of-pipe technologies to abate pollution. Yet, in many situations, the end-of-pipe equipment is technologically unavailable or economically unaffordable (Rødseth and Romstad, 2013). For by-production model, in the practical application, the separation of the inputs’ into polluting and nonpolluting ones would need to be made a priori before conducting any efficiency assessment, but it might be hard to decide whether some inputs can be classified into the polluting or non-polluting group (Dakpo et al., 2016). Therefore, the applied range of byproduction model is limited. As stated in Dakpo et al. (2016), the weak G-disposability also fails to correctly represent and capture the different trade-offs in a pollution-generating technology. Because this paper is to discuss the impacts of CNG2020 strategy on airline efficiency, and natural disposability and managerial disposability can analyze the adaptive behaviors of DMUs to changes in environmental regulations, we apply the natural disposability and managerial disposability in Sueyoshi and Goto (2012) as the basic methods to discuss the topic. Sueyoshi and Goto (2012) proposed the natural disposability and managerial disposability. For natural disposability, a decrease in the vector of inputs implies a decrease in the vectors of both

desirable outputs and undesirable outputs. This disposability is also termed the “natural reduction” of pollution. The idea is that the aim of a manager is to increase his/her firm’s operational efficiency in a way that, given a vector of reduced inputs, the firm increases the desirable outputs as much as possible. No environmental managerial effort needs to be undertaken in order to meet the objective of pollution reduction. For managerial disposability, a firm increases its consumption of inputs in order to increase the volume of desirable outputs and simultaneously decrease the levels of undesirable outputs. This can be achieved through some managerial effort such as the adoption of new technologies that can mitigate pollution. Sueyoshi and Goto (2012) proposed the detailed models based on the Range Adjusted Measure (RAM) model. In order to illustrate the models in Sueyoshi and Goto (2012), we firstly introduce the basic RAM model. The RAM model is proposed by Aida et al. (1998) and Cooper et al. (1999), and has been widely applied to evaluate efficiency, such as RAM in Network DEA (Avkiran and McCrystal, 2012) and RAM in Dynamic DEA (Li et al., 2016a). The basic RAM model is:

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M  X s

1 q ¼ 1  max MþN s:t: xm0 ¼

K X

m0 R m¼1 m

þ

N þ X s

1 q ¼ 1  max MþNþJ

!

n0 Rþ n¼1 n

s:t: xm0 ¼

yn0 ¼

lk xmk þ s m0 ; m ¼ 1; 2; /; M ðC1Þ

lk ynk  sþ n0 ; n ¼ 1; 2; /; N

(1)

ðC2Þ

k¼1 K P

yn0 ¼

K X

ðC3Þ

k¼1;2;/;K

k¼1;2;/;M

lk ulk þ s l0 ; l ¼ 1; 2; /; L K P

k¼1;2;/;K

ðynk Þ are the ranges of the inputs and

þ outputs. s m and sn stand for the slacks of the m th input and the n th output. l is the intensity variable. s m denotes the input redundancy, P if the inputs of DMU k are on the optimal frontier ð Kk¼1 lk xmk Þ, it is 0; and if the inputs are not on the optimal frontier, it is larger than 0. sþ n is the output deficiency, if the outputs of DMU k are on the P optimal frontier ð Kk¼1 lk ynk Þ, it is 0; and if the inputs are not on the optimal frontier, it is larger than 0. Under natural disposability, the undesirable outputs are considered as the inputs. The natural disposability in RAM model is

q ¼ 1  max s:t: xm0 ¼

1 MþNþJ

K X

lk xmk þ

M  X s

m0

m¼1

s m0 ; m

Rm

þ

N þ X s

n0

n¼1

Rn

¼ 1; 2; /; M

þ

L X s

!

l0

l¼1

K X

lk ynk  sþ n0 ; n ¼ 1; 2; /; N

K X

lk ulk þ s l0 ; l ¼ 1; 2; /; L

ðC1Þ ðC2Þ

s:t: xm0 ¼

j

(2)

j

lk ¼ 1

Mj j X s

m0 R m¼1 m

þ

N jþ X s

n0 R n¼1 n

þ

L sj X

!

l0 R l¼1 l

ljk xjmk þ sj m0 ; m ¼ 1; 2; /; Mj ; j ¼ 1; 2; /; J ðC1Þ

ljk yjnk  sjþ n0 ; n ¼ 1; 2; /; Nj ; j ¼ 1; 2; /; J

ðC2Þ

j

K X

ljk ujlk þ sj ; l ¼ 1; 2; /; Lj ; j ¼ 1; 2; /; J l0

ðC3Þ

k¼1

k¼1;2;/;K

K P

k¼1;2;/;K

max

ðynk Þ 

max

ðujk Þ 

k¼1;2;/;K

K X

K X

ul0 ¼

ðC4Þ

and for the slacks of the inputs, desirable outputs and the undesirable outputs. ulk denotes the l th undesirable output of DMU k, ul0 denotes the l th undesirable output of the evaluated DMU. L is the number of the undesirable outputs. Rm ¼ max ðxmk Þ  min ðxmk Þ,

Rj ¼

ðC4Þ

k¼1

s stand l

k¼1;2;/;K

(3)

k¼1

yn0 ¼

ðC3Þ

þ  lk ; s m0 ; sn0 ; sl0  0

Rn ¼

ðC2Þ

The variables in model (3) are same as model (2). In model (3), the inputs and the desirable outputs are modeled as outputs, as shown in constraints (C1) and (C2). The undesirable outputs are considered as inputs, as shown in constraint (C3). From the detailed models of natural disposability and managerial disposability, we can know that natural disposability is same as strong disposability, in which the undesirable outputs are considered as the inputs. In the managerial disposability, the inputs are modeled as desirable outputs and the undesirable outputs are considered as the inputs. But the models in Sueyoshi and Goto (2012) have limitations too. In the process of evaluating efficiency, it does not consider the internal structure relative to the measures characterizing the operations performance of DMUs. Most of the DMUs consist of many divisions and divisional efficiency is important when exploring the development of airline efficiency (Li et al., 2015). Therefore, the detailed Network RAM model with natural disposability and the Network RAM model with managerial disposability are proposed. The Network RAM model with natural disposability is J P

k¼1

þ s m , sn

Rl

þ  lk ; s m0 ; sn0 ; sl0  0

k¼1 K P

l¼1

ðC3Þ

lk ¼ 1

wj q ¼ 1  max M þ Nj þ Lj j j¼1

k¼1

ul0 ¼

þ

Rl

k¼1

yn0 ¼

Rn

!

l0

k¼1

k¼1;2;/;K

min

n¼1

L X s

k¼1

yn0 and xm0 are the n th output and the m th input of evaluated DMU. xmk ; ynk denote the m th input and the n th output of DMU k, k ¼ 1; 2; /; K. M; N; K are the number of the inputs, the outputs and max ðxmk Þ  min ðxmk Þ and the DMUs. R m ¼ ðynk Þ 

þ

lk ynk  sþ n0 ; n ¼ 1; 2; /; N

K X

þ lk ; s m0 ; sn0  0

max

Rm

n0

lk xmk  s m0 ; m ¼ 1; 2; /; M ðC1Þ

k¼1

Rþ n ¼

m¼1

N þ X s

k¼1

ul0 ¼

lk ¼ 1

m0

k¼1

k¼1 K X

K X

M  X s

min

min

K X

lhk zkðh;jÞ ; j; h ¼ 1; 2; /; J

ðC4Þ

k¼1 K P k¼1

ljk ¼ 1; j ¼ 1; 2; /; J

ðC5Þ

jþ j ljk ; sj m0 ; sn0 ; sl0  0

and

(4)

ðujk Þ are the ranges of the inputs,

J is the number of the divisions. R m ¼ maxðxm Þ  minðxm Þ,  Rþ n ¼ maxðyn Þ  minðyn Þ and Rl ¼ maxðul Þ  minðul Þ are the ranges of the inputs, desirable outputs and undesirable outputs.

k¼1;2;/;K

k¼1;2;/;K

ðynk Þ

k¼1

ljk zðj;hÞ ¼ k

desirable outputs and the undesirable outputs. l is the intensity variable. In model (2), it can be found that the undesirable outputs are considered as inputs, as shown in constraint (C3). The managerial disposability in RAM model is

zðj;hÞ stands for the intermediate products between Division j and Division h.wj is the weight of Division j. Nj , Mj and Lj stand for the

Q. Cui, Y. Li / Journal of Cleaner Production 164 (2017) 1289e1300

number of the desirable outputs, the inputs and the undesirable outputs of Division j.xjmk , yjnk and ujlk denote the m th input, the n th desirable output and the l th undesirable output of DMU k of Division j. lj , lh are the intensity variables, K is the number of the þ  DMUs. s m , sn and sl stand for the slacks of the inputs, desirable outputs and the undesirable outputs. The divisional efficiency of Division j is

1 q ¼ 1  max Mj þ Nj þ Lj

Mj j X s

m0 R m¼1 m

þ

N jþ X s

n0 R n¼1 n

þ

L sj X

!

l0 R l¼1 l

(5)

The Network RAM model with managerial disposability is J P

wj q ¼ 1  max M þ Nj þ Lj j¼1 j s:t: xjm0 ¼

K X

Mj jþ X s

m0 R m¼1 m

þ

N jþ X s

n0 R n¼1 n

þ

L sj X

!

l0 R l¼1 l

ljk xjmk  sjþ m0 ; m ¼ 1; 2; /; Mj ; j ¼ 1; 2; /; J ðC1Þ

k¼1

yjn0 ¼

K X

ljk yjnk  sjþ n0 ; n ¼ 1; 2; /; Nj ; j ¼ 1; 2; /; J

ðC2Þ

k¼1

ujl0 ¼

K X

ljk ujlk þ sj ; l ¼ 1; 2; /; L; j ¼ 1; 2; /; J l0

ðC3Þ

k¼1 K P

ljk zkðj;hÞ ¼

K X

lhk zkðh;jÞ ; j; h ¼ 1; 2; /; J

ðC4Þ

1293

much ASK as possible when the resources are pre-determined. The service divisions need to satisfy the travel demand of passengers from origins to destinations in a safe, punctual, convenient and comfortable mode. For this purpose, they need to supply aircrafts and seat loads to produce passenger traffic. The aircrafts and seat loads can be thought as the inputs of airline services and the passenger traffic can be seen as the outputs of airline services. In order to produce efficient services, the service division needs to minimize the aircrafts and seat loads when the passenger traffic (Revenue Passenger Kilometers, RPK) are certain, or to maximize the passenger traffic when the aircrafts, seat loads are certain. Hence, ASK can be treated as the linking activities between operation divisions and sale divisions. When the airlines apply aircrafts and seat loads to convert ASK into RPK, the aircrafts need to consume fuels and discharge greenhouse gases, so greenhouse gases can be considered as an undesirable output of services stage. The sale divisions need to sell airline’s services as much as possible to produce revenues, in which the services can be treated as its inputs and the revenues can be seen as its outputs. Certainly, the sales process needs some sale cost. An efficient sale requires the airline to produce most revenues when the services and sale cost are certain, or to minimize the services and sale cost when the revenues are fixed. In this sense, passenger traffic can be treated as the linking activities between service divisions and sale divisions. Combining with Mallikarjun (2015) and Li et al. (2015), we choose the inputs, outputs and intermediate products as follows:

(7)

Operations Stage: Inputs 1 ¼ Operating Expenses (OE) Outputs 1 ¼ Available Seat Kilometers (ASK) Services Stage: Inputs 2 ¼ Available Seat Kilometers (ASK) and Fleet Size (FS) Output 2 ¼ Revenue Passenger Kilometers (RPK) Undesirable output: Greenhouse Gases Emission (GHG) Sales Stage: Inputs 3 ¼ Revenue Passenger Kilometers (RPK) and Sales Costs (SC) Output 3 ¼ Total Revenue (TR) Intermediate products: Link (Operations Stage to Services Stage): Available Seat Kilometers (ASK) Link (Services Stage to Sales Stage): Revenue Passenger Kilometers (RPK)

In this paper, based on the previous literature review, a new theoretical model of airline efficiency is constructed, in which the inputs and the outputs are newly set to be suitable to discuss the impacts of the CNG2020 strategy on airline efficiency. According to the structure of Mallikarjun (2015), Li et al. (2015, 2016b), airline production process is divided into Operations Stage, Services Stage and Sales Stage. As introduced in Li et al. (2015), most airlines comprise several divisions, such as operations division, services division and sales division. These divisions are known as stages in Li et al. (2015). Their operation divisions need to fully use the operating expenses increase its passenger service capacity, which can be reflected by Available Seat Kilometers (ASK). The resources can be treated as the inputs of operation divisions, ASK can be seen as the output. An efficient operation requests the airline to produce as

Similar with Li et al. (2016b), Greenhouse Gases Emission (GHG) is set as the undesirable output of Services stage. The emissions from aviation include CO2, H2O, NOx, SOx and soot, CO2 is the most important greenhouse gas (Sausen et al., 2005). As stated in the Introduction, when the airlines apply aircrafts and seat loads to convert ASK into RPK, the aircrafts need to consume fuels and discharge greenhouse gases, so greenhouse gases can be considered as an undesirable output of services stage. For an aircraft, its emission amount has a close relationship with the real aircraft load and the flight distance. In addition, the emission amount is also decided by the Fleet Size. Therefore, the Greenhouse Gases Emission should be the output of Services Stage. Operating Expenses (OE) is defined as the input of Operations stage, and the value of Operating Expenses (OE) does not include Sales Costs (SC). What’s more, OE is chosen to discuss the influence of buying carbon emission rights. Total Revenue contains passenger service revenue, freight service revenue, postal service revenue and other revenue. Since the topic of this paper is to discuss the impacts of the CNG2020 strategy on the efficiency of the airlines, and some airlines’ emission may be less than the limits, then these airlines may sell the surplus rights to added revenue to Total Revenue. Therefore,

k¼1

k¼1 K P k¼1

ljk ¼ 1; j ¼ 1; 2; /; J

ðC5Þ

jþ j ljk ; sj m0 ; sn0 ; sl0  0

(6) The variables in model (6) are same as model (4). In model (6), the inputs and the desirable outputs are modeled as outputs, as shown in constraints (C1) and (C2). The undesirable outputs are considered as the inputs, as shown in constraint (C3). The divisional efficiency of Division j is

1 q ¼ 1  max Mj þ Nj þ Lj

Mj jþ X s

m0 R m¼1 m

þ

N jþ X s

n0 R n¼1 n

þ

L sj X l0 R l¼1 l

!

4. Empirical study 4.1. The efficiency model

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Total Revenue is chosen as the output, rather than Passenger Revenue. The detailed three-stage structure is shown in Fig. 1. 4.2. The data The empirical data are obtained from 29 global airlines: Aeroflot, Air Berlin, Air France-KLM, Lufthansa Airlines, Scandinavian Airlines, Iberia, Ryanair, British Airways, TAP Portugal, Norwegian, Finnair, Turkish Airlines, EasyJet, Virgin Atlantic Airways, China Eastern Airlines, China Southern Airlines, Korean Air, Qantas Airways, Delta Air Lines, Air China, Hainan Airlines, Emirates Airline, Air Canada, Cathay Pacific Airways, Singapore Airlines, All Nippon Airways, Eva Air, Thai Airways and Garuda Indonesia. Among these airlines, seven airlines’ passenger turnover volume ranks top 10 all over the world (Delta Air Lines, Emirates Airline, China Southern Airlines, Lufthansa Airlines, British Airways, Air France-KLM and Air China). These airlines are from Asia, Europe, Oceania and America; they can represent the global airlines to a certain extent. Therefore, these airlines are chosen as the samples to analyze the impacts of CNG2020 strategy. An empirical study in this paper will be performed with the data of an eight-year period, from 2008 to 2015. According to the principle of CNG2020 strategy, there are three phases: pilot phase (from 2021 through 2023), first phase (from 2024 through 2026) and second phase (2027 through 2035). The average level of CO2 emissions from international aviation covered by the scheme between 2019 and 2020 represents the basis for CNG2020 strategy. Therefore, the inputs and outputs in the years after 2015 must be predicted. Considering that the accuracy of the forecast data may be poorer as the forecast period become longer, we assume that all these 29 airlines will participate in 2021 and only consider the efficiencies of these airlines in pilot phase from 2021 to 2023. Hence, the inputs and outputs from 2016 through 2023 must be predicted based on the data in the period of 2008e2015. The data on Operating Expenses, Available Seat Kilometers, Fleet Size, Sales Costs, Total Revenue and Revenue Passenger Kilometers are collected from the annual reports. The data on Greenhouse Gases Emission is taken from the sustainability, environment and corporate social responsibility reports of the 29 companies. Among the 29 airlines, some airlines are low cost carriers. Because the CNG2020 strategy has no special measures for low-cost carriers and its impacts on airline efficiency are same for low-cost carriers and full service carriers, this paper has not considered the difference between full service carriers and low-cost carriers. BP neural network is applied to predict the inputs and outputs. BP neural network has been widely used in predicting and the detailed method can be found in many existing papers (Sadeghi, 2000; Yu et al., 2008; Zhang and Wu, 2009; Ren et al., 2016). BP neural network is composed of input layer, hidden layer and output layer. The detailed parameters of BP neural network are shown in

Table 2. In order to improve the forecast accuracy, the data of the first seven year is defined as the input set of the training set and set the last year as the goal set of the training set. Then the BP neural network is trained and the data of the latter seven years is defined as the test set to predict the data of the next year. This step is repeated and the forecast data of 2016e2023 is gotten. It is worth pointing out that the forecast value of fleet size has referred to the fleet upgrade plans in the airlines’ annual reports. Descriptive statistics of inputs, outputs and intermediate products during 2008e2015 are provided in Table 3. It is assumed that all these 29 airlines will participate in 2021 and the data of 2021e2023 will be applied to discuss the efficiency change. The descriptive statistics of inputs, outputs and intermediate products from 2021 to 2023 are shown in Table 4. Table 5 shows the Pearson correlation coefficients between the inputs and the outputs from 2021 to 2023 (Cui et al., 2013; Mi et al., 2017). As shown in Table 5, most of the coefficients are positive and relatively high, which ensures that the relations between the inputs and outputs are tight. 4.3. Results According the Network RAM model with natural disposability and Network RAM model with managerial disposability model in Section 3, the weights of the three stages have direct impacts on the results and they must be provided in advance. Many papers have focused on exploring the method to determine the stage weights, such as Analytic Hierarchy Process in Wang et al. (2010), Malmquist Index in Kao and Hwang (2014) and efficiency ratio in Kao (2014). However, the results of these methods are different and none of them is commonly recognized. In existing efficiency papers, Yu
rrez (2014) and Li et al. (2016b) had set (2010), Lozano and Gutie equal weights for each stage. Following these papers, the weights of   the three stages are set as 13; 13; 13 and introduce the detailed models. The efficiency of the detailed Network RAM model with natural disposability is

qnatural

!! sOE sFS sGHG sSC sTR 1 0 0 0 0 0 þ þ þ þ ¼ 1  max * 5 ROE RFS RGHG RSC RTR

X 8 lk OEk þ sOE OE0 ¼ 0 > > > k > P > > lk ¼ 1 > > > > k > P > > ðlk  mk ÞASKk ¼ 0 > > > > k X > > > > mk FSk þ sFS FS0 ¼ > 0 > > > k > X > > > mk GHGk þ sGHG < GHG0 ¼ 0 k s:t: P > mk ¼ 1 > > >P k > > > > ðmk  hk ÞRPKk ¼ 0 > > > k > X > > > SC0 ¼ hk SCk þ sSC > 0 > > > k > X > > > hk TRk  sTR > 0 > TR0 ¼ > > k >P > : hk ¼ 1

(8)

k

Fig. 1. Network structure.

The efficiency of the detailed Network RAM model with managerial disposability is

Q. Cui, Y. Li / Journal of Cleaner Production 164 (2017) 1289e1300

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Table 2 The parameters of BP neural network. Input neurons

Hidden neurons

Output neurons

Maximum steps

Training goal

Learning rate

29

60

1

50000

0.0005

0.01

Table 3 Descriptive statistics of the inputs and outputs during 2008e2015. Variable The inputs Operating Expenses (1,000,000 dollar) Fleet Size Sales Costs (1,000,000 dollar) The desirable outputs Total Revenue (1,000,000 dollar) The undesirable outputs Greenhouse gases emission (1000 tons) Intermediate products Available Seat Kilometers (1,000,000) Revenue Passenger Kilometers (1,000,000 person-kilometers)

Meana

Std.dev.a

Mina

Maxa

11,838.02 283.49 719.97

8939.14 208.94 518.03

1161.00 53.00 65.00

44,135.00 809.00 2387.00

13,461.94

9697.06

1271.00

42,609.00

15,018.35

8529.73

3280.00

42,150.00

136,153.16 108,408.26

95,406.45 82,138.15

2791.48 1943.04

559,878.00 485,690.00

Note: Operating Expense, Sales Costs and Total Revenue are expressed in Purchasing Power Parity dollars. a Mean stands for the mean value of these 29 airlines and Std.dev. denotes the standard deviation of these 29 airlines. Min and Max represent the minimum value and the maximum value of these 29 airlines, respectively.

Table 4 Descriptive statistics of the inputs and outputs from 2021 to 2023. Variable The inputs Operating Expenses (1,000,000 dollar) Fleet Size Sales Costs (1,000,000 dollar) The desirable outputs Total Revenue (1,000,000 dollar) The undesirable outputs Greenhouse gases emission (1000 tons) Intermediate products Available Seat Kilometers (1,000,000) Revenue Passenger Kilometers (1,000,000 person-kilometers)

qmanagerial ¼ 1  max

sOE sFS sGHG sSC sTR 1 * 0 þ 0 þ 0 þ 0 þ 0 5 ROE RFS RGHG RSC RTR

X 8 lk OEk  sOE OE0 ¼ 0 > > > k > P > > lk ¼ 1 > > > > k > P > > ðlk  mk ÞASKk ¼ 0 > > > > k X > > > > mk FSk  sFS FS0 ¼ > 0 > > > k > X > > > mk GHGk þ sGHG < GHG0 ¼ 0 k s:t: P > mk ¼ 1 > > >P k > > > > ðmk  hk ÞRPKk ¼ 0 > > > k > X > > > SC0 ¼ hk SCk  sSC > 0 > > > k > X > > > hk TRk  sTR > 0 > TR0 ¼ > > k >P > : hk ¼ 1 k

In the model, all variables are non-negative. The variables are:

Mean

Std.dev.

Min

Max

17,502.68 522.98 1769.95

13,730.90 324.57 1213.40

2876.00 102.00 438.00

55,850.00 1321.00 5547.00

24,021.10

16,236.32

5502.00

62,451.00

32,148.26

14,754.76

5661.00

65,897.60

261,728.99 206,918.72

214,178.63 172,162.12

13,565.86 10,927.61

946,427.34 761,225.23

!!

Table 5 Input-output correlations. ASK OE ASK FS RPK SC

RPK

GHG

0.996 0.582

0.678 0.575

TR

0.721

0.520 0.769

Note: All correlation coefficients are statistically significant at the 1% level.

(9)

OEk Operating Expenses of airline k; FSk Fleet Size of airline k; ASKk Available Seat Kilometers of airline k; GHGk Greenhouse gases emission of airline k; SCk Sales Costs of airline k; RPKk Revenue Passenger Kilometers of airline k; TRk Total Revenue of airline k; ROE Range of Operating Expenses; RFS Range of Fleet Size; RSC Range of Sales Costs; RGHG Range of Greenhouse gases emission; RTR Range of Total Revenue; l; m; h The intensity variables of the three stages. The MATLAB R2012b software is applied to realize models (8)e(9) to get the efficiencies of these 29 airlines during

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2021e2023, as shown in Table 6. Comparing natural disposability and managerial disposability in Table 6, we can conclude that the mean value in efficiency score of natural disposability is larger than that of managerial disposability. This result shows that the efficiency has a certain degree of decline when some managerial efforts are made. The standard deviation of managerial disposability is larger than that of natural disposability, indicating that the efficiency difference can be shown more evidently in managerial disposability. This manifests that the managerial efforts can make the efficiency difference more prominent. The efficient airlines are different under natural disposability and managerial disposability from 2021 to 2023. Scandinavian is the only efficient airline under natural disposability, while under managerial disposability the efficient airlines from 2021 to 2023 are Delta, All Nippon and Emirates. The predicted data of Services stage is applied to illustrate the difference of the two disposability models and the comparison can be found in Table 7. From Table 7, it can be concluded that for Scandinavian, both its indices related with undesirable output (GHG) and the indices related with desirable output (TR) have low rankings. Greenhouse gases emission (GHG) is an undesirable output, if one airline has a small GHG per input, its efficiency should be high and it should be regarded as the benchmarking airline. Total Revenue (TR) is a desirable output, if one airline has a small TR per input, its efficiency should not be high and it should not be regarded as the benchmarking airline. This result indicates that under natural disposability, the indices related with undesirable output have a larger role in deciding benchmarking airline. As shown in Table 7, for Delta in 2021, All Nippon in 2022 and Emirates in 2023, both their indices related with undesirable output (GHG) and the indices related with desirable output (TR)

have middle rankings. However, they are set as the benchmarking airlines under managerial disposability. Therefore, in the aspect of treating greenhouse gases emission, natural disposability is relatively more reasonable than managerial disposability in these airline samples. 4.4. CNG2020 strategy discussion According to the principle of CNG2020 strategy, if a member state participates in the strategy, each airline of this state will be assigned an emission limit according to its international aviation emissions, and if the airline’s emission exceeds the limit, the airline must buy emission rights. The carbon emission limit is calculated based on the average international emissions of 2019 and 2020. In this paper, it is assumed that all these 29 airlines will participate in the CNG2020 strategy in the pilot phase (from 2021 through 2023), so the efficiency change of these 29 airlines during the period from 2021 to 2023 will be discussed. Because the 29 airlines have different proportion of international aviation, the emission limits of the 29 airlines should be firstly calculated. According to the principle, the formula of the emission limit for each airline can be gotten. These 29 airlines will participate in the CNG 2020 strategy during 2021e2023, so according to the principle of CNG2020 strategy, the emission limit is set as the average emission of these airlines’ international routes, which is calculated based on the value of Revenue Tonne Kilometers.

L¼

 29   1 X 1   cj2019 *Ej2019 þ cj2020 *Ej2020 29 j¼1 2

(10)

cj2019 and cj2020 stand for the proportion of international aviation of airline j in 2019 and 2020, this value is predicted through BP Neural

Table 6 The efficiency during 2021e2023. Airlines

Aeroflot Air Berlin Air France-KLM Lufthansa Scandinavian Iberia Ryanair British TAP Portugal Norwegian Finnair Turkish EasyJet Virgin Atlantic China Eastern China Southern Korean Air Qantas Delta Air China Hainan Emirates Air Canada Cathay Pacific Singapore All Nippon Eva Air Thai Garuda Indonesia Mean Std.dev.

2021

2022

2023

Average

natural

managerial

natural

managerial

natural

managerial

natural

managerial

0.556 0.469 0.775 0.739 1.000 0.553 0.716 0.698 0.769 0.687 0.890 0.599 0.869 0.824 0.642 0.612 0.790 0.867 0.626 0.621 0.789 0.664 0.870 0.689 0.918 0.682 0.885 0.884 0.783 0.740 0.128

0.697 0.784 0.683 0.844 0.834 0.845 0.641 0.727 0.409 0.730 0.623 0.570 0.494 0.501 0.551 0.648 0.593 0.559 1.000 0.589 0.469 0.683 0.556 0.658 0.650 0.897 0.559 0.402 0.447 0.643 0.148

0.553 0.447 0.774 0.732 1.000 0.545 0.717 0.835 0.739 0.672 0.890 0.586 0.800 0.807 0.631 0.602 0.789 0.863 0.597 0.598 0.780 0.666 0.872 0.693 0.919 0.683 0.886 0.876 0.762 0.735 0.132

0.690 0.789 0.667 0.768 0.852 0.835 0.620 0.710 0.362 0.753 0.618 0.574 0.475 0.452 0.531 0.637 0.598 0.543 0.813 0.567 0.460 0.704 0.548 0.648 0.650 1.000 0.544 0.387 0.422 0.628 0.151

0.538 0.443 0.785 0.742 1.000 0.555 0.719 0.833 0.753 0.671 0.899 0.567 0.780 0.796 0.627 0.604 0.790 0.865 0.623 0.591 0.774 0.909 0.872 0.700 0.929 0.683 0.845 0.867 0.748 0.742 0.135

0.705 0.788 0.665 0.755 0.851 0.847 0.647 0.718 0.385 0.780 0.645 0.596 0.505 0.463 0.532 0.640 0.633 0.569 0.799 0.564 0.483 1.000 0.572 0.665 0.677 0.893 0.552 0.403 0.435 0.647 0.152

0.549 0.453 0.778 0.737 1.000 0.551 0.717 0.789 0.753 0.677 0.893 0.584 0.816 0.809 0.633 0.606 0.790 0.865 0.616 0.604 0.781 0.746 0.872 0.694 0.922 0.683 0.872 0.876 0.765 0.739 0.129

0.697 0.787 0.672 0.789 0.846 0.842 0.636 0.719 0.385 0.754 0.628 0.580 0.491 0.472 0.538 0.642 0.608 0.557 0.871 0.573 0.471 0.796 0.558 0.657 0.659 0.930 0.552 0.397 0.435 0.639 0.146

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Table 7 The comparison of the different efficient airlines. Year

Airlines

Disposability

GHG/OE

Ranking

GHG/FS

Ranking

GHG/SC

Ranking

2021 2021 2022 2022 2023 2023

Scandinavian Delta Scandinavian All Nippon Scandinavian Emirates

Natural Managerial Natural Managerial Natural Managerial

16.16 13.44 16.03 14.22 16.76 11.80

18 22 18 19 18 26

3.54 5.01 3.55 4.48 3.67 13.25

27 19 27 23 27 6

61.39 236.39 56.73 29.83 48.47 1082.75

27 12 27 29 28 1

Year

Airlines

Disposability

TR/OE

Ranking

TR/FS

Ranking

TR/SC

Ranking

2021 2021 2022 2022 2023 2023

Scandinavian Delta Scandinavian All Nippon Scandinavian Emirates

Natural Managerial Natural Managerial Natural Managerial

1.98 1.48 2.06 1.68 2.16 1.10

8 12 8 11 7 20

0.43 0.55 0.46 0.53 0.47 1.24

17 14 17 16 17 4

7.54 25.97 7.30 3.53 6.24 101.25

23 5 23 29 24 2

Network. Ej2019 and Ej2020 are the overall emission volume of airline j in 2019 and 2020. The data on the proportion of international aviation is from annual reports, and is calculated by the proportion of Revenue Tonne Kilometers based on the principle of CNG2020 strategy. According to our predicted data, the predicted emission limit during 2021e2023 is calculated and the result is 13.879 million tons. The predicted exceeding emissions of the airlines can be denoted as

EEjt ¼ cjt Ejt  L; t ¼ 2021; 2022; 2023; j ¼ 1; 2; /; 29:

(11)

The predicted exceeding emissions of each airline during 2021e2023 are shown in Table 8. As shown in Table 8, the emissions of some airlines are less than the emission limits in some years. Furthermore, the exceeding emissions become larger as the restrictions get stricter and stricter. Although CNG2020 strategy can affect airlines’ whole production process, we simplify this influence and propose the following important assumptions: 1. All of these airlines whose emissions are larger than the emission limits can buy sufficient emissions, and these expenses will increase their Operating Expenses (OE). 2. All of these airlines whose emissions are less than the emission limits will sell all of the rest emissions rights, and these revenues will increase their Total Revenue (TR). 3. The condition has not been considered that the airlines may pass through the emission costs from CNG2020 strategy to passengers through air fares. 4. It is assumed that these 29 airlines have participated in the CNG 2020 strategy during 2021e2023, so according to the principle of CNG2020 strategy, the emission limit is set as the average emission of these airlines’ international routes, which is calculated based on the value of Revenue Tonne Kilometers. Because the Operating Expenses (OE) and the Total Revenue (TR) during 2021e2023 are the value when these 29 airlines will not be included into CNG2020 strategy, we should add the impacts of CNG2020 strategy to get the new efficiencies and discuss the difference. Hence, if the price of unit emission right is decided, the inputs and the outputs of each airline will change and the efficiencies are calculated again to compare the changes, and then the impacts of CNG2020 strategy on airline efficiency can be analyzed. According to the prediction result of International Energy Agency, the carbon price in 2020 will be between 8 $/ton CO2-eq and 20 $/ton CO2-eq. First, the mean value, 14 $/ton CO2-eq, is taken to discuss the difference. The results are shown in Table 9.

Table 8 The predicted exceeding emissions of the airlines (104 tons). Airlines

2021

2022

2023

Aeroflot Air Berlin Air France-KLM Lufthansa Scandinavian Iberia Ryanair British TAP Portugal Norwegian Finnair Turkish EasyJet Virgin Atlantic China Eastern China Southern Korean Air Qantas Delta Air Lines Air China Hainan Emirates Air Canada Cathay Pacific Singapore All Nippon Eva Air Thai Garuda Indonesia

480.76 485.05 926.25 784.24 1055.35 1384.16 1094.25 1154.27 1717.56 130.64 347.07 1562.31 271.31 382.80 213.57 477.80 478.42 294.56 534.00 102.17 966.35 2800.92 817.24 189.81 330.96 710.78 317.81 11.05 24.35

516.41 554.04 945.89 851.78 1050.43 1464.13 1216.40 1243.89 2257.62 143.46 331.70 1619.91 209.06 237.66 140.06 439.54 470.80 241.43 617.00 46.57 949.70 2837.70 800.36 157.64 324.14 682.71 258.58 17.16 188.17

764.36 785.42 990.14 945.70 1029.10 1563.06 1429.83 1489.94 2388.77 198.53 315.92 2041.29 148.25 51.15 15.22 363.33 433.70 175.14 704.13 243.89 911.18 3328.27 768.57 230.53 309.95 645.60 102.13 125.21 450.25

Comparing Tables 6 and 9, it can be conclude that most airlines’ efficiencies have little change when the CNG2020 strategy is considered, which indicates that CNG2020 strategy has few effects on most airlines’ efficiencies. Although the impacts of CNG2020 strategy are small, there are some differences in the efficiency change between under natural disposability and under managerial disposability, such as in 2021, when CNG2020 strategy is considered, the efficiency under natural disposability increases while that under managerial disposability declines. We summarize and analyze the reasons leading to these differences, as shown in Table 10. According to the assumptions above, it can be known that the CNG2020 strategy mainly has direct impacts on Operating Expenses (OE) and Total Revenue (TR), so the range change of OE and TR in model (14) should be firstly analyzed. When CNG2020 strategy has not been considered, the ranges of OE from 2021 to 2023 are 480.4, 484.46 and 529.68, and those of TR are 482.44, 497.35 and 560.43. When the CNG 2020 strategy is considered, the ranges of OE from 2021 to 2023 are 482.79, 486.73

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Table 9 The efficiency when CNG2020 strategy is considered and the price is 14 $/ton CO2-e. Airlines

2021

Aeroflot Air Berlin Air France-KLM Lufthansa Scandinavian Iberia Ryanair British TAP Portugal Norwegian Finnair Turkish EasyJet Virgin Atlantic China Eastern China Southern Korean Air Qantas Delta Air China Hainan Emirates Air Canada Cathay Pacific Singapore All Nippon Eva Air Thai Garuda Indonesia

2022

2023

Average

natural

managerial

natural

managerial

natural

managerial

natural

managerial

0.557 0.470 0.775 0.740 1.000 0.553 0.716 0.698 0.768 0.688 0.891 0.599 0.870 0.825 0.643 0.613 0.790 0.867 0.627 0.622 0.790 0.663 0.871 0.689 0.918 0.683 0.885 0.885 0.783

0.697 0.783 0.684 0.844 0.835 0.844 0.642 0.728 0.409 0.730 0.623 0.570 0.495 0.501 0.550 0.648 0.593 0.559 1.000 0.588 0.470 0.683 0.557 0.658 0.651 0.897 0.559 0.403 0.447

0.553 0.447 0.774 0.732 1.000 0.545 0.717 0.835 0.738 0.673 0.891 0.585 0.801 0.807 0.631 0.602 0.790 0.864 0.598 0.598 0.781 0.666 0.873 0.693 0.920 0.684 0.886 0.878 0.762

0.690 0.788 0.667 0.768 0.853 0.836 0.621 0.711 0.364 0.753 0.618 0.575 0.476 0.452 0.531 0.637 0.599 0.543 0.813 0.567 0.461 0.704 0.548 0.648 0.651 1.000 0.544 0.387 0.422

0.538 0.444 0.785 0.742 1.000 0.555 0.719 0.833 0.752 0.671 0.900 0.567 0.780 0.797 0.627 0.605 0.791 0.866 0.624 0.592 0.775 0.909 0.873 0.700 0.930 0.684 0.845 0.868 0.749

0.705 0.788 0.666 0.755 0.851 0.848 0.648 0.719 0.387 0.780 0.645 0.597 0.505 0.464 0.532 0.640 0.634 0.570 0.799 0.564 0.483 1.000 0.572 0.665 0.678 0.893 0.552 0.403 0.435

0.549 0.454 0.778 0.738 1.000 0.551 0.717 0.788 0.753 0.677 0.894 0.584 0.817 0.810 0.634 0.607 0.790 0.866 0.616 0.604 0.782 0.746 0.872 0.694 0.923 0.684 0.872 0.877 0.765

0.697 0.786 0.672 0.789 0.846 0.843 0.637 0.719 0.387 0.754 0.629 0.581 0.492 0.472 0.538 0.642 0.609 0.557 0.871 0.573 0.472 0.796 0.559 0.657 0.660 0.930 0.552 0.398 0.435

and 532.34, and those of TR become 482.41, 497.35 and 560.43. Therefore, when CNG2020 strategy is considered, the range of OE will increase and that of TR will decrease. These results are useful in discussing the efficiency change of Operations and Sales stage. From Table 10, it can be conclude that because CNG2020 strategy directly affects Operating Expenses and Total Revenue, although it has little influence on the efficiency, it has important impacts on the efficiency change of Operations stage and Sales stage. Comparing Tables 8 and 10, we find an interesting result that although the emissions of TAP Portugal are larger than the emission limits and it needs to buy emission rights to increase Operating Expenses in 2022 and 2023, both of its slacks of Operating Expenses and Total Revenue have changed whether under natural disposability or under managerial disposability. This indicates that the CNG2020 strategy also influences its slacks of Total Revenue. The same phenomenon happens on China Eastern and Air China too. In 2021, their emissions are less than the limits and they can sell rights

to increase Total Revenue, but their slacks of Operating Expenses have changed. We set the price as the minimum value 8 $/ton CO2-eq and the maximum value 20 $/ton CO2-eq, to discuss the difference, respectively. The results can be found in Table 11. From Table 11, it can be found that the efficiency scores have little difference between the condition when the price is set as 8 $/ton CO2-eq and the condition when the price is set as 20 $/ton CO2-eq. And comparing Tables 6 and 11, we can conclude that most airlines’ efficiencies have little change when CNG2020 strategy is considered, which indicates that CNG2020 strategy has few effects on most airlines’ efficiencies. This is because the efficiency is defined to reflect the relationship between inputs and outputs. Although some airlines can sell the surplus emission rights to get a lot of revenues to increase their Total Revenue and some airlines must pay a mass of expenses to buy the emission rights to augment their Operating Expenses, the effects on the relationships between the inputs and the outputs are very small.

Table 10 The reasons for the different change. Airlines

Year

Change of natural disposability

Reasons

Change of managerial disposability

Reasons

Air Berlin

2021

Increase

Decrease

TAP Portugal

2022

Decrease

TAP Portugal

2023

Decrease

Turkish

2022

Decrease

China Eastern

2021

Increase

Air China

2021

Increase

Slacks of OE decrease, Operations efficiency increase Slacks of OE and TR increase, Operations and Sales efficiency decrease Slacks of OE and TR increase, Operations and Sales efficiency decrease Slacks of OE increase, Operations efficiency decrease Slacks of OE decrease, Operations efficiency increase Slacks of OE decrease, Operations efficiency increase

Slacks of OE increase, Operations efficiency decrease Slacks of OE decrease, Operations efficiency increase Slacks of OE decrease, Operations efficiency increase Slacks of OE decrease, Operations efficiency increase Slacks of OE increase, Operations efficiency decrease Slacks of OE increase, Operations efficiency decrease

Increase Increase Increase Decrease Decrease

Q. Cui, Y. Li / Journal of Cleaner Production 164 (2017) 1289e1300

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Table 11 The efficiency when the price is 8 $/ton CO2-eq and 20 $/ton CO2-eq. Airlines

2021

2022

natural

Aeroflot Air Berlin Air France-KLM Lufthansa Scandinavian Iberia Ryanair British TAP Portugal Norwegian Finnair Turkish EasyJet Virgin Atlantic China Eastern China Southern Korean Air Qantas Delta Air China Hainan Emirates Air Canada Cathay Pacific Singapore All Nippon Eva Air Thai Garuda Indonesia

managerial

2023

natural

managerial

natural

managerial

8

20

8

20

8

20

8

20

8

20

8

20

0.557 0.470 0.775 0.740 1.000 0.553 0.716 0.698 0.768 0.687 0.891 0.599 0.869 0.825 0.643 0.612 0.790 0.867 0.627 0.621 0.790 0.663 0.871 0.689 0.918 0.682 0.885 0.885 0.783

0.557 0.470 0.776 0.740 1.000 0.553 0.716 0.698 0.768 0.688 0.891 0.599 0.870 0.825 0.643 0.613 0.791 0.867 0.627 0.622 0.790 0.663 0.872 0.690 0.919 0.683 0.886 0.886 0.784

0.697 0.784 0.684 0.844 0.834 0.844 0.642 0.728 0.409 0.730 0.623 0.570 0.494 0.501 0.550 0.648 0.593 0.559 1.000 0.588 0.470 0.683 0.556 0.658 0.651 0.897 0.559 0.402 0.447

0.697 0.783 0.684 0.844 0.835 0.844 0.643 0.728 0.409 0.730 0.623 0.570 0.495 0.502 0.550 0.647 0.594 0.559 1.000 0.588 0.471 0.683 0.557 0.659 0.651 0.897 0.560 0.403 0.447

0.553 0.447 0.774 0.732 1.000 0.545 0.717 0.835 0.738 0.672 0.890 0.585 0.801 0.807 0.631 0.602 0.789 0.864 0.597 0.598 0.780 0.666 0.872 0.693 0.920 0.684 0.886 0.877 0.762

0.553 0.447 0.774 0.732 1.000 0.545 0.716 0.835 0.737 0.673 0.891 0.585 0.802 0.808 0.632 0.602 0.790 0.864 0.598 0.598 0.781 0.666 0.873 0.693 0.920 0.685 0.886 0.878 0.762

0.690 0.788 0.667 0.768 0.853 0.836 0.621 0.711 0.363 0.753 0.618 0.574 0.476 0.452 0.531 0.637 0.598 0.543 0.813 0.567 0.461 0.704 0.548 0.648 0.650 1.000 0.544 0.387 0.422

0.690 0.788 0.668 0.768 0.853 0.836 0.621 0.712 0.365 0.753 0.618 0.575 0.476 0.452 0.531 0.637 0.599 0.543 0.813 0.567 0.462 0.704 0.549 0.649 0.651 1.000 0.544 0.387 0.422

0.538 0.443 0.785 0.742 1.000 0.555 0.719 0.833 0.753 0.671 0.899 0.567 0.780 0.797 0.627 0.604 0.791 0.866 0.623 0.592 0.774 0.909 0.873 0.700 0.930 0.684 0.845 0.868 0.749

0.538 0.444 0.785 0.743 1.000 0.555 0.719 0.833 0.752 0.671 0.900 0.566 0.780 0.797 0.628 0.605 0.791 0.866 0.624 0.592 0.775 0.908 0.873 0.700 0.930 0.684 0.846 0.868 0.749

0.705 0.788 0.665 0.755 0.851 0.847 0.648 0.719 0.386 0.780 0.645 0.597 0.505 0.464 0.532 0.640 0.633 0.570 0.799 0.564 0.483 1.000 0.572 0.665 0.678 0.893 0.552 0.403 0.435

0.705 0.787 0.666 0.755 0.851 0.848 0.649 0.720 0.387 0.780 0.645 0.598 0.506 0.464 0.532 0.640 0.634 0.570 0.799 0.565 0.484 1.000 0.572 0.666 0.678 0.893 0.553 0.403 0.436

5. Conclusions In this paper, we focus on analyzing the different impacts of the CNG2020 strategy on airline efficiency under natural disposability and managerial disposability. The process is divided into three stages: Operations, Services and Sales. Operating Expenses is chosen as the input to the Operations stage. This input produces Available Seat Kilometers in the Operations stage. Available Seat Kilometers and Fleet Size are the inputs to the Services stage to produce Revenue Passenger Kilometers and Greenhouse gases emission. The Revenue Passenger Kilometers and Sales Costs are the inputs of the Sales stage to generate Total Revenue. Two new model, Network RAM model with natural disposability and Network RAM model with managerial disposability are proposed to analyze the impacts of CNG2020 strategy on 29 airlines from 2021 to 2023. Following the real principle, we calculate the real emission limits for each airline. Among these airlines, some airlines’ emissions are less than the limits and other ones exceed. For the airlines whose emissions are less than the limits, such as Scandinavian, Finnair, EasyJet, Virgin Atlantic, China Eastern, China Southern, Korean Air, Qantas, Hainan, Air Canada, Singapore, All Nippon and Eva Air, the revenue on selling emission rights add their total revenue. And for the other airlines whose emissions are larger than the limits, the expenses on buying emission rights add their operation expenses. The efficiency change between when the CNG2020 strategy is considered and when CNG2020 strategy is not considered is discussed. Overall, the contribution of this paper to the literature is embodied in two aspects. First, a new three-stage strategic operating framework of airline production process is proposed, whose inputs and outputs are chosen suitably to discuss the impacts of CNG2020 strategy. The concept in this paper enriches the theory and method of airline management research and supplies a new

viewpoint for evaluating the environmental performance of airlines. Second, two new model, Network RAM model with natural disposability and Network RAM model with managerial disposability, are proposed to analyze airline efficiency difference. The network models consider the internal structure relative to the measures characterizing the operations performance of DMUs. Most of the DMUs consist of many divisions and the divisional efficiency is important when exploring the development of airline efficiency. Therefore, compared with existing models, the network models can depict the overall production process of airlines more accurately. Some interesting conclusions are gotten. First, the efficient airlines are different between under natural disposability and under managerial disposability. Scandinavian is the only efficient airline under natural disposability, while under managerial disposability the efficient airlines from 2021 to 2023 are Delta, All Nippon and Emirates. Second, compared with natural disposability, the efficiency differences are more obvious under managerial disposability. Third, under natural disposability, the indices related with undesirable output have a larger role in deciding benchmarking airline. Fourth, CNG2020 strategy has little influence on the efficiencies of most of the 29 airlines, regardless of the price is 8 $/ton CO2-eq, 14 $/ton CO2-eq or 20 $/ton CO2-eq. Finally, the efficiency change of natural disposability and that of managerial disposability are different. However, we have not considered the airlines’ capacity on passing through the costs to passengers. Future research will focus on discussing the impacts under the consideration of the airlines’ passing through costs to passengers. Acknowledgements We are grateful to the three anonymous reviewers for their

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