Performance evaluation of Chinese port enterprises

Transport Policy 60 (2017) 75–86

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Transport Policy journal homepage: www.elsevier.com/locate/tranpol

Performance evaluation of Chinese port enterprises under significant environmental concerns: An extended DEA-based analysis Jiasen Sun a, Yang Yuan a, Rui Yang a, Xiang Ji b, *, Jie Wu b a b

Research Center for Smarter Supply Chain, Dongwu Business School, Soochow University, Suzhou, Jiangsu 215021, China School of Management, University of Science and Technology of China, Hefei 230026, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Port enterprise Environmental performance Directional distance function (DDF)

The rapid development of China’s port industry has caused numerous problems, among which the most significant is environmental pollution. A major goal for government and port enterprises alike is the achievement of both effective environmental protection and operational efficiency. Therefore, environmental factors should be considered when evaluating port efficiency. However, most of the available data envelopment analysis (DEA) models that have been used in previous studies enable each decision-making unit (DMU) to choose its favorite weight-combination with a rather careless approach. This may have caused some DMUs with very good economic indexes but very poor environmental indexes to be evaluated as fully efficient. This paper proposes a non-radial DEA preference model, based on the assumption of variable returns to scale (VRS) and the Directional Distance Function (DDF). The proposed model has been used to evaluate and analyze the efficiency of Chinese-listed port enterprises. The efficiency results showed the average efficiency to be low for all ports when environmental factors are considered. The regression results show that port assets, berth quantity, and the geographical location can significantly impact the environmental performance of Chinese port enterprises. This study also classified all ports into four categories, based on throughput and efficiency. Recommendations for the implementation for improvements of different environmental policies have been made for the individual ports in each category based on the actual situation of each port.

1. Introduction Economic globalization is increasing the importance of maritime traffic and port industries more than ever before (Kavussanos and Talley, 2004; Talley and Ng, 2016). Using China and the data within the China Ports Yearbook (2013) as example, Chinese national port enterprises delivered 11.767 billion tons of cargo and 190 million twenty-foot equivalent units (TEU) of container throughput during 2013. These figures represent increases of 9.2% (for cargo) and 7.2% (for TEU) over the previous year. However, the rapid development of the global port industry has also caused severe environmental problems, such as gas emissions and water pollution (Psaraftis and Kontovas, 2010; Dessens et al., 2014). Due to the important role of the port industry in promoting economic development, the question of how to suitably integrate environmental factors1 into the evaluation of port efficiency has attracted

increasing attention both from academics and the industry itself. Many scholars have studied the concept of port efficiency evaluation in depth, involving various methods. Among these qualitative and quantitative methods, the use of data envelopment analysis (DEA) has increased in popularity for use in situations where multiple indexes of inputs and outputs had to be converted into a combined efficiency score (Ji et al., 2017a). Additionally, compared to other analytical tools (e.g. stochastic frontier analysis), DEA imposes neither a specific functional relationship between production output and input, nor any assumptions regarding the statistical distribution of error terms (Ji et al., 2017b, 2017c). Roll and Hayuth (1993) first used the DEA method to evaluate port efficiency. Subsequently, a series of scholars applied the DEA method to evaluate and analyze port efficiency. However, most DEA models that have been used in previous studies do not restrict the weights of indexes. This allows each decision-making

* Corresponding author. E-mail addresses: [email protected] (J. Sun), [email protected] (Y. Yuan), [email protected] (R. Yang), [email protected] (X. Ji), [email protected] (J. Wu). 1 Here environmental factors refer to different pollutant emissions. http://dx.doi.org/10.1016/j.tranpol.2017.09.001 Received 20 January 2017; Received in revised form 18 July 2017; Accepted 6 September 2017 0967-070X/© 2017 Elsevier Ltd. All rights reserved.

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different groups on the evaluation of port efficiency. Later, Wu et al. (2010a), (2010b) developed a quantitative method (based on the conventional DEA theory) where efficient ports could retain efficiency, while inefficient ports could improve efficiency. Wu and Goh (2010) published a DEA-based port efficiency comparison between emerging markets (BRIC and the Next-11) and more advanced markets (G7). This study revealed that none of the ports of the advanced markets could be effective representatives for the field of port efficiency. Wanke et al. (2011) applied the DEA and SFA methods to assess the efficiencies of 25 port enterprises in Brazil. They concluded that due to the lack of investment, operational capacities of Brazilian ports were insufficient to not meet the export demand. Through a hybrid analysis based on DEA and SFA, Panayides et al. (2011) decomposed port efficiency into market efficiency and operating efficiency. Their study found that the market and operating efficiencies of shipping firms are not consistent. Niavis and Tsekeris (2012) employed both DEA and bootstrapped parametric techniques to analyze major determinants of port efficiency in the region of southeastern Europe. These scholars concluded that the low efficiency of some ports could be attributed to both a lack of managerial skills and effects of scale. To explore the internal structure of the port, Wanke (2013) divided the operation process of the port into two stages; these were defined as physical infrastructure and shipment consolidation stage. Then, a network-DEA model was employed to optimize the efficiencies of both stages. The results have shown that private administration actively affected port efficiency of the first stage, while both the hinterland scale and operation mode positively influenced the second stage efficiency of the port. Barros et al. (2016) studied the effects of cost and operation of the Chinese ports during the period from 2002 to 2012, using a stochastic frontier method. Their results indicated that China's ports were heterogeneous, impacting their cost efficiency. Medal-Bartual et al. (2016) applied DEA and the Malmquist productivity index (MPI) to investigate how the economic crisis affected port efficiency changes in Spain from 2005 to 2011. Their study indicated that the economic crisis did not have the same impact on all Spanish ports. Most studies assumed that inputs and outputs had been provided with precision. However, this assumption is not always true. To deal with the uncertainties involved in inputs and outputs, Wanke et al. (2016) used Fuzzy-DEA models and the bootstrapped regression technique to measure the efficiency of Nigerian airports. The authors concluded that Nigerian airports should focus on third-party capacity management (e.g. privatization). Wanke and Barros (2016) used a bootstrapped DEA method to assess the efficiencies of 27 Brazilian ports for the years of 2007–2011. They not only found that the overall port capacity of Brazil was inadequate, but also concluded that infrastructure had a positive impact on scale efficiency, while private administration positively impacted management efficiency. Our study is also related to the emerging literature that integrates emissions (or pollutants) into a conventional DEA framework to evaluate the degree of efficiency of different transportation industries. During recent years, an increasing number of scholars have focused on how environmental factors, such as emissions or pollutants, affect transportation performance. Oum et al. (2013) for example, combined the nonparametric directional output distance function (DODF) with DEA models to measure the social efficiency of three major railroads and two major airlines in Japan from 1999 to 2007. The results indicated that railroads are more socially efficient than airlines. Based on a DDF-DEA model in combination with a bootstrapping procedure, Martini et al. (2013) reported that fleet mix significantly influenced the environmental efficiency of Italian airports between 2005 and 2008, while low-cost carriers failed to do so. Chang et al. (2013) used a SBM-DEA model to analyze the environmental efficiency of the entire transportation sector of China. Their results indicated that most of China's provinces had a rather low degree of eco-efficiency in the transportation industry. In 2014, Chang et al. (2014) developed a weak disposability SBM-DEA model to measure both the economic and environmental efficiencies of 27 global airlines during 2010. Chang's study revealed that poor fuel

unit (DMU) to choose favorable weight-combinations with a careless approach. One possible drawback of this practice is that, it can evaluate DMUs as fully efficient due to their very good economic indexes, yet neglecting their very poor environmental indexes. This is rather unfair for those DMUs that invest extensive efforts to reduce emissions, even at the cost of a portion of their economic profits. Additionally, such evaluation results may signal to DMUs (or at least give the impression) that high profits outweigh emissions (i.e. high emissions lead to a better result than low emissions, if combined with high profits), thus incentivizing the DMUs to only pursue economic profit, without concern for emission controls. Here, we offer a solution for this problem and propose a non-radial DEA preference model. The proposed model incorporated preference weights into environmental indicators. Our model can thus provide decision makers with indicators for pollution reduction potential. Additionally, in comparison to previous models, our proposed model furthermore provides a complete ranking system. This study also applied the proposed model to evaluate the performance of Chinese port enterprises. The results show that the average efficiency of all ports is low if environmental factors are considered. To further provide valuable suggestions for Chinese ports, we applied a regression model and analyzed how the port's assets, berth quantity, and geographical location affect efficiency. Finally, all ports were divided into four types. Unfortunately, no strong-type port enterprises were found in China. Hence, port enterprises should be encouraged to fully utilize existing equipment and resources, and to take effective actions to reduce their pollution emissions. The remainder of this paper is organized as follows: Section 2 reviews important literature relevant to our research. Section 3 presents the traditional DEA model as well as a modified DEA model. Section 4 empirically analyzes port enterprise performance. Our conclusions are presented in Section 5. 2. Literature review This study broadly relates to existing literature pertaining to DEAbased port efficiency evaluations. Using DEA to measure port efficiency has aroused significant concern of several scholars. Roll and Hayuth (1993) first used the DEA method to evaluate port efficiency. Their theoretical work paved the way for using the DEA method for the evaluation of port efficiency. By dividing 26 Spanish ports into three types (based on their scale), Martinez-Budria et al. (1999a), (1999b) employed DEA to evaluate port efficiency over a five-year period. The study concluded that the efficiency of large-scale ports followed a decreasing trend over time. Turner et al. (2004) used DEA and the Tobit regression to measure port efficiency variations in North America. The study covered a period from 1984 to 1997, and explored the relationships between port efficiency and industrial structure and conduct. Turner's results showed that the longstanding relationship between ports and the rail industry dominated port efficiency in North America during the investigated time span. Barros and Athanassiou (2004) used both CCR-DEA (Charnes et al., 1978) and BCC-DEA (Banker et al., 1984) to compare port efficiencies between Greek and Portuguese ports. The authors pointed out that economic scale should be the principal target of adjustment for inefficient ports. Cullinane et al. (2005) used DEA to measure the efficiency of the world's most important container ports and terminals. The author reported that an appropriate definition of input and output factors played a significant role for a meaningful application of DEA. Rios and Maçada (2006) applied a BCC-DEA model to analyze the degree of port efficiency of Mercosur from 2002 to 2004. This study found that 60% of the ports were efficient during the investigated three-year period. Barros (2006) used DEA to measure the port efficiency of Italian ports from 2002 to 2003. Barros combined both operational and financial factors and investigated the roles of size, containerization, and labor in port efficiency variations. By integrating port heterogeneity, Wu et al. (2009) applied a cross-efficiency DEA model to measure the port efficiency of East Asia. Their study further explored the impact of 76

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Transport Policy 60 (2017) 75–86 (continued )

consumption can be a major cause for both economic and environmental inefficiency. Azadi et al. (2014) applied two-stage target-setting DEA models to help public transportation service providers (TSPs) to improve efficiency by forcing TSPs to pay closer attention to emission control and a green supply chain management. Lee et al. (2014) used a slacks-based data envelopment analysis (SBM-DEA) model to evaluate the environmental efficiency of 11 port cities. The results showed that the port cities of New York, Kaohsiung, Busan, Rotterdam, Antwerp, and Singapore have the highest environmental efficiency, while Tianjin in China was the least environmentally efficient. Cui and Li (2015) proposed a virtual frontier DEA model to evaluate the transportation carbon efficiency of 15 countries. Their study showed that the influencing degree of a structural factor was relatively small. Voltes-Dorta and Martín (2016) combined a DDF-DEA model with the Malmquist-Luenberger index to benchmark the noise-oriented efficiency of major European airports. This study indicated that the degree of noise efficiency could be improved by using larger aircraft. However, more stringent night movement could limit this practice. Based on the classic DEA theory, Ji et al. (2016) presented a novel eco-design for transportation service procurement (TSP). Their model allows centralized decision makers to obtain a Pareto optimal TSP combination when emission control requires special attention. This work was a first attempt to use a DEA method to solve a multi-criteria eco-friendly TSP problem. The study and methods by Ji et al. provide an effective paradigm for further DEA-based multi-criteria TSP studies. Furthermore, Song et al. (2016) applied a non-radial DEA model under managerial disposability and a panel beta regression to identify how railway transportation affects regional environmental efficiency. The authors found a significantly positive correlation between railway transportation and environmental efficiency. Specifically, our study was closely related to literature that addresses DEA-based port environmental efficiency evaluations. Compared to other transportation sectors (e.g. airport and railway), literature of DEA-based port environmental efficiency evaluations is comparatively rare. Recent examples of such evaluations however, include Chin and Low (2010), who applied a standard output-oriented SBM-DEA model to investigate whether production efficiency implies environmental efficiency for Asian ports. In addition, Haralambides and Gujar (2012) employed the standard CCR-DEA model to evaluate the environmental efficiency of India's dry port sector. Chang (2013) applied the standard SBM-DEA model to analyze the environmental efficiencies of ports in Korea and elsewhere. Our contribution to the above-named studies is the proposal of a nonradial DEA preference model. Our model assigns preference weights to environmental indicators, thus providing decision makers with the scope and pathway to reduce pollution. Additionally, compared to previous models, our proposed model also provides an inclusive ranking result. Furthermore, we applied the proposed model to evaluate the performance of Chinese port enterprises. To provide valuable suggestions for China's port authorities, we divided the country's ports into four types (based on different scales and regions) to present a deep analysis of Chinese port efficiency.

X Y B gX gY gB xi;nþ1 yr;nþ1 bt;nþ1 Variables λj λnþ1 β

α ν Ei θi

Weight of DMUj Weight of DMUnþ1 In model (4), it represents the proportion of input reduction, desirable output increase and undesirable output reduction. In model (7), it represents the proportion of desirable output increase The proportion of input reduction in model (7) The proportion of undesirable output reduction in model (7) The efficiency of DMUi obtained by model (4) The efficiency of DMUi obtained by model (7)

We assumed existence of n DMUs. Each DMU produces s different desirable outputs and k undesirable outputs using m different inputs, which

are

denoted

via

yj ¼ ðy1j ; ⋯; ysj ÞT ,

bj ¼ ðb1j ; ⋯; bkj ÞT

and

T

xj ¼ ðx1j ; ⋯; xmj Þ . The production technology could then be conceptually described as:

T¼




    xj ; yj ; bj : yj ; bj can be generated by xj

(1)

Aiming to reasonably and realistically model a production process, which produces both desirable and undesirable outputs, F€are et al. (1989) imposed the following two assumptions on the production technology T: (1) Outputs are weakly disposable (for example, if ðxj ; yj ; bj Þ 2 T and 0  θ  1, then the following must be true: ðxj ; θyj ; θbj Þ 2 T). (2) The properties of the joint production of desirable and undesirable output are such that both of these two variables are null-joint (i.e. if ðxj ; yj ; bj Þ 2 T and bj ¼ 0, then yj ¼ 0). Consequently, as described in F€are and Grosskopf (2004), T can be characterized via the following piecewise linear combination of the observed data: n 9  X 8 λj xij  xi0 ; i ¼ 1; ⋯; m > xj ; yj ; bj : > > > > > > > j¼1 > > > > n > > X > > = < λj yrj  yr0 ; r ¼ 1; ⋯; s T¼ j¼1 > > n > > > > X > > > > > > λ b ¼ b ; t ¼ 1; ⋯; k > > j tj t0 > > ; : j¼1 λj  0; j ¼ 1; ⋯; n

(2)

Both of these assumptions are reasonable, since the first one indicates that the desirable and undesirable outputs can either decrease or increase in the same proportion, while the second assumption implies that the only method to eliminate all undesirable outputs would be if all production activities stopped. This so-called environmental DEA technology has widely been used for a variety of environmental issues (Wang et al., 2015a, 2015b; Wu et al., 2015). Thus, we adopted this technology for this paper as well. Additionally, we restricted this research to the area where the assumption of variable returns to scale (VRS) had always been established. Compared to the assumption of constant returns to scale (CRS), the VRS assumption was a more applicable means with which to characterize a situation where a change in inputs does not result in the same change of outputs. Such situations are widespread in the real world. Thus, the VRS assumption has been widely applied in numerous studies. Examples include Banker et al. (1984), Li (1998), Sharma et al. (1999), Tsai and Molinero (2002), Butler and Li (2005), Chen et al. (2015), and F€are and Zelenyuk (2015).

3. Methodology 3.1. Preliminaries In order to construct above models, the notations used in this study are listed below. Data xj yj bj xij yrj btj xi0 yr0 bt0

Input Desirable output Undesirable output Improvement direction of input Improvement direction of desirable output Improvement direction of undesirable output Input i consumed by DMUnþ1 Desirable output r produced by DMUnþ1 Undesirable output t emitted by DMUnþ1

Input consumed by DMUj Desirable output produced by DMUj Undesirable output emitted by DMUj Input i consumed by DMUj Desirable output r produced by DMUj Undesirable output t emitted by DMUj Input i consumed by DMUo Desirable output r produced by DMUo Undesirable output t emitted by DMUo

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3.2. Radial DDF-VRS model

3.3. Proposed non-radial DDF-VRS preference model

The directional distance function had first been developed by Chambers et al. (1996, 1998). This function was firstly introduced for the evaluation of environmental efficiency by Chung et al. (1997), assuming that desirable outputs increase, while inputs and undesirable outputs decrease at an identical rate. The main advantage of the directional distance function is that it allows for a simultaneous contraction of inputs/undesirable outputs and the expansion of desirable outputs. To contract redundant inputs and undesirable outputs, while simultaneously expanding potentially desirable outputs during the production process, we used the radial DDF-VRS model in our study that uses the radial directional distance function (Eq. (3)), and which was constructed by F€ are and Grosskopf (2010). Through this equation, a linear and flexible combination for optimizing the latent improvement capacity of both input and output factors could be obtained:

Most studies that address the application of DEA to environmental performance measurement apply the concept of radial efficiency measures (Zhou et al., 2007). However, this practice has several drawbacks. Firstly, radial efficiency measures may overestimate efficiency (Fukuyama and Weber, 2009), since the inputs and outputs in radial DEA models must proportionally change. In addition, remaining slacks are not accounted for in the resulting efficiency scores (Cooper et al., 2006; Liu and Tone, 2008). Secondly, using radial efficiency measures often leads to cases where many DMUs arrive at the same efficiency score of 1. Hence, these results lead to difficulties in ranking the environmental performance of DMUs when their rank is only based on the DMUs’ efficiency scores (Zhou et al., 2007). Finally, the environmental problems of ports have become increasingly severe, which in turn has had serious negative effects on sustainable economic growth. From a sustainable development point of view, port enterprises must pay better attention to the reduction of undesired outputs. Hence, for using the DEA model, undesired outputs should receive a preference weight. Due to the above reasons, this paper proposes a preference non-radial DDF-VRS model, shown as Model (7). Compared to Model (4), the new proposed model features two main improvements. The first improvement is the incorporation of a virtual positive ideal DMU within the non-radial DDF-VRS model. As Sun et al. (2013) pointed out, the traditional DEA model enables each DMU to measure the efficiency by using favorable weights of that DMU, to calculate its maximum efficiency score. This practice prevents to compare and rank DMUs on the same basis. In addition, more than one DMU will always be evaluated as efficient. To solve this problem, this paper adds a virtual positive ideal DMU into the DEA model. The second improvement is that the proposed model adds a preference weight to environmental indicators. Continued environmental deterioration and the proposed strategy for socially sustainable development will force companies to pay closer attention to environmental protection. Thus, preference weights can be utilized to clarify the importance of environmental protection for management decision makers. Based on the above summary, this paper proposes a non-radial DDFVRS preference model (7), which is based on the earlier work of Fukuyama et al. (2011) and Barros et al. (2012). Before proposing our model however, we will first introduce the virtual positive ideal DMU. The input data of all DMUs form a matrix with m rows and n columns. Desirable output data form a matrix with s rows and n columns, while undesirable output data form a matrix with k rows and n columns. The smallest data of each row of the input matrix was selected as input of the virtual positive ideal DMU. The biggest data of each row of the desirable output matrix was selected as the desirable output of the virtual positive ideal DMU, while the smallest data from each row of the undesirable output matrix was selected as the undesirable output of the virtual positive ideal DMU. We can show the positive ideal DMU via DMUnþ1. The inputs, desirable outputs and undesirable outputs of DMUnþ1 can be

! D ðX; Y; B; gÞ ¼ supfβ : ðX þ βgX ; Y þ βgY ; B þ βgB Þ 2 Tg

(3)

The variable g ¼ ðgX ; gY ; gB Þ is the direction vector with the function to reduce inputs and undesirable outputs, while increasing desirable outputs. The corresponding proportion was defined as β. When β ¼ 0, both input and output factors of the DMU are optimal, with no potential for additional latent improvement capacity. To identify the latent improvement capacity of the environmental performance, the radial DDF-VRS model was used as follows.

Maxβ n X λj xij  xi0  βxi0 ; i ¼ 1; ⋯; m s:t: n X j¼1 n X

j¼1

λj yrj  yr0 þ βyr0 ; r ¼ 1; ⋯; s (4) λj btj  bt0  βbt0 ; t ¼ 1; ⋯; k

j¼1

n X

λj ¼ 1

j¼1

λj  0; j ¼ 1; ⋯; n In addition, for the ith port, the efficiency relative to the frontier can be defined as:

Ei ¼ 1  βi

(5)

For any feasible β that satisfies the constraints contained in model (4), it is trivial to show that β belongs to ½0 ; 1Þ. Then, the following two complementary conditions for the value ofMax β apply: (1) If an A exists that satisfies β  A < 1 for all feasible β, then Max β ¼ А. (2) If for any B < 1, there always exists a feasible β that satisfies B < β < 1, then Max β does not exist, but Sup β¼ 1.

denoted as:xnþ1 ¼ ðx1;nþ1 ; ⋯; xi;nþ1 ; ⋯; xm;nþ1 ÞT , ynþ1 ¼ ðy1;nþ1 ; ⋯; yr;nþ1 ; ⋯; ys;nþ1 ÞT , and bnþ1 ¼ ðb1;nþ1 ; ⋯; bt;nþ1 ; ⋯; bk;nþ1 ÞT where xi;nþ1 ¼ minðxi1 ; ⋯; xin Þ, yr;nþ1 ¼ maxðyr1 ; ⋯; yrn Þ, and bt;nþ1 ¼ minðbt1 ; ⋯; btn Þ. The production possibility set for DMUs is represented as follows:

To mathematically complete our research, we defined Max β as 1 when condition (2) establishes, i.e.:

 Max β ¼

Max β 1

condition ð1Þ condition ð2Þ

 X n n X  T2 ¼ fðX; Y; BÞ λj Xj þ λnþ1 Xnþ1  X; λj Yj þ λnþ1 Ynþ1  Y;  j¼1 j¼1 n n X X λj Bj þ λnþ1 Xnþ1  Bj ; λj þ λnþ1 ¼ 1; λj  0; j ¼ 1; 2; ⋯; n; n þ 1g

If condition (2) was established as above, the under-evaluated DMU would need to reduce almost all inputs and emissions to become efficient. This further implies that this DMU would be almost entirely inefficient. Thus, the DMU should be given an efficiency score of almost 0. Therefore, it is reasonable to let Max β be 1 in condition (2).

j¼1

j¼1

(6) Then, we constructed the following Model (7):

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be further reduced, while the desirable outputs can be further increased. This means that we obtain an imaginary DMU with fewer inputs than that of DMUo, thus producing more desirable outputs, while reducing undesirable outputs. Then, α* > 0, or β* > 0, or ν* > 0. This contradicts the known condition that α* ¼ 0, β* ¼ 0 and ν* ¼ 0. Therefore, DMUo is efficient.

Max ν þ ε*ðα þ βÞ n X s:t: λj xij þ λnþ1 xi;nþ1  xi0  αxi0 ; i ¼ 1; ⋯; m n X j¼1 n X j¼1 n X

j¼1

λj yrj þ λnþ1 yr;nþ1  yr0 þ βyr0 ; r ¼ 1; ⋯; s (7)

The following proves the necessary condition that DMUo is efficient. Following the constraint conditions of Model (7), we obtained the following Formula (8):

λj btj þ λnþ1 bt;nþ1  bt0  νbt0 ; t ¼ 1; ⋯; k λj þ λnþ1 ¼ 1

n X

j¼1

λj  0; j ¼ 1; ⋯; n; n þ 1

j¼1 n X

Here, ε > 0 is a non-Archimedean element that is smaller than any positive real number. Similar to Model (4), Model (7) might also not be feasible in some extreme situations. To mathematically complete our methodology, we added an additional definition, as shown below:

8 maxv þ ε*ðmaxα þ maxβÞ > > < 1 þ ε*ðmaxα þ maxβÞ Max v þ ε*ðα þ βÞ ¼ maxv þ ε*ð1 þ maxβÞ > > : 1 þ ε*ð1 þ maxβÞ

condition condition condition condition

j¼1 n X

λj xij þ λnþ1 xi;nþ1  ð1  αÞxi0  xi0 ; i ¼ 1; ⋯; m λj yrj þ λnþ1 yr;nþ1  ð1 þ βÞyr0  yr0 ; r ¼ 1; ⋯; s λj btj þ λnþ1 bt;nþ1  ð1  νÞbt0  bt0 ; t ¼ 1; ⋯; k

j¼1

n X

ð10 Þ ð20 Þ ð30 Þ ð40 Þ

λj þ λnþ1 ¼ 1

j¼1

λj  0; j ¼ 1; ⋯; n; n þ 1 If at least one of the estimated parameters (α* , β* and ν* ) is not equal to 0, then, at least one of the first three inequalities in Formula (8) can have strict inequality. For example, when β* > 0, the formulas in (8) evolve into the following form:

(*) Here Conditions (10 ) to (40 ) were defined as follows: Condition (10 ): If ðA; CÞ exists that satisfies α  A < 1; v  C < 1 for all feasible ðα; vÞ. Condition (20 ): If A exists that satisfies α  A < 1 for all feasible α, while for any D < 1, a feasible v always exists that satisfies D < v < 1. Condition (30 ): If C exists that satisfies v  C < 1 for all feasible v, while for any B < 1, a feasible α always exists that satisfies B < α < 1. Condition (40 ): If for any B < 1; D < 1, a feasible ðα; vÞ always exists that satisfies B < α < 1 and D < v < 1. Then, we used the following Proposition 1 to show that, with the definition (*), Model (7) can always be feasible.

n X j¼1 n X j¼1 n X

λj xij þ λnþ1 xi;nþ1  ð1  αÞxi0  xi0 ; i ¼ 1; ⋯; m λj yrj þ λnþ1 yr;nþ1  ð1 þ βÞyr0 > yr0 ; r ¼ 1; ⋯; s λj btj þ λnþ1 bt;nþ1  ð1  νÞbt0  bt0 ; t ¼ 1; ⋯; k λj þ λnþ1 ¼ 1

j¼1

λj  0; j ¼ 1; ⋯; n; n þ 1

Proof. For any under-evaluated DMU0 , a feasible β in Model (7) must 2 3

Since:

max

fyrj g

5. Consequently, max β can always be

0

obtained. Then, similar to Model (4), for any feasible ðα; β; vÞ that satisfies the constraints contained in Model (7), v þ ε*ðα þ βÞ belongs to ½0 ; 1 þ ε*ð1 þ maxβÞÞ. In addition, the four conditions for the Max v þ ε*ðα þ βÞ value are as follows:

B B B B B B B B B @

r¼1;⋯;s

j¼1;⋯;nþ1

yr0

0

(1 ) If ðA; CÞ exists that satisfies α  A < 1; v  C < 1 for all feasible ðα; vÞ, then Max v þ ε*ðα þ βÞ ¼ C þ ε*ðA þ maxβÞ. 0 (2 ) If A exists that satisfies α  A < 1 for all feasible α, while for any D < 1, a feasible v always exists that satisfies D < v < 1, then Max v þ ε*ðα þ βÞ does not exist, but Sup v þ ε*ðα þ βÞ ¼ 1 þ ε*ðA þ maxβÞ. 0 (3 ) If C exists that satisfies v  C < 1 for all feasible v, while for any B < 1, a feasible α always exists that satisfies B < α < 1, then Max v þ ε*ðα þ βÞ does not exist, but Sup v þ ε*ðα þ βÞ ¼ B þ ε*ð1 þ maxβÞ. 0 (4 ) If for any B < 1; D < 1, a feasible ðα; vÞ always exists that satisfies B < α < 1 and D < v < 1, then Max v þ ε*ðα þ βÞ does not exist, but Sup v þ ε*ðα þ βÞ ¼ 1 þ ε*ð1 þ maxβÞ. After ensuring that Model (7) is mathematically complete, we present the following Proposition 2 for the identification of efficient DMUs: λ*j ðj

(9)

j¼1

n X

Proposition 1. Under definition (*), Model (7) is always feasible.

belong to 40; min

(8)

n X

λj x1j þ λnþ1 x1;nþ1 ; ⋯;

n X

! 1

λj xmj þ λnþ1 xm;nþ1 ; C C ! C C C λj y1j þ λnþ1 y1;nþ1 ; ⋯; λj ysj þ λnþ1 ys;nþ1 ; C 2 T C j¼1 j¼1 ! C C n n X X A λj b1j þ λnþ1 b1;nþ1 ; ⋯; λj bkj þ λnþ1 bk;nþ1

j¼1 n X

j¼1 n X

j¼1

j¼1

(10)

We can obtain a DMU in the production possibility set that can use no more inputs than those of DMUo, to produce more desirable outputs. In addition, the undesirable outputs of the DMUs are not more than those of DMUo. This indicates that DMUo is not efficient, which contradicts the known condition. The same is true for situations such as α* > 0 or β* > 0. Above all, the estimated parameters are equal to 0, i.e., α* ¼ β* ¼ ν* ¼ 0. Thus, the necessary condition could be proven. After solving Model (7), for the ith port, the efficiency relative to the frontier can be defined as:

θi ¼ 1  νi  ε*ðαi þ βi Þ

Proposition 2. If the estimated parameters ¼ 1; 2; ⋯n; n þ 1Þ, α , β* , and ν* are the optimum solution of Model (7), then the necessary and sufficient condition for DMUo to be efficient, is that α* ¼ 0, β* ¼ 0 and ν* ¼ 0. *

(11)

From Model (7), we can obtain that n DMUs under evaluation all consider a virtual ideal unit as their reference object. In other words, the model must satisfy the constraint that the efficiency value of the virtual ideal unit is equal to the value 1 when all DMUs were attempting to maximize their own efficiency scores. The main difference between Models (7) and (4) is that Model (4) requires inputs and desirable and

Proof. First, we prove that the sufficient condition for DMUo is efficient. If DMUo is inefficient, the inputs and the undesirable outputs can 79

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from the China ports year book (Du, 2013) and Annual Report of 17 Listed ports. All the Annual Report of 17 Listed ports can be downloaded from the Shanghai Stock Exchange and Shenzhen Stock Exchange. Table 1 presents descriptive statistics for the 17 investigated port enterprises.

undesirable outputs to change in the same proportion, while Model (7) removes this restriction. The economic significance of Model (7) is that both inputs and undesirable outputs can be decreased, while desirable outputs can simultaneously be increased for DMUo. This in turn enables the coexistence of economic development and environmental protection.

4.2. Results and analysis of the resource-utilization efficiency of ports 4. Efficiency analysis To help the Chinese port enterprises improve their resourceutilization performance from an efficiency perspective, thus generating more profit for the port enterprises, we used the classical CCR-DEA model (proposed by Charnes et al., 1978) to evaluate the efficiency of each port in the respective subsection. The results are shown in Fig. 1. The Classical CCR-DEA model has been popularly used to analyze resource-utilization efficiency in numerous areas, such as product line design with sustainability (Ji et al., 2017d), in-store logistics of grocery retailing (Reiner et al., 2013), and IT-enabled production capability (Ayabakan et al., 2017). In this subsection and through the use of the classical CCR-DEA model, we wished to explore whether the input resources of the port were fully utilized to make desirable outputs. To concentrate on this research focus, we temporarily excluded undesirable outputs in this section.

The following section contains a demonstration of an empirical study of an environmental performance evaluation for the main Chinese port enterprises, using the proposed non-radial DDF-VRS models. The detailed descriptions of collected raw data and explanations for the selection of indexes are presented in Section 4.1. To further enrich the content of this empirical study, we applied the classical CCR-DEA model to analyze whether the involved Chinese port enterprises had sufficiently good performance in their resource utilization, which is presented in Section 4.2. The main content of the empirical study, i.e. the environmental performance evaluation for the Chinese port enterprises, is presented in Section 4.3. Sections 4.4 and 4.5 then present the accompanied regression analysis and managerial implications, respectively.

4.1. Data description

4.2.1. General characteristics of port resource-utilization efficiency Based on the CCR model, we only found one efficient port enterprise with an efficiency score of 1.00 (Yantian Port). The mean efficiency score for all port enterprises was 0.361. This finding implied that these ports did not fully utilize their resources. The CCR model showed that the five ports with the highest efficiency were Yantian Port, Zhuhai Port, Yingkou Port, Tangshan Port, and Shanghai International Port, respectively. Common characteristics of these five ports were that they are all coastal ports. China has implemented an export oriented economic growth model for the past few decades, because export trade is an important driving force to promote China's economic development. Most of the produced goods are shipped through coastal ports to other countries in the world. This has led to further development of coastal ports, thus increasing the CCR efficiency of coastal ports. Fig. 1 shows that there are

Considering both representativeness and data availability, this study used data pertaining to 17 port enterprises (listed within the Shenzhen and Shanghai market) to empirically analyze their efficiency during 2013. The study samples include 14 coastal port enterprises (Shanghai International Port, Tianjing Port, Jinzhou Port, Yingkou Port, Lianyungang Port, Dalian Port, Tangshan Port, Ningbo Port, Rizhao Port, Beihai Port, Xiamen Port, and Zhuhai Port) and three inland port enterprises (namely Chongqing Port, Nanjing Port, and Wuhu Port). Existing studies considering port enterprise performance select fixed assets, operational costs, and staff number as input indicators. Throughput, profits, and other relevant figures were selected as output indicators (Liu, 1995). In our study, the inputs were staff number, operational costs, and fixed assets. Outputs were net profit, cargo throughput, and NOx emissions. The number of staff (which is widely accepted as a meaningful input) changed due to business expansion or contraction. Therefore, staff number can (to a certain extent) reflect operational performance. Operational costs represented the financial resources of each port enterprise, and were used to measure the cost of selling goods or for providing services. Fixed assets were material resources, which referred to the investment for non-monetary assets. As the most essential output of a port enterprise, net profit can measure operational performance from a financial perspective. Cargo throughput is an important statistical indicator for the port industry and reflects operational performance from a non-financial perspective. Therefore, net profit and cargo throughput were selected as representatives of desirable outputs. Undesirable outputs generally included environmental pollution, such as wastewater, waste gas, and waste residue. Among gas emissions from port enterprises, NOx emissions formed the main volume. Hence, this study selected NOx emissions to represent undesirable outputs. The data for each of the port enterprises used in this study was taken

1.0000 CCR Efficiency

0.8000 0.6000 0.4000

Shenzhen Chiwan Port

Nanjing Port

10 11 12 13 14 15 16 17

Wuhu Port

9

Yantian Port

8

Chongqing Port

7

Zhuhai Port

Beihai Port

6

Xiamen Port

Rizhao Port

5

Ningbo Port

4

Dalian Port

3

Tangshan Port

Lianyungang Port

2

Jinzhou Port

1

Yingkou Port

Tianjing Port

0.0000

Shanghai International Port

0.2000

Fig. 1. CCR efficiency of all investigated ports.

Table 1 Descriptive statistics for input and output variables. Inputs

Mean S.D. Median Maximum Minimum

Outputs

Staff number (persons)

Fixed assets (100 million RMB)

Operating cost (100 million RMB)

Net profit (100 million RMB)

Cargo throughput (ten thousand tons)

NOx (tons)

4726.41 4669.45 3465.00 19842 531.00

79.48 88.47 59.65 349.31 1.77

63.83 90.79 30.47 337.98 1.71

9.88 15.39 5.35 62.76 0.16

178.58 158.37 100.83 543.00 18.07

932.34 833.53 528.73 2847.36 85.92

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three ports with minimal CCR efficiency, which are Lianyungang Port, Chongqing Port, and Wuhu Port. The reason for this low efficiency of Lianyungang port lies in its geographical location. The level of economic development of the area where the Lianyungang port is located is low. Chongqing Port and Wuhu Port are both inland ports, located in the central region of China. The economic level of Central China is generally lower than in China's coastal areas. Therefore, poor hinterland economic levels have negatively affected the development of these three ports. In addition, the infrastructural development of Central China is not ideal, and the transportation capacity is relatively low. These factors also affected the efficiency of Chongqing Port and Wuhu Port.

Mean of CCR efficiency scores 0.39 0.38 0.37 0.36 0.35 Large-sized port Medium-sized Small-sized port enterprises port enterprises enterprises Fig. 3. Mean CCR efficiency values for three types of ports.

4.2.2. Resource-utilization efficiency comparison of different sized ports Here, we compare the resource-utilization efficiency of different sized port enterprises. To classify all ports according to size, this study adopted the hierarchical cluster analysis method, which has been widely used by scholars to classify various DMUs (Song et al., 2013; Wu et al., 2010a,b). The main idea of cluster classification is to first calculate the mean Euclidean distance between any two ports based on three inputs and three outputs. Then, the ports with approximate mean Euclidean distances are merged into one classification. With this hierarchical cluster analysis method (using SPSS software), 17 listed port enterprises were classified into three classifications based on their sizes. The results can be represented in a tree diagram. Fig. 2 shows that the investigated port enterprises were divided into three categories: 1) large-sized port enterprises (Shanghai International Port and Ningbo Port), 2) mediumsized port enterprises (Yingkou Port, Rizhao Port, Tianjing Port, and Dalian Port), and 3) small-sized port enterprises (Xiamen Port, Wuhu Port, Beihai Port, Jingzhou Port, Zhuhai Port, Shenzhen Chiwan, Yantian Port, Lianyungang Port, Tangshan Port, Chongqing Port, and Nanjing Port). Fig. 3 shows the mean efficiency scores for the three types of port enterprises. According to the traditional DEA model, the mean value of large-scale port enterprises is maximal, while the mean value of smallscale port enterprises is minimal. This finding indicates that large port enterprises have advantage of scale, contributing to their high-efficiency ranking. During the early stage of port development, its technical level is relatively under-developed. At this stage, the main objective of the port is to maximize profit and throughput, and no economic condition would improve the technical level. This explains the low performance of smallscale ports at this stage. Once assets expand to a certain degree, technological and production processes will greatly improve. Under these conditions, performance also improves significantly.

4.3. Results and analysis of environmental efficiency of ports 4.3.1. Results of environmental efficiency of port Table 2 lists the efficiencies and ranking results of the ports. The third column contains the efficiencies of all ports Model (4), including undesirable output. The fifth column shows the efficiency scores obtained via our proposed Model (7), which does include undesirable outputs. Compared to Model (4), Model (7) is a non-radial model. To test whether the efficiencies of ports obtained via different models had significant differences, we adopted the spearman ranking correlation method. Table 3 shows a p-value of 0.903 as the result, indicating that the efficiencies of ports obtained by two models are significantly different. The third column of Table 2 lists the efficiencies of all ports obtained via traditional models including undesirable output (Model (4)). In the model, the undesirable output was considered as an input. The results revealed two main findings: The first finding is that the efficiencies of ports obtained by Model (4) are generally higher than efficiencies calculated with other models. The second finding is that nine ports were evaluated as efficient. The number of efficient ports via Model (4) is far higher than what other models obtained. The reason for these two findings is that Model (4) cannot reflect the actual production process. In some processes of production, undesirable outputs (e.g. pollutants) are always accompanied by desirable outputs and regardless of the adopted technology, undesirable outputs are unavoidable (Chung et al., 1997; Zhou et al., 2006; Feng, 2015). However, Model (4) adopts a radical approach, using the same proportions to reduce inputs and undesirable outputs, while increasing the desirable outputs. Seiford and Zhu (2002) pointed out that the same proportion for inputs, desirable outputs, and undesirable outputs was not always possible. In addition, non-zero slacks will exist in the process of efficiency evaluation (Fukuyama and Weber, 2009). Thus, Model (4) may overestimate the efficiency of ports, and

Table 2 Environmental efficiency evaluation results for Chinese listed port enterprises. DMU

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Fig. 2. Clustering result of ports based on all inputs and outputs. 81

Port enterprise

Shanghai International Port Tianjing Port Jinzhou Port Yingkou Port Lianyungang Port Dalian Port Tangshan Port Ningbo Port Rizhao Port Beihai Port Xiamen Port Zhuhai Port Shenzhen Chiwan Port Yantian Port Chongqing Port Wuhu Port Nanjing Port

Model (4)

Model (7)

Efficiency

Ranking

Efficiency

Ranking

1.0000

1

0.0302

17

0.9960 0.9916 1.0000 0.9876 0.9990 0.9974 1.0000 0.9979 0.9909 0.9943 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

13 15 1 17 10 12 1 11 16 14 1 1 1 1 1 1

0.0526 0.1920 0.0650 0.2628 0.0491 0.1221 0.0335 0.0678 0.1489 0.1820 0.1625 0.2486 0.2197 0.5830 0.1940 0.9998

14 7 13 3 15 11 16 12 10 8 9 4 5 2 6 1

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vertical axis. Using a mean efficiency of 0.21 and an average throughput of 17857.91 as thresholds, the matrix was divided into four parts (representing the four different types of port enterprises). As shown in Fig. 3, Zones A, B, C, and D were defined as obesity type, thin type, lean type, and strong type, respectively. Ports with higher throughput and smaller efficiency were defined as obesity-type ports. These ports include Shanghai International Port, Tianjing Port, Yingkou Port, Dalian Port, Ningbo Port, and Rizhao Port. This type of port receives strong support from the economic hinterland; however, the large throughput results from the high rate of investment. Due to irreversible characteristics of port investment facilities, these obesity-type ports should fully utilize existing resources and equipment to improve port output via implementation of reasonable strategies. Furthermore, this type of port enterprise takes effective actions for the control of pollutant emissions. Thin-type ports utilize small-scale throughput, but low environment efficiency levels. In our study, thin-type ports mainly include Jinzhou Port, Tangshan Port, Beihai Port, Xiamen Port, Zhuhai Port, and Wuhu Port. Most ports of this type excessively invest in port infrastructure to pursue bigger and stronger ports. The result however, is not high efficiency, but excessive emissions. The lean-type ports, by definition, have small throughput. However, the efficiency levels of lean-type ports are higher. In our study, the leantype group included such ports as Lianyungang Port, Shenzhen Chiwan Port, Yantian Port, Chongqing Port, and Nanjing Port. The throughput of this type of port is small, but emissions can be efficiently reduced. Currently, they focus on how to expand throughput, while maintaining existing input and emission levels. Ports with larger throughput and higher operating efficiency were defined as strong-type port enterprises. Here, it should be noted that there were no strong-type port enterprises in our matrix. Such ports would be maximally competitive. Therefore, how to make full use of existing input levels, while controlling emissions and increasing cargo throughput remains a very important issue for all port enterprises.

Table 3 Spearman ranking correlation between three models.

Model (4) Model (7)

Model (4)

Model (7)

1.000 0.032 Sig. (two-tailed) 0.903

– 1.000

obtain more efficient ports. The seventh column of Table 2 lists the efficiency results of ports obtained via Model (7). Although Model (4) and Model (7) both consider undesired outputs, the ways to treat undesired outputs differ. The first difference is that Model (4) is a radial model, while Model (7) is a nonradial model. Through a radial model, inputs and outputs (including the undesired outputs) must be reduced/increased at identical proportions. This may not only overestimate the efficiencies of ports (Fukuyama and Weber, 2009), but also assess many ports as efficient (Zhou et al., 2007). The second difference is that Model (7) assigns a preference weight to the undesired output. Environmental problems of ports have become increasingly prominent (Psaraftis and Kontovas, 2010; Dessens et al., 2014). For a sustainable development, the local government or managers of ports must pay increased attention to the reduction of undesired outputs. Therefore, this study assigned a preference weight to undesired outputs. The efficiency of ports obtained by Model (7) was significantly lower than the efficiency obtained via Model (4), which is consistent with the theoretical analysis. 4.3.2. Environmental efficiency comparison of ports of different sizes Using the size classification results as in Section 4.2.2, this section discusses the environmental efficiency comparison of differently sized port enterprises. Fig. 4 shows that the environmental efficiency of largescale ports is lower than that of both other types of ports. The reason for this phenomenon is that port pollution has not received sufficient attention from the Chinese government. Based on a report from the Natural Resource Defense Council (2014), half of the world's 20 largest ports are in China. Among the 10 busiest ports in the world, 7 ports are also in China. Since the Chinese government is blindly pursuing economic development and GDP growth, government officials have focused little attention to formulate and implement emission reduction policies and regulations for the port industry (Wang et al., 2015). For a port, large amounts of capital have been invested in infrastructure to increase throughput and profits. However, few laws and regulations relating to the port environment were issued for restricting the pollution emission of ports. Consequently, ports rarely focus on emission reduction technologies or on controlling their emissions. For these reasons, larger scale of the port will lead to a lower environmental performance.

4.4. Efficiency analysis via regression model To further verify whether port size, port location, or the port hinterland will affect their environmental efficiency (obtained via Model (7)), we applied a multiple regression method in this section. Similar techniques have widely been used in port efficiency analyses, such as Martinez-Budria et al. (1999a), (1999b), Tongzon (2001), Barros and Managi (2008), and Cullinane and Wang (2010). Referring to these studies, this subsection used similar variables, as shown in Table 4.

4.3.3. Throughput-efficiency matrix of port enterprises Ports should improve their efficiencies based on their own circumstances. To further refine specific strategies to improve efficiency, this study constructed a throughput-efficiency matrix based on the results of Model (7), as shown in Fig. 5. The matrix used the rate of environmental efficiency as horizontal axis, while cargo throughput was used as the

0.35

Mean of efficiency scores of model (7)

0.28 0.21 0.14 0.07 0 Large-sized port Medium-sized Small-sized port enterprises port enterprises enterprises Fig. 4. Mean environmental efficiency values of three types of ports.

Fig. 5. Throughput-efficiency matrix for port enterprises. 82

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covering half of Bohai by sea ice, severely affecting port production (Su and Wang, 2012). The winter temperatures in the Sea of Southern China are higher, and free of freezing. Tables (1), (2) and (5) were obtained via ordinary least squares estimation. Two explanatory variables (e.g. traditional CCR efficiency and environmental efficiency) were used for this study. If ordinary least squares estimation was adopted, two regression models were constructed. However, the covariance of the residuals of both regression models was not equal to zero (Cov ðε1 ; ε2 Þ≠0), indicating that there were errors if the ordinary least squares estimation was used. Therefore, we used the three-stage least squares estimation (For more details, please refer to Nelson and Olson (1978) and Belsley (1988)) to obtain effective estimation results. The results are shown in (3) and (4) of Table 5. Table 5 shows that variable FA had a significant positive impact on CCR efficiency, while impacting environmental efficiency significantly negative. In other words, the larger the port size, the higher its CCR efficiency and the lower its environmental efficiency. This conclusion is consistent with the conclusion presented in Section 4.2.2 and 4.3.2 As a result, the regression result of this variable further validated our view in Section 4.2.2 and 4.3.2. The BQ variable had a significant negative impact on CCR efficiency. Berth was the scarce resource of the port, BQ directly determined the input of port infrastructure, and the utilization rate of berths reflected the level of port services. The BQ variable had a significantly negative impact on CCR efficiency, indicating that the utilization rate of berths in China is not high, thus resulting in inactivity and waste of berth resources. During past decades, China has heavily invested in the construction of ports and built a large number of berths. However, since the outbreak of the financial crisis in 2008, China's actual import and export was strongly impacted, leading to a significantly decline. In 2015, China's import and export volume fell by 7% relative to that of 2014, and import and export volumes fell by 2% in 2016 compared to 2015. These data show that China's import and export trade may enter a relatively low growth period and will stay there a considerable time. Berth construction not only has a long cycle, but also requires high investment cost. Therefore, China's port should focus on the rational allocation and use of existing berths, to improve the comprehensive service level of the ports. The main source of port pollution is ship emissions. In addition, many types of equipment such as containers, trailers, and other equipment will emit a considerable amount of pollutants during work. Due to the decline of China's actual import and export volume in recent years, the actual port business growth is showing a downward trend. Therefore, the total amount of pollutants emitted by port has not increased. This explains why the BQ variable has a significant positive impact on environmental efficiency. Table 5 shows that the CCR efficiency of northern ports is lower than that of southern ports. Two main reasons cause the lower CCR efficiency of northern ports. (1) The development of industry in Northern China slows down. In 2015, the total profits of industrial enterprises above a

Table 4 Statistical description of variables. Variable

Mean

Std. Dev.

Min

Max

Model (7) Traditional model (CCR-DEA) FA BQ PSI NIE LP (1 ¼ North China; 0 ¼ South China)

0.213 0.361 79.479 198.647 50.480 3198.765 0.471

0.242 0.214 88.471 272.307 6.261 2732.736 0.514

0.030 0.097 1.770 9 38.920 175 0

1.000 1.000 349.310 1183 65.870 9772 1

Differences between our study and the above studies mainly lie in two aspects. On one hand, the research objects of this paper are Chinese ports. On the other hand, this article considers environmental efficiency as an explanatory variable, and analyzed the impacts of other variables on environmental efficiency. This paper explains the differences of port efficiency from two directions, namely the supply and the demand of the port. Referring to the studies of Wanke (2013), Yuen et al. (2013), Wanke and Barros (2015, 2016), variables of the supply dimension have been treated as fixed assets (FA) of a port (in 100 million RMB) and as berth quantity (BQ). Table 4 shows that the average FA of these ports is 79.479 (100 million RMB). The standard deviation is 88.5 (100 million RMB), indicating the existence of a significant difference between the assets of the ports. The BQ describes both the throughput capacity of the port and the infrastructure conditions of the port. The maximum BQ was 1183, and the minimum BQ was 9, which shows that the BQ between ports is very different. The main function of a port is to serve the industrial development of a near hinterland city. The proportion of the secondary industry of the hinterland city to the GDP (abbreviated as PSI) and number of industrial enterprises in the hinterland city (abbreviated as NIE) was used to describe demand differences and demand diversity of the hinterland city in relation to the port. The standard deviations of both variables were 6.261 and 2732.736, respectively. The large standard deviation shows apparent differences of the demand of hinterland cities to the ports. In addition, to verify whether the location of the port (LP) affected the efficiency of the port, all ports were divided into northern Chinese ports and southern Chinese ports for this study, considering the Yangtze River as a dividing line. There were two main reasons for this division. First, major industrial types differ between northern and southern China. Northern China is rich in mineral resources; therefore, the proportion of heavy industry and machinery manufacturing industry is comparatively large. In the south of China, the population intensifies. Therefore, laborintensive industries are more developed, mainly including various processing industries as well as light industries. Second, a wide gap exists between winter temperatures in southern China and Northern China. In Northern China, the sea often freezes during winter. For example, in 2010, the Bohai sea ice area has reached 36 thousand square kilometers,

Table 5 Regression analysis results. Variable

(1)

(2)

(3)

CCR-DEA model

Model (7)

CCR-DEA model

Model (7)

FA BQ LP PSI NIE Constant chi2 F p R-squared

0.00174* (0.000895) 0.000844* (0.000401) 0.210 (0.122) 0.0115 (0.00812) 1.32e-05 (2.95e-05) 1.027** (0.465)

0.00426** (0.00141) 0.00102** (0.000458) 0.175* (0.0886) 0.00237 (0.00518) 1.91e-05 (1.59e-05) 0.325 (0.321)

0.00174* (0.00101) 0.000844** (0.000336) 0.210** (0.105) 0.0115 (0.00808) 1.32e-05 (2.20e-05) 1.027** (0.443) 9.42

0.00426*** (0.000856) 0.00102*** (0.000284) 0.175** (0.0889) 0.00237 (0.00683) 1.91e-05 (1.86e-05) 0.325 (0.374) 30.38

1.35 0.3148 0.357

3.58 0.0364 0.641

0.0935 0.357

0.0000 0.641

Model (1)–(2): Robust standard errors in parentheses. Model (3)–(4): Standard errors in parentheses. *** (p < 0.01), ** (p < 0.05), * (p < 0.1). 83

(4)

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ports we examined, belonged to the strong-type group, which shows strong competitiveness. Three of the examined port enterprises however, were close to being strong-type ports: In the obesity-type, Shanghai International Port and Ningbo Port (with their large throughput) should improve their environmental efficiency by strengthening the control of pollutant emissions. As a lean-type port, Nanjing Port should focus on finding proper niches and increase throughput by relying on resources of the hinterland. Furthermore, the government should improve the transportation network (with the port at the center of the network) to increase the collection and distribution capacity of ports. Regarding the port enterprises themselves, they should fully utilize surrounding resources and rely on their economic hinterland. The ports should also strive to accurately identify their core businesses and main target markets. In addition, ports should improve their core technology and management ability, rather than devoting all their efforts to increasing throughput. Furthermore, all ports should strengthen mutual cooperation in terms of technology and management levels; Chinese ports should also learn from foreign benchmarking port enterprises. In terms of environmental protection, local governments should finetune relevant regulations related to ship emissions and port environmental protection as soon as possible, including striving to build at least one green port. As key participants in energy savings and emissions reduction, port enterprises should implement energy saving and emission reduction policies as part of their daily operations. Port enterprises should also be forward-looking. In the process of port planning, they should consider to become resource-saving ports, using environmental factors as the key factors for their own performance evaluation.

designated size in China reached 635.54 billion RMB, signifying a decrease of 2.3% over the previous year. Industrial profits in northern China have significantly declined. For example, the economy of the northeastern Chinese area “cliff” declined, and industrial added value above designated size continued to grow negatively. This showed that the economic and industrial development in the hinterland of the Northern China has reduced the port's operation volume, thus reducing the CCR efficiency of affected ports. (2) Freezing days in the northern ports of China further affect productivity. For example, Yingkou Port has a frozen period of three months, Dalian port has a frozen period of two months, and Tianjin port has a frozen period of three months (the specific freezing period of ports can be found at http://www.port.org.cn, which is sponsored by the China Port Association). These ports will close and stop operations when they encounter cold weather conditions. This explains the significant impact of the location of the port on CCR efficiency due to the external environment. Due to the above two reasons, the speed of the actual business development of northern ports is apparently behind that of southern ports; therefore, the actual emissions of pollutants of northern ports are less than those of southern ports. This explains why the impact of the LP variable on environmental efficiency was also significant. 4.5. Discussion and management implications This study evaluated Chinese ports using both the traditional DEA model and a proposed model. We found that the efficiency results and the ranking of these ports changed significantly when environmental factors were considered. A port, which was considered efficient based on the traditional model, was not necessarily considered efficient under the evaluation of the model proposed in this paper. China has achieved tremendous success in economic development over the past several decades. However, China is also facing an urgent problem of environmental degradation. Consequently, to protect the environment from pollution, and to implement sustainable development, pollution emissions cannot be ignored when studying the performance of Chinese ports. Using a cluster analysis, this study divided ports into three groups, according to their scale. Based on the traditional model, efficiency scores of large-scale and medium-sized port enterprises were higher than efficiency scores of small-scale port enterprises. However, based on the nonradial model, small-scale port enterprises ranked first, while both medium-sized and large-scale port enterprises suffered from extremely low environmental efficiency scores. Therefore, different port enterprises should improve their performance in different ways. Medium-sized and large-scale port enterprises should take measures to reduce pollution emissions, while small-scale port enterprises should consider how to fully utilize existing resources. Based on a multiple regression model, the number of berths had a significant negative impact on resource-utilization efficiency. This indicates a relatively low utilization rate of berths in China. Under the current global economic downturn environment, Chinese ports are not suitable for large-scale infrastructure constructions. They should focus more on reasonable allocation and use of existing berths, while improving the comprehensive service level of ports. The regression results show that the utilization of resources in the northern ports of China was less efficient, compared to the ports of South China. This was mainly due to the lethargic economic development in the hinterland and external climate factors. Therefore, on one hand, the local government should actively introduce economic policies to stimulate the development of both economy and industry. On the other hand, ports in northern China should improve port outputs through a rational marketing strategy, such as by expanding the scope of their economic hinterland, cooperating with each other to reduce unnecessary competition or by implementing merger, and by eliminating backward and inefficient ports. All port enterprises could be divided into four different types, according to our throughput-efficiency matrix. Unfortunately, none of the

5. Conclusion This study aimed to evaluate and analyze the operational performance of Chinese port enterprises by considering environmental factors. For this purpose, a non-radial DEA model was proposed. Compared to traditional models, the proposed model considers the environmental factors of a port, and this consideration could provide a complete and more accurate ranking for all ports. With the proposed model, this paper evaluated and analyzed the efficiency of 17 Chinese listed port enterprises. The results show low average efficiency of all ports (when considering environmental factors). According to the scale classification, large and medium-scale ports perform worse. Via multiple regression model, we found that fixed assets, berth quantity, and the geographical location significantly affected the efficiency of a port. This paper also divided all ports into four categories, based on their throughput and efficiency. The ports within different categories should implement different policies to improve their own shortcomings. This study can be extended in at least the following two directions: First, the proposed model is a static model; a dynamic version of this model could be developed. To develop a dynamic DEA model, potential technical improvements must be considered, taking random variables into account. In addition, the proposed model of this paper was applied to evaluate the environmental efficiencies of China's ports. However, our model can also be extended to evaluate the environmental efficiency of enterprises in other industries. Acknowledgements The authors would like to thank the financial supports from National Natural Science Funds of China (Nos. 71501139 and 71402111), Natural Science Funds of Jiangsu Province (No. BK20150307), and Research project of philosophy and Social Sciences in Universities of Jiangsu (2015SJB525). Dr. Xiang Ji and Prof. Jie Wu also would like to thank partially financial support from National Natural Science Funds of China (No. 71571173).

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