Evaluating the Sustainable Development Performance of China’s International Commercial Ports Based on Environmental, Social and Governance Elements

Abstract

An environmental, social and governance (ESG) evaluation system can focus on the value of enterprises more comprehensively and better scrutinize the development premise of enterprise. As a novel investment concept, both domestic and foreign investors widely acknowledge the significance of ESG. With the implementation of “carbon peak”, “carbon neutral” and other national strategies, an increasing number of transportation enterprises in China’s international commercial ports have started to focus on the role of ESG evaluation. This not only facilitates self-examination and correction within enterprises but also helps in adjusting the strategic direction toward sustainable development. This shift toward ESG evaluation is crucial for promoting environmental sustainability and corporate social responsibility within the transportation industry. In this regard, this study aims to evaluate the sustainable development performance of China’s international commercial ports based on ESG elements. A data envelopment analysis (DEA) is considered to be a non-parametric performance evaluation method that can effectively solve for multi-criteria decision-making units, so this study mainly selects the DEA model for the performance evaluation. This study conducted research to select eight benchmarking companies within the industry and found that efficient units excelled in their ability to complete capacity levels with high quality and quantity at ports. In contrast, less efficient units scored lower in the domain of social responsibility.

1. Introduction

The concept of environmental, social and governance (ESG) was first proposed in a report on Who Cares Wins, commissioned by former UN Secretary-General Koffi Anan and compiled by 20 financial institutions. ESG is an abbreviation for environmental, social and governance, which encompasses the impact of production and operations on the environment (environmental considerations), a company’s support for employees and the community (societal aspects), as well as internal management and supervision within a company, along with communication and cooperation between the enterprise and its shareholders and stakeholders (corporate governance) [1]. The new ESG index is a useful indicator that can be used to measure a company’s sustainability and long-term social value. It integrates a company’s environmental, social and governance responsibilities and aims to help investors understand the company’s social and environmental impact, as well as the effectiveness of the company’s internal governance.

In recent years, as economic globalization and internationalization have progressed, China has also begun to pay attention to the implementation of ESG concepts and actively promote ESG-related corporate practices [2,3]. In China, high-quality development has always been the top priority for the country to build a modern socialist country in an all-round way. The concept of new development should be carried out in the whole process of economic and social development and in all fields. To this end, enterprises should work together with the government to accelerate the green transformation of enterprises; promote the prevention and control of environmental pollution; look forward to enhancing the diversity, stability and sustainability of the ecosystem; and promote the “Carbon to Peak” concept in a positive and steady manner. This new concept means that at a certain point, the growth of carbon dioxide emissions no longer peaks and then gradually falls back. It is the historical turning point of carbon dioxide emissions from growth to decline, marking the decoupling of carbon emissions and economic development, and the peak target includes the peak year and the peak. Nowadays, these strategic goals are being actively and steadily promoted in China. Despite China’s late start in ESG development compared to the United States and Europe, there has been increasing acceptance and attention to ESG across various fields and enterprises in China, particularly under the guidance of the “dual-carbon” policy objective. As a result, ESG development has gained momentum in China. From green finance to the construction of the ESG indicator system, ESG development in China has greatly accelerated and is moving from concept to practice.

From a sustainable development perspective, the Chinese government will integrate ESG considerations into its policy-making process, which hopes to promote green development and transformation across various industries in China. This will be achieved through policy-led initiatives aimed at improving environmental quality while considering environmental, social and economic benefits.

As far as China’s international transportation industry is concerned, in order to serve the country’s strategic goal of “dual-carbon”, the industry urgently needs to improve its energy efficiency or reduce the intensity of its carbon emissions. It should not be overlooked that China’s carbon emissions are relatively high, and according to United Nations statistics, China’s international transportation industry has ranked ninth in the world in terms of CO₂ emissions in the last decade, which is highly correlated with the market share of shipping tonnage and the structure of the types of ships used in international transportation [4]. For this reason, Chinese shipping port managers and enterprises need to pay attention and make the corresponding changes. First of all, government regulation plays a crucial role in incorporating ESG into industry norms within China’s international transportation sector. This inclusion can lead to improved ESG performance, enhanced energy efficiency, reduced carbon emissions and mitigated carbon neutral risks, as well as establishing national-specific ESG disclosure standards for shipping ports that align gradually with global norms. Secondly, from the perspective of the strategic development of enterprises, ESG can effectively measure the sustainable development ability of enterprises in responding to climate change and realizing carbon neutrality. Since the proposal of the “carbon peak” and “carbon neutrality” national strategic objectives, energy saving and emission reduction have been made possible. Since the country has proposed the strategic goals of “carbon peak” and “carbon neutrality”, energy conservation and emission reduction have not only become the responsibilities that enterprises should assume for environmental and social reasons but also an important driving force to assist enterprises in low-carbon process innovation and digital transformation.

After years of development in the international transportation industry, most traditional businesses have reached a bottleneck. In China, listed companies in this field have been exploring sustainable development by expanding and integrating into the supply chain, as well as actively constructing green, low-carbon and smart ports. However, among the 30 listed port enterprises in China’s A-share international transportation industry, only about 70% disclose their ESG reports, indicating room for improvement to align with international standards.

To this end, this study hopes to carry out ESG performance evaluation research on China’s international transportation industry, in order to find out benchmarking enterprises in the industry for other enterprises to follow and learn from and further help inefficient enterprises to analyze their advantages and disadvantages in the operation process and propose corresponding improvement countermeasures, so as to enhance the competitiveness of China’s international transportation industry in the international arena and contribute to the sustainable development of green ports and the landing of the national strategy of “carbon peak” and “carbon neutrality”.

The remaining parts of this paper will be organized as follows: Section 2 reviews the literature on ESG applications and DEA performance evaluation with ESG. Section 3 introduces the primary method of the DEA used in this study, based on the super slack-based measure (SBM). A real-world numerical example in 17 international commercial ports of Chinese A-share-listed companies is presented in Section 4. And the last section concludes the highlights and some managerial implications of this study.

2. Literature Review

2.1. ESG Performance for Sustainable Development

ESG is one of the important ways for contemporary investors to assess corporate risk, and half of the assets in the European market are allocated through strategies that consider ESG concepts [5]. Good management of ESG factors can not only help companies in the international transportation industry to reduce their operational risks and enhance their brand reputation but also improve their operational efficiency and strengthen their competitiveness in the international market. Additionally, the ESG performance of international transportation-related companies may directly affect their financing costs and stock price performance. Numerous studies have found that enterprises with good ESG performance tend to obtain lower financing costs and ensure that their share prices are more stable. For this reason, research on the ESG applications in the international transportation industry has gained the increasing attention of scholars and the managers of the related enterprises [6,7,8,9,10,11].

Uyar et al. [12] defined their study as the relationship between corporate social responsibility (CSR) performance and CSR reporting in the international logistics and transportation industry, where CSR performance was represented by the scores of the three ESG composite indicators of environment, society and governance. Its study showed that enterprises with higher CSR performance tended to have a greater likelihood of releasing that CSR report to the general public. At the same time, the number of CSR reports released by that enterprise would be higher. This conclusion also verified the positive impact of ESG evaluation scores on enterprise development. In the case of international logistics industry, Govindan et al. [13] focused on exploring the relationship between board ownership structure and corporate social responsibility (CSR) performance. By collecting ESG scores from evaluated firms between 2011 and 2018, they used regression analysis and least squares regression tests to explore the correlation between drivers of social responsibility performance and corporate value for firms related to the international logistics industry. Their findings showed that gender diversity among board members exhibits a significant positive correlation with overall CSR performance. While Murillo and Fábio [5] alerted that seaports typically account for about 80% of the international trade of coastal countries and are crucial to national economic development, few studies related to the port industry or terminals have introduced the concept of ESG. To this end, they proposed a method to quantify the ESG performance of international commercial ports by conducting an empirical case study of three global hub ports. They declared that ESG scores were effective in categorizing ports. Then, Tsang et al. [14] claimed that the popularity of ESG performance measurement was increasingly popular, especially for listed companies. Satisfactory ESG performance could help enterprises obtain more investment decisions and resource support. However, they found that in the practice of logistics and supply chain management (LSCM), when listed companies conducted business cooperation with external port logistics companies, implementing existing ESG evaluation systems is difficult for small and medium-sized enterprises with unstructured and non-standard business data. For this reason, they constructed a new ESG development prioritization and performance measurement framework to address this issue.

It is evident that an increasing number of countries are recognizing the important role of ESG assessment performance for sustainable enterprise development. Consequently, selecting scientific and effective assessment methods has become a key issue. Therefore, this study specifically chose a relatively objective non-parametric research method to assess the ESG performance of the international transportation industry in China.

2.2. ESG Performance Evaluation with DEA

DEA is an approach to the boundary of operations research and economic production, which explicitly considers the use of multiple inputs (i.e., resources) and the generation of multiple outputs (i.e., services). It is used to compare the final efficiency of multiple service decision-making units (DMUs) providing similar services. Efficiency measured by DEA can provide a clear picture of the input–output mix, yielding more trustworthy efficiency values than operating ratios or profitability metrics. Generally, enterprise managers will use the DEA method to compare a group of service units, identify relatively inefficient units and explore ways to reduce inefficiency by comparing and learning from efficient units. As a result, DEA applications for performance evaluation have found relevance across diverse areas [15,16,17,18,19].

In recent years, a number of scholars have tried to use DEA models to carry out application research on the ESG performance evaluation of the international transportation industry (as diverse as industry, finance, education and international commercial port transportation). Stefanoni and Voltes-Dorta [20] discovered that in the highly competitive automotive industry, managers are under increasing pressure to achieve sustainability and environmental performance. Traditional studies usually only consider financial metrics, but they used a DEA–Malmquist methodology to assess the technical efficiency of 33 prominent global automakers (2014 to 2017) with three ESG scores as key variables. They believed their research would help inform the creation of sustainable portfolios. Pham et al. [21] claimed that there were many studies nowadays focusing on exploring the relationship between ESG scores on firms’ operational performance. They believed that it was impossible to obtain more comprehensive evaluation results by only using a single indicator to measure performance. Therefore, their research mainly explored the relationship between ESG scores and operational performance comprehensively by combining data envelopment analysis (DEA) and OLS regression methods. A study of 56 firms found that environmental and social dimension scores could directly and positively affect the business performance. Cheng et al. [22] pointed out that after the outbreak of the COVID-19 epidemic, it stimulated a strong interest in ESG ratings and corporate economic sustainability research in academia. Thus, they collected ESG data from 1,108 Chinese firms (2015–2019) to explore the relationship between the impact of the efficiency of corporate ESG ratios and financial performance, and their study found that firms tend to sacrifice their governance performance in order to improve their environmental performance and social performance.

3. Methodology

Suppose that n evaluated DMUs produce m inputs and s outputs. Let $x_{ij}(i=1,\dots ,m)$ and $y_{rj}(r=1,\dots ,s)$ denote the ith input and rth output of the jth DMU$(j=1,\dots ,n)$, respectively. The production possible set (PPS) can be described as follows by these DMUs:

$$T=\left\{(x_1,\dots ,x_i,\dots ,x_m,y_1,\dots ,y_r,\dots ,y_s)|{\displaystyle \sum _{i=1}^m{v}_i{x}_{ij}}\le x_{ik},i=1,\dots ,m;{\displaystyle \sum _{i=1}^m{u}_r{y}_{rj}}\ge y_{rk},r=1,\dots ,s\right\}$$

(1)

where $v_i$ and $u_r$ are non-negative intensity vectors, indicating that the above definition corresponds to the situation of constant returns to scale (CRSs). In 1978, Charnes et al. [23] proposed a nonlinear programming model, which is called the original DEA-CCR model. This model was traditionally used to analyze all positive data. Through the Charnes–Cooper transformation [24], formulating the efficiency of DMU-k can be drafted as follows:

$$\begin{array}{l}\max {\displaystyle \sum _{r=1}^s{u}_r{y}_{rk}}\\ s.t.{\displaystyle \sum _{i=1}^m{v}_i{x}_{ik}}=1;\\ \frac{{\displaystyle \sum _{r=1}^s{u}_r{y}_{rj}}}{{\displaystyle \sum _{i=1}^m{v}_i{x}_{ij}}}\le 1;j=1,\dots ,n;\\ v_i\ge 0,i=1,\dots ,m;\\ u_r\ge 0,r=1,\dots ,s.\end{array}$$

(2)

Model (2) is the original DEA-CCR model in multiplier form. And its dual model presented in the envelopment form can be described as follows:

$$\begin{array}{l}\max \theta \\ s.t.{\displaystyle \sum _{j=1}^n{\lambda }_j{x}_{ij}}\le \theta \cdot x_{ik};\\ {\displaystyle \sum _{j=1}^n{\lambda }_j{y}_{rj}}\ge y_{rk};\\ {\lambda }_j\ge 0,j=1,\dots ,n.\end{array}$$

(3)

Subsequently, Banker et al. [25] extended the model (3) to cover variable returns to scale (VRSs). However, the two basic DEA approaches may be limited by some of the inefficient components not being reflected in the results of a performance assessment (such as the mix inefficiencies). In order to solve this problem, Tone [26] first proposed the following DEA-SBM model:

$$\begin{array}{l}\min \rho =\frac{1-\frac{1}{m}{\displaystyle \sum _{i=1}^m{s}_i^{-}/x_{ik}}}{1+\frac{1}{s}{\displaystyle \sum _{r=1}^s{s}_r^{+}/y_{rk}}}\\ s.t. x_{ik}={\displaystyle \sum _{j=1}^n{\lambda }_j{x}_{ij}}+s_i^{-},i=1,\dots ,m\\ y_{rk}={\displaystyle \sum _{j=1}^n{\lambda }_j{y}_{rj}-s_r^{+}, }r=1,\dots ,s\\ {\lambda }_j\ge 0,j=1,\dots ,n\\ s_i^{-}\ge 0,i=1,\dots ,m\\ s_r^{+}\ge 0,r=1,\dots ,s\end{array}$$

(4)

where $s_i^{-},s_r^{+}$ denote the inefficient components, which represent the waste of inputs and the insufficiency of outputs, respectively. In model (4), Tone [26] declared that DMUs were evaluated to be efficient if and only if the optimal solution of $s_i^{-*}=s_r^{+*}=0$ for all $i$ and $r$ (or equivalently, the efficiency ${\rho }^{*}=1$). In model (4), the new PPS can be defined as

$$PPS=\left\{(\tilde{\mathrm{x}},\tilde{\mathrm{y}})|\tilde{\mathrm{x}}\ge {\displaystyle \sum _{j=1}^n{\lambda }_j{\mathrm{x}}_j,\tilde{\mathrm{y}}\le {\displaystyle \sum _{j=1}^n{\lambda }_j{\mathrm{y}}_j,\tilde{\mathrm{y}}\ge 0,{\lambda }_j\ge 0,j=1,\dots ,n}}\right\}$$

(5)

In fact, the original SBM-DEA model involved computing the ratio of the average input decrease to the average output increase when assessing efficiency. That is, the objective function of the SBM-DEA model is to explore the most appropriate extent of improvement between the inputs and outputs. Therefore, the SBM-DEA model can be seen as a non-oriented or non-radial model. Model (4) has one advantage in that it allows the analyst to access the efficiency by analyzing the maximum adjustable quantity of each vector, which is instead of only considering the improvement in one dimension (inputs or outputs) alone. However, its shortcomings are also very evident. Model (4) cannot further distinguish the effective units, and all of the evaluation performance values are 1. To further enhance the discrimination of all efficient units, Tone [27] proposed a new super-SBM model to identify the super-efficiency of all efficient DMUs, which can be described as follows:

$$\begin{array}{l}\min \delta =\frac{\frac{1}{m}{\displaystyle \sum _{i=1}^m{\tilde{\mathrm{x}}}_i/{\mathrm{x}}_{ik}}}{\frac{1}{s}{\displaystyle \sum _{r=1}^s{\tilde{\mathrm{y}}}_r/{\mathrm{y}}_{rk}}}\\ s.t. {\tilde{\mathrm{x}}}_i\ge {\displaystyle \sum _{j=1,j\ne k}^n{\lambda }_j{\mathrm{x}}_{ij}},i=1,\dots ,m\\ {\tilde{\mathrm{y}}}_r\le {\displaystyle \sum _{j=1,j\ne k}^n{\lambda }_j{\mathrm{y}}_{rj},r=1,\dots ,s}\\ {\lambda }_j\ge 0,j=1,\dots ,n,j\ne k\\ {\tilde{\mathrm{x}}}_i\ge {\mathrm{x}}_{ik} ,i=1,\dots ,m\\ {\tilde{\mathrm{y}}}_r\ge k,{\tilde{\mathrm{y}}}_r\le {\mathrm{y}}_{rk},r=1,\dots ,s\end{array}$$

(6)

Note that it is a non-oriented or non-radial model. For the inefficient DMUs, the efficiency computed by model (6) is necessarily equal to 1. Because model (6) can only be effective for distinguishing the efficient DMUs. Typically, model (4) and model (6) must be used together. However, Fang et al. [28] noted that Equation (5) did not incorporate the slacks explicitly; then, they proposed adding two other slack variables ($w_i^{-},w_r^{+}$) to account for these incorporated slacks of the first two constraints of model (6). Their model to evaluate the super-efficiency can be described as follows:

$$\begin{array}{l}\min \rho =\frac{1+\frac{1}{m}({\displaystyle \sum _{i=1}^m{w}_i^{-}/x_{ik})}}{1-\frac{1}{s}({\displaystyle \sum _{r=1}^s{w}_r^{+}/y_{rk})}}\\ s.t. x_{ik}\ge {\displaystyle \sum _{j=1,}^n{\lambda }_j{x}_{ij}}-w_i^{-};\\ y_{rk}\le {\displaystyle \sum _{j\ne k}^n{\lambda }_j{y}_{rj}+w_r^{+}};\\ {\lambda }_j\ge 0,j=1,\dots ,n;\\ w_i^{-}\ge 0,i=1,\dots ,m;\\ w_r^{+}\ge 0,w_r^{+}\le y_{rk},r=1,\dots ,s.\end{array}$$

(7)

where $w_i^{-} \mathrm{and} w_r^{+}$ denote the incorporate slacks (or super-efficient components) of inputs and outputs, respectively. In model (7), the constraints $w_r^{+}\le y_{rk}(r=1,\dots ,s)$ can ensure that the computed super-efficiency value is always non-negative.

Similar to model (6), if the efficiency value of DMU-k evaluated by model (7) was greater than 1, this DMU can be viewed as efficient. That is, model (7) can determine the minimum distance ($w_i^{-} \mathrm{and} w_r^{+}$) between the evaluated DMU and efficient frontier. It is a non-oriented or non-radial model. However, for any evaluated DMU-k, the minimum distance ($w_i^{-} \mathrm{and} w_r^{+}$) is necessarily zero; in other words, model (7) cannot determine the gap between the evaluated DMU and its reference target. Thus, they proposed the following model to calculate the efficiency of inefficient DMUs:

$$\begin{array}{l}\min \rho =\frac{1-\frac{1}{m}({\displaystyle \sum _{i=1}^m{s}_i^{+}/x_{ik})}}{1+\frac{1}{s}({\displaystyle \sum _{r=1}^s{s}_r^{-}/y_{rk})}}\\ s.t. x_{ik}={\displaystyle \sum _{j=1,}^n{\lambda }_j{x}_{ij}}-w_i^{-*}+s_i^{+};\\ y_{rk}={\displaystyle \sum _{j\ne k}^n{\lambda }_j{y}_{rj}+w_r^{+*}-s_r^{-}};\\ {\lambda }_j\ge 0,j=1,\dots ,n;\\ s_i^{+}\ge 0,i=1,\dots ,m;\\ s_r^{-}\ge 0,r=1,\dots ,s.\end{array}$$

(8)

where $w_i^{-*} \mathrm{and} w_r^{+*}$ are the optimal solutions calculated by model (7), and the optimal solution of the new variables $s_i^{+*} \mathrm{and} s_r^{-*}$ can denote the inefficient components of the evaluated DMU-k. Therefore, in this study, we formulate efficiency as follows:

$${\varphi }^{*}=\{\begin{array}{l}\frac{1+\frac{1}{m}({\displaystyle \sum _{i=1}^m{w}_i^{-*}/x_{ik})}}{1-\frac{1}{s}({\displaystyle \sum _{r=1}^s{w}_r^{+*}/y_{rk})}}, \mathrm{if} \frac{1+\frac{1}{m}({\displaystyle \sum _{i=1}^m{w}_i^{-*}/x_{ik})}}{1-\frac{1}{s}({\displaystyle \sum _{r=1}^s{w}_r^{+*}/y_{rk})}}>1\\ \frac{1-\frac{1}{m}({\displaystyle \sum _{i=1}^m{s}_i^{+*}/x_{ik})}}{1+\frac{1}{s}({\displaystyle \sum _{r=1}^s{s}_r^{-*}/y_{rk})}}, \mathrm{otherwise}\end{array}$$

(9)

In this study, if the optimal solution of Equation (9) was larger or equal to 1, the evaluated DMU could be viewed as efficient. Otherwise, the evaluated DMU would be inefficient. In order to ensure the possibility of this application, this study draws an unambiguous usage flow chart in Figure 1, as follows:

Step 1: Firstly, the main research subjects of this study are determined.

Step 2: Some appropriate variables are selected (such as the throughput of the port, ESG rating scores, etc.) through the literature review.

Step 3: The suitable super-SBM model for the performance evaluation of this study is constructed.

Step 4: Based upon the above research results, this study should interview some relevant enterprises to investigate the practical operational data of the selected variables.

Step 5: The DEA model chosen in this study is used to conduct performance evaluation processing on the collected data. If some in-feasibility problem occurs, it is necessary to go back to Step 3 and make the appropriate adjustments to the constructed modified super-SBM DEA model. After doing that, repeat Step 5.

Step 6: Each DMU can be evaluated under model (7) and (8) and then their super-efficiency is formulated by using Equation (9).

Step 7: An in-depth analysis of the most suitable adjustment for each inefficient DMU is conducted.

Step 8: According to the comprehensive evaluation results of this study and taking into account the ESG rating scores, all the evaluated units in this study will be further grouped. It is used to identify the benchmarking enterprises of this industry and provide improvement directions for the inefficient enterprises.

4. Empirical Research

4.1. Model Variables

Generally, the selection of input or output variables in DEA applications is critical. It significantly influences the variability of evaluation results when the set of research variables changes. According to the literature review, this study summarizes the variable selection of recent research for the port performance evaluation in

Table 1. The definition of variables selected in this study.

Variables		Definition
X1	Length of shoreline of the port (kilometer) [26,27,28]	It mainly refers to the boundary between the port waters and the land, which is usually delimited and managed by the state or local government in accordance with laws and regulations. The setting of the port shoreline mainly involves port construction, channel maintenance, the ship entering and leaving the port, etc., which has important economic and security significance for port management. This is selected as one important input variable in this study.
X2	Number of berths in the port [26,27,28]	It mainly refers to the position of ships that can be docked in the port area. And the number and size of berths in the port is one of the important indicators to measure the scale of a port. This is selected as another important input variable in this study.
Y1	Throughput (hundred million tons) [26,27,28,29,30]	The container throughput is the sum of the number of imported and exported containers in a port over a period of time, usually in TEU. The container throughput can usually effectively reflect the important role played by ports in international material exchange and foreign trade transportation and is an important basis for port planning and development construction. This is chosen as the key output variable in this study.
Y2	Environmental performance of ESG [15,16,17]	The ESG evaluation system is usually used to evaluate how the commitment, performance and business model of the enterprise are in line with the sustainable development goals of this enterprise, and they are often used by investment companies to screen or evaluate the future development sustainability of the enterprise. Environmental aspects refer to carbon emissions, waste pollution and management policies, energy use/consumption, natural resource use and management policies, biodiversity, compliance and employee environmental awareness. It is emphasized that enterprises should pay attention to ecological and environmental protection. Thus, it is selected as one of output variables in this study.
Y3	Social performance of ESG [15,16,17]	This index emphasizes that enterprises should fulfill their social responsibilities. Social aspects refer to gender balance policies, human rights, community health and safety, labor norms, product responsibility and supply chain responsibility management. It is also selected as one of output variables in this study.
Y4	Governance performance of ESG [15,16,17]	This index emphasizes that enterprises should improve their own governance, and its evaluation results can reflect the operating level of enterprises. Governance refers to corporate governance policies, anti-corruption and anti-bribery measures, risk management, tax transparency, fair labor practices, ethical codes of conduct, board independence and diverse organizational structures. Thus, it is also selected as one of output variables in this study.

. Thus, through considerations of the characteristics of port operations and consultations with experts, this study selected five variables, including two inputs (the length of the shoreline of the port and the number of berths in the port) and four outputs (the throughputs of each port, environmental performance of ESG, social performance of ESG and governance performance of ESG). The definitions of these selected variables are described in Table 1.

4.2. Research Data

This study aimed to explore the impact of ESG performance on the operation performance and sustainable development of China’s international commercial port enterprises. To this end, this study collected the various input and output variables needed for this study through a data investigation and literature search. The research objects identified in this study are mainly 17 international commercial port enterprises of Chinese A-share-listed companies. The study variables, including the infrastructure input data and annual output data of each port, were primarily sourced from the China Port Yearbook. The ESG performance data were obtained from official releases in 2023 (https://www.wind.com.cn/portal/zh/ESG/index.html, accessed on 4 May 2024). The research data are summarized in

Table 2. The research data collected for this study.

Ports	WindESG Rating	Input Variables		Output Variables
		X1 (Kilometer)	X2	Y1 (Hundred Million Tons)	Y2	Y3	Y4
Zhaoshang	A	14.20	98	0.13	6.59	6.89	8.94
Qingang	A	13.41	59	2.01	5.16	5.41	8.26
Zhuhai	BBB	110.04	141	1.28	4.42	5.44	7.27
Tianjin	BBB	45.85	212	5.30	4.05	5.72	6.90
Beibuwan	BBB	40.34	274	3.58	6.60	3.33	7.13
Qindao	BBB	31.97	124	6.30	4.08	4.95	6.96
Shanggang	BBB	109.15	1075	7.76	2.36	4.38	8.97
Xiamen	BBB	32.60	180	2.28	2.47	3.71	7.51
Guangzhou	BBB	26.50	208	3.70	3.14	3.69	6.94
Tangshan	BBB	36.91	143	7.22	3.76	4.55	5.21
Rizhao	BB	24.00	89	5.41	2.39	2.24	6.42
Ningbo	BB	102.92	712	6.23	1.87	2.45	6.72
Liaoning	BB	53.73	217	4.95	1.52	2.13	7.11
Jinzhou	BB	14.02	161	1.04	1.36	1.70	7.11
Lianyungang	BB	20.40	97	2.77	2.45	1.61	5.91
Chongqing	BB	54.92	591	1.98	1.91	0.95	6.68
Nanjing	B	24.52	181	0.89	2.43	1.95	5.00

.

4.3. Evaluation Results

Under the evaluation by models (7–9), the final results of the operating efficiency of all the evaluated ports are summarized in Table 3. In this table, there are eight evaluated DMUs obtaining an efficiency value of more than 1, including Qinggang, Zhaoshang, Shanggang, Rizhao, Qindao, Beibuwan, Tangshan and Tianjin. In other words, nearly half of the port’s daily operations are efficient. These selected efficient ports can be regarded as the industry benchmarks, and they can be used for learning by the other operating inefficient units. Among them, the top three ports are Qingang (1.4888), Zhaoshang (1.1454) and Shanggang (1.1149), respectively. The average performance value of all eight operationally efficient assessed units is 1.1237.

On the other hand, this study also assessed nine DMUs that are operationally inefficient, namely, Guangzhou, Lianyungang, Xiamen, Jinzhou, Liaoning, Zhuhai, Nanjing, Ningbo and Chongqing, respectively. Based on the ESG rating scores, these units are relatively inefficient to operate in 2023, because all of their assessed performance values are less than 1. Of these, the three units with the most unsatisfactory operational performance include Nanjing (0.1982), Ningbo (0.1536) and Chongqing (0.0646), respectively. And the average performance value of all nine operationally inefficient assessed units is 0.2583. The comparison shows that the average performance value of the efficient assessed units is 4.35 times higher than this value.

4.4. Suitable Adjustment Analysis (Trade-Off Analysis)

Simply providing port managers with the operating performance based on ESG elements can only determine whether their operations are efficient or not, and it fails to allow them to understand the quantitative analysis of the strengths and weaknesses of each port being evaluated. In order to explore the advantages of efficient ports and some disadvantages of inefficient ones, this study further provides an in-depth analysis of the most suitable adjustment for all the evaluated international commercial ports in China. Then, managers can effectively address or improve their operational weaknesses, which also highlights the practical management implications and value of this study. The detailed corresponding analyses are presented in Table 3.

Table 3 presents the quantitative results of all the selected variables for each international commercial port. The analysis results have identified the target that should be met for each variable in future production and the extent of suitable adjustment (expressed as an adjustment rate) in the optimal trade-off. To input variables, the adjustment ratio was calculated by subtracting the original resource value from its target scores and then dividing this gap by the original values. A positive adjustment ratio denotes the learning benchmark is not yet as good as the assessed unit’s current performance in that direction. That is, a positive adjustment ratio can be interpreted as denoting the advantage of this evaluated port.

Conversely, if the results of the adjustment ratio for one variable are negative, it represents the disadvantage degree of the given port. For output variables, this study used reverse processing, in which the original data value was subtracted from the target scores and then this gap was divided by its target. This was performed to allow the positive numbers to also denote the advantages of the evaluated port.

Therefore, all the positive change rates in Table 3 can denote the advantage of each variable while the negative ones denote the disadvantage. For example, the top-ranked Qingang clearly demonstrates its perfect advantages in both input and output levels, especially in the input variables, which are as high as 60.91%(X2) and 30.77%(X1), respectively. Of course, it also presents a relatively perfect performance in outputs as well, including throughput (Y1, 4.10%), governance performance (Y4, 2.16%) and social performance (Y3, 1.91%), respectively. From the results presented in this table, not only is each operationally efficient unit analyzed, but also a detailed analysis of each inefficient unit is presented. It can help relevant managers uncover the key aspects that affect the sustainability of the enterprise. For Chongqing Port, the least desirable port, its operation in 2023 has not reached a relatively ideal operating state in terms of both input and output. For example, the worst performing dimensions are social performance (Y3, −469.47%), environmental performance (Y2, −170.16%) and the utilization efficiency of the berth in the port area (X2, −90.02%). Analyzing suitable adjustments for each variable is intended to help inefficient units make more targeted optimizations in future operations. This not only provides the direction to be optimized (qualitative analysis) but also offers an actionable quantitative optimization analysis for the reference of the relevant managers. To this end, through Table 3, we can conduct a detailed analysis for each assessed unit, help the relevant managers layout the future sustainable development of its port and provide them with reference opinions.

Table 3 provides a detailed customized analysis for each assessed unit and measures the average valued strengths and weaknesses analysis for both efficient and inefficient units. This project further clustered all the assessed units mainly based on the performance values evaluated in this study and the ESG composite scores from the social evaluation, hoping that the results can be used as a reference for other non-efficient ones. The advantage of doing so is that it can help the managers of each port to understand the advantages and disadvantages of the development of its entire industry from a macro perspective. For example, in terms of efficient ports, the three levels in which they perform best on average are throughput (Y1, 8.21%), the utilization efficiency of the berth in the port area (X2, 7.61%) and governance performance (Y4, 5.92%), respectively. At the same time, for inefficient operations, the three worst performance areas on average are social performance (Y3, −157.60%), environmental performance (Y2, −126.98%) and the utilization efficiency of the berth in the port area (X2, −62.88%), respectively. Therefore, managers should strengthen the scientific supervision at these three levels in the future operation.

Table 3. Evaluating efficiencies and suitable adjustment of each variable for all DMUs.

DMUs	Efficiency	X1		X2		Y1		Y2		Y3		Y4
		Target	Change Rate *	Target	Change Rate *	Target	Change Rate *	Target	Change Rate *	Target	Change Rate *	Target	Change Rate *
Qingang	1.4888	17.54	30.77%	94.93	60.91%	1.93	4.10%	5.16	0.00%	5.31	1.91%	8.08	2.16%
Zhaoshang	1.1454	14.20	0.00%	98.00	0.00%	0.13	0.00%	5.16	21.70%	5.41	21.48%	8.26	7.61%
Shanggang	1.1149	109.15	0.00%	1075.00	0.00%	6.30	18.81%	2.36	0.00%	4.38	0.00%	6.96	22.41%
Rizhao	1.0702	24.00	0.00%	89.00	0.00%	3.99	26.25%	2.39	0.00%	2.24	0.00%	6.42	0.00%
Qindao	1.0624	31.97	0.00%	124.00	0.00%	6.03	4.21%	4.08	0.00%	4.75	4.09%	5.90	15.18%
Beibuwan	1.0555	40.34	0.00%	274.00	0.00%	3.58	0.00%	5.21	21.02%	3.33	0.00%	7.13	0.00%
Tangshan	1.0318	36.91	0.00%	143.00	0.00%	6.33	12.34%	3.76	0.00%	4.55	0.00%	5.21	0.00%
Tianjin	1.0203	45.85	0.00%	212.00	0.00%	5.30	0.00%	4.05	0.00%	5.26	7.96%	6.90	0.00%
Average for efficient DMUs		39.99	3.85%	263.74	7.61%	4.20	8.21%	4.02	5.34%	4.40	4.43%	6.86	5.92%
Guangzhou	0.4716	20.72	−21.81%	84.61	−59.32%	3.70	0.00%	4.73	−50.78%	5.23	−41.70%	7.75	−11.64%
Lianyungang	0.4016	16.70	−18.15%	70.52	−27.30%	2.77	0.00%	4.97	−102.80%	5.33	−230.96%	8.03	−35.87%
Xiamen	0.2845	14.25	−56.29%	61.38	−65.90%	2.28	0.00%	4.94	−100.00%	5.16	−39.04%	8.11	−8.04%
Jinzhou	0.2627	13.41	−4.35%	59.00	−63.35%	2.01	−93.27%	5.16	−279.41%	5.41	−218.24%	8.26	−16.17%
Liaoning	0.2627	26.13	−51.37%	103.55	−52.28%	4.95	0.00%	4.42	−190.78%	5.09	−139.19%	7.37	−3.64%
Zhuhai	0.2251	13.43	−87.80%	59.79	−57.60%	1.97	−54.05%	5.19	−17.40%	5.44	0.00%	8.27	−13.81%
Nanjing	0.1982	13.41	−45.31%	59.00	−67.40%	2.01	−125.84%	5.16	−112.35%	5.41	−177.44%	8.26	−65.20%
Ningbo	0.1536	31.67	−69.23%	122.94	−82.73%	6.23	0.00%	4.10	−119.12%	4.96	−102.35%	6.98	−3.89%
Chongqing	0.0646	13.41	−75.58%	59.00	−90.02%	2.01	−1.52%	5.16	−170.16%	5.41	−469.47%	8.26	−23.65%
Average for inefficient DMUs		18.12	−47.77%	75.53	−62.88%	3.10	−30.52%	4.87	−126.98%	5.27	−157.60%	7.92	−20.21%
Total average		28.42	−23.48%	164.10	−29.71%	3.62	−12.29%	4.47	−64.71%	4.86	−81.35%	7.42	−7.91%

Note: Change rate * = positive values denote the advantage of each input and undesirable output, and negative values denote the disadvantage.

Table 3. Evaluating efficiencies and suitable adjustment of each variable for all DMUs.

DMUs	Efficiency	X1		X2		Y1		Y2		Y3		Y4
		Target	Change Rate *	Target	Change Rate *	Target	Change Rate *	Target	Change Rate *	Target	Change Rate *	Target	Change Rate *
Qingang	1.4888	17.54	30.77%	94.93	60.91%	1.93	4.10%	5.16	0.00%	5.31	1.91%	8.08	2.16%
Zhaoshang	1.1454	14.20	0.00%	98.00	0.00%	0.13	0.00%	5.16	21.70%	5.41	21.48%	8.26	7.61%
Shanggang	1.1149	109.15	0.00%	1075.00	0.00%	6.30	18.81%	2.36	0.00%	4.38	0.00%	6.96	22.41%
Rizhao	1.0702	24.00	0.00%	89.00	0.00%	3.99	26.25%	2.39	0.00%	2.24	0.00%	6.42	0.00%
Qindao	1.0624	31.97	0.00%	124.00	0.00%	6.03	4.21%	4.08	0.00%	4.75	4.09%	5.90	15.18%
Beibuwan	1.0555	40.34	0.00%	274.00	0.00%	3.58	0.00%	5.21	21.02%	3.33	0.00%	7.13	0.00%
Tangshan	1.0318	36.91	0.00%	143.00	0.00%	6.33	12.34%	3.76	0.00%	4.55	0.00%	5.21	0.00%
Tianjin	1.0203	45.85	0.00%	212.00	0.00%	5.30	0.00%	4.05	0.00%	5.26	7.96%	6.90	0.00%
Average for efficient DMUs		39.99	3.85%	263.74	7.61%	4.20	8.21%	4.02	5.34%	4.40	4.43%	6.86	5.92%
Guangzhou	0.4716	20.72	−21.81%	84.61	−59.32%	3.70	0.00%	4.73	−50.78%	5.23	−41.70%	7.75	−11.64%
Lianyungang	0.4016	16.70	−18.15%	70.52	−27.30%	2.77	0.00%	4.97	−102.80%	5.33	−230.96%	8.03	−35.87%
Xiamen	0.2845	14.25	−56.29%	61.38	−65.90%	2.28	0.00%	4.94	−100.00%	5.16	−39.04%	8.11	−8.04%
Jinzhou	0.2627	13.41	−4.35%	59.00	−63.35%	2.01	−93.27%	5.16	−279.41%	5.41	−218.24%	8.26	−16.17%
Liaoning	0.2627	26.13	−51.37%	103.55	−52.28%	4.95	0.00%	4.42	−190.78%	5.09	−139.19%	7.37	−3.64%
Zhuhai	0.2251	13.43	−87.80%	59.79	−57.60%	1.97	−54.05%	5.19	−17.40%	5.44	0.00%	8.27	−13.81%
Nanjing	0.1982	13.41	−45.31%	59.00	−67.40%	2.01	−125.84%	5.16	−112.35%	5.41	−177.44%	8.26	−65.20%
Ningbo	0.1536	31.67	−69.23%	122.94	−82.73%	6.23	0.00%	4.10	−119.12%	4.96	−102.35%	6.98	−3.89%
Chongqing	0.0646	13.41	−75.58%	59.00	−90.02%	2.01	−1.52%	5.16	−170.16%	5.41	−469.47%	8.26	−23.65%
Average for inefficient DMUs		18.12	−47.77%	75.53	−62.88%	3.10	−30.52%	4.87	−126.98%	5.27	−157.60%	7.92	−20.21%
Total average		28.42	−23.48%	164.10	−29.71%	3.62	−12.29%	4.47	−64.71%	4.86	−81.35%	7.42	−7.91%

Note: Change rate * = positive values denote the advantage of each input and undesirable output, and negative values denote the disadvantage.

4.5. Grouping Analysis

In order to identify the comprehensive learning benchmark enterprises in China’s international transportation industry, this study further clustered all the assessed units mainly based on the performance values evaluated in this study and the ESG rating scores from the social evaluation, hoping that the results can be used as a reference for other non-efficient ones. Thus, this study applies the evaluated results of the “efficiency” as the abscissa and the “ESG scores” as the ordinate to construct a new grouping analysis of all the evaluated DMUs, as shown in Figure 2. And the two red dashed lines in this figure represent the average of their respective coordinates.

• Group 1 (Benchmark type)

Enterprises in the first group not only perform relatively well in their daily operations but also pay attention to maintaining their social values. They are the genuine benchmark enterprises in China’s international transportation industry with outstanding overall performance and are worthy of learning by other enterprises. This group consists of four companies, namely, Qingang, Zhaoshang, Beibuwan and Tianjin.

• Group 2 (Operational-oriented type)

Although enterprises in this group have achieved relatively good performance at the operational level, they are relatively less impressive in terms of external ESG rating performance. In other words, these companies tend to pursue the value of corporate economic returns more in their past operations and management and should strengthen the degree of their contribution to social value in their future production process. This group also consists of four companies, namely, Shanggang, Rizhao, Qingdao and Tangshan.

• Group 3 (Social-oriented type)

Compared with enterprises in the second group, the ones in this group often achieve relatively excellent scores for the ESG rating, but their daily operation management has potential resource waste or other problems, resulting in their comprehensive business performance being not satisfactory. Only Zhuhai Port belongs to this group. In the future, this port should pay more attention to the management and utilization of the shoreline and berths in order to create more satisfactory economic value.

• Group 4 (Learning type)

Enterprises in this group not only need to strengthen their day-to-day operational management but also pay more attention to the contribution of social value. They have not achieved satisfactory results in both dimensions. Therefore, in their future production processes, they should endeavor to learn from the experience of the benchmark enterprises in group 1 regarding the expectation of achieving creative economic and social value for their enterprises in China’s international transportation industry.

5. Conclusions and Implications

5.1. Conclusions

ESG evaluation, as an investment approach, takes corporate social responsibility and sustainability into account. It focuses not only on monetary returns but also on the environmental, social and governance impacts of the company. Among them, the environmental aspect mainly refers to the resource utilization, emission, carbon footprint, etc. Social aspects mainly refer to employee relations, human rights issues, anti-corruption and so on. Governance mainly refers to the management organization, board structure, information transparency and so on. The important significance of the ESG-based investment method is that it can improve investment returns and promote sustainable development. At the same time, investment based on an ESG performance evaluation can also help investors assess the long-term risks and benefits of enterprises, encouraging enterprises to achieve comprehensive sustainable development.

With the growing popularity of ESG issues, more and more investors are realizing its importance, establishing their own ESG investment standards and influencing corporate social responsibility behavior through actions, such as voting and investing. Particularly in China, with the introduction of national strategic goals such as “carbon peak” and “carbon neutrality”, domestic capital markets have begun to change rapidly while many domestic port enterprises are accelerating their transformation and upgrading efforts. These developments have elevated ESG as a frequently mentioned topic among regulators and enterprises alike. Thus, this study aims to explore the potential effects of ESG performance on operating performance and sustainability development within international commercial ports in China. In summary, through this research, we find out that, first of all, for 17 international commercial port enterprises of Chinese A-share-listed companies, nearly half of them show the efficient operation performance in 2023, which can be used as a benchmark for other inefficient enterprises to learn. They are Qinggang, Zhaoshang, Shanggang, Rizhao, Qindao, Beibuwan, Tangshan and Tianjin. Secondly, for efficient ports, the best performance levels on average are summarized as follows: throughput, the utilization efficiency of the berth in the port area and governance performance. These are the advantages of these efficient enterprises, which should continue to consolidate these levels’ performance in future development and seek new breakthroughs in the remaining levels. Finally, in terms of relatively inefficient enterprises, the worst aspects of their average performance are social performance, environmental performance and the utilization efficiency of the berth in the port area, respectively. These items are the shortcomings of the inefficient enterprises (their weak projects), which need to attract the high attention of the relevant managers. In corporate strategic planning in the future, managers should pay more attention to the optimization of and rectifying these aspects in order to improve the rapid growth and sustainable development of relevant enterprises.

5.2. Managerial Implications

This study is also primarily expected to assist the managers of the relevant port enterprises in China’s international transportation industry in gaining a better understanding of the development frontier dynamics of their industry, grasping the advantages and disadvantages of industrial development from a macro perspective and learning from the benchmarking enterprise in this industry. It aims to help enterprises develop their sustainable development strategy based on the characteristics of their own organizations, in order to enhance their competitiveness in the international market and, ultimately, create unexpected gains for enterprises.

5.3. Future Directions

Future research can expand the evaluation model to the network structure to deal with the more complex operational management and performance evaluation issues of the international commercial port enterprises of Chinese A-share-listed companies. In addition, some other mathematical models for performance evaluation can also be combined with the model of this study to verify the realistic value of the evaluation results together, which may be a new path for future research.