Health evaluation of a regional logistics industrial ecosystem in China based on fuzzy matter-element analysis method

Abstract

The construction of a regional logistics industrial ecosystem elicits increasing attention because of the growing awareness of environment protection, and its health evaluation by considering environmental impacts is one of the kernel problems that must be addressed to promote the development of regional logistics. In this study, a comprehensive methodology based on fuzzy mathematics and matter-element analysis theory was presented to accurately evaluate the health status of the regional logistics industrial ecosystem in China. First, the forming mechanism and influencing factors of regional logistics industrial ecosystem were analyzed. Second, a fuzzy matter-element model was constructed to evaluate the regional logistics industrial ecosystem, and the weights of characteristic values of evaluation indexes for fuzzy matter-element were obtained using entropy decision method. Finally, this proposed method was applied to evaluate the logistics industrial ecosystem health of eight provinces in China. In this case, these evaluation indexes include energy consumption, logistics cost, and CO₂ emissions. The results show that this method is feasible and effective, and demonstrate the promising application of the proposed model in evaluating the health status of a regional logistics industrial ecosystem and in supplying reliable data for the environmental protection of regional logistics activities.

Keywords

Logistics industrial ecosystem ecological health evaluation entropy weight matter–element analysis method

1 Introduction

Growing concerns about climate change and the local and regional impacts of air, ground, and water pollution from industrial activities have significantly expanded the interaction between environmental management and operations; such an expansion has led to an attempt to provide a new conceptual framework on industrial ecology to understand the impacts of industrial systems on the environment [1, 2]. This framework serves to identify and implement strategies to reduce the environmental impacts of products and processes associated with industrial systems, with an ultimate goal of sustainable development.

Logistics industry cluster, as a useful way to promote regional economic growth, has been a prominent focus of society. The development of ecological theory provides a new perspective for research on logistics industrial cluster development. Industrial ecology posits that a regional logistics industrial cluster is an organic unity, which is an adaptive, self-coordinating, and self-organizing organic system in a changing environment [3, 4]. With the growing awareness of environmental protection and natural resources in the context of their decline, building environmental symbiosis systems for logistics industry clusters, namely, regional logistics industrial ecosystems, has become an inevitable development of regional logistics and an important part of promoting a socio-economic sustainable development society.

The rapid development of regional economy in China has caused the regional logistics industry to become a large consumer of energy and fuel; its subsequent emissions have caused serious damage to the environment. A conflict occurs between the goal of meeting consumers’ needs and creating commodity effectiveness and that of maintaining natural ecological balance and protection of natural resources. Given the increasing diversity and dynamics of regional logistics activities, environmental issues of regional logistics industry become increasingly significant. Social, political, and economic demands for sustainable development force all regional logistics organizations to consider the natural and social environment when taking logistics activities to reduce the impacts on the environment and jointly develop the industrial ecosystem. In accordance with the ultimate goal of regional logistics industrial ecosystem, following the law of development of green logistics activities and constructing a sustainable regional logistics industrial system based on industry ecology theory have become important issues in China’s logistics industry development.

In the past several decades, interest in industrial ecology development has significantly grown. Hannan and Freeman published papers on “organization population ecology” in 1977 [5], and Robert Frosch and Nicholas Gallopoulos introduced industrial ecology through an article called “Strategies for Manufacturing” (1989), which appeared in a special issue of Scientific American devoted to managing planet earth [6]. Since then, the system and the scientific exposition on ecological application in social and economic organizations began to elicit attention. The concepts of closing production loops [7], networks and food-webs [8], and bio-mimicry inform the design of eco-industrial parks [9] and, more generally, the design for environment procedures for use in industrial settings.

However, scant attention has been paid to theory-based research in the development of regional logistics industry ecology. The existing literature focuses on logistics industrial cluster symbiosis [10], carbon emission calculation of transportation and logistics [11], green supply chains [12], reverse logistics [13], and governance patterns and policies in the logistics industry. The complete health evaluation of the regional logistics industry that considers environmental impacts lacks research. Thus, the main purpose of this paper is to develop a comprehensive methodology for the health evaluation of a regional logistics industrial ecosystem.

To accomplish these tasks, this paper is divided into five sections. The remainder of this paper is organized as follows: Section 2 describes the forming mechanism and influencing factors of a regional logistics industrial ecosystem. Section 3 describes the fuzzy matter–element model based on the entropy weight method to evaluate the health of the similarity measurement of a regional logistics industrial ecosystem. Section 4 presents a case study to evaluate the performance of the model, including the results, analysis, and discussion. Section 5 gives the conclusion.

2 Analysis of a regional logistics industrial ecosystem

2.1 Forming mechanism of a regional logistics industrial ecosystem

Active interactions occur among regional logistics, regional environment, and natural resources. Regional logistics is the integrated management of all the activities required to move products through the supply chain. For a typical product, this supply chain extends from a raw material source through the production and distribution system to the point of consumption and associated reverse logistics. The regional logistics activities comprise freight transport, storage, inventory management, materials handling, and all related information processing, which all significantly affect the environment[14].

In the interaction process between regional logistics agents and the environment, the relationship among regional logistics agents is not only competition but also communication, collaboration, and coordination to optimize transportation routes and loads, reduce waste, reduce fossil fuel consumption, and increase energy efficiency and revenue [15].

Moreover, these agents work together to evolve into a complex logistics industry symbiosis system, namely, a regional logistics industrial ecosystem. A regional logistics industrial ecosystem is not only important in environmental protection and socio-economic sustainable development but also brings huge economic benefits for its internal subsystems and organizations. A high-performing regional logistics industrial ecosystem results in increased revenue, reduced costs and waste, improved asset utilization, and enhanced customer service.

2.2 Influencing factors of the health of a regional logistics industrial ecosystem

A regional logistics industrial ecosystem comprises logistics enterprise communities and ecology environment. Therefore, its health status is affected by internal and external influencing factors. Internal influencing factors consist of the complex competition and cooperation among logistics enterprise communities, which comprise communities of transportation enterprises, warehousing enterprises, loading and unloading enterprises, packaging, enterprise distribution processing enterprises, dispatching enterprises, logistics information management enterprises, and so on. The external influencing factors mainly include factors of political ecology, economic ecology, social ecology, science and technology ecology, cultural ecology, and natural resource ecology[16].

3 Health evaluation model of a regional logistics industrial ecosystem based on fuzzy matter-element analysis

3.1 Definition of a fuzzy matter-element

Matter–element analysis is a new theory used to solve the problem of incompatible problems. Such an analysis is suitable for a multi-index evaluation, and its main idea is to describe objects with three element factors, namely, unit, characteristic, and quantity value [17]. The ordered three-tuple that includes these element factors is regarded as the basic element of things, which is referred to as a matter–element. If the quantity value is with the characteristic of ambiguity, the matter–element is called a fuzzy matter–element.

With R is defined as a fuzzy matter–element, M is defined as an evaluation unit, V is defined as the characteristic of unit M, and the fuzzy quantity value that corresponds to the characteristics of unit M is expressed by μ (x) and is referred to as the membership degree of the quantity value x characteristic index C corresponding to object M. The fuzzy matter–element is illustrated in Equation (1). $R = | \begin{matrix} M \\ C & μ (x) \end{matrix} |$ (1)

3.2 Definition of a fuzzy composite matter–element

In this study, “evaluation unit, evaluation index, and measured index value of regional logistics industrial ecosystem” are treated as the basic elements for the health assessment of a regional logistics industrial ecosystem. If m regional logistics industrial ecosystem evaluation units are present, their common n evaluation indexes include C₁, C₂, …, C_n, and the fuzzy values that correspond to these evaluation indexes are μ₁ (x_1i) , μ₂ (x_2i) , …, μ_m (x_mi) (i = 1, 2, ⋯ , n), and R_mn is regarded as an n-dimensional fuzzy composite matter-element for m evaluation units, which is denoted by Equation (2). $R_{mn} = | \begin{matrix} M_{1} & M_{2} & M_{m} \\ C_{1} & μ_{1} (x_{11}) & μ_{2} (x_{21}) & μ_{m} (x_{m 1}) \\ C_{2} & μ_{1} (x_{12}) & μ_{2} (x_{22}) & μ_{m} (x_{m 2}) \\ C_{n} & μ_{1} (x_{1 n}) & μ_{2} (x_{2 n}) & μ_{m} (x_{mn}) \end{matrix} |$ (2)

In the above formula, M_j (j = 1, 2, ⋯ , m) denotes evaluation unit J, X_ji denotes the characteristic value of index I for evaluation unit j, and μ_j (x_ji) represents the membership degree of X_ji.

3.3 Determining the preferential membership degree

The membership degree of fuzzy values of various evaluation indexes, which are subordinate to the fuzzy values of the corresponding evaluation index that corresponds to the standard scheme, is called the preferential membership degree. Given that the maximum membership degree is generally positive, the following calculation formula of indicators can be used.

For positive index, the preferential membership degree is determined by Equation (3). $μ_{j} (x_{ji}) = \frac{x_{ji}}{max x_{ji}}$ (3)

For negative index, the preferential membership degree is determined by Equation (4). $μ_{j} (x_{ji}) = \frac{min x_{ji}}{x_{ji}}$ (4)

In Equations (3 and 4), X_ji denotes the characteristic value of index i for evaluation unit j, max x_ji is the maximum value for all characteristic values, and min x_ji is the minimum value for all characteristic values.

3.4 Constructing a standard fuzzy matter–element and a difference square compound fuzzy matter–element

Based on the preferential membership degree principle, the optimized evaluation index membership degrees of all evaluation indexes in R_mn are chosen to construct n-dimensional standard fuzzy composite matter-element R_0n for the standard scheme denoted by Equation (5). $R_{0 n} = | \begin{matrix} M_{0} \\ C_{1} & μ_{0} (X_{01}) \\ C_{2} & μ_{0} (X_{02}) \\ C_{n} & μ_{0} (X_{0 n}) \end{matrix} |$ (5)

If Δ_ji (j = 1, 2, ⋯ , m ; i = 1, 2, ⋯ , n) denotes the difference square between the optimal value of a standard fuzzy composite matter–element R_0n and the corresponding measured value of fuzzy complex matter-element R_mn, a difference square composite fuzzy matter-element exists, namely R_Δ, which is expressed by Equation (6). $R_{Δ} = | \begin{matrix} M_{1} & M_{2} & M_{m} \\ C_{1} & Δ_{11} & Δ_{21} & Δ_{m 1} \\ C_{2} & Δ_{12} & Δ_{22} & Δ_{m 2} \\ C_{n} & Δ_{1 n} & Δ_{2 n} & Δ_{mn} \end{matrix} |$ (6) where Δ_ji = (u_oi - u_ji) ².

3.5 Determining the weight based on the entropy decision method

In the process of comprehensive evaluation, the contributions of different evaluation indexes are different for the evaluation unit, and their weights must be determined on the basis of their contributions to the evaluation unit. When determining the weight of the evaluation index, subjective determination methods such as Delphi and analytic hierarchy process are commonly used in the actual evaluation process. These methods can be easily influenced by the preference of experts and then lead to the objectivity and scientific problems of the evaluation. The entropy decision weight method is based on the entropy weight of the decision-making power, and it can effectively avoid the subjective judgment error and eliminate human disturbance [18]. The calculation process of the entropy decision weight method is as follows:

(1) Build the judgment matrix of n evaluation indexes for m evaluation units, which are expressed by A_ji = (x_ji) _mn (j = 1, 2, ⋯ , m ; i = 1, 2, ⋯ , n).

(2) After the judgment matrix A_ji is normalized, the normalized judgment matrix B_ji is obtained.

For the positive index, the normalized process is illustrated in Equation (7). $b_{ji} = \frac{x_{ji} - min x_{ji}}{max x_{ji} - min x_{ji}}$ (7)

For the negative index, the normalized process is illustrated in Equation (8). $b_{ji} = \frac{max x_{ji} - x_{ji}}{max x_{ji} - min x_{ji}}$ (8)

In the above equations, max x_ji and min x_ji respectively denote the most satisfactory and the most unsatisfactory of the same evaluation for different evaluation units.

(3) The definition of entropy indicates that the entropy value of evaluation index i can be expressed as H_i, which is calculated by Equations (9 and 10). $H_{i} = - (\sum_{j = 1}^{m} f_{ji} ln f_{ji}) / - ln m$ (9) $f_{ji} = \frac{1 + b_{ji}}{\sum_{j = 1}^{m} (1 + b_{ji})}$ (10) where H_i denotes the entropy value of index i, m denotes the quantity of evaluation units, and n denotes the quantity of evaluation index number.

(4) To make ln f_ji meaningful and consistent with the meaning of entropy, the entropy weight of evaluation index i is defined as w_i, which is calculated by Equation (11). $ω_{i} = \frac{1 - H_{i}}{n - \sum_{i = 1}^{n} H_{i}}$ (11) where 0 ≤ ω_i ≤ 1, $\sum_{i = 1}^{n} ω_{i} = 1$ . After calculating Formula (12), the index of weight vector W_i = (ω_i) _1×n is obtained.

3.6 Calculating the closeness degree and making a comprehensive evaluation

The Euclidean closeness degree is the closeness degree between the evaluation units and the standard solution (optimal scheme). All evaluation units can be classified and sorted on the basis of the size of this closeness degree. A greater value results in the two becoming closer; a smaller value corresponds to the two being more distant. Given that the health status evaluation of a regional logistics industrial ecosystem is a comprehensive evaluation, the Euclidean closeness degree can be calculated by Equation (12). $ρ H_{j} = 1 - \sqrt{\sum_{i = 1}^{n} ω_{i} • Δ_{ji}}$ (12) where ρH_j is the mutual closeness degree between evaluation unit j and the standard composite fuzzy element; this Euclidean closeness degree can evaluate the health status of regional logistics industrial ecosystem units. R_ρH denotes the compound fuzzy matter–element of the Euclidean closeness degree, which is expressed by Equation (13). $R_{ρ H} = [\begin{matrix} M_{1} & M_{2} & M_{m} \\ ρ H_{j} & ρ H_{1} & ρ H_{2} & ρ H_{m} \end{matrix}]$ (13)

4 Case study

4.1 Selecting the evaluation indexes

China is a developing country, and it faces the dual tasks of developing the economy and protecting the environment. Consequently, creating a regional logistics industrial ecosystem suitable to China’s economy is essential. Its economy is characterized by a huge market, mass production, and high levels of consumption. According to the actual situations of the study areas and other relevant study information [19, 20], the principles of the evaluation index selection of a regional logistics industrial ecosystem in China are determined.

These principles are as follows [21, 22]:

Selecting the factors of variation in the evaluation units;

Focusing on the stability factors but also considering the factors that significantly affect regional logistics ecological instability;

Selecting only one of them if a high correlation between factors exists;

Selecting the measurable factors as much as possible to realize the quantitative evaluation.

Based on the above principles, the evaluation indexes include total value of social logistics goods (C₁), added value of the logistics industry (C₂), gross regional product (C₃), investment in fixed assets of the related logistics industry (C₄), social logistics total cost (C₅), energy consumption of the related logistics industry (C₆), CO₂ emissions of the related logistics industry (C₇), and volume of freight (C₈).

These values of evaluation indexes are taken from the raw data of eight provinces in the China Economic Statistics Yearbook and China Energy Statistical Yearbook in 2013. These values are illustrated in Table 1.

4.2 Building the evaluation model based on fuzzy matter-element analysis method

Based on the principle of the fuzzy matter–element method, the process of building a health evaluation model of a regional logistics industrial ecosystem is as follows:

(1) The compound fuzzy matter–element that corresponds to eight units and seven indexes is constructed.

(2) Based on the principle of the preferential membership degree and the natures of the index, the compound fuzzy matter–element is constructed as follows: $R_{mn} = [\begin{matrix} M_{1} & M_{2} & M_{3} & M_{4} & M_{5} & M_{6} & M_{7} & M_{8} \\ C_{1} & 1.0000 & 0.2468 & 0.2111 & 0.8981 & 0.4202 & 0.1717 & 0.1155 & 0.2694 \\ C_{2} & 0.9853 & 0.3674 & 0.2519 & 1.0000 & 0.5963 & 0.2901 & 0.2348 & 0.3656 \\ C_{3} & 1.0000 & 0.3703 & 0.2424 & 0.9243 & 0.4784 & 0.2430 & 0.1353 & 0.4439 \\ C_{4} & 0.7909 & 0.7377 & 0.2293 & 0.9644 & 0.9962 & 0.5260 & 0.4785 & 1.0000 \\ C_{5} & 0.1952 & 0.4666 & 0.6425 & 0.1830 & 0.3162 & 0.7052 & 1.0000 & 0.3543 \\ C_{6} & 0.3246 & 0.6375 & 1.0000 & 0.1859 & 0.5117 & 0.5602 & 0.7615 & 0.4169 \\ C_{7} & 0.3128 & 0.6107 & 1.0000 & 0.1735 & 0.5130 & 0.6091 & 0.7473 & 0.4064 \\ C_{8} & 0.7317 & 0.2774 & 0.4081 & 1.0000 & 0.7297 & 0.0852 & 0.1733 & 0.5512 \end{matrix}]$

(3) Using Equation (6), the differential fuzzy complex element is constructed as follows: $R_{Δ} = [\begin{matrix} M_{1} & M_{2} & M_{3} & M_{4} & M_{5} & M_{6} & M_{7} & M_{8} \\ C_{1} & 0.0000 & 0.5672 & 0.6223 & 0.0104 & 0.3362 & 0.6860 & 0.7823 & 0.5337 \\ C_{2} & 0.0002 & 0.4001 & 0.5596 & 0.0000 & 0.1630 & 0.5039 & 0.5855 & 0.4025 \\ C_{3} & 0.0000 & 0.3965 & 0.5740 & 0.0057 & 0.2721 & 0.5730 & 0.7476 & 0.3093 \\ C_{4} & 0.0437 & 0.0688 & 0.5939 & 0.0013 & 0.0000 & 0.2247 & 0.2720 & 0.0000 \\ C_{5} & 0.6477 & 0.2845 & 0.1278 & 0.6675 & 0.4676 & 0.0869 & 0.0000 & 0.4169 \\ C_{6} & 0.4561 & 0.1314 & 0.0000 & 0.6628 & 0.2385 & 0.1934 & 0.0569 & 0.3400 \\ C_{7} & 0.4722 & 0.1516 & 0.0000 & 0.6831 & 0.2372 & 0.1528 & 0.0638 & 0.3523 \\ C_{8} & 0.0720 & 0.5222 & 0.3503 & 0.0000 & 0.0731 & 0.8369 & 0.6834 & 0.2015 \end{matrix}]$

(4) The entropy decision method is used to calculate the weight of each index. The judgment matrix is obtained after the actual values of all indexes are processed with the normalization method by Equation (7) and Equation (8). The entropy of each index is calculated by Equation (9). $H_{i} = (0.9837, 0.9815, 0.9860, 0.9889, 0.9900, 0.9911, 0.9911, 0.9881)$

The weight of each index is obtained by Equation (11). $w_{i} = (0.1594, 0.1806, 0.1370, 0.1090, 0.0981, 0.00874, 0.0870, 0.1165)$

(5) The closeness degree of each sample can be obtained by Equation (13). $R_{ρ H} = [\begin{matrix} M_{1} & M_{2} & M_{3} & M_{4} & M_{5} & M_{6} & M_{7} & M_{8} \\ ρ H_{i} & 0.6029 & 0.4187 & 0.3699 & 0.5694 & 0.5350 & 0.3370 & 0.3272 & 0.4300 \end{matrix}]$

4.3 Evaluation results

The evaluation result of M₁ is the optimized value in all evaluation units, and it denotes that the health of the logistics ecosystem in Jiangsu Province is better than those in the other units in 2013. The evaluation result of M₇ is the lowest, and this rank indicates that the ecological health of the logistics ecosystem in Guizhou Province is worse than those of other units in 2013. The result shows that the logistics ecological health statuses of M₂, M₄, M₅, and M₈ are in the range of between good and bad in 2013.

The total results indicate that the grade values of most provinces are in the sub-health status, which comprehensively reflects the regional logistics development of China and is in agreement with the actual monitoring data. At present, China is in the process of promoting its overall modernization program, has made environmental protection one of its basic national policies, has regarded the realization of sustainable development as an important strategy, and has conducted country-wide large-scale measures for pollution prevention and control as well as ecological environment protection. To achieve carbon emission reduction targets, regional logistics in all provinces of China must significantly contribute toward achieving the environmental protection and climate change objectives. To this end, CO₂ emissions from the logistics industry and the reliance on fossil fuels must be reduced in China. In this context, the railways and inland waterways have a major role to play in the future regional logistics industrial ecosystem in China.

5 Conclusion

Applying fuzzy mathematics and matter-element analysis theory, this paper proposed a comprehensive methodology to accurately evaluate the health status of regional logistics industrial ecosystems. The main conclusions of this paper are as follows:

According to the structure and compositions of a regional logistics industrial ecosystem, the factors that influence regional logistics industrial ecosystem health are classified into internal influence factors and external environment influence factors, and its health evaluation indexes considering environmental effects include energy consumption, logistics cost, and CO₂ emissions.

Based on fusion of multi-indexes, a fuzzy matter-element model is constructed to evaluate the health of a regional logistics industrial ecosystem by matter-element transform. This model, which is based on the entropy weight method, can comprehensively process industrial statistics data, results of numerical analysis, measurement data, experiential knowledge, and so on.

The case study shows that the model can accurately assess and project the health status of a logistics ecological system with higher precision. The total results reflect that the grade values of many provinces in China are in a sub-health status. Thereby, different measures should be encouraged for implementation to improve the environmental performance of the regional logistics industry. These measures include the provision of financial assistance for cleaner heavy goods vehicles and cleaner engines in inland waterway transports as well as the better organization of distribution operations in cities and conurbations such as through freight villages.

The measurement results have significant potential to support the research on environmental protection and the sustainable development of a regional logistics industrial ecosystem. Further research on the sustainable development of a regional logistics industrial ecosystem is necessary, especially for measuring procedures, evaluation methods, and policy making.

Footnotes

Acknowledgments

This work was financially supported by the National Natural Science Foundation of China (Grant No. 71403096), the Science Foundation of the Chinese Education Commission (Grant No. 15YJC630185), and the Research Project of Ministry of Housing and Urban–Rural Development of China (Grant No. 2015-K1-033).

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