[Retracted] Measurement of Coordinated and Coupled Development and Evaluation of Sustainable Development for Marine Economic-Ecological Complex System

Wen, Mingming; Liu, Chang; Mamonov, K. A.; Du, Jun

doi:https://doi.org/10.1155/2021/2043635

Discrete Dynamics in Nature and Society

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Volume 2021 | Article ID 2043635 | https://doi.org/10.1155/2021/2043635

[Retracted] Measurement of Coordinated and Coupled Development and Evaluation of Sustainable Development for Marine Economic-Ecological Complex System

Mingming Wen,^1,2Chang Liu,³K. A. Mamonov,⁴and Jun Du^1,2

Academic Editor: Ahmed Farouk

Received15 Jul 2021

Revised16 Aug 2021

Accepted24 Aug 2021

Published07 Sept 2021

Abstract

The development of marine economy has stimulated the need for marine natural products and services, as well as the demand and utilization intensity of marine resources. As a result, the coastal regions start to discharge more and more pollutants, posing a serious threat to marine ecoenvironment. Because of the interactions between marine economy and marine ecology, it is of practical significance to study the marine economic-ecological complex system (MEECS). However, the relevant studies have several common defects: the incomplete data statistics system, the limited attention on development law or action mechanism, and the lack of long-term quantitative and empirical analysis. To overcome these defects, this paper tries to measure the coordinated and coupled development (CCD) and evaluate the sustainable development of MEECS. Specifically, an ecology-dominated coordinated development model was proposed for the complex system, and a differential equation model was established to describe the dynamic coordinated development of the complex system. Through data envelopment analysis (DEA), the sustainable development of the complex system was evaluated based on its input-output efficiency. Through experiments, the outputs of decision-making units (DMUs) of our model were obtained, and the development of the subsystems in MEECS was evaluated comprehensively to verify the effectiveness of our algorithm.

1. Introduction

The ocean provides humans with an abundance of food and rich chemical, power, and mineral resources. It is an important link in global ecological balance [1–4]. In recent years, there is a growing demand of natural products/services and resources and a rise in the utilization intensify of natural resources. As a result, the coastal regions in China discharge more and more pollutants, which seriously threat the marine ecoenvironment. Therefore, it is of practical significance to measure the coordinated and coupled development (CCD) and evaluate the sustainable development of the marine economic-ecological complex system (MEECS) [5–10].

The coordinated development of marine economy and ecoenvironment is critical to the national strategies for ocean and regional coordinated development [11–14]. Broszeit et al. [15] empirically analyzed the coordinated development level of the regional MEECS, constructed a coordination measurement model for the complex system based on core influencing factors, and carried out principal component analysis (PCA) and regression fitting, producing the trend map for the coordination level of the MEECS in 2015–2020. Paoli et al. [16] probed deep into the relationship between the ecoenvironment and the three industries of regional marine economy, created an entropy-based coordination measurement model that combines the merits of various econometric models, and performed feature analysis and simulation of different stages of development, namely, uncoordinated recession, coordinated development, and transition. Beaumont et al. [17] examined the problems in the industrial structure of marine economy and the surrounding environment, analyzed the environmental evolution and the coordination of the MEECS, using artificial neural network (ANN) and variable fuzzy evaluation, and plotted the ecological and economic outcomes of the complex system.

The destruction of the marine ecoenvironment will seriously restrict the development of major marine economic industries, such as marine fisheries, marine tourism, and marine biomedical industry [18–20]. Woods et al. [21] treated marine economy, society, and ecoenvironment as three dimensions of the same complex system, analyzed the system features such as action, penetration, and dependence, and constructed a nonradial, nonangular slack-based measure (SBM) model for the complex system with a ring structure. Jacob and Rao [22] set up a marine ecological service database covering coastal city development index, marine ecology-economy investment, and labor input of marine environmental protection, carried out phased analysis on the development efficiency of the MEECS, and measured the development level and overall efficiency of the complex system in each stage, with the aid of data envelopment analysis (DEA). Bicho and Haws [23] combined the improved entropy method with DEA into a second-order relative evaluation model for the subject effort of coastal provinces in China for high-quality economic development and compared the measured SBM superefficiencies of eight coastal cities.

So far, researchers engaging in marine development have analyzed several problems in marine ecology, resources, and economy from the perspectives of different disciplines and industries. However, the relevant studies have several common defects: the incomplete data statistics system, the limited attention on development law or action mechanism, and the lack of long-term quantitative and empirical analysis. To overcome these defects, this paper tries to measure the CCD and evaluate the sustainable development of MEECS. Section 2 proposes an ecology-dominated coordinated development model for the MEECS and establishes a differential equation model that describes the dynamic coordinated development of the complex system. Section 3 carries out DEA to evaluate the sustainable development of the complex system based on its input-output efficiency. Section 4 conducts several experiments to determine the output of the decision-making units (DUs) in our model and the composite development level of each subsystem in the complex system.

This paper attaches equal importance to marine ecological development and marine economic growth and treats the MEECS as an organic whole. The coordinated development of the complex system was discussed in detail from the perspective of each subsystem. In addition, DEA was introduced to enhance the pertinence and timeliness of the research.

2. Complex System CCD Analysis

Figure 1 illustrates the ecology-dominated coordinated development model for the complex system. As shown in Figure 1, marine economy cannot grow properly and the ocean cannot realize sustainable development, which features resource conservation, balance, orderliness, and coexistence, unless the resources are developed properly and the environmental policies are implemented reasonably. To adopt the model, the coastal provinces must first clarify the objectives of the MEECS, stop putting consumerism above anything else, and seek regulation and institutional guarantee that conform to the law of sustainable development.

This paper constructs a differential equation model to describe the dynamic coordinated development of the MEECS. Let EC₁ and EC₂ be the development state of marine economy and the damage state of marine ecoenvironment, respectively, β₁ and β₂ be the growth rates of marine economy and ecoenvironmental damage, respectively, and α₁₂ and α₂₁ be the degree of impact from marine ecoenvironment on marine economy and that from marine economy on marine ecoenvironment, respectively. Then, the coordination σ between the development of the two subsystems of the MEECS and the coordination of subsystem development can be established based on the above parameters:

Figure 2 provides the coordination indices of subsystem development in the complex system. Let and be the growth rate and inhibition effect of the i-th factor affecting system development, respectively, be the competitive rate reflecting the proportion of the i-th factor affected by the j-th factor, and i = 1 and j = 2 are the development levels of marine economy subsystem and marine ecology subsystem, respectively. Then, model (1) can be written in the differential form:

Formula 2 can be further simplified aswhere whether and are greater than zero determines the type of competition between subsystems. (1) If both and are greater than zero, then the i-th and j-th factors are pure competitors. The development of one factor is constrained by the development of the other. (2) If both and are smaller than zero, then the two factors promote each other. (3) If both and are zero, the two factors do not affect each other. (4) If > 0 and < 0, the i-th factor suppresses the development of the j-th factor, while the j-th factor hinders the development of the i-th factor, creating a supplier-receiver relationship. (5) If > 0 and = 0, there is an amensalism between the two factors: the former suppresses the development of the latter, but the latter does not affect the development of the former. (6) If < 0 and = 0, there is a symbiosis between the two factors: the latter promotes the development of the former, but the former does not affect the development of the latter.

To facilitate the calculation of parameters for subsequent simulation, the simplified model (3) can be written as a discrete model with piecewise continuous derivative solutions:

According to the properties of the solutions to the discrete model, the solutions of formula 4 are not absolutely differentiable when and u change continuously. If i = 1 or 2, the parameters can be rounded:where m is a positive integer. Transposing the terms, we obtain

Multiplying both sides with 1/a², we obtain

Multiplying both sides with , we obtain

Formula 8 is equivalent to

Integrating with respect to formula 9, we obtain

Transposing the terms, we obtain

By arranging formula 11, we obtain

Taking the reciprocal of formula 12, we obtain

Multiplying both sides with , we obtain

If τ approximates m + 1, formula 14 becomes

Making e^βi = β_i and ((e^βi−1)/β_i) u_ij = α_ij, the continuous-time model that characterizes the development and changes of the MEECS subsystems can be converted into the following discrete-time model:

MEECS services are mainly produced by marine resource components, nonresource components, and system functions. With society and people as the main service objects, the relevant services are mostly realized through the two subsystems: marine economy and ecoenvironment. The connotations of the MEECS in Figure 3 reflect the potential services, produced services, and realized services of each subsystem. It can be inferred that the marine economy subsystem EC₁ consists of three parts: the total output EC₁₁ of the primary industry, the total output EC₁₂ of the secondary industry, and the total output EC₁₃ of the tertiary industry; the marine ecoenvironment subsystem EC₂ also consists of three parts: degree of environmental pollution EC₂₁, resource reserves EC₂₂, and degree of environmental protection EC₂₃.

3. Sustainability Development Evaluation

Under the effects of the development indices of economic and ecoenvironment subsystems and external development indices, the MEECS converts various resources and nonresources into economic outputs and delivers the outputs to the marine and costal land ecosystems. Figure 4 presents the input-output energy flows of the complex system. It can be seen that based on the basic economic inputs, the economic and ecoenvironment subsystems provide feedback to the complex system through their respective economic activities, such as production, operation, and consumption. The waste generated is discharged back to the marine and costal land ecosystems.

Suppose the MEECS is composed of parts. Then, there are comparable DUs, each of which has q inputs and t outputs. This paper treats the ocean as a complex system that receives marine ecoenvironment development indices and outputs marine economic development indices. The sustainable development of the complex system was evaluated by analyzing its input-output efficiency through DEA. Figure 5 describes the input-output of DEA.

Let a_ij be the economic input of the j-th DU, DU_j, on the i-th input; b_lj be the economic output of DU_j on the l-th output; o_i be the weight of the i-th input; and p_l be the weight of the l-th output. Then, the corresponding sets can be denoted as follows: A_j = (a_1j, a_2j, …, a_qj)^T, B_j = (b_1j, b_2j, …, b_tj)^T, o = (o₁, o₂, …, o_q)^T, and p = (p₁, p₂, …, p_t)^T. The data of A_j and B_j can be obtained from historical statistics.

For weight vectors o = (o₁, o₂, …, o_q)^T and p = (p₁, p₂, …, p_t)^T, the efficiency index ξ_j of DU_j can be calculated by

Formula 17 shows that ξ_j is the ratio of input o^TA_j to output p^TB_j. The weights o and p can be adjusted to make ξ_j ≤ 1. For DEA, the C²R model has a clear modeling path, a simple form, and a solid theoretical basis. Suppose is the efficiency index of the DU to be evaluated, . Then, a fractional programming model C²R can be established with as the objective and the efficiency indices of all DUs as the constraints

Let h = > 0, θ = ho, and λ = hp, and formula 18 can be converted into a linear programming model:

The dual programming model of formula 19 can be described as

The linear programming model (19) is DEA effective, provided that both parameters and are greater than zero and the optimal objective value O_C2R, i.e., , equals 1. Hence, it is difficult to judge if the model is DEA effective.

The dual programming model can be rewritten as

The dual programming model is DEA effective, provided that all the optimal solutions , , , and are known, and are both zero, and equals 1. It is also rather difficult to directly judge if the model is DEA effective.

To overcome the above difficulties, this paper proposes a C²R model with non-Archimedean infinitesimals to determine whether the DEA of the target DU is effective:

The dual programming model of NAI can be expressed as

Let δ = be the efficiency index of the target DU and γ = be the sum of redundant economic input and insufficient economic input . Then, the weak effectiveness and effectiveness of the DEA on can be judged by the following criteria:(1)If < 1, then the DEA on is not weakly effective(2)If = 1 and γ > 0, i.e., at least one of and is positive, then the DEA on is weakly effective(3)If = 1 and γ = 0, i.e., the sum of and is zero, then the DEA on is effective

Suppose and belong to the possible production set (PPS) of all probable marine production activities. Then, the input-oriented C²R dual programming model can be established as

The PPS can be established as

In the PPS, the input should be minimized without changing the output . If A₀ is minimized, then the optimal value of the dual programming equals 1. In this case, the DEA on is effective.

4. Analysis of Experiments and Results

This paper sets up a database of Chinese coastal regions through a survey on marine economy and marine ecology in the research period and uses the database for analysis of experiments and results. Table 1 shows the outputs of the DUs in our MEECS. The radial and relaxation motions refer to the gap from the DEA efficient frontier and the correction needed to reach the frontier. Estimated value stands for the corrected value, i.e., the sum of original value, radial motion, and relaxation motion.

Calculations show that nitrate content and ammonia content had the largest relaxation motions in the sample period, suggesting that marine ecological and economic development is mainly constrained by the excessive discharge of nitrogenous pollutants. Among the outputs, coastal tourism output, marine transport output, and offshore oil and gas output were far from the zero value. Hence, the three industries have a good development potential and should receive more economic inputs.

Figure 6 presents the double exponential curves of the interaction between the subsystems in the research period, which reflect the evolution of suppression/inhibition effects between the subsystems. It can be learned that the marine ecoenvironment exerted a high stress on marine economy; in return, marine economy acted as a strong constraint on marine ecoenvironment.

The proposed model was applied to measure the coordination between MEECS subsystems of China’s coastal provinces in the sample period. On this basis, the phase and type of the coordinated development between the subsystems were derived for each province in the period. As shown in Table 2, the synchronous development between marine economy and ecoenvironment proceeded under clear mutual constraints. Before 2018, the coordination between subsystems remained in the antagonistic state. To this day, the coordination between subsystems is still ecologically vulnerable. In recent years, with the advancement of marine science and technology, the adjustment of the marine industry structure, the rising quality of residents in coastal cities, and the effective implementation of marine environmental protection policies, the coordination of the complex system has been improved to a certain extent, and the coordination has gradually shifted from synchronized development to comprehensive and coordinated development.

Figure 7 presents the composite score of subsystem development in the MEECS. From 2010 to 2020, the score of the MEECS in China increased from 0.353 to 0.421. The system coordination quickly increased from the initial antagonism to synchronous coordination. Meanwhile, the social feedback system score rose from 0.211 to 0.635, and the system level improved from the initial microfeedback to early feedback, a sign of good development state.

Figures 8 and 9, respectively, show the score of each state layer in the ecoenvironment system and the economy system. It can be seen that the scores of marine ecoenvironment condition and marine ecoenvironment pressure in China decreased year by year, indicating that the human and natural factors faced by the ecoenvironment system are increasing step by step. The scores of marine economy scale and marine economy vitality continued to increase, and the marine economy developed with vigor and vitality. However, the marine economy structure score fluctuated greatly and dropped despite the growing proportion of the tertiary industry. Therefore, the marine economy is heavily reliant on marine resources, although the marine industries are increasingly diverse.

The coastal provinces might differ significantly in MEECS development state, owing to factors such as ecoenvironment conditions, resource reserves, and economic foundation. Figures 10 and 11 display the composite scores of the complex system and the economy subsystem of coastal provinces in 2010–2020, respectively. It can be seen that the composite scores of the complex system in all coastal provinces increased to different degrees, exhibiting an overall upward trend. Shanghai, Zhejiang, and Jiangsu have gradually evolved into the core growth provinces of MEECS in China. The evolution trend of other provinces was similar. As for the development trend of the ecoenvironment subsystem, the ecoenvironment deteriorated in all coastal provinces by varied degrees. However, some provinces, namely, Shanghai, made an increasingly strong marine ecological response, stepped up their efforts in environmental protection, and achieved positive results on marine ecological protection and restoration. On the contrary, some provinces failed to make up for the ecological losses with ecological responses. Hence, the ecoenvironment subsystem developed poorly in these provinces, causing a continued growth in ecological stress. Furthermore, although the ecoenvironment subsystem in most provinces belonged to the level of synchronized coordination, their scores were poised to decrease, an evidence of weak sustainable development ability.

5. Conclusions

This paper mainly discusses the CCD measurement of the MEECS and the sustainable development evaluation of the complex system. First, the authors constructed an ecology-dominated coordinated development model for the MEECS and derived a differential equation model to describe the dynamic coordinated development of the complex system. Next, DEA was implemented to analyze the input-output efficiency of the MEECS and evaluate the sustainable development of the complex system. Through experiments, the outputs of each DU in our model were obtained and the development of each subsystem in the MEECS was evaluated comprehensively. The results show a certain development potential in coastal tourism output, marine transport output, and offshore oil and gas output. In addition, the double exponential curves of the interaction between the subsystems in the sample period were presented, which reflect the basic evolution of the promotion and inhibition between the subsystems. Finally, the composite scores of the MEECS, the subsystems, and the complex system of each coastal province were displayed and used to analyze the significant regional difference in the development state of the complex system.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest regarding the publication of this paper.

Acknowledgments

This article was supported by the Guangdong Philosophy and Social Sciences “13th Five Year Plan” 2020 Project (GD20XYJ10), the project of Enhancing School with Innovation of Guangdong Ocean University (230420023), the project for scientific research start-up funds of Guangdong Ocean University (R20067), and the project of Guangdong Provincial Science and Technology Department’s Subsidy for People’s Livelihood in 2020 and Other Institutional Development Expenditure Funds (overseas famous teachers) (2020A1414010281).

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Copyright © 2021 Mingming Wen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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