A novel cooperative game network DEA model for marine circular economy performance evaluation of China
Introduction
With the high-speed development of the marine economy in China, there are increasing environmental issues due to the overuse of marine resources and pollution from manufacturing production. According to the China Marine Ecological Quality Bulletin, large amounts of untreated wastewater have been discharged into the ocean, and total marine wastewater emissions reached 6.36 billion tons in 2017. The excessive consumption of limited marine resources leads to uncertainty about China’s marine economy (Qu et al., 2016). Exploring how to carry out sustainable development between the marine economy and the ecological environment has become a hot topic in academia and government sectors.
The development pattern of the circular economy (CE) has been considered an approach for more appropriate economic and environmental management. In the CE, the economy and the environment are not characterized by linear inter-linkages but by a circular relationship (Boulding, 2013). In 2002, the Chinese government formally proposed a CE strategy for sustainable development, and in 2012, China began to implement the development mode of the marine circular economy (MCE) in ocean areas. Following the circular economy framework, the MCE aims to balance sustainable economic benefits with long-term ocean health and to achieve a shift from cleaner, recycling-based industrial production to sustainable marine development and management (Keen et al., 2018). It is necessary to scientifically evaluate whether the MCE achieves desirable results in different ocean and coastal regions by providing a mode of sustainable growth.
There are research gaps in previous studies on the MCE. While current research has mainly focused on defining the MCE (Keen et al., 2018, Mulazzani and Malorgio, 2017), related challenges (Silver et al., 2015, Sarker et al., 2018) and implementation frameworks (Voyer et al., 2018), few studies have paid attention to evaluating the MCE. As an important part of and practice related to CE in coastal areas, to the best of our knowledge, there is no study that carries out a reasonable and effective assessment of MCE. The major challenge is how to evaluate the subsystems, i.e., marine production or the marine environment to promote MCE development. In addition, the regional differences in the marine economy, which are responsible for the heterogeneity of MCE performance, should not be neglected.
The aim of this study is to systematically assess MCE performance in China’s 11 coastal regions through quantitative analysis, and the contributions of this paper are presented as follows. While many studies have separately discussed the marine economy production process and the environmental treatment process, they have neglected the bidirectionality and links between them. In contrast to such studies, this paper first presents a comprehensive MCE system containing an economic production (EP) subsystem and an environmental treatment (ET) subsystem. Both subsystems are linked with a closed loop to illustrate resource recycling and pollution treatment in the MCE. Then, a novel two-stage cooperative game DEA is proposed for evaluating MCE performance. In contrast to the single-flow network structure, the proposed model not only considers a complex internal structure with a bidirectional closed loop flow but also introduces cooperative game theory to maximize the potential performance growth of both the EP and ET subsystems by following a centralized control philosophy from the decision makers in maritime sectors. In addition, the proposed model can decompose the subsystem efficiency into the aggregation of factor efficiencies, which will provide a guidance for the factor dominance of those subsystem efficiencies.
As another contribution, based on the evaluation results, this paper explores some interesting findings concerning MCE performance. First, the overall performance of China’s MCE is poor due to the worse efficiency score of the ET system. In addition, obvious regional disparities and stage characteristics exist in China’s 11 coastal regions. Both the EP and ET subsystems of coastal areas present a fluctuation stage in the middle of the sample period and a decline stage at the end of the sample period. Second, the convergence analysis shows that although the disparities in MCE performance across coastal regions will not shrink in the whole sample period, the catch-up effect still exists in different areas, and the MCE performance levels of different regions will also converge to their respective equilibrium states. Third, this paper finds that the worse performance of the ET subsystem is due to too many inefficient environmental treatment inputs, indicating a contradiction between the achievement of industrial pollution treatment in the coastal region and the huge resources and equipment required for marine environmental protection.
The rest of this paper is organized as follows. Three aspects of the literature are presented in the literature review in Section 2. Section 3 describes the methodology, indicators and data. Section 4 reports the empirical results of marine circular economy evaluation. In Section 5, the conclusions and policy implications are provided.
Section snippets
Circular economy and marine circular economy
Since the circular economy was introduced in the 1960s, researchers and practitioners have obtained many rich results. Definitions of the CE are provided from many perspectives. The CE is so popular that in the World Economic Forum (2014), the CE is a concept that emphasizes a restorative and regenerative approach to business process management (Gupta et al., 2019). At the macro level, the CE aims to achieve economic growth, resource utilization, pollution reduction and material recycling based
The structure of the MCE system
The material flows from marine resource utilization, production, and consumption to renewable resource utilization run through the whole MCE system. Since these four processes form a closed loop feedback cycle, the MCE system can be divided into an economic product subsystem and an environmental treatment subsystem. To better understand how these two subsystems work based on a closed cycle, we present some variables and symbols to demonstrate the flow of the inputs and outputs involved in the
Efficiency disparity and stage characteristics
The efficiencies of the MCE system and EP and ET subsystems of China’s coastal regions are obtained from model (3) and (5), (6), (7), as shown in Table A in the appendix. We further draw a bar chart of the average scores of EMCE, EEP and EET, as shown in Fig. 3.
The performance of China’s MCE is lower than desired. The average annual efficiency score of the MCE system in China is 0.784, indicating that there is still 21.6% room for improvement. Jointly comparing the efficiencies of the EP
Conclusions and policy implications
The authorities and policymakers of China’s maritime sector are committed to the circular development of the marine economy. They want to know the performance of the integrated marine circular economy to uncover problems related to resource utilization, material recycling and pollution treatment. Few studies have evaluated the performance of MCE within a closed flow network framework to provide more comprehensive results. Although the competitive game DEA model has been adapted for circular
Acknowledgments
The authors would like to thank the editor and the three anonymous reviewers for the constructive comments on improving an early version of this paper. This work is supported by The National Social Science Fund of China (19VHQ002).
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