Coordinated Development of Marine Economy and Ecological Environment in Coastal Areas of China: Development Level, Coupling Coordination Measurement, and Obstacle Analysis

Abstract

The ecological environment (EE) is an important factor affecting the sustainable development of the marine economy (ME): achieving coordinated development between the ME and EE remains a problem to be explored. From three perspectives—comprehensive development level, coupling coordination degree spatiotemporal analysis, and obstacle degree recognition—this paper explores the development level and spatiotemporal evolution characteristics of ME and EE coupling coordination in 11 coastal areas in China from 1999 to 2019. The results indicate the following: (1) During the research period, the comprehensive development levels of the ME and EE in China’s coastal areas continued to improve, with relatively high levels of economic structure subsystems and pressure subsystems. (2) There was a clear coupling relationship between the ME and EE, with a high degree of coupling, mainly consisting of high-quality coupling and good coupling. The increase in coupling coordination was significant, especially in Guangdong, Shandong, and Liaoning. (3) The analysis of the obstacle degree found that the crucial obstacle elements affecting the coordinated development of the ME and EE included the ME vitality subsystem and the EE response subsystem. Finally, policy suggestions are put forward, including promoting the development of an ecotype ME, promoting marine technology innovation, and adopting differentiated EE governance policies according to local conditions. This study helps evaluate the development capacity of the ME in China’s coastal areas and provides a basis for formulating targeted ME development strategies.

1. Introduction

The marine economy (ME) is a significant component of world economic development. According to the Organization for Economic Cooperation and Development (OECD), the total global output value of the ME will exceed $3 trillion by 2030 [1]. The development of the ME, however, has had a certain impact on the marine ecological environment (EE), which restricts the sustainable development of the ME. Therefore, achieving sustainable development of the ME and EE is a major challenge. In order to address the issues faced by the ME and EE, various countries have introduced corresponding ocean policies, such as the EU’s “Guidelines for the Marine Strategic Framework” (2008), the UK’s “Marine Protected Area Policy” (2013) [2], and Norway’s “Norwegian Ocean Strategy” (2017) [3]. These policy documents aim to accelerate the sustainable development of the ME and EE, protect and restore marine ecosystems, and promote marine science and technology research. Recently, the rapid development of ME has become an increasingly significant aspect of the national economy in China. In accordance with the 2020 China Marine Economic Statistics Bulletin, the total national marine production value in 2020 exceeded 8 trillion yuan, comprising 14.9% of the total coastal area production value [4]. However, with the rapid development of the ME, the marine EE has been damaged, and environmental issues such as increased wastewater and waste emissions have gradually become prominent, seriously hindering the continuous development of the ME [5]. The 20th National Congress of China emphasized that maritime power should be strengthened, the ME should be developed, and the EE of the ocean should be protected. Since then, the protection of both the ME and marine EE has been elevated to the height of national strategy [6]. To sustainably develop the ME, this paper conducted empirical research on 11 coastal regions in China from the perspective of coupling coordination. The paper further explores the current situation and problems of coordinated development between the ME and EE in China.

Scholars from various countries have focused on several aspects of research into the ME and EE, including the relationship between them. For instance, Kildow et al. compared the contributions of the EE from different countries to their ME, hoping to find the most accurate and effective research method [7]. Ehlers noted that the ME poses a threat to the EE, and sustainable ocean governance is necessary [8], and Martinez et al. studied the significance of EE security for social and economic development, suggesting strategies for conducting marine ecological and economic assessment work. Using the Kuznets curve econometric model [9], Qin et al. conducted empirical research into the existence of a Kuznets curve between China’s expanding ME and marine environmental pollution [10]. Wang et al. constructed a VAR model for ME development and marine environmental pollution and investigated the connection between the increasing size of the ME and various indicators of ocean environmental pollution in the Bohai Rim region of China [11]. Research into integrated management of the ME and EE, such as by Garmendia et al., concludes that integrated ocean management cannot rely solely on traditional methods: it also requires the introduction of new methods and technological means [12]. Du et al. used spatial econometric models to survey the influence of marine environmental policies and proposed that increasing the intensity of environmental policies has a promoting effect on the development of the ME [13]. Research into the relationship between ME benefits and EE performance, such as that undertaken by Hoagland et al., who analyzed the relationship between ME benefits and environmental costs in major sea areas around the world, provides referenced gist for the healthy and sustainable development of the ME [14]; and Zheng used the CCR model to calculate the economic and environmental efficiency of coastal areas, providing guidance for sustainable development of the ME [15].

In summary, research into the ME and EE mostly focuses on their relationship, management, and performance, with very little measurement and spatiotemporal analysis of the coupling coordination relationship between them. In view of this, this paper used panel data from China’s coastal areas (not including Macao, Hong Kong, or Taiwan) from 1999 to 2019. The comprehensive assessment model and coupling coordination degree (CCD) model were used to empirically test the comprehensive development level and CCD level of the ME and EE. The models were also used to further explore the obstacles to coupling and coordinated development, with a view to providing empirical support for the application and deepening of the policies of ME development pilots. The contribution of this paper is that, on the one hand, it helps evaluate the development capacity of the ME in China’s coastal areas and provides a better understanding of the health status of the marine EE. On the other hand, this study helps accelerate the coordinated development of both the ME and EE, providing a basis for formulating targeted ME development strategies, thereby contributing to the sustainable development of the ME.

2. Materials and Methods

2.1. Study Area

This paper takes the coastal areas of China (not including the Macao, Hong Kong, and Taiwan regions) as the research area. There are 11 coastal regions or cities, including Hebei, Liaoning, Tianjin, Shandong, Jiangsu, Zhejiang, Shanghai, Guangdong, Fujian, Guangxi, and Hainan, with a gross area of 1.2873 million square kilometers and a total coastline length of approximately 18,000 km [16], as shown in Figure 1.

2.2. Data Source and Indicator Selection

This study selected relevant data on the marine industry and ecological environment from 1999 to 2019 as the study subjects. The data come from the “China Ocean Statistical Yearbook”, “China Environmental Statistical Yearbook”, “China Energy Statistical Yearbook”, and “China Agricultural Statistical Data”, as well as from economic operation reports for the 11 regions. Most of the data were processed through the aforementioned channels, while a small amount of incomplete data were calculated.

A scientific and effective evaluation index system should accurately reflect ME and EE development and problems. Therefore, based on the existing research literature [17,18], this study determined the marine economy index system based on three aspects: economic scale, economic structure, and economic vitality. Growth on the economic scale indicates that an ME has a relatively fast development speed and makes a positive contribution to economic growth and employment creation. The indicators for ocean scale include the total value of ocean industry [19], the ratio of ocean production value to regional GDP, the ratio of the added value of ocean industry to GDP [20], ocean fishing yield, mariculture yield, and sea-salt yield [21]. Economic structure refers to the industrial structure and components of the ME, reflecting the status and contribution of different industries in the ME. Economic structure indicators include the ratio of output value for the marine primary industry, the ratio of output value for the marine secondary industry, and the ratio of output value for the marine tertiary industry [22]. Economic vitality refers to ME innovation ability, market competitiveness, and attractiveness, reflecting its development potential and sustainability. Economic vitality indicators include the number of marine scientific research institutions [23], the number of marine scientific research practitioners [24], the number of marine scientific research projects, the number of marine scientific research papers published [25], the number of marine scientific patent authorizations [26], the internal expenditure of marine scientific research funds, and the number of students majoring in marine science [27].

EE indicators include status, stress, and response. State indicators are key indicators for measuring the quality of the EE, reflecting its natural state. Status indicators include per capita sea area, total coastal wetland area, mariculture area [28], total salt field area, per capita water resources, number of marine coastal ecosystems, number of marine natural relics, and amount of marine biodiversity [29]. Stress indicators measure the degree to which an EE is affected by human activities. Common pressure indicators include industrial wastewater, exhaust gas, solid waste, etc. The pressure indicators in this study include the total discharge of industrial wastewater [30], the total industrial waste gas emission, and the output of industrial solid waste [31]. Response indicators are indicators that measure the government and social organizations’ abilities and actions to respond to EE issues, such as the treatment rate of industrial wastewater, reflecting the degree of attention and response-ability of the government and social organizations to EE issues. Response indicators include industrial wastewater discharge and treatment rates [32], industrial solid waste disposal rate [33], area of marine nature reserves, and the number of marine nature reserves [34]. This indicator can better reflect the dynamic trends of China’s ME and EE, evaluate the development status of China’s ME and EE, and provide data support for relevant departments to plan and deploy comprehensively. The indicators are shown in

Table 1. Evaluation index system of ME and EE development level.

Type	Level I Indicators	Secondary Indicators	Indicator Attribute	Weight
Marine economy	Economic scale	Total value of ocean industry (billion CNY) (X1)	+	0.0773
		The ratio of ocean production value to regional GDP (X2)	+	0.0382
		The ratio of added value of ocean industry to GDP (X3)	+	0.0383
		Ocean fishing yield (tons) (X4)	+	0.0705
		Mariculture yield (tons) (X5)	+	0.0753
		Sea-salt yield (10,000 tons) (X6)	+	0.1394
	Economic structure	The ratio of output value for the marine primary industry (%) (X7)	+	0.0547
		The ratio of output value for the marine secondary industry (%) (X8)	+	0.0152
		The ratio of output value for the marine tertiary industry (%) (X9)	+	0.0207
	Economic vitality	Number of marine scientific research institutions (X10)	+	0.0291
		Number of marine scientific research practitioners (X11)	+	0.0449
		Number of marine scientific research projects (X12)	+	0.0744
		Number of marine scientific research papers published (X13)	+	0.0630
		Number of marine scientific patent authorizations (X14)	+	0.1151
		Internal expenditure of marine scientific research funds (X15)	+	0.1038
		Number of students majoring in marine science (X16)	+	0.0401
Ecology environment	State	Per capita sea area (hectares) (Y1)	+	0.0751
		Total coastal wetland area (10,000 hectares) (Y2)	+	0.0360
		Mariculture area (hectares) (Y3)	+	0.0637
		Total salt field area (hectares) (Y4)	+	0.0766
		Per capita water resources (m3/person) (Y5)	+	0.0576
		Number of marine coastal ecosystems (Y6)	+	0.0714
		Number of marine natural relics (Y7)	+	0.1458
		Amount of marine biodiversity (Y8)	+	0.1161
	Pressure	Total discharge of industrial wastewater (10,000 tons) (Y9)	−	0.0061
		Total industrial waste gas emission (10,000 tons) (Y10)	−	0.0038
		Output of industrial solid waste (10,000 tons) (Y11)	−	0.0058
	Response	Industrial wastewater treatment rates (%) (Y12)	+	0.0041
		Industrial solid waste disposal rate (%) (Y13)	+	0.0521
		Area of nature reserve (10,000 hectares) (Y14)	+	0.2077
		Number of marine nature reserves (Y15)	+	0.0781

.

2.3. Research Method

2.3.1. Comprehensive Evaluation

(1) Standardization

Standardized processing is a commonly used data processing method that allows for the reasonable allocation of weights for each indicator, thereby more accurately reflecting the true situation of the data. In Formulas (1) and (2), x_ij represents the original indicator value, and X_ij represents the standardized value.

$$X_{ij}=\frac{x_{ij}-\min (x_{1j},x_{2j},\cdots ,x_{nj})}{\max (x_{1j},x_{2j},\cdots ,x_{nj})-\min (x_{1j},x_{2j},\cdots ,x_{nj})}$$

(1)

$$X_{ij}=\frac{\max (x_{1j},x_{2j},\cdots ,x_{nj})-x_{ij}}{\max (x_{1j},x_{2j},\cdots ,x_{nj})-\min (x_{1j},x_{2j},\cdots ,x_{nj})}$$

(2)

(2) Entropy method calculation

At present, the commonly used evaluation methods include Analytic Hierarchy Process, Index Analysis, Fuzzy Comprehensive Evaluation Analysis, etc. However, these methods present problems such as strong subjectivity and rough grading. In comparison, the entropy method not only overcomes the shortcomings of subjective weighting but also objectively reflects the differences and changes between indicators. Therefore, this paper uses the entropy method to calculate the comprehensive scores for the ME and EE. The specific formula process is as follows:

$$W_i=\ln \frac{1}{n}{\displaystyle \sum _{j=1}^n\left[\left(S_{ij}\right)\ln \left(S_{ij}\right)\right]},S_{ij}=\frac{X_{ij}}{{\displaystyle \sum _{j=1}^n{X}_{ij}}}$$

(3)

(3) Index weight calculation

$${\lambda }_{ij}=\left(1-W_i\right)/{\displaystyle \sum _{i=1}^m\left(1-W_i\right)}$$

(4)

(4) Comprehensive evaluation index

The larger the index, the higher the capability of annual ME and EE development (and vice versa).

$$u_1={\displaystyle \sum _{i=1}^m{\lambda }_{ij}}{A}_{ij},u_2={\displaystyle \sum _{i=1}^m{\lambda }_{ij}}{B}_{ij},{\displaystyle \sum _{i=1}^m{\lambda }_{ij}}=1$$

(5)

2.3.2. Coupling Coordination Model

The coupling coordination model can reflect the degree of interaction between ME and EE and is an effective evaluation tool. In Formula (6), C represents the degree of coupling; in Formula (7), D represents coupled co-scheduling.

$$C=\frac{\sqrt{\left(u_1\times u_2\right)}}{\left(u_1+u_2\right)},T\left(u_1,u_2\right)=a{u}_1+b{u}_2$$

(6)

$$D=\sqrt{CT}$$

(7)

2.3.3. Obstacle Degree Model

The obstacle level model can be applied to compare obstacle levels. U represents the obstacle value of the subsystem, and O is the O value of the indicator layer. The larger the O and U values, the larger the obstacle degree severity of the item and the greater its impact.

$$U={\displaystyle \sum {{\displaystyle o}}_j},O_j=\frac{F\times I}{{\displaystyle {\sum }_{j=1}^m\left(F\times I\right)}}$$

(8)

3. Results

3.1. Analysis of the Comprehensive ME and EE Evaluation Levels

In order to present the changes in the comprehensive level of ME and EE and compare the comprehensive level of the ME subsystem and EE subsystem, the mapping surface diagram was drawn using the drawing software Origin 2022. The trend in the comprehensive evaluation index for ME development in various coastal regions of China is shown in Figure 2a and

Table 2. Ranking of ME comprehensive level index.

	1999	Index	2003	Index	2007	Index	2011	Index	2015	Index	2019	Index
Shandong	1	0.23	1	0.28	1	0.45	1	0.57	1	0.74	2	0.82
Guangdong	2	0.22	2	0.27	2	0.36	2	0.47	2	0.65	1	0.93
Hainan	3	0.21	4	0.22	6	0.25	7	0.27	8	0.28	9	0.35
Fujian	4	0.21	3	0.26	4	0.30	4	0.34	3	0.48	3	0.72
Shanghai	5	0.18	6	0.21	3	0.34	3	0.38	4	0.43	4	0.57
Zhejiang	6	0.18	5	0.22	5	0.26	5	0.30	5	0.39	5	0.52
Jiangsu	7	0.12	7	0.15	7	0.20	8	0.24	7	0.30	7	0.40
Liaoning	8	0.12	8	0.14	8	0.20	6	0.27	6	0.33	6	0.40
Hebei	9	0.11	9	0.13	9	0.18	11	0.14	10	0.21	10	0.31
Guangxi	10	0.09	11	0.11	11	0.13	10	0.14	11	0.19	11	0.27
Tianjin	11	0.08	10	0.11	10	0.16	9	0.18	9	0.28	8	0.36

. The figure shows that the ME comprehensive evaluation index improved in all the regions. From the comprehensive evaluation index and its ranking from 1999 to 2019, Guangdong increased from second place to first place; over the preceding 20 years, the comprehensive evaluation index for Guangdong increased 4.2 times. During the same period, however, Hainan gradually decreased from third place in 1999 to ninth place, with a comprehensive evaluation index growth of only 1.7. The rankings for Shandong, Shanghai, Fujian, Zhejiang, Jiangsu, and Guangxi remained relatively stable, with the comprehensive evaluation index increasing by 3.6, 3.2, 3.5, 2.9, 3.3, and 3, respectively. Liaoning rose from eighth to sixth place; Hebei dropped from ninth to tenth place; and Tianjin rose from eleventh to eighth place. Overall, the development of ME in Guangdong, Shandong, Fujian, Shanghai, and Zhejiang has always been the antecedent of the 11 coastal regions. In terms of ME development, Shandong, Guangdong, and Fujian all have superior geographical and resource conditions, placing a strong emphasis on the development of their MEs. Guangdong Province, in particular, has achieved outstanding ME development, and in 2020, its gross marine product was ranked first in the country for 25 consecutive years. In 2020, the total value of Guangdong’s ME accounted for approximately 21% of the country’s total marine production value. Guangdong Province has a complete range of marine industries and an optimized industrial structure covering multiple emerging industries, such as clean marine energy, comprehensive utilization of seawater, marine biomedicine, and advanced marine equipment manufacturing.

As shown in Figure 2b–d, based on the comprehensive evaluation index for the three subsystems of the ME, economic structure > economic scale > economic vitality. In the economic structure subsystem, the average indices for Hainan and Guangxi are higher, while the average indices for Shanghai and Tianjin are lower. In the economic scale subsystem, the average indices for Shandong and Fujian are higher, while the average indices for Guangxi and Hebei are lower. In the economic vitality subsystem, Guangdong and Shandong have higher evaluation indices, while Hainan, Guangxi, and Hebei have lower evaluation indices. It can be seen that the comprehensive evaluation index for economic scale and economic vitality in Shandong is relatively high, making the overall ME comprehensive evaluation index for Shandong rank top, while the economic scale and economic vitality of Guangxi and Hebei are both relatively low, which has lowered the comprehensive ME evaluation index for both regions. ME development in Hebei is mainly based on an extensive growth model, leading to insufficient technological innovation capabilities and a low level of research in the high-tech marine field, thus limiting ME economic vitality. Compared with other regions, Guangdong has a relatively high level of economic vitality, with research institutions such as Guangdong Ocean University and Zhanjiang Bay Laboratory, which have a high level of marine technology strength. In 2020, Guangdong Province still ranked first in the coastal areas of China for indicators such as marine technology patents, publishing scientific works, and publishing scientific papers. In addition, the provincial government attaches great importance to ME development, vigorously supporting the development of the high-tech marine industry and attracting a large number of high-tech marine companies to establish themselves there.

The trends in the comprehensive evaluation index for EE development in various coastal regions of China are shown in Figure 3a and

Table 3. Ranking of EE comprehensive level index.

	1999	Index	2003	Index	2007	Index	2011	Index	2015	Index	2019	Index
Jiangsu	1	0.26	1	0.27	1	0.35	5	0.22	4	0.20	4	0.20
Fujian	2	0.22	2	0.24	5	0.19	6	0.10	6	0.14	6	0.14
Liaoning	3	0.19	3	0.20	4	0.28	1	0.54	1	0.54	1	0.57
Shandong	4	0.18	4	0.20	2	0.32	2	0.28	2	0.51	2	0.52
Guangdong	5	0.12	5	0.14	3	0.32	3	0.24	3	0.23	3	0.21
Tianjin	6	0.09	7	0.09	10	0.04	10	0.03	10	0.02	10	0.02
Zhejiang	7	0.09	6	0.10	8	0.10	8	0.06	9	0.08	9	0.08
Hebei	8	0.08	8	0.09	6	0.14	7	0.09	7	0.13	7	0.13
Guangxi	9	0.08	9	0.08	9	0.08	9	0.06	8	0.10	8	0.09
Hainan	10	0.06	10	0.05	7	0.13	4	0.22	5	0.19	5	0.19
Shanghai	11	0.02	11	0.02	11	0.02	11	0.01	11	0.02	11	0.01

. It can be seen that the comprehensive evaluation indices for EE in the 11 coastal regions of China fluctuate slightly, and there is no overall upward trend: the comprehensive EE evaluation index is far lower than the comprehensive ME development evaluation index. Specifically, (1) the composite indices for the EE in some regions show a downward trend, including for Jiangsu, Fujian, Tianjin, and Zhejiang. Jiangsu’s EE comprehensive evaluation index ranked top in 1999, with an index of 0.26: by 2019, it had dropped to fourth place, with an index of 0.20. Fujian dropped from second (0.22) in 1999 to sixth place (0.14) in 2019; Tianjin from sixth (0.09) in 1999 to tenth place (0.02) in 2019; and Zhejiang from seventh (0.09) in 1999 to ninth place (0.08) in 2019. (2) The comprehensive EE evaluation index has improved in some regions, including Liaoning, Shandong, Guangdong, and Hainan. The comprehensive evaluation index for Liaoning climbed from third place (0.19) in 1999 to first place (0.57) in 2019; Shandong climbed from fourth (0.18) in 1999 to second place (0.52) in 2019; Guangdong from fifth place (0.12) in 1999 to third place (0.21) in 2019; and Hainan from tenth place (0.06) in 1999 to fifth place (0.19) in 2019. (3) The indices for Hebei and Guangxi have improved slightly, but their rankings have both decreased. The comprehensive EE evaluation index for Shanghai has been at the bottom for 20 years. Overall, the comprehensive level of EE development in Liaoning, Shandong, and Guangdong initially ranked among the top 11 coastal regions, while the comprehensive EE level indices for Zhejiang, Tianjin, and Shanghai were ranked at the bottom. The ranking of the comprehensive evaluation index in 2019 is Liaoning > Shandong > Guangdong > Jiangsu > Hainan > Fujian > Hebei > Guangxi > Zhejiang > Tianjin > Shanghai. Liaoning has a vast sea area and abundant marine resources. Although its GDP is not as high as that of Guangdong, its proportion of marine GDP is high. Guangdong has relatively few large-scale polluting heavy industries, which have a relatively small influence on the environment. Shandong has excellent port resources, and the marine industry is dominated by the tertiary industry, with relatively little damage to the EE.

As shown in Figure 3b–d, based on the comprehensive evaluation index of the three EE subsystems, pressure > state > response. In the pressure subsystem, the regions with higher average evaluation index values include Hainan (0.99), Tianjin (0.96), and Shanghai (0.92), while the regions with lower evaluation index values include Hebei (0.67) and Liaoning (0.75). The pressure subsystem is a negative indicator, which was reversed in the standardization process of this study. Therefore, the larger the calculated pressure subsystem index, the better. Hainan and Tianjin have the highest stress index, indicating that compared with the other nine regions, their ecological pressure is relatively small. However, the pressure indices for Hebei and Liaoning are relatively small, indicating that the two regions face relatively greater pressure from the EE among all 11 regions. In the state subsystem, the regions with higher average evaluation index values include Shandong (0.30) and Guangdong (0.28), while the regions with lower evaluation index values include Shanghai (0.02) and Tianjin (0.03). In the response subsystem, the regions with higher average evaluation index values include Guangdong (0.28) and Shandong (0.18), while the regions with lower evaluation index values include Shanghai (0.03) and Tianjin (0.02). Among the 11 regions, the pressure on the EE in Shanghai is relatively low, but the EE status and response are relatively weak. The status and response subsystems in Shandong and Guangdong are relatively high, while the levels of the pressure subsystem are in the middle. Hainan was the first city in China to propose the construction of an ecological province. In 1999, the Chinese State approved Hainan to become the first ecological demonstration province in the country. In terms of industrial project selection, Hainan always adheres to the principles of green, low-carbon, and sustainable development, resulting in the lowest ecological pressure index in the country. In addition, Guangdong and Shandong have implemented multiple action plans to reduce ecological pressure, achieving breakthrough improvements in nearshore water quality and achieving the unity of pollution control and habitat restoration. Therefore, their response subsystem development levels are relatively high.

3.2. Analysis of Coupling Degree (CD) between ME and EE

CD refers to the degree of benign coupling in the interaction between ME and EE, which can reflect the coordination between ME and EE. Based on the comprehensive index obtained earlier, the coupling degree model was used to obtain the coupling degree (CD) of coordinated development for the ME and EE in the 11 coastal areas from 1999 to 2019. Cross-sectional time data from 1999, 2003, 2007, 2011, 2015, and 2019 were intercepted to draw a spatiotemporal distribution map of the CD, as shown in Figure 4. This study divided the CD into ten levels to obtain the level classification (

Table 4. Coupling level classification.

Level	Coupling Level	Index	1999	2003	2007	2011	2015	2019
10	High-quality coupling	0.9~1.0	Zhejiang, Fujian, Shandong, Hainan, Guangdong	Hebei, Zhejiang, Fujian, Shandong, Guangdong, Hainan	Tianjin, Hebei, Zhejiang, Fujian, Shandong, Guangdong, Guangxi, Hainan	Hebei, Jiangsu, Fujian, Shandong, Guangdong, Guangxi, Hainan	Hebei, Jiangsu, Fujian, Shandong, Guangdong, Guangxi, Hainan	Hebei, Jiangsu, Shandong, Guangxi, Hainan
9	Good coupling	0.8~0.9	Hebei	Tianjin, Guangxi	Liaoning	Tianjin, Liaoning, Zhejiang	Liaoning, Zhejiang	Liaoning, Zhejiang, Fujian, Guangdong
8	Intermediate coupling	0.7~0.8	Liaoning, Shanghai, Guangxi	Liaoning, Shanghai, Jiangsu	Jiangsu	—	—	—
7	Primary coupling	0.6~0.7	Jiangsu	—	Shanghai	—	Tianjin	—
6	Barely coupled	0.5~0.6	—	—	—	—	—	Tianjin
5	Near decoupling	0.4~0.5	Tianjin	—	—	—	Shanghai	—
4	Mild decoupling	0.3~0.4	—	—	—	Shanghai		Shanghai
3	Moderate decoupling	0.2~0.3	—	—	—	—	—	—
2	Severe decoupling	0.1~0.2	—	—	—	—	—	—
1	Extreme decoupling	0.0~0.1	—	—	—	—	—	—

).

From the CD distribution shown in Figure 4, it can be seen that (1) from 1999 to 2019, the overall CD of ME and EE in coastal areas was relatively high, showing an upward trend. The CD for each region reached its maximum value during the sample period in 2007, followed by a downward trend, but still increased compared with 1999. (2) In the mean distribution map of CD, the order of CD is Shandong > Hainan > Guangdong > Hebei > Fujian > Guangxi > Zhejiang > Jiangsu > Liaoning > Tianjin > Shanghai. Based on the CD classification shown in Table 4, it can be seen that the CD for Shandong and Hainan reached 1, followed by Jiangsu (0.999), Hebei (0.995), and Guangxi (0.977), which all demonstrate high-quality coupling. The CD in Shanghai continued to decline, from intermediate coupling in 1999 to mild decoupling in 2019, with a CD value of 0.325. The CD in Tianjin was also relatively low, reaching a barely coupled level in 2019. The coupling levels of the other nine coastal areas are concentrated in good coupling and high-quality coupling levels. Shanghai and Tianjin are noteworthy regions, having the most active economies in China, but their coupling is relatively low among the 11 coastal regions. Therefore, further exploration is needed in conjunction with the CCD.

3.3. Analysis of Coupling Coordination Degree (CCD) between ME and EE

On the basis of the comprehensive index and CD obtained in the previous sections, the CCD model was used to finally obtain the coupling co-dispatch for the coordinated development of the ME and EE in all 11 coastal areas from 1999 to 2019. Time sectional data from 1999, 2003, 2007, 2011, 2015, and 2019 were intercepted to draw the spatiotemporal distribution diagram of the co-dispatch, as shown in Figure 5. This study further divided the coordination degree into ten levels, resulting in

Table 5. Coordination level classification.

Level	Coordination Level	Index	1999	2003	2007	2011	2015	2010
10	High-quality coordination	0.9~1.0	—	—	—	—	Shandong	Shandong
9	Good coordination	0.8~0.9	—	—	—	—	—
8	Intermediate coordination	0.7~0.8	—	—	Shandong	Shandong	Liaoning, Guangdong	Liaoning, Guangdong
7	Primary coordination	0.6~0.7	—	—	Guangdong	Liaoning, Guangdong	—	Jiangsu, Fujian
6	Barely coordinated	0.5~0.6	—	Fujian, Shandong	Liaoning, Jiangsu, Fujian	Jiangsu, Hainan	Jiangsu, Fujian, Hainan	Hebei, Zhejiang, Hainan
5	Near Dysfunction	0.4~0.5	Jiangsu, Fujian, Shandong, Guangdong	Liaoning, Jiangsu, Zhejiang, Guangdong	Hebei, Zhejiang, Hainan	Zhejiang, Fujian	Hebei, Zhejiang	Guangxi
4	Mild disorders	0.3~0.4	Liaoning, Zhejiang, Hainan	Hebei, Hainan	Shanghai, Guangxi	Hebei, Guangxi	Shanghai, Guangxi	Tianjin, Shanghai
3	Moderate imbalance	0.2~0.3	Hebei, Shanghai, Guangxi	Tianjin, Shanghai, Guangxi	Tianjin	Tianjin, Shanghai	Tianjin	—
2	Severe imbalance	0.1~0.2	Tianjin	—	—	—	—	—
1	Extreme imbalance	0.0~0.1	—	—	—	—	—	—

for level classification.

In combining Figure 5 and Table 5, it can be observed that (1) the overall CCD shows an upward trend. The coordination levels in Shandong and Guangdong improved the fastest, rising from a level of near imbalance in 1999 to a level of high-quality coordination and intermediate coordination in 2019. This was followed by Liaoning, which rose from a mild imbalance in 1999 to an intermediate coordination level; in addition, the coordination levels in the other eight coastal areas also increased. (2) The regions with lower coordination levels include Tianjin, Shanghai, and Guangxi. In 1999, the coordination degrees for Tianjin and Shanghai were 0.322 and 0.312, respectively, which were at a mild imbalance level. Guangxi had a coordination degree of 0.431, which was at the brink of the imbalance level. The average ranking of coordination degree is Shandong > Guangdong > Liaoning > Fujian > Jiangsu > Hainan > Zhejiang > Hebei > Guangxi > Shanghai > Tianjin. The CD and CCD for Tianjin and Shanghai are at the bottom level among the 11 coastal areas in China. These two areas have obvious geographical advantages, e.g., rich marine natural resources and complete categories of marine industries, but they have shortcomings, including being at the lower end of the marine industrial structure. Although a number of leading marine economic enterprises exist in these areas, they are basically labor-intensive industries, having high energy consumption, producing environmental pollution, and possessing a low technology-added value. The high-end marine technology industry is in its early stages, and the number of high-end industries is significantly insufficient. This situation, therefore, restricts the ME and EE coupling coordinated development in Tianjin and Shanghai.

3.4. Analysis of Obstacle Factors in Coupling Coordination between ME and EE

For further analysis of the obstacle factors that hinder the coordinated development of ME and EE coupling in coastal areas, this study uses an obstacle degree model to demonstrate, numerically, the change in obstacle degree for the criterion layer and the ranking of obstacle degree for the indicator layer for both, at the six time points. At the same time, to more intuitively display the changes in obstacle degrees, this study applied the drawing software Origin2022 to draw a percentage stacked bar chart of obstacle degree change, including:

(1) Analysis of Obstacle Factors in the Criterion Layer

The obstacle level changes for the EE criterion layer are shown in Figure 6. Overall, the obstacle level for the criterion layer that hinders the development of ME is in the order of economic vitality > economic scale > economic structure. The decreasing impact of obstacles to economic structure indicates that the economic structure of coastal areas is gradually becoming more reasonable. The proportion of obstacles to economic scale and vitality is gradually increasing. In 2019, the regions with the highest proportions of economic vitality barriers included Hebei, Tianjin, Liaoning, Zhejiang, Shanghai, Shandong, Guangxi, Fujian, and Hainan, while the regions with the highest proportion of economic scale barriers only comprised Jiangsu and Guangdong. From this, it can be seen that the vitality of the ME in China’s coastal areas is a limiting factor for the coordinated development of ME and EE coupling.

The change in obstacle level for the EE is shown in Figure 7. The obstacle level for state and response in various regions fluctuates slightly and remains the main obstacle factor. The degree of stress disorder increased significantly from 1999 to 2019: the proportion of stress disorder in Shandong increased from 5% to 29%; in Guangdong from 4% to 24%; in Hebei from 6% to 18%; in Liaoning from 6% to 16%; and in Jiangsu from 4% to 17%. In 2019, the obstacle ranking of the criterion layer was Response > State > Pressure in Liaoning, Guangdong, Shanghai, Jiangsu, Guangxi, and Hainan, while the regions ranked as Status > Response > Pressure in Hebei, Shanghai, Zhejiang, and Fujian. From this, the response subsystem is the main limiting factor for the coupling and coordinated development of the two.

(2) Analysis of obstacle factors in the indicator layer

From

Table 6. Ranking of Obstacle Factors in the Indicator Layer of ME.

	1999	2003	2007	2011	2015	2019
Tianjin	X9 > X6 > X14	X9 > X6 > X14	X9 > X6 > X14	X9 > X6 > X14	X9 > X6 > X14	X9 > X6 > X14/X15
Hebei	X9 > X6 > X14	X9 > X6 > X14	X9 > X6 > X14	X9 > X6 > X14	X9 > X6 > X14	X6 > X14 > X15
Liaoning	X9 > X6 > X14	X9 > X6 > X14/X15	X9 > X6 > X14	X9 > X6 > X14	X9/X6 > X1/X3	X6 > X15 > X14
Shanghai	X9 > X6 > X14	X9 > X6 > X7/X15	X6 > X9 > X14	X6 > X7 > X4/X5/X9/X14	X6 > X7 > X5	X6 > X14 > X15/X7
Jiangsu	X6 > X9 > X14	X6 > X9 > X14	X6 > X9 > X14	X6 > X9 > X14	X6 > X9 > X14	X9 > X6 > X14/X15
Zhejiang	X9 > X6 > X14	X9 > X6 > X14	X9 > X6 > X14	X9 > X6 > X14	X6 > X9 > X14	X6 > X15 > X14
Fujian	X6 > X9 > X14	X6 > X9 > X14	X6 > X9 > X14	X6 > X9 > X14	X6 > X9 > X14	X6 > X14 > X15
Shandong	X9 > X6/X14/X15	X9 > X14/X15	X9 > X14 > X15	X9 > X14 > X7	X9 > X7 > X14	X9 > X15 > X7
Guangdong	X6/X9 > X14	X6 > X9 > X14	X6 > X9/X14	X9 > X6 > X14	X6 > X9 > X7	X6 > X7/X5
Guangxi	X9 > X6 > X14	X9 > X6 > X14	X9 > X6 > X14	X9 > X6 > X14	X9 > X6 > X14	X6 > X9 > X14
Hainan	X9 > X6 > X14	X9 > X6 > X14	X6/X9 > X14	X6 > X14 > X15	X6 > X14 > X9/X15	X6 > X14 > X15

, the top three obstacle factors in the indicator layer of the 11 coastal regions are defined as the main obstacle factors. In the ME indicator layer, from 1999 to 2019, there was no significant change in the main obstacle factors in the coastal areas studied, including the proportion of marine tertiary industry output value (X9), sea-salt production (X6), number of marine technology patent authorizations (X14), and marine scientific research expenditure (X15). Among them, X9 and X6 were the main obstacle factors with the highest frequency of occurrence, indicating that the proportion of marine tertiary industry output and sea-salt production are important bottlenecks that constrain the coordinated development of ME and EE coupling.

In terms of obstacle ranking in the EE indicator layer, as shown in

Table 7. Ranking of Obstacle Factors in the Indicator Layer of EE.

	1999	2003	2007	2011	2015	2019
Tianjin	Y14 > Y15 > Y7	Y14 > Y15 > Y7	Y14 > Y7/Y15	Y14 > Y7/Y15	Y14 > Y7/Y15	Y14 > Y7/Y15
Hebei	Y14 > Y7/Y15	Y14 > Y7/Y15	Y14 > Y10 > Y7/Y15	Y14 > Y11 > Y7/Y15	Y14 > Y11 > Y7/Y15	Y14 > Y11 > Y7/Y15
Liaoning	Y14 > Y7 > Y15	Y14 > Y7 > Y15	Y14 > Y7 > Y15	Y14 > Y11 > Y7	Y14 > Y11 > Y7	Y14 > Y7 > Y11
Shanghai	Y14 > Y7/Y15	Y14 > Y7/Y15	Y14 > Y7/Y15	Y14 > Y7/Y15	Y14 > Y7/Y15	Y14 > Y7/Y15
Jiangsu	Y14 > Y15 > Y7/Y8	Y14 > Y15 > Y7	Y14 > Y7/Y15	Y14 > Y7/Y15	Y14 > Y7/Y9/Y15	Y14 > Y9 > Y7
Zhejiang	Y14 > Y7/Y15	Y14 > Y15 > Y7	Y14 > Y7/Y15	Y14 > Y7/Y15	Y14 > Y7 > Y15	Y14 > Y7 > Y8/Y9
Fujian	Y14 > Y7 > Y15	Y14 > Y7 > Y15	Y14 > Y7 > Y15	Y14 > Y7 > Y15	Y14 > Y7 > Y8	Y14 > Y7 > Y8
Shandong	Y14 > Y7 > Y15	Y14 > Y7 > Y15	Y14 > Y7 > Y15	Y14 > Y7 > Y9/Y11/Y15	Y14 > Y7 > Y8	Y14 > Y11 > Y9
Guangdong	Y14 > Y7 > Y15	Y7 > Y15 > Y13	Y14 > Y7 > Y13	Y14 > Y9 > Y7	Y14 > Y9 > Y7	Y14 > Y9 > Y7
Guangxi	Y14 > Y7/Y15	Y14 > Y7 > Y15	Y14 > Y7 > Y15	Y14 > Y7/Y15	Y14 > Y7/Y15	Y14 > Y7/Y15
Hainan	Y14 > Y7/Y8	Y14 > Y7/Y8	Y14 > Y7 > Y8/Y13	Y14 > Y8 > Y13/Y15	Y14 > Y8 > Y13/Y15	Y14 > Y8 > Y13/Y15

, the main obstacle factors for each region were the area of marine nature reserves (Y14), the number of marine natural heritage sites (Y7), the number of marine nature reserves (Y15), the production of industrial solid waste (Y11), the amount of marine biodiversity (Y8), and the total amount of industrial wastewater discharge (Y9). Among them, Y14, Y7, and Y15 occurred most frequently as the main obstacle factors during the research period, indicating that the area and quantity of marine-type nature reserves, as well as the number of marine natural relics, are limited, becoming the key factors restricting the coupling and coordinated development of the ME and EE.

4. Discussion

The ME is a new increase field in China’s economic development. Driven by the Maritime Power Strategy, ME development in China has been rapid, and the conflict with the EE is becoming increasingly prominent. Investigating the coordinated coupling development of both ME and EE has, therefore, become a new way to implement sustainable development of the ME. By drawing on relevant research achievements, this paper explored ME and EE coordinated development in China and has innovated a combination of research perspectives and methods compared with previous studies. In terms of research perspective, there were few studies involving the coupling and coordination of the ME and EE in 11 coastal regions in China. This paper, therefore, chose the research perspective of coupling coordinated development. The research into the ME and related systems from the point of coupling coordination also includes several papers. These include Zhang et al.’s research on coupling coordination between the ocean industry and ocean technology talents [35]; Li’s evaluation of the coupled development of the marine industry and marine technology [36]; Zhao et al.’s coupling and coordination study on the marine and regional economies [37]; and Wan et al.’s research into the coupling coordination between the ME and urbanism [38]. In terms of research method combination, based on the coupling coordination model, this study added obstacle model analysis to further distinguish the key obstacle factors affecting coupling coordination, and ultimately provides a specific basis for effective policy recommendations.

This paper measured and studied the development, coupling, and coordination levels, as well as ME and EE obstacle factors, in coastal areas of China from 1999 to 2019, which, to some extent, enriches empirical study on the sustainable development of the ME. Sustainable development of the ME and EE is a complex issue. The following shortcomings of this study require further exploration. Firstly, due to the fact that some data from 2020 and beyond have not yet been officially released, to ensure the accuracy and consistency of the calculation results, the sample period of this paper was 1999 to 2019. In future research, the sample period will be expanded. Secondly, the range of indicators for the comprehensive ME and EE evaluation index systems in this paper was not sufficiently wide, so the coverage of indicators will be expanded in the future.

5. Conclusions and Recommendations

This study constructed an indicator system for the comprehensive evaluation of the ME and EE and, based on this, measured and evaluated the comprehensive development level, coupling coordination level, and obstacle factors for the ME and EE in 11 coastal areas of China during the sample period. The research conclusions are as follows:

(1) From 1999 to 2019, the comprehensive development levels for the ME and EE in China’s coastal areas showed an upward trend, with the ME exceeding the EE. Specifically, the development level for each subsystem showed an economic structure > economic scale > economic vitality and pressure > state > response. The three regions with the highest ME comprehensive levels in 1999 were Guangdong, Shandong, and Fujian, while Liaoning, Shandong, and Guangdong had the highest comprehensive EE levels.

(2) For the coupling level measurement, 82% of the regions were at both high-quality and good coupling levels. The level of coupling coordination was significantly lower than that of coupling coordination, with only Shandong being at a high-quality coordination level, while other regions were concentrated between intermediate coordination at level eight and mild imbalance at level four. This indicates that although there is a clear coupling relationship between the ME and EE in China, further promotion is necessary to improve coupling coordination scheduling.

(3) By measuring the degree of obstacles, it was found that the vitality subsystem of the ME and the response subsystem of the ecological environment were the obstacle factors constraining the coordinated development of ME and EE coupling. Further analysis found that the ratio of the tertiary industry in the ME and the situation of marine-type nature reserves were the obstacle factors with the highest degree of obstacles in the indicator layer.

In accordance with the study above, this paper proposes the following: (1) Adhere to the coordinated development of the ME and EE and promote the development of an ecotype ME. An ecotype ME refers to an ecotype marine industry with low contamination, lower energy consumption, high technology and high efficiency, including marine power, coastal tourism, new marine materials, and marine energy, as well as services related to the ME. This involves undertaking exchanges and cooperation between the ME and EE and establishing a marine cultural and economic circle that integrates marine culture and the development of tourism and cultural industries. (2) The lack of economic vitality in ME is the main obstacle to coupled and coordinated development, and in response to this obstacle factor, efforts should be made to accelerate marine technological creation. The development of marine technology plays a significant part in protecting the marine EE and promoting the sustainable development of the ME. Advanced technology can effectively reduce the loss rate of the marine environment and resources and have a significant active impact on marine ecological efficiency. It is, therefore, necessary to accelerate the improvement of policies and systems to inspire innovation; this would promote innovation in marine technology, guide and encourage the entry of corporate capital, promote the implementation of marine scientific research achievements, and better and more quickly integrate scientific and technological innovation achievements into enterprise production practices, thereby assisting in the implementation of the Maritime Power Strategy. (3) In response to the obstacle factor of insufficient response to EE, differentiated EE governance policies should be adopted according to local conditions. The ecological security forms and environmental pollution issues in different coastal areas are different, and differentiated environmental governance systems could help improve the efficiency of EE governance.