Coordinated Development of the Marine Environment and the Marine Fishery Economy in China, 2011–2020

Abstract

The marine environment is the material basis for the survival and development of fishery resources, and changes in the marine environment affect the fishery economy. Therefore, against the background of sustainability and environmental uncertainty, it is important to investigate the development of the marine environment and the marine fishery economy to improve the quality of both. Taking the panel data for 11 coastal cities in China from 2011 to 2020, we use several methods, including the entropy method, a coupling harmonious degree model, and a Tobit model, to measure the marine-environment quality and marine-fishery-economy quality, their coordination, and the factors affecting that coordination. We find that (1) the marine-environment quality and marine-fishery economy quality show a significant upward trend over time, but they are spatially unbalanced, with obvious interprovincial differences. (2) Coordination between the marine-environment quality and marine-fishery-economy quality has risen steadily, but the level of coordination is still low, remaining at the primary level in most areas. (3) The important factors affecting coordination between the marine-environment quality and marine-fishery-economy quality include the strength of the marine fishery industry, scale of the marine fishery economy, production capacity of marine fisheries, marine-environment quality, and quality of the marine environment and its resources. In light of these findings, we should increase the coordination between the marine-environment quality and marine-fishery-economy quality by upgrading the marine fishery industry, modernizing marine fisheries, linking the environmental governance of marine and land areas, and strengthening the ecological construction of the marine environment.

1. Introduction

The marine environment is an important source and guarantee for the development of the marine fishery economy. However, with the ongoing expansion of human activity into the marine space, the marine fishery economy and the marine environment face a dilemma of mutual restriction and impact. The extensive development of traditional marine fisheries and marine overfishing have resulted in the extinction of many species and the increasing vulnerability of marine ecosystems. According to the FAO report on the State of World Fisheries and Aquaculture in 2017, the proportion of fish stocks caught within the sustainable limit showed a downward trend, from 90.0% in 1974 to 66.9% in 2015. The proportion of fish stocks caught at unsustainable levels increased from 10% to 33.1% over the same period [1]. The discharge of pollutants from aquaculture and marine fishery has harmed and seriously polluted the marine environment, leading to the oxidation of marine resources, frequent marine disasters, the pollution of coastal environments, marine ecosystem degradation, and the spread of marine garbage. Meanwhile, changes in the marine climate, storm surges, coastal erosion, rising sea levels, seawater warming, and acidification have harmed marine fisheries, restricting the development of marine fishery economies. In 2019, researchers found that from 1930 to 2010, the total output of global fishing grounds decreased by 4.1% owing to the effects of climate change [2]. According to the latest FAO’s State of World Fisheries and Aquaculture in 2022, the sustainability of marine fishery resources is still a matter of deep concern. In 2019, the proportion of sustainable catch stocks will decline to 64.6%, 1.2% lower than the level in 2017 [3].

Promoting the sustainable development of the marine fishery economy and improving marine-environment quality have, therefore, attracted considerable attention. In a 2022 report, the Intergovernmental Panel on Climate Change noted that climate change has a great impact on humans and ecosystems and that it spreads across regions through interconnected systems [4]. The practice of global environmental governance has confirmed that the development mode of “pollution first, then treatment, and pollution during treatment” is unsustainable. With increasing risks from external uncertainty and the intensifying climate crisis, focusing only on the development of the marine fishery economy is likely to exacerbate marine-environment deterioration and ultimately hinder development. Likewise, focusing only on marine environmental protection will hamper the development of the marine fishery economy. Therefore, promoting benign interaction between the two is important for the economic development of coastal nations. The development of the marine environment and the marine fishery economy does not have to be a contradictory zero-sum game; rather, it can reflect a mutually beneficial coexistence. On the one hand, the marine environment provides the resource carrying capacity and development space for the marine fishery economy. On the other hand, the marine fishery economy provides support for marine environmental protection and governance. Protecting the marine environment can, therefore, promote the sustainable development of the marine fishery economy, as opposed to harming it. Thus, studying the coordination between marine-environment quality and marine-fishery-economy quality has important theoretical and practical significance for improving the marine fishery economic structure, optimizing the allocation of marine fishery resources, enhancing the functions of the marine environment, and improving the marine-environment quality.

A growing body of related literature has emerged in recent years, focusing on issues of the sustainable development of the fishery economy, fishery economic efficiency, common fishery policy, and regional fishery governance [5,6,7]. Globally, marine fisheries play crucial economic, social, and cultural roles; they support human well-being through employment in fishing, processing, and retail services [8], as well as food security [9]. Fisheries are prone to uncertainty because environmental, institutional, economic, and social changes are not easily foreseen or determined [10]. Gordon noted that unless controls are placed on fishing, fisheries are susceptible to problems associated with open-access arrangements, such as over-exploitation and over-capitalization [11]. Hartmann et al. investigated the economic optimality of implementing Marine Protected Areas (MPAs) to obtain more informative data about fish populations, thereby allowing for better management strategies [12]. Sun et al. examined the specific manifestations of the sustainable utilization of marine fishery resources from the perspectives of time and space [13]. Unregulated fishing practices result in the overexploitation of resources, both in biological and economic terms. Fishery resources comprise five different groups with different problems and issues: small pelagics, large pelagics, demersal fish, bivalves (e.g., mussels, oysters, and clams), and others (e.g., sponges, coral, and algae) [14]. Lauria noted that fish consumption varies from country to country depending on the local traditions and the supply of fish. For example, fish is a key component of people’s diets in many developing countries because it is often the only affordable and readily available source of animal protein [15]. Building on Gordon’s insights, Smith developed a predictive theory of how the dynamics of open-access fishery will unfold [16]. Recent research on fishery economics has examined incentives across many margins, including the within-season effects, incentives to harvest different ages and sizes of fish, responses to ecological disturbances, spatial choices, and multispecies interactions [17]. Interestingly, research has revealed the factors affecting marine environments, including the overexploitation of fishery resources, coastal pollution, fishing’s effect on marine ecosystems, marine ecosystem management, and protected marine areas [18,19,20,21]. Fishing intensification and its related environmental effects have led to a massive reduction in targeted species as well as the extinction, through indirect ecological effects, of other species in the marine food web; however, the effect of fishing practices on other species and habitats is still poorly understood and is likely to remain so for some time [22]. Research on marine-environment protection is mainly based on the effect of the environment on the marine economy [23], focusing on the relationship between balancing economic growth and ecological restoration according to the local conditions. Previous research on the coordination between the marine environment and the marine economy has constructed systems for sustainable economic and environmental development, analyzed their operational mechanisms and levels of coordinated development, evaluated their degree of coordination, and proposed measures for coordinated development [24,25,26,27,28].

In contrast to the abovementioned research, few studies have considered the relationship between the marine environment and the fishery economy. Although a general framework for monitoring and assessing the fishery economy and the marine environment has been developed, only a few empirical studies have been conducted [29,30,31,32,33,34,35]. In short, although there is substantial research on the marine fishery economy and the marine environment, most studies examine the two independently, without considering their coordinated development characteristics, spatial patterns, and related influencing factors. This study, therefore, takes 11 coastal provinces/cities in China as the research object, analyzes the spatio-temporal evolution of the coordination between the marine environment and the marine fishery economy, reveals the characteristics of such coordination, and identifies the factors affecting that coordination. This can enrich the research on the development quality of marine environments and marine fishery economies, provide a theoretical framework and path choice for improving coordination between the two, and provide a reference for formulating related policies.

2. Study Design and Methods

2.1. Study Area

We selected 11 major coastal provinces/cities in China as the research object (Hong Kong, Macau, and Taiwan were excluded): Liaoning, Tianjin, Hebei, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Guangxi, and Hainan. We acquired data related to their marine environments and fishery economies and investigated the internal coordination relationships. The study period was 2011–2020. The data sources included the China Fisheries Statistical Yearbook (2011–2020), China Statistical Yearbook (2011–2020), China Environmental Statistical Yearbook (2011–2020), Bulletin on the State of China’s Marine Ecological Environment (2011–2020), and a number of research articles published in professional journals. Missing data were obtained by linear interpolation or calculated by the authors.

2.2. Index Selection

We carefully selected quality indicators related to marine fishery economies and environments to evaluate the coordination between the two. The strength of the marine fishery industry (SFI), the scale of the marine fishery economy (SFE), and the capacity of marine fishery production (SFP) are important indicators for measuring the quality of the marine fishery economy; therefore, we selected those three as the primary indicators for measuring the quality of the marine fishery economy. Five secondary indicators were selected under each primary indicator, resulting in a total of 15 indicators for evaluating the fishery economic quality. Referring to the literature, the strength of the marine fishery industry is measured by the proportion of marine fishery in the fishery economy, the output value of that marine fishery, the output value of mariculture, the output value of marine fishing, and the per capita income of marine fishermen. The economic scale of marine fishery is measured by the output of marine products, marine aquaculture, output value of pelagic fishery, marine fishing output, and mariculture area. The production capacity of the marine fishery is measured by the number of motorized marine fishing vessels, number of marine production fishing vessels, ownership of marine fishing vessels, number of fishery practitioners, and processing capacity of marine products. The index system for evaluating the marine-environment quality was mainly constructed in the two dimensions of marine resource environment quality (SCQ) and marine ecological environment quality (SBQ). Five indicators were selected for each dimension, for a total of 10 measurement indicators. The marine resource environment quality was measured by the direct economic loss from marine disasters, relative annual variation in the sea level, proportion of nearshore Class I and II water quality, coastal wetland areas, and nearshore and coastal areas. The marine ecological environment quality was measured by the direct discharge of marine wastewater, chemical oxygen demand, petroleum, ammonia nitrogen, and total phosphorus. These indicators reflect the marine ecological pollution and have a negative effect on the quality of the marine ecological environment as negative indicators. The entropy method was used for the evaluation, and the weighted scores were used to calculate the scores for the marine-environment quality and marine-fishery-economy quality in the research areas (

Table 1. Evaluation index system of marine-environment quality and marine-fishery-economy quality.

Target Layer	Rule Layer	Weight	Index Layer	Index (Positive/Negative)	Weight
Quality of the marine fishery economy			Proportion of marine fishery in fishery economy (%)	positive	0.0299
			Marine fishery output value (CNY 10,000)	positive	0.0593
	Marine Fishery industry strength	0.2440	Output value of mariculture (CNY 10,000)	positive	0.0730
	(SFI)		Marine fishing output value (CNY 10,000)	positive	0.0560
			Per capita income of fishermen (CNY)	positive	0.0258
			Seafood output (10,000 tons)	positive	0.0605
			Mariculture yield (10,000 tons)	positive	0.0780
	Marine Fishery economy scale	0.4043	Pelagic fishery output (10,000 tons)	positive	0.1028
	(SFE)		Marine fishing yield (10,000 tons)	positive	0.0636
			Marine aquaculture area (hectares)	positive	0.0994
			Ownership of marine mobile fishing vessels (total tons)	positive	0.0695
			Ownership of marine production fishing vessels (tons)	positive	0.0631
	Marine Fishery production capacity	0.3517	Number of marine mobile fishing vessels (units)	positive	0.0557
	(SFP)		Fishery practitioners (persons)	positive	0.0604
			Total processing amount of seawater products (tons)	positive	0.1030
Marine- environment quality	Marine	0.7853	Direct economic loss from marine disasters (CNY 100 million)	negative	0.0281
	Resource		Relative annual variation in sea level (millimeters)	negative	0.0970
	Environment		Proportion of nearshore Class I and II water quality (%)	positive	0.1394
	quality		Coastal wetland area (10,000 hectares)	positive	0.2916
	(SCQ)		Nearshore and coastal area (square kilometers)	positive	0.2292
	Marine- Ecological Environment quality (SBQ)	0.2147	Direct discharge of marine wastewater (100 million tons)	negative	0.0403
			Chemical oxygen demand (tons/year)	negative	0.0620
			Petroleum (tons/year)	negative	0.0286
			Ammonia nitrogen (tons/year)	negative	0.0282
			Total phosphorus (tons/year)	negative	0.0556

Note: Weights are calculated according to the research methods of the entropy method.

).

2.3. Research Methods

2.3.1. Data Standardization

To eliminate the influence of the dimensions and the positive and negative directions of the index data on the results, we used the range method to standardize the data. The details are given below.

The processing method for the larger and better positive indicators is:

$$A_{ij}^{+}=(x_{ij}-x_{min})/(x_{max}-x_{min})$$

(1)

For the smaller and better negative positive index, the processing method is:

$$A_{ij}^{-}=(x_{max}-x_{ij})/(x_{max}-x_{min})$$

(2)

In the model, $A_{ij}$ represents the standardized data matrix, i(i = 1, …, n) represents the province/city, j(j = 1, …, n) represents the index, $x_{ij}$ is the original data matrix, and $x_{max}$ and $x_{min}$ represent the maximum and minimum values of $x_{ij}$, respectively.

2.3.2. Entropy Method

To improve the objectivity and credibility of the index weights, we used the entropy method to calculate the index weights and comprehensive scores of the marine-environment quality and marine-fishery-economy quality. The specific calculation steps of the entropy method are as follows:

Calculation of the index entropy:

$$e_j=-k{{\displaystyle \sum }}_{i=1}^n{p}_{ij}\ln (p_{ij})$$

(3)

In the model $p_{ij}$ = $A_{ij}$/${\displaystyle \sum }_{i=1}^n{A}_{ij}$, $e_j$ represents the index entropy ($0 \le e_j\le 1$), n represents the number of indexes, and k = 1/$\ln m$, k > 0, and m represents the number of evaluation objects.

Index weight determination:

$$w_j=(1-e_{ij})/{{\displaystyle \sum }}_{i=1}^n\left(1-e_{ij}\right)$$

(4)

In the model, $w_{ij}$ represents the index weight, $e_{ij}$ represents the index entropy, and $w_j^{’}$=${\displaystyle \sum }_{i=1}^m{w}_j$ represents the rule layer weight.

Calculation of the comprehensive score:

$$S={{\displaystyle \sum }}_{j=1}^n{w}_j{A}_{ij}$$

(5)

2.3.3. Coupling Harmonious Degree Model

The coupling coordination model is used to measure two or more systems in physics. We used this model to build a coordination measurement model for the marine-environment quality and marine-fishery-economy quality. The formula is as follows:

$$M_{ij}={\left[\frac{H_{ij}\ast K_{ij}}{{\left(H_{ij}+\frac{K_{ij}}{2}\right)}^2}\right]}^{\frac{1}{2}}$$

(6)

where $M_{ij}$ represents the coupling value, $M_{ij}ϵ$(0,1), $H_{ij}$ indicates the economic quality of the marine fishery, and $K_{ij}$ represents the marine-environment quality. To further measure the coordination between the marine-environment quality and marine-fishery-economy quality, the following model is established:

$$N_{ij}=\mathsf{\theta }{H}_{ij}+\mathsf{\lambda }{K}_{ij}$$

(7)

$$T_{ij}={\left(M_{ij}*N_{ij}\right)}^{\frac{1}{2}}$$

(8)

where $T_{ij}$ represents the comprehensive coordination index between the marine-environment quality and marine-fishery-economy quality of province j in year i, $T_{ij}ϵ$ [0,1], θ represents the weight of the marine-fishery-economy quality, and λ is the weight of the marine-environment quality. Since the contributions of the two systems are the same, the values assigned to them are the same: θ = λ = 1/2. The value of $T_{ij}$ reflects the relationship between the two systems. The larger the value, the higher the coordination degree between the marine-environment quality and marine-fishery-economy quality. The converse is also true. To more intuitively reflect the coordination relationship between the two, we used existing classification methods to divide the coordination values of the two systems, as shown in

Table 2. Evaluation standard of coordination grade.

Coordination Grade	RHC	Coordination Grade	RHC
0 < D ≤ 0.2	Serious disorder	0.4 < D ≤ 0.6	Primary coordination
0.2 < D ≤ 0.3	Mild disorder	0.6 < D ≤ 0.8	Intermediate coordination
0.3 < D ≤ 0.4	Barely coordinated	0.8 < D ≤ 1	Senior coordination

Note: RHC: rank of harmony coefficient.

.

2.3.4. Tobit Model

The coordination between the marine-environment quality and marine-fishery-economy quality is characterized by a random distribution, and the value is between 0 and 1. If the ordinary least-squares (OLS) method was used for a regression, it would be unable to obtain a consistency estimate and the conclusion would be biased; therefore, we used the maximum likelihood intercept regression model—that is, the Tobit model. The Tobit estimator was proposed by James Tobin in 1958 to analyze estimations with censored dependent variables. A fixed-effects Tobit model is not feasible because there is no sufficient statistic whereby the fixed effects are conditioned out of the likelihood [36]. The formula of the Tobit model is as follows:

$$\mathrm{Y}=\left\{\begin{array}{c}a+\beta X_{ij}+u_i+e_{ij}, Y>0,\forall i,t\\ 0, Y>0,\forall i,t\end{array}\right.$$

(9)

where Y is the coordination value vector of the marine fishery and marine environment, X is the independent variable vector, a is the intercept item, $\beta$ is the parameter vector, u is the random variable, and e is the residual.

3. Spatio-Temporal Evolution of Marine-Environment Quality and Fishery-Economy Quality

Based on the entropy method (Formulas (3)–(5)), we calculated the marine fishery economy level of the selected provinces/cities from 2011 to 2020.

Table 3. Marine-fishery-economy quality index in China’s coastal provinces/cities (2011–2020).

Province/ City	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	Mean Value
Liaoning	0.3673	0.4043	0.4385	0.4689	0.4613	0.4507	0.4053	0.3936	0.3859	0.3889	0.4165
Tianjin	0.0124	0.0181	0.0298	0.0364	0.0360	0.0354	0.0325	0.0331	0.0371	0.0299	0.0301
Hebei	0.0800	0.0909	0.0928	0.0980	0.0999	0.1126	0.1122	0.1185	0.1179	0.1255	0.1048
Shandong	0.5271	0.6059	0.6357	0.7072	0.7422	0.7574	0.7264	0.7266	0.6971	0.7022	0.6828
Jiangsu	0.1353	0.1452	0.1645	0.1675	0.1729	0.1717	0.1770	0.1925	0.1657	0.1929	0.1685
Shanghai	0.0379	0.0429	0.0465	0.0552	0.0573	0.0585	0.0661	0.0731	0.0821	0.0769	0.0596
Zhjiang	0.3947	0.4455	0.4664	0.5091	0.5233	0.4808	0.5160	0.5528	0.5378	0.5604	0.4987
Fujian	0.4069	0.4549	0.4848	0.5083	0.5397	0.5651	0.5855	0.6217	0.6407	0.6541	0.5462
Guangdong	0.3169	0.3380	0.3486	0.3564	0.3583	0.3651	0.3736	0.3835	0.3855	0.3872	0.3613
Guangxi	0.1304	0.1451	0.1525	0.1581	0.1625	0.1756	0.1821	0.1886	0.1874	0.1757	0.1658
Hainan	0.1428	0.1587	0.1686	0.1789	0.1851	0.1909	0.1855	0.1856	0.1863	0.1786	0.1761
Mean value	0.2320	0.2590	0.2753	0.2949	0.3035	0.3058	0.3057	0.3154	0.3112	0.3157	0.2919

Data sources: China Marine Yearbook (2011–2020), and China Marine Economic Statistical Bulletin (2011–2020). (According to Formulas (3)–(5)).

and Figure 1 show the detailed results.

3.1. Spatio-Temporal Evolution of Fishery Economy Quality

In terms of the time trend, from 2011 to 2020, the overall quality of China’s fishery economy was generally good, showing a continuous upward trend with an average annual increase of 6%, as shown in Table 3 and Figure 1; however, the development quality of the marine fishery economy was low, and its resilience was insufficient. As shown in Figure 2, 2016 was an important turning point for the development of the marine fishery economy. In terms of a segmented development, the quality of the marine fishery economies in the coastal provinces/cities showed a linear upward trend, with an increase rate of about 30% from 2011 to 2016. In particular, the growth rate in Liaoning, Shandong, Fujian, and Guangdong was significantly higher than that in other the provinces/cities. In 2016, the quality of the marine fishery economy in Liaoning, Tianjin, Hebei, Shandong, Hainan, and other provinces/cities declined significantly; in particular, the rate of decline in Liaoning reached 12%. The quality of the marine fishery economies in most coastal provinces/cities shows a straight upward trend, with growth rates exceeding 10%.

Regarding the spatial evolution, the marine-fishery-economic quality presents a distribution pattern of a large gap between the north and south and a small gap between areas in the east. In the Northern Marine Economic Circle, the marine-fishery-economic quality of Shandong was far higher than that of the other provinces/cities, with an average of six times and twenty-two times that of Hebei and Tianjin, respectively. This shows that the quality of the marine fishery economy in Shandong had primacy, the quality of the marine fishery economy in the surrounding provinces was low, and clustered development had not yet formed in this region. In the Eastern Marine Economic Circle, Zhejiang had the highest level of marine-fishery-economic quality, with an average of three and nine times that of Jiangsu and Shanghai, respectively. The regional development gap was large. In the Southern Marine Economic Circle, Fujian had the highest level of marine-fishery-economic quality, with an average of three times that of Guangxi and Hainan. There were large gaps in the quality of the marine fishery economies in the region.

3.2. Spatio-Temporal Evolution of Marine-Environment Quality

Using the entropy method (Formulas (3)–(5)), we obtained the marine environment–related data of the selected coastal provinces/cities from 2011 to 2020.

Table 4. Marine-environment quality level in China’s coastal provinces/cities (2011–2020).

Province/City	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	Mean Value
Liaoning	0.5891	0.5627	0.5686	0.6047	0.6227	0.6146	0.6747	0.7063	0.6964	0.6916	0.6331
Tianjin	0.3216	0.2942	0.3046	0.3211	0.3477	0.3441	0.3780	0.3605	0.4150	0.4133	0.3500
Hebei	0.5776	0.5478	0.5284	0.5167	0.5187	0.5275	0.5559	0.5407	0.5487	0.5433	0.5405
Shandong	0.7969	0.6594	0.6786	0.6687	0.6801	0.7024	0.7297	0.7271	0.7020	0.6999	0.7045
Jiangsu	0.7461	0.6815	0.8794	0.8520	0.8825	0.8951	0.8996	0.8892	0.8329	0.8669	0.8425
Shanghai	0.4299	0.3335	0.4151	0.3424	0.3732	0.3675	0.4147	0.4327	0.4227	0.4112	0.3943
Zhejiang	0.3487	0.2824	0.3509	0.3201	0.3681	0.4030	0.4425	0.4916	0.4397	0.4890	0.3936
Fujian	0.4301	0.3926	0.5066	0.5197	0.4987	0.5045	0.5610	0.5567	0.5439	0.5781	0.5092
Guangdong	0.6382	0.6188	0.6648	0.6533	0.7000	0.6832	0.6292	0.6745	0.6813	0.7147	0.6658
Guangxi	0.5280	0.4941	0.5032	0.4878	0.5199	0.5504	0.5238	0.5634	0.5370	0.5213	0.5229
Hainan	0.4406	0.4097	0.4168	0.4098	0.4441	0.4736	0.4456	0.4872	0.4825	0.4837	0.4494
Mean value	0.5851	0.5309	5805	0.5728	0.5980	0.6073	0.6299	0.6487	0.6362	0.6459	0.6035

Note: The results were calculated using the authors’ formula. (According to Formulas (3)–(5)).

and Figure 2 show the results.

Regarding time trends, from 2011 to 2020, the development of the marine-environment quality showed a wave-like upward trend. As shown in Figure 2, the marine-environment quality fluctuated from 2011 to 2013. Since 2014, the marine-environment quality improved at an increasing rate. Although some provinces/cities experienced a temporary decline, they subsequently entered a new, relatively strong growth period. According to the marine environment development index, the marine-environment quality of the coastal provinces/cities is good, with the average marine-environment quality exceeding 0.5 in 10 years. Jiangsu had the best marine-environment quality, with an average of 0.8, while Zhejiang and Fujian had the fastest marine-environment growth, with a rate of more than 30%.

Regarding the spatial dimension, the marine-environment quality presents a spatial distribution pattern of large gaps in the east and small gaps in the north and south. In the Eastern Marine Economic Circle, Jiangsu had the highest marine-environment quality, twice that of Shanghai. The gap is obvious. The marine-environment quality of Shandong in the Northern Marine Economic Circle was the highest and was not much different from that of the other provinces/cities. Guangdong had the best marine-environment quality in the Southern Economic Circle, showing a small gap with the other provinces/cities. At the end of 2020, the average level of marine-environment quality among the regions was very close, all around 0.5. This indicates that regional marine-environment quality in China is gradually changing from an unbalanced state to a more balanced one; however, the differences in the marine-environment quality between provinces are gradually widening. Among them, Liaoning, Shandong, Jiangsu, and Guangdong were in the leading position, being significantly higher than the other provinces/cities, while the averages for Tianjin, Zhejiang, and Shanghai were low.

4. Coordinated Development of Marine-Environment Quality and Marine-Fishery-Economy Quality

Based on the coupling coordination model formula (Formulas (6)–(8)), we calculated the coordination degree between the marine-environment quality and marine fishery economy quality from 2011 to 2020.

Table 5. Coordination between marine-environment quality and marine-fishery-economy quality (2011–2020).

Province/City	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	Mean Value
Liaoning	0.4823	0.4883	0.4997	0.5160	0.5177	0.5130	0.5113	0.5135	0.5091	0.5092	0.5060
Tianjin	0.1778	0.1910	0.2183	0.2325	0.2365	0.2349	0.2355	0.2336	0.2491	0.2357	0.2245
Hebei	0.3278	0.3340	0.3327	0.3354	0.3373	0.3490	0.3534	0.3558	0.3566	0.3613	0.3443
Shandong	0.5693	0.5622	0.5731	0.5864	0.5960	0.6039	0.6033	0.6029	0.5914	0.5920	0.5881
Jiangsu	0.3986	0.3966	0.4361	0.4346	0.4419	0.4427	0.4467	0.4548	0.4310	0.4522	0.4335
Shanghai	0.2527	0.2446	0.2635	0.2622	0.2704	0.2708	0.2877	0.2986	0.3051	0.2982	0.2754
Zhejiang	0.4307	0.4211	0.4497	0.4493	0.4685	0.4691	0.4888	0.5105	0.4931	0.5116	0.4692
Fujian	0.4573	0.4597	0.4978	0.5069	0.5093	0.5167	0.5353	0.5424	0.5433	0.5545	0.5123
Guangdong	0.4742	0.4782	0.4906	0.4912	0.5004	0.4997	0.4923	0.5043	0.5062	0.5128	0.4950
Guangxi	0.3622	0.3659	0.3721	0.3726	0.3812	0.3943	0.3929	0.4037	0.3983	0.3890	0.3832
Hainan	0.3542	0.3570	0.3641	0.3680	0.3786	0.3877	0.3792	0.3878	0.3872	0.3833	0.3747
Mean value	0.3897	0.3908	0.4089	0.4141	0.4216	0.4256	0.4297	0.4371	0.4337	0.4363	0.4188

Note: The results were calculated using the authors’ formula. (According to Formulas (6)–(8)).

and Figure 3 show the results.

4.1. Temporal Evolution of Coordination between the Marine-Environment Quality and Marine-Fishery-Economy Quality

Figure 3 shows that the coordination between the marine-environment quality and marine-fishery-economy quality rose in waves from 2011 to 2020. This shows that China had attached importance to improving the marine-environment quality and the sustainable development of the fishery economy during the study period. The linkage between the marine fishery economy and the marine environment has now become increasingly obvious, and a new situation of integrated development has taken shape. Fujian and Zhejiang showed the fastest rate of coordinated growth, with an average annual growth rate of about 2%. As shown in Table 5, although the coordination between the marine-environment quality and marine-fishery-economy quality steadily improved during the study period, the overall level was still low, with the average coordination rising from 0.39 to 0.42. The highest coordination value was for Shandong, reaching 0.5920 in 2020, with an average of only 0.5881. However, the coordination values of Tianjin, Hebei, Shanghai, Guangxi, Hainan, and others were far lower than the average, with an average value of about 0.3, while the average value of coordination in the other provinces was around 0.5. This shows that the coordination between the marine-environment quality and marine-fishery-economy quality was at a low level. The difference was still large from the perspective of an interprovincial coordination value. Shandong had the highest average coordination value, while Tianjin had the lowest, with a difference of more than 2.6 times. This shows that the coordinated development of the marine-environment quality and marine-fishery-economy quality was highly unbalanced.

4.2. Spatial Evolution of Coordination between the Marine-Environment Quality and Marine-Fishery-Economy Quality

To explain the spatial evolution of the coordination between the marine-environment quality and marine-fishery-economy quality, we depict the coordination levels of typical years and map them according to the classification in Table 2. As shown in Figure 4, from 2011 to 2020, the coordination between the marine-environment quality and marine-fishery-economy quality showed a spatial distribution pattern of large differences between the east and north and small differences in the south.

The patterns are summarized as follows:

(1) Serious disorder: In 2011, the coordination between the marine-environment quality and marine-fishery-economy quality in Tianjin was seriously disordered. As shown in Table 3 and Table 4, the marine-environment quality and marine-fishery-economy quality in Tianjin were at the lowest level among the selected provinces/cities during the study period and far below the average level. This shows that the development of the marine-environment quality and marine-fishery-economy quality in Tianjin was low.

(2) Mild disorder: Shanghai was in a state of mild disorder in 2011. Tianjin was added in 2014. According to Table 3 and Table 4, the level of the marine-environment quality and marine-fishery-economy quality in Shanghai was low, being only slightly higher than that in Tianjin. This shows that the development of the marine environment and the marine fishery economy in Shanghai was relatively slow, the marine-environment carrying capacity was declining, and the resilience of the fishery economy was fragile. In the process of fishery economy development, a coordination between the marine-environment quality and marine-fishery-economy quality had been ignored, hindering the integrated development of the two.

(3) Barely coordinated: In 2011, Hebei, Jiangsu, Guangxi, and Hainan were in a state of being barely coordinated and by 2014, Jiangsu had been removed. By 2020, Guangxi had also been removed. This shows that the coordination between the marine-environment quality and marine-fishery-economy quality in most coastal provinces/cities in China was dynamically adjusting and gradually improving. The development of the marine fishery economy was promoting the gradual improvement of the marine-environment governance capacity and quality. The improvement of the marine-environment quality was also promoting a fishery economy development. However, the coordination between the marine-environment quality and marine-fishery-economy quality in some provinces/cities was still at a low level in terms of their overall coordination.

(4) Primary coordination: In 2011, Liaoning, Shandong, Zhejiang, Fujian, and Guangdong were in a state of primary coordination. By 2014, Jiangsu was added; by 2016, Shandong had been removed. By 2019, Shandong and Guangxi were added, and there were seven provinces in a state of primary coordination. More generally, many provinces/cities had reached the primary coordination state, and these were in a relatively stable state, with small fluctuations and a longer duration. This shows that the primary coordination stage belonged to the stable running period of the marine environment and fishery economy development; however, it was difficult to break through the constraints and enter a new state in the short term.

(5) Moderate coordination: Among the selected provinces/cities, only Shandong was in a moderate coordination state from 2016 to 2018. This shows that Shandong’s marine environment and fishery economy had initially formed a benign interactive development situation. Table 3 and Table 4 show that the level of the marine-environment quality and marine-fishery-economy quality in Shandong far exceeded that of the other selected areas during the study period. This is mainly attributable to Shandong taking the lead in transforming and upgrading its fishery industry, cultivating new industries, optimizing the spatial structure of its fisheries, and building a modern fishery industry system. Ideally, China should strengthen its construction of marine ecological civilization, improve its marine-environment protection mechanisms, and promote the coordinated development of the marine environment and the fishery economy. In terms of the overall coordination, Shandong was at the midpoint of an intermediate coordination.

(6) Senior coordination: A coordination between the marine-environment quality and marine-fishery-economy quality had yet to reach the senior coordination state in China. After China’s “reform and opening up”, its fishery economy entered a period of rapid development, resulting in a depletion of its fishery resources, pollution in the coastal waters, and a sub-healthy or unhealthy marine environment. Based on the concept of green, sustainable development, the traditional fishery industry is undergoing a structural adjustment, optimization, and innovation. Moreover, the restoration and conservation of marine resources have been intensified, helping to improve the marine environment. Furthermore, a coordination between the two is also being accelerated and will eventually reach a state of senior coordination.

5. Factors Affecting Coordination between the Marine-Environment Quality and Marine-Fishery-Economy Quality

5.1. Model Specification

Coordination between the marine-environment quality and marine-fishery-economy quality is affected by many internal and external factors and accurately identifying the various factors is important for improving the coordination between the two. Based on the existing situation in China and the available literature, we selected the marine fishery industry strength (SFI), marine fishery economy scale (SFE), marine fishery production capacity (SFP), marine resource environment quality (SCQ), and marine-environment quality (SBQ) as the main influencing factors. We also took the output value of marine fishery, the output of marine products, the year-end ownership of marine production fishing vessels, the relative annual change in the sea level, and the total amount of phosphorus in directly discharged marine wastewater as the specific indicators. The following random-effect Tobit model was established:

$$Cor={\beta }_0+{\beta }_{1}Ln\left(SFE\right)+{\beta }_{2}Ln\left(SFE\right)+{\beta }_{3}Ln\left(SFP\right)+{\beta }_{4}Ln\left(SCQ\right)+{\beta }_{5}Ln\left(SBQ\right)+\epsilon$$

(10)

where Cor is the dependent variable, which represents the coordination between the marine-environment quality and marine-fishery-economy quality. The value is [0, 1], i = (0, 1, 2, ….., 5) is an undetermined coefficient, and ε is the random error term. In order to explain the results, the percentage of the coefficients and independent variables were logarithmized. In order to evaluate the robustness of the Tobit model, the fixed effect least square method (Model 1), the random effect least square method (Model 2), the mixed model Tobit (Model 3) and the random adaptive model Tobit (Model 4) with the same dependent and independent variables were used for a comparison.

5.2. Descriptive Statistics

As shown in

Table 6. Descriptive statistics of the variables.

Variable	Symbol	Observations	Mean Value	Standard Deviation	Minimum Value	Maximum Value
Coordination level	Cor	110	0.418749	0.106973	0.177825	0.603891
Strength of fishery industry	SFI	110	4.679636	1.107592	1.850000	6.370000
Scale of the fishery economy	SFE	110	5.003798	1.514934	1.343961	6.652475
Fishery production capacity	SFP	110	3.719994	1.164299	0.826672	5.534104
Quality of marine resource environment	SCQ	110	4.349157	0.399793	3.218876	5.036952
Quality of marine ecological environment	SBQ	110	4.909854	1.139169	1.609438	6.772165

, the average coordination between the marine-environment quality and marine-fishery-economy quality from 2011 to 2020 was 0.4187. This indicates that the overall coordination between the marine fishery economy and the marine environment was low. The overall standard deviation of all indicators was small, which means that the sample statistics and the overall parameter values were relatively close, and the sample was representative.

5.3. Empirical Results Analysis

As shown in

Table 7. Model regression results.

Variable	Model 1 Fixed-Effect OLS	Model 2 Random-Effect OLS	Model 3 Hybrid Model Tobit	Model 4 Random-Effect Tobit
SFI	0.0295 **	0.0329 ***	0.0130	0.0330 ***
	(0.00768)	(0.00731)	(0.0202)	(0.00523)
SFE	0.0497 **	0.0412 **	0.0806 ***	0.0411 ***
	(0.0198)	(0.0144)	(0.0150)	(0.00863)
SFP	0.0359 *	0.0240 *	−0.0248	0.0238 **
	(0.0178)	(0.0125)	(0.0223)	(0.0104)
SCQ	−0.0122 ***	−0.0129 ***	−0.0130	−0.0129 ***
	(0.00183)	(0.00189)	(0.0128)	(0.00268)
SBQ	−0.0107 ***	−0.0107 ***	−0.0115	−0.0107 ***
	(0.00172)	(0.00190)	(0.00735)	(0.00163)
Constant term	0.00410	0.0788	0.160 **	0.0788 **
	(0.101)	(0.0596)	(0.0604)	(0.0382)
var(e.y) sigma_u sigma_e				0.0402 ***
			0.000992 **	(0.00986)
			(0.000248)	0.00958 ***
				(0.000692)
N	110	110	110	110

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

, the significance of the estimated values of the SFI, SFE, SFP and C in Model 1 was significantly lower than that in Model 4, and the significance of the estimated values of the SFE, SFP and C in Model 2 was significantly lower than that in Model 4. Meanwhile, the SFI, SFP, SCQ and SBQ in Model 3 passed the significance test, and the estimated values of each independent variable in Model 4 well passed the statistical significance test; therefore, the calculation effect of Model 4 was the best. This shows that the random effect Tobit model is reasonable and feasible. The empirical results of Model 4 show that the strength of marine fishery industry, the scale of the marine fishery economy and the production capacity of marine fishery have a positive effect on the coordination of a marine fishery economy and marine environmental quality. Among them, the influence coefficient of the strength of the marine fishery industry was the highest, reaching 0.041 and passing the 1% significance test, which means that for every 1 percentage point increase, the coordination between the marine-fishery economy and marine-environmental quality would increase by 0.041 percentage points. The impact coefficient of the scale of the marine fishery economy was 0.033, which was the second largest factor affecting the coordination between the marine fishery economy and marine environmental quality, and which also passed the 1% significance test of statistics. This shows that if the scale of the marine fishery economy increased by 1 percentage point, the coordination between the marine fishery economy and marine environmental quality would increase by 0.033 percentage points. The influence coefficient of the marine fishery production capacity reached 0.023, and passed the 5% significance test. This means that the coordination between the marine fishery economy and marine environmental quality would increase by 0.023% for each percentage point increase in the marine fishery production capacity. In general, the strength of the marine fishery industry, the scale of the marine fishery economy and the production capacity of marine fishery are statistically important and highly stable determinants of the coordination between a marine fishery economy and marine environmental quality, and they play an important role in improving the coordination between the two.

The impact coefficient of the marine resource environment and marine ecological environment on the coordination of the marine fishery economy and marine environment quality was negative. Therefore, this paper adopted negative indicators for the data of the marine resource environment and marine ecological environment, while they also had a positive effect on the coordination of the marine fishery economy and marine environment quality. Among them, the impact coefficient of marine resources and environment was −0.0129, and this passed the 1% significance test. This shows that if the quality of the marine resources and environment increased by 1 percentage point, the coordination between a marine fishery economy and marine fishery environment quality would increase by a 0.0129 percentage point. The impact coefficient of the marine ecological environment quality was −0.0107, which means that if the marine ecological environment quality increased by 1 percentage point, the coordination between the marine fishery economy and marine environment quality would increase by a 0.0107 percentage point. This shows that the marine resources environment and the marine ecological environment are also statistically important and highly stable determinants of the coordination between a marine fishery economy and the quality of a marine environment. This is consistent with the expectation that improving the quality of the marine environment can increase the coordination between the marine fishery economy and the quality of the marine environment, which fully demonstrates that it is necessary to improve the quality of the marine environment.

6. Countermeasures and Suggestions

Given the current complex, severe situation, to improve the coordination between the marine-environment quality and marine-fishery-economy quality and to promote a coordinated evolution to an advanced stage, we should do the following:

(1) Promote the transformation and upgrading of the marine fishery industry. First, accelerate the innovation of fishery green science and technology, build a fishery green science and technology system, reduce the energy consumption in the marine fishery industry, form an effective linkage with the construction of the marine environment, and achieve the green, sustainable development of the marine fishery economy. Second, promote the digitalization of the marine fishery industry, and build a big data platform for the marine fishery economy. Third, transform the resource-dependent fishery development model, increase investment in marine environmental resource recovery and offshore fishery habitat restoration, and build a new model of marine resource conservation and fishery production with the coordinated development of fisheries, resources, and ecology. Fourth, promote the integrated development of marine fishery production, marine manufacturing, coastal tourism, and marine environmental protection industries, and cultivate new integrated cross-border businesses, such as marine leisure fisheries, marine biological products, and marine environmental protection.

(2) The scale of the marine fishery economy should be enhanced. First, develop modernized marine fishery and aquaculture. Innovate marine fishery breeding technology, build green marine ranches, promote fishery proliferation and release, promote healthy aquaculture, and improve the scale of marine fishery breeding. Moreover, guide the offshore and deepwater expansion of marine aquaculture and explore large-scale offshore deepwater cages, offshore aquaculture vessels, deepwater bottom seeding, and three-dimensional ecological aquaculture. Second, expand the space for marine fishing. Strengthen the cooperation among countries and regions in deep-sea fishing, and develop new deep-sea fishery resources, while also improving and upgrading deep-sea fishery equipment. Third, improve the quality and scale of marine-fishery-product processing. Innovate the processing of marine fishery products, cultivate new forms of marine fishery processing, and expand the industrial chain of marine fishery processing. Develop the deep processing of ocean aquatic products, innovate product forms, and extend product functions.

(3) Improve the marine-environment quality. First, link the land and sea environmental governance. Improve the overall planning system for the land and marine environments, strengthen the control of land-source pollution, and curb marine pollution from the source. Deepen the marine environmental governance, prioritize ecology, improve the marine-environment quality, and establish an integrated land-and-sea environmental governance system. Second, adhere to joint prevention and control, and utilize high technology to strengthen marine-environment monitoring and supervision. Promote marine-environment restoration and marine environmental protection, and strengthen the supervision and management of the marine environment. Third, improve the marine-environment compensation mechanism. Improve the laws and regulations related to marine-environment compensation; clarify the main body, responsibilities, methods, and standards of marine-environment compensation; and implement protection and compensation for typical ecosystems in important bays. Fourth, implement cross-regional joint ecological defense and governance, and strengthen the regional marine-environment space protection and governance. Strengthen cooperation with neighboring countries in the governance of the marine environment and promote the common governance of the international marine environment.

6.1. Discussion

The United Nations General Assembly pointed out in “Changing Our World: 2030 Agenda for Sustainable Development”, that “global warming, sea level rise, ocean acidification and other impacts of climate change have seriously affected coastal areas and low-lying coastal countries, including many least developed countries and small island developing States. The survival of many societies and various biological systems that support the earth is threatened” [37]. The change in marine environments has a significant impact on the marine economy of coastal countries and regions in the world, especially on the development of marine fishery economies. UNESCO pointed out that “At present, the degradation of the marine environment is intensifying, which has a negative impact on the structure and function of the marine ecosystem. By 2050, the global population is expected to reach 9 billion, which will exert greater pressure on the marine ecosystem” [38]. At the same time, the extensive development mode of the marine fishery economy has led to problems such as sea water oxidation, and marine garbage and pollutant concentration, which have caused serious damage to the marine environment and had a huge impact on world food security. How to improve the quality of the marine environment and promote the high-quality development of marine fisheries has become the focus of attention of all countries in the world. Consequently, the quality of the marine environment and marine-fishery-economy development should be improved from a diversified perspective. Promoting the coordinated development of the marine environment and marine fishery economy is not only a scientific issue, but also a practical issue. Studying the coordination of China’s marine environment and marine fishery quality is of great value to other countries and regions in the world, mainly reflected in the following two aspects:

First, studying the coordination of China’s marine environment and marine fishery quality provides a new research framework for other countries and regions to improve the quality of their marine environments and marine fishery economies. At present, the research on the economic quality of marine fisheries and the quality of marine environments is as shown in the previous literature review [39], where we have mostly analyzed the economic marine-fishery-quality and the marine-environment-quality as separate entities in a single analysis, or discussed the impact of changes in the marine environment on the development of marine fisheries. We believe that changes in the marine environment can affect the economic development of marine fisheries, and that extensive aquaculture and the overfishing of marine fisheries can lead to the deterioration of the marine environment [40,41]. We seldom discuss the coupling and coordination of the marine-fishery economic quality and marine environmental quality. The marine fishery economic system and the marine environmental system are two closely related systems that interact with each other, and they work together to form a diversified organism; therefore, we need to analyze the coupling and coordination relationship between the two from a systematic perspective to promote their common development and form a positive resultant force. We selected several indicators to construct the indicator system of the marine fishery economic quality and marine environmental quality, we measured their coordination using the coupling coordination model in physics, and selected the Tobit measurement model to measure the important factors affecting their coordination, revealing the coordinated evolution state, laws and regional differences of China’s marine fishery economy and marine environmental quality. This was not only conducive to breaking the traditional thinking of separating the marine environment and marine fishery development, giving play to the synergistic effect, but it also helped to provide a new research framework and method for other coastal countries and regions to study their marine fishery economies and marine environment quality.

Second, the study of the coordination of China’s marine environment and marine fishery quality provides decision-making reference for other countries and regions to improve the coordination of marine environment and marine fishery quality. We used the model to measure the scale of the marine fishery economy, the strength of the marine fishery industry and the production capacity of marine fishery. These aspects have a far greater impact on the coordination of a marine fishery economy and marine environmental quality than on the marine environmental quality, but this does not mean that the marine environment plays a small role in improving the coordination of a marine fishery economy and marine environment quality. On the contrary, the quality of the marine resources and marine ecological quality had a strong, statistically significant impact on improving the coordination of the marine fishery economy and marine environment quality, which means that to improve the coordination of the marine fishery economy and marine environment quality, we must pay close attention to the level of the marine environment quality. If we want to improve the coordination between the marine fishery economy and marine environment quality, therefore, we should not only promote the high-quality development of the marine fishery economy, but also accelerate the governance and protection of the marine environment. Only by deeply integrating these two aspects can we improve the level of coordination. This is an important reference for other coastal countries and regions in the development of economic and marine environmental policies for marine fisheries.

Of course, we have constructed an evaluation index system for the marine fishery economy and marine environment quality based on the existing research results, which reflects the strength of the marine fishery economy development and the marine environment construction level in a more comprehensive way. However, due to the limitation of the data and materials, some impact indicators have not been included in the evaluation indicator system, which will inevitably lead to certain limitations in the research, such as changes in the natural environment, labor quality, etc.; consequently, we need to further explore and improve the indicator system. At the same time, we focused on the spatio-temporal evolution characteristics of the coordination between the marine fishery economy and marine environmental quality. The spatial agglomeration analysis of the coordination between the marine fishery economy and marine environmental quality was weak, which will also be the focus of future research. In addition, we considered other work using a qualitative or different statistical method. We note the coupling relationship between the marine fishery economy and marine environmental quality at the global scale and recognize that policies and governance strategies are also valuable.

6.2. Conclusions

By measuring the coordination between the marine-environment quality and marine-fishery-economy quality, we analyzed the pattern of their coordination over time. The following conclusions were obtained.

First, the marine-environment quality and marine-fishery-economy quality are on the rise overall, showing an obvious spatial heterogeneity. In the initial stage, in terms of the quality of the marine fishery economy, during the study period of 10 years, the marine fishery economic quality of Shandong, Zhejiang, and Fujian remained at a high level, with a rapid development rate in a time sequence evolution. In terms of the spatial evolution, the marine fishery economy presented a distribution pattern of a large gap between the south and north and small gaps in the east. The average marine fishery economy of Shandong in the Northern Marine Economic Circle was 22 times that of Tianjin, and the gap was obvious. In the next stage, in terms of the marine-environment quality, during the study period of 10 years, the marine-environment quality of Jiangsu, Shandong, and Guangdong remained at a high level. In terms of the spatial evolution, the marine-environment quality presented a spatial distribution pattern of a large gap in the east and a small gap in the north and south. The marine-environment quality of Jiangsu in the Eastern Marine Economic Circle was twice that of Shanghai. The gap between the provinces in the marine-environment quality gradually widened. By 2020, the marine-environment quality of Jiangsu was 2.5 times that of Tianjin.

Second, the coordination between China’s marine fishery economy and the quality of its marine environment shows obvious volatility, but the overall trend is wave like, gradually moving to the intermediate stage. Liaoning, Shandong, Jiangsu, Zhejiang, Fujian, Guangxi and Guangdong had reached the primary level of coordination by 2020. Among them, Shandong and Guangdong were approaching the intermediate level. A coordination between the marine-environment and fishery-economy quality was biased. The marine-environment quality and coordination level were relatively high, but the marine-fishery economic quality was at a low level. Relying on the advantages of the marine-environment quality, this shows a high coordination, forming the negative effect of a high coordination value but a low economic quality. In terms of the spatial evolution, there was a significant difference between the coordinated evolution of the marine-environment quality and the marine-fishery-economy quality, showing a small difference in the south. The difference between the east and north was large. The interprovincial coordination of the marine-environment quality and the marine-fishery-economy quality was in a dynamic adjustment period. Most provinces/cities were in the barely coordinated and primary coordination stages, and only Shandong was in the intermediate coordination stage. Third, the scale of the marine fishery economy and the strength of the marine fishery industry are important factors that affect the coordination between the marine-environment quality and marine-fishery-economy quality. Through a model calculation, we found that the strength of the marine fishery industry, the scale of the marine fishery economy, the production capacity of marine fishery, the marine-environment quality, and the quality of marine resources and the environment had a positive effect on coordination, and that all passed the significance test. Among them, the impact coefficient of the marine fishery economic scale was the highest, reaching 0.04, followed by that of the strength of the marine fishery industry, reaching 0.03.