Spatial and Temporal Differentiation of the Coordination and Interaction among the Three Fishery Industries in China from the Value Chain Perspective

Abstract

The efficiency change, mutual cooperation, and interaction among the three fishery industries in China can accurately reflect the level of economic development within the industry. Studying the relationships between the three fishery industries under the existing structural system is conducive to enhancing the endogenous power and steady progress of the industry. Using the DEA-Malmquist model, gray correlation, impulse response, and variance decomposition methods, this paper focuses on the specific value appreciation process of the three fishery industries, namely, fishery capture and aquaculture (primary industry), aquatic processing (secondary industry), and recreational fishery activities (tertiary industry), in order to analyze the synergy and interactive response relationship among the three fishery industries during the period of 2003 to 2020 based on the value chain. We propose specific policy suggestions regarding the overall efficiency level and integration degree of the three fishery industries. The results show the following: (1) the efficiency of fishery capture and aquaculture (primary industry) and aquatic processing (secondary industry) show significant regional differences, and the change in trend in the efficiency of recreational fishery activities (tertiary industry) is better than that of the other two. (2) Most of the synergy degrees of fish capture and aquaculture efficiency, aquatic processing efficiency, and recreational fishing efficiency, are medium and above. (3) The interactions among the efficiencies of the three fishery industries in the country and that in different regions vary. From a national perspective, the efficiency of the fishery industries can be dependent on economic inertia. There is a regional heterogeneity among the interactive responses to the efficiency of the three fishery industries in China; the interaction of fishery value chain efficiency in the four economic regions differs in both strength and direction. Exploring the synergy and interactive response among the three fishery industries in China from the value chain perspective can provide a basis for the precise governance of different regional characteristics and help to modernize the fishery industry.

1. Introduction

From the acquisition, aquaculture, processing, and storage of fishery resources to the transportation, circulation, and sale of fish, the activities of the fishery industry provide fishery products for human consumption and provide input for other sectors, which directly or indirectly yields improved social and economic benefits. However, from 2000 to 2022, “the world fisheries and aquaculture status” released by the Food and Agriculture Organization (FAO) of the United Nations showed that the proportion of biologically sustainable levels ranged from 90% in 1974 to 64.6% in 2019, of which 57.3% reached the upper limit of sustainable fishing. The fishing industry cannot afford for fishery resources to be uncontrollably exploited and utilized. To cope with the resource-related environmental constraints faced by increasing grain production in land areas, the building “blue granaries” relying on vast sea areas and promoting of the development of fishery in inland water have become important measures for ensuring food security [1]. As a major global fishery producer, China plays an important role in fishery income, food security, nutrition, and employment. The “14th Five-Year National Fishery Development Plan” states that the level of modernization in the fishery industry needs to be improved, and the integration of the fishery industry needs to be enhanced so that all links within the fishery industry can be effectively connected and coordinated. In 2022, the “No. 1 Central Document” emphasized the need for the continuous integrational development of the primary, secondary, and tertiary industries in rural areas. Thus, central and local governments will have to focus on promoting the integration of the fishery industry and the connection between production and marketing. However, in the post-pandemic landscape, the endogenous impetus of the fishery industry is insufficient [2,3], all sections of the fishery industry are inefficiently interlinked, and the transformation and improvement of the fishery faces a potential impasse. In the future, there will be an urgent need to improve the overall construction of the fishery industrial chain and to accelerate the integrated development of the three industries. Based on the discussion above, studies that can effectively examine the correlation among the three fishery industries, understand the interactive relationship among them, and assess whether they are adequately suited for implementing the aforementioned enhancements in the coordination and mutual promotion of the three fishery industries, are of great importance.

The concept of industry convergence was first proposed in the foreign information industry [4]. In the information age, its essence is a kind of industry innovation in which the interaction and integration of related industries create a new emerging industry that uses the information industry as a platform. In recent years, the integrated development of the agricultural industry and other industries (namely the convergence of the three fishery industries in rural areas) has attracted the attention of some scholars. The convergence of the three industries in rural areas refers to the integration of the largest agricultural industries (the planting industry, forestry, animal husbandry, and aquaculture), the rural processing industry, and nonagricultural industries that are closely related to agricultural production. At present, research on the integration of these industries is mainly qualitative, exploring the impact of the integration of agriculture (and related industries) on rural poverty to provide a basis for developing countries with a less robust/established agricultural foundation to choose the optimal rural development path [5]. The existing research focuses on the process of integration in the three rural industries from the perspective of symbiosis and generally aims to identify a symbiotic model to realize the rural industrial integration system [6]. It combines e-commerce and rural finance with the integration of rural industries, analyzes the development dilemma of the agricultural industry, and then investigates the path e-commerce and rural finance could take to drive rural industrial integration [7,8]. Existing research on rural industrial integration in China mainly focuses on qualitative analysis, starting from large agricultural industries and focusing less on a specific agricultural industry. There are few quantitative studies on the integration of the three rural industries, and there is no unified way to measure the degree of integration. At present, the measurement of the degree of rural industrial integration mostly focuses on the entropy index and the coupling coordination degree [9,10]. In addition, considering technology, products, markets and other factors, the quantitative research methods for integration include the Herfindahl index method [11], the horizontal and vertical pull open grade method [12], and gray correlation analysis [13].

The value chain can help realize a final commodity’s value multiplier effect in the process of the integration of the rural primary, secondary, and tertiary industries. Rural industrial integration should be viewed from the value chain perspective, as this perspective makes it easier to reveal the interconnection of the three industries’ market subjects in real economic activities. Although foreign studies that analyze rural industrial integration from the value chain perspective are common, domestic studies in China are less common [14,15]. Fishery industry integration is an important part of rural industry integration. Studying the interaction among the three fishery industries from the value chain perspective holds important practical significance for realizing the integration of fishery and rural industries.

According to the standard presented in the Industry Classification of the National Economy (GB/T4754-2002), fishery economic activities can be divided into the following three categories: The primary fishery industry including fishing, breeding and aquaculture, which controls the growth and reproduction process of organisms by artificially utilizing the natural growth and self-reproduction process of fish, resulting in products that can be consumed without deep processing. The secondary fishery industry, also known as the fishery and construction industry, includes fishery processing, the manufacturing of fishery machinery and tools, the production of fishery feed and drugs, construction, etc. It is the industrial sector for the primary fishery industry and the products (raw materials) provided by the industry. The tertiary fishery industry, also known as the fishery circulation and service industry, refers to other fishery activities outside of the primary and secondary industries of fishery, including recreational fishing, fishery circulation, storage and transportation, etc. Fishery capture and aquaculture, aquatic products processing, and recreational fishery activities, respectively, belong to first, second, and third industry categories. Among them, fishery capture and aquaculture are the most important activities of the primary fishery industry. Fishery capture in this paper includes both marine fishery capture and freshwater fishery capture, and aquaculture mainly includes mariculture and freshwater aquaculture. Rough and fine processing for aquatic products are both important means of fishery industrialization [16]. Recreational fishery activities can use natural and human fishery resources to fulfill the leisure tourism function of a fishing village, improve fishery income and enrich the development space of the fishery. On the one hand, since fishery capture and aquaculture can be used to provide recreational resources such as fishery appreciation and production experience, the aquatic processing industry can support the development environment of the recreational fishery by providing characteristic fishery processing products and recreational experience projects [17]. This stage is greatly influenced by the primary and secondary industries and is promoted by the government. Recreational fishery activity plays an important role in further utilizing existing resources, in increasing fishery related products’ added value, and in extending the value chain. Therefore, the three fishery industries’ interconnection occupies a crucial position in the value chain comprising fishery capture and aquaculture, aquatic products processing, and recreational fishery. Value chain theory holds that the value chain is a process of integrating production factors together to shape various input links, forming a final product through assembly and finally completing the value cycle through market trade and consumption [18]. This paper takes labor, land, capital, and technology as the input elements, and the output value and output as the output elements. Then, it establishes the input-output relationships of the three fishery industries (Figure 1) based on the value appreciation process. From the perspective of input and output, the three fishery industries are inseparable. The literature generally takes the specific stage of fishery production or the whole fishery economy as the framework for measuring the correlation among different fishery industries and for exploring the degree of influence and the induction effect of a certain fishery industry on the whole fishery. Specifically, the literature includes the dual influence between fishery capture and aquaculture and recreational fishery [19], the driving effect of the aquatic processing industry on the upstream and downstream fishery industries [20], the influence of each fishery industry on the overall fishery economy from the value chain perspective [21,22], and so on. However, the academic community inadequately describes the interactions among the three fishery industries, and a quantitative analysis of the relationships among the three industries in the value chain is lacking.

This paper incorporates the primary, secondary, and tertiary fishery industries into the analysis framework, selects three specific appreciation processes in the fishery industries, i.e., fishery capture and aquaculture, aquaculture processing, and recreational fishery, and reveals the interconnection of the three industries from the value chain perspective. To make more targeted policy recommendations to improve the overall efficiency and degree of integration, it is necessary to systematically measure the efficiency of China’s three fishery industries and to further analyze the spatial differentiation of the efficiency of the interactive response in each stage of this specific value chain with the help of the impulse response and variance decomposition. Studying the coordination and interactive response among the three fishery industries in China can more accurately reflect the quality of fishery economic development in various regions, which is an important basis for accelerating industrial integrated development, improving the overall efficiency level and risk response ability of fishery, and dispensing precise governance based on different regional characteristics.

2. Materials and Methods

2.1. Research Methods

2.1.1. Calculating the Efficiency of the Three Fishery Industries

At present, China’s three fishery industries are facing the problem of transformation and upgrading. The area of fishery capture and aquaculture is gradually shrinking, and the scale of aquatic processing and recreational fishery is constantly expanding. As one of the main description parameters, efficiency can reflect the factor flow intensity and industrial agglomeration, which is the endogenous power and exogenous thrust of industry-coordinated development and regional economic integration construction. Therefore, using economic efficiency as a characteristic to research the interactions among fishery industries can fully reflect the direction, speed, and depth of future economic development [23]. With the help of DEAP 2.1, this paper chooses the Data Envelopment Analysis (DEA)-BBC model and Malmquist productivity index for efficiency analysis, which is in line with the purpose of analyzing the dynamic change in efficiency in each province. The input data and output data corresponding to n decision-making units are set as follows:

$$x_j=(x_{1j},x_{2j},\dots ,x_{mj}),j=1,2,\dots ,n$$

(1)

$$y_j=(y_{1j},y_{2j},\dots ,y_{rj}),j=1,2,\dots ,n$$

(2)

In the formula, $x_j$ represents the input element data vector of the $jth$ decision-making unit. $x_{mj}$ represents the $mth$ input element data of the $jth$ decision-making unit. $y_j$ represents the output factor data vector of the $jth$ decision-making unit. $y_{rj}$ represents the $rth$ output element data of the $jth$ decision-making unit. The BBC model with variable returns to scale is as follows:

$$\begin{gathered} \min \theta -\epsilon ({\displaystyle \sum _{j=1}^m{s}^{-}+{\displaystyle \sum _{j=1}^r{s}^{+})}} \\ s.t.\left\{\begin{array}{c}{\displaystyle \sum _{j=1}^n{\lambda }_j{x}_j+s^{-}=\theta x_{j0}}\\ {\displaystyle \sum _{j=1}^n{\lambda }_j{y}_j-s^{+}=y_{j0}}\\ {\displaystyle \sum _{j=1}^n{\lambda }_j=1}\\ {\lambda }_j,s^{+},s^{-}\ge 0,j=1,2,\dots ,n\end{array}\right. \end{gathered}$$

(3)

In the formula, $\theta$ is the efficiency value of the decision-making unit, $\epsilon$ is a non-Archimedean infinitesimal, $s^{+}$ and $s^{-}$ are the input and output relaxation variables, respectively, and ${\lambda }_j(j=1,2,\dots ,n)$ is the planning decision variable weight. If $\theta =1$, the $j0$ decision unit has weak DEA efficiency; if $\theta =1$, $s^{+}=s^{-}=0$, the $j0$ decision unit has DEA efficiency.

According to the results obtained from the DEA-BBC model, the Malmquist productivity index (total factor productivity (TFP)) is calculated based on the current frontier from period t to period t + 1:

$$M(x_{t+1},y_{t+1},x_t,y_t)={[\frac{D^t(x_t,y_t)}{D^t(x_{t+1},y_{t+1})}\times \frac{D^{t+1}(x_t,y_t)}{D^{t+1}(x_{t+1},y_{t+1})}]}^{-\frac{1}{2}}$$

(4)

In the formula, $D^t(x_t,y_t)$ represents the efficiency of the decision-making unit based on the frontier of stage t under the production of period t, and $D^{t+1}(x_t,y_t)$ represents the efficiency of the decision-making unit based on the frontier of stage t + 1 under the production of period t. When the scale reward is unchanged, TFP can be decomposed into the product of technological progress (TC) and technical efficiency (TE). When the scale return is variable, TE can be further decomposed into the product of pure technical efficiency (PE) and scale efficiency (SE), as follows:

$$M(x_{t+1},y_{t+1},x_t,y_t)=TFP=TC\times TE=TC\times (PE\times SE)$$

(5)

Assume that the base period efficiency is 1. Through changing the inefficiency value from 0 to 0.001 to facilitate the following calculation, the efficiency values of the three fishery industries in each year can be obtained.

2.1.2. Calculation of the Synergy of the Three Fishery Industries

The academic views on industrial synergy mainly focus on the synergy of industry with other factors, the synergy among industries, and the collaborative agglomeration among industries. Through the mutual cooperation of various industries, regional industrial agglomeration and integrated development can be effectively realized. For example, Ding [24] theoretically found, on the basis of analysis, that the improvement of traffic conditions can effectively promote the optimization of the industrial structure and a reasonable division of labor, and enhance industrial competitiveness; conversely, the stable and large-scale transportation demand resulting from industrial development ensures the efficient utilization of transportation infrastructure, so there is a certain degree of synergy between transportation and industry. In practice, combined with the operation of the Silk Road Economic Belt and the China-Europe freight train, it is found that the new transportation channel can support the industrial development model, and the optimized layout of the industry can be used to establish a multi-level comprehensive transportation network, confirming the synergistic effect. By evaluating the coupling coordination degree of digital infrastructure, industrial digitalization and digital industrialization, Li and Li [25] assess the synergy development level of the digital economy industry in the middle and lower reaches of the Yangtze River and, in turn, strengthen the internal synergy level of urban agglomerations. Zhong [26] claimed that through overall optimization and efficient integration, each industrial unit can achieve dynamic optimization and evolve from primary to advanced, from simple to complex, and from disordered to orderly, which can form an endogenous growth mechanism of reciprocity and win-win cooperation, realize the efficient development mode of the integration of each industrial unit, and achieve the goal of becoming an industrial collaborative agglomeration.

According to the input-output relationship and efficiency calculation method of the value chain process, there is a certain degree of association among the efficiencies of the three fishery industries. This paper adopts the concept of industrial synergy to discuss the relationships among three fishery industries, and this is essentially based on the convergence of coordination among industries to analyze the correlation. Here, this paper uses the gray correlation degree model to measure the degree of synergy, and the gray correlation degree result is defined as the synergy degree. Considering three sequences $x_i=[x_i(1),x_i(2),\dots ,x_i(n)],i=1,2,3$, $x_i(n)$ represents the $nth$ value in the $ith$ sequence. Let $x_1$ be the parent sequence and the rest be the subsequence. Then, the calculation formula for the synergy degree between sequence $x_1$ and sequence $x_r(r=2,3)$ is as follows (similarly, the synergy degree between the parent sequence ($x_2$, $x_3$) and other sequences can be obtained):

$$x_i^{\prime }(k)=\frac{x_i(k)-\min (x_i)}{\max (x_i)-\min (x_i)},i=1,2,3,k=1,2,\dots ,n$$

(6)

$$z_r(k)=|x_1^{\prime }(k)-x_r^{\prime }(k)|,r=2,3$$

(7)

$${\xi }_{1r}=\frac{1}{n}{\displaystyle \sum _{k=1}^n\frac{{\min }_k(z_2,z_3)+\rho {\max }_k(z_2,z_3)}{z_r+\rho {\max }_k(z_2,z_3)}}$$

(8)

In the formula, the value of the resolution coefficient $\rho$ is generally 0.5, and the range of the synergy degree is [0, 1]. The larger the value is, the higher the degree of synergy between two sequences. The synergy degree is usually considered low when the value range is [0, 0.4], medium when the value range is (0.4, 0.6], high when the value range is (0.6, 0.8], and strong when the value range is (0.8, 1] [27].

2.1.3. Calculation of the Interactive Impulse Response of the Three Fishery Industries

Signal, system and circuit theory contains the concept of function, while economics generally expresses the degree to which a variable is impacted and affected by other variables as the impulse response, which specifically refers to the impact and influence of a one standard deviation change in a variable on other variables. Namely, the impulse response function describes the short-term influence of the impact of one endogenous variable on other endogenous variables in a panel vector autoregressive (PVAR) model and it shows how the variables in the model respond to the shock. In this paper, the efficiencies of the three fishery industries (e1, e2, and e3) are selected as variables to study the interactive response of efficiency among the three industries. Variance decomposition quantitatively grasps the influence among variables, showing the long-term dynamic change relationship among the variables. The PVAR model can specifically demonstrate the short-term effects of the variables in impulse response analysis. With the aid of stata16, the PVAR model is described as follows:

$$Y_{i,t}=A_0+{\displaystyle \sum _{j=1}^s{A}_i{Y}_{i,t-j}+{\eta }_i+{\gamma }_t+{\mu }_{i,t}}$$

(9)

In the formula, $Y_{i,t}={(e{1}_{i,t},e{2}_{i,t},e{3}_{i,t})}^{\prime }$, $i$ represents the province, $t$ represents time, ${\eta }_i$ is the individual effect column vectors, ${\gamma }_t$ is the time effect column vector, ${\mu }_{i,t}$ is white noise, $s$ is the optimal lag order, $A_0$ is the intercept term, and $A_j$ is the parametric coefficient matrix of lag order $j$.

2.2. Data Sources and Description

This paper takes 31 provinces as samples. In view of China’s first official proposal of “recreational fishery” in 2001, the statistical data for this index have only been available since 2003. Thus, economic fishery data from 2003 to 2020 are selected, and the statistical yearbook has no data after 2020.

In the index selection of the DEA-Malmquist model, the input elements include land, capital, labor, and technology. To reasonably evaluate the difference in the price of fish, the output is measured by the output value (

Table 1. Provincial input–output indicators.

Stage	I/O	Factor	Indicator
Fishery Capture and Aquaculture	Input	Land	Aquaculture area
		Labor	Number of workers in the labor force
		Capital	Number of fishing vessels
	Output	Value	Fishery capture and aquaculture value
Aquatic Product Processing	Input	Substance	Number of aquatic products used for processing
		Capital	Aquatic processing capacity
		Technique	Technology promotion agency
	Output	Value	Aquatic product processing value
Recreational Fishery	Input	Value	Fishery capture and aquaculture value
		Value	Aquatic product processing value
		Capital	Fixed asset investment in agriculture, forestry, animal husbandry and fishery
	Output	Value	Recreational fishery value

). In terms of fishery capture and aquaculture, the aquaculture area, the year-end ownership of fishing vessels, and the labor force are selected as land, capital, and labor inputs, and the output value of fishery capture and aquaculture is taken as the output factor [28,29]. It is necessary to explain the following: the aquaculture area focuses on the description of the input factor in the aquaculture industry. The year-end ownership of fishing vessels includes both motorized and nonmotorized fishing vessels, focusing on the description of the input factor in the fishing industry. The labor force refers to the fishery labor force from 2003 to 2007 (including professional and part-time employees; there are no temporary employee statistics in the yearbook) and the 2008–2020 fishery employees (including professional, part-time and temporary employees); the two indicate the number of workers in the fishery population who actually participate in fishery activities and obtain physical or monetary income because labor force heterogeneity is difficult to measure. In addition, less than 20% of temporary fishery employees live on fishery economic activities; therefore, temporary fishery practitioners from 2003 to 2007 can be ignored, and the labor force should be treated as the fishery labor force from 2003 to 2007, and fishery practitioners from 2008 to 2020. There are no significant differences between the two types of data. In terms of aquatic processing, the most important input elements are processing raw materials and processing enterprises. Limited by statistical data, processing raw materials are measured by the amount of aquatic products used for processing, and processing enterprises are measured by the aquatic product processing capacity, listing aquatic technology extension institutions as technical input, and the total value of aquatic processed products as the output factor. In terms of recreational fishery, this stage is greatly influenced by the primary and secondary industries and promoted by the government. Thus, the output value of the first two processes of the specific value chain is taken as the material and technical input in recreational fishery. Fixed-asset investment in agriculture, forestry, animal husbandry, and fishery is taken as the capital factor [30,31], and the output value of recreational fishery is taken as the output factor. The indicators above are from the China Fishery Statistical Yearbook, China Fishery Yearbook, and China Statistical Yearbook, with a time span from 2003 to 2020. A few missing data are filled in through interpolation.

3. Interpretation of the Results

3.1. Analysis of Efficiency Changes and the Characteristics of the Three Fishery Industries

3.1.1. Comparison of the Three Fishery Industries

Table 2. Average efficiency of the three fishery industries from the provincial perspective.

	Fishery Capture and Aquaculture				Aquatic Product Processing				Recreational Fishery
Province	TC	PE	SE	TFP	TC	PE	SE	TFP	TC	PE	SE	TFP
Beijing	1.677	0.953	1.157	1.028	1.249	1.146	1.000	1.227	1.115	1.186	1.008	1.041
Tianjin	1.237	1.000	1.078	1.093	1.636	2.687	0.824	2.056	1.575	1.379	1.396	1.557
Hebei	1.257	1.001	1.082	1.084	1.073	1.020	1.000	1.053	1.097	1.098	1.493	1.251
Shanxi	1.489	1.018	1.187	1.089	0.061	0.060	0.060	0.061	4.156	1.650	1.019	1.305
InnerMongolia	1.509	0.983	1.231	1.055	1.074	1.019	1.000	1.066	1.711	1.215	1.154	2.265
Liaoning	1.235	0.975	1.085	1.066	1.151	1.102	0.970	1.247	1.332	1.877	1.745	2.576
Jilin	1.480	1.073	1.155	1.118	1.043	1.054	1.000	1.090	1.327	1.201	1.195	1.441
Heilongjiang	1.447	1.001	1.196	1.096	1.188	2.349	1.009	2.170	1.132	1.438	1.076	1.021
Shanghai	1.212	1.000	1.081	1.080	1.466	1.051	1.000	1.855	2.032	1.001	1.911	5.856
Jiangsu	1.394	1.019	1.116	1.239	1.145	1.037	1.016	1.158	1.619	1.676	1.026	1.113
Zhejiang	1.317	1.009	1.078	1.099	1.070	1.000	0.987	1.056	2.064	1.355	1.025	1.063
Anhui	1.377	1.004	1.175	1.104	1.088	1.094	1.009	1.104	1.076	1.690	1.109	1.166
Fujian	1.451	1.002	1.092	1.072	1.093	1.005	0.986	1.062	1.991	0.989	1.103	0.948
Jiangxi	1.710	0.977	1.328	1.082	1.118	1.015	1.010	1.153	1.328	1.114	1.140	1.258
Shandong	1.365	0.977	1.116	1.072	1.091	1.000	0.965	1.051	1.419	3.227	1.064	1.456
Henan	1.881	0.910	1.544	0.955	1.130	0.997	1.000	1.043	1.051	1.225	1.269	1.318
Hubei	1.793	1.001	1.359	1.142	1.147	1.111	0.982	1.140	1.375	2.408	1.143	1.601
Hunan	1.786	1.007	1.428	1.121	1.050	1.103	1.011	1.123	0.994	1.158	1.163	1.379
Guangdong	1.443	1.000	1.106	1.084	1.093	0.989	1.005	1.056	1.761	1.664	2.319	2.324
Guangxi	1.641	1.015	1.099	1.081	1.135	1.063	1.003	1.093	1.159	0.956	1.878	1.081
Hainan	1.411	1.000	1.086	1.105	1.155	1.071	1.000	1.220	2.344	1.496	1.688	2.624
Chongqing	2.294	1.028	1.812	1.131	0.946	1.513	1.019	1.301	1.423	1.135	1.050	1.418
Sichuan	4.203	1.000	2.484	1.168	1.332	1.169	1.012	1.188	1.007	1.628	1.752	4.934
Guizhou	2.026	1.162	1.305	1.351	1.332	3.291	1.000	3.686	1.258	1.072	1.325	2.322
Yunnan	2.136	1.020	1.647	1.155	1.248	1.159	1.000	1.124	1.242	0.995	0.923	0.879
Tibet	0.935	1.000	0.901	0.963	0.001	0.001	0.001	0.001	7.690	1.000	0.520	7.677
Shaanxi	1.698	1.162	1.324	1.305	0.500	5.899	0.706	5.916	2.089	1.958	1.448	1.351
Gansu	1.703	1.237	1.344	1.051	0.234	0.236	0.236	0.234	1.011	1.448	0.839	1.273
Qinghai	1.237	2.493	1.063	1.470	0.001	0.001	0.001	0.001	0.001	1.055	0.001	0.001
Ningxia	1.451	1.000	1.213	0.991	0.413	0.412	0.412	0.408	1.100	1.046	1.123	1.303
Xinjiang	1.394	1.056	1.148	1.148	0.819	1.082	0.824	1.023	0.945	1.339	0.963	1.210
Averages	1.619	1.067	1.259	1.116	0.975	1.255	0.841	1.298	1.683	2.223	1.655	1.893

shows the dynamic changes in the efficiency of the three fishery industries at the provincial level. It should be noted that the fishery capture and aquaculture efficiency representing the primary fishery industry is not simply the sum of aquaculture efficiency and fishery capture efficiency. The reason why these activities are referred to as fishery capture and aquaculture efficiency is that, on the one hand, fishery capture and aquaculture are important parts of the primary fishery industry. On the other hand, the traditional academic measure of fishery economic efficiency is achieved by bringing fishery capture elements and aquaculture elements into the input and output calculations of the fishery economy. Moreover, to maintain biodiversity and to restore ecology, the momentum behind China’s fishery capture has been reduced, thus the growth of the primary fishery industry is largely driven by the aquaculture industry. Therefore, for inland areas, the efficiency of fishery capture and aquaculture, representing the efficiency of the primary industry, can, at the right moment, be reflected solely by aquaculture efficiency.

From 2003 to 2020, the efficiency of the three fishery industries in China progressed by varying degrees. The annual growth rate of provincial fishery capture and aquaculture efficiency is 11.6%, that of aquatic processing efficiency is 29.8%, and the efficiency growth of recreational fishery is the fastest, with an average annual growth rate of 89.3%. For fishery capture and aquaculture, the rate of TC increases by approximately 61.9% annually, that of SE increases by approximately 25.9%, and PE grows slightly slower, at 6.7% per year. Compared with developed fishery areas, our country lacks mature industrial technology. Thus, the future growth of efficiency needs to be realized through intensive cultivation and high yields. For aquatic product processing, the average annual aquatic processing efficiency in a province is higher than that of fishery capture and aquaculture, but the rate of TC decreases by 2.5% and that of SE decreases by 15.9%, indicating that the current aquatic processing industry is in a bottleneck of scale expansion and technological innovation. With the continuous development of aquatic processing, there are many problems, such as the insufficient depth of processing and transformation of aquatic products as well as limited added value promotion. However, the accelerating research and development of aquatic processing technology, and the deep processing of aquatic products, will limit the further expansion of the aquatic processing scale. Therefore, finding the balance between scale expansion and technology improvement is key for improving the efficiency of aquatic product processing. Among the three fishery industries’ efficiencies in each province, the efficiency of recreational fishery improves the fastest, which is reflected in TC, PE, and SE increasing by 68.3%, 122.3%, and 65.5%, respectively. Since 2006, China has successively issued the policy of “building a resource-conserving and environmentally friendly society”, the strictest water resource management system, a series of fishing ban systems, “joint protection, not large-scale development”, and other environmental regulation policies. Fishermen have gradually returned to shore, and related enterprises have been looking for new fishery development paths. As a model of the integration of entertainment, production, and education, recreational fishery has a strong employment-driving effect. Compared with other industries, recreational fishery is more closely related to production and consumption in manufacturing, wholesale, and retail.

3.1.2. Regional Differences in the Efficiency of the Three Fishery Industries

(1)

Changes in fishery capture and aquaculture efficiency

The annual average growth rate of fishery capture and aquaculture efficiency in Qinghai is the highest, reaching 47%, and the annual average growth rate in Henan is the lowest, at −4.5%. The gap between the two is 51.5%, indicating that the provincial fishery capture and aquaculture efficiency in China has obvious regional differences. During the study period of this paper, apart from Qinghai, where the efficiency has increased year by year, the average annual change rates of efficiency in Jiangsu, Shaanxi, and Guizhou are all above 20%. However, the annual growth rates of efficiency in Henan, Xizang, and Ningxia are negative. The fishery structure system in these provinces is not perfect, the investment is insufficient, the overall production capacity is low, and local attention to the fishery industry is generally not high. In addition, this paper finds that the average annual growth rate of fishery capture and aquaculture efficiency in the inland provinces of China is generally higher than that in the eastern coastal areas. By evaluating the degree of comprehensive development in fishery modernization, China’s current fishery development mode has transitioned from catching to aquaculture [32]. The fishing industry in coastal developed areas is nearly saturated, and combined with the losses from fishery disasters, diminishing aquaculture input elements (such as the reduced aquaculture area and personnel losses), coastal fishery capture, and aquaculture industry, development is underpowered. In contrast, inland provinces are crisscrossed by rivers and dotted with lakes, making local freshwater fishery expand rapidly.

(2)

Changes in aquatic product processing efficiency

The average annual growth of aquatic processing efficiency in Shaanxi is nearly five times, which is closely related to the formation of the whole ecological industrial chain pattern. However, the aquatic processing industry in Tibet has no efficiency level, indicating that the polarization of aquatic processing efficiency in China is serious. Apart from Shaanxi, the average annual growth rates of aquatic processing efficiency in Guizhou, Heilongjiang, Tianjin, Shanghai, Chongqing, Liaoning, Beijing, and Hainan are all above 20%. Tianjin, Shanghai, Chongqing, and Beijing have given full play to the capital aggregation function of the municipalities directly under the central government, with a high utilization rate of resources and large market demand, providing a solid foundation for the intensive processing of fishery and aquatic products. Conversely, Tibet, Qinghai, Shanxi, Gansu, and Ningxia are either in a state of inefficiency or are declining year by year in aquatic processing efficiency. These provinces are deep inside the mainland, and for the aquatic product processing industry, which depends heavily on talent, capital, and technology, the efficiency loss is large. The reflux effect of capital is greater than the diffusion effect, and the natural geographical location, and social and economic conditions, seriously restrict the development of the fishery processing industry.

(3)

Changes in the efficiency of recreational fishery

The efficiency change trend in recreational fishery is good. Except for Qinghai, which is inefficient, and Yunnan and Fujian, which have small decreases in efficiency, the efficiency of recreational fishery in most provinces of China shows an increasing trend. The foundation of recreational fishery in Qinghai is weak, and there is no corresponding fiscal support. For these reasons, insufficient investment in the development and promotion of new varieties and technologies hinders local recreational fishery. The efficiency of recreational fishery in Fujian and Yunnan has improvement space of 5.2% and 12.1% through an improvement in the input-output structure and a reasonable expansion of scale, respectively. Inland areas can make full use of the advantages of lake resources and create income from accommodation, entertainment, and catering while increasing the unit value of fishery products. As the places of recreational fishery spring up, coastal areas should explore representative recreational methods on the basis of traditional recreational fishery. With a change in residents’ ideas, an increase in disposable income and an extension of leisure and entertainment time, recreational fishery will create greater vitality in the future.

3.2. Analysis of the Synergy Degree of the Three Fishery Industries’ Efficiency

3.2.1. Time Dimension

Based on the usual division of the stages of the synergy degree, this paper takes 0, 0.4, 0.6, and 0.8 as the breakpoints to divide the degree of correlation of the three fishery industries.

Table 3. Synergy degree of the efficiency of the three national fishery industries.

Year	e1 as Parent Sequence		e2 as Parent Sequence		e3 as Parent Sequence
	e1 and e2	e1 and e3	e1 and e2	e2 and e3	e1 and e3	e2 and e3
2004	0.510	0.481	0.548	0.756	0.520	0.756
2008	0.870	0.864	0.873	0.820	0.867	0.820
2012	0.650	0.631	0.650	0.858	0.637	0.858
2016	0.724	0.673	0.740	0.873	0.692	0.873
2020	0.739	0.700	0.738	0.880	0.700	0.880

shows the synergy degree among e1, e2, and e3 from the national perspective (since the base period efficiency is set to 1, the synergy degrees of the three fishery industries’ efficiency in 2003 are not considered). The results show that the vast majority of pair-to-pair coordination is highly synergistic or above and that a few cases are medium synergistic. Among them, the synergy of e1 and e2, and of e1 and e3, shows an “up-down-up” trend, reaching a strong synergistic state in 2008, then decreasing, and showing a slow rising trend. The synergy of e2 and e3 is stable in each year, maintaining a strong synergistic state except in 2004. The results above fully indicate that the interaction among the efficiencies of the three fishery industries is more obvious under the value chain. In addition, the synergy among e1, e3, and e3 is relatively stable, and currently, high and stable fishery industry efficiency coordination demonstrates the organic combination and coordinated development of the three fishery industries under the appropriate scale and appropriate proportion.

3.2.2. Spatial Dimension

(1)

In all provinces, the synergy among the efficiencies of the three fishery industries is medium and above, and only a few provinces have extremely high synergy. According to the calculation method, a high synergy degree only indicates that the interaction between the two is strong and does not mean that the interaction is developing in a positive direction. Although the synergy degree in Qinghai and Tibet is in a strong range, owing to the inefficiency level state in the two provinces, we judge that the efficiency coordination between industries should be negative. This is manifested in the strong mutually inhibitory relationship, leading to the inefficient state of fishery development. If the two places can promote the construction of the three fishery industries in the future, the synergy degree of mutual inhibition among the three fishery industries is expected to become mutually reinforcing and accelerate fishery development. The synergy in the eastern coastal provinces, including Hebei, Liaoning, Jiangsu, Zhejiang, Fujian, and Shandong, shows high synergy in the forward direction. The situation of extremely high synergy is less common. Fujian and Shandong are constrained to a certain degree of sustainable development, and the construction scale in seed multiplication farms and germplasm resource protection areas is lower than the national average level. In Liaoning, the promotion of fishery technology is low, the proportion of nontraditional fishermen is small, and the constraint of the fishery management system is high. Other provinces are more or less limited in terms of fishery funding support, the industrial scale and the degree of mechanization [33,34,35].

(2)

The efficiency synergy of the three fishery industries from the provincial perspective shows significant regional differences (Figure 2). Firstly, regarding the efficiency between fishery capture and aquaculture and aquatic product processing, spatially, provinces with a high degree of coordination are concentrated in the eastern coastal areas (Jiangsu 0.798, Zhejiang 0.667, Fujian 0.902, Shandong 0.748, Guangdong 0.679, Hainan 0.808) and the Yangtze River basin (Anhui 0.660, Jiangxi 0.646, Hubei 0.734, Hunan 0.675, Sichuan 0.768). The industrial gradient of these regions is high, and the driving effect of the regional growth pole makes the regional economy obtain overall growth. The fishery economy improves the input-output efficiency and pulls the relationship between fishery capture and aquaculture and aquatic processing. Secondly, regarding the efficiency between fishery capture and aquaculture and recreational fishery, most of the provinces with high coordination are gathered in northern China (Heibei 0.657, Shanxi 0.606, Inner Mongolia 0.674, Liaoning 0.678, Jilin 0.668, Heilongjiang 0.671) and southwest China (Hunan 0.724, Chongqing 0.696, Sichuan 0.768, Guizhou 0.696). On the basis of the efficiency calculation method in this paper, the main reason for the high synergy between e1 and e3 may be that the market supply of recreational fisheries in these areas stems from fishery capture and aquaculture. The investment of intermediate products forms the input elements for the recreational fishery sector and the demand market for the fishery capture and aquaculture sector. Thirdly, regarding the efficiency between aquatic product processing and recreational fishery, the developed eastern coastal areas (Heibei 0.626, Liaoning 0.691, Jiangsu 0.646, Zhejiang 0.649, Fujian 0.640, Shandong 0.649) have a high degree of synergy through the intermediate input mechanism and the regional pull mechanism. Southwest China (Chongqing 0.746, Sichuan 0.669, Guizhou 0.708, Yunan 0.662) is mountainous, the water level changes rapidly, and the fishery capture and aquaculture industry is highly constrained. However, the agglomeration of enterprises and its externalities in this region have formed a mutually promoting effect between technological innovation and the promotion of leading enterprises and can make full use of the primary and secondary fishery industries to drive the tertiary industry.

It is necessary to further analyze the direction and degree of interaction among the efficiencies of fishery capture and aquaculture, aquatic processing, and recreational fishery. This study focuses on exploring the interactive response of the efficiency of the three fishery industries in different regions to accelerate industrial integrated development and improve the overall efficiency level and risk response ability of fishery.

3.3. Interactive Response Analysis of the Efficiencies of the Three Fishery Industries

3.3.1. National Dynamic Transmission Process of Efficiency among the Three Fishery Industries

The sequence data including e1, e2, and e3 were tested for stationarity by using the Levin–Lin–Chu (LLC) and the Im–Pesaran–Shin (IPS) methods, and we obtained stable data for each variable. Before the impulse response, the optimal lag order of the model was determined to be 3 by using the Akaike information criterion (AIC), Bayesian information criterion (BIC), and Hannan–Quinn information criterion (HQIC) minimization criteria. The PVAR (3) model was established, and the impulse response function was analyzed in Figure 3. In the figure, the horizontal axis s is the number of lag periods, the middle dotted line is the zero scale line, the red solid line is the degree of the impulse response function, and the solid lines on both sides are 95% confidence intervals. The figure shows that the interactive influence of the efficiency of the three fishery industries is weak and that the effect is limited in the short term. Next, the exogenous impact and interrelationships among e1, e2, and e3 in the country as a whole were investigated.

(1)

Influence analysis of the exogenous impact. Firstly, fishery capture and aquaculture serve as an exogenous impact factor: e1 has no significant impact on e2. e1 has a continuous positive impact response on e3, which reaches the highest intensity in the first lag phase and then gradually decreases, showing a drag-tail trend. These results show that improving fishery capture and aquaculture efficiency can promote recreational fishery efficiency in the short term but, in the long term, this positive promoting effect gradually decreases and tends toward stability. The impact of fishery capture and aquaculture on the efficiency of aquatic product processing is not fully reflected. Secondly, aquatic processing efficiency serves as an exogenous impact factor: e1 has a weak negative response after being impacted by e2. The impact of e2 on e3 is the opposite to the effect of e1 on e3 and is an obvious negative effect. Improving e2 suppresses the progress of e3, and the negative effect is maximized in the first phase, then gradually decreases to 0 in the fifth phase. That is, aquatic processing efficiency has a great inhibitory influence on recreational fishery efficiency in the early stage but, in the long term, this effect will gradually converge to 0. Finally, recreational fishery efficiency serves as an exogenous shock factor: e3 promotes both e1 and e2, reaching peaks in the first and third phases, respectively. Compared with e1, e3 has a longer action range and a longer influence on e2. In the past decade, the state has vigorously promoted the structural reform of the supply side of fishery, extended the value chain of fishery, and continuously expanded new functions of fishery so that, in turn, the efficiency of the tertiary industry supports the further improvement in the efficiency of the primary and secondary industries.

(2)

Impact analysis among the efficiencies of the three industries. The national perspective reflects the weak interaction between fishery capture and aquaculture efficiency and aquatic processing efficiency. However, according to the synergy analysis above, there is no obvious interaction between the two. The reason for the lack of an obvious interaction between e1 and e2 may be regional heterogeneity, as the interaction direction and intensity for e1 and e2 are different in various areas, and there will be an offset scenario at the national level. The efficiency of the primary and tertiary industries reflects the influence of two-way mutual promotion, indicating that the integration of the fishery industry is mainly the integration of the primary and tertiary industries. The efficiency of the secondary and tertiary industries is reflected in the negative inhibitory effect of e2 on e3 and the positive promoting effect of e3 on e2. The aquatic processing industry in the middle of the value chain lacks power and is still in a running-in state with a recreational fishery stage. On the whole, the development of recreational fishery can effectively promote the progress of the overall fishery industry.

3.3.2. Variance Decomposition of the Efficiencies of the Three National Fishery Industries

Table 4. Variance decomposition.

Period	Variable	e1	e2	e3	Period	Variable	e1	e2	e3
1	e1	1.000	0.000	0.000	15	e1	0.997	0.003	0.000
1	e2	0.000	1.000	0.000	15	e2	0.010	0.987	0.003
1	e3	0.002	0.000	0.998	15	e3	0.016	0.080	0.904
5	e1	0.999	0.001	0.000	20	e1	0.997	0.003	0.000
5	e2	0.003	0.994	0.003	20	e2	0.011	0.986	0.003
5	e3	0.012	0.080	0.908	20	e3	0.017	0.080	0.903
10	e1	0.998	0.002	0.000	30	e1	0.997	0.003	0.000
10	e2	0.008	0.989	0.003	30	e2	0.011	0.986	0.003
10	e3	0.015	0.080	0.905	30	e3	0.017	0.080	0.903

Note: The horizontal variables are the decomposing variables, and the vertical variables are the variables being decomposed. The same applies below.

gives the variance decomposition results in periods 1, 5, 10, 15, 20, and 30 from e1 to e3, and each variable is stable for all error terms separately. Specifically, the variance contributions of all variables are mostly influenced by their own, up to 99.7%, 98.6%, and 90.3%, which means that the long-term development of the efficiency of the fishery industry is influenced by itself and is relatively weakly influenced by the role of the efficiency of other industries. The long-term impact relationship of the efficiency of the three fishery industries is generally stable from a national perspective, but the impact relationship is weak, and the interaction degree among the three industries is low. The reason for this phenomenon is most likely that there is regional heterogeneity in the interactive response of the efficiency of the three fishery industries in China. Thus, the interactions among the three fishery industries should be further studied by region.

3.3.3. Dynamic Interaction Analysis of the Efficiency of the Three Fishery Industries in Each Region

To scientifically reflect the social and economic development of different regions, based on economic policy according to the standard of national statistics, China is divided into four economic regions: Eastern, Central, Western, and Northeast China. The eastern region includes Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. The central region includes Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan. The western region includes Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. Northeast China includes Liaoning, Jilin, and Heilongjiang. All regional fishery value chain efficiency samples were subjected to stability tests and cointegration tests, after differences if necessary, and were then used for interaction analysis. e1_1, e2_1, and e3_1 are the first-order lags of e1, e2, and e3, respectively, which can still show the interactive relationships among the variables after differencing. The impulse response diagram of the efficiency interaction of each industry (Figure 4) and the contribution degree of phase 30 (

Table 5. Phase 30 contribution degree in each region.

Region	Variable	e1	e2	e3	Region	Variable	e1	e2	e3
East	e1	0.805	0.190	0.005	West	e1	0.990	0.004	0.006
	e2	0.021	0.964	0.015		e2	0.393	0.604	0.003
	e3	0.124	0.247	0.629		e3	0.003	0.003	0.994
Midland	e1	0.749	0.101	0.149	Northeast	e1	0.928	0.039	0.033
	e2	0.166	0.796	0.038		e2	0.196	0.791	0.013
	e3	0.082	0.061	0.858		e3	0.307	0.013	0.679

) were obtained. Compared with the weak long-term interaction of the overall fishery efficiency in China, the efficiency of the three fishery industries has a significant interaction in the long term in each region.

The eastern region has obvious advantages in terms of its location, foundation, and factor aggregation [36]. It is the only region among the four economic regions where the efficiency interaction is basically positive, showing the two-way mutual promotion of efficiency (Figure 4a, Table 5) and reflecting the advantages of fishery economic development in the eastern and coastal areas of China. The efficiency of recreational fishery is most strongly promoted by the remaining efficiencies, with e1 and e2 contributing 12.4% and 24.7% to e3. Secondly, the efficiency of fishery capture and aquaculture is greatly affected by the efficiency of aquatic processing, contributing 19%, and the efficiency of recreational fishery has less influence on fishery capture and aquaculture efficiency. The efficiency of aquatic processing is more responsive to its own impact, and the other efficiencies have less impact on e2. The eastern region is currently dominated by positive interactions between e2 and e3 in the direction of the integration of the three fishery industries. On the one hand, the region has a rich fishery resource endowment, fishery industry integration has good momentum, and the development of the tertiary fishery industry is strongly supported by the primary and secondary industries. On the other hand, for the aquatic processing industry, which is the middle of the fishery industry link, its efficiency change is less affected by the efficiency stage of the other two industries. This result reflects the lack of effective connection among fishery capture and aquaculture, aquaculture processing, and recreational fishery in the three stages of the value chain. The integration of the three fishery industries needs to be further consolidated.

The central region shows two-way mutual promotion between e1 and e2, bidirectional inhibition between e1 and e3, the first negative and then positive effect of e2 on e3, and the negative effect of e3 on e2 (Figure 4b, Table 5). Anhui, Jiangxi, Hunan, and Hubei in Central China are located in the inland areas of the middle and lower reaches of the Yangtze River. Although there are many tributaries of the water system, the degradation of freshwater fishery resources has made the fishing industry relatively small in recent years. Combined with various fishing regulations implemented since 2003, the area is mainly dominated by freshwater aquaculture and aquatic product processing. Moreover, because of the close proximity to the Yangtze River Economic Belt, intensive colleges and universities, and large commodity demand diversity intensity, various positive external effects highlight the economies of scale and aggregate economic effects in this region [37], which realizes the optimal allocation of different production and management factors. All of this information indicates that fishery capture and aquaculture and aquatic product processing are the key points in this area. However, the lack of guidance of the tertiary industry in this region cannot lead to the positive integration of recreational fishery and the other two industries; e3 and e1, e3 and e2 have more or less mutual inhibition mechanisms. Going forward, the central region should try to establish all kinds of fishery industrial parks on the basis of the first two stages of fishery development. Through the development and utilization of the whole industry chain, a single and scattered recreational fishery will be introduced to guide the establishment of a positive connection mechanism among the three fishery industries and further promote the development of the tertiary fishery industry.

In the western region, the efficiency of the three fishery industries has the weakest interaction degree, which is mainly manifested as the interaction between fishery capture and aquaculture and aquatic product processing (Figure 4c, Table 5). Different from the central region, the interaction between fishery capture and aquaculture and recreational fishery and between aquatic product processing and recreational fishery in this region is low or not significant. However, according to the above description of the western fishery industry synergy degree, e1 and e3, and e2 and e3, have medium synergy and above. Therefore, within the mainland, the fishery industry is not an advantageous industry. Combined with low market demand and insufficient regional attention, external adverse factors restrict fishery modernization, coordination, and mutual promotion. In recent years, Sichuan, Yunnan, and Guizhou have successively built the whole industrial chain pattern of characteristic fishery. On the one hand, they help people out of poverty; on the other hand, they break the location stickiness and explore a new industrial development path through industrial transfer and undertaking. In the future, through a series of industrial assistance and support policies, the western region is expected to further establish the influence mechanism of interaction among the three fishery industries.

In the regional layout of modern fishery, Northeast China belongs to the functional expansion area, and its main direction is the development of suitable fishery resources and recreational fishery [38]. According to Figure 4d and Table 5, Northeast China shows a significant correlation between the efficiency of the primary and tertiary industries, and the contribution of e1 to e3 is as high as 30.7%. Northeast China can make better use of its local superior fishery resources to develop characteristic recreational fishery (such as ice fishing). Secondly, the final direction of interaction between e1 and e2 is all negative. The agricultural economic growth of the three northeastern provinces is slowing down, and the proportion of the fishery output value in Heilongjiang and Jilin is relatively low. Thus, the development of fishery largely depends on Liaoning. Fishery in Liaoning Province has a regional growth advantage but no department growth advantage. Given the hard economic system transformation in recent years, the main subjects of the fishery industry, such as large fisheries, cooperatives, and fishery processing enterprises, are eager to seek new development. The problems of information asymmetry and low efficiency of cooperation among various subjects lead to difficulties in the connection between fishery capture and aquaculture and aquatic product processing, thus resulting in the response relationship of mutual inhibition of efficiency.

4. Study Conclusions and Policy Recommendations

4.1. Study Conclusions

Through the typical value-added process of fishery capture and aquaculture—aquatic processing—recreational fishery, which reveals the coordination and interaction process among the three fishery industries in China, can help improve the fishery industry chain and enhance the value chain, realize the coordinated development of the three fishery industries, and promote fishery modernization. Based on the development level of the provincial fishery economy, this paper systematically calculates the difference in the efficiency of the three fishery industries from 2003 to 2020, analyzes the spatial differentiation of the interactive response with the help of the impulse response function and variance decomposition, and draws the following conclusions:

(1)

From 2003 to 2020, the efficiency of the three fishery industries in China improved to varying degrees, indicating that the overall performance of China’s fishery economy is good. Among them, recreational fishery, as an emerging industry, is based on the primary and secondary industries, and its efficiency is growing the fastest. The efficiency of fishery capture and aquaculture needs to be increased through intensive cultivation and high yields. The improvement in aquatic processing efficiency falls into a bottleneck in terms of scale expansion and technological innovation. The key to improving efficiency is to find the balance between scale expansion and technological improvement in the aquatic processing industry. In addition, the efficiency of fishery capture and aquaculture and aquatic product processing shows significant regional differences, the change trend in recreational fishery efficiency is good, and the efficiency of recreational fishery in most provinces shows a rising trend.

(2)

The synergy degree of the efficiency of the three fishery industries in China reflects the close connection and coordinated development of all links in the value chain of the fishery economy. In the temporal dimension, the efficiency of fishery capture and aquaculture, aquatic processing and recreational fishery in pairs shows high synergy and above. The high and stable synergy among the three fishery industries fully indicates the organic combination and coordinated development of the three fishery industries under the appropriate scale and appropriate proportion. In the spatial dimension, the synergy of pair-to-pair efficiency in all provinces is medium and above.

(3)

The interactions among the efficiencies of the three fishery industries perform distinctly in China and its different regions, so it can be seen that the interaction process of each link of the value chain in the fishery economy is dynamically changing and has regional differentiation. From the perspective of the country as a whole, fishery industry efficiency has a certain economic inertia in the short term, and the long-term mutual influence is generally stable. However, the influence relationship is weak, and the degree of coordinated development among the three industries is low. There is regional heterogeneity in the interactive response of the three fishery industries in China. The interactions of the efficiencies of the three fishery industries are different both in terms of strength and influence direction.

4.2. Policy Recommendations

The efficiency of China’s three fishery industries is insufficient in terms of scale expansion and technological innovation, and the coordination and interaction of the three industries show significant regional differences. Based on the conclusions above, this paper aims to provide policy suggestions for relevant government departments to coordinate the development level of the three fishery industries and to release the potential of the fishery economy.

(1)

The government should focus on balancing the development level among the three fishery industries and realizing an improvement in the overall efficiency level of fishery. Firstly, attention should be paid to optimizing the primary fishery industry production structure through the construction of marine pastures. The government needs to speed up the adjustment of the structure and layout of fishery capture and aquaculture, improve the structure of marine fishing operations, and give full play to the demonstration and promotion role of representative aquaculture and fishing pilot projects. Secondly, it is necessary to bring into full play the intermediate connecting role of the secondary fishery industry. It is necessary to strengthen the connection mechanism of the three fishery industries based on technological, market, and transportation aspects, and improve the overall efficiency level and resilience. In detail, the government should improve fishery processing technology and upgrade fishery equipment and facilities; consolidate the construction of the aquatic product trading market; cultivate the leading enterprises of aquatic products to extend the fishery industry chain; increase the density of the transportation network of land, water, and air; and promote the production and circulation of fishery through consumption. Thirdly, steady progress should be made in the tertiary fishery industry, thereby improving core competitiveness in the fishery industry. For example, the government can explore the integration points of fishery and related industries; deeply integrate fishery with technology, education and culture; cultivate various types of fishery management entities; and give full play to the vitality and creativity of each entity.

(2)

The government should deepen regional cooperation on the basis of policy support and technical assistance, implement the concept of synergetic development, and improve the synergy degrees of the three fishery industries in the country as a whole and in various regions. For instance, fishery counterpart assistance policies and mechanisms should be established. Specifically, it is important to give full play to the demonstration effect and spillover effect of provinces and regions with a high level of fishery economy. The central and western regions should simultaneously implement the strategy of “bringing in” and “going out”, optimize the fishery operation environment, introduce advanced fishery equipment and talent, and send professionals to learn key technologies. Through these measures, the sharing of resources and technologies among fishery enterprises, cooperatives and research institutes in the eastern and western regions will be strengthened, increasing vigilance against risks in order to lead the fishery economy in underdeveloped areas out of the bottleneck period.

(3)

Regional fisheries policies should be different to release the potential for the diversified development of local fisheries. Firstly, because of its high efficiency and basically positive interaction direction, Eastern China should make use of the “blue granaries” and “marine pasture” projects to push forward to the deep sea and develop high-quality mariculture. It is also clearly important to process aquatic products deeply with the help of advanced technological means, improve the level of fishery foreign trade, and promote pelagic fishery in an orderly manner. Moreover, the diversified agglomeration of the fishery economy needs to be accelerated through the eastern regional spillover effect, driving the progress of the fishery economy in the central and western regions. Secondly, under the present situation of mainly freshwater aquaculture, on the one hand, the central region should establish a fishery big data platform and promote the depth of environmentally friendly fishery development to make the fishery economy realize scale, precision, and intelligence. On the other hand, this area should focus on the development of the tertiary fishery industry and optimize the fishery industry structure around rural tourism, ecological protection, innovation, and entrepreneurship. Thirdly, with a low fishery industry efficiency level and fragile ecological environment, the western region should give priority to the development of fishery specialization. Examples include breeding local rare fish with alpine ice and snow melt water, purifying rivers through fishery multiplication and release, promoting ecologically healthy aquaculture models, building characteristic fishery industrial parks, and cultivating local brands. Fourthly, Northeast China should make full use of various water resources to develop a special tertiary fishery industry. It would also be a good choice to build a fishery cooperation platform with the countries and regions along the Ice Silk Road for the purpose of realizing the complementarity of fishery industries between China and the Arctic region.

5. Conclusions

Through the typical value-added process of fishery capture and aquaculture, aquatic processing, and recreational fishery activities, which reveal the coordination and interaction process among the three fishery industries in China, the fishery industry chain can be improved, the value chain can be enhanced, the coordinated development of the three fishery industries can be realized, and the modernization of China’s fisheries can be promoted. The study shows that the efficiency of the three fishery industries in China has improved to varying degrees, that most of the synergy degrees are at a medium level and above, and that the interactions among the efficiencies of the three fishery industries vary, both in the country and across different regions. Therefore, the government should focus on balancing the development level among the three fishery industries and on deepening regional cooperation on the basis of policy support and technical assistance. Regional fisheries policies should be customized to release the potential for the diversified development of local fisheries.