Spatial–Temporal Characteristics and Influencing Factors of Marine Fishery Eco-Efficiency in China: Evidence from Coastal Regions

Abstract

Marine fishery is an important part of China’s maritime power strategy. Improving the ecological efficiency of marine fishery is the inevitable way to achieve the sustainable development of fishery. Based on the perspective of industrial sustainable development, this study used the Super-SBM model to evaluate the ecological efficiency of marine fishery in 11 coastal provinces of China from 2011 to 2020. Combined with Malmquist index, Moran index and other methods, the spatial and temporal evolution characteristics were analyzed. On this basis, the Tobit panel model was used to explore the influencing factors of marine fishery eco-efficiency. The results show that: (1) From 2011 to 2020, the marine fishery eco-efficiency in the 10 coastal provinces and cities of China shows a clear trend of improvement, and the efficiency values in high-efficiency areas remain basically stable. The relative gap between efficient and inefficient regions remains significant. (2) From the perspective of spatial distribution characteristics, the ecological efficiency of marine fishery in coastal provinces and cities in China had no obvious spatial correlation, and showed a trend of cross-distribution between high-efficiency regions and low-efficiency regions. (3) The change of marine fishery eco-efficiency is the result of a variety of influencing factors. Fishery industrial structure, scientific and technological support levels and environmental regulation play a role in promoting the improvement of marine fishery eco-efficiency. Therefore, optimizing the structure of the fishery industry, improving environmental regulation and increasing investment in science and technology are all effective measures for local governments to improve the eco-efficiency of marine fisheries.

1. Introduction

The marine economy serves as a crucial driver for regional development in the modern era and significantly contributes to enhancing national strength. The 19th National Congress of the Communist Party of China (CPC) introduced the strategy of “adhering to the overall planning of land and sea and accelerating the establishment of maritime power”. Integrating the development of maritime power into the contemporary economic system, embracing the new concept of green development and enhancing marine ecological efficiency are pivotal to achieving sustainable progress in the marine economy. Modern marine fishery stands as one of the world’s four primary marine industries, with China being the largest global producer of marine aquatic products. In 2020, China’s marine product output reached 33.1438 million tons, accounting for 50.6% of the total global output. Of this, marine fishing accounted for 11.7907 million tons, and mariculture accounted for 21.3531 million tons [1]. The total value of China’s marine fishery production reached 87.471 billion dollars, underscoring its increasingly prominent role in bolstering the nation’s marine economic strength. However, with the rapid economic development in coastal areas of China and the deepening of marine resource exploitation, the traditional extensive development mode of marine economy is no longer suitable for the current concept of conservation and intensive and recycling of resources. Problems such as the depletion of fishery resources and deterioration of marine ecological environment appear [2], which seriously restrict the sustainable development of China’s marine fishery; these have become urgent problems that need to be solved in the development of China’s fishery economy.

Ecological efficiency, based on efficient resource utilization and minimal environmental damage, can assess the level and quality of economic development. As a result, it has emerged as a crucial indicator for measuring the sustainable development of the economy, resources and environment. It comprehensively reflects the degree of coordinated development among regional resources, economy, and ecosystems [3]. Schaltegger et al. introduced the concept of “ecological efficiency”. They defined it as the ratio between economic added value and environmental impact, emphasizing the coordination of economic and environmental benefits to create greater social benefits with reduced resource consumption and environmental impact [4]. The growing focus on sustainable development has led to an increased attention toward ecological efficiency. Currently, research on ecological efficiency primarily centers around the following aspects: (1) Research objects of ecological efficiency. Scholars have combined the concept of eco-efficiency with economic production practices, involving industry [5,6,7,8], energy [9,10], marine [11,12,13,14], agriculture [15,16], tourism [17,18], urban development [19,20,21,22] and many other fields. The economic and environmental performance was evaluated via an empirical method, and ecological efficiency was used as an optimization strategy in the actual production process. (2) Research methods of ecological efficiency. Scholars used analysis tools such as Principal Component Analysis [23], environmental resource ratio method [24], Ecological Footprint Analysis method [25], Stochastic Frontier method (SFA) [26,27] and Data Envelopment Analysis method (DEA) [28,29] to measure the ecological efficiency of different research objects. DEA and SFA are the most widely used methods, but DEA has obvious advantages: it does not need to set a production function and can handle multi-input and multi-output simultaneously. Tone et al. [30] incorporated relaxation variables into the objective function on the basis of the DEA model and built a non-radial and non-angular Slacks-Based Measure model (SBM), which has gradually become the mainstream model for measuring ecological efficiency. After a series of extensions, models such as the super-efficiency SBM model and non-expected output SBM model are formed. (3) Study on regional differences of eco-efficiency. Early studies mainly described and analyzed the results of eco-efficiency in different regions [31]. However, due to the differences of production technology in different time sections, it is difficult to reveal the complete change process and the evolution characteristics of regional differences by static evaluation of eco-efficiency only from the perspective of space. With the deepening of research and the maturity of research methods, spatial analysis methods, such as data spatial visualization, Kernel density, Theil index, spatial Markov chain, and exploratory spatial analysis, have been applied to investigate regional disparities and dynamic evolution of eco-efficiency distribution [32,33,34]. Research levels also involve different scales, such as country [35], province [36], special economic zone [37], and city [38]. (4) Study on influencing factors of ecological efficiency. Existing studies have covered industrial structure, economic level, government regulation and opening to the outside world, etc., and the DEA-Tobit model is usually used to investigate the impact degree of the above factors on ecological efficiency [39].

Throughout the relevant literature, ecological efficiency has been applied not only to the environmental performance evaluation of specific industries, especially high energy consumption and high-pollution industries, but also to the evaluation of overall economic and environmental coordination at different spatial scales. The concept of ecological efficiency has been well applied and developed. Specifically speaking, in the field of marine fisheries, ecological efficiency of marine fisheries refers to maximizing the benefits of marine fisheries and minimizing negative ecological benefits on the premise that the output and quality of marine fisheries meet social needs. Currently, scholars have basically reached a consensus on the resource and environmental issues facing the development of marine fisheries, but there is still no precise definition of the ecological efficiency of marine fisheries. The perspective on the ecological performance of marine fisheries is mostly limited to resource management [40], ecosystem [41], or carbon emissions [42], and few scholars have conducted systematic research on the ecological efficiency of marine fisheries from the overall perspective of industrial sustainable development. The theory of ecological efficiency has not been applied to the study of the sustainable development of marine fisheries. Based on this, from the perspective of the sustainable development of the marine fishery industry, this paper uses the super-efficiency SBM model to measure the ecological efficiency of marine fisheries in 11 coastal provinces in China from 2011 to 2020, and analyzes its spatiotemporal evolution characteristics using methods such as the Malmquist index and the Moran index. On this basis, in order to provide a feasible reference for improving the level of marine fishery ecological efficiency and realizing the sustainable development of Marine fishery economy, the Tobit panel model was used to analyze the factors influencing the ecological efficiency of Marine fishery.

2. Theoretical Analysis, Research Methods and Index Construction

2.1. Theoretical Analysis

In a broad sense, marine fishery includes not only marine fishing and mariculture, but also the value-added links of the industrial chain such as aquatic product processing, storage, transportation, circulation and service, as well as the supporting links such as seed breeding, fishery feed and fishery drug processing and fishery machinery manufacturing. It is an industrial network with horizontal and vertical systems. With the expansion of the scale of the marine fishery industry, energy resource consumption and pollutant emissions have gradually increased. At the same time, due to the decline in fishery resources and the deterioration of the offshore environment, the carbon sink function of fisheries has weakened, which has a negative impact on the ecosystem balance of coastal areas. According to previous studies, combined with the current situation of China’s marine fishery development, the impact of marine fishery industry on its eco-efficiency mainly comes from four aspects: The first is the resource consumption caused by offshore fishing and fuel burning of fishing vessels. The second is the energy consumption and pollutants of mariculture, including the fuel consumption of aquaculture fishing vessels, the power consumption of oxygen supply and feeding in ponds and factories, and the eutrophication of water bodies caused by fishing drugs and feeding. The third is the three wastes (waste gas, waste water and industrial residue) produced in the processing of aquatic products due to low raw material utilization and low-level processes. Lastly, the fourth is the loss of products in the transportation process, as well as the environmental pollution caused by the consumption of related service industries. In order to show the ways or means that different links in the marine fishery industry chain may affect eco-efficiency under the constraints of resources and environment, a schematic diagram of the impact process of the marine fishery industry’s eco-efficiency is constructed (Figure 1).

Based on this, the ecological efficiency of marine fishery is defined as the premise of reducing the consumption of resources and energy in the operation of the marine fishery industry, reducing the negative impact of the whole environment and maintaining a high level of income. The process of improving the ecological efficiency of marine fisheries is the process of internalizing negative externalities through a series of industrial optimization methods.

2.2. Research Methods

2.2.1. Super-SBM Model

By combing various works from the existing literature, it is found that the commonly used measurement methods of ecological efficiency mainly include Stochastic Frontier Analysis (SFA), Data Envelopment Analysis (DEA), etc. Considering that the SBM model can obtain a more accurate efficiency value than the traditional DEA model and can make up for the lack of CCR model in the case of when the multiple DMU is 1. This article regards marine fisheries as a complete industrial sector and uses the super-efficiency SBM model to calculate the ecological efficiency of marine fisheries in different provinces and cities along the coast of China. The super-efficiency SBM model with constant returns to scale is expressed as:

$$\begin{gathered} \mathrm{m}\mathrm{i}\mathrm{n}\mathsf{\rho }=\frac{\frac{1}{m}\sum _{i=1}^m\overline{x_i}/x_{ik}}{\frac{1}{s}\sum _{r=1}^s\overline{y_r}/y_{rk}} \\ \mathrm{s}.\mathrm{t}.\overline{x_i}\ge \sum _{j=1,j\ne k}^n{x}_{ij}{\theta }_j \\ \overline{y_r}\le \sum _{j=1,j\ne k}^n{y}_{rj}{\theta }_j \\ \overline{x_i}\ge x_{ik} \\ \overline{y_r}\le y_{rk} \\ \mathsf{\theta },s^{-},s^{+},\overline{y}\ge 0 \\ I=1, 2, \dots , m; r=1, 2, \dots , q; j=1, 2, \dots , n (j \ne k) \end{gathered}$$

(1)

where n decision-making units (DMUs) are composed of input m and output s. The vector form is expressed as x ∈ R^m and y ∈ R^s; x and y are matrices. s⁻ and s⁺ represent the slack of input and output.

2.2.2. Malmquist Index

The Malmquist index is mainly used to analyze the change of production efficiency after efficiency evaluation. In order to analyze the changes of a marine fishery eco-efficiency, this paper chooses the Malmquist index used by Zhou et al. (2010) [43]:

$$\mathrm{M}\left(x^{t+1},y^{t+1},x^t,y^t\right)={\left[\frac{E^t(x^{t+1},y^{t+1})}{E^t(x^t,y^t)}\times \frac{E^{t+1}(x^{t+1},y^{t+1})}{E^{t+1}(x^t,y^t)}\right]}^{1/2}$$

(2)

The index involves two single-period distance functions $E^t(x^t,y^t) \mathrm{a}\mathrm{n}\mathrm{d} E^{t+1}(x^{t+1},y^{t+1})$ with constant returns to scale, and two inter-period output distance functions $E^t(x^{t+1},y^{t+1}) \mathrm{a}\mathrm{n}\mathrm{d} E^{t+1}(x^t,y^t)$. If $\mathrm{M}\left(x^{t+1},y^{t+1},x^t,y^t\right)$ > 1, it indicates the technological progress. Moreover, the Malmquist index can be decomposed into technical efficiency change and technical change. Thus, (2) can be written as:

$$\mathrm{M}\left(x^{t+1},y^{t+1},x^t,y^t\right)=\frac{E^{t+1}(x^{t+1},y^{t+1})}{E^t(x^t,y^t)}\times {\left[\frac{E^t(x^{t+1},y^{t+1})}{E^{t+1}(x^{t+1},y^{t+1})}\times \frac{E^t(x^t,y^t)}{E^{t+1}(x^t,y^t)}\right]}^{1/2}$$

(3)

2.2.3. Moran’s Index

Moran’s index is a coefficient [44] used to judge the correlation between entities in a certain space. The value is usually in [−1, 1]. When Moran’s index is between [0, 1], it shows that there is spatial correlation, that is, adjacent entities have similar attributes or trends; Moran’s index is negatively correlated when it is less than 0; when the Moran index is equal to 0, it presents spatial randomness. The Moran index is divided into Global Moran’s I and Local Moran’s I, which are used to measure the existence of spatial autocorrelation and the specific location of spatial agglomeration.

2.2.4. Tobit Regression Model

The Tobit regression model is a model with limited dependent variables. When the value of the variable is cut or truncated, the Tobit regression model following the maximum likelihood method is a better choice. Since the efficiency values calculated by the data envelopment method are greater than 0, which belongs to the truncated case, the panel Tobit regression method is used to analyze the influencing factors.

2.3. Indicator Selection and Data Sources

2.3.1. Index System Construction

Combined with the connotation of marine fishery ecological efficiency, the input index is constructed from three aspects of economy, resources and environment, and the total output value of marine fishery is taken as the output index to construct the quantitative evaluation index system of marine fishery ecological efficiency (

Table 1. The quantitative evaluation index system of marine fishery ecological efficiency.

Target Layer	Criterion Layer	Variable	Indicator Layer
Eco-efficiency of marine fishery	Input indicators	Labor input	Marine fishery practitioners
		Fixed asset investment	Fixed asset stock of marine fishery
		Current asset investment	Intermediate consumption of marine fishery
		Natural resources input	Area of mariculture
		Environmental pollution input	Economic losses of marine fishery caused by pollution
	Output indicators	Output value	Total economic output value of marine fishery

). In terms of input indicators, at the economic level, refer to existing research (Sun Kang et al., 2017) [45] selecting the number of marine fishery employees as labor input, marine fishery fixed assets stock and marine fishery intermediate consumption as capital input; at the resource level, marine aquaculture area represents natural resource input; at the environmental level, because it is difficult to measure the direct environmental investment in marine fishery, the economic loss of marine fishery caused by pollution is used to reflect the input of environmental pollution. Among them, ① there is no direct data on the investment in fixed assets of marine fisheries from 2011 to 2020 in the ‘China Fisheries Statistical Yearbook’. This paper uses the investment in fixed assets of agriculture, forestry, animal husbandry and fishery to indirectly obtain the investment in fixed assets of marine fisheries. Considering the lag of the role of fixed assets, the perpetual inventory method is used to measure the stock of fixed assets over the years with 2011 as the base period. The specific formula is:

$${{\displaystyle k}}_{i,t}=(1-\delta ){{\displaystyle k}}_{i,t-1}+\lambda {{\displaystyle I}}_{i,t}$$

(4)

Among them, K_i,t and K_i,t−1 represent the stock of marine fishery fixed assets in region i in t and t − 1 years, respectively; I_i,t represents the constant price fixed asset stock of region i in year t; δ_i,t represents the depreciation rate of fixed assets; and λ represents the capital formation rate of fixed assets. The values of the two are based on the practice of Yu et al. (2020) [46]. ② The data of intermediate consumption of marine fisheries cannot be obtained directly from the yearbook. Drawing on the ideas of Qin et al. (2018) [47], the intermediate consumption of fisheries is used for conversion. The specific conversion formula is:

$$\begin{gathered} \mathrm{M}\mathrm{a}\mathrm{r}\mathrm{i}\mathrm{n}\mathrm{e} \mathrm{f}\mathrm{i}\mathrm{s}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{y} \mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{m}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{t}\mathrm{e} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{u}\mathrm{m}\mathrm{p}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}=\mathrm{F}\mathrm{i}\mathrm{s}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{y} \mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{m}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{t}\mathrm{e} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{u}\mathrm{m}\mathrm{p}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\times \frac{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{o}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t} \mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e} \mathrm{o}\mathrm{f} \mathrm{m}\mathrm{a}\mathrm{r}\mathrm{i}\mathrm{n}\mathrm{e} \mathrm{f}\mathrm{i}\mathrm{s}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{y}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{f}\mathrm{i}\mathrm{s}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{y} \mathrm{o}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t} \mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}} \end{gathered}$$

The results are converted into comparable prices in 2011 according to the price index of agricultural means of production. Since the data of marine fishery pollution indicators are difficult to obtain directly, the economic loss of marine fishery caused by pollution is used instead. Based on the idea of Xu et al. (2022) [48], the specific formula is:

$$\begin{gathered} \mathrm{M}\mathrm{a}\mathrm{r}\mathrm{i}\mathrm{n}\mathrm{e} \mathrm{f}\mathrm{i}\mathrm{s}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{y} \mathrm{e}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{c} \mathrm{l}\mathrm{o}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{s} \mathrm{c}\mathrm{a}\mathrm{u}\mathrm{s}\mathrm{e}\mathrm{d} \mathrm{b}\mathrm{y} \mathrm{p}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}=\mathrm{E}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{c} \mathrm{l}\mathrm{o}\mathrm{s}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{a}\mathrm{q}\mathrm{u}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{c} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{s} \mathrm{c}\mathrm{a}\mathrm{u}\mathrm{s}\mathrm{e}\mathrm{d} \mathrm{b}\mathrm{y} \mathrm{p}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\times \frac{\mathrm{E}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{c} \mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e} \mathrm{a}\mathrm{d}\mathrm{d}\mathrm{e}\mathrm{d} \mathrm{o}\mathrm{f} \mathrm{f}\mathrm{i}\mathrm{s}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{y}}{\mathrm{G}\mathrm{D}\mathrm{P}}\times \frac{\mathrm{S}\mathrm{e}\mathrm{a}\mathrm{w}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t} \mathrm{o}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{o}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{a}\mathrm{q}\mathrm{u}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{c} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{s}} \end{gathered}$$

In terms of output indicators, the total output value of marine fisheries was selected and reduced to a comparable price in 2011 according to the agricultural producer price index. Furthermore, the super-efficiency SBM model with constant returns to scale is used to measure the ecological efficiency of marine fishery.

2.3.2. Data Source

The period of this study is from 2011 to 2020. The research objects included 10 coastal provinces of Tianjin, Hebei, Liaoning, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Guangxi and Hainan in mainland China. Due to the serious lack of data in Shanghai, it was easy for the empirical results to deviate greatly. Therefore, this is not included in the study. The relevant data in the study are mainly derived from the “China Fisheries Statistical Yearbook (2012–2021)”, “China Fisheries Yearbook (2012–2020)”, “China Marine Statistical Yearbook (2012–2017)”, “China Environmental Statistical Yearbook (2012–2021)”, “China Rural Statistical Yearbook (2012–2021)” and “China Statistical Yearbook (2012–2021)”. A small amount of missing data is supplemented by the moving average method. The statistical software used in this study includes DEA-solver, DEAP 2.1, Stata and Eviews 8.0, and the chart visualization software includes Origin2021, ArcMap10.8 and GeoDA.

3. Empirical Analysis and Results

3.1. Marine Fishery Ecological Efficiency Calculation

According to Formula (1), the DEA-solver software is used to calculate the ecological efficiency of marine fisheries in China’s coastal areas from 2011 to 2020 (

Table 2. Ecological efficiency value of marine fishery in 2011–2020.

	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	Average Value	Ranking
Tianjin	1.0551	1.0818	1.1232	1.1789	1.1832	1.0988	1.1389	1.1375	1.0714	1.0380	1.1107	4
Hebei	0.2561	0.2810	0.3460	0.2989	0.3145	0.3034	0.2993	0.3190	0.3532	0.3812	0.3153	10
Liaoning	0.6867	0.7188	1.0878	1.0035	1.0264	1.0565	1.0571	0.6907	1.0185	1.0248	0.9371	6
Jiangsu	0.4205	0.4222	0.4325	0.4593	0.4638	1.0645	1.0454	1.0422	1.0757	1.1015	0.7528	7
Zhejiang	1.2387	1.0709	1.0128	1.0426	1.0257	1.0975	1.1403	1.1442	1.0952	1.1368	1.1005	5
Fujian	1.0873	1.1351	1.2681	1.2451	1.2832	1.2812	1.2870	1.3110	1.3232	1.4304	1.2652	2
Shandong	1.4754	1.4336	1.2280	1.2747	1.2223	1.1743	1.1534	1.1818	1.1918	1.1875	1.2523	3
Guangdong	0.5337	0.5515	0.6343	0.7364	0.6766	0.7288	0.7161	0.7623	1.0006	1.0166	0.7357	8
Guangxi	0.3715	0.4666	0.5333	0.5033	0.4719	0.5384	0.5522	0.5562	0.5612	0.5773	0.5132	9
Hainan	1.7520	1.6816	1.7141	1.6923	1.3578	1.4412	1.3371	1.3697	1.3823	1.2362	1.4964	1

). According to the relevant research and the situation of this paper, it is considered that the efficiency value less than 0.4 is relatively ineffective, 0.4–0.8 is relatively inefficient, 0.8–1 is relatively effective, 1–1.2 is weakly effective, and 1.2 is highly effective.

According to the changes of marine fishery eco-efficiency values and mean values over the years, the trend chart of marine fishery eco-efficiency values in coastal provinces from 2011 to 2020 has been drawn (Figure 2).

From the average calculated over the years in Figure 1, it can be seen that the eco-efficiency of marine fisheries shows a fluctuating upward trend, and the efficiency values are obviously different among regions. According to the characteristics of the data, it is divided into five categories: ① Highly effective, including Hainan, Shandong and Fujian provinces, showing that the efficiency value remains above 1.2 for a long time. The efficiency value of Hainan over the years has shown a fluctuating downward trend. The year 2014 is an important node for efficiency reduction, but the overall average is still 1.49 high. Shandong’s efficiency value continued to decline steadily, with a large decline from 2012 to 2013, and began to stabilize in 2016, with an average of about 1.25 over the years. Fujian’s efficiency value rises in gentle fluctuations, surpassing Hainan and Shandong in 2020 and ranking first in the country. From 2011 to 2013, the marine industry in various provinces and cities was in a period of rapid development. The degradation of offshore resources and environment has threatened the industrial sustainability in some areas. The high ecological efficiency of Hainan benefits from the superior marine environment and the tertiary industry structure. But, long-term low energy consumption and low output route will reduce the vitality of the industry, thereby reducing ecological efficiency. Shandong Province is a major marine fishery province, rich in fishery industry and marine scientific research strength. As a pioneer in the construction of marine ranching, Shandong has explored and practiced a systematic ecological farming model to restore offshore fishery resources to a certain extent, and further combined leisure tourism and modern service industries to create new economic growth points so that marine fishery ecological efficiency can be maintained at a high level for a long time. Fujian is also a traditional marine fishery province, the coastline length accounts for about 1/5 of the country, with the development of marine fishery resources, location and industrial base. In addition, Fujian’s import and export trade of aquatic products is active and the output value contribution is large. The integration with international standards is conducive to forcing the green and high-quality development of the fishery industry. ② Effective efficiency refers to the state where the average efficiency is close to 1, including Tianjin, Zhejiang and Liaoning provinces. The average eco-efficiency of Tianjin from 2011 to 2020 is about 1.11, which indicates that the input and output of marine fishery are coordinated. The eco-efficiency value of marine fishery in Zhejiang Province decreased first and then increased slowly, but the efficiency value remained stable at about 1.1, reflecting a high level of marine fishery ecological performance. In addition to maintaining a good marine resource environment, Zhejiang has also formulated strict regulations on the conservation of fishery resources and habitat protection, creating a beneficial industrial development environment. Marine fisheries in Liaoning Province were relatively inefficient in 2011 and 2012, and remained effective after growing to 1.09 in 2013. Until 2018, with the reduction in the scale of aquaculture and the reduction in total output value, it entered a painful period of structural adjustment of the fishery industry, followed by a new growth trend in 2019. ③ From invalid to effective, including Guangdong and Jiangsu provinces, the efficiency value is between 0.4 and 0.8. Guangdong and Jiangsu are provinces with more developed fisheries. The ecological efficiency of marine fisheries in Guangdong has fluctuated during the study period, with an average annual growth rate of about 7.4%. In 2019, the efficiency was effective, disposing of the disparity in the environmental impact of marine fishery activities among cities, and achieving an overall improvement in ecological efficiency. The efficiency value of Jiangsu Province rose from 0.42 in 2011 to 1.1 in 2020, with an average annual growth rate of 11.29%. In 2016, it completed a leap from relatively inefficient to efficient. ④ Relatively inefficient. Guangxi’s resources and environment are in good condition, but the scale of the marine fishery industry is small, the development mode is extensive, and the industrialization level is low, all of which reduce the ecological efficiency of marine fishery. ⑤ Relatively ineffective, with an efficiency value less than 0.4. Hebei is located in the Bohai Rim region, the scale of marine fisheries is not large, the marine pollution is serious and the ecological environment is fragile, causing the ecological efficiency of marine fisheries to remain at a low level for a long time.

3.2. Time Trend of Marine Fishery Eco-Efficiency Change

Based on the selected input and output indicators of marine fishery, DEAP 2.1 is used to measure the Malmquist index. The results obtained using the calculation Formulas (2) and (3) are shown in

Table 3. Total factor productivity index of marine fishery in China from 2011 to 2020.

Area	Comprehensive Technical Efficiency	Technological Progress	Pure Technical Efficiency	Scale Efficiency	Total Factor Productivity
Tianjin	1.000	1.056	1.000	1.000	1.056
Hebei	1.039	1.000	1.034	1.006	1.039
Liaoning	1.004	1.020	1.001	1.004	1.024
Jiangsu	1.079	1.035	1.074	1.005	1.117
Zhejiang	1.000	1.022	1.000	1.000	1.022
Fujian	1.000	1.062	1.000	1.000	1.062
Shandong	1.000	1.016	1.000	1.000	1.016
Guangdong	1.042	0.987	1.041	1.001	1.029
Guangxi	1.040	0.984	1.029	1.011	1.023
Hainan	1.000	1.023	1.000	1.000	1.023
Average value	1.020	1.020	1.017	1.003	1.041

and

Table 4. Dynamic analysis of marine fishery eco-efficiency.

Time	Comprehensive Technical Efficiency	Technological Progress	Pure Technical Efficiency	Scale Efficiency	Total Factor Productivity
2011–2012	1.0540	0.9480	1.0330	1.0200	0.9990
2012–2013	1.0530	0.9380	1.0220	1.0300	0.9880
2013–2014	0.9900	1.0510	1.0080	0.9820	1.0410
2014–2015	1.0200	1.0710	1.0220	0.9980	1.0920
2015–2016	1.0510	1.0970	1.0650	0.9870	1.1540
2016–2017	0.9940	1.0300	1.0020	0.9920	1.0240
2017–2018	1.0090	1.0160	1.0040	1.0060	1.0260
2018–2019	1.0020	0.9910	1.0020	0.9990	0.9920
2019–2020	1.0100	1.0510	1.0000	1.0100	1.0610
Average value	1.0200	1.0200	1.0170	1.0030	1.0410

. The changes of marine fishery eco-efficiency are analyzed from static and dynamic perspectives.

3.2.1. Static Analysis

In terms of regions, the technological progress of Tianjin, Zhejiang, Fujian, Shandong and Hainan is higher than the comprehensive technical efficiency, indicating that technological progress has a high contribution to the improvement of marine fishery ecological efficiency, and each province relies on technological innovation to achieve the goal in industrial ecology. At the same time, the pure technical efficiency of Tianjin, Liaoning, Zhejiang, Fujian, Shandong and Hainan is less than the technical progress, indicating that the digestion of existing technologies in the field is still insufficient, and it is necessary to use various factors efficiently to consolidate the development foundation. The scale efficiency of Hebei, Jiangsu, Guangdong and Guangxi is less than the pure technical efficiency, which is in the stage of increasing returns to scale. It can be moderately expanded to improve efficiency, while the scale advantage of the Guangdong marine fishery industry is gradually replaced, and technological innovation has become the source of industrial power.

3.2.2. Dynamic Analysis

From the perspective of time series, the dynamic changes of the decomposition indexes of marine fishery eco-efficiency from 2011 to 2020 are further analyzed, as shown in Table 4.

From the mean point of view, the total factor productivity of marine fisheries in 10 coastal provinces and cities in China from 2011 to 2020 was 1.041, indicating that total factor productivity has become the main driving force for the improvement of the ecological efficiency of marine fisheries in China, and the technological progress and total factor productivity in 2011–2020. The trend of change is basically consistent. It can be said that the main reason for the change of total factor productivity is technological progress. The comprehensive technical efficiency, technical progress, pure technical efficiency and scale efficiency are 1.02, 1.02, 1.017 and 1.003, respectively. It also shows that the total factor productivity of marine fishery is gradually driven by scale efficiency to technological progress, and pure technical efficiency has a significant effect on the improvement of comprehensive technical efficiency. In addition, the reason why the technological progress from 2011 to 2013 was less than 1 was that China did not pay enough attention to the ecological problems of marine fisheries during this period, and the fishery proliferation technology was relatively backward.

3.3. Spatial–Temporal Evolution Characteristics of Marine Fishery Eco-Efficiency

3.3.1. Kernel Density Estimation

In order to analyze the time evolution trend of marine fishery eco-efficiency, the four representative time nodes of 2011, 2014, 2017and 2020 were selected, and Eviews 8.0 was used to draw the corresponding kernel density curve (Figure 3).

Firstly, from the position point of view, the density function center of the four years gradually moved to the right. Among them, 2014 has a slight right shift compared with 2011, and 2017 has a significant left shift compared with 2014. The trend in 2020 and 2017 is similar. Compared with 2014, it has a tendency to converge to the high-efficiency interval. After 2014, with the adjustment of fishery policy and the upgrading of fishery structure, the development of marine fishery industry and the ecological environment have been gradually coordinated, and the ecological efficiency has been improved.

Secondly, in terms of shape, the four sample intervals showed different distribution characteristics of high-efficiency and low-efficiency areas. The slopes in 2011 and 2014 were relatively gentle. In 2017, the density value increased, and the high density was concentrated in areas with high ecological efficiency values. In 2020, the density value will continue to rise, high density will be more concentrated in high-efficiency areas, and the density of low-efficiency areas will decrease, indicating that the relative gap of ecological efficiency of marine fisheries in China is quite pronounced.

Thirdly, from the peak point of view, the ecological efficiency of marine fisheries gradually evolved from a wide peak to a sharp peak from 2011 to 2020. The peak value of high-efficiency areas increased significantly, the vertical distance of each year widened, and the peak value of high-efficiency areas was much higher than that of low-efficiency areas, indicating that the ecological efficiency of marine fisheries has been improved to a certain extent, but there is still a large gap between provinces.

3.3.2. Moran’s Index

Global Moran Index

We used the GeoDa [49] software to calculate the global Moran index of marine fishery eco-efficiency in China from 2011 to 2020 on the basis of adjacency weight, see

Table 5. The global Moran index of marine fishery eco-efficiency in China from 2011 to 2020.

Time	Global Moran Index	Expected Value	Z-Statistic	p-Value
2011	−0.4122	−0.1250	−0.9182	0.1940
2012	−0.4985	−0.1250	−1.1664	0.1370
2013	−0.6146	−0.1250	−1.4424	0.0650
2014	−0.6308	−0.1250	−1.5161	0.0560
2015	−0.5777	−0.1250	−1.3476	0.0890
2016	−0.2510	−0.1250	−0.4006	0.3770
2017	−0.2583	−0.1250	−0.4250	0.3590
2018	0.0389	−0.1250	0.4770	0.3010
2019	−0.2207	−0.1250	−0.3383	0.3970
2020	−0.0868	−0.1250	0.1092	0.4430

.

From the numerical characteristics, it can be seen (Table 5) that the Moran index of China’s marine fishery ecological efficiency is negative in all years except 2018, and all of them fail to pass the 5% significance test, indicating that China’s marine fishery ecological efficiency presents the characteristics of cross-distribution between high-value areas and low-value areas. From the trend of change (Figure 4), the ecological efficiency of marine fishery in 2011–2020 showed a trend of segmented change. The global Moran’s index decreased significantly from 2011 to 2014, indicating that the eco-efficiency value of marine fishery was more dispersed in spatial distribution during this period. To a certain extent, it reflects that as China has entered a new era of marine development, various regions have focused on the development of marine fisheries while ignoring inter-regional exchanges and cooperation, even in the development of beggar-thy-neighbors, resulting in serious border effects, forming a situation in which the ecological efficiency of marine fisheries is uneven. After 2015, the global Moran’s index fluctuated and rose until 2018, from negative to positive, reaching the highest concentration state in recent years, indicating that the similarity of marine fishery eco-efficiency values in adjacent areas was rising higher and higher. In 2015, China’s fishery economy stabilized at a high level. At the same time, new progress has been made in the conservation of fishery resources and the action of fishery energy conservation and emission reduction. The central government continues to increase its efforts to support the implementation and construction of proliferation and release, marine ranching, etc. At the same time, it provides guarantee for fishermen to reduce their boats and transfer their jobs, and has achieved remarkable results in the improvement of marine fishery ecology. The spillover effect of high eco-efficiency provinces expanded, so that the eco-efficiency of marine fisheries in neighboring provinces has been significantly improved. Since entering the ‘13th Five-Year’, the development speed of the marine industry has accelerated, the fishery industry is facing the challenge of industrial transformation and upgrading, and the ecological efficiency of the marine fishery has fluctuated, but the trend is good.

Local Moran Index

In order to further reveal the spatial evolution characteristics of inter-regional marine fishery eco-efficiency, the GeoDa software was used to calculate the local Moran index from 2011 to 2020, and the scatter plot (Figure 5) and LISA clustering map (Figure 6) of 2011, 2014, 2017 and 2020 were generated.

From the analysis of Figure 5, it can be seen that the eco-efficiency of China’s marine fishery has been on the rise in the past 10 years, but the spatial pattern of cross-distribution has not improved significantly. In 2011, 2014 and 2017, the provinces distributed in high–low and low–high regions accounted for a large proportion, accounting for more than 50% of the study provinces. By 2020, the provinces distributed in high–high regions have accounted for about 44% of the research provinces, indicating that the inter-provincial spatial agglomeration of marine fishery eco-efficiency has increased, and the efficiency differences between coastal provinces and cities have further narrowed. From the LISA clustering map of marine fishery eco-efficiency in Figure 6, it can be seen that Tianjin and Shandong were surrounded by low-efficiency areas in 2011 and 2014, Jiangsu was surrounded by high-efficiency areas in 2011, and the efficiency of Liaoning, Tianjin and neighboring areas was significantly different in 2014, 2017 and 2020. Combined with the distribution map, it can be seen that high-efficiency areas and low-efficiency areas are staggered from north to south. The zonal shape of China’s coastal areas makes it difficult for marine fisheries to form a regional ‘agglomeration’ like the industrial sector. Therefore, the spatial spillover effect of high eco-efficiency areas of marine fisheries is not significant. The eco-efficiency values of Hebei and Guangxi are low for a long time, but Hebei is bordered by Tianjin and Shandong, which are high-efficiency areas, and Guangxi is close to Guangdong, which is growing rapidly. Therefore, it is necessary to explore a new model of interregional marine fishery cooperation, so that low-efficiency areas can fully accept the radiation effect from high-efficiency areas and achieve coordinated development.

4. Analysis of Influencing Factors of Marine Fishery Eco-Efficiency

4.1. Identification of Influencing Factors

Tobit regression model is used to analyze the main factors affecting the change of marine fishery eco-efficiency in China and describe its influencing mechanism and degree of action. According to the relevant research results and the principles of scientificity, generality and data availability, the factors that may affect the ecological efficiency of marine fisheries are selected to construct the index system of influencing factors, as shown in

Table 6. Influencing factors of marine fishery eco-efficiency.

Variable	Code	Computing Method
Industrial structure	SYS	Output value of marine fishery secondary industry and tertiary industry/primary industry
Scientific support	TEC	Number of marine fishery R&D institutions/Number of fishery R&D institutions
Degree of opening-up	OPE	Total imports and exports of aquatic products/GDP of fishery economy
Environmental regulation	POL	Investment in marine environmental governance

.

Industrial structure determines the direction and scale of resource flows, thus affecting the level of resource consumption; the industrial structure determines the type and quantity of energy use, thus affecting the environmental situation. Some scholars have confirmed that industrial structure optimization has a positive impact on eco-efficiency [50]. Therefore, this paper expects that the optimization of marine fishery industrial structure will be conducive to the improvement of marine fishery eco-efficiency. Based on this, the proportion of the second industry, the third industry output value and the first industry output value of marine fishery in each province is selected to characterize the industrial structure of marine fishery.

Science and technology investment can promote scientific and technological innovation, thereby improving the utilization efficiency of marine fishery resources and the level of production technology, thus reducing pollutant emissions. At the same time, technological progress is also conducive to improving the level of pollutant control of related enterprises. Based on this, the proportion of the number of marine fishery research and promotion institutions in each province in the total number of national fishery research institutions is selected to characterize the scientific and technological support for marine fishery.

The implementation of fishery ‘going out’ is an important strategy to promote the sustainable development of fishery. Although opening to the outside world has expanded people’s demand for marine aquatic products to a certain extent, the gradual expansion of import and export will lead to the excessive consumption of resources and increase in water pollution in specific sea environments; through the deep participation in international aquatic products trade, the quality of domestic fishery enterprises can be improved. Based on this, the proportion of import and export trade of aquatic products in the total output value of fishery economy in each province is selected to characterize the openness of marine fishery.

Environmental regulation refers to the direct or indirect intervention of local or central governments to related enterprises for the purpose of environmental protection and resource conservation. Under the pressure of environmental regulations, marine fishery operators will invest more resources in ecological environmental protection and carry out environmental technology innovation and upgrading in order to seek long-term development. The governments of different provinces attach different importance to the ecological environment of marine fishery, which leads to the different impacts of marine fishery industry on environmental pollution. Based on this, the investment funds of local governments in marine environmental governance are selected to represent environmental regulation.

4.2. Regression Analysis

The Tobit regression model is a model with limited dependent variables. When the value of the variable is cut or truncated, the Tobit regression model following the maximum likelihood method is a better choice. Since the efficiency values calculated using the data envelopment method are greater than 0, which belongs to the truncated case, the panel Tobit regression method is used to analyze the influencing factors.

Based on each influencing factor index, the following model is established:

$${EFF}_{it}={\beta }_0+{\beta }_{1}SYS+{\beta }_{2}TEC+{\beta }_{3}OPE+{\beta }_{4}POL+\delta +\epsilon$$

(5)

In the formula: EFF_it represents the eco-efficiency value of marine fisheries in the t year of the i province, β₀, β₁, β₂, ……, β₄ are regression coefficients of each explanatory variable, δ is the individual effect and ε is the residual term.

Panel Tobit regression of random effects of influencing factors was performed using Stata 14.0 software. The results are shown in

Table 7. Regression results of influencing factors of marine fishery eco-efficiency.

Variable	Regression Coefficient	Z-Statistic
SYS	0.2860 ***	3.10
POL	0.0463 *	1.09
TEC	0.0040 **	2.54
OPE	−0.2212 ***	−3.03
Constant term	1.9712 ***	3.69

Note: ***, ** and *, respectively, indicate that the variables are significant at the level of 1%, 5% and 10%.

.

LR test results strongly reject the ‘$H_0:{\sigma }_u=0$’; the individual effects are existed and random effects panel Tobit regression should be used, so this model is reasonable.

According to the calculation results shown by the model:

Firstly, the industrial structure of marine fisheries has a significant positive effect on the eco-efficiency of marine fisheries, with an impact coefficient of 0.286 and a 1% significance test. This shows that the adjustment of marine fishery industrial structure in China’s coastal provinces is conducive to the improvement of marine fishery ecological efficiency. The reason is that the primary industry of marine fisheries has a high degree of resource dependence, which is an industrial sector that directly interacts with the marine ecological environment, while the secondary and tertiary industries of marine fisheries have a lower resource consumption, higher technical content and added value. With the increase in the proportion of the secondary and tertiary industries, labor, capital and technology are concentrated in the fields of marine aquaculture processing, circulation and service, reducing the environmental pressure of the primary sector while obtaining more green output value, which promotes the ecological efficiency of marine fisheries.

Secondly, the level of fishery opening to the outside world has a certain negative constraint on the ecological efficiency of marine fisheries, with an impact coefficient of −0.221, and passed the 1% significance test. The higher the level of the opening up of coastal provinces and regions, the greater the demand for export and import of aquatic products. At the same time, due to the of deep processing of aquatic products, low added value of products and low processing technology, many drawbacks and structural problems are highlighted. Relying solely on marine fishery production to enhance international competitiveness has caused the deterioration of the marine fishery water environment. At present, the level of opening to the outside world is a negative indicator, but coastal provinces and regions can selectively import and export trade according to their actual situation, while paying attention to the combination of advanced technology and the protection of the ecological environment, so as to realize the benign interaction of aquatic products import and export trade, the positive effect of opening to the outside world will gradually appear.

Thirdly, science and technology support has a significant positive effect on marine fishery eco-efficiency, the influence coefficient is 0.004, and has passed the 5% significance test. The reason is that, on the one hand, the increase in R&D funding has gradually transformed the marine fishery industry from labor and capital-intensive to technology and knowledge-intensive, making full use of marine fishery resources and greatly reducing pollutant emissions. On the other hand, coastal provinces and regions have continuously improved the introduction and training mode of marine fishery talents, established scientific research institutions for marine fishery, and improved the conversion rate of achievements, which has played an important role in improving the ecological efficiency of marine fisheries.

Fourthly, environmental regulation has a positive effect on the ecological efficiency of marine fisheries, with an impact coefficient of 0.0463, and has passed the 10% significance test. Under the background of the current ecological civilization construction and green development, the coastal provincial governments pay more and more attention to the protection of the ecological environment of marine fishery waters, and attach importance to the improvement of environmental quality. Environmental regulation can directly and strongly manage and restrict the unreasonable discharge of marine fishery enterprises, and urge them to consider the cost of environmental pollution and other stakeholders while obtaining the economic benefits of marine fishery. Reasonable environmental regulation can stimulate the new demand of marine fishery-related enterprises, R&D bias green technology innovation, improve the level of cleaner production technology, upgrade pollution treatment technology and indirectly improve the ecological environment of marine fisheries.

5. Discussion

China is a maritime power, and marine fisheries are an important component of modern agriculture and the marine economy. In recent years, marine fisheries have rapidly developed, with the continuous optimization of their structure and a substantial increase in marine product output. However, the development of China’s marine fisheries still follows an extensive approach, and the traditional extensive development model is no longer suitable for the current intensive and sustainable resource perspective. Problems such as excessive nearshore fishing, the depletion of fishery resources and marine environmental pollution have emerged [2]. In 2013, the Chinese government introduced “Several Opinions on Promoting the Sustainable and Healthy Development of Marine Fisheries,” actively promoting the sustainable and healthy development of marine fisheries and advancing marine environmental restoration projects. The marine fisheries have gradually shifted toward ecological health development. In the future, the marine fisheries will further uphold the concept of ecological civilization construction, forging a path toward China’s distinctive marine fisheries ecological development [41].

In this paper, the Super-SBM model is used to measure the ecological efficiency of marine fisheries in 10 coastal provinces and cities in China from 2011 to 2020. The Malmquist index is used to analyze its static characteristics and dynamic changes. Kernel density estimation, GIS technology and Moran index are used to describe the temporal and spatial evolution of marine fisheries’ ecological efficiency. On this basis, the panel Tobit regression model is used to analyze various factors affecting the ecological efficiency of marine fisheries.

According to the measured results of efficiency value, the marine fishery eco-efficiency value of China’s 10 coastal provinces is ranked as follows: Hainan, Fujian, Shandong, Tianjin, Zhejiang, Liaoning, Jiangsu, Guangdong, Guangxi and Hebei. The ecological efficiency of marine fisheries in Hainan, Fujian and Shandong has been kept above 1.2 for a long time, which belongs to the high efficiency type. The average efficiency of Tianjin, Zhejiang and Liaoning provinces is close to 1, which belongs to the relatively efficient type. The efficiency values of Jiangsu and Guangdong are between 0.4 and 0.8, which belong to the relatively low efficiency type. Guangxi belongs to the low efficiency type. The average efficiency value of Hebei Province is less than 0.4, which belongs to the relatively invalid type.

From the static and dynamic analyses, technological progress has become an important support to enhance the ecological efficiency of marine fisheries. Each province should realize the digestion of existing technologies through the scientific allocation of elements, and explore technological innovation on this basis. On the premise of maintaining a stable ecological environment, provinces with low ecological efficiency such as Hebei and Guangxi should pay more attention to the expansion of the marine fishery scale.

From the perspective of time evolution, the marine fishery eco-efficiency in the 10 coastal provinces and cities of China shows a clear trend of improvement, and the efficiency values in high-efficiency areas remain basically stable. The efficiency values in some regions have improved rapidly, and efficiency types have been upgraded. For example, Jiangsu and Guangdong provinces have increased from low efficiency types to relatively low efficiency types, while Fujian has increased from relatively low efficiency types to high efficiency. The relative gap between efficient and inefficient regions remains significant.

From the perspective of spatial distribution, the marine fishery eco-efficiency of coastal provinces and cities in China has no obvious spatial correlation, showing a cross-distribution of high-efficiency and low-efficiency regions. Overall, the strip-shaped geographical distribution of China’s coastal provinces is not conducive to the agglomeration and development of the marine fishery industry.

The change of marine fishery eco-efficiency is the result of a variety of influencing factors. The structure of the fishery industry, the level of scientific and technological support and environmental regulations have a positive effect on the improvement of marine fishery eco-efficiency, and the effect intensity is more significant. The level of opening to the outside world has a negative effect on the eco-efficiency of marine fishery at the significance level of 1%, which means that the higher the degree of opening to the outside world, the more unfavorable the improvement of the ecological efficiency of marine fisheries. Therefore, when formulating policies to improve the ecological efficiency of marine fisheries, multiple approaches must be comprehensively considered. Optimizing the structure of the marine fishery industry, enhancing the local government regulation of the marine fishery ecological environment, and increasing investment in scientific and technological funds are all effective measures to improve the ecological efficiency of marine fisheries.

6. Conclusions

To sum up, our study calculated the marine fishery eco-efficiency in China’s coastal areas from 2011 to 2020, explored the rules and characteristics of its spatiotemporal changes, and analyzed the influencing factors of such changes. The results emphasize that the ecological efficiency of marine fisheries in China’s coastal areas has been significantly improved, and technological progress is an important reason for the improvement of marine fishery ecological efficiency. Furthermore, the study highlights the importance of considering regional differences and acknowledges that in the future, such differentiated developments will persist for a long time. In future studies, it is necessary to continue to explore other factors that may influence the eco-efficiency of marine fisheries and to evaluate the applicability of our findings in different contexts. It is also possible to further consider the redundancy rate and deficiency rate of ecological efficiency inputs and outputs of marine fisheries.