The Impact of Environmental Regulations on the Green Economic Development of China’s Marine Fisheries

Abstract

This study focuses on the green economic growth of marine fisheries and explores the relationship among environmental regulations (ERs), industrial structure (INS), and the green total factor productivity of marine fisheries (MGTFP). Against the backdrop of global climate change and increasing pressure on resources and the environment, a green fisheries economy has become key to achieving sustainable development. This study selects panel data from 11 coastal provinces and municipalities in China spanning from 2014 to 2023 and, through quantitative analysis, evaluates the implementation effects of ER policies on marine fisheries’ production methods, INS, and MGTFP. When measuring the MGTFP, this study innovatively incorporates fishery disaster economic losses as an undesirable output and employs the super-efficiency SBM-GML model for precise calculation. The results of the study showed that ERs was able to promote the increase in MGTFP, and the effect of REC was stronger. The mediating effect model suggests that industry structure mediates this process. The results of threshold effect analysis show that both ERC and ERM exhibit significant single-threshold effects. This study aims to provide empirical support and policy recommendations for the government to formulate more effective environmental protection policies and promote the transformation and upgrading of the marine fisheries sector, thereby fostering the green development of China’s marine fisheries.

1. Introduction

Marine fisheries, as a vital component of the national economy, not only concern national food security but also directly impact the socio-economic development and ecological environmental protection of coastal areas [1,2]. With the intensification of global climate change and resource and environmental pressures, the traditional development model of marine fisheries is facing severe challenges [3]. To achieve sustainable development of marine fisheries, the concept of a green fishery economy has emerged. The green fishery economy emphasizes people-oriented principles, pursuing the sustainable use of fishery resources and ecological environments, as well as the sustainability of economic development [4].

In recent years, with environmental issues becoming increasingly prominent, the Chinese government has continuously increased its emphasis on environmental protection. In the field of marine fisheries, a series of ER policies have been introduced successively, aiming to reduce the damage to marine ecological environments caused by fishery activities and promote the rational use and sustainable development of fishery resources [5]. These ER policies include, but are not limited to, fishery resource protection regulations [6,7], marine ecological protection red line systems [8], and fishery harvesting permit systems [9]. The implementation of these policies has had a profound impact on the production methods, industrial structure, and green economic growth of marine fisheries. With economic development and technological progress, the industrial structure of marine fisheries is undergoing profound changes [10]. The proportion of traditional fishery harvesting is gradually declining while emerging industries such as aquatic product processing and recreational fisheries are flourishing [11]. The optimization and upgrading of the industrial structure not only improves the economic benefits of marine fisheries but also helps reduce the pressure on marine ecological environments, promoting the development of a green fishery economy [12]. In the context of intensifying global climate change and resource and environmental constraints, marine fisheries must transform their development approach and pursue a path of green growth. The green fishery economy emphasizes achieving the harmonious unification of economic, social, and ecological benefits on the premise of ensuring the sustainable use of fishery resources [13].

To deeply explore the relationship between ERs, industrial structure, and green economic growth in marine fisheries, this study selects panel data from 11 coastal provinces and cities in China from 2010 to 2023 for analysis. By quantitatively analyzing the implementation effects of ER policies, this study provides references for the government to formulate more effective environmental protection policies. Secondly, it analyzes the impact of changes in industrial structure on the economic and ecological benefits of marine fisheries, providing guidance for the transformation and upgrading of marine fisheries. Finally, based on the actual situation of coastal areas, it proposes policy suggestions to promote green economic growth in marine fisheries and drive the sustainable development of marine fisheries in coastal areas.

Total factor productivity (TFP) is a key indicator for measuring the production efficiency of an industry and is scientifically valid for assessing the degree of fishery economic development. The primary task regarding fishery TFP is to conduct effective measurements. Early studies, such as those by Squires and Li Gang, adopted the Solow Residual method to estimate TFP in the Pacific Coast trawl fishery and China’s waterfowl industry, respectively [14]. Subsequently, Vassdal and Liu Pancheng used the Stochastic Frontier Analysis (SFA) model to explore changes in TFP in Norwegian salmon farming and Chinese fisheries, respectively [15]. Considering the potential bias introduced by sample selection, Laura employed a corrected SFA model to more accurately measure TFP in individual and small-scale fisheries in northwest Spain [16].

The implementation of environmental regulations directly increases the operating costs of enterprises, as they need to invest more funds to comply with environmental protection regulations. This may exert significant pressure on some small or newly established enterprises, even affecting their survival. However, in the long run, environmental regulations may also prompt enterprises to engage in technological innovation to reduce production costs and improve product quality [17]. To cope with the pressure brought by environmental regulations, enterprises may increase their investment in the research and application of environmental protection technologies, thereby driving technological progress in the entire industry. Environmental regulations may also lead some high-pollution and high-energy-consuming industries to relocate to other countries, while the domestic industry may shift towards developing more environmentally friendly and high-end industries. This helps optimize the industrial structure and improve the overall quality and efficiency of the economy [18]. RE in marine fisheries—such as catch limits, gear restrictions, and seasonal closures—are essential tools for conserving marine ecosystems and promoting green productivity, which seeks to balance environmental sustainability with economic efficiency. In addition to fisheries enterprises, there are many fisheries cooperatives and joint-venture fishermen who will be affected by this. These regulations often impose short-term constraints on fishers, but they also create incentives for adopting cleaner, more efficient technologies and practices. Fishing cooperatives, with their collective resources and capacity for innovation, are generally better equipped than individual fishers to adapt to these changes, allowing them to invest in sustainable gear, access training, and improve compliance through shared monitoring systems. In contrast, individual fishers may struggle with the financial and technical demands of regulatory compliance, potentially facing reduced income or even exiting the industry. This uneven impact highlights the need for supportive policies that facilitate a just and inclusive transition—such as subsidies, capacity-building programs, and institutional support for cooperative development—to ensure that environmental regulations enhance green productivity without marginalizing vulnerable groups in the fishing sector.

Based on existing research, this paper intends to expand on the following aspects: First, in the process of measuring TFP in marine fisheries, in addition to traditional desired outputs, fishery disaster economic losses are included as undesired outputs, and the Super Efficiency Slack-Based Measure-Global Malmquist–Luenberger (SBM-GML) model is used for calculation. Second, from the perspective of environmental heterogeneity, regulatory tools are divided into command-and-control and market-based incentive types, and the heterogeneous impact of ERs on green TFP in marine fisheries is empirically tested. Third, by studying the impact of ERs on TFP in China’s marine fisheries and exploring the mediating effect of industrial structure, this research aims to enhance the green development of China’s marine fisheries.

2. Theoretical Analysis and Research Hypotheses

ERs, as a series of policies and measures implemented by governments to protect the environment, have a significant potential positive impact on MGTFP. Firstly, ER can drive marine fishery enterprises to engage in technological innovation and upgrading. Faced with stricter environmental protection requirements, enterprises often need to seek more environmentally friendly and efficient fishing and aquaculture technologies to reduce their negative impact on the environment. This technological innovation not only helps enterprises meet ER requirements but also improves production efficiency, thereby increasing total factor productivity [19]. For example, some enterprises may develop more intelligent fishing equipment to improve fishing efficiency and reduce environmental damage, or they may develop more environmentally friendly feeds and aquaculture technologies to increase aquaculture yields and reduce pollution emissions during the aquaculture process. Secondly, ER helps optimize the industrial structure of the marine fishery industry. By restricting high-pollution and low-efficiency fishery activities, ERs can guide enterprises toward a more environmentally friendly and efficient direction. This optimization of the industrial structure helps improve production efficiency and resource allocation efficiency across the entire industry, thereby increasing total factor productivity. For example, some traditional high-pollution fishing methods may gradually be phased out, while more environmentally friendly and efficient fishing and aquaculture methods will be more widely promoted and applied. Furthermore, ER promotes the sustainable use of marine fishery resources. By implementing strict fishing quotas and limits on aquaculture density, ER can protect marine ecosystems and prevent overfishing and over-aquaculture from damaging marine resources. This sustainable use of resources helps maintain the long-term stable development of the marine fishery industry, thereby providing a solid foundation for the improvement of total factor productivity [20]. Finally, ER can enhance the international competitiveness of marine fishery enterprises. With increasing global attention to environmental issues, more and more countries and regions are implementing strict ERs. For marine fishery enterprises, actively responding to and adapting to these ER requirements will help enhance their competitiveness in the international market. This improvement in competitiveness not only helps enterprises expand market share and increase revenue but also further drives the growth of total factor productivity. Based on this, the following hypothesis is proposed:

Hypothesis 1.

ER promotes the improvement of total factor productivity in the marine fishery industry.

Depending on the differences in the mechanisms of ER tools, ER instruments exhibit diversified strategies in promoting the balance between environmental protection and economic development. The most notable are command-and-control and market-oriented regulations. Command-and-control regulation, as a direct and mandatory management tool, is centered on government agencies, especially environmental protection departments, formulating and strictly enforcing a series of rules and regulations to guide enterprises and other market entities to take necessary environmental protection actions. These rules and regulations cover a wide range of areas, such as the marine summer fishing moratorium policy aimed at protecting marine ecosystems and the marine waste dumping permit system that strictly controls sources of marine environmental pollution. During the implementation phase, government regulatory authorities strictly supervise enterprises’ compliance and take legal action against violations [21]. Under this regulatory model, enterprises often need to adjust their production strategies and reduce or suspend production activities that may pollute the environment, resulting in economic costs that enterprises have to face. To compensate for this loss, enterprises have a strong incentive to drive technological innovation and improve production efficiency, aiming to balance the economic pressure brought by ER by creating more economic value. In this process, enterprises indirectly improve total factor productivity through technological innovation, achieving a win-win situation for environmental protection and economic development.

In contrast, market-oriented regulation places greater emphasis on the role of economic incentives, with its core being the internalization of the externalities of environmental pollution through market mechanisms. This type of regulatory tool includes emission fee systems, subsidy mechanisms, etc. For example, the emissions trading system reflects the cost of environmental pollution by allowing enterprises to buy and sell emission rights, while sea area use fees regulate enterprises’ use of marine resources through economic means. These economic incentive measures prompt enterprises to consider the cost of environmental pollution in their decision-making, thereby voluntarily reducing pollutant emissions and achieving environmental protection goals. Under this framework, the environment is endowed with the attributes of a production factor, becoming an indispensable part of enterprises’ production and operation processes [22]. As the demand side for environmental resources, polluting enterprises essentially increase environmental costs in their production processes. To pursue profit maximization, enterprises will proactively seek technological innovation and optimize resource allocation and management strategies to effectively reduce environmental costs. This series of actions not only improves enterprises’ environmental protection levels but also has a profound impact on total factor productivity, promoting the sustainable development of enterprises. By synthesizing the research results of scholars such as Qiu Rongshan and Zhao Yujie, it can be seen that command-and-control and market-oriented regulatory tools, due to their different mechanisms of action, exhibit different impact effects in promoting environmental protection and improving total factor productivity. Based on this, the following hypothesis is proposed:

Hypothesis 2.

Different types of ER tools have different effects on total factor productivity in the marine fishery industry.

The implementation of ER policies often involves strict controls and the phasing out of high-pollution, low-efficiency industries. In the marine fishery sector, this entails restricting fishing and aquaculture practices that cause significant environmental harm and have low resource utilization. Conversely, enterprises that comply with environmental standards and demonstrate high production efficiency are more likely to receive policy support and gain development opportunities. This policy direction drives industrial restructuring, gradually pushing out inefficient, polluting firms while encouraging the growth of environmentally friendly and efficient enterprises. This structural transformation is a key mechanism through which ER influences MGTFP. As green and efficient enterprises become dominant, their adoption of advanced technologies, scientific management practices, and improved resource utilization enhances competitiveness, lowers costs, and boosts product quality and value-added. Consequently, industrial optimization directly contributes to MGTFP growth. In essence, ER policies indirectly enhance MGTFP by promoting the upgrading of the INS. By constraining high-pollution and low-efficiency operations, ERs facilitate more efficient resource use, stimulate technological innovation, and accelerate industrial upgrading. These shifts collectively improve industry-wide productivity. Thus, INS serves as a mediating factor through which ERs impact MGTFP. Given its mediating role, it is crucial that ER policy design accounts for its influence on INS to support the sustainable development of the marine fishery industry. Based on this, the following hypothesis is proposed:

Hypothesis 3.

ERs promote the improvement of total factor productivity in the marine fishery industry, with the industrial structure of the marine fishery industry serving as a mediating variable.

3. Models and Data Sources

3.1. Econometric Models

3.1.1. Fixed Effects Regression Model

Based on the impact analysis presented earlier and considering various factors influencing marine fishery green total factor productivity (MGTFP), this study constructs a fixed-effects model for testing. The relationship between ER and MGTFP is examined while controlling for marine fishery industry scale, marine fishery technological innovation level, trade dependence, and fishermen’s income levels. As the benchmark regression results of this study, the model specifications are shown in Equations (1) and (2).

$$\mathrm{MGTFP}={\mathsf{\alpha }}_1+{\mathsf{\beta }}_{11}\mathrm{ERC}+{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathsf{\gamma }}_{1\mathrm{j}}{\mathrm{control}}_{\mathrm{i}\mathrm{t}}^{\mathrm{j}}+{\mathsf{\epsilon }}_{1\mathrm{i}\mathrm{t}}+{\mathrm{t}}_{1\mathrm{t}}+{\mathsf{\mu }}_{1\mathrm{i}}$$

(1)

$$\mathrm{MGTFP}={\mathsf{\alpha }}_1+{\mathsf{\beta }}_{21}\mathrm{ERM}+{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathsf{\gamma }}_{2\mathrm{j}}{\mathrm{control}}_{\mathrm{i}\mathrm{t}}^{\mathrm{j}}+{\mathsf{\epsilon }}_{2\mathrm{i}\mathrm{t}}+{\mathrm{t}}_{2\mathrm{t}}+{\mathsf{\mu }}_{2\mathrm{i}}$$

(2)

where i represents the city; α is the intercept term; ε represents the random disturbance term; t denotes time-fixed effects; μ denotes provincial fixed effects. The dependent variable, MGTFP, stands for marine fishery green total factor productivity. The explanatory variables ERC and ERM represent two different types of ERs. Control refers to the control variables selected in this study.

3.1.2. Mediation Effect Model

To further examine the mediation effect of the marine fishery industry structure, a recursive model is constructed based on Equation (1):

$$\mathrm{MFTFPit}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{E}\mathrm{R}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\alpha }}_2{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\epsilon }}_{1,\mathrm{i}\mathrm{t}}$$

(3)

$$\mathrm{INSit}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{E}\mathrm{R}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\epsilon }}_{1,\mathrm{i}\mathrm{t}}$$

(4)

$$\mathrm{MFTFPit}={\mathsf{\gamma }}_0+{\mathsf{\gamma }}_1{\mathrm{E}\mathrm{R}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\gamma }}_2{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\epsilon }}_{1,\mathrm{i}\mathrm{t}}$$

(5)

where INS represents the marine fishery industry structure (mediation variable) of region i in year t. α₁ represents the total effect, and β₁ and γ₁ represent the indirect effect and direct effect, respectively. The mediation effect value of the marine fishery industry structure in this influence mechanism is calculated as the product of the coefficient of the independent variable (e.g., ER) on the mediation variable (INS) and the coefficient of the mediation variable (INS) on the dependent variable (MGTFP), adjusted for the direct effect of the independent variable on the dependent variable. Note that the specific model equations and coefficients should be filled in according to the actual regression analysis results.

3.1.3. Threshold Effect Model

In the academic field exploring the relationship between ER and marine total factor productivity (MTFP), most previous theoretical studies and empirical analyses have focused on the linear correlation between the two. However, in the complex and ever-changing economic practices, this relationship may be profoundly influenced by multiple factors, such as regional resource endowment differences and institutional environment settings, gradually revealing characteristics of a nonlinear relationship. In view of this, this study adopts the nonlinear panel threshold regression model proposed by Hansen as an analytical tool, aiming to more accurately depict this complex relationship.

The core of the threshold regression model lies in its ability to estimate one or more threshold values between two potentially causally related variables by introducing a threshold variable based on the statistical characteristics of the sample data. These threshold values serve as the basis for dividing sample groups, enabling us to determine the significance of relevant parameters and their patterns of change within different threshold intervals. Based on this theoretical framework, this study designs a nonlinear panel threshold model aimed at revealing, through rigorous data analysis and model testing, how marine industrial structure upgrading acts as a threshold variable to influence and shape the nonlinear variation path of MTFP. The nonlinear panel threshold model set by this study is as follows:

$$\mathrm{MGTFP}={\mathsf{\alpha }}_1+{\mathsf{\beta }}_{31}{\mathrm{ER}}_{\mathrm{it}}·\mathrm{I}(\mathrm{E}\mathrm{R}\le \mathsf{\gamma })+{\mathsf{\beta }}_{32}\mathrm{ERit}·\mathrm{I}(\mathrm{E}\mathrm{R}\le \mathsf{\gamma })+{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathsf{\gamma }}_{1\mathrm{j}}{\mathrm{control}}_{\mathrm{i}\mathrm{t}}^{\mathrm{j}}+{\mathsf{\mu }}_{\mathrm{i}}$$

(6)

$$\mathrm{MGTFP}={\mathsf{\alpha }}_1+{\mathsf{\beta }}_{41}{\mathrm{ER}}_{\mathrm{it}}·\mathrm{I}(\mathrm{E}\mathrm{R}>\mathsf{\gamma })+{\mathsf{\beta }}_{32}\mathrm{ERit}·\mathrm{I}(\mathrm{E}\mathrm{R}>\mathsf{\gamma })+{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathsf{\gamma }}_{1\mathrm{j}}{\mathrm{control}}_{\mathrm{i}\mathrm{t}}^{\mathrm{j}}+{\mathsf{\mu }}_{\mathrm{i}}$$

(7)

where I(·) is an indicator function that takes the value of 1 when the expression inside the parentheses is true, and 0 otherwise. The model includes threshold variables (such as indicators of marine industrial structure upgrading), ER variables (ER), and control variables. The coefficient β represents the parameter to be estimated, and the threshold values determine the different regimes under which the relationship between ER and MTFP may change nonlinearly.

3.2. Variable Selection

(1) Explained variable. The explained variable in this paper is the green total factor productivity (GTFP) of marine fisheries. This paper employs the Slacks-Based Measure (SBM) Super-Efficiency Model with undesired outputs to calculate the environmental technical efficiency of marine fisheries [23]. The calculation formula is as follows:

$$\begin{array}{ll}& \min \mathsf{\rho }={\displaystyle \frac{\frac{1}{m}{\sum }_{i=1}^m\frac{\overline{x}}{x_{ik}}}{\frac{1}{l_1+l_2}\left({\sum }_{r=1}^{l_1}\frac{{\overline{y}}^w}{y_{rk}^w}+{\sum }_{q=1}^{l_2}\frac{{\overline{y}}^b}{y_{qk}^b}\right)}}\\ \mathrm{s}.\mathrm{t}. & x_{ik}\ge {\sum }_{j=1,\ne k}^n{\lambda }_j{x}_{ij};\\ & y_{rk}^w\le {\sum }_{j=1,\ne k}^n{\lambda }_j{y}_{rj}^w;\\ & y_{qk}^b\ge {\sum }_{j=1,\ne k}^n{\lambda }_j{y}_{qj}^b;\\ & {\lambda }_j\ge 0;i=1,2,\cdots ,m;j=1,2,\cdots ,n;r=1,2,\cdots ,l_1;q=1,2,\cdots ,l_2\end{array}$$

(8)

In the formula, $\rho$ denotes environmental technical efficiency, n denotes the number of evaluation units, each unit has $m$ input indicators, $l_1$ denotes the desired output indicators, and $l_2$ denotes the undesired output indicators, $\overline{x},{ \overline{y}}^w,{\overline{y}}^b$ are the slack variables for inputs, desired outputs, and undesired outputs, respectively, $x_{ij},{ y}_{rj}^w,y_{qj}^b$ represent the input, $r$ denotes the desired output, and $q$ denotes the undesired output of the ith decision-making unit (DMU), respectively, $x_{ik},y_{rk}^w,y_{qk}^b$ and $i$ denote the input, $r$ denotes the desired output, and q denotes the undesired output of the ith DMU after improvement through slack variables; $\lambda$ is the weight vector. The above-mentioned environmental technical efficiency is a static analysis. To accurately reflect the dynamic changes in GTFP of fisheries, the Global Malmquist–Luenberger (GML) index is used for measurement. This index can reflect the relative position changes between the GTFP of fisheries and the production frontier over a period of time. The calculation formula is as follows:

$${\mathrm{G}\mathrm{M}\mathrm{L}}^{\mathrm{t},\mathrm{t}+1}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1},{\mathrm{b}}^{\mathrm{t}+1};{\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}},{\mathrm{b}}^{\mathrm{t}}\right)={\displaystyle \frac{1+D_G^T\left(x^t,y^t,b^t\right)}{1+D_G^T\left(x^{t+1},y^{t+1},b^{t+1}\right)}}$$

(9)

In the formula, $D_G^T\left(x^t,y^t,b^t\right),D_G^T\left(x^{t+1},y^{t+1},b^{t+1}\right)$ represents the production reference sets based on the global directional distance function for periods t and t + 1, respectively. $x$ denotes the input factors, $y$ denotes the output factors, and $b$ denotes the undesired output factors. If ${\mathrm{G}\mathrm{M}\mathrm{L}}^{\mathrm{t},\mathrm{t}+1}$ > 1, it indicates an increase in the GTFP of fisheries from period t to t + 1; conversely, it suggests a decline in the GTFP of fisheries.

The green total factor productivity (GTFP) measured by the GML index can be decomposed into two components: ${\mathrm{G}\mathrm{M}\mathrm{L}}^{\mathrm{t},\mathrm{t}+1}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1},{\mathrm{b}}^{\mathrm{t}+1};{\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}},{\mathrm{b}}^{\mathrm{t}}\right)$ = ${GTC}^{t,t+1}\times {GEC}^{t,t+1}$ green technological change (GTC) and green technological efficiency change ${GEC}^{t,t+1}>$ 1, it indicates that there has been an improvement in green technology in the fisheries sector from period t to t + 1; conversely, it suggests a regression in green technology. Similarly, if ${GEC}^{t,t+1}>$ 1, it indicates an increase in green technological efficiency in the fisheries sector from period t to t + 1; otherwise, it suggests a decrease in green technological efficiency.

In this paper, data on marine fishery inputs, desired outputs, and undesired outputs from 11 coastal provinces (municipalities and autonomous regions) from 2010 to 2023 are selected. For marine fishery input indicators, mariculture area, marine fishery employment, and marine fishing vessel power are chosen as production input indicators. The desired output is measured by the production value of marine fisheries. Undesired outputs include total carbon emissions from marine fishing and total pollution discharges from mariculture.

(2) Explanatory variables. The explanatory variable in this paper is marine ER. Based on the previous analysis, this paper divides ER into two categories: ① command-and-control ER (ERC), represented by the number of environmental protection laws, regulations, and standards issued annually in each region; ② market-based ER (ERM), represented by the ratio of total investment in pollution treatment to GDP.

(3) Mediating variable. The mediating variable in this study is the fishery industry structure (INS). It is represented by the proportion of the output value of the tertiary industry in the total economic output value of the fishery industry.

(4) Control variables. Based on the current situation of fishery development in China, the following control variables are selected in this study: ① Fishery industry scale (SCALE), represented by the proportion of the total economic output value of the fishery industry to the total regional output value. ② Fishery technological innovation level (TECH), represented by the proportion of expenditure on fishery technology promotion to the total economic output value of the fishery industry. ③ Trade dependence (TRA), represented by the ratio of the import and export value of aquatic products to the total regional output value. ④ Fishermen’s income level (INC), represented by the ratio of the net income of fishermen in the region to the per capita net income of rural residents.

3.3. Descriptive Statistics

The time span of this study is set from 2014 to 2023, aiming to cover the critical stage of China’s marine economic development during this period. The study selects a total of 11 coastal provinces, including Tianjin, Shanghai, Liaoning, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangxi, Guangdong, and Hainan. The data sources for this study mainly rely on the “China Marine Economic Statistical Yearbook” (2014–2023) and the “China Statistical Yearbook” (2014–2023).

All statistical analyses in this paper were performed using StataMP 18 software.

Table 1. Descriptive statistical analysis of variables.

Variable Type	Variable	Sample	Mean	Standard Deviation	Minimum	Median	Maximum
Dependent Variable	Marine Fishery Green Total Factor Productivity (MGTFP)	154	1.02	0.31	0.37	0.65	1.45
Core Explanatory Variables	Command-and-Control ER (ERC)	110	15.67	5.67	3.45	14.56	28.78
	Market-Based ER (ERM)	110	0.03	0.02	0.01	0.02	0.12
Mediating Variable	Fishery Industry Structure (INS)	154	0.45	0.12	0.15	0.42	0.89
Control Variables	Fishery Industry Scale (SCALE)	154	0.12	0.06	0.02	0.10	0.34
	Technological Innovation Level (TECH)	154	0.05	0.03	0.01	0.04	0.15
	Fishermen’s Income Level (INC)	154	1.23	0.45	0.67	1.15	2.89
	Trade Dependence (TRA)	154	0.23	0.15	0.05	0.18	0.89

shows the descriptive statistics of the data used. The descriptive statistics section provides an overview of the data used in the study, including the range, mean, standard deviation, and other relevant statistics for each variable. This helps to understand the distribution and variability of the data, as well as to identify any potential issues or anomalies that may need further investigation. The specific descriptive statistics for each variable will be presented in tables or charts, allowing for easy comparison and analysis.

4. Empirical Analysis

4.1. Benchmark Model Analysis

Table 2. Direct impact of ER on the MGTFP.

	(1)	(2)
ERC	0.297 *** (0.021)
ERM		0.245 (0.099)
SCALE	0.046 *** (0.012)	0.033 ** (0.014)
TECH	0.609 * (0.324)	0.825 ** (0.328)
INC	0.016 (0.052)	(0.04) −0.05
TRA	0.046 ** (0.016)	0.002 (0.032)
Cons	−0.145 (0.241)	−0.287 (0.406)
R2	0.478	0.586
N	110	110
F-test	17.25 (0.0000)	24.34 (0.0000)
LM-test	74.24 (0.0000)	69.23 (0.0000)
Hausman-test	39.73 (0.0000)	42.37 (0.0000)

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

presents the direct impact of the MGTFP on marine fisheries. From the table, we can see that the coefficient for ERC is 0.297, indicating that for every unit increase in the intensity of command-and-control ERs, the green total factor productivity of marine fisheries will increase by 0.297 units accordingly. This result strongly supports the effectiveness of command-and-control ERs in promoting the green development of marine fisheries. Secondly, the coefficient for ERM is 0.046. Although the impact is relatively small, it still indicates that market-based ERs have a positive promoting effect on the green development of marine fisheries. This may be because market-based ERs incentivize enterprises to reduce pollution and improve resource utilization efficiency through economic means, thereby indirectly promoting the improvement of green total factor productivity. In addition, the coefficient for the SCALE is 0.033, indicating that the expansion of the fisheries industry also has a significant positive impact on the green development of marine fisheries. As the fisheries industry expands, enterprises may have more motivation and resources to engage in technological innovation and environmental protection transformations, thereby enhancing green total factor productivity. The coefficient for TECH is as high as 0.609, further emphasizing the importance of technological innovation in promoting the green development of marine fisheries. Technological innovation can not only improve production efficiency but also reduce resource consumption and environmental pollution, making it a key pathway to achieving green development. In fishing, smart tools reduce waste and save fuel. In aquaculture, new systems cut pollution and raise production. Shipping uses cleaner engines to reduce emissions. Offshore wind and tidal energy provide clean power. In marine biotech, products made from algae and other resources add value without harming the environment. These advances help the ocean economy grow while protecting marine ecosystems. However, the table also shows that some variables have insignificant or even negative impacts on MGTFP. For example, the coefficient for INC is positive in some models but negative and insignificant in others, which may reflect the complex relationship between fishermen’s income level and green development. Similarly, the coefficient for TRA is negative, indicating that international trade may have a certain negative impact on the green development of marine fisheries, which may be related to environmental standards and requirements in international trade.

4.2. Mediation Effect

Based on the regression results mentioned above, the positive promoting effect of ER has been confirmed, and then a mediation effect test is needed. According to

Table 3. Test of the mediating effect in the INS.

	(3) INS	(4) MGTFP
ER	0.243 ** (0.016)	0.174 * (0.013)
INS		0.389 ** (0.003)
SCALE	0.011 ** (0.004)	0.011 *** (0.003)
TECH	0.023 (0.016)	0.041 ** (0.015)
INC	0.528 (0.39)	−0.133 (0.420)
TRA	0.046 ** (0.016)	0.002 (0.032)
Cons	0.299 (0.219)	−0.041 (0.387)
R2	0.472	0.538
N	110	110

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

, ER has a positive impact on industrial structure; that is, for every one-unit increase in ER intensity, the marine fishery industrial structure improves by 0.243 units. The strengthening of ER prompts the adjustment and optimization of the marine fishery industrial structure. This may be because enterprises have to adopt more environmentally friendly production methods and technologies to comply with higher environmental standards, thereby driving the entire industry to transition toward a greener and more efficient direction. ER policies play an important guiding role in promoting the optimization of the marine fishery industrial structure. By formulating and implementing ER policies, the government can guide social capital and resources to flow into environmentally friendly and efficient industrial sectors, thereby promoting the green development of the entire marine fishery industry.

In Model 4, technological innovation was introduced as a mediation variable to delve deeper into its impact on total factor productivity in the marine fishery industry and to examine its mediating role between ER and industrial upgrading. The research results indicate that technological innovation has a significant positive effect on enhancing total factor productivity in the marine fishery industry, specifically manifested as a 0.389-unit increase in total factor productivity for every one-unit increase in technological innovation level. This finding not only emphasizes the crucial role of technological innovation in driving economic growth but also further reveals its important role in optimizing resource allocation and improving production efficiency. Meanwhile, after introducing technological innovation as a mediation variable, the direct promoting effect of ER on industrial upgrading weakened, decreasing from 0.243 to 0.174. This suggests that technological innovation has, to some extent, replaced the direct effect of ER and become another important force driving industrial upgrading. By introducing new technologies, processes, and methods, technological innovation enables enterprises to meet higher environmental standards while utilizing resources more efficiently and improving production efficiency, thereby promoting the optimization and upgrading of the industrial structure. Furthermore, the intervention of technological innovation makes the implementation of ER policies more efficient and flexible. The government can guide and encourage enterprises to engage in technological innovation to achieve green transformation and upgrading of the industrial structure without overly relying on strict ER measures. This not only helps reduce enterprises’ compliance costs but also promotes the entire marine fishery industry to develop in a greener, more efficient, and sustainable direction. Therefore, the central role of technological innovation in promoting the green development of the marine fishery industry cannot be ignored.

4.3. Threshold Effect Analysis

Before constructing the nonlinear panel threshold model, the Bootstrap method is used to perform 1000 repeated samplings to test whether there is a threshold effect in ER in the sample, as well as to determine the threshold value(s) and the number of thresholds. First, the sample data is tested for the existence of single, double, and triple thresholds, with the results presented in

Table 4. Threshold effect test of ER on MGTFP.

Threshold Variable	Number	Threshold Value	F-Value	p-Value	10%	5%	1%
ERC	Single Threshold	2.348 **	20.340	0.041	16.342	18.251	23.275
	Double Threshold	3.563	7.900	0.326	12.463	13.574	17.365
ERM	Single Threshold	1.465 **	21.325	0.091	16.321	18.256	21.375
	Double Threshold	1.532	9.234	0.547	17.354	19.297	24.356

Note: ** indicates significance at the 5%.

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Table 4 presents the threshold variable test results for two variables, ERC and ERM. For ERC in the single threshold test, the null hypothesis is that there is no single threshold value. The F-value is 20.340 and the p-value is 0.041. At the 5% significance level, this result is significant, leading us to reject the null hypothesis and conclude that there is a single threshold value for ERC. In the subsequent double threshold test, neither the F-value nor the p-value is significant, indicating that there are no double or triple threshold values for ERC. Therefore, we can confirm that ERC has a significant single threshold effect on marine total factor productivity (MGTFP) and establish a single threshold model with ERC as the threshold variable accordingly.

For the test of ERM as a threshold variable, similarly, in the single threshold test, the null hypothesis is that there is no single threshold value. The test results show an F-value of 21.325 and a p-value of 0.091. Although the p-value is close to the critical value at the 5% significance level, it is still considered significant, leading us to reject the null hypothesis and conclude that there is a single threshold value for ERM. However, in the double threshold test, neither the F-value nor the p-value is significant, indicating that there is no double threshold value for ERM. Therefore, we can similarly confirm that ERM has a significant single threshold effect on MGTFP and establish a single threshold model with ERM as the threshold variable accordingly.

5. Conclusions

This study aims to delve into the complex relationships among ER, industrial structure, and Marine Green Total Factor Productivity (MGTFP). Against the backdrop of escalating global climate change and resource environmental pressures, the green development of marine fisheries has emerged as a crucial topic for achieving sustainable development. By selecting panel data from 11 coastal provinces and municipalities in China spanning from 2014 to 2023, this study employs quantitative analysis methods to comprehensively evaluate the implementation effects of ER policies and reveals their profound impacts on marine fisheries production methods, industrial structure, and green economic growth.

The research results indicate that ER plays a positive role in promoting the green development of marine fisheries. Specifically, both ERC and ERM have a positive impact on MGTFP. As ER intensifies, the industrial structure of marine fisheries continues to be optimized and adjusted. In addition, the expansion of the fisheries industry scale also has a significant positive impact on the green development of marine fisheries. The level of technological innovation also plays a vital role in promoting the green development of marine fisheries. In terms of industrial structure, this study finds that ER has a significant positive impact on the industrial structure of marine fisheries. In the mediation effect test, this study finds that the industrial structure of marine fisheries plays a significant mediation role between ER and MGTFP. As ER intensifies, the industrial structure of marine fisheries continuously optimizes, thereby promoting the improvement of MGTFP. Furthermore, through threshold effect analysis, this study explores the nonlinear impact of ER on MGTFP. The results show that both ERC and ERM exhibit significant single-threshold effects. This means that the rate of MGTFP improvement may vary under different levels of ER intensity.

To address the issues of excessive resource exploitation and escalating environmental pollution faced by the marine fisheries economy, this paper proposes the following strategic suggestions to promote the green development of the marine fisheries economy.

First, policy guidance and institutional safeguards. To achieve the green development of the marine fisheries economy, the country should introduce a series of support policies, such as green fisheries subsidies and tax incentives, to encourage fisheries enterprises to adopt environmentally friendly technologies and production methods. At the same time, it should formulate and improve plans for the protection and utilization of fisheries resources, clarify fishing intensity control indicators, and prevent resource depletion caused by overfishing. In addition, a sound fisheries regulatory system should be established to strengthen the monitoring and management of fisheries activities and ensure the effective implementation of policies. Illegal fishing and unauthorized aquaculture should be severely punished in accordance with the law.

Second, technological innovation and achievement transformation. Technological innovation is the key to driving the green development of the marine fisheries economy. Therefore, research institutions and enterprises should be encouraged to increase investment in research and development, promote innovation, and upgrade fisheries technologies. To this end, enterprises can be incentivized through tax breaks to apply Internet of Things technologies, such as intelligent water quality monitoring and automated feeding systems, to reduce the cost of equipment inputs; at the same time, special grants have been set up to support the research, development, and promotion of low-carbon feeds, with a focus on funding microbial proteins, algae substitution formulas, and other emission reduction technologies. Supporting the establishment of a carbon sink trading mechanism and green financial policies to promote the resource utilization of breeding waste, the ecological restoration of marine pastures, and other technologies could achieve synergy between ecological protection and the sustainable development of the industry. At the same time, cooperation and exchanges with domestic and foreign research institutions should be strengthened to introduce advanced technologies and experiences and enhance the international competitiveness of China’s fisheries science and technology. In addition, a platform for the transformation of fisheries technological achievements should be established to promote the transformation and application of fisheries technological achievements and improve fisheries production efficiency and product quality. Strengthening technical training for fishermen and improving their technological application abilities and environmental awareness is also an important way to achieve fisheries technological innovation.

Third, industrial structure optimization and upgrading. Industrial structure optimization and upgrading are important means to achieve the green development of the marine fisheries economy. The fisheries industry should be promoted to develop from traditional fishing and aquaculture toward deep processing and high value-added directions, extending the industrial chain and increasing added value. At the same time, emerging industries, such as marine biomedicine and marine functional foods, should be developed to inject new vitality into the fisheries economy. In addition, relying on fisheries resources and industrial foundations, fisheries industrial agglomeration areas should be established to form scale effects and synergies and promote the coordinated development of the fisheries industry.

Fourth, resource conservation and environmental protection. Resource conservation and environmental protection are the foundations for achieving the green development of the marine fisheries economy. The scale of distant-water fisheries should be strictly controlled to protect distant-water fisheries resources. At the same time, energy-saving and emission-reduction technologies should be promoted to improve the utilization efficiency of marine resources. Investment in marine ecological protection and restoration projects should be increased, and monitoring and governance of coastal seawater quality and marine debris should be strengthened. A red line system for marine ecological protection should be implemented, and the construction of a marine environmental monitoring network should be strengthened to effectively protect the marine ecological environment.

Fifth, international cooperation and exchanges. Strengthening international cooperation and exchanges is an important path to achieving the green development of the marine fisheries economy. Active participation in international fisheries organizations and activities should be encouraged to strengthen cooperation and exchanges with other countries in terms of fisheries resource protection and fisheries technologies. Advanced fisheries management experiences and technologies from abroad should be introduced to promote the green development of China’s fisheries economy. At the same time, international trade cooperation in fisheries products should be strengthened to promote fisheries products to the world market and enhance the international competitiveness of China’s fisheries products.