Can the Digital Economy Improve the Quality of the Marine Environment? Empirical Evidence from Coastal Provinces and Cities in China

Abstract

Studying the impact of digital economy development on marine environmental quality has important theoretical and practical significance for achieving a win–win situation between high-quality economic development and high-level ecological environment protection. This article selects the marine environment of coastal provinces and cities in China from 2011 to 2022 as the research object and uses the entropy method to comprehensively evaluate the quality of marine environment and the level of digital economy. Also, we construct intermediary and threshold effect models to deeply explore the impact mechanism of digital economy development on marine environmental quality. We find that digital economy and marine environmental quality both show a wave-like rising trend, but the comprehensive level is relatively low. The development of the digital economy can effectively improve the level of marine environmental quality, and the digital economy promotes the improvement of marine environmental quality by improving the level of marine economy. The level of economic development and industrial scale has created a threshold effect in the process of promoting the development of marine environmental quality through the digital economy. Therefore, strengthening the digital governance of the marine environment and promoting the industrialization of marine ecology and the ecologicalization of marine industries will help promote the integrated development of the digital economy and marine environment.

1. Introduction

The marine environment can not only provide a broad space carrier and rich material energy for human beings but also plays an important role in promoting economic development and ensuring ecological security [1]. However, with the increasing problems of global warming, indiscriminate exploitation of resources, and increasing pollution sources, the quality of marine environment is facing unprecedented challenges [2]. According to the World Meteorological Organization’s “State of the Global Climate 2021” report, marine pollution is increasing at an alarming rate, and if current trends continue, more than half of the world’s marine species may be almost extinct by 2100 [3]. The continuous decline in marine environmental quality has led to the depletion of offshore resources and the tightening of development space of marine industries, which seriously restricts sustainable economic development. Meanwhile, the extensive development of the traditional marine economy, characterized by high pollution, high energy consumption, and low output, has led to many marine environmental quality problems such as rising sea temperatures, abnormal marine biological structures, ocean acidification, and “plastic encirclement” [4]. The marine environment is currently at the peak of pollution emissions and a period of multiple risk pressures. According to data from the United Nations Environment Programme, as of 2018, the world generates approximately 300 million tons of plastic waste annually, with over 8 million tons of plastic entering the oceans, causing losses to marine ecosystems worth up to USD 8 billion annually [5]. The crisis of the marine ecological environment has become a major practical problem faced by coastal countries and regions around the world [6]. China attaches great importance to the construction of marine ecological civilization and has implemented the strategic plan of “developing the marine economy, protecting the marine ecological environment, and accelerating the construction of a strong maritime country” [7]. It has continuously strengthened the prevention and control of marine environmental pollution, protected marine biodiversity, and realized the orderly development and utilization of marine resources. Although China has accelerated the promotion of the construction of marine ecological civilization and the quality of the marine environment has been greatly improved, the development of the marine environment still faces difficulties such as carbon emissions, non-point source pollution, and overfishing. At the same time, there are bottlenecks in the development of the marine economy, such as weak leadership of scientific and technological innovation, low efficiency of resource allocation, and insufficient supply of new production factors, which restrict the effective improvement of the quality of the marine environment. An effective way to address this predicament is to drive high-end technological innovation, which can provide sustained and effective impetus for the development of the marine economy as well as the governance and protection of the marine environment. This will enable the green and low-carbon development of the marine economy and foster a new pattern of coordinated and symbiotic development between marine ecological benefits and economic quality. In this way, the green and low-carbon development of the marine economy can be realized, and a new pattern of coordinated and symbiotic development between marine ecological benefits and economic quality can be formed.

In recent years, with the mass breakthrough of digital technology, the digital economy has become an important engine for economic power change, efficiency change, and quality change. According to the Digital China Development Report (2024), the scale of China’s digital economy reached CNY 53.9 trillion, accounting for 42.8% of the GDP. In 2023, the contribution rate of the growth of the digital economy to the GDP growth reached 66.45% [8]. Based on data flow, technology flow, material flow, and capital flow, the digital economy has rapidly penetrated into all fields of the economy and society, restructured the resource allocation mode and organizational form of traditional industries, and responded to the great changes in the internal endowment of the economy and the external environment in a timely manner by virtue of its high permeability, scale effect, and network effect [9]. The development of digital technologies has promoted the deep integration of information technology and industrial technology, as well as the digital economy and the real economy, endowing productivity with the contemporary attributes of digitalization and greening. Driven by emerging technologies such as the digital marine and smart marine, the marine industry is evolving from a traditional one into an innovative industry that seeks potential from science and technology, resources from the open ocean, and a future from business model innovation, and it has given rise to new production factors. The digital economy has profoundly transformed the development model of the marine economy, promoting the coordinated and symbiotic development between the marine economy and the marine environment. At the same time, a variety of digital technologies have been widely applied to the governance and protection of the marine environment, improving the quality and efficiency of protection. Gradually, the digital economy has become an “ecological restoration tool”. In this context, can the digital economy exert a reshaping effect to help improve the quality of the marine environment? If so, how does the digital economy affect the quality of the marine environment? Does the impact of the digital economy on marine environmental quality exhibit nonlinear characteristics? In-depth exploration of these issues is of great theoretical and practical significance for effectively solving the problem of mutual constraints between space, resources, and economy, accelerating the construction of digital and green coordinated development of marine ecological civilization, and achieving a win–win situation between high-quality marine economy development and high-level marine ecological environment protection.

At present, domestic and foreign studies on the relationship between the digital economy’s impact on marine environmental quality mainly focus on two aspects; namely, the influencing factors and development of the digital economy’s inhibition of environmental pollution and the comprehensive effect of the digital economy on the improvement of environmental quality. In these studies, a superefficient DEA model, intermediary effect, coupling coordination degree, and other methods are used to measure the relationship between them. As for the influencing factors of the digital economy to curb environmental pollution, Ishida (2015) and Inani and Tripathi (2017), respectively, studied the impact of ICT investment in Japan and India and reached a consistent conclusion that the digital economy reduced the intensity of energy consumption [10]. Li et al. (2021) found that the digital economy significantly reduced PM2.5 through direct and technological effects, and improved China’s urban environmental quality [11]. Jahanger (2023) pointed out that digital economy can effectively promote the degree of industrial agglomeration in various regions, and the development of digital economy can reduce air pollution through the positive externalities of industrial agglomeration [12]. Li Guanghao et al. (2021) believe that promoting the development of digital economy can reduce environmental pollution by releasing the driving force of innovation, promoting the intensive transformation of industrial production mode and the online transformation of residents’ lifestyle, and also point out that the improvement effect of digital economy development on environmental pollution is asymmetrical [13]. He Weida et al. (2022) calculated by constructing a two-way fixed-effect model that the development of China’s digital economy greatly promoted the improvement of green ecological efficiency [14]. Deng Rongrong et al. (2022) believe that the development of the digital economy contributes to the optimization of industrial structure and the improvement of the level of green innovation, and reduces urban environmental pollution emissions through the effect of green innovation and industrial structure optimization [15]. Zhu Yunxuan et al. (2023) believe that the development of the digital economy has an inhibitory effect on the emission of three major pollutants, and summarize the three major factors that affect this inhibitory effect, including the level of economic development, environmental regulations, and government support [16]. Liu Qiang (2022) confirmed from the two dimensions of digital industrialization and industrial digitalization that the digital economy has significantly promoted the improvement of green economic efficiency, and the impact of the digital economy on green economic efficiency shows regional heterogeneity [17].

Scholars generally believe that the development of the digital economy has a positive significance for improving overall environmental quality. In terms of the dialectical relationship between digital economy and environmental pollution, Loh (2022) pointed out that the digital economy promotes technological innovation and is an important mechanism to curb PM2.5 pollution [18]. Failler (2022) believes that digital economy inhibits environmental pollution through the effect of green development and innovative development, and environmental pollution inhibits the development of digital economy through the effect of talent crowding out and policy tightening [19]. The research results of Wang Xuxia et al. (2023) show that the development of the digital economy can play a complementary role in green finance in controlling environmental pollution [20]. Zhou Hanmei et al. (2024) believe that the development of the digital economy will not only help reduce the haze pollution in the region, but also improve the environmental quality of surrounding areas [21]. Liu Yang (2023) calculated the coordination between marine economy and environment of China’s coastal provinces and cities and confirmed that the coordination between marine economy and marine environmental quality has steadily increased, showing a spatial distribution pattern of “large gap between the south and the north, and small gap in the east”. There are two levels of differentiation between regional marine economy and marine environmental quality [22]. Li Hua analyzed the development levels of China’s marine economy and ecological environment, as well as their characteristics of spatial and temporal evolution. It was concluded that the development of the marine economy has exerted a significant “coercive” impact on the ecological environment. The progress of marine science and technology is the main factor influencing the evolution of the response of China’s marine ecological environment. Factors such as the optimization of the marine industrial structure, the reduction in pollutant emissions, and the increase in environmental protection investment also play a certain role in promoting the alleviation of the degree of coercion [23]. Through empirical tests, Ji Jianye studied the impact mechanism of environmental regulation and technological innovation on the total factor productivity of the marine economy. Environmental regulation has a significant double threshold effect on the total factor productivity of the marine economy. Different levels of technological innovation determine that “offset effect” and “compensation effect” dominate [24]. Liu Surong et al. believe that digital economy can significantly promote the high-quality development of marine economy, especially at the level of innovation and opening up, but there is a nonlinear decreasing trend of marginal effect and regional distribution differences [25]. Li Guanghao (2021) pointed out that the development of the digital economy plays an important role in technological innovation and industrial structure optimization, and technological innovation and structural optimization are two important ways to reduce environmental pollution [26].

Through the review of relevant literature, it can be seen that domestic and foreign scholars have used a variety of empirical methods to study the impact relationship between the digital economy and marine economic quality, as well as marine economy and marine environmental quality from multiple perspectives, and these achievements have provided an important theoretical basis and research methods for this study. However, it rarely involves the impact pathways and structural relationships of the digital economy and marine environmental quality. Specifically, what is the comprehensive level of digital economy and marine environmental quality, and what are the characteristics and rules of its space–time evolution? Is there a causal relationship between them and what is the mechanism of action? In-depth analysis of these problems is the urgent focus and difficulty of the coordinated development of marine environment and economy in the world. The solution of these problems will not only help to improve the quality and stability of the marine ecosystem, but also help to give full play to the effectiveness of the digital economy and empower the quality development of the marine environment. In view of this, this paper takes China’s coastal provinces and cities as the research scale; explores the impact of the digital economy on marine environmental quality; reveals the transmission mechanism of the digital economy on marine environmental quality; clarifies the structural relationship of the digital economy in affecting marine environmental quality, so as to provide a new perspective for studying the relationship between the digital economy and marine environmental quality; and provides valuable reference information for practitioners and policymakers to formulate policies for the sustainable development of marine environment and economy.

The possible marginal contributions of this study are as follows.

First, from the perspective of digital empowerment of marine environmental quality, it analyzes the role of the digital economy in improving marine environmental quality, enriching the research perspectives and connotations of the digital economy and marine environmental governance.

Second, it explores the dynamic evolution laws and operational mechanisms of the marine environment, marine economy, and digital economy quality; verifies the action path of the digital economy in improving marine environmental quality; and analyzes the transmission mechanism of the marine economy in the impact of the digital economy on the marine environment. This fills the research gap regarding the mutual influence between the digital economy and the marine environment, providing a reliable empirical basis for the digital economy to assist in marine environmental pollution control.

Third, it analyzes the nonlinear structure of the digital economy’s impact on marine environmental quality, reveals the role of economic development level and industrial scale in the digital economy’s impact on the marine environment in different periods, and provides policy recommendations for other countries and regions to promote the quality and efficiency of the digital economy and optimize marine environmental quality. Meanwhile, it offers policy references for giving full play to the role of the digital economy in reducing pollution and emissions in the marine environment.

2. Study Design

2.1. Study Area and Design

We selected 11 coastal provinces (cities) in China as the research objects: Liaoning, Tianjin, Hebei, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Guangxi, and Hainan. We calculated the comprehensive quality level of each province’s marine ecological environment, marine economy, and digital economy, and focused on exploring the impact of digital economy on marine environmental quality. The data used mainly come from the China Marine Environment Bulletin, China Statistical Yearbook, China Information Industry Yearbook, and China Marine Statistical Yearbook from 2011 to 2023. The missing data in the paper were calculated and obtained using the interpolation method.

2.2. Research Hypotheses and Model Construction

2.2.1. Research Hypotheses

The digital economy is widely applied to marine environmental governance through digital technology, which can effectively improve the quality level of marine environment. Digital technology innovation has become one of the effective measures to deal with marine environmental quality problems, and many scholars’ research conclusions have confirmed this point. On the one hand, digital technology is adopted to deal with marine environmental pollutants, and green development and protection of marine resources are implemented through digital technology. Digital clean technology is used to enhance the ability of marine environment to reduce pollution and carbon emissions. On the other hand, the development of the digital economy can promote green technology innovation; assist relevant parties in accurately estimating the development trend, investment amount, and expected returns of green innovation in marine technology; help enhance the innovation capability of environmental protection technology; strengthen the marine environmental protection supervision of coastal enterprises; improve the marine ecological environmental protection mechanism; and improve the government’s level of marine environmental governance. At the same time, with the increasing improvement of marine digital infrastructure, it provides a more environmentally friendly carrier for marine environmental protection and quality. For example, techniques and devices such as satellite remote sensing and IoT sensors can monitor ocean temperature, acidification, algal blooms, and illegal fishing activities in real time, as well as deploy buoys and unmanned ships to collect ocean data. Therefore, this article proposes the following hypothesis.

Hypothesis H1: 

The development of the digital economy promotes marine environmental quality.

The development of the digital economy can facilitate the optimal allocation of marine resources, enhance marine economic efficiency and benefits, and achieve innovative-driven development through resource optimization. For example, marine digital platforms integrate aquaculture, environmental protection, and shipping data, which not only improves the level of marine economy but also mitigates the risks of marine environmental pollution, thereby promoting the improvement of marine environmental quality. The digital economy empowers traditional marine industries to advance their optimization and upgrading, realizing green and ecological development of marine industries and innovative circular economic models. This reduces the harm of traditional marine industries to marine environmental quality and decreases marine environmental pollution. For instance, the development of smart marine fisheries, intelligent shipping, and smart ports can effectively alleviate marine environmental pressure, enhance resource utilization efficiency, and provide new solutions for marine resource protection. Moreover, the development of the digital economy brings forth new forms of marine economy, achieving green and low-carbon development of industries, which in turn enhances the effectiveness of marine environmental governance and sustainable development. Examples include the marine big-data industry, marine information technology services, and submarine data centers. The development of these new formats can effectively improve the collection and processing of marine industry and environmental data. Big data analysis supports ecosystem assessment, biodiversity protection, and climate change research, providing precise decision-making support for governments to govern marine environmental quality. Based on the above analysis, the following hypothesis is proposed:

Hypothesis H2: 

The digital economy can promote the improvement of marine environmental quality by enhancing the level of marine economy.

The development of the digital economy is a long-term and complex process, which is influenced by various factors and will have different impacts on the improvement of marine environmental quality at different times. On the one hand, the development of the digital economy will be influenced by the level of economic development and the scale of industries. When the economic level is in the early stage of development, the strength is relatively weak, and the coordination between digital economy and economic development is low, making it difficult to form a fusion and symbiotic pattern. Therefore, the impact on the quality of the marine environment is relatively weak, especially as digital technology cannot be widely applied to marine environment governance and protection. When the economy develops to the mid to late stage, the digital economy also enters the stage of innovative development, and data elements become important production factors. The integration of digital economy and economic development level is high, and digital technology is widely used in various industries, promoting the efficiency improvement of marine environmental governance and protection. The impact of the digital economy on marine environmental quality is significantly enhanced. On the other hand, the development of the digital economy also has an important impact on the scale of the marine industry. When the scale of the marine industry is in the early stages of development, the ability of digitalization to empower traditional marine industries is low, the transformation and upgrading of the marine industry is slow, the industrial scale is difficult to expand significantly, and it cannot continuously stimulate new blue energy. Therefore, the impact on improving the quality of the marine environment is relatively weak. As the scale of the marine industry develops in the middle and later stages, digital technology is also becoming increasingly mature. Digitization empowers the comprehensive improvement of the marine industry’s capabilities, which can not only promote the rapid transformation and upgrading of traditional marine industries, but also stimulate new marine business models, utilizing big-data AI. Tools such as the Internet of Things can become powerful engines for expanding the scale of the ocean. At the same time, the expansion of the ocean industry also provides more application scenarios for the development of the digital economy, promoting digital technology innovation and the development of the digital economy. At this stage, the linkage between the digital economy and the scale of the marine industry can further enhance the impact on the quality of the marine environment. Therefore, this article proposes the following hypothesis:

Hypothesis H3: 

The level of economic development and industrial scale can lead to a threshold effect, rather than a linear structure, on the impact of the digital economy on the quality of the marine environment.

2.2.2. Model Construction

The marine environment, marine economy, and digital economy are all complex systems, and scientific evaluation of them needs to build a comprehensive evaluation system for accurate evaluation. Therefore, we selected a number of indicators to build an evaluation index system and used the entropy method to measure their comprehensive scores. The comprehensive assessment system of marine environmental quality divides marine environmental quality into two levels: marine resource environmental quality and marine ecological environmental quality. For the marine resource environment quality, five indicators were selected for measurement, namely the relative annual variation in the sea level, the proportion of water quality of Class I and Class II in coastal areas, the area of coastal wetlands, the area of inshore and coastal regions, and the seawater eutrophication index. The marine ecological environment quality was measured by five indicators: station proportion (%), phytoplankton diversity index, zooplankton diversity index, benthic biodiversity index, and annual mean value of fecal coliform in water. The comprehensive evaluation system of marine economic level is constructed by dividing marine economy into two dimensions: marine economic strength and marine economic potential. The strength of the marine economy is measured by six indicators: total output value of marine economy, added value of marine tertiary industry, marine-related industries, output value of marine fishery, output of ocean-going fishery, and throughput of standard container. The marine economic potential was measured by six indicators: the number of coastal tourists, the number of marine motor fishing vessels, the number of marine production fishing vessels at the end of the year, the number of marine fishing vessels, the area of mariculture, and the number of ocean-going fishing vessels at the end of the year. The comprehensive evaluation system of digital economy is constructed by dividing the digital economy into two categories: industrial digitalization and digital industrialization. Industrial digitalization is measured by five indicators: information I transmission, fixed asset investment in software and information technology services, number of digital economy enterprises, express delivery volume, telecommunications business volume, and information technology consulting service income. However, digital industrialization is measured by five indicators, such as the income of electronic information manufacturing industry, the income of software industry, the number of Internet domain names, the penetration rate of telephone, and the length of long-distance optical cable lines. The specific indicators are shown in

Table 1. Index system for marine environment, marine economy, and digital economy.

Target Layer	Rule Layer	Weight	Index Layer	Index (Positive/Negative)	Weight
Marine environmental quality level	The quality of marine resources and environment		Relative annual variation in sea level (mm)	Negative	0.0835
			Proportion of water quality of Class I and Class II in coastal areas (%)	Positive	0.1039
		0.6594	Area of coastal wetlands (1000 hectares)	Positive	0.2409
			Eutrophication Index of Seawater	Negative	0.0431
			Area of coastal and shore areas (square kilometers)	Positive	0.1859
	The quality of marine ecological environment		The sediment condition of the sea area conforms to the first type of sea site ratio of sediment quality criteria (%)	Positive	0.0431
			Phytoplankton diversity index	Positive	0.0826
		0.3406	Zooplankton diversity index	Positive	0.0371
			Benthic biodiversity index	Positive	0.1652
			Annual mean value of fecal coliforms in water bodies (per liter)	Negative	0.0126
Marine economy level	Marine economic strength		Total output value of marine economy (billion CNY)	Positive	0.0601
			Output value of the tertiary industry (billion CNY)	Positive	0.0661
		0.4492	Marine related industries (billion CNY)	Positive	0.0596
			Output value of marine fishery (billion CNY)	Positive	0.0729
			Pelagic fishery (10,000 tons)	Positive	0.0669
			Standard container throughput (10,000 TEU)	Positive	0.1237
	Marine economic potential		Number of coastal tourists (tens of thousands)	Positive	0.0664
			Marine motor fishing vessel ownership (gross tons)	Positive	0.0849
		0.5508	Number of marine production fishing vessels at the end of the year (tons)	Positive	0.0772
			Number of marine fishing motor fishing vessels (vessels)	Positive	0.0684
			Mariculture area (ha)	Positive	0.1202
			Number of ocean-going fishing vessels at year end (kW)	Positive	0.1337
Digital economy level	Industrial digitization		Investment in fixed assets in information transmission, software and information technology services (billion CNY)	Positive	0.0462
			Number of digital economy enterprises (PCS)	Positive	0.0482
			Number of authorized patent applications (pieces)	Positive	0.0942
		0.5618	Online retail sales (billion CNY)	Positive	0.1061
			Digital Financial Inclusion Index	Positive	0.0211
			Express volume (ten thousand pieces)	Positive	0.1429
			Information technology consulting service revenue (ten thousand CNY)	Positive	0.1031
	Digital industrialization		Telecommunications business volume (billion CNY)	Positive	0.1047
			Electronic information manufacturing revenue (billion CNY)	Positive	0.1157
			Software industry revenue (billion CNY)	Positive	0.0797
		0.4382	Number of Internet domain names (thousands)	Positive	0.0783
			Telephone penetration rate (units/100 people)	Positive	0.0139
			Internet broadband access users (10,000 households)	Positive	0.0459

Note: The weights in the table are calculated according to Formulas (3) and (4) in research method, Section 2.3.2 Entropy Method. The biodiversity index is a comprehensive manifestation of the number of biological species and the evenness of individual quantity distribution among species, which is characterized by the Shannon–Wiener diversity index. The calculation formula is $H^{’}=-{\displaystyle \sum \left(P_i\ast {\log }_i{P}_i\right)}$, where $P_i$ is the proportion of the number of individuals of the i-th species in the sample to the total number of individuals in the sample.

.

2.3. Research Methods

2.3.1. Range Method

Due to the different dimensions and positive and negative directions of each indicator data, it is necessary to standardize the original data. This article uses the range method to process the original data, and the specific processing method is as follows.

The processing method for larger and better positive indicators is

$$P_{ij}^{+}={\displaystyle \frac{x_{ij}-\min (x_{ij}\cdot \cdot \cdot \cdot \cdot x_{nj})}{\max (x_{ij}\cdot \cdot \cdot \cdot \cdot x_{nj})-\min (x_{ij}\cdot \cdot \cdot \cdot \cdot x_{nj})}}$$

(1)

The method for handling negative indicators that are smaller and better is as follows:

$$P_{ij}^{-}={\displaystyle \frac{\max (x_{ij}\cdot \cdot \cdot \cdot \cdot x_{nj})-x_{ij}}{\max (x_{ij}\cdot \cdot \cdot \cdot \cdot x_{nj})-\min (x_{ij}\cdot \cdot \cdot \cdot \cdot x_{nj})}}$$

(2)

In the formula, $i(i=1,\cdot \cdot \cdot \cdot ,n)$ represents the city; $j(j=1,\cdot \cdot \cdot \cdot ,n)$ represents the indicator; and $A_{ij}$ is the standardized data matrix. $x_{ij}$ is the raw data matrix; the $\max (x_{ij}\cdot \cdot \cdot \cdot \cdot x_{nj})$ and $\min (x_{ij}\cdot \cdot \cdot \cdot \cdot x_{nj})$ represent the maximum and minimum values, respectively.

2.3.2. Entropy Method

The entropy method is an objective weighting method, which is used to evaluate the development level of the object with multiple indexes. This method is based on the original information from the objective environment and determines the weight of indicators according to the amount of information. This method has been widely used in various fields such as society and economy. In this paper, the entropy method is used to measure the weight and comprehensive score of each index of marine environment, marine economy, and digital economy. The specific calculation steps are as follows:

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

(3)

where $p_{ij}=A_{ij}/{\displaystyle \sum _{i=1}^n{A}_{ij}}$, $e_j$ represents the index entropy value, where $0\le e_j\le 1$; $n$ represents the number of indicators; $k=1/\ln m$, $k>0$; and $m$ represents the number of evaluation objects.

Index weight calculation:

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

(4)

where $w_j$ represents the index weight and $e_{ij}$ represents the index entropy value. The criterion layer weight is ${w^{’}}_j={\displaystyle {\sum }_{i=1}^m{w}_j}$ Comprehensive score calculation:

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

(5)

2.3.3. Panel Regression Model

The panel regression model combines the characteristics of cross-sectional regression and time series regression, enabling it to better capture the complex relationships within the data. The fixed-effects regression is a commonly used panel regression model. It can examine the impact of explanatory variables on the explained variables for different individuals at different times, thereby obtaining valuable information and conclusions, which provides a basis for economic decision-making, policy formulation, and so on. It is achieved by adding some virtual observations, and some variables can be controlled to report the performance of the model more accurately [27]. To examine the impact of digital economy development on marine environmental quality, this study constructs the following baseline regression model:

$$\ln Me{q}_{i,t}={\alpha }_0+a_1\ln Dg{e}_{i,t}+a_{2}Co{n}_{i,t}+{\mu }_i+{\phi }_t+{\epsilon }_{i,t}$$

(6)

where $i$ is individual, representing different regions, and t is time, representing different years. Explained variable: $Me{q}_{i,t}$ represents the economic quality of marine; ${\alpha }_0$ is the intercept term vector; the core explanatory variable $Dg{e}_{i,t}$ represents the quality level of digital economy; $Co{n}_{i,t}$ represents a set of control variables related to the level of marine environmental quality; ${\mu }_i$ is the individual fixed effect term; ${\phi }_t$ is the time fixed effect term; and ${\epsilon }_{i,t}$ is the random disturbance term.

2.3.4. Mediating Effect Model

The mediating effect model is a statistical method used to explore how an independent variable influences a dependent variable through a mediating variable, so as to reveal the internal mechanism or principle of the relationship between the two variables. In simple terms, mediating effect analysis can help us understand whether the relationship between the independent variable and the dependent variable is realized indirectly through the mediating variable, or it is a direct influence of the independent variable on the dependent variable. When considering the influence of the independent variable X on the dependent variable Y, if X affects Y by influencing the variable M, then M is said to be an intermediate variable (mediator or mediating variable). The influence of X on Y through the mediating variable M is the mediating effect (mediation effect). In order to verify the specific channel mechanism of digital economy development on marine environmental quality level, this study constructs the following equation:

$$\ln Me{q}_{i,t}={\beta }_0+{\beta }_1\ln Dg{e}_{i,t}+{\beta }_2\ln Co{r}_{i,t}+{\mu }_i+{\phi }_t+{\epsilon }_{i,t}$$

(7)

$$\ln Oe{d}_{i,t}={\lambda }_0+{\lambda }_1\ln Dg{e}_{i,t}+{\lambda }_2\ln Co{r}_{i,t}+{\mu }_i+{\phi }_t+{\epsilon }_{i,t}$$

(8)

$$\ln Me{q}_{i,t}={\gamma }_0+{\gamma }_1\ln Dg{e}_{i,t}+{\gamma }_2\ln Oe{d}_{i,t}+{\gamma }_3\ln Co{r}_{i,t}+{\mu }_i+{\phi }_t+{\epsilon }_{i,t}$$

(9)

where $Oe{d}_{i,t}$ represents the mediating variable, which is represented by selecting the level of marine economy, and the remaining variables are the same as those in Formula (4).

2.3.5. Threshold Effect Model

In order to test whether there is a nonlinear structural relationship between the digital economy and the variables of the marine environmental quality level, this paper employs the panel threshold regression model proposed by Hansen (1999) to conduct an examination of the above-mentioned nonlinear relationship [28]. “Threshold regression”, as a statistical model used to analyze the structural changes in the relationships between variables within different intervals, is widely applied in fields such as economics, finance, and sociology. Its essence is to search for threshold variables among the variables that reflect causal relationships. The threshold value is estimated based on the sample data, and it is also necessary to test whether there are significant differences in the parameters of the sample groups divided according to the threshold value. The panel threshold regression model set in this paper is as follows:

$$\begin{array}{l}\ln Me{q}_{it}={\varphi }_0+{\varphi }_1\ln De{g}_{it}·\mathrm{I}(\ln Ep{m}_{it}\le \delta )+{\varphi }_2\ln De{g}_{it}·\mathrm{I}(\ln Ep{m}_{it}>\delta )\\ +\theta \ln Co{r}_{it}+{\mu }_1\end{array}$$

(10)

$$\begin{array}{l}\ln Me{q}_{it}={\phi }_0+{\varphi }_1\ln De{g}_{it}·\mathrm{I}(\ln St{m}_{it}\le \kappa )+{\varphi }_2\ln De{g}_{it}·\mathrm{I}(\ln St{m}_{it}>\kappa )\\ +\theta \ln Co{r}_{it}+{\mu }_1\end{array}$$

(11)

In the formula, I(·) is an indicative function. When the expression in parentheses is false, the value is 0; otherwise, it is 1. According to whether the threshold variable $\ln Epm$ and $\ln Stm$ is greater than the threshold value $\delta$ and $\kappa$, the sample interval can be divided into two regimes. The slope values ${\varphi }_1$ and ${\varphi }_2$ are used to distinguish the two regimes.

Similarly, based on the first-order threshold value model, the situation where there are multiple thresholds in the model can also be considered. Next, taking the two-order threshold value model as an example, the above-mentioned model is as follows:

$$\begin{array}{l}\ln Meq=b_0+{\beta }_1\ln Deg·I(\ln Epm\le {\delta }_1)+{\beta }_2\ln Deg·I({\delta }_1<\ln Epm\le {\delta }_2)\\ +{\beta }_3\ln Deg·I(\ln Epm\le {\delta }_2)+\theta \ln Cor+{\mu }_1\end{array}$$

(12)

where ${\delta }_1<{\delta }_2$. The calculation process of the second-order threshold model is similar to that of the single-threshold model, which is to estimate the second threshold value under the condition that the first threshold value is fixed.

2.4. Variable Description

(1) Explained variable: We take the marine environmental quality level ($\ln Meq$) y as the explained variable, use the marine environmental quality index system constructed shown in Table 1 to measure, and take the logarithm of these values. (2) Explanatory variable: This study takes the digital economy level ($\ln Deg$) x as the explanatory variable, uses the digital economy index system constructed shown in Table 1 for calculation, and takes the logarithm of these values. (3) Intermediate variable: We take the marine economic quality level ($\ln Oed$) x₁ as the regulating variable, use marine economic quality index system shown in Table 1 to measure, and take the logarithm of these values. (4) Threshold variable: The level of economic development ($\ln Epm$) x₆ and marine industry scale level ($\ln Stm$) x₅ are used as threshold variables to measure whether they have a nonlinear relationship with the marine fishery economy. Among them, the level of economic development is measured by the per capita net income of fishermen, while the scale level of the marine industry is measured by the output of marine aquatic products. (5) Control variable: We select factors that might affect marine environmental quality level as control variables, including the development level of marine tertiary industry, which is represented by ocean passenger throughput ($\ln Otr$) x₂. The scale and capacity of marine traffic is expressed by the ocean freight volume ($\ln Ogt$) x₃. The scale and development of maritime trade is expressed by the cargo throughput of ocean ports ($\ln Ocz$) x₄ and the logarithm of these variables is taken to eliminate heteroscedasticity and multicollinearity (According to

Table 2. Descriptive statistics of the variables.

stats	Explained Variable	Explanatory Variable	Intermediate Variable	Threshold Variable		Control Variable
	lnMeq	lnDeg	lnOed	lnEpm	lnStm	lnOtr	lnOgt	lnOcz
max	−0.273	−0.059	−0.394	2.154	0.262	−3.039	0.211	−1.127
min	−1.703	−5.271	−3.059	−3.270	−2.388	−10.126	−4.455	−5.258
mean	−0.754	−2.190	−1.622	0.334	−1.637	−5.179	−1.466	−2.602
P50	−0.681	−2.145	−1.628	0.522	−1.626	−4.571	−1.528	−2.477
sd	0.325	0.990	0.817	1.512	0.345	1.836	1.055	0.823
N	132	132	132	132	132	132	132	132

).

In order to avoid pseudo-regression, this paper uses variance inflation factor (VIF) to comprehensively test the multicollinearity of each variable before the baseline regression. The results are shown in

Table 3. Variable inflation factor test table.

Variable	VIF	1/VIF
lnDeg	8.79	0.1137
lnOed	4.03	0.2478
lnOtr	5.05	0.1981
lnOgt	5.09	0.1965
lnOcz	3.23	0.3094
lnStm	2.81	0.3556
lnEpm	1.95	0.5133
Mean VIF	4.42

, where the maximum value of VIF in variables is 8.79, the minimum value is 1.95, and the average value is 4.42, which is far less than 10, fully indicating that there is no obvious multicollinearity between variables.

3. Analysis of Empirical Results

3.1. Analysis of Marine Environment Quality Level

Using the entropy method mentioned above to calculate the comprehensive level of marine environmental quality in China from 2011 to 2022, and the results are shown in Figure 1.

According to Table 1, during the study period, China’s marine environmental quality level had been steadily increasing, and the overall level was relatively good, but the growth rate was relatively slow, with the average value increasing from 0.45 to 0.56, and the average annual increase was only 2%. This shows that as China accelerates the construction of marine ecological civilization and actively builds a system of marine ecological civilization institutions, the marine environment is gradually moving towards a track of sound development.

In terms of horizontal comparison, the level of marine environmental quality in China’s coastal provinces and municipalities during the period 2012–2014 was highly volatile, especially in Shanghai, where the decline was the most obvious, with a drop of 40%. The quality of the marine environment in most of the provinces and municipalities during the period 2014–2019 showed a significant growth, with an average annual growth rate of more than 4%. Among them, except for Liaoning, where the level of marine environmental quality declined, the level of marine environmental quality in the rest of the provinces and cities grew significantly. Zhejiang and Shanghai had the highest growth rate in marine environmental quality, with an average annual growth rate of more than 5%. This was mainly due to China’s comprehensive promotion of marine ecological environmental protection and governance, accelerating the transformation and upgrading of marine industries, and implementing the green and low-carbon development of the marine economy, which greatly improved the quality of the marine ecological environment. The quality of the marine environment of most provinces and municipalities during the period of 2019–2020 was in a decreasing trend, especially in the provinces and municipalities of Liaoning, Tianjin, Hebei, Shandong, and Jiangsu, with the highest decline in Tianjin, with a decline of close to 20%. This was mainly due to the slow transformation of the marine industry in the Bohai Sea region, and the constraints imposed by the high energy consumption and pollution of the traditional marine industry on the improvement of the quality of the marine environment. The quality of the marine environment in most provinces and municipalities showed a significant increase in the period 2020–2022, with an increase of about 8%. In addition to a small decline in the quality of the marine environment in Guangxi, other provinces and cities had a substantial increase, especially Hebei, Shanghai, and Zhejiang provinces and cities with a large increase in more than 10%.

In terms of vertical comparison, the quality of marine environment shows a distribution pattern of “high in the north and south and low in the east”, with significant differences between provinces. The average value of the marine environmental quality in the southern and northern regions reaches more than 0.5, while the average value of the marine environmental quality in the eastern region is only about 0.4, which is not balanced between the regions, but there is a trend of gradual reduction. During the study period, Jiangsu had the highest marine environmental quality, with an average value of more than 0.7, while Tianjin had the lowest average value of marine environmental quality, only about 0.4, with a difference of nearly double, and the phenomenon of bifurcation was more obvious. The difference in the level of marine environmental quality within the region is also relatively obvious, among which, the marine environmental quality of Shandong in the Northern Marine Economic Circle is the highest, with an average value of 0.7, and the marine environmental quality of Tianjin is the lowest, only half of the level of the marine environmental quality of Shandong; the level of the marine environmental quality of Jiangsu in the Eastern Marine Economic Circle is the highest, and the level of the marine environmental quality of Shanghai is the lowest, only half of the level of the marine environmental quality of Jiangsu; and the level of the marine environmental quality of Guangdong in the Southern Marine Economic Circle is the lowest. The highest level of marine environmental quality was in Guangdong, within the Southern Marine Economic Circle, with an average value of 0.6, while the average value of marine environmental quality water in Hainan is about 0.4. There is a relatively significant gap.

3.2. Analysis of the Quality Level of the Digital Economy

Using the entropy method mentioned above to calculate the comprehensive level of digital economy development in coastal provinces and cities of China, and the results are shown in Figure 2.

The overall development of the quality of China’s digital economy has shown a steep rise, with the average value rising from 0.06 to 0.32, an increase of five times. However, the overall level is still low, and the polarization is serious. This fully demonstrates that China’s digital economy development is still in its infancy, and there is an urgent need to accelerate the cultivation of digitized new industries and new business forms, continuously stimulate the potential and vitality of digital economy development, promote the deep integration of the digital economy with a variety of traditional industries, and form a new pattern of synergistic development.

In terms of horizontal comparison, the quality of the digital economy in coastal provinces and municipalities was on the rise during 2011–2014, especially in Hainan and other provinces, where the growth rate was faster, with an average annual growth rate of more than 40%, while the average annual growth rate of the digital economy in other provinces was around 20%. This is mainly confined to the fact that China has begun to comprehensively implement the digital development strategy, accelerate the digital development of industries, and deeply promote the digital transformation of traditional industries, and there is an initial development of the digital economy during the period of 2015–2018, except for in Liaoning, where there is a significant decline in the quality of the digital economy; the rest of the provinces have shown a relatively fast upward trend, with an average annual growth rate of more than 10%, especially in Guangdong, where the average annual growth rate of the digital economy is more than during 2019–2022, except for in the Fujian and Hainan provinces, which have seen a slight decline in the quality of their digital economies; the rest of the provinces have shown a relatively large increase in their digital economies. This shows that China has accelerated the integration of the digital economy and the real economy and implemented the “cloud computing and digital empowerment”, and the new development pattern of the digital economy led by digitization and intelligence is gradually taking shape.

In terms of vertical comparison, Figure 3 shows that the quality of the digital economy shows a distribution pattern of “high in the East and South and low in the North”, with more serious polarization within the region. Among them, the average value of the quality of the digital economy in the northern region is only 0.11, the average value of the quality of the digital economy in the eastern region is about 0.19, and the average value of the quality of the digital economy in the southern region is about 0.18, while the development differences between regions are more obvious. During the study period, the digital economy quality level difference of the eastern region of the provinces is significant; in Jiangsu, the average value of the digital economy reached 0.32, while in Shanghai the average value of the digital economy is only 0.17. The difference is close to double. The southern region of the provinces within the digital economy differences are also large, of which the highest quality of the digital economy is in Guangdong, with an average value of more than 0.48, while the average value of the quality of the digital economy in Hainan is the lowest level of the average value of the digital economy, only 0.03 or so. With an almost 16-times difference between the two, the phenomenon of the two levels of polarization is very serious.

3.3. Benchmark Regression Results

As shown in

Table 4. Benchmark regression results table.

variant	OLS (1)	Fixed Effects (2)	Random Effects (3)	Two-Way Fixed Effects (4)
	lnMeq	lnMeq	lnMeq	lnMeq
lnDeg	0.1628 (***)	0.1741 (***)	0.1858 (***)	0.2092 (***)
lnOtr	0.1146 (***)	0.1093 (***)	0.0847 (***)	0.1242 (***)
lnOgt	−0.2133 (***)	0.0333 (0.34)	−0.002 (0.94)	0.0555 (*)
lnOcz	0.0968 (**)	−0.0642 (*)	−0.051 (0.16)	−0.0724 (**)
-Cons	0.134 (0.164)	0.0749 (***)	−0.045 (***)	0.7917 (***)
AIC	3.224	−238.054
Hausman		Chi2(5) = 42.72 (***)
Sample Size	132	132	132	132
Adjustment of R2	0.469	0.568	0.562	0.9305

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

, the results of the baseline regression of the impact of the digital economy on the level of marine environmental quality are presented. Column (1) shows the regression results of the least squares model, which indicates that the digital economy has a significant positive effect on the quality level of the marine environment, with a regression coefficient of 0.1628 and passes the 1% level of significance test. Column (2) shows the regression results of the fixed-effects model, which indicates that the digital economy also has a positive driving effect on the quality level of the marine environment, with a regression coefficient of 0.1741 and passes the 1% level of significance test. By comparing the AIC results, the AIC value of the fixed-effect model is −238.054, which is obviously smaller than the AIC value of the least squares method, while the adjusted R-squared value of the fixed-effects model is 0.568, which is higher than that of the ordinary least squares (OLS) method. Therefore, the fixed-effect model has significant advantages over the least-squares model. Column (3) is the regression result of the random-effect model, with a regression coefficient of the regression coefficient of 0.1858, and passes the significance test at the 1% level, and it can be seen from the test result of Hausman that it is better to choose the fixed-effect model. Column (4) is the regression result of the two-way fixed-effect model, which shows that the digital economy has a significant positive effect on the level of the marine environmental quality. The regression coefficient is 0.2092 and passes the significance test at the 1% level, and the adjusted R² is 0.2092 and passes the 1% level, with R being 0.9305, which was higher than the adjusted R of the fixed-effect model. Therefore, when compared with the regression results of the fixed-effects model in column (2), the two-way fixed-effects model in column (4) has obvious advantages. In conclusion, it can be seen that the estimated coefficient of the impact of the digital economy on the level of marine environmental quality is significantly positive at the 1% level, with a result of 0.2092. This fully demonstrates that the development of the digital economy can effectively improve the development level of marine environmental quality.

3.4. Analysis of Robustness Test

To test the reliability of the results regarding the impact of the development of the digital economy on the quality of the marine environment, a robustness test is conducted on the regression results. This paper employs three methods for the robustness test: (1) replacing the regression model, (2) substituting the explained variable, (3) replacing the explanatory variable. The specific results are shown in

Table 5. Robustness test results.

Variable	(1) Tobit		(2) Replace the Explained Variable		(3) Replace the Explanatory Variable
	Meq		lnMeq		lnMeq
	Coef	Std. Err	Coef	Std. Err	Coef	Std. Err
lnDeg	0.2265 (***)	0.0444	0.2146 (***)	0.0645	0.1978 (**)	0.0507
lnOtr	0.04647 (***)	0.0112	0.1312 (***)	0.0230	0.1272 (***)	0.0304
lnOgt	0.0351 (***)	0.0143	0.057 (0.112)	0.0358	0.0616 (*)	0.0338
lnOcz	0.02 (0.194)	0.0155	−0.0766 (**)	0.0388	−0.0703 (**)	0.0361
-cons	0.7986 (***)	0.0837	0.8335 (***)	0.2043	0.8211 (***)	0.2657
Province fixed			YES		YES
Year fixed			YES		YES
R2			0.929		0.9327
N	132

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

. First, the measurement model was replaced. A Tobit model is used for regression analysis. Since all the data used are comprehensively measured by the entropy method and the data values all fall between [0, 1], which are censored data, this data is selected for regression analysis using the Tobit model. The regression results are shown in Table 5 (1). Among them, the regression coefficient of the digital economy is 0.2265, which passes the test at the 1% significance level. This indicates that the digital economy plays a promoting role in improving the quality of the marine environment, demonstrating the robustness of the regression analysis. Second, regression analysis was conducted by replacing the explained variable. To avoid misestimation caused by indicator selection, this paper excludes the annual average value of fecal coliform bacteria in water from the marine environmental quality indicator system, recalculates the weights and the comprehensive level, and uses the calculated results as the explained variable for regression analysis. The regression results are shown in Table 5 (2). The regression coefficient of the digital economy on the marine environmental quality is 0.2146, and it passes the significance test at the 1% level. This indicates that the digital economy has a significantly positive effect on improving the level of marine environmental quality. Finally, the method of replacing the explanatory variable is adopted. In this paper, the indicator of telephone penetration rate is removed from the indicator system of the digital economy level, and the weights and the comprehensive level are recalculated. Then, the calculated results are used as the explanatory variable for regression analysis. The regression results are shown in Table 5 (3). The regression coefficient of the digital economy on the marine environmental quality is 0.1978, and it passes the significance test at the 1% level. Similarly, it confirms that the digital economy has a significantly positive effect on improving the level of marine environmental quality. This fully demonstrates that the conclusion that the development of the digital economy has a significant promoting effect on the quality of the marine environment is robust.

3.5. Analysis of Mediation Effect Results

In order to further explore the transmission mechanism of the digital economy on the quality of the marine environment, this paper uses a mediating effect model to test the role of the quality of the marine economy in the process of the impact of the digital economy on the quality of the marine environment (Table 3). Models (1–3) report the regression results of the mediation effect model with marine economic quality as the mediating variable. The digital economy in model (1) has a significant positive driving effect on marine environmental quality, while the estimated coefficient of the digital economy on marine economic quality in model (2) is 0.2705 and passes the significance test at the 1% level, which fully indicates that the development of the digital economy level enhances the level of marine economic quality to some extent. The regression coefficient of marine economic quality on the level of marine environmental quality in model (3) is 0.2259 and passes the significance test at the 1% level, which indicates that the level of marine economy is an important driving factor of marine environmental quality. The regression coefficient of the digital economy changes from 0.2092 in the baseline regression model (1) to 0.3078 in model (3), indicating that the transmission path of “digital economy–marine economic quality–marine environmental quality” exists significantly, and that the digital economy enhances the level of marine environmental quality by improving the productivity of the marine economy and optimizing the allocation of resources (According to

Table 6. The measurement results of the mediating effect.

Variable	(1)		(2)		(3)
	lnMeq		lnOed		lnMeq
	Coef	Std. Err	Coef	Std. Err	Coef	Std. Err
lnDeg	0.2092 (***)	0.0618	0.2705 (***)	0.0457	0.3078 (**)	0.0697
lnOed					0.2259 (***)	0.1285
lnOtr	0.1242 (***)	0.0311	−0.0279 (0.225)	0.0230	0.1328 (***)	0.0306
lnOgt	0.0555 (*)	0.0343	0.0736 (***)	0.0253	0.0328 (0.35)	0.0349
lnOcz	−0.0724 (**)	0.0372	0.071 (***)	0.0275	−0.0943 (***)	0.0375
-Cons	0.7917 (***)	0.2764	−0.2157 (0.291)	0.2043	0.8582 (***)	0.2718
Province fixed	YES		YES		YES
Year fixed	YES		YES		YES
R2	0.9305		0.994		0.9341
N	132

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

).

3.6. Analysis of Threshold Effect Results

The impact of the digital economy on the quality of the marine environment can be influenced by a variety of factors, especially economic and industrial development, leading to major significant shifts and the formation of turning points or sudden changes, rather than gradual linear development. Based on this, this paper examines whether the impact of the digital economy on the quality of the marine environment is constrained by the level of regional per capita income and the level of the scale of the marine industry from the dimensions of economic development and industrial scale using the threshold model. The bootstrap method was utilized to conduct 1000 tests on the threshold effect of the sample data. As a result, both economic development and industry scale passed the single threshold test, as shown in

Table 7. Threshold effect model estimation results.

Category	lnEpm		lnStm
Number of Thresholds	1	2	1	2
F-value	23.87	7.74	17.25	11.54
P-value	0.03	0.55	0.084	0.253
10% threshold level	17.539		16.813
5% threshold level	21.408		18.207
1% threshold level	28.945		26.437
threshold value	−1.311		−2.538
lnOST	0.1041 (***) 0.0252		0.1016 (***) 0.0271
lnOIP	0.0548 (*) 0.0330		0.0355 (0.29) 0.0334
lnOMC	−0.0213 (0.53) 0.337		−0.0803 (**) 0.0364
lnDeg − 1 (lnEpm ≤ −1.311)	0.1131 (***) 0.029
lnDeg − 1 (lnEpm > −1.311)	0.1617 (***) 0.0258
lnDeg − 1 (lnStm ≤ −2.538)			0.1272 (***) 0.0365
lnDeg − 1 (lnStm > −2.538)			0.1638 (***) 0.0241
-Cons	0.174 (0.261) 0.154		1.515 (***) 0.163
R-sq	0.679		0.618
N	132		132

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

. This indicates that there is a nonlinear relationship between the digital economy and the level of marine environmental quality, influenced by the level of economic development and the scale of marine industry.

The truthfulness test of the threshold is to verify whether the estimated value is equal to the true value. To determine whether there is a threshold effect, the results of the test are shown in Table 7, where the F statistic is significant at the 1% level, i.e., the p-value is less than 0.01 in the one-medium threshold model; therefore, there is a threshold value in both models. The red dotted line in Figure 3 represents the threshold value of the non-central deviation distribution at a significance level of 0.01. In this case, the lowest point of the LR statistic is the corresponding true threshold value, and the dashed line indicates that the critical value is 7.35. Figure 3 shows the LR plot of the Epm critical value estimate. According to the principle of the threshold model, the threshold estimate is the value corresponding to the Likelihood Ratio statistic LR as it tends to 0. Therefore, −1.311 was determined to be the single threshold value. Figure 4 shows the LR plot of the Stm threshold estimate, showing that the threshold passes the test of truthfulness within the 99% confidence interval. Again, −2.538 is identified as the single threshold. Figure 3 and Figure 4 show that the critical value passes the test of truthfulness within the 99% confidence interval and the critical value of 7.35 is significantly larger than the threshold value; thus, the above threshold value can be considered to be true and valid.

Figure 3 and Figure 4, respectively, represent panel threshold regression models with economic development and industry scale as threshold variables used to estimate the threshold effect of the digital economy on marine environmental quality. When the threshold variable is the level of economic development (LnEpm), there are large differences in the effects of values on marine environmental quality. When the level of economic development is in the middle and early stage (LnEpm ≤ −1.311), the coefficient of the digital economy’s impact on the quality of the marine environment is 0.1131, which is significant at the 1% level, and the impact of the digital economy on the quality of the marine environment is significantly positive, but it shows a trend of gradual reduction. This is mainly due to the early stage of economic development. Although the digital economy has the advantages of digital technology, digital technology and economic development have not yet reached a deep integration state, and the role of digital momentum has not been fully utilized. The promotion of green and low-carbon economic development is limited, and the development of the digital economy has certain negative effects (such as ecological interference in submarine cable construction and maintenance, energy consumption and heat pollution in data centers, and emerging digital technologies’ impact on submarine noise pollution). For example, electronic waste pollution generates a huge amount of electronic waste, some of which contains toxic substances such as lead and mercury. If not handled properly (such as illegal dumping or landfilling), they may eventually flow into the ocean through rivers or groundwater, poisoning marine life and entering the food chain. Therefore, at this time, the role of the digital economy in improving the quality of the marine environment will show a downward trend. When the level of economic development reaches the middle and late stage (−1.311 ≤ LnEpm), the digital economy’s impact on the marine environmental quality is 0.1617; at this stage, the impact of digital economy on the quality of the marine environment is also significantly positive, and it is significantly higher than that of the previous stage. This is mainly due to the current situation where the digital economy and economic development form a coordinated development trend. The digital economy can fully leverage the advantages of data elements and digital technology, promote economic transformation and development, achieve a green and low-carbon development model, reduce pollution to the marine environment, optimize the utilization of marine resources and reduce waste, and promote the optimization and emission reduction in shipping efficiency. For example, satellite remote sensing can monitor real-time ocean temperature, salinity, sea level changes, chlorophyll concentration, pollutant diffusion, and coral bleaching. Unmanned equipment goes deep into dangerous or difficult-to-reach areas such as the deep sea and polar regions for exploration, biological surveys, shipwreck archeology, and pipeline inspections, reducing the risks and costs of direct human intervention. At this time, the impact of the digital economy on the improvement of marine environmental quality is gradually increasing. It can be seen that, limited by the level of economic development, the impact of digital economy development on the quality of the marine environment presents a “U” state; when the level of economic development exceeds the threshold value of −1.311, the impact of the digital economy on the quality of the marine environment will gradually increase with the continuous improvement of the level of economic development.

When the threshold variable is LnStm, there is a significant difference in the impact of values on the quality of the marine environment. When the level of marine industry scale is in the middle and early stage (LnStm ≤ −2.538), the coefficient of the influence of digital economy on the quality of the marine environment is 0.1272, which is significant at the level of 1%, indicating that the digital economy has a significant positive effect on the quality of the marine environment. This is mainly because in the early stage of the development of the marine industry, the scale of the marine industry is relatively small, and the advantages of digital empowerment of the marine industry are difficult to fully utilize. The digital technology leading the development of the marine industry is not sufficient. In addition, the negative impact of the digital economy on the marine environment may exist. Therefore, although the digital economy has a positive promoting effect on the quality of the marine environment at this time, the level of impact is not high. When the level of the scale of the marine industry is in the middle and late stage (−2.538 < LnStm), the regression coefficient changes to 0.1638, and it passes the significance test at 1% level, indicating that the level of digital economic development has a significant promotion effect on the quality of the marine environment, and the level of influence has increased significantly compared with the middle and early stage of the industry scale. This is mainly due to the full play of the role of the digital economy in the scale of the marine industry at this time, with obvious advantages in industrial transformation, infrastructure construction, resource allocation, and green transformation. For example, digital technology can digitally transform traditional marine manufacturing, marine ports and shipping, and marine fisheries that have high added value, high technology intensity, and good growth potential, accelerating the transformation and upgrading of traditional marine industries. The use of technologies such as big data, the Internet of Things, and artificial intelligence can promote the development of emerging industries such as marine drugs and biological products, marine renewable energy, deep-sea high-end instruments and equipment, and new materials. The gradual improvement of marine digital infrastructure will further promote the aggregation and sharing of data resources, reducing unnecessary intermediate costs. By establishing a smart management platform for the operation of the marine economy, aggregating marine economic data, and smoothing the data chain, it is possible to optimize the allocation of marine resources. Therefore, at this time, the digital economy is accelerating the upgrading of the scale of the marine industry, gradually achieving green and sustainable development, and its impact on the quality of the marine environment is also increasing. In summary, the effect of the scale level of the marine industry’s digital economic development on the quality of the marine environment presents a “U”-type state; when the scale level of the marine industry is less than the threshold value of −2.538, the digital economy will have a positive roll in promoting the level of marine environmental quality, but the impact effect will gradually weaken when the scale level of the marine industry goes above this. When the scale level of marine industry exceeds the threshold value of −2.538, the enhancement effect of the digital economy on the quality of the marine environment will show an upward trend with the increasing scale level of marine industry.

4. Discussion

In this paper, by measuring the level of digital economy and marine environmental quality and its mechanism in China’s coastal provinces and cities, the following conclusions are drawn:

(1)

The quality of marine environment and digital economy both show a fluctuating upward trend, but the overall level is relatively low, and the digital economy can effectively promote the improvement of marine environment quality. The reason for this is that, firstly, marine environmental pollution has not been fundamentally controlled from the source, and a forward-looking, systematic, and collaborative management system for marine pollution prevention and control has not yet been established. In addition, the high investment, high energy consumption, high emissions, and low efficiency development of the marine economy pose a huge threat to the quality of the marine environment. Secondly, due to the early stage of the industry lifecycle of the digital economy, there is insufficient integration with the marine industry, and the supporting role of digital technology in marine environmental protection and governance has not been fully utilized. Previous studies have confirmed that the marine economy is one of the important factors affecting the quality of the marine environment. The pollutants emitted during the development of the marine economy have a significant “coercive” impact on the ecological environment. The coordination level between the marine economy and the marine environment is relatively low, but there is a significant improvement. [6] These research conclusions are similar to the overall low level of China’s marine environmental quality and the negative impact of the marine economy on the marine environment calculated in this article from 2011 to 2022. Firstly, the digital economy promotes the improvement of marine environmental quality, and the marine economy plays a transmitting role in this process. Firstly, according to the regression analysis results in Table 3, the impact coefficient of the digital economy on marine environmental quality is 0.2092, which has passed the statistical significance level. This is mainly because the digital economy can effectively avoid the excessive consumption of resources and energy by traditional marine industries, causing harm such as marine environmental pollution and ecological degradation; promote the deep integration of greening, ecologicalization, and digitization; and intelligently promote the high-quality development of marine economy and environment. This is similar to previous research findings that the digital economy can effectively reduce environmental pollution and improve environmental quality and efficiency [6,7,8,12]. Secondly, previous studies mainly focused on the impact of the development of the marine economy on the quality of the marine environment [13,20,23,24], while this study focuses on analyzing the impact and mechanism of the digital economy on the improvement of marine environmental quality, expanding the research on the development of the marine environment and digital economy. Real problems such as the deterioration of the marine environment, the frequent occurrence of marine accidents, and the overexploitation of fishery resources are forcing the development of a “smart ocean”. The question of how to improve the quality of the marine economy through the construction of marine informatization and digitalization, broaden the application of digital technology in the field of marine environmental governance and protection, and promote the sustainable development of the marine environment has become an important issue that needs to be solved globally. This paper studies the transmission mechanism of the digital economy on the quality of the marine environment from a new perspective, and reveals the constraints of the digital economy on the quality of the marine environment, which not only helps to provide theoretical references for deepening the synergistic mechanism of the elemental allocation, information sharing, spatial layout, regional cooperation, and other factors of the marine environment and the digital economy, but also helps to change the way of thinking of the mutual division of marine ecological environment and economic development. Through this study, the synergistic amplification effect will be realized, providing a theoretical framework for other countries and regions to study the synergistic unification of digital economy and ecological benefits.

(2)

The driving effect of the digital economy on marine environmental quality will be influenced by structural changes in economic development and the scale of marine industries. As shown in Table 3, when economic development and the scale of marine industries are at different stages, the impact coefficient of the digital economy on the marine environment varies, and is always higher than the previous period. When economic development acts as a threshold variable, the impact coefficient of the digital economy on the marine environment increases from 0.1131 to 0.1617. When the scale of marine industries acts as a threshold variable, the impact coefficient of the digital economy on the marine environment rises from 0.1272 to 0.1638. The main reason for this is that when the economy operates in a crude mode with low green levels, the advantages and empowering role of the digital economy cannot be fully realized. Moreover, the digital economy has not yet deeply integrated with various industries, and its impact on marine environments is relatively low. In particular, if the construction of digital economy facilities relies on fossil fuels, it will exacerbate carbon emissions, leading to ocean acidification and warming, threatening coral reefs and marine biodiversity. The laying of submarine cables and the construction of offshore wind power facilities may damage seafloor sediments, disrupt benthic habitats, and affect fish migration routes. Therefore, the role of the digital economy in improving marine environmental quality will gradually diminish. When the economy is developing in a high-quality, green, and sustainable manner, the digital economy integrates deeply with various industries. For example, smart fisheries are rapidly advancing, using big data to optimize fishing quotas and reduce overfishing; blockchain technology tracks seafood supply chains to combat illegal fishing. Intelligent shipping systems are becoming more sophisticated, with AI optimizing routes to reduce fuel consumption and digital management minimizing the spread of invasive species in ballast water. Digital grid technology enhances the utilization efficiency of clean energy sources such as offshore wind and tidal power, reducing the carbon footprint on marine ecosystems. The scale of the marine industry plays a similar role in the impact of the digital economy on the marine environment. Therefore, in-depth research into how different threshold variables affect the digital economy’s influence on the marine environment is crucial for promoting green digital infrastructure and building global marine data sharing platforms to strengthen international cooperation, especially for countries and regions advancing the empowerment of the marine environment through the digital economy.

Although the development of the marine environment and digital economic quality of different coastal countries and regions will form their own particularities due to a variety of factors such as geographic location, economic structure, level of science and technology, resource endowment and history and culture, the question of how to use digitization to crack the problem of the marine environment, improve the quality of the marine economy, and promote the sustainable development of the ocean has always been the focus of the attention of the world’s coastal countries and regions. For example, in June 2023, dead fish stretching for thousands of meters appeared in the waters near Chumphon in southern Thailand and in the Gulf of Mexico in the United States, which was caused by the death of fish trapped in shallow waters due to the lack of oxygen in the ocean heat wave. The massive fish kills further affect the seabirds that feed on them, with warming of the Pacific surface waters off the west coast of North America from 2013 to 2016 leading to the tragic deaths of an estimated one million seabirds due to lack of food [29]. The United Nations has declared 2021 the start of the Decade of the Ocean. One of the program’s ten challenges is to create an integrated digital virtual body of the oceans, with the aim of helping the international community implement Sustainable Development Goal 14: “Conserve and sustainably use the oceans, seas and marine resources [30]”. Promoting the development of the digital economy and thus the development of the marine environment is the way of the future. For example, Singapore uses digital twins and tidal and pollution simulation systems to establish a 3D model of the national coastline to predict the impact of land reclamation projects and sea level rise, monitor port vessel emissions in real time, and automatically optimize shipping routes to reduce pollution; Australia has deployed a sensor network to monitor water temperature, acidity, and pollutants, and predict coral bleaching events; and The Global Fisheries Monitoring Platform uses satellite remote sensing, AIS vessel positioning, and AI data analysis to track global fishing vessel activities in real-time and combat illegal fishing, particularly when used in cooperation with the governments of Indonesia, Peru, and other countries. It helped Indonesia reduce illegal fishing by over 90% in 2020. This indicates that the digital economy is not only a technological tool, but also a key lever for reshaping the paradigm of ocean governance and balancing ecology and economy. But there is insufficient consideration of digital governance related to the development of a high-quality marine environment, and so far there are no research papers on the link between the marine environment and the digital economy. Based on the influence mechanism of the marine environment, marine economy and digital economy, this paper proposes accelerating the digital governance of the marine environment, building a marine monitoring network, constructing an “intelligent ocean” and “transparent ocean”, promoting the ecological and eco-industrialization development of marine industry, and constructing a blue economic industrial belt with horizontal synergy and vertical linkage, as well as carrying out comprehensive management of sea and land environment, strengthening the pollution prevention and ecological restoration of the sea area, and establishing a comprehensive ecological management system of the coastal zone, etc. These measures can provide scientific policy references for other countries and regions to accelerate the development of the digital economy to enhance the marine environment.

Although this paper utilizes the entropy method, mediation effect, and other models to evaluate the quality level of China’s marine environment and digital economy and the impact path of the digital economy on the quality of the marine environment, respectively, due to the availability of data, it is inevitable that some of the indicators fail to be included in the evaluation index system, which will have a certain impact on the accuracy of the empirical results. For example, the indicators of government digital governance and the amount of marine plastic waste are not included in the model calculation because of data unavailability. Moreover, this article is limited by research methods and geographical regions, which may also affect the accuracy of the study on the impact of digital economy on marine environmental quality. Further improvement is needed to enhance the credibility of scientific research. At the same time, this paper is relatively weak in analyzing whether there is heterogeneity in the impact of the digital economy on the quality of the marine environment and has not explored whether there is a spatial effect between the quality of the marine environment and the digital economy, which will also be the focus of future research.

5. Conclusions

Taking the development of the marine environment in China’s coastal provinces and cities as an entry point, this paper measures the relationship between the digital economy and the quality level of the marine environment and the impact of the digital economy on the quality of the marine environment in each province and city from 2011 to 2022, and draws the following conclusions:

(1)

The level of marine environmental quality in China’s coastal provinces and cities has been steadily rising, and the overall level is good, but the growth rate is relatively slow, with the average value rising from 0.45 to 0.56, an average annual increase of only 2%, and with large differences between regions. Spatially, the quality of marine environment shows a distribution pattern of “high in the north and south and low in the east”; the quality of digital economy shows a steep upward trend, with the average value rising from 0.06 to 0.3, an increase of 5 times, but the overall level is low, and there is a serious polarization phenomenon among the provinces. During the study period, the quality of the digital economy showed an upward trend in most provinces and cities, except for individual provinces and cities such as Liaoning and Fujian, which showed a significant decline in the quality of the digital economy. Spatially, the quality of the digital economy shows a distribution pattern of “high in the east and low in the south and north”, with serious polarization within the region.

(2)

The development of the digital economy has a significant role in promoting the improvement of marine environmental quality. The estimated coefficient of the impact of the digital economy on the level of marine environmental quality is significantly positive at the 1% level, and the result is 0.2092, which fully indicates that the development of the digital economy can effectively improve the level of development of marine environmental quality. As measured by the mediating effect model, the marine economy plays a conduction role in the process of the impact of digital economy on the quality of the marine environment. The regression coefficient for the level of marine environmental quality is 0.2259 and passes the 1% level significance test, indicating that the transmission path of “digital economy-marine economic quality-marine environmental quality” exists significantly, and that the digital economy enhances the level of marine environmental quality by improving the productivity of the marine economy and optimizing the allocation of resources.

(3)

Both economic development and industrial scaling produce threshold effects in the process of digital economy affecting the quality of the marine environment. Among them, economic development produces a single-threshold effect, with a threshold value of −1.311. When the level of economic development is lower than the threshold value, the impact coefficient of the digital economy on the quality of the marine environment is 0.1311; when the level of economic development is higher than the threshold value, the impact coefficient of the digital economy on the quality of the marine environment is 0.1617. The scale of the industry also produces a single-threshold effect, with a threshold value of −2.538. When the scale level of the marine industry is lower than the threshold, the impact coefficient of the digital economy on the quality of the marine environment is 0.1272; when the scale level of the marine industry is higher than the threshold, the impact coefficient of the digital economy on the quality of the marine environment is 0.1638, and the effect of the digital economy on the improvement of the quality of the marine environment will be on the rise with the increasing scale level of the industry.