Digital Empowerment of the China’s Marine Fishery for High-Quality Development: A Total Factor Productivity Perspective

Abstract

In the context of the era where the maritime power strategy converges with the wave of the digital economy, the digital economy provides a critical transformational opportunity for marine fisheries to break through the traditional extensive model and achieve high-quality development. Based on panel data from 41 coastal cities in China from 2003 to 2022, this study empirically examines the enabling effect of the digital economy on marine fisheries from the perspective of total factor productivity. The findings are as follows: First, the development of the digital economy promotes the improvement of total factor productivity in marine fisheries, but this is primarily achieved through “innovation-driven” expansion of the production frontier, while its potential in “efficiency catch-up” has not yet been fully realized. Second, the enabling effect exhibits distinct spatial heterogeneity, with its positive impact concentrated in cities in the South China Sea region, where industrial foundations and policy environments are more aligned. Third, the influence of the digital economy demonstrates nonlinear threshold characteristics; when technology promotion and industrial collaboration surpass specific thresholds, the enabling effect significantly strengthens, but as innovation capability improves, its marginal contribution shows a diminishing trend. Accordingly, it is recommended to deepen the application of digital technologies in core processes, transitioning from “isolated applications” to “systematic integration.” Simultaneously, tailored regional development strategies should be formulated to align with the resource endowments and development stages of each maritime region. On this basis, efforts should be made to improve technology promotion and industrial support systems, construct a collaborative and efficient digital fishery ecosystem, and facilitate the sustainable transition of marine fisheries from factor-driven to innovation-driven growth.

1. Introduction

As China advances its maritime power strategy, marine fisheries have become a key pillar of the blue economy and an important engine for ocean-oriented development. Improving quality and efficiency in this sector strengthens the foundation for marine biomedicine and equipment manufacturing, while also supporting the growth of marine information services and coastal tourism. In the context of persistent land and resource constraints and rising food demand, marine fisheries, as an important component of modern agriculture, are playing an increasingly strategic role in ensuring food security and promoting rural revitalization [1]. With an extensive coastline, numerous islands and a vast maritime jurisdiction, China has prioritized the release of development potential in its marine fishery sector. Policies related to livelihood protection, equipment upgrading, resource conservation and offshore expansion have been progressively introduced [2,3]. Supported by policy incentives, technological progress and market demand, the sector has experienced steady growth, continuous structural improvement and significant efficiency gains [4,5]. However, the long-term reliance on an extensive development model has resulted in spatial imbalance, homogenized regional competition, excess low-end capacity, insufficient high-end supply and rising ecological pressure. These challenges increasingly hinder the transition toward innovation driven, green and coordinated high-quality development [1,6]. As traditional advantages weaken and new growth drivers remain limited, it is essential to explore transformation pathways that balance development and conservation and integrate traditional practices with innovation in order to consolidate foundations and cultivate new momentum.

Amid the digital economy wave, technologies such as cloud computing, big data, artificial intelligence, and the Internet of Things are rapidly integrating into all sectors of the economy and society. This not only spurs new business forms, models, and growth drivers but also fuels the transformation, upgrading, and quality enhancement of traditional industries. As a representative industry characterized by high resource dependency, low risk resilience, and significant untapped potential, marine fisheries are facing a strategic opportunity to overcome development constraints and achieve profound transformation. To seize the initiative and gain a leading edge, major maritime nations worldwide are actively advancing digital transformation in fisheries [7,8,9,10,11]. Facing intensifying international competition and growing domestic demand, China has also prioritized the digital-intelligent empowerment of marine fisheries. Key policy documents such as the 14th Five-Year National Fisheries Development Plan emphasize the widespread application of modern information technologies across the entire chain of fishery production, management, processing, and distribution. With technological innovation as the core driver, the aim is to achieve systemic industrial upgrading and sustainable resource utilization. During an inspection in Guangdong, President Xi Jinping further stressed the need to “vigorously develop deep-sea aquaculture equipment and smart fisheries, and promote the informatization, intellectualization, and modernization of marine fisheries.”

In response to contemporary trends and cutting-edge developments, the academic community has increasingly focused on the deep integration of the digital economy and marine fisheries. Scholars have conducted a series of theoretical explorations and empirical studies on key issues such as resource allocation optimization, production paradigm innovation, and governance efficiency improvement, providing a growing body of academic insights and policy references to clarify the intrinsic logic and practical pathways of “digital-intelligent empowerment.” For instance, Lennox (2022) highlighted the use of social media, online logs, video platforms, and search engines as robust foundations for resource assessment and governance in recreational fisheries, effectively addressing the limitations of traditional data sources in coverage, timeliness, and cost-efficiency [10]. Zhang (2023) elaborated on the application and prospects of novel digital technologies in mariculture, emphasizing the need to synergize IoT, big data, AI, and blockchain to establish an integrated “collection-storage-analysis-decision-encryption” framework for real-time water quality monitoring, fish behavior tracking, precision feeding, and full-process traceability [12]. Examining the sustainability of marine biological resources in Europe, Le Thanh Ha (2024) revealed a U-shaped relationship between digital transformation and marine fisheries, where initial adaptation costs, knowledge barriers, and path dependencies may cause short-term disruptions, but positive effects dominate once application levels and infrastructure cross critical thresholds, driving sustained economic performance [13]. Grounded in resource-based theory, Wang (2025) argued that the digital economy systemically empowers marine fisheries by extending value chains, accelerating knowledge spillovers, and enhancing talent competencies, thereby optimizing the allocation and value realization of industrial resources and facilitating a transition from resource dependency to a resilient complex system characterized by knowledge accumulation and dynamic adaptation [14]. Employing a Spatial Durbin Model, Zhang (2025) evaluated the direct and spillover effects of the digital economy on carbon emissions in marine fisheries [15]. While affirming the positive role of digital transformation in promoting cleaner production, the study also highlighted potential “pollution transfer” risks stemming from industrial agglomeration and resource competition, advocating for stronger regional collaboration and balanced planning to foster a “digital symbiosis–green win-win” development paradigm.

In summary, while existing scholarship has closely followed emerging trends and conducted valuable research, providing a solid foundation for understanding the multidimensional mechanisms through which the digital economy empowers marine fisheries, further refinement is needed. Most empirical studies use provinces or countries as the unit of analysis [16,17,18], capturing overall trends and macro effects but overlooking internal variations within coastal regions and heterogeneity across cities. This study narrows the focus to the city level, employing high-resolution panel data to examine how digital economy development influences total factor productivity in marine fisheries. This approach reduces aggregation bias and provides more accurate quantitative evidence of digital empowerment effects. Moreover, unlike prior studies that focus on the digital economy’s role in promoting growth or optimizing structure [19,20], this study decomposes total factor productivity to distinguish whether its impact arises from improved resource-use efficiency or expanded production frontiers. The results indicate that digital empowerment primarily drives “innovation-led frontier expansion,” while potential gains in “efficiency catching-up” and “management optimization” remain underutilized. This suggests that integration of digital technologies is still at an early stage, dominated by technology adoption and paradigm reconstruction, offering empirical guidance for coastal cities seeking to advance digital governance and efficiency-driven growth in fisheries.

2. Hypothesis

Based on local practice and academic research, the digital economy empowers marine fisheries through data integration, intelligent technologies, and industrial ecosystem innovation, forming a systemic pathway from efficiency improvement to value enhancement. First, data elements optimize production logic [12,14,16]. Traditional operations rely on experience and manual intervention, making decisions often subjective and delayed, which limits efficient input allocation. The digital economy transforms production into a data-driven system that is collectable, analyzable, and feedback-enabled, allowing real-time, science-based management. In aquaculture, IoT sensors monitor water conditions and organism health, while AI algorithms adjust feeding schedules to maximize growth and minimize waste. In fishing, satellite remote sensing, acoustic detection, and navigation technologies visualize and predict fish distributions, enabling optimized routes and reducing fuel and time costs. Second, digital technologies optimize industrial structure [21,22]. The rise of the digital economy dissolves barriers and information asymmetries across fisheries, transforming loosely organized, experience-driven units into highly coordinated, resource-integrated modern industrial communities. Community-based digital collaboration networks connect dispersed vessels, ports, facilities, and stakeholders, facilitating coordinated production planning, unified capacity allocation, and shared market channels. Blockchain and industrial internet technologies further integrate the fishing, processing, distribution, and sales stages, enhancing transparency, traceability, and broader interactions among capital, technology, and data. Third, digital ecosystems expand value space [13,23]. Online platforms lower the cost of accessing and applying knowledge, accelerating best-practice diffusion and local innovation, fostering rapid industrial knowledge accumulation. Data-driven credit systems expand financing access, allowing operators to leverage operational data, while financial institutions use intelligent models for risk assessment and pricing. Consumer big data enables targeted marketing and customized services, shifting business models from standardized supply to personalized, high-quality seafood and value-added services. Open digital ecosystems attract cross-sector collaboration, with technology firms, data providers, and creative organizations contributing knowledge and forming innovation communities with shared risks and benefits. Based on the above, this study proposes Hypothesis 1:

H1. 

The development of the digital economy enhances total factor productivity in marine fisheries.

China’s coastal regions, spanning temperate, subtropical, and tropical zones, show pronounced spatial gradients in substrate, seascape, and ecology, shaping differentiated resource structures, environmental capacities, and development pathways that influence how the digital economy empowers marine fisheries [14,15,24]. Based on natural endowments, economic patterns, and administrative boundaries, coastal cities can be grouped into the Bohai–Yellow Sea, East China Sea, and South China Sea regions. In the Bohai–Yellow Sea, established large-scale aquaculture and fishing systems, together with developed cold-chain logistics, provide a strong foundation for digital technology adoption. Supply chains support integration of IoT, blockchain, and sensors for intelligent management. Yet, the semi-enclosed sea limits water exchange, pushing ecological capacity toward its threshold, while weak local digital innovation hinders development of context-specific solutions, reducing the conversion of technology into industrial competitiveness. The East China Sea benefits from both advanced external technologies and rich local industrial infrastructure. Access to the Yangtze River Delta innovation hub allows early adoption of AI and IoT, and the abundant Zhoushan fishing grounds offer ideal conditions for application. Still, coastal development pressures and transboundary river pollution create complex ecological constraints, weakening environmental foundations and causing imbalances between technology investment and returns. The South China Sea combines unique ecological conditions and regional digital advantages. Tropical waters, coral reefs, and mangroves favor ecological and specialty aquaculture, while the Guangdong–Hong Kong–Macau Greater Bay Area provides technical and financial support, promoting innovations in e-commerce and smart fisheries-tourism. However, gaps in digital infrastructure and ecosystem vulnerability, compounded by typhoons, raise costs and risks for technology deployment. These regional differences form the basis for Hypothesis 2.

H2. 

The impact of the digital economy on the total factor productivity of marine fisheries exhibits spatial heterogeneity.

The digital economy’s impact on marine fisheries may be condition-dependent, with empowerment pathways shaped by the technological environment, industrial base, and regional support, leading to stage-specific effects. Technology adoption, industry development, and regional innovation act as key factors and potential thresholds for structural change [25,26]. First, the breadth and depth of digital technology adoption in fisheries depend on the effectiveness of extension systems. In regions with weak technical services, limited guidance can hinder fishers from effectively using smart monitoring devices or converting IoT data into production decisions, reducing the potential of digital tools. As extension systems improve across pre-production, production, and post-production stages, digital technologies can more effectively support resource monitoring, precision feeding, and disease warning. Second, the development of related industries shapes the scope and synergy of digital applications. When processing, cold-chain logistics, e-commerce, and fisheries-tourism services are underdeveloped, digital interventions often remain confined to isolated production improvements. With stronger industrial linkages, digital tools can optimize resource allocation and enable integrated innovations, such as cross-regional distribution via e-commerce or market expansion through smart fisheries-tourism platforms. Third, regional innovation capacity guides the direction of digital empowerment. In early-stage innovation regions, limited local knowledge leads the digital economy to primarily import external solutions and optimize resource allocation. In regions with advanced innovation capacity, digital tools increasingly integrate with local resources and industrial characteristics, supporting coordinated R&D, refined production management, and platform-based business ecosystems. This shift from external adoption to endogenous system integration results in stage-specific empowerment effects. Based on this reasoning, Hypothesis 3 is proposed.

H3. 

The digital economy’s effect on marine fisheries’ total factor productivity may exhibit significant threshold effects depending on technology adoption, industry coordination, and regional innovation.

3. Materials and Methods

3.1. Model Settings

To analyze the causal relationship between the digital economy and the total factor productivity of marine fisheries, this study employs a two-way fixed effects model. The model controls for unobserved factors across both individual and time dimensions, effectively mitigating omitted variable bias. The specification is presented in Model (1).

$$MFTF{P}_{it}={\beta }_0+{\beta }_{1}digita{l}_{it}+{\beta }_{2}Control{s}_{it}+{\lambda }_i+{\delta }_t+{\xi }_{it}$$

(1)

In this specification, total factor productivity of marine fisheries (MFTFP) is the dependent variable in this study, digital denotes the level of digital economy development, and Controls are control variables, including economic level (eco), industrial structure (ind), urbanization rate (urban), financial development (fina), environmental pollution (pollu), and science and education support (sei). λ_i and δ_t capture city and time fixed effects, respectively, ξ_it is the random error term, i denotes cities, and t represents time.

Traditional linear modeling frameworks often fail to adequately capture the underlying nonlinear relationships between variables. It is therefore necessary to introduce threshold regression models for further investigation. This methodology effectively identifies differential effects of influencing factors across specific threshold values, enabling more precise detection of the heterogeneous impact of the digital economy on the total factor productivity of marine fisheries. Thereby, it provides empirical support and decision-making basis for marine farming and fishing practices. The specification is presented in Model (2).

$$MFTF{P}_{it}={\beta }_0+{\beta }_{1}digita{l}_{it}\times I\left(threshold\le \theta \right)+{\beta }_{2}digita{l}_{it}\times I\left(threshold>\theta \right)+{\beta }_{3}Control{s}_{it}+{\gamma }_i+{\delta }_t+{\xi }_{it}$$

(2)

Here, where I (.) is an indicator function that takes the value 1 if the condition inside the parentheses is satisfied, and 0 otherwise. threshold denotes the threshold variable, and θ is the threshold value. Other variables are defined as above. This model considers a single-threshold case but can be extended to multiple thresholds based on sample test results, with a similar specification.

3.2. Variable Selection

3.2.1. Dependent Variable

This study uses total factor productivity of marine fisheries (MFTFP) as the core indicator, as it both reflects fisheries development quality and aligns with the digital economy’s empowerment logic. On the one hand, distinct from the limitations of traditional indicators such as output value and yield per unit, which focus narrowly on the scale of production, the core essence of total factor productivity (TFP) lies in isolating the scale effects of input factors such as capital, labor, and resources. It not only reflects efficiency gains arising from optimized resource allocation and improved organizational management in the production process but also highlights sustainable growth driven by technological innovation, diffusion, and application. This dimension precisely constitutes the key distinction between TFP and singular efficiency metrics, encompassing technological iteration and breakthroughs across the entire fishery production chain—including the breeding of improved aquaculture varieties, intelligent upgrading of fishing equipment, innovation in disease prevention and control technologies, and digital transformation of production processes. On the other hand, with data factor penetration, intelligent technology application, and industrial ecosystem restructuring as its core drivers, the empowerment logic of the digital economy for traditional fisheries aligns closely with the dual dimensions of TFP. The “technology spillover” effect of digital technologies directly promotes the outward expansion of the production technology frontier in fisheries, while data-driven “efficiency transformation” enhances resource utilization efficiency and management capabilities. Together, these two aspects form the core drivers of TFP growth. Based on prior studies [27,28,29,30], this study develops a quantitative evaluation index system that balances the completeness of factor inputs and adaptability to technological dimensions, based on input-output theory, production function models, and the principles of data availability and reliability. As shown in

Table 1. Indicators of total factor productivity in marine fisheries.

Indicator Type	Primary Indicator	Specific Indicator
Input Indicators	Resource Input	Marine Aquaculture Area (hectares)
	Labor Input	Marine Fishery Workforce (persons)
	Capital Input	Power of Marine Motorized Fishing Vessels (kilowatts)
Output Indicators	Expected Output	Marine Fishery Output (tons)
		Marine Fishery Output Value (10,000 yuan)

, the input indicators include seawater aquaculture area (resource input), marine fishery workforce (labor input), and the power of motorized marine fishing vessels (capital input), while the output indicators consist of marine fishery yield (physical output) and marine fishery output value (monetary output). This system not only captures the direct outcomes of technological progress and efficiency improvements but also accounts for product quality enhancements and value appreciation driven by technological iteration. Following Pastor (2005) and Oh (2010) [31,32], under variable returns to scale (VRS), the GML (Global Malmquist-Luenberger) index is defined as:

$$\begin{array}{l}GM{L}^{t,t+1}\left(x^t,y^t,x^{t+1},y^{t+1}\right)={\displaystyle \frac{1+D^G\left(x^t,y^t\right)}{1+D^G\left(x^{t+1},y^{t+1}\right)}}\\ ={\displaystyle \frac{1+D^t\left(x^t,y^t\right)}{1+D^{t+1}\left(x^{t+1},y^{t+1}\right)}}\times {\displaystyle \frac{\frac{1+D^G\left(x^t,y^t\right)}{1+D^t\left(x^t,y^t\right)}}{\frac{1+D^G\left(x^{t+1},y^{t+1}\right)}{1+D^{t+1}\left(x^{t+1},y^{t+1}\right)}}}\\ ={\displaystyle \frac{T{E}^{t+1}}{T{E}^t}}\times {\displaystyle \frac{BP{G}_{t+1}^{t,t+1}}{BP{G}_t^{t,t+1}}}\\ =E{C}^{t,t+1}\times BP{C}^{t,t+1}\end{array}$$

(3)

GML_t,t+1 measures the change in total factor productivity of marine fisheries from period t to t + 1, with GML >1 indicating productivity growth, GML < 1 indicating a decline, and GML = 1 representing no change. D(x,y) measures the distance between actual efficiency and the production frontier given specific inputs and outputs. Superscripts t and t + 1 denote reference to current or next period technology, and G represents the global production technology set. Furthermore, the GML index can be decomposed into efficiency change (EC) and technological progress change (BPC). EC captures improvements in internal management and resource allocation, while BPC reflects iterations and breakthroughs in external technology. EC > 1 indicates efficiency improvement, EC < 1 indicates decline; BPC > 1 indicates technological progress, and BPC < 1 indicates regression.

3.2.2. Independent Variable

Relying on the extensive application and cross-sector integration of digital technologies, the digital economy drives the transformation and upgrading of traditional industries and the emergence of new business models. The development level of this sector across cities cannot be captured by a single indicator, and existing literature has yet to reach a consensus on constructing a comprehensive measurement system. Drawing on the approaches of Zhao (2020) [33], Zhang (2024) [34], and Shen (2025) [35], this study selects indicators related to infrastructure, policy support, industrial scale, and inclusive services. These include government attention to the digital economy (measured by the frequency of “digital economy” terms in government work reports), the proportion of administrative villages with broadband access, broadband users per 100 people, postal service revenue, telecommunications revenue, mobile phone users per 100 people, and the digital inclusive finance index. The entropy method is used to assign objective weights to these multidimensional indicators, avoiding subjective bias from arbitrary weighting, providing a scientific and systematic assessment of digital economy development, and offering reliable support for the empirical analysis of its impact on total factor productivity in marine fisheries.

3.2.3. Control Variables

To mitigate omitted variable bias and enhance the reliability of estimation results, this study selects the following control variables with reference to existing research:

(1)

Economic level (eco), measured by the logarithm of GDP per capita to alleviate the impact of outliers and reduce heteroscedasticity. The level of regional economic development serves as a fundamental supporting condition for the deep integration of the digital economy and marine fisheries, influencing the total factor productivity of the marine fishery sector throughout the entire process of technology application, industrial upgrading, and resource allocation. From the perspective of prerequisites for implementing digital technologies, a higher level of economic development signifies stronger local fiscal capacity, enabling greater investment in improving digital infrastructure, such as marine communication base stations and offshore IoT sensing networks. This provides the hardware foundation for deploying intelligent aquaculture equipment, transmitting data for offshore operations, and establishing fishery e-commerce platforms. Simultaneously, market entities in economically developed regions are better positioned to bear the initial costs of digital technology R&D and the procurement of intelligent equipment, thereby lowering the barriers to digital transformation.

(2)

Industrial structure (ind), represented by the proportion of the secondary industry, influences resource allocation and may indirectly affect marine fisheries through factor competition or technological spillovers. The regional industrial structure is deeply intertwined with factor allocation and policy orientation. The development of the secondary industry may, on one hand, compete for resources such as capital and labor, potentially constraining the input available for the marine fisheries sector. On the other hand, it can also support the digital and mechanized upgrading of fisheries through technological spillover effects in areas such as equipment manufacturing and technological R&D. By influencing the input structure and production efficiency of marine fisheries through these dual channels—factor competition and technology diffusion—it indirectly affects the sector’s dynamics. Controlling for this variable helps avoid its interference with the accuracy of estimating the core relationships under study.

(3)

Urbanization rate (urban), measured by the proportion of urban population. Urbanization constitutes a critical process of spatial agglomeration of factors and optimized resource allocation. Its advancement drives the transfer of labor to urban areas, expands consumer markets, and facilitates the upgrading of both soft and hard infrastructure. These developments not only provide marine fisheries with a more abundant labor force and a broader consumer market but also create conditions for the digital and industrial transformation of the fishery sector through improved infrastructure, thereby profoundly influencing the enhancement of industrial quality and efficiency.

(4)

Financial development (fina), gauged by the ratio of total deposits and loans to GDP. Financial deepening serves as a critical nexus linking capital supply with industrial demand, providing essential credit support for the modernization of marine fisheries—such as the acquisition of intelligent aquaculture equipment and the construction of deep-sea farming facilities—by broadening financing channels and alleviating credit constraints. Concurrently, the development of the digital economy involves substantial and sustained investments in building digital infrastructure, technological research and development, and the application of digital solutions, all of which rely on a well-developed financial system to furnish funding security and risk-sharing mechanisms.

(5)

Environmental pollution (pollution), indicated by industrial wastewater discharge per unit of GDP. The sustainable development of marine resources relies on long-term ecological conservation. Industrial wastewater discharge undermines aquatic ecosystems, directly damages the foundational conditions for fishery survival and growth, and constrains the enhancement of total factor productivity. Concurrently, environmental regulatory pressures may compel fishery operators to adopt digital monitoring and intelligent control technologies to comply with environmental requirements.

(6)

Science and education investment (sei), measured by the ratio of government expenditure on science and education to GDP. This indicator directly reflects the degree of emphasis a region places on human capital accumulation and innovation-driven development. A higher level of investment in science and education not only enhances the quality of the fishery workforce and strengthens talent reserves by cultivating professionals, but also promotes technological innovation and the commercialization of research outcomes in the fishery sector, thereby providing intellectual support for the integration of digital technologies and fisheries.

3.2.4. Threshold Variables

To further examine the potential nonlinear impact of the digital economy on the total factor productivity of marine fisheries, this study defines three threshold variables.

(1)

Aquaculture technology extension (ATE) is measured by the ratio of funding for aquaculture technology extension institutions to fisheries output value. Technology adoption is a key link in transforming scientific achievements into productive capacity. Only when the input intensity and service capacity of the extension system reach a certain level can it effectively lower the cost and barriers for fishers to adopt digital technologies, accelerate knowledge diffusion and technology application, and provide the necessary support environment for the digital economy to enhance total factor productivity.

(2)

Related industry coordination (FAR) is represented by the share of fisheries-related circulation, services, industry, and construction in total fisheries output. The development of related industries provides broad market space, essential infrastructure, and efficient supporting services for digital technology applications. As a threshold variable, FAR allows exploration of whether the digital economy’s impact on marine fisheries varies with the maturity of the industrial ecosystem.

(3)

Regional Innovation Capacity (RIC) is measured by the number of patent applications per 10,000 people. As a technology-intensive economic form, the digital economy’s integration with fisheries requires regions to possess an innovative environment and technological learning capacity. Regions with low innovation may struggle to absorb digital technology spillovers, whereas high-innovation regions can adapt more quickly to technological changes and drive digital transformation of production.

It should be noted that due to data availability, ATE and FAR are measured at the provincial level corresponding to the sample cities. Given the relatively uniform technology extension policies and industrial planning within provinces, provincial data can reasonably reflect the industrial environment of the cities and satisfy the requirements for threshold effect analysis.

3.3. Data Sources

This study conducts an empirical analysis using data from 41 coastal cities in China over the period 2003–2022. Data on seawater aquaculture area, marine fisheries labor, marine motorized vessel power, fisheries output, and output value are sourced from provincial and municipal statistical yearbooks, as well as the Guangdong Rural Statistical Yearbook and the Shandong Fisheries Statistical Yearbook. Government attention to the digital economy is quantified by the frequency of “digital economy” terms in government work reports. The digital inclusive finance index is compiled jointly by the Digital Finance Research Center at Peking University and Ant Group. Raw data for digital infrastructure, digital industry, industrial structure, financial level, environmental pollution, science and education investment, innovation capacity, and technology extension are obtained from the China City Statistical Yearbook, China Urban Construction Statistical Yearbook, China Urban-Rural Construction Statistical Yearbook, and China Fisheries Statistical Yearbook. Notably, although China has 55 coastal cities (excluding Hong Kong, Macao, and Taiwan), some regions have severe data gaps, so the final sample includes 41 cities. Additionally, data on fisheries labor and motorized vessel power in some cities are not distinguished between marine and freshwater fisheries; in this study, the share of marine fisheries output in total fisheries output is used as a coefficient to estimate the marine fisheries portion.

4. Results

4.1. Benchmark Regression Analysis

As shown in

Table 2. Benchmark estimation results.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
digital	0.481 ***	0.459 ***	0.460 ***	0.499 ***	0.537 ***	0.517 ***	0.543 ***
	(0.109)	(0.109)	(0.104)	(0.103)	(0.103)	(0.094)	(0.103)
eco		−0.058	−0.118	−0.126 *	−0.096	−0.084	−0.068
		(0.059)	(0.072)	(0.071)	(0.073)	(0.070)	(0.072)
ind			0.435 *	0.479 **	0.566 **	0.548 **	0.569 **
			(0.236)	(0.234)	(0.237)	(0.244)	(0.244)
urban				0.159	0.160	0.180	0.221 *
				(0.112)	(0.110)	(0.109)	(0.113)
fina					0.045 **	0.046 **	0.042 **
					(0.021)	(0.021)	(0.020)
pollution						−2.380	−2.818
						(2.119)	(2.228)
sei							4.133 *
							(2.271)
Urban fixed effect	YES	YES	YES	YES	YES	YES	YES
Time fixed effect	YES	YES	YES	YES	YES	YES	YES
N	779	779	779	779	779	779	779
R2	0.104	0.106	0.107	0.109	0.113	0.114	0.118

Note: *, **, *** represent significance levels of 10%, 5%, and 1%, respectively; the values in parentheses are cluster-robust standard errors.

. Regardless of whether control variables are included, the estimated coefficient of the digital economy is positive and statistically significant at the 1% level, indicating that the vigorous development of the digital economy contributes to a steady increase in the total factor productivity of marine fisheries, thus supporting Hypothesis 1. Drawing on local practices and the theory of social reproduction, this study analyzes the empowerment pathways of the digital economy on marine fisheries across four key stages: production, distribution, exchange, and consumption.

(1)

Production stage. Digital technologies are progressively transforming traditional production models in marine fisheries by optimizing the allocation efficiency of three key input factors to enhance output. In terms of resource inputs, Internet of Things (IoT) sensors enable continuous monitoring of critical environmental parameters such as water temperature, salinity, and dissolved oxygen levels in aquaculture zones. Combined with artificial intelligence algorithms, this allows for dynamic adjustments in feed volumes and stocking densities, thereby reducing inefficient occupation of mariculture areas and minimizing resource wastage, while improving output efficiency per unit of aquaculture area. Regarding labor inputs, the deployment of automated feeding systems and intelligent sorting devices reduces the burden of repetitive tasks for fishery workers, redirecting labor toward more efficient roles such as technical operation and data analysis. As for capital inputs, the integration of satellite remote sensing, acoustic detection, and navigation technologies enables precise identification of fish distribution patterns and optimization of operational routes, which helps lower fuel consumption and decrease unproductive sailing time for motorized fishing vessels, thus enhancing the utilization efficiency of capital. Moreover, the introduction of automated vessels and intelligent processing equipment reduces reliance on traditional labor and enhances the standardization and controllability of production processes.

(2)

Distribution stage. The rise of the digital economy has accelerated the optimization of resource and value distribution structures, thereby facilitating the efficient allocation of input factors and the equitable distribution of output value. By leveraging big-data-based credit assessment and supply chain finance, platforms aggregate transaction records, aquaculture data, and other relevant information from fishery operators. This enables more precise and efficient credit support for small and medium scale aquaculture farmers and fishers, assisting them in expanding mariculture operations, upgrading motorized fishing vessels, and alleviating financial constraints on factor inputs. Meanwhile, digital channels such as remote expert systems and industry-specific mobile applications facilitate the low-cost dissemination of aquaculture techniques, market information, and management experience. This helps narrow the skill gaps among practitioners across different regions, allowing the labor value of marine fishery workers to be more fully realized. Furthermore, the expansion of online channels lowers the barriers to participating in modern large-scale markets, enabling small and medium-sized fishery operators to engage directly with end-market demand. This promotes a more equitable distribution of industrial returns across production, processing, and circulation stages, thereby indirectly stimulating greater production input incentives among all stakeholders.

(3)

Exchange stage. Leveraging digitalized market infrastructure, the circulation efficiency of seafood products has been steadily improved, accelerating the transformation of production inputs into final output value. B2B e-commerce platforms and fresh food e-commerce channels directly connect producers with wholesalers, retailers, and even end consumers, compressing traditional multi-layer intermediate distribution links, reducing transaction costs, and significantly enhancing the market conversion efficiency of marine fishery yields. Meanwhile, traceability systems based on IoT and blockchain technologies enable end-to-end tracking and information transparency throughout the entire process of seafood products—from catch, transportation, and storage to sales—effectively reducing quality losses and trust costs, providing verification means for premium quality and pricing, and thereby promoting the premiumization of marine fishery output value. In this process, the synergy between digital technologies and cold chain logistics further ensures the consistent quality of seafood products, allowing the outputs formed from inputs in mariculture and fishing activities to maximize their value across spatial constraints.

(4)

Consumption stage. The development of digital platforms has facilitated a higher-level dynamic equilibrium characterized by “demand-driven supply and supply-created demand,” thereby compelling the optimization of production factor input structures and the upgrading of output. Platforms such as social media and live-streaming e-commerce transcend traditional geographical constraints, extending the reach of seafood products—originally confined to coastal or regional markets—to broader consumer bases, thereby directly expanding the market absorption capacity for marine fishery yields. By aggregating and analyzing browsing, search, and transaction data, these platforms can accurately identify shifts in consumer preferences. The timely and efficient feedback of such information to the supply side incentivizes producers to adjust aquaculture species composition and refine processing standards—for instance, by increasing the proportion of high-value-added species or enhancing the sophistication of product processing—thus driving both qualitative improvement and efficiency gains in the output value of marine fisheries. In this process, digital technologies steer the allocation of production factors toward areas of concentrated market demand, ensuring that inputs such as mariculture area and fishing vessel power are better aligned with consumption upgrading trends, thereby enriching the qualitative dimension of total factor productivity.

4.2. Robustness Check

To ensure the robustness of the baseline results, this study conducts the following robustness checks:

(1)

Excluding key cities. To reduce potential sample selection bias, provincial capitals and centrally administered municipalities are excluded, retaining only ordinary prefecture-level cities. These cities usually have superior resources, infrastructure, and policy support, making their digital economy development and fisheries modernization patterns potentially unrepresentative and prone to over-influence on the estimates.

(2)

Handling outliers. The dependent variable is winsorized at the 1st and 99th percentiles to limit the effect of extreme TFP fluctuations caused by natural disasters, policy changes, or other shocks. This approach preserves most of the original sample while improving the robustness and reliability of the regression estimates.

(3)

Replacing explanatory variable. Urban digital economy levels are recalculated using the coefficient of variation method instead of the entropy method. By weighting indicators according to their relative dispersion, this method reduces subjectivity and measurement bias. Consistent regression results indicate that conclusions do not depend on the specific weighting approach used.

(4)

Replacing dependent variable. To test sensitivity to production technology assumptions, TFP is re-estimated using the constant returns to scale (CRS) SBM-GML model rather than variable returns to scale (VRS). The CRS assumption provides a more constrained efficiency benchmark, allowing verification of whether empirical results are robust to different theoretical frameworks.

(5)

Adjusting sample period. To avoid the temporary disruption caused by the COVID-19 pandemic in 2020, which may have affected factor flows, market order, and fisheries productivity, the sample period is limited to 2003–2019. Re-estimating the model within this interval ensures that extreme exogenous events do not bias the results.

As shown in

Table 3. Robustness test.

	(1)	(2)	(3)	(4)	(5)
	Excluding Key Cities	Handling Outliers	Replacing Explanatory Variable	Replacing Dependent Variable	Adjusting Sample Period
digital	0.586 *	0.245 **	0.602 ***	0.643 ***	1.632 ***
	(0.317)	(0.116)	(0.124)	(0.152)	(0.282)
Controls	YES	YES	YES	YES	YES
Urban fixed effect	YES	YES	YES	YES	YES
Time fixed effect	YES	YES	YES	YES	YES
N	646	779	779	779	656
R2	0.110	0.134	0.115	0.095	0.139

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively; clustered standard errors are reported in parentheses.

, the results of these robustness checks are generally consistent with the baseline findings, reaffirming the validity of the main conclusions.

4.3. Endogeneity Treatment Analysis

When examining the impact of the digital economy on the total factor productivity of marine fisheries, potential endogeneity issues such as bidirectional causality and omitted variables must be considered. To address these concerns and ensure the robustness of the findings, this study employs Two-Stage Least Squares (2SLS) and System Generalized Method of Moments (SYS-GMM) for endogeneity testing, drawing on methodologies from existing research (Gu, 2022; Wu, 2025) [36,37]. The empirical results are presented in

Table 4. Endogeneity treatment.

	(1)	(2)
	2SLS	SYS-GMM
digital	0.637 ***	0.815 **
	(0.112)	(0.302)
Controls	YES	YES
Kleibergen–Paap rk LM statistic	8.764 **
Kleibergen–Paap rk Wald F statistic	46.394 > 22.30 (10% critical value)
Hansen J statistic	3.052 (p-val = 0.2174)
AR (1)-p-val		0.015
AR (2)-p-val		0.807
Hansen test-p-val		0.174
Urban fixed effect	YES	YES
Time fixed effect	YES	YES
N	697	738
R2	0.035

Note: ** and *** denote significance at the 5% and 1% levels, respectively; clustered standard errors are reported in parentheses.

.

(1)

The instrumental variable (IV) approach uses lagged values of digital economy (digital) from one to three periods as its own instruments. The validity of the instruments requires two conditions: relevance (correlated with the endogenous variable) and exogeneity (uncorrelated with the error term). These conditions are strictly tested: the Kleibergen-Paap rk LM rejects the null hypothesis of underidentification at the 5% level; the Kleibergen-Paap rk F statistic is 46.39, well above the Stock-Yogo critical value for weak instruments at the 10% level, indicating no weak instrument problem; and the Hansen J overidentification test yields a p-value of 0.217, failing to reject the null hypothesis that all instruments are exogenous. With valid instruments, the estimated coefficient of the digital economy is 0.637, remaining positively significant at the 1% level.

(2)

To further address potential endogeneity, the SYS-GMM method treats the core explanatory variable, digital economy, and the lagged dependent variable, marine fisheries TFP, as endogenous, using higher-order lags as instruments. Diagnostic tests confirm the model’s validity: the Arellano-Bond test indicates first-order autocorrelation in the differenced residuals but no second-order autocorrelation, while the Hansen test supports instrument exogeneity. The estimation shows that after controlling for individual and time effects as well as other covariates, the digital economy (digital) remains positively significant at the 5% level, further confirming its role in promoting marine fisheries TFP. This demonstrates that the baseline results are robust even after accounting for endogeneity and dynamic panel bias.

4.4. Heterogeneity Analysis Results

The vast maritime space and abundant resource endowment provide a solid foundation for China’s coastal cities to develop their blue economy. However, the extensive territory, combined land–sea environment, and complex climate create significant regional differences in natural conditions, economic base, and cultural practices. These differences not only shape diverse development paths for marine fisheries but also affect the actual empowerment effects of the digital economy. To explore potential heterogeneity, this study divides China’s coastal provinces into the Bohai–Yellow Sea region (Tianjin, Hebei, Liaoning, Shandong), the East China Sea region (Jiangsu, Shanghai, Zhejiang, Fujian), and the South China Sea region (Guangdong, Guangxi, Hainan). The empirical results (

Table 5. Heterogeneity analysis.

	(1)	(2)	(3)
	Bohai–Yellow Sea	East China Sea	South China Sea
digital	0.444	0.294	0.514 ***
	(0.627)	(0.467)	(0.159)
Controls	YES	YES	YES
Urban fixed effect	YES	YES	YES
Time fixed effect	YES	YES	YES
N	247	247	285
R2	0.303	0.193	0.180

Note: *** denotes significance at the 1% levels; clustered standard errors are reported in parentheses.

, Columns 1–3) show positive coefficients for the digital economy in all regions, but its significant promotion of marine fisheries TFP appears only in the South China Sea region, supporting Hypothesis 2.

In the Bohai–Yellow Sea and East China Sea regions, traditional fisheries are relatively mature, dominated by nearshore fishing and aquaculture, with high input of conventional production factors and strong industrial inertia. Under these conditions, digital technology faces institutional barriers and path dependence, limiting marginal productivity gains. Moreover, long-term exploitation has led to resource depletion and ecological pressures in cities like Dalian, Qingdao, and Zhoushan, so current policies prioritize ecological restoration and catch regulation. Consequently, digital technology is largely applied to resource monitoring, environmental management, and compliance, with limited direct impact on productivity.

By contrast, the South China Sea cities demonstrate a more pronounced enabling effect of the digital economy, attributable to two equally important factors: On one hand, these cities have undergone earlier industrial transformation toward technology-intensive sectors like offshore fisheries and deep-sea aquaculture. Guangdong leads nationally in gravity-based deep-water cage deployment and deep-sea species reserves, while Hainan provides the strongest fiscal incentives for large aquaculture equipment and vessel development. This industrial orientation creates inherent demand for digital technologies, allowing direct integration of digital elements into core value chain segments—from satellite-based fishing ground exploration to intelligent feeding systems and full-process cold chain traceability. On the other hand, the region benefits from uniquely favorable policy environments shaped by national strategies. The Guangdong-Hong Kong-Macao Greater Bay Area provides robust support for digital fishery technology through its concentrated innovation resources and industrial conversion capacity, while Hainan Free Trade Port offers privileged arrangements in cross-border data flow and high-end equipment imports. These institutional advantages systematically reduce barriers to technological innovation while creating an attractive ecosystem for talent, capital, and advanced technologies.

4.5. Impact Mechanism Results

To elucidate the specific pathways through which the digital economy enhances the quality and efficiency of marine fisheries, this study decomposes total factor productivity (TFP) growth into changes in technical efficiency (EC) and technical progress (BPC). This allows for a deeper examination of whether the driving force stems from optimizing the management and resource-use efficiency of existing production units, or from innovating and expanding the industry’s production possibility frontier. As shown in

Table 6. Impact mechanism.

	(1)	(2)
	EC	BPC
digital	−0.526	0.344 **
	(0.668)	(0.152)
Controls	YES	YES
Urban fixed effect	YES	YES
Time fixed effect	YES	YES
N	779	779
R2	0.087	0.144

Note: ** denotes significance at the 5% levels; clustered standard errors are reported in parentheses.

, the digital economy exhibits a negative yet statistically insignificant impact on EC, while demonstrating a significantly positive effect on BPC. This indicates that at the current stage, the digital economy primarily drives marine fisheries through “innovation-driven frontier expansion” rather than “efficiency catch-up” or managerial optimization.

The significantly positive coefficient for BPC reflects the digital economy’s capacity to expand the production frontier by facilitating knowledge creation and paradigm innovation. Specifically, it reduces the costs of knowledge acquisition, dissemination, and recombination through big data-driven genetic breeding in aquaculture, AI-enabled environmental prediction and disease diagnosis, and IoT-optimized decision-making in offshore fishing operations. These innovations promote cross-disciplinary integration and continuous outward shifting of the technological frontier in marine fisheries. Conversely, the insignificant negative relationship with EC reveals structural challenges during the digital transition. EC measures the ability to narrow the gap between actual and potential output through learning and optimizing existing technologies. The current underperformance suggests two main constraints: First, the integration of digital technologies with traditional fisheries entails adaptation costs, where new equipment and technological updates may disrupt existing production processes and cause resource allocation frictions in the short term. Second, persistent obstacles in data collection, standardization, and interoperability, such as maritime operational constraints, inconsistent data formats, and departmental information silos, hinder the synergistic potential of data elements in optimizing resource allocation.

Therefore, beyond focusing on technological R&D and adoption, policymakers should address funding pressures, cultivate interdisciplinary talent, and break down information barriers to alleviate short-term disruptions and activate long-term efficiency gains. This dual approach will enable the digital economy to both “create new momentum” and “optimize existing resources,” ultimately driving high-quality development in marine fisheries through coordinated technological and efficiency improvements.

4.6. Threshold Effect

To further investigate whether the promoting effect of the digital economy on marine fisheries exhibits structural changes based on specific threshold conditions and to capture potential nonlinear impacts, this study focuses on technology diffusion, industrial coordination, and regional innovation, constructing a threshold effect model for analysis. This approach aims to deepen understanding of the logic and boundaries of digital economy empowerment. Following Hansen (1999) [38], it is first necessary to test the existence and number of thresholds. The results show that when using technology diffusion (ATE), industrial coordination (FAR), and regional innovation capability (RIC) as threshold variables, the model passes a single-threshold test under 500 bootstrap resamples, with threshold values of 0.0024, 0.5954, and 1.4915, respectively. Based on this, a single-threshold model is adopted to identify possible differences in marginal effects. The regression results, presented in

Table 7. Threshold effect results.

	(1)	(2)	(3)
	ATE	FAR	RIC
digital × I (threshold ≤ θ)	0.527	0.666 *	5.036 ***
	(0.337)	(0.346)	(1.145)
digital × I (threshold > θ)	1.874 ***	1.549 ***	0.925 ***
	(0.387)	(0.383)	(0.343)
Controls	YES	YES	YES
N	779	779	779
R2	0.072	0.040	0.055

Note: * and *** denote significance at the 10% and 1% levels, respectively; clustered standard errors are reported in parentheses.

, indicate that once technology diffusion and industrial coordination exceed their respective thresholds, the promoting effect of the digital economy is significantly enhanced. In contrast, when regional innovation capability is relatively weak, the digital economy exhibits a stronger driving effect. As innovation capability reaches a higher level, although the coefficient remains positive and significant, the marginal effect declines noticeably, thus validating Hypothesis 3.

The reasons are twofold. First, the effectiveness of digital economy empowerment heavily relies on the foundational conditions of the fisheries sector. Aquaculture technology extension reflects the capacity for adopting and disseminating new technologies. When underdeveloped, practitioners may struggle to operate intelligent equipment or interpret data reports, leading to a disconnect between digital tools and practical applications. However, once a threshold is surpassed, improved extension channels enable workers to transition from initial adaptation to deep integration of digital tools, enhancing the complementary relationship between human capital and digital technologies. Similarly, industrial linkages form the ecosystem for digital technology application. Below a critical scale, fragmented value chains and a lack of specialized services prevent the realization of network effects and economies of scale essential for digital transformation. Beyond this point, well-developed industrial support systems such as cold chain logistics, e-commerce platforms, and equipment maintenance provide a solid foundation for comprehensive digital penetration, fostering synergistic progress between digital technology and industrial ecology. Second, the role of the digital economy varies significantly across development stages. When regional innovation capacity is low, internal knowledge creation and technological iteration may stagnate in inefficient equilibria. Here, the digital economy acts as a powerful exogenous driver, helping to break through local innovation bottlenecks by introducing external knowledge, optimizing resource allocation, and enabling new business models. This generates a “leapfrogging effect” that compensates for capability gaps and yields high marginal returns. As innovation capacity improves, however, regions gradually establish more mature endogenous innovation systems with diversified growth drivers. The digital economy then shifts from a “breakthrough catalyst” to a “platform-based enabler,” functioning as a general-purpose technology that enhances the efficiency and coordination of existing R&D, production processes, and management practices. Consequently, its direct contribution diminishes relatively, exhibiting typical diminishing marginal effects.

5. Discussion

Amidst the wave of the digital economy, the digital transformation of marine fisheries has become a critical pathway to overcome traditional development bottlenecks and achieve high-quality advancement. Based on panel data from China’s coastal cities spanning 2003 to 2022, this study adopts total factor productivity as the central analytical lens to systematically examine the policy effects, spatial heterogeneity, and threshold characteristics of the digital economy’s enabling role in marine fisheries. The core findings not only align with and extend existing research but also achieve a degree of breakthrough in analytical scale and mechanism decomposition, primarily reflected in the following aspects: First, in terms of the overall impact, this study confirms that the digital economy has a significant positive effect on the total factor productivity of marine fisheries. This finding aligns with the existing academic focus on how digital technologies optimize mariculture processes and enhance the efficiency of marine fisheries through improved resource allocation, collectively validating that digital transformation serves as a crucial pathway for activating new drivers of fishery development. The key distinction, however, lies in the analytical scale. While prior research has predominantly adopted provincial-level analysis, which may obscure intra-regional variations due to data aggregation [13,15,16]. This study zooms in to the city level. This finer-grained approach enables a more precise capture of the specific outcomes and nuanced differences in digital practices across cities, thereby offering higher-resolution empirical evidence for evaluating the actual effects of digital enablement and enhancing the practical relevance of the conclusions.

Secondly, in the deeper dissection of the impact mechanism, this study validates the core logic of how the digital economy empowers marine fisheries. Specifically, the promoting effect of the digital economy on total factor productivity primarily stems from technological progress rather than improvements in technical efficiency. This finding stands in contrast to existing research, which predominantly emphasizes the direct role of digital technologies in optimizing production processes and enhancing management efficiency [20,21]. However, through the decomposition of the GlobalMalmquistLndex, this study reveals that, at the current stage, the core value of the digital economy lies in expanding the production frontier—from big-data-driven innovations in aquaculture breeding to AI-assisted intelligent disease diagnosis, and further to IoT-supported precise decision-making in offshore fishing. Digital technologies are continuously breaking through the traditional limits of fishery production. Conversely, in terms of narrowing the gap between actual efficiency and the potential optimal level, the role of the digital economy has not yet been fully realized. This observation also indicates that the integration of digital technologies and fisheries remains at an early stage characterized by technological adoption and paradigm restructuring. In this way, this study addresses a gap in the existing literature regarding the internal structure of the enabling pathways.

Furthermore, the regional heterogeneity analysis further delineates the spatial landscape of digital economy enablement: its positive effects are concentrated in cities within the South China Sea region. This observation aligns with the scholarly consensus on the spatial differentiation of regional digital economic development [13,15,25]. Building on this, the present study provides a more in-depth clarification of the key underlying drivers—namely, the triadic interaction of marine resource endowments, industrial foundations, and policy environments. In contrast, the Bohai and Yellow Sea regions are constrained by the ecological carrying capacity thresholds of semi-enclosed sea areas, while the East China Sea region is entangled in the complex dilemma of balancing ecological preservation and development, coupled with industrial path dependence. The South China Sea region, however, leverages its first-mover advantages in capital- and technology-intensive industries such as deep-sea aquaculture and pelagic fisheries. This is further amplified by the innovation spillovers from the Guangdong-Hong Kong-Macao Greater Bay Area and the policy dividends of the Hainan Free Trade Port. Together, these factors create a distinctive advantage for adopting digital technology enablement, positioning the South China Sea region as a pioneering zone for the deep integration of the digital economy and marine fisheries.

Finally, the exploration of threshold effects clarifies the conditional dependencies of digital economy enablement. This study finds that once technology promotion and industrial collaboration surpass certain critical thresholds, the enabling effect of the digital economy is significantly enhanced. In contrast, as regional innovation capacity improves, its marginal contribution tends to exhibit a diminishing trend. This conclusion aligns with existing literature suggesting the existence of critical thresholds in digital transformation [14,26,27]. While prior research has often focused on threshold characteristics in a single dimension, the multi-threshold analysis in this study expands the dimensions of threshold variables beyond a single condition. It simultaneously considers three key factors: technology diffusion, industrial ecosystems, and innovation capacity, thereby offering a more comprehensive depiction of the complex logic behind digital economy enablement. The study reveals the differential impacts of these three key factors on digitalization outcomes: technology promotion helps overcome R&D bottlenecks, industrial collaboration strengthens the ecological foundation, and regional innovation must be aligned with different stages of development. This provides a more solid basis for formulating tiered and targeted policies for fishery digitalization.

6. Conclusions

This study uses data from 41 Chinese coastal cities (2003–2022) to examine how the digital economy empowers marine fisheries, focusing on total factor productivity. The findings are: First, the digital economy steadily improves productivity, mainly through “innovation-driven” expansion and “frontier breakthroughs,” while its potential in efficiency catch-up and management optimization remains limited. Second, its effect shows clear spatial differences. The South China Sea region, with early offshore and deep-sea aquaculture advantages and policy support from the Greater Bay Area and Hainan Free Trade Port, leads in digital-fisheries integration. Third, the digital economy’s impact varies with external conditions. Once technology diffusion and industrial coordination cross thresholds, its effect strengthens, but as innovation capability rises, marginal returns decline. Based on these insights, the following policy recommendations are offered:

(1)

Deepen the application of digital technologies in core processes. Currently, the digital economy mainly empowers marine fisheries through technological progress, while its effect on technical efficiency remains limited. Efforts should focus on moving digital technology from “isolated applications” to “systemic integration.” In production, low-cost, high-sensitivity water sensors, drones, and automated feeding systems should be deployed to monitor and regulate key indicators such as temperature, dissolved oxygen, and pH in real time. Big data-based disease warning models can reduce risks at the source and improve production stability. In management and allocation, integrated fishery cloud platforms should be developed to combine vessel tracking, electronic catch logs, supply chain finance, and remote expert consultation, breaking data silos and enabling fishers to make informed, real-time decisions that optimize resources and reduce costs. The government should guide and support digital skills training for fishers through visual manuals and offshore practical training to improve proficiency in using smart equipment and interpreting data.

(2)

Implement precise regional development strategies tailored to resource endowments and development stages. In the South China Sea, deep-sea aquaculture foundations and policy support from the Greater Bay Area and Hainan Free Trade Ports should be leveraged to develop digital management systems for large smart aquaculture vessels and gravity-based deep-water cages, and explore international cross-border e-commerce and traceability platforms to establish a “smart offshore” benchmark. In the East China Sea, there should be a focus on “digital conservation” by combining satellite remote sensing, drone patrols, and AI image recognition to build a dynamic monitoring network for fisheries and ecosystems, supporting scientific catch quotas and seasonal closures, and guiding the industry from “production-oriented” to “management-oriented.” In the Bohai-Yellow Sea, filling digital gaps in the supply chain should be prioritized by integrating IoT temperature monitoring and blockchain traceability in cold chain logistics, while encouraging leading enterprises to unify production standards, branding, and supply chain services through industrial internet platforms, enhancing overall competitiveness.

(3)

Overcome key thresholds and strengthen supporting systems to build a healthy digital fishery ecosystem. For technology diffusion, the “last-mile” capacity of local aquaculture extension services should be enhanced by establishing county- and township-level digital service stations and allocating more funds to digital projects, providing equipment support, data interpretation, and troubleshooting for fishers. In industrial coordination, cultivate modern processing, smart cold chain, and e-commerce enterprises to ensure that digital data from fishing and aquaculture is effectively translated into value through mature backend support, forming a virtuous “digital-industry” cycle. For regional innovation, strategies should be stage-specific: in areas with weak innovation, there should be a focus on adopting mature domestic and international digital fishery solutions to catch up quickly; in areas with strong innovation, deep integration of digital technology with seed breeding and equipment manufacturing be promoted, and digital innovation centers should be built to shift the digital economy from external stimulus to endogenous driving force, ensuring sustained and high-impact empowerment.