The Impact of Digital Economy Policy on Mariculture Green Total Factor Productivity in China

Abstract

Mariculture plays a crucial role in the marine industry, holding significant importance for global food provision, coastal economic growth, and marine ecological preservation. However, mariculture encounters challenges such as resource scarcity, environmental contamination, and market instabilities. The broad adoption of digital technology presents valuable growth prospects for mariculture. Employing the SBM-GML model, this study assesses the green total factor productivity of mariculture across ten coastal provinces in China from 2006 to 2022 and investigates the influence of digital economy policies on the sector’s green total factor productivity. The results reveal an overall fluctuating upward trend in the green total factor productivity of Chinese mariculture, ranging between 0.975 and 1.074, with variations in technical efficiency surpassing those in technological progress. This underscores that enhancing the green total factor productivity in China’s mariculture sector primarily hinges on technical efficiency. Noteworthy regional disparities point to an imbalance in regional mariculture advancement. Additionally, this study illustrates the favorable impact of digital economic strategies on the sector’s green total factor productivity, with varying effects observed across diverse regions. These findings provide empirical support and policy recommendations which will help government authorities formulate and implement effective policies, fostering the green transformation of mariculture amid the evolving digital economy landscape.

1. Introduction

The degradation of ecosystems, the worsening climate crisis, and the increasing loss of biodiversity continually threaten global food security. According to “The State of World Fisheries and Aquaculture 2022” compiled by the FAO, in 2020, 811 million people worldwide were still suffering from hunger, and 3 billion people were unable to afford healthy diets. In 2020, the world’s per capita fishery product supply was 20.2 kg, more than double that in the 1960s, and it was found to be growing considerably faster than the world’s population growth rate, mainly due to the rapid growth of aquaculture production [1]. Meanwhile, developing aquaculture is an effective way to address food security. China is the largest country contributing to aquaculture. In 2020, 65.49 million tons of aquaculture was produced, with farmed aquatic products accounting for 79.8% of this total. Chinese-farmed aquatic products account for over 60% of the world’s total aquaculture production [2]. Therefore, studying Chinese aquaculture is highly significant for alleviating the global food security crisis and upgrading people’s healthy diets.

In recent years, China has implemented measures to protect its ecological environment and regulate pollution stemming from aquaculture activities. Efforts include dismantling extensive farming nets in inland areas, backfilling shrimp and fishponds, sealing wells, and enforcing farming bans, leading to an annual decline in the freshwater aquaculture area of China. The freshwater aquaculture area reduced from 6197.62 thousand hectares in 2016 to 4983.87 thousand hectares in 2021 [3]. Despite this decline, China’s abundance of marine resources positions mariculture as an essential strategy. Data from the China Fisheries Statistical Yearbook show a decrease in the proportion of seawater products from 57.7% in 1986 to 50.4% in 2022. Furthermore, the Chinese sea area allocated for aquaculture shrank by approximately 14% in 2020 compared to 2015 [4].

Enhancing the efficiency of mariculture has emerged as a prominent societal concern. The China Fishery Yearbook offers a precise delineation of mariculture. Mariculture is a method of fishery production that encompasses the deliberate introduction of seedlings or the natural capture of juveniles, followed by artificial feeding and management. It encompasses offshore farming, intertidal farming, and various other farming methods. The efficiency of mariculture serves as a comprehensive indicator for assessing the harmonized development of resource utilization, economic benefits, and environmental impact within the mariculture industry. The Chinese mariculture industry currently encounters notable challenges, including outdated development practices, extensive offshore aquaculture, and rising pollution in aquaculture waters, falling short of the standards for resource-efficient and environmentally friendly development [5].

At the beginning of the 21st century, China vigorously promoted the development of the digital economy. However, in this period, the digital economy had yet to be clearly defined. It was not until 2016 that the G20 put forward the Digital Economy Development and Cooperation Initiative, which clearly defined the digital economy: the digital economy takes digital information and knowledge as production factors, uses information networks as carriers, and utilizes information and communication technologies as the main economic form, aiming to promote efficiency improvement and structural optimization. Following this, the Chinese government considers the advancement of the digital economy to be a top priority and has implemented a range of pertinent policies. Since the 2018 Chinese government report introduced the construction of Digital China, digital technology and the digital economy have empowered the traditional economy [6], improved production efficiency [7,8], and promoted industrial upgrading [9,10]. In 2023, the scale of China’s digital economy reached CNY 53.9 trillion, comprising 42.8% of the GDP. The digital economy’s contribution to GDP growth stood at 66.45%, significantly bolstering economic stability and expansion [11]. Therefore, studying the impact of digital economy policy on green total factor productivity (GFP) in the mariculture industry has become essential, as it can assist in promoting the sustainable and intensive development of global aquaculture. It can also contribute to global food security and the consumption of a healthy diet.

This paper focuses on a study encompassing 10 coastal provinces, autonomous regions, and municipalities of the central government of China from 2006 to 2022. The SBM-GML model is utilized to evaluate the green total factor productivity of mariculture, accounting for undesired outputs. The research investigates the influence of the digital economy on green total factor productivity in mariculture and proposes corresponding countermeasure suggestions. The objective is to offer empirical evidence for enhancing the green total factor productivity of the mariculture sector.

The possible innovations are as follows. Firstly, innovatively incorporating mariculture losses due to environmental pollution to measure the undesired output of mariculture affected by environmental factors. Secondly, exploring the impact of the digital economy on the green total factor productivity of mariculture can advance the research scope of the digital economy field.

2. Literature Review

The significant role of marine food production as a supplement to traditional land-based food sources has garnered widespread recognition among scholars. Mariculture emerges as the fastest-growing sector in food production and is poised to become the leading marine food source in the future [12]. However, the environmental impact of marine food production is concerning. Mariculture operations produce substantial pollutants such as nitrogen, phosphorus, and carbon through fish respiration, excretion, and feed waste, posing a considerable threat to ecosystems and the environment [13]. Nutrient and organic pollution, heavy metals, and antibiotics are major pollutants in the mariculture industry, necessitating effective source management [14]. Therefore, responsible governance of the mariculture sector is essential to address the ecological and environmental challenges associated with marine food production.

There is extensive research using total factor productivity (TFP) to measure mariculture efficiency. There are three main approaches to calculating TFP. The first method is the Solow residual method. Hakan and Ragnar (2013) evaluate the productivity of fisheries in Iceland, Norway, and Sweden using this method [15]. However, this approach considers only two factor inputs—capital and labor—and may not accurately measure the efficiency of mariculture. The second method is stochastic frontier analysis (SFA). Based on SFA, Singh (2008) and Zhu & Ping (2023) [16,17] study the economic and green production efficiency of aquaculture. SFA accounts for the effect of random errors but has stringent assumptions regarding the distribution of error and inefficiency terms. The third method is the non-parametric method based on the Malmquist total factor productivity index. Liu (2015) [18] analyzes the TFP of the Chinese mariculture industry using the DEA–Malmquist method, and this method has also been used multiple times in research on the productivity of the ocean economy in China and its coastal regions (Alam, Khan and Huq, 2012) [19]. This method does not require the establishment of functional relationships between variables, avoids biased results from inappropriate modeling, and considers multifactor inputs with desired and undesired outputs. The abovementioned studies have overlooked the environmental pollution caused by mariculture, which fails to objectively reflect the relationship between inputs and outputs and contradicts the concept of green development. Xu et al. (2022) [20] address this by using the equivalent pollutant outputs of nitrogen, phosphorus, and COD in mariculture as undesired outputs, which were successfully resolved using the SBM-GML model.

Studies on the factors influencing total factor productivity in fisheries offer concrete approaches to promoting efficiency and sustainability in the industry. Asche et al. (2013) discovered that technological progress is the main driver of total factor productivity in Norwegian salmon farming [21]. Xu et al. (2022) and Sabbag and Costa (2018) utilized a three-stage DEA model to measure the technical efficiency of mariculture and found that scale inefficiency was a significant hindrance to improving technical efficiency [20,22]. Ji et al. (2021) [23] examined the efficiency of green technology in Chinese mariculture through a stochastic frontier model beyond the logarithmic function. Their findings reveal that mariculture’s green technology efficiency is mainly impacted by aquaculture waters and the level of regional development.

The digital economy plays a crucial role in boosting total factor productivity by enhancing innovation capabilities, facilitating the integration and advancement of various industries, optimizing resource allocation efficiency, and reducing costs through multiple avenues. Firstly, the adoption of digital technology drives the shift in traditional production factors and methods towards digitization, leading to decreased search and communication expenses in enterprise activities (del Águila et al., 2003) [24]. Lower search and communication costs enable more efficient utilization of idle resources, thereby improving overall production efficiency (Nocke, 2007) [25]. Secondly, digital transformation motivates enterprises to increase innovation expenditures, establish a collaborative innovation ecosystem, enhance overall productivity and technological influence, and achieve high-quality development. Thirdly, digital transformation empowers enterprises to engage in cross-border collaborations, overcome asset specificity constraints, evolve into comprehensive service platforms, diversify the range of supplied products, and enhance resource utilization and performance simultaneously (Ding et al., 2022) [26]. Additionally, companies can effectively maximize and reallocate idle resources through digital platforms (Horton and Zeckhauser, 2016) [27], ensuring precise resource matching and efficient allocation (Zervas, 2017) [28]. Furthermore, the Internet reduces information asymmetry among enterprises, leading to a more specialized division of labor and increased efficiency (Hollenbeck, 2018) [29].

The impact of the digital economy on land economic development has been well substantiated (del Águila et al., 2003; Ding et al., 2022; Wang et al., 2023) [24,26,30]. Fang et al. (2024) have expanded their research to explore the marine economy, concluding that advancing the digital economy within a specific region will boost high-quality development not only in the local marine economy but also in the neighboring areas [31].

3. Materials and Methods

3.1. SBM Model

The non-parametric DEA model is used to measure the efficiency of DMU by constructing the production front of data. The traditional DEA model ignores the influence of relaxation variables and cannot accurately calculate the efficiency value containing unexpected output. Therefore, the non-radial and non-angle SBM model proposed by Tone (2001) [32] can make up for the deficiency and avoid the deviation caused by the different radial and angle, ensuring that the measured results are more consistent with the actual production. Zhang et al. (2021) [33] constructed the SBM measurement model considering the undesirable output as follows:

$${\lambda }^{*}(x^{t,k^{\prime }},y^{t,k^{\prime }},b^{t,k^{\prime }},g^x,g^y,g^b)=max{\displaystyle \frac{1}{2}}\left\{{\displaystyle \frac{1}{N}}{\sum }_{n=1}^N{\displaystyle \frac{S_n^x}{g_n^x}}+{\displaystyle \frac{1}{M+1}}({\sum }_{m=1}^M{\displaystyle \frac{S_m^y}{g_m^y}}+{\sum }_{i=1}^I{\displaystyle \frac{S_i^b}{g_i^b}})\right\}$$

$$s.t.\left\{\begin{array}{c}\begin{array}{c}\begin{array}{c}\sum _{k=1}^K{\lambda }_k^t{x}_{kn}^t+S_n^x=x_{k^{\prime }n}^t,\forall n;\\ {\sum }_{k=1}^K{\lambda }_k^t{y}_{km}^t-S_m^{yx}=y_{k^{\prime }m}^t,\forall m;\end{array}\\ {\sum }_{k=1}^K{\lambda }_k^t{a}_{ki}^t+S_i^a=x_{k^{\prime }i}^t,\forall i;\\ {\sum }_k^K{\lambda }_k^t=1,{\lambda }_k^t\ge 0,\forall n;\end{array}\\ S_i^b\ge 0,\forall i;\\ \begin{array}{c}{S}_n^x\ge 0,\forall n;\\ S_m^y\ge 0,\forall m;\end{array}\end{array}\right.$$

(1)

where ${\lambda }^{*}$ is the objective function; ${\mathrm{x}}^{\mathrm{t},{\mathrm{k}}^{\mathrm{\prime }}}$, ${\mathrm{y}}^{\mathrm{t},{\mathrm{k}}^{\mathrm{\prime }}}$, ${\mathrm{b}}^{\mathrm{t},{\mathrm{k}}^{\mathrm{\prime }}}$, ${\mathrm{g}}^{\mathrm{x}}$, ${\mathrm{g}}^{\mathrm{y}}$ and ${\mathrm{g}}^{\mathrm{b}}$ refer to the respective vector of factor inputs, desired outputs, undesired outputs, input compression directions, and desired output expansion directions. Additionally, $S_n^x$, $S_m^y$ and $S_i^b$ are slack variables for inputs, desired outputs, and undesired outputs. The time period is $\mathrm{t}=\mathrm{1,2}\cdots \mathrm{T}$, and $\mathrm{N}$, $\mathrm{M}$, and $\mathrm{I}$ are quantities of input factors of production, desired outputs, and undesired outputs, respectively.

3.2. Global Malmquist–Luenberger (GML) Index Model

The Global Malmquist–Luenberger (GML) index is an enhanced version of the traditional ML index, enabling intertemporal efficiency comparisons [34,35]. This study employs the GML index to conduct a dynamic analysis of green total factor productivity in marine aquaculture, presenting the GML index from period t to t + 1 as follows:

$${GML}_{t+1}^t(x^t,y^t,b^t,x^{t+1},y^{t+1},b^{t+1})={\displaystyle \frac{1+{\lambda }^G(x^t,y^t,b^t)}{1+{\lambda }^G(x^{t+1},y^{t+1},b^{t+1})}}$$

$$={\displaystyle \frac{1+{\lambda }^t(x^t,y^t,b^t)}{1+{\lambda }^{t+1}(x^{t+1},y^{t+1},b^{t+1})}}\times \left[{\displaystyle \frac{1+{\lambda }^G(x^t,y^t,b^t)}{1+{\lambda }^t(x^{t+1},y^{t+1},b^{t+1})}}\times {\displaystyle \frac{1+{\lambda }^{t+1}(x^{t+1},y^{t+1},b^{t+1})}{1+{\lambda }^G(x^{t+1},y^{t+1},b^{t+1})}}\right]$$

$$={GMLEC}_{t+1}^t\times {GMLTC}_{t+1}^t$$

(2)

where ${GML}_{t+1}^t$, ${GMLEC}_{t+1}^t$ and${GMLTC}_{t+1}^t$, respectively, represent the change in total factor productivity, technical efficiency and technical progress from period t to period t + 1. When the total factor productivity index is greater than 1, it indicates that the green total factor productivity shows an upward trend. Conversely, when it is less than 1, it indicates a downward trend in green total factor productivity.

$GMLEC$ and $GMLTC$ represent the technical efficiency and technological progress of marine aquaculture, respectively. Specifically, $GMLEC$ reflects the application and dissemination level of advanced technologies in marine aquaculture. When $GMLEC$ > 1, it indicates that the unit scale effect of the current technological level in marine aquaculture is optimal, and the technology dissemination effect is good. $GMLTC$ reflects whether the current technological conditions have been updated compared to the previous period and whether the input factors of marine aquaculture activities are effectively utilized. If $GMLTC$ > 1, it indicates an increase in the unit technological level and the utilization of input factors, and vice versa.

3.3. Econometric Model

To examine the impact of digital economy policy on the green total factor productivity in Chinese mariculture, we try to construct a multiple linear model.

$${tecch}_{it}={\alpha }_0+{\alpha }_1{digital}_{it}+\gamma Z_{it}+{\mu }_t+{\mu }_j+{\epsilon }_{it}$$

(3)

where the subscript $i$ denotes the province and t denotes the year; ${tecch}_{it}$ denotes the green total factor productivity in aquaculture; ${digital}_{it}$ denotes the core explanatory variable digital economy; $Z_{it}$ is a set of other control variables; $u_t$and ${\mu }_j$ are the year fixed effect and province fixed effect, respectively; and ${\epsilon }_{it}$is the error term. ${\alpha }_0$ is a constant term and ${\alpha }_1$ is the estimated coefficient of $digital$$;\gamma$ is the estimated coefficient of control variables.

3.4. Indicator Selection

3.4.1. Selection of Productivity Measurement Indicators

Green total factor productivity builds upon traditional total factor productivity accounting methodologies, incorporating factors such as resource consumption and pollution emissions to more fully capture unexpected outputs. By integrating economic benefits with ecological resource protection, green total factor productivity provides a more comprehensive evaluation of economic development [23]. The measurement of green total factor productivity mainly includes input elements (land, labor, and capital) [36], output elements (output), and unexpected output elements (environmental pollution) [23].

This research paper presents a comprehensive and systematic framework for evaluating green total factor productivity in the mariculture industry. The framework integrates essential resource inputs, desired outputs, and undesired outputs, which have been carefully selected based on established literature and data integrity.

In our study, these indicators are utilized to assess resource factor inputs. For land inputs, we measure the total mariculture area in each coastal province, encompassing offshore aquaculture, mudflat aquaculture, and other aquaculture, as a comprehensive reflection of land resources essential for mariculture activities. Labor inputs are evaluated based on the number of mariculture employees directly engaged in production activities. Additionally, mariculture capital inputs are examined using the year-end ownership of fishing vessels as a pertinent indicator.

Regarding outputs, we account for both desired and undesired outcomes. The mariculture output serves as the desired output indicator, independent of price influence, providing an overall measure of industry productivity. To gauge undesired outputs, we incorporate the lost production of aquatic products due to seawater aquaculture pollution. This indicator assesses water quality in the vicinity and quantifies production loss from seawater pollution [37].

In summation, we have carefully selected a set of indicators to evaluate green total factor productivity in the mariculture sector. These indicators were chosen based on literature insights, data completeness, and a nuanced comprehension of the mariculture industry. Further details and statistical analyses of the selected indicators are detailed in

Table 1. Input–output indicators of mariculture.

Panel A: Indicators
Type	Indicators	Definition			Unit of Measurement
Input	Land	Mariculture area			Hectare
	Labor	Number of mariculture professionals			Person
	Capital	Fishing vessel ownership at the end of the Period			Kilowatts
Desired output	Farming output	Output of mariculture			Tons
Undesired output	Pollution losses	Amount of pollution loss in fishery			Tons
Panel B: Statistical Analysis
Type	Indicators	Mean	Std.Dev.	Min	Max
Input	Land (Hectare)	202,549	224,679	813	942,050
	Labor (Person)	224,970	149,326	1718	482,760
	Capital (Kilowatts)	1.583 × 106	1.205 × 106	46,625	4.543 × 106
Desired output	Farming output (Tons)	1.793 × 106	1.607 × 106	5155	5.561 × 106
Undesired output	Pollution losses (Tons)	8842	32,441	2	383,104

.

3.4.2. Selection of Factors Influencing GTFP in Mariculture

Based on Equation (3), the explanatory variable is the green total factor productivity in Chinese mariculture (${\mathrm{t}\mathrm{e}\mathrm{c}\mathrm{c}\mathrm{h}}_{\mathrm{i}\mathrm{t}}$).

The core explanatory variable (${\mathrm{d}\mathrm{i}\mathrm{g}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l}}_{\mathrm{i}\mathrm{t}}$) is digital economy policy. Following Tao and Ding’s (2022) [38] institutional supply method to measure the policy supply intensity of the digital economy. Specifically, the method is as follows: first, by consulting the provincial government work report from 2007 to 2022, the keywords of the digital economy are extracted to build a key dictionary of digital economic policies. Second, on the Peking University Magic website, these keywords are used to conduct a search of digital economy policy texts issued by 10 coastal provinces in China during the sample period. Third, the word segmentation of the government work report is carried out with the help of Python 3.13.0 software, and the keyword frequency related to the digital economy in the provincial government work report is counted, and finally, the word frequency is used to determine the intensity of the digital economy policy.

The control variables (${\mathrm{Z}}_{\mathrm{i}\mathrm{t}}$) are other characteristics that affect the green total factor productivity in Chinese mariculture. The control variables are as follows: Aquaculture technology extension funds ($\mathrm{l}\mathrm{n}\mathrm{a}\mathrm{q}\_\mathrm{f}\mathrm{u}\mathrm{n}\mathrm{d}\mathrm{s}$) is measured by the logarithm of aquaculture technology extension funds. The intensity of aquatic technology popularization ($\mathrm{l}\mathrm{n}\mathrm{a}\mathrm{q}\_\mathrm{p}\mathrm{o}\mathrm{p}\mathrm{u}$) is measured by the logarithm of the actual amount of aquatic technology popularization. Fishers’ income ($\mathrm{l}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}$) is measured by the logarithm of the total income of fishers’ households. The level of fishery human capital ($\mathrm{l}\mathrm{n}\mathrm{h}\mathrm{u}\mathrm{m}\mathrm{a}\mathrm{n}$) is measured by the logarithm of the number of marine fishery professionals. Regional economic development level ($\mathrm{l}\mathrm{n}\mathrm{p}\mathrm{g}\mathrm{d}\mathrm{p}$) is measured by the logarithm of regional per capita GDP.

3.5. Data

This study concentrates on mariculture production in 10 coastal provinces in China: Liaoning, Hebei, Tianjin, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, Guangxi, and Hainan. Shanghai, the Hong Kong Special Administrative Region, the Macau Special Administrative Region, and Taiwan Province were excluded due to data insufficiencies and the limited scale of mariculture production. From 2006 to 2022, pertinent data for measuring green total factor productivity were extracted from the China Fishery Statistical Yearbook and China Statistical Yearbook. When data were missing, interpolation and geometric growth rate methods were employed to supplement the records based on data fluctuation patterns.

Descriptive statistics of key variables for each coastal province in China are outlined in

Table 2. Descriptive statistics of main variables.

Variable	Observation	Mean	Std.Dev.	Min	Max
$tecch$	117	1.004	0.017	0.976	1.039
$tfpch$	117	1.012	0.079	0.775	1.768
$tech$	117	0.999	0.013	0.956	1.051
$digital$	117	8.034	8.702	0	30
$lnaq\_funds$	117	9.232	1.171	5.820	12.106
$lnaq\_popu$	117	6.625	1.069	4.369	8.145
$lnincome$	117	10.200	1.264	7.208	12.390
$lnhuman$	117	11.560	1.572	7.172	13.060
$lnpgdp$	117	10.790	0.530	9.354	11.840

Note: The missing value variable was removed.

. The average green total factor productivity in Chinese mariculture (1.066) indicates a 6.6% growth rate on average from 2007 to 2022.

4. Results and Discussions

4.1. Measurement and Analysis of GTFP in Mariculture

4.1.1. Trends Analysis

Figure 1 displays the time trend of green total factor productivity in mariculture, along with its corresponding decomposition index. In 2022, the green total factor productivity index value amounted to 1.074, indicating an increase of 7.4% and a significant upward trend. Specifically, the technical efficiency index value rose by 7.6% to 1.076, while the technical progress index value decreased by 0.2% to 0.998. These results demonstrate that the advancement of green total factor productivity in China’s mariculture industry primarily depended on the enhancement of technical efficiency. However, pure technical progress experienced a slight decline, which has become a hindrance to the improvement of green total factor productivity in China.

In terms of temporal trends, three noteworthy increases in mariculture green total factor productivity have been observed. The first occurred in 2009 and was likely attributable to the formal implementation of the Water Pollution Prevention and Control Law proposed by China in 2008. This law strengthened the protection and management of water resources, which had a positive impact on the marine aquaculture industry. The implementation of the regulations encouraged aquaculture enterprises to pay more attention to environmental protection, reducing water pollution during the breeding process and thus improving breeding efficiency and product quality. The law marked an elevation of the country’s endeavors to manage its water resources and was indicative of the coastal provinces’ pursuit of water body conservation. The second rise transpired in the year 2011. This was due to adjustments in fisheries’ policies and planning with regard to the marine economy. In 2011, the Chinese government adjusted its fishery policies, emphasizing the principle of ecological priority, which helped guide the development of marine aquaculture in a more sustainable and efficient direction. The government had implemented a series of policies to encourage technological innovation and improve resource utilization efficiency, thereby promoting an increase in total factor productivity. During this period, China was implementing strategic plans such as the 12th Five-Year Plan for the Development of the National Marine Economy. These plans emphasized the comprehensive development of marine economy and proposed specific measures to promote technological progress and improve production efficiency in marine aquaculture. In 2022, the third significant increase occurred with the official launch of the National Fisheries Development Plan for the 14th Five-Year Plan. This plan highlights that China’s fishing industry is at a pivotal juncture in accelerating high-quality development, supported by the emergence of new information technology and agricultural modernization. Coastal provinces are actively striving towards green, scientific, and technological advancements, leading to a substantial rise in mariculture operations.

4.1.2. Analysis of Regional Variations in Green Total Factor Productivity in Mariculture

The fluctuations in green total factor productivity within the aquaculture industry across different regions are depicted in

Table 3. Green total factor productivity index for mariculture industry by region.

Province	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	Mean
Tianjin	1.002	0.918	1.097	0.991	1.453	0.775	0.940	0.975	0.964	0.988	1.092	1.090	1.013	1.047	1.028	1.768	1.071
Hebei	0.894	0.942	1.013	0.940	1.043	0.976	1.006	0.992	1.019	0.992	1.007	0.983	0.985	1.012	1.005	1.027	0.990
Liaoning	0.994	0.986	1.004	1.005	1.002	0.998	1.007	1.005	1.003	1.006	1.002	0.995	1.006	1.002	1.014	1.005	1.002
Jiangsu	0.986	1.066	0.992	1.014	0.997	1.020	1.009	1.010	1.006	1.005	1.004	1.002	1.000	1.001	0.968	0.967	1.003
Zhejiang	1.081	0.994	0.995	0.963	1.011	1.009	1.000	1.001	1.010	1.020	1.012	1.001	1.111	0.975	1.115	0.967	1.017
Fujian	1.009	0.988	0.992	0.995	0.999	0.999	0.999	0.999	0.999	1.000	1.000	1.000	1.000	1.000	1.001	0.999	0.999
Shandong	1.022	1.019	1.017	1.015	1.012	1.010	1.008	1.006	1.003	1.001	0.999	0.996	0.994	0.992	0.989	0.987	1.004
Guangdong	1.071	0.967	1.005	1.010	1.005	1.000	1.002	1.003	1.003	1.003	0.999	1.005	1.008	0.999	0.998	0.998	1.005
Guangxi	1.004	0.962	1.005	1.057	0.994	0.969	1.019	1.020	1.003	1.040	0.997	0.995	1.052	1.000	0.983	0.988	1.005
Hainan	1.044	0.975	0.995	1.030	1.002	1.004	1.008	1.010	1.004	1.011	1.003	1.019	0.952	1.051	1.053	1.039	1.013
Mean	1.011	0.982	1.011	1.002	1.052	0.976	1.000	1.002	1.001	1.007	1.011	1.009	1.012	1.008	1.016	1.074

. It is noteworthy that the average green total factor productivity index in Hebei Province and Fujian Province falls below 1, contrasting with other regions where this index surpasses 1. This suggests an overall increasing trend in green total factor productivity for most regions. Analyzing the average growth rates reveals that Tianjin Municipality stands out with a substantial increase exceeding 5%, placing it in the top tier. Meanwhile, Zhejiang Province and Hainan Province exhibit growth rates exceeding 1%, placing them in the second tier, whereas Liaoning Province, Jiangsu Province, Shandong Province, Guangdong Province, and Guangxi Zhuang Autonomous Region show growth rates below 1%, positioning them in the third tier. Notably, there are slight decreases in green total factor productivity in Hebei Province and Fujian Province.

The findings align with the research conclusions drawn by Zhu and Ping (2023) [17]. Despite the differing methods used to calculate total factor productivity between the two studies, the input–output factors exhibit similarities, yielding negligible discrepancies in the outcomes. The superior efficiency in green total factor productivity in marine aquaculture in Tianjin Municipality primarily arises from its advanced specialization in this field. The primary products of marine aquaculture in Tianjin Municipality include fish and crustaceans, with the fish specialization index ranking second (0.5527) and crustaceans ranking second (1.4473), boasting a notable advantage over the third-ranked index (0.9051) (Zhao et al., 2020) [39]. The heightened total factor productivity in marine aquaculture in Hainan Province can be attributed to the favorable ecological conditions in the South China Sea waters and the robust production capacity, which facilitates reduced resource input for seawater breeding (Zhang, Han, and Qin, 2022) [40]. Additionally, Hainan demonstrates noteworthy comparative advantages in overall fish farming (Zhao et al., 2020) [39].

Overall, within the confines of resource and environmental limitations, coastal regions in China have prioritized enhancing green total factor productivity in order to enhance the marine aquaculture industry, thereby enhancing the industry’s growth quality. The disparities in green total factor productivity in marine aquaculture across regions are shaped by a multitude of factors, primarily stemming from China’s extensive marine expanse, which encompasses the Bohai Sea, Yellow Sea, East China Sea, and South China Sea, characterized by climates ranging from temperate to subtropical zones. Varied provinces encounter diverse resource constraints during industrial progress, resulting in notable diversities in industrial growth trajectories [41].

4.1.3. Further Decomposition of Green Total Factor Productivity in Mariculture

This study further dissects the green total factor productivity of Chinese mariculture into the technical efficiency index and the technical progress index. This approach enables a more precise and comprehensive appraisal of the factors influencing variations in green total factor productivity.

Table 4. Technical progress index of mariculture industry by region.

Province	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	Mean
Tianjin	0.992	0.965	1.045	0.998	1.002	1.000	1.000	1.000	0.984	0.967	1.051	1.000	0.994	1.006	1.000	1.000	1.000
Hebei	0.997	0.990	1.007	0.956	1.052	0.977	1.000	0.989	1.008	0.993	1.002	0.992	0.990	1.007	0.998	1.019	0.999
Liaoning	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Jiangsu	0.958	1.044	0.979	1.009	0.999	1.010	1.000	1.003	1.000	1.000	1.000	1.000	0.999	1.001	0.984	0.999	0.999
Zhejiang	1.044	0.976	0.992	0.977	1.007	1.005	0.994	0.998	1.004	1.008	1.001	0.999	1.041	0.959	1.043	0.963	1.001
Fujian	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Shandong	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Guangdong	1.011	0.995	1.001	1.007	1.000	1.000	1.000	1.000	1.000	1.000	0.998	1.001	1.001	1.000	1.000	1.000	1.001
Guangxi	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Hainan	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	0.999	1.001	1.000	0.966	1.023	1.011	1.000	1.000
Mean	1.000	0.997	1.003	0.995	1.006	0.999	0.999	0.999	1.000	0.997	1.005	0.999	0.999	1.000	1.004	0.998

and

Table 5. Technical efficiency index of mariculture industry by region.

Province	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	Mean
Tianjin	1.010	0.952	1.050	0.993	1.450	0.775	0.940	0.975	0.980	1.022	1.039	1.090	1.018	1.041	1.028	1.768	1.071
Hebei	0.897	0.951	1.005	0.984	0.991	0.999	1.006	1.002	1.011	0.999	1.005	0.991	0.995	1.006	1.007	1.008	0.991
Liaoning	0.994	0.986	1.004	1.005	1.002	0.998	1.007	1.005	1.003	1.006	1.002	0.995	1.006	1.002	1.014	1.005	1.002
Jiangsu	1.029	1.021	1.013	1.006	0.998	1.009	1.009	1.008	1.006	1.005	1.004	1.002	1.001	0.999	0.984	0.969	1.004
Zhejiang	1.035	1.019	1.003	0.986	1.003	1.004	1.006	1.002	1.006	1.012	1.011	1.003	1.068	1.017	1.069	1.004	1.015
Fujian	1.009	0.988	0.991	0.995	0.998	0.999	0.999	0.999	0.999	0.999	1.000	1.000	1.000	1.000	1.001	0.999	0.999
Shandong	1.022	1.019	1.017	1.015	1.012	1.010	1.008	1.006	1.003	1.001	0.999	0.996	0.994	0.992	0.989	0.987	1.004
Guangdong	1.059	0.971	1.003	1.003	1.005	1.000	1.002	1.003	1.003	1.003	1.001	1.004	1.007	0.999	0.998	0.998	1.004
Guangxi	1.004	0.962	1.005	1.057	0.994	0.969	1.019	1.020	1.003	1.040	0.997	0.995	1.052	1.000	0.983	0.988	1.005
Hainan	1.044	0.975	0.995	1.030	1.002	1.004	1.008	1.010	1.004	1.012	1.002	1.019	0.986	1.027	1.041	1.039	1.012
Mean	1.010	0.985	1.009	1.007	1.046	0.977	1.000	1.003	1.002	1.010	1.006	1.010	1.013	1.008	1.012	1.076

indicate that, over the sample period, the cumulative change in the technical efficiency index surpasses that of technical progress when considering the average cumulative values of different decomposition indices across various regions. The limited variation in the technical progress index plays a crucial role in hindering the enhancement of green total factor productivity, highlighting the inadequate emphasis placed by provinces on technological innovation.

The results of our technical progress index measurements for the mariculture industry in each region are depicted in Table 4. From the perspective of average values, only two provinces have a technical progress efficiency index greater than 1, including Zhejiang Province (1.001) and Guangdong Province (1.001). The extent of technological progress variations in the marine aquaculture industry among diverse regions is rather limited, with no notable technological progress observed within these regions throughout the sample period. This lack of substantial change signifies that technological progress has a minor influence on green total factor productivity (Zhang and Ji, 2022) [41]. The technical progress indices of Hebei Province and Jiangsu Province are below 1, indicating that the technological advancements in these provinces have impeded the improvement of their fisheries’ green total factor productivity.

Over a 16-year period, the average technical progress index exceeded 1 only in four specific years (2009, 2011, 2017, and 2021), during which technological progress facilitated the improvement of China’s fishery green total factor productivity. However, in most years, the average technical progress index was below 1. This trend suggests that despite the ongoing advancement of China’s marine aquaculture industry, the impact of enhancing aquaculture technological progress on improving green total factor productivity is not substantial. This finding differs from that of Ji and Li (2019) [42]; the discrepancy could be attributed to variances in input–output factors resulting in disparate outcomes.

Overall, the technological advancement in China’s mariculture sector has shown a slight downturn, contributing to a marginal hindrance of green total factor productivity. Moving forward, China should prioritize enhancing technological progress within the mariculture industry, as it plays a pivotal role in boosting green total factor productivity.

The technical efficiency index results for each region are outlined in Table 5. In Hebei and Fujian Provinces, the mean technical efficiency index was below 1. Notably, Hebei Province witnessed the most substantial decline of around 1%, while Fujian Province experienced a 0.1% decrease. These findings suggest a minor decrease in technical efficiency in these provinces, which had a slight inhibitory effect on green total factor productivity. Among the regions, Tianjin exhibited the most marked increase in the technical efficiency index, registering a 7.1% growth over the specified period. Following Tianjin, Zhejiang and Hainan provinces showed the second- and third-largest improvements in technical efficiency, with average increases of 1.5% and 1.2%, respectively. On the other hand, modest technical efficiency advancements of less than 1% were noted in Liaoning, Jiangsu, Shandong, Guangdong, and Guangxi provinces, indicating sustained technical efficiency levels over the study period. Overall, the changes in technical efficiency surpassed mere technical progress, implying that enhancements in technical efficiency mainly drove the enhancements in green total factor productivity within the Chinese mariculture industry.

4.1.4. Characterization of the Spatial and Temporal Features of MGTFP

Using arcgis 10.8 and adobe illustrator CC 2019 software, spatial patterns were analyzed by mapping the annual average MGTFP by region from 2007 to 2014 and 2015 to 2022. Regions were classified based on MGTFP’s annual average cumulative efficiency index values, with thresholds set at 0.900 ≤ p ≤ 0.999 for low-efficiency areas, 1.000 ≤ p ≤ 1.099 for comparatively low-efficiency areas, 1.010 ≤ p ≤ 1.029 for relatively high-efficiency areas, and p ≥ 1.3 for high-efficiency areas (Figure 2).

Over the entire period, MGTFP exhibited significant variations across regions, indicating gradual and moderate development in China. This trend is evidenced by an increase in high-efficiency areas, a continuous decrease in low-efficiency areas, and a consistent reduction in MGTFP disparity within regions. From 2007 to 2014, the distribution of low-, comparatively low-, comparatively high-, and high-efficiency MGTFP locations in China followed a ratio of 2:5:3:0. Specifically, Fujian and Hebei demonstrated lower efficiency levels, whereas Tianjin, Shandong, and Jiangsu exhibited higher efficiency levels. Significant regional disparities and limited inter-regional coordination were apparent during this period. Between 2015 and 2022, the efficiency ratio of regional MGTFP shifted to 2:5:2:1, with a noticeable increase in regions that demonstrated higher efficiency levels. Apart from Shandong and Jiangsu, other regions showed a consistent enhancement in green mariculture development, considering local circumstances and resource endowment advantages in alignment with relevant policies. Shandong and Jiangsu stand as prominent provinces in China’s marine economy. The swift initial growth, characterized by high resource consumption and exacerbated marine environmental pollution, has had significant repercussions for marine fisheries development due to deteriorating marine ecology. Consequently, the green total factor productivity of marine aquaculture has seen a decline.

4.2. An Empirical Analysis of the Influence of Digital Economy Policy on the Green Total Factor Productivity in Chinese Mariculture

4.2.1. Correlation Test

A scatter plot was generated to visually represent the trend in the relationship between digital economic policies and green total factor productivity in aquaculture. Analysis of Figure 3 and Figure 4 reveals a positive correlation between digital economic policies and green total factor productivity, suggesting that advancements in the digital economy can enhance green total factor productivity in aquaculture.

Pearson correlation analysis was performed, and STATA 17.0 software was used to conduct the initial investigation of the relationship between variables in the study sample, as outlined in

Table 6. Pearson correlation test results.

	$\mathit{t}\mathit{e}\mathit{c}\mathit{c}\mathit{h}$	$\mathit{d}\mathit{i}\mathit{g}\mathit{i}\mathit{t}\mathit{a}\mathit{l}$	$\mathit{l}\mathit{n}\mathit{a}\mathit{q}\_\mathit{f}\mathit{u}\mathit{n}\mathit{d}\mathit{s}$	$\mathit{l}\mathit{n}\mathit{a}\mathit{q}\_\mathit{p}\mathit{o}\mathit{p}\mathit{u}$	$\mathit{l}\mathit{n}\mathit{i}\mathit{n}\mathit{c}\mathit{o}\mathit{m}\mathit{e}$	$\mathit{l}\mathit{n}\mathit{p}\mathit{g}\mathit{d}\mathit{p}$	$\mathit{l}\mathit{n}\mathit{h}\mathit{u}\mathit{m}\mathit{a}\mathit{n}$
$tecch$	1
$digital$	0.357 **	1
$lnaq\_funds$	−0.070	0.334 ***	1
$lnaq\_popu$	0.206 **	0.014	0.158 ***	1
$lnincome$	−0.072	−0.300 ***	−0.284 ***	0.135	1
$lnpgdp$	0.132 *	0.226 ***	0.329 ***	0.053	−0.169 ***	1
$lnhuman$	−0.279 ***	0.018	0.411 ***	0.289 ***	−0.026	−0.108	1

Note: * p < 0.10, ** p < 0.05, *** p < 0.01.

. The digital economic policy demonstrates a positive correlation with the total factor productivity of mariculture, showing a correlation coefficient of 0.357, thereby confirming the theoretical analysis. Additionally, the correlation coefficients’ absolute values for both the core explanatory variables and control variables in the table are below 0.5, indicating minimal correlation among these variables, an absence of multicollinearity, and consequently, no impact on the regression results.

4.2.2. Baseline Regression

We begin by testing the original data to determine the appropriate model. First, the multicollinearity problem is tested. The VIF value is 2.29, which is much lower than 10, meaning that there is no serious collinearity problem. Panel data are likely to have heteroscedasticity and autocorrelation. We then conduct the Modified Wald test, Wooldridge test, and Friedman test as shown in Equation (3), and the results all reject the null hypothesis at the significance level of 1%. In summation, the least squares method of panel data is appropriate for our study.

To examine the impact of digital economy policy on the green total factor productivity in Chinese mariculture, we begin by conducting a regression analysis of Equation (3). The estimated results are shown in column (1) of

Table 7. The impact of digital economy policy on the green total factor productivity in mariculture: baseline regression.

Variables	(1)	(2)	(3)	(4)	(5)
	$\mathbf{t}\mathbf{e}\mathbf{c}\mathbf{c}\mathbf{h}$	$\mathbf{t}\mathbf{e}\mathbf{c}\mathbf{c}\mathbf{h}$	$\mathbf{t}\mathbf{e}\mathbf{c}\mathbf{c}\mathbf{h}$	$\mathbf{t}\mathbf{f}\mathbf{p}\mathbf{c}\mathbf{h}$	$\mathbf{t}\mathbf{e}\mathbf{c}\mathbf{h}$
$\mathrm{d}\mathrm{i}\mathrm{g}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l}$	0.015 **	0.038 **	0.036 **	0.036 **	−0.000
	(0.01)	(0.02)	(0.02)	(0.02)	(0.00)
$\mathrm{l}\mathrm{n}\mathrm{a}\mathrm{q}\_\mathrm{f}\mathrm{u}\mathrm{n}\mathrm{d}\mathrm{s}$			−0.000	−0.000	−0.000
			(0.00)	(0.00)	(0.00)
$\mathrm{l}\mathrm{n}\mathrm{a}\mathrm{q}\_\mathrm{p}\mathrm{o}\mathrm{p}\mathrm{u}$			0.649 ***	0.653 ***	0.004
			(0.24)	(0.24)	(0.01)
$\mathrm{l}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}$			0.304 ***	0.307 ***	0.003
			(0.09)	(0.09)	(0.00)
$\mathrm{l}\mathrm{n}\mathrm{h}\mathrm{u}\mathrm{m}\mathrm{a}\mathrm{n}$			−1.843 ***	−1.864 ***	−0.021 *
			(0.41)	(0.41)	(0.01)
$\mathrm{l}\mathrm{n}\mathrm{p}\mathrm{g}\mathrm{d}\mathrm{p}$			0.751	0.759	0.007
			(0.91)	(0.91)	(0.02)
$\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t}$	0.946 ***	1.030 ***	7.618	7.719	1.105 ***
	(0.08)	(0.26)	(8.37)	(8.35)	(0.21)
Year	No	Yes	Yes	Yes	Yes
Province	No	Yes	Yes	Yes	Yes
N	117	117	117	117	117
R2	0.043	0.191	0.379	0.382	0.141

Note: Standard errors in parentheses.* p < 0.10, ** p < 0.05, *** p < 0.01. The following table is the same.

. According to the regression coefficients in column (1), the core explanatory variable digital economy policy ($\mathrm{d}\mathrm{i}\mathrm{g}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l}$) has a significant positive impact on the green total factor productivity in mariculture. According to the regression coefficients in columns (1) and (2), the marginal value of the variable digital economy policy ($\mathrm{d}\mathrm{i}\mathrm{g}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l}$) is 0.015, which means that if $\mathrm{d}\mathrm{i}\mathrm{g}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l}$ increases by one unit, the green total factor productivity in mariculture will increase by about 0.015. Column (2) is to control the year fixed-effect and province fixed-effect. The coefficient of the core explanatory variable ($\mathrm{d}\mathrm{i}\mathrm{g}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l}$) is still significantly positive at a 5% level, showing that the digital economy has a positive effect on the green total factor productivity in mariculture. Column (3) further controls other control variables, and the coefficient of the core explanatory variable ($\mathrm{d}\mathrm{i}\mathrm{g}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l}$) is still significantly positive, which verifies that our conclusion is robust and reliable. Digital economy policy has a positive impact on the green total factor productivity of mariculture by promoting technological progress, optimizing resource allocation, improving the management level, and promoting sustainable development [7,8]. Digital economy policy drives technological progress in mariculture by supporting the development and application of digital technologies [43]. For example, policies can encourage enterprises to increase investment in digital technologies such as the Internet of Things, big data, and artificial intelligence, improve the refinement and intelligence level of mariculture, and reduce the extensive development model of traditional mariculture, to improve green total factor productivity. The digital economy policy optimizes the resource allocation of mariculture by promoting the development of the digital economy. Policies could encourage enterprises to use e-commerce platforms to expand sales channels and improve product visibility and competitiveness so as to achieve optimal allocation of resources and improve the total factor productivity of mariculture [31,44]. The digital economy policy can also improve the management level of mariculture enterprises by promoting the digital management model. Policies can encourage enterprises to adopt Internet of Things technology and big data analysis to achieve remote monitoring and intelligent management, thereby improving the management efficiency and management level of enterprises and improving the green total factor productivity of mariculture. In addition, digital economy policies promote the sustainable development of mariculture by supporting the development of digital environmental technologies. Enterprises adopt energy-saving and emission-reducing digital environmental protection technologies, reducing environmental pollution and resource consumption, to achieve the coordination and unification of economic and ecological benefits and improve green total factor productivity [45].

Columns (4) and (5) in Table 7 examine the impact of the digital economy policy on technological efficiency and technological progress in mariculture, respectively. As can be seen from the regression results of columns (4) and (5), the digital economy policy ($\mathrm{d}\mathrm{i}\mathrm{g}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l}$) has a significant positive impact on technological efficiency ($\mathrm{t}\mathrm{f}\mathrm{p}\mathrm{c}\mathrm{h}$), while it has no significant impact on technological progress ($\mathrm{t}\mathrm{e}\mathrm{c}\mathrm{h}$). Therefore, the digital economy policy mainly promotes green total factor productivity growth in mariculture by improving technological efficiency.

From the perspective of other control variables, according to the results of column (3) in Table 7, aquatic technology extension funds ($\mathrm{l}\mathrm{n}\mathrm{a}\mathrm{q}\_\mathrm{f}\mathrm{u}\mathrm{n}\mathrm{d}\mathrm{s}$) have no significant impact on the green total factor productivity in Chinese mariculture. There is a significant positive correlation between aquatic technology extension intensity ($\mathrm{l}\mathrm{n}\mathrm{a}\mathrm{q}\_\mathrm{p}\mathrm{o}\mathrm{p}\mathrm{u}$) and green total factor productivity in mariculture, i.e., the higher the aquatic technology extension intensity, the higher the green total factor productivity level in mariculture. Aquatic technology promotion promotes green total factor productivity in mariculture by improving the technical efficiency of mariculture as seen in column (4). The coefficient of fishers’ income ($\mathrm{l}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}$) is significantly positive at the 1% level, which means that the increase in fishers’ income can significantly improve the green total factor productivity in Chinese mariculture. The reason is that increasing fishers’ income can make it easier for them to purchase high-quality fish fry and production resources and introduce new aquaculture technology and equipment, promoting an improvement in green total factor productivity in mariculture. There is a significant negative correlation between the level of fishery human capital ($\mathrm{l}\mathrm{n}\mathrm{h}\mathrm{u}\mathrm{m}\mathrm{a}\mathrm{n}$) and the green total factor productivity in mariculture. This may be because the professional and technical personnel working in the mariculture industry have not deeply integrated research and development technology with the practice of mariculture and have not given full play to the positive spillover effect of knowledge [23]. Regional economic development level ($\mathrm{l}\mathrm{n}\mathrm{p}\mathrm{g}\mathrm{d}\mathrm{p}$) has no significant impact on green total factor productivity in mariculture. In summation, aquatic technology promotion and fishers’ income have significant positive effects on the green total factor productivity in Chinese mariculture. Therefore, policies should focus more on strengthening aquatic technology promotion and increasing fishers’ income to maximize the positive spillover effect. Moreover, it is of great practical significance for China to enhance the green total factor productivity in mariculture by encouraging fishery technical experts to guide and serve mariculture, deeply integrating knowledge and technology into mariculture practice, and serving the development of fisheries.

4.2.3. Heterogeneity Analysis

The previous paper discussed the effect of the digital economy policy on the green total factor productivity in Chinese mariculture, but the effect may be different in different provinces. We further introduce heterogeneity analysis based on Equation (3). Provinces are divided into categories: the Bohai Rim economic circle, Yellow Sea and East Sea economic circle, and South Sea economic circle. From

Table 8. Heterogeneity results based on different sea economic circles ¹.

Variables	(1)	(2)	(3)
	$\mathbf{t}\mathbf{e}\mathbf{c}\mathbf{c}\mathbf{h}$	$\mathbf{t}\mathbf{e}\mathbf{c}\mathbf{c}\mathbf{h}$	$\mathbf{t}\mathbf{e}\mathbf{c}\mathbf{c}\mathbf{h}$
	The Bohai Rim Economic Circle in China	Yellow Sea and East Sea Economic Circle in China	South Sea Economic Circle in China
$\mathrm{d}\mathrm{i}\mathrm{g}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l}$	0.046 *	0.001 **	−0.000
	(0.03)	(0.00)	(0.00)
$\mathrm{l}\mathrm{n}\mathrm{a}\mathrm{q}\_\mathrm{f}\mathrm{u}\mathrm{n}\mathrm{d}\mathrm{s}$	0.000	−0.000	−0.000
	(0.00)	(0.00)	(0.00)
$\mathrm{l}\mathrm{n}\mathrm{a}\mathrm{q}\_\mathrm{p}\mathrm{o}\mathrm{p}\mathrm{u}$	0.687	−0.042 ***	−0.002
	(0.52)	(0.01)	(0.00)
$\mathrm{l}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}$	0.127	−0.007	0.003
	(0.16)	(0.01)	(0.00)
$\mathrm{l}\mathrm{n}\mathrm{h}\mathrm{u}\mathrm{m}\mathrm{a}\mathrm{n}$	−0.271 *	−0.033 ***	−0.008
	(0.15)	(0.01)	(0.01)
$\mathrm{l}\mathrm{n}\mathrm{p}\mathrm{g}\mathrm{d}\mathrm{p}$	0.174	−0.013	0.006
	(0.74)	(0.01)	(0.01)
$\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t}$	−3.601	1.917 ***	1.031 ***
	(10.03)	(0.20)	(0.10)
Year	No	No	No
Province	No	No	No
N	45	25	47
R2	0.210	0.731	0.062

Note: Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. The following table is the same. ¹ The Bohai Rim Economic Circle in China includes Liaoning Province, Tianjin City, and Hebei Province. The Yellow Sea and East Sea Economic Circle in China encompasses Jiangsu Province, Zhejiang Province, Fujian Province, and Shandong Province. The South Sea Economic Circle in China comprises Guangdong Province, Guangxi Province, and Hainan Province.

, we can see that digital economy policies have a positive effect on the green total factor productivity in mariculture in the Bohai Rim economic circle and the Yellow Sea and East China Sea economic circle. Digital economy policy has no significant effect on the green total factor productivity in mariculture in the South China Sea economic circle. Moreover, the effect is more noticeable in the Bohai Rim economic circle than in the Yellow Sea and East China Sea economic circle.

All in all, the digital economy policy has a significant positive effect on the green total factor productivity of mariculture. The conclusions of this paper provide a theoretical basis and practical guidance for promoting the sustainable development of mariculture whilst considering the background of the current digital economy development strategy. At present, there are relatively few studies on the impact of digital economy policy on the green total factor productivity of mariculture. Due to the particularity of the digital economy policy and the mariculture industry, the relevant data collection and processing methods also present certain difficulties and challenges. Therefore, in the future, it is necessary to further explore the calculation of the policy intensity of digital economy and the green total factor productivity of mariculture, improve relevant research methods, data collection, and processing methods, and clarify the micro-mechanism of digital economy affecting the green total factor productivity of mariculture.

5. Conclusions and Policy Recommendations

5.1. Conclusions

This study uses the SBM-GML model to measure the green total factor productivity in the Chinese mariculture industry, considering undesired outputs. The results show that in recent years, the total factor productivity in the Chinese mariculture industry has fluctuated and increased. From the perspective of regional heterogeneity, green total factor productivity in most regions of China shows an overall upward trend. The study further decomposes the green total factor productivity in Chinese mariculture into the technical efficiency index and technical progress index. According to the average cumulative values of the different decomposition indexes during the sample period, the cumulative change in the technical efficiency index in each region is greater than that of technological progress. In general, the range of change in technical efficiency is greater than that of technological progress, which means that the improvement of green total factor productivity in the Chinese mariculture industry mainly depends on technical efficiency.

The paper further uses a panel data regression model to empirically test the influence of digital economy policies on the green total factor productivity in the Chinese mariculture industry. The results show that the digital economy policies enhance green total factor productivity in the mariculture industry by improving technical efficiency. Both aquatic technology promotion and fishers’ income positively impact green total factor productivity in aquaculture. Moreover, digital economy policies affect green total factor productivity in aquaculture sectors of the Bohai Rim economic circle and the Yellow Sea and East China Sea economic circle but have no significant effect on the South China Sea economic circle.

5.2. Policy Recommendations

Based on the above research findings, this paper puts forward the following policy recommendations.

Firstly, the government should attach great importance to the application of digital technology in the marine aquaculture industry. The government focuses on promoting the digital economy, accelerating the development of digital technology, promoting the application and popularization of digital technology in the marine aquaculture industry, and assisting in the upgrading of the fishing industry. Simultaneously, it promotes the integration of digital economies in various regions, breaks through regional barriers, and eliminates vested interests and policy obstacles.

Secondly, the government should enhance the innovation and popularization of marine aquaculture technology. It should focus on implementing key technologies, raise the international level of technology, and promote the comprehensive development of marine aquaculture technology. Specialized technical promotion teams should be established, technical training for aquaculture professionals should be strengthened, and marine ecological protection should be emphasized, and the government should actively advocate for ecological aquaculture modes.

Thirdly, the government should expedite the development of marine information technology infrastructure to promote the digital transformation of the marine aquaculture industry. It should focus on infrastructure construction, invest in new types of infrastructure such as artificial intelligence and the Internet of Things, establish a public service platform for the marine sector, and optimize resource flow. It should also utilize marine information technology to protect and develop marine resources, upgrade the marine aquaculture industry chain, cultivate professional talents, and promote high-quality development of the marine economy.

6. Limitations and Further Research

This study has several limitations that need to be addressed. First, the study encounters challenges due to the absence of a standardized definition for the digital economy, including concepts like industrial digitization and digital industrialization. Consequently, the current measurement system utilized in this research lacks the capacity to comprehensively and accurately represent digital economic policies and the development of the digital economy. Recommendations for future research include investigating the digital economy from various perspectives and analyzing its impact on the green total factor productivity of marine aquaculture. Secondly, due to constraints related to data availability, this study concentrated on the provincial level to evaluate the green development status, spatial variations, and dynamic changes in marine aquaculture across China. While the provinces are considered in this analysis, it is essential to recognize that cities and counties serve as specific units for implementing agricultural policies. Exploring the green development of marine aquaculture from the viewpoint of cities and counties can enhance the adaptation to local agricultural production conditions and environmental features, facilitating the development of more practical green policies and actions. Future research should aim to delve into green development studies at the city and county levels. Lastly, this research solely employed provinces as the research units and did not account for the spatial spillover effects of the digital economy. Future studies should focus on exploring related studies on spatial effects.