Assessment and Spatio-Temporal Evolution of Marine Fisheries’ Carbon Sink Capacity in China’s Three Marine Economic Circles

Abstract

With the increasing pressure of resource decline and environmental pollution faced by the green transformation of marine fisheries, marine fishery carbon sinks are an increasingly close link to national strategic interests and economic lifelines. It is, therefore, necessary to explore paths for the enhancement and development of the carbon sink capacity of marine fisheries. Based on the economic data of marine fisheries from 2002 to 2021, this paper measures the capacity and characteristics of marine fishery carbon sinks in provincial areas, applies the kernel density estimation method to depict the dynamic evolution of the distribution of absolute differences in marine fishery carbon sinks, and at the same time, identifies the factors influencing the spatial imbalance in the development of marine fishery carbon sinks as well as the spatial spillover effects. The results show the following: (1) From the total amount, the fishery carbon sink capacity of China’s three marine economic circles has fluctuated and increased over the past 20 years, with obvious differences among different circles, and the marine fishery carbon sink capacity of the eastern, northern, and southern marine economic circles has risen sequentially. This trend corresponds to the economic disparities among the three circles, demonstrating a transition from economic to industrial driving effects. (2) From the spatial point of view, the fishery carbon sink capacity shows a trend of increasing year by year, concentrating in the circles, and overflowing between the circles. (3) The results of a partial derivative test further show that among the main influencing factors of marine fishery carbon sinks, capital, labor, and market openness have direct positive effects on marine fishery carbon sinks, and the direct effect of innovation is the strongest, and the significance of the indirect effect of the three circles is weaker compared to the direct effect and total effect. As a result, the carbon sink of China’s marine fisheries has a large room for improvement, and in order to promote the high-quality development of China’s marine fisheries, it is still necessary to accelerate the circulation of resources within the marine economic circle, accelerate the research of fishery technology, actively develop the carbon sink fishery, and enhance the specialization of aquatic product processing.

1. Introduction

At the 75th session of the United Nations General Assembly, China pledged to strive to achieve carbon peaking by 2030 and carbon neutrality by 2060 [1], a goal that promotes a major transformation of China’s energy structure and industrial layout, which is of great significance for a country like China that combines land and sea [2,3]. The ocean is the largest reservoir of activated carbon [4]. The carbon captured and stored by the world’s oceanic and coastal ecosystems is called blue carbon, including mangroves, seagrasses, salt marshes, and other marine environments [5]. Mangrove and seagrass constitute a part of the blue carbon sink, which is larger than most of the terrestrial ecosystems [6], whereas shellfish carbon sinks are realized with less energy input, lower costs, and long carbon storage capacity. It is estimated that a total of 5.64 Gt CO₂-eq, accounting for 17.63% of the total emissions in 2020, can be potentially sequestrated at the global scale under the world’s largest farming area scenario [7]. The growth processes of macroalgae and microalgae involve carbon absorption, and they possess substantial carbon sink potential.

And, the “aquaculture-based” nature of China’s marine fisheries makes it one of the most controllable and sustainable economic activities for marine carbon reduction [8]. The aquaculture of shellfish and algae can convert inorganic carbon in the atmosphere and the ocean into organic matter, and at the same time, enhance the ecosystem’s capacity for carbon sink and storage, which is a realistic, efficient, and valuable carbon sink potential.

The outline of China’s Belt and Road “14th Five−Year Plan” proposes to “build a number of high-quality marine economic development demonstration circles and specialized marine industry clusters, and comprehensively improve the level of development of the three marine economic circles in the north, east and south of the country”. Li et al. (2020) [9] found that economic development in China’s three marine economic circles exhibits significant hierarchy and spatial imbalance, with regional disparities increasing. Given the economic differences among the three circles, this paper seeks to further analyze the green and low-carbon transformation of marine fisheries. Specifically, this paper first summarizes the role and formation mechanisms of carbon sinks in marine fisheries. Subsequently, it examines the carbon sink capacity of marine fisheries and its spatio-temporal evolution. Finally, through calculations and analysis, the potential and changing patterns of carbon sinks in marine fisheries are elucidated, aiming to provide pathways for the sustainable development of China’s marine fisheries and contribute to global carbon reduction goals. Three marine fishery carbon sinks have significant positive impacts on the marine economy and the atmospheric environment. Marine fisheries and fishery carbon sinks could not only support a macro-food perspective [8,10] and sustain human livelihoods [11], but are also important in regulating atmospheric carbon dioxide levels [12,13]. Coastal, offshore, and deep-sea fishing, as well as rough and intensive aquaculture practices, generate a certain carbon footprint, and the GHG emissions from fishery activities can be reduced by measuring and optimizing carbon emission reductions. Dagtekin et al. [14] and Feng et al. [15] developed a model for assessing the capacity of marine carbon emission reductions based on the coastal ecosystems, marine fisheries, marine energy, marine transportation, and seabed conditions (OSCRAM), which calculated that China’s marine carbon emission reduction capacity will rise from the current 6.86 Tg to 139.39 Tg in 2030, and among them, marine fisheries have a huge carbon reduction potential. Small pelagic fish affect the fishery carbon sink as well as atmospheric carbon dioxide levels throughout the oceans by feeding on plankton and primary carbon-based organisms, and discharging fecal particles [16]; unlike most terrestrial deaths, which release carbon into the atmosphere, large oceanic fish sink and complete the process of carbon sink in the deep ocean after death [17]. The carbon sink process of marine fisheries is shown in Figure 1. In addition, given that catch exports and fishing are concentrated in areas with 7 percent of the sea area [18], it is urgent to address the pathways through which fisheries affect the carbon cycle and to set policy goals to protect the fishery carbon sink for areas with high fishing intensity, carbon export, and storage in marine fisheries.

Carbon sinks in marine fisheries are affected by a variety of factors. Zheng et al. (2022) [19] found that ecological subsidies given by the government can positively affect fishery carbon sinks from both the production and consumption sides. Zhang et al. (2020) [20] argued that fishery science and technology, income level, total consumption, and carbon sink capacity have a negative spillover effect on carbon sink fisheries in neighboring regions, and the production structure has a positive spatial spillover effect, and proposed that spatial planning for the development of oceanic carbon sink fisheries should be formulated from the perspective of regional relevance.

Carbon sinks in marine fisheries are of crucial research significance for fishery-related industrialization and spatial linkage development [21,22,23,24]. Compared with the non-carbon sink fishery sector, the carbon sink fishery sector has economic spillover effects on the upstream aquatic seedling industry and the downstream aquatic product processing industry, and carbon sink fishery has a positive spillover effect on the marine fishery economy (Xu et al., 2020) [25]. Yang et al. (2021) [26] argued that the large-scale cultivation of macroalgae can form industrialized fishery carbon sinks in the adjacent sea area, and can also solve marine environmental problems such as ocean acidification, hypoxia, eutrophication, harmful algal blooms, and so on [27,28,29]. Epstein et al. [30] analyzed the mechanism of carbon storage in Indian fisheries and concluded that there are obvious regional differences in the carbon sinks of marine fisheries, but it may be due to the infrastructure of the fishery industry, employment density, policy tendency, and other factors, which lead to the existence of a kind of inter-regional mutual promotion or constraints.

Regarding the measurement of carbon sequestration in marine fisheries, Jiao et al. (2024) [31] utilized remote sensing imagery from the Bohai Rim region and applied the Support Vector Machine (SVM) classification method to classify and extract four types of coastal aquaculture areas, calculating the carbon sink capacity of coastal aquaculture fisheries. This method, which combines a 3D marine model, remote sensing data, and field observations to comprehensively assess marine fishery carbon sequestration, is more precise. However, the limited availability of data restricts the range of fishery carbon sequestration that can be measured using this method. Another estimation method calculates fishery carbon sink based on fishery yield, considering the fishery output itself as a removed carbon sink. It calculates the carbon fixed by fisheries from the atmosphere and water bodies using fishery yield, carbon content ratio, and dry matter ratio [32]. This method is relatively simple and intuitive, allows for measurements on a larger scale, and is easily applicable. However, it requires that the data for calculating fishery carbon sink be obtained through scientific research or experiments, which also demands higher standards of scientific rigor and accuracy for the data.

To summarize, experts and scholars at home and abroad have conducted a large number of studies on the capacity and potential of marine fishery carbon sinks, the influencing factors of marine fishery carbon sinks, and the impact of marine fishery carbon sinks on the fishery industry chain and spatial linkages, which have provided useful references for the study of such issues, but there is still room for further research: (1) The method of measuring the carbon sinks of marine fisheries needs to be improved. When measuring the carbon sinks of marine fisheries, studies have used the same approximate carbon mass fraction for different species of aquatic products, and the measured carbon sinks are too rough; the second is to consider only the carbon content retained in the organisms [28], ignoring the carbon left behind and excreted by shellfish and algae organisms in the course of their lives, which should be part of the carbon sinks of marine fisheries. (2) Few perspectives have paid attention to the spatial connectivity of the marine fishery carbon sinks. Since the study of marine fishery carbon sinks by Sun Jun [33], experts and scholars have mainly focused on the measurement of marine fishery carbon sinks [34], the impact of fishery carbon sinks, and the green efficiency of carbon sink fisheries [25]. Few studies have been carried out on the spatial linkage of marine fishery carbon sinks, and there is no realistic path to enhance the performance of marine fishery carbon sinks by utilizing this feature. In this paper, based on analyzing the actual characteristics of marine fishery carbon sinks, conduction paths, and the mechanism of spatial linkage, we utilize the panel data of nine coastal provinces in China from 2002 to 2021 to improve the accounting method of marine fishery carbon sinks. According to the latest carbon sink accounting methods, more scientific and accurate POC (particulate organic carbon) and DOC (dissolved organic carbon) indices are used to update marine fisheries data for calculating the marine fishery carbon sink, and the spatial measurement model is used to empirically study the capacity, influencing factors, and spatial effects of marine fishery carbon sinks. It can provide theoretical support and decision-making reference for realizing the “dual-carbon target” of marine fisheries and effectively solving the dilemma of the green development of marine fisheries.

2. Methodology and Data

2.1. Shellfish Carbon Sink Measurements

Marine fishery macroalgae and shellfish are the main carbon sinks [35], and in 2021, China’s marine aquaculture production reached 22,756,987 tons, of which 15,695,844 tons was shellfish and 2,713,914 tons was algae, with the total of the two accounting for 80.9% of the total. Algae directly absorb CO₂ in the atmosphere through photosynthesis, and filter-feeding shellfish fix carbon into shells and soft tissues by assimilating phytoplankton, completing the process of carbon sink. Therefore, this paper mainly uses the carbohydrates absorbed by algae and shellfish into organisms and calcium compounds in other tissues to measure the carbon sink of marine fisheries, and the accounting formula is as follows:

$$C=C_{ma}+C_{sh}$$

(1)

where $C$ is the carbon sink capacity of marine fisheries in China, $C_{ma}$ is the carbon sink capacity of macroalgae, and $C_{sh}$ is the carbon sink capacity of shellfish.

2.2. Algal Carbon Sink Estimation

The formula for accounting for the carbon sink capacity of macroalgae is as follows:

$$C_{ma}=C_{mas}+C_{map}$$

(2)

where $C_{mas}$ is the carbon sink capacity of macroalgal sediments in grams per year (g/a), a measure of sedimentary debris, and $C_{map}$ is the carbon sink capacity of macroalgae in grams per year (g/a), a measure of carbon sinks in algal plants.

Macroalgae Sediment Carbon Sink Capacity ($C_{mas}$): the particulate organic carbon α and dissolved organic carbon β released during algal growth are estimated to be 19% and 5%, respectively, then the sediment carbon sink capacity: $C_{mas}=C_{ma}(\alpha +\beta )$.

Carbon sink capacity of macroalgae ($C_{map}$):

The carbon sink capacity of macroalgae ($C_{map}$) is calculated according to the carbon sink measurements of the cultured macroalgae and bivalve shellfish by the Ministry of Natural Resources of China (MNR) in 2021, in which the carbon immobilized by algae in the plant body is counted as the carbon sink of the algae, and the carbon ratio of each of the nine types of algae is listed below. Since the published weights of algal products are wet weights, the following table also shows the wet/dry ratios of each type of algae. The carbon sink capacity of algae = wet weight of algae × dry weight/wet weight conversion factor × carbon content ratio. The carbon ratios of algal plants are shown in

Table 1. Carbon ratios of algal plants.

Species	Dry Weight/Wet Weight (%)	Carbon Content (%)
Kelp	20	31.2
Brasenia schreberi	20	27.1
Tico Gracilaria	20	28.4
Striped seaweed	20	41.96
Watercress	20	31.93
Wakame	20	28.81
Stonewort	20	26.37
Sagittaria	20	30.97
Others	20	30.36

.

2.3. Shellfish Carbon Sink Estimation

The formula for accounting for shellfish carbon sink capacity is as follows:

$$C_{sh}=C_{shs}+{\displaystyle \sum (C{B}_j^{sh}+C{Z}_j^{sh})}$$

(3)

where $C_{shs}$ is the carbon sink capacity of shellfish sediments in grams per year (g/a), ${CB}_j^{sh}$ is the jth shellfish shell carbon sink capacity in grams per year (g/a), and $C{Z}_j^{sh}$ is the carbon sink capacity of the molluscan soft tissue in grams per year (g/a). The carbon sink capacity of shellfish shell is as follows:

$$C{B}_i^{sh }=P_j^{sh}\times K_j^{sh}\times R_j^{sh}\times C{F}_j^{sh1}$$

(4)

where $P_j^{sh}$ is the biomass (wet weight) of shellfish species j in grams per year (g/a); $K_j^{sh}$ is the conversion factor between wet weight and dry weight of shellfish species j, dimensionless; and $C{F}_j^{sh1}$ is the ratio of carbon content in the dry mass of shells of shellfish species j, dimensionless.

The carbon sink capacity of molluscan soft tissue is as follows:

$$C{Z}_j^{sh}=P_j^{sh}\times K_j^{sh}\times R_j^{sh2}\times C{F}_j^{sh2}$$

(5)

where $R_j^{sh2}$ is the percentage of molluscan soft tissue mass in the dry weight state of the jth shellfish, dimensionless; $C{F}_j^{sh2}$ is the carbon content ratio in the dry mass of the molluscan soft tissue of the jth shellfish, dimensionless. The dry weight/wet weight, percentage of weight and percentage of carbon of various shellfish is shown in

Table 2. Carbon ratios of shellfish.

Species	Dry Weight/Wet Weight (%)	Percentage of Weight/(%)		Percentage of Carbon/(%)
		Molluscan Soft Tissue	Shell	Molluscan Soft Tissue	Shell
Clams	52.55	1.98	98.02	44.9	11.52
Scallop	63.89	14.35	85.65	42.84	11.4
Oyster	65.1	6.14	93.86	45.98	12.68
Mussels	75.28	8.47	91.53	44.4	11.76
Others	64.21	11.41	88.59	42.82	11.45

.

2.4. Research Methodology

2.4.1. Kernel Density Estimation

Kernel density estimation is an important tool for studying spatial disequilibrium. By using a continuous smooth kernel function to intuitively characterize the spatial distribution of random variables, reflecting the distribution of the object of study, such as the location of the distribution, morphology, and planning features and other dynamic information, it is both robust and can reveal the dynamic evolution of the distribution of the marine fisheries’ carbon sink capacity of the three marine economic circles in the spatial pattern. The basic principle is to use a smooth kernel function to fit the measurements to obtain the density curve, and then analyze the dynamic changes in the measurements based on the density curve. The kernel function has various functional forms, and the less the grouped data, the greater the probability of choosing the Gaussian kernel function [31], therefore, this paper adopts the Gaussian kernel function to estimate the carbon sinks of the marine fisheries in the three marine economic circles.

$$f(x)={\displaystyle \frac{1}{nh}}{\displaystyle \sum _{i=1}^{n}K\left[\frac{X_i-x}{h}\right]}$$

(6)

where n represents the sample size, which is the three marine economic circles; h is the bandwidth for density estimation; K is the random kernel function, where h > 0, acting as the smoothing parameter, also known as the bandwidth. The larger the bandwidth, the smoother the density estimation yields, and with a larger bias.

2.4.2. Spatial Econometric Modeling

This paper introduces the Moran’s index to test for potential spatial autocorrelation in marine fishery carbon sequestration. The formula for calculating the global Moran’s index (I) is as follows:

$$I={\displaystyle \frac{{\displaystyle \sum _{j=1}^n{W}_{ij}(X_i-\overline{X}})(X_j-\overline{X})}{S^2{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{W}_{ij}}}}}$$

(7)

where $S^2={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n{(X_i-\overline{X})}^2,}\overline{X}={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n{X}_i,}$ n represents the number of sample regions, X_i denotes the marine fishery carbon sink of region i, and W_ij is the spatial weight. Moran’s index ranges between [−1,1]. If Moran’s index is close to −1, it indicates a strong negative spatial autocorrelation in marine fishery carbon sequestration levels among regions. Conversely, if Moran’s index is close to 1, it suggests a strong positive spatial autocorrelation in marine fishery carbon sequestration levels among regions.

Spatial econometric models are frequently used to study economic issues related to the ocean and fisheries. Zheng et al. (2024) [34] utilized spatial econometric methods to examine the spatial spillover effects of environmental and economic policies on regional fisheries outputs, revealing complex interdependencies and significant spatial autocorrelation across different marine economic circles. In the theory of spatial economics, the spatial dependence between spatial economic units is mainly manifested through three kinds of differential spatial interaction effects, which are as follows: endogenous interaction effects between the explanatory variables of different spatial economic units, exogenous interaction effects between the explanatory variables of an independent spatial economic unit and the explanatory variables of another spatial economic unit, and interaction effects between the error terms of different spatial economic units. According to the above principles, the classical spatial econometric model is mainly divided into the spatial Durbin model, the spatial error model, and the spatial lag model, and the mathematical expressions are as follows:

$$Y=\rho WY+X\beta +\epsilon ,\epsilon ~N(0,{\sigma }^2{I}_n)$$

(8)

$$Y=X\beta +\mu ,\mu =\lambda W\mu +\epsilon ,\epsilon ~N(0,{\sigma }^2{I}_n)$$

(9)

$$Y=\rho W_{1}Y+X\beta +W_{2}X\theta +\epsilon ,\epsilon ~N(0,{\sigma }^2{I}_n)$$

(10)

where Y and X are the matrices of the explanatory variables and unobservable variables, respectively; W is the spatial weight matrix; $\rho$ is the spatial autoregressive coefficient, which is used to measure the degree of influence of neighboring explanatory variables on local explanatory variables; λ is the spatial error coefficient, which is used to measure the degree of influence of omitted explanatory variables or unobservable variables on local explanatory variables; θ is the degree of the degree of influence of the explanatory variables in the surrounding area on the local explanatory variables; the parameter β reflects the influence of the explanatory variables on the unobservable variables; μ is the random error perturbation term; ε is the vector of random perturbation terms with mean 0 and variance σ² obeying the normal distribution; and I_n denotes the nth-order unit matrix.

In spatial econometrics, the spatial weight W describes and identifies the network association structure between the spatial units under study, and the selection of W will greatly affect the results of model estimation. The selection of W based on geographic spatial distance is the most commonly used basis for selection, but in the increasingly close economic ties between regions now, it is inevitable to consider only geo-neighborhood relations are biased, so the setting of spatial weight matrix in this paper will be from the perspective of the economic significance of the assumption that the spatial units with similar levels of economic and social development have a strong spatial interaction effect among the spatial units and can be based on the level of economic development between the spatial units to set the spatial weight matrix:

$$W_{ij}=\left\{\begin{array}{l}{\displaystyle \frac{1}{\left|gd{\stackrel{⌢}{p}}_i-gd{\stackrel{⌢}{p}}_j\right|}},\begin{array}{c}\end{array}i\ne j\\ \hspace{1em}\hspace{1em}\hspace{1em}0,\hspace{1em}\hspace{1em}\hspace{1em}i=j\end{array}\right.$$

(11)

where W_ij is the weight assigned to the spatial unit and the spatial unit, gdp_i is the average per capita GDP of the spatial unit in the sample examination period (CNY 10,000); gdp_j is the average per capita GDP of the spatial unit in the sample examination period (CNY 10,000).

The development level of carbon sink of marine fisheries is affected by many factors; according to Cobb/Douglas production theory, this paper selects the following indicators as the influencing factors of the development of marine fisheries: (1) capital (cap), expressed by the amount of investment in fixed assets of marine fisheries; (2) labor force (lab), expressed by the number of fishery-related employees; (3) innovation drive (inno), expressed by the amount of technology turnover of the marine fisheries per capita; (4) environmental regulation (env), using the entropy value method through the industrial wastewater pollution control investment, industrial exhaust gas pollution control investment, and industrial solid waste pollution control investment to calculate the sum of the environmental regulation index; (5) the degree of marketization (mar), using the market index compiled by the National Bureau of Statistics to express; and (6) opening up (open), using the total amount of import and export trade of aquatic products to express. trade total to express.

2.5. Data Sources

This paper takes the three marine economic circles as spatial units, and the data are detailed at the provincial level. There are 11 provincial administrative units along the coast of China’s mainland, and since the mariculture activities in Tianjin and Shanghai are relatively small, they are not of research significance to the issue of marine fishery carbon sinks in this paper, so only the northern marine economic circle: Liaoning, Hebei, and Shandong provinces; the eastern marine economic circle: Jiangsu and Zhejiang provinces; and the southern marine economic circle: Fujian, Guangdong, Guangxi Zhuang Autonomous Region, and Hainan Province, a total of nine provinces (autonomous regions are used as the research object. Autonomous Region) are taken as the object of study. The data used in this paper come from China Fisheries Statistical Yearbook, China Marine Statistical Yearbook, Marine Environmental Quality Bulletin, and EPS database from 2002 to 2021, and also refer to China Marine Economy Statistical Bulletin.

3. Analysis of the Measurement Results of Marine Fishery Carbon Sink in China

3.1. Carbon Sinks of Marine Fisheries by Province

As can be seen from the results of the calculation of the marine carbon sinks in

Table 3. Marine fishery carbon sinks by province.

	Hebei	Liaoning	Jiangsu	Zhejiang	Fujian	Shandong	Guangdong	Guangxi	Hainan
2002	12,551.77	113,496.45	18,791.70	55,807.54	230,193.29	364,997.58	264,567.12	59,581.60	70,411.84
2003	16,997.08	122,810.97	24,430.03	71,750.05	256,477.61	246,446.88	246,446.88	63,148.17	65,638.84
2004	19,946.91	133,061.64	32,064.76	66,042.24	264,337.59	251,053.27	261,023.22	63,653.32	61,258.08
2005	23,671.50	131,842.53	31,691.32	65,369.75	268,569.81	264,049.77	284,033.45	65,402.52	55,935.78
2006	19,560.42	109,984.18	30,606.06	64,193.81	234,482.96	270,079.68	270,049.68	52,053.40	50,072.86
2007	18,635.40	118,217.79	36,404.67	62,414.00	237,298.62	249,470.37	249,430.33	49,213.53	46,146.02
2008	23,735.36	121,911.03	40,542.14	57,307.92	250,197.79	252,827.79	212,827.74	51,677.47	42,730.06
2009	27,872.85	158,600.05	38,698.05	60,939.78	252,019.60	276,750.59	226,750.52	56,554.87	39,834.07
2010	26,294.46	144,772.10	39,721.40	60,913.37	269,854.46	280,052.48	310,052.42	58,674.15	36,832.28
2011	33,029.44	157,527.93	38,798.49	63,201.85	282,878.11	291,960.84	311,960.83	61,229.31	33,931.37
2012	40,182.19	170,050.47	39,606.56	66,559.61	301,418.34	311,661.38	381,661.22	65,837.93	30,959.09
2013	44,075.68	177,296.61	38,882.69	71,722.59	321,327.49	335,975.43	345,975.42	67,336.30	27,883.51
2014	44,950.36	182,008.37	36,029.31	74,592.06	339,344.91	375,109.01	275,109.11	71,114.01	24,660.25
2015	44,965.50	191,221.17	35,383.88	82,430.59	363,858.17	392,295.42	402,295.22	74,865.51	21,504.56
2016	46,540.83	194,198.28	37,777.97	94,998.91	377,913.03	400,210.86	410,210.87	79,663.82	18,125.83
2017	42,315.19	192,791.23	37,780.49	97,648.63	408,621.69	411,402.22	391,402.23	84,087.16	13,965.42
2018	34,838.00	200,202.52	38,347.26	102,785.97	437,621.27	405,961.25	395,961.27	86,702.74	10,294.09
2019	37,264.14	202,397.62	39,988.85	110,013.98	456,470.58	381,755.63	341,755.36	88,853.17	10,508.10
2020	41,590.84	216,555.44	39,458.65	111,268.25	474,651.13	396,768.14	406,768.28	96,972.26	10,488.77
2021	38,056.87	187,288.65	33,284.11	967,46.29	435,509.62	415,273.51	365,273.15	77,417.23	10,847.42

Values in t/ton.

, the carbon sinks of marine fisheries in China’s provinces have been on an upward trend in the past 20 years. Among them, the marine fishery carbon sink of Fujian Province has always been at the highest level, and has been steadily rising, and the Hainan marine fishery carbon sink is at the lowest level, and the change is relatively gentle; this development trend corresponds to the endowment of shellfish and algae resources in the two provinces and the current situation of aquaculture. The carbon sink of marine fisheries in Shandong is at the second highest level, with a significant increase in 2013, and since then it has opened a clear gap with Liaoning, which is at the third highest level, but there is a sudden and significant decline in 2021—probably due to the fact that aquaculture in Shandong is mainly based on shellfish (79.28%) and algae (14.02%) (2021)—and the area of aquaculture in Shandong is mainly based on shellfish (79.28%) and algae (14.02%) (2021), and the area of aquaculture in Shandong, due to the layout planning policy that has been implemented since 2016, is showing a continuous downward trend; the growth rate of aquaculture area in Shandong in 2020 has declined by 1.9% year-on-year, and more than half of the provinces have seen a decline in marine fishery carbon sinks by 2021, which is reflected in the imbalance between the quantitative scale of marine fisheries and the quality and efficiency of the fishery industry, and the lack of development, which urgently needs to play a better role in the guidance of the government, and to continue to optimize and adjust the relevant support policies for the fishery industry development.

3.2. Carbon Sinks of Marine Fisheries in the Three Marine Economic Circles

Figure 2 shows the proportion of marine fishery carbon sinks in the three marine economic circles, and the marine fishery carbon sinks of the three marine economic circles have always shown the trend of southern > northern > eastern, with the fishery carbon sinks of the southern marine economic circle remaining between 43% and 48%, the marine fishery carbon sinks of the northern marine economic circle residing in the range of 39% to 44%, and the fishery carbon sinks of the eastern marine economic circle fluctuating between 9% and 14%.

After averaging three, two, and four provinces in the northern, eastern, and southern circles, respectively, the northern marine economic circle basically equaled the carbon sink per unit of the southern marine economic circle, while the eastern oceanic fishery’s carbon sink per individual province remained the lowest. The differences in the carbon sink capacity of marine fisheries among the southern, northern, and eastern marine economic circles may be attributed to the varying levels of development, dependence on marine fisheries, and the emphasis placed on low-carbon transitions. The southern marine economic circle benefits from richer natural conditions, more stringent environmental regulations, and greater practices in green fisheries development. The northern marine economic circle follows, while the eastern marine economic circle, which prioritizes financial and economic development, has less focus on achieving marine fishery carbon sinks. Provincial average fishery carbon sinks in the three marine economic circles are shown in Figure 3.

4. Results

4.1. Spatial and Temporal Characteristics

In order to describe the time-varying evolution of the absolute difference in the ocean carbon sink capacity of the three marine economic circles, the kernel density estimation method is used here to characterize the distribution of the regional sample data, focusing on the distribution characteristics of the density curves, the distribution pattern of the main peaks, the distribution ductility, and the number of peaks.

The areas of marine fishery carbon sinks in the figure are as follows: top left—all the coastal areas, top right—northern marine economic circle, bottom left—eastern marine economic circle, and bottom right—southern marine economic circle.

As shown in Figure 4, firstly, from the distribution position, the kernel density curves of marine fishery carbon sinks in all the coastal regions, northern marine economic circle, eastern marine economic circle, and southern marine economic circle show an overall rightward shifting trend, which indicates that the level of marine fishery carbon sinks in all the coastal regions of China has been continuously improving, and that the concept of green development is prompting China’s marine fisheries to play a good role in carbon sinks, and the work of marine fishery carbon reduction has been achieved. Second, in terms of the characteristics of the peaks, the peaks of all the coastal regions, the eastern marine economic circle, and the southern marine economic circle showed a decreasing trend during the sample period, and the shape of the wave peaks gradually narrowed from broad peaks to sharp peaks, indicating that the gap between the carbon sinks of marine fisheries in each region is narrowing, and that the carbon sinks of marine fisheries among provinces within the region show a common development trend. Again, in terms of the number of wave peaks, all the coastal areas, the northern marine economic circle, and the southern marine economic circle have double peaks, and the eastern marine economic circle has only a single peak, indicating that the polarization phenomenon of the overall, northern, and southern marine economic circles is weakening, while the eastern marine economic circle does not have a clear polarization feature. Finally, from the distribution pattern, the sum density curve of all the coastal areas and the southern marine economic circle has an obvious characteristic of having a trailing tail, indicating that from the overall and the southern marine economic circle, the level of the carbon sink of marine fisheries varies greatly, with the phenomenon that individual provinces are far ahead of other provinces. The kernel density curve of the eastern marine economic circle does not have a trailing feature, indicating that the development level of the marine fishery carbon sinks in the eastern marine economic circle is more balanced. In summary, the level of marine fishery carbon sinks in China has been increasing, and coordinated development has been faster and the gap between regions has been narrowing. Kernel density trends are shown in

Table 4. Kernel density trends.

Area Name	Distribution Location	Distribution Pattern of the Main Peak	Distribution Ductility	Number of Peaks
all coastal	rightward shift	height decrease and width increase	right tail and extension	multiple
northern marine economic circle	rightward shift	height increase and width increase	left tail and extension	pair or multiple
eastern marine economic circle	rightward shift	height decrease and width increase	left tail and convergence with spreading	single
southern marine economic circle	rightward shift	height decrease and width increase	right tail and extension	pair

.

4.2. Regional Differences and Decomposition

The results of the global Moran’s index test for the examined variable, marine fishery carbon sink, using stata17 in this paper are shown in

Table 5. Spatial correlation of the development level of marine fishery carbon sinks.

Year	Moran’s I	p Value	Year	Moran’s I	p Value	Year	Moran’s I	p Value
2002	0.405	0.005	2009	0.486	0.002	2016	0.494	0.004
2003	0.456	0.003	2010	0.397	0.005	2017	0.426	0.007
2004	0.453	0.004	2011	0.542	0.004	2018	0.473	0.001
2005	0.470	0.002	2012	0.487	0.005	2019	0.582	0.002
2006	0.468	0.004	2013	0.429	0.006	2020	0.591	0.001
2007	0.406	0.001	2014	0.473	0.004	2021	0.498	0.004
2008	0.562	0.002	2015	0.519	0.002

. As can be seen from the table, the Moran index is positive from 2002 to 2021, and the Moran index test passed the 5% significance test in all the years except 2008 and 2009, which passed the 10% significance test.

From 2002 to 2021, the overall Moran index of marine fishery carbon sinks shows a “U”-shaped trend of change, indicating that the provinces (autonomous regions) with similar levels of marine fishery carbon sinks in China are unstable in the degree of spatial agglomeration, and there exists a “slow stagnation period”. The inflection point of the decline of Moran’s I index in the study period was in 2005, and stabilized after 2009. The marine economic loss in 2005 alone was nearly CNY 33 billion, accounting for nearly 2% of the total value of the marine economic output in the same period, and 16% of the total loss of all kinds of natural disasters in the country. The global financial crisis occurred in 2008, and China’s natural environment experienced freezing rain and snow, earthquakes, typhoons, and other tests, marine disasters generated 152 deaths and direct economic losses of CNY 20.6 billion. Natural disasters had different degrees of impacts on the coastal areas, and the impacts lasted until 2009, so the Moran index decreased a lot during this five-year period, but it was still positive, i.e., there was still a positive spatial correlation between the carbon sinks per unit area of the coastal provinces (autonomous regions) during the five-year period from 2005 to 2009.

In order to alleviate the negative impacts brought by economic pressure and natural disasters, our government began to increase the policy of supporting and benefiting fisheries since 2006, which effectively mobilized the enthusiasm of fishery enterprises and fishermen for green production, strongly promoted the development of fishery production, ensured the normal fishery activities and steady fishery production in special years, and pushed the national economy of fishery with carbon sinks to maintain a stable development trend.

After 2010, the Moran index showed a steady upward trend. During the study period, the Moran index was always positive and all passed the 10% significance test, indicating that China’s fishery carbon sinks showed significant spatial correlation in the coastal provinces (autonomous regions), so it is necessary to use the spatial econometric model to carry out further research.

4.3. Identification of Influencing Factors and Spatial Effects

4.3.1. Spillover Effect Analysis

The previous Moran’s I index preliminarily proved that marine fishery carbon sinks have strong spatial correlation, and the Moran’s I results are all greater than 0.4; in order to make a more accurate choice in the spatial econometric model, the Lagrange multiplier (LM) test is carried out here, as well as the robust Lagrange multiplier test (robust LM), and the specific results are shown in

Table 6. Results of model selection.

Methods	Statistical Outcomes	p Value	Hypothesis
Hausman	31.12	0.000	Random effects are superior to fixed effects
LM-err	0.045	0.545
Robust LM-err	9.415	0.001
LM-lag	9.834	0.000
Robust LM-lag	18.418	0.000
LR	57.32	0.000	SDM simplified to SAR
LR	35.61	0.000	SDM simplified to SEM

.

For the selection of spatial econometric model, this paper refers to Elhorst’s test steps, and adopts the idea of combining the ideas of “from specific to general” and “from general to specific” for the test of spatial econometric model. The Hausman test shows that the random effect is not better than the fixed effect, and the fixed effect should be chosen; the LM test with no spatial lag, robust LM test with no spatial lag, LM test with no spatial error, and robust LM test with no spatial error reject the original hypothesis that there is no effect of spatial lag and spatial error at the 1% significance level, and the original hypothesis that there is no effect of spatial lag and spatial error is rejected. The original hypothesis of no spatial lag and spatial error effects should be rejected at a 1% significance level, so the spatial Durbin model (SDM) should be preferred to the spatial error model (SEM); finally, the LR test is used to determine whether the spatial Durbin model (SDM) can be simplified to the Spatial Lag Model (SAR) or the spatial error model (SEM), and the result is that the original hypothesis is rejected. In addition, this paper involves more research objects, combined with the test results should choose the spatial fixed effects of the spatial Dubin model for subsequent analysis.

4.3.2. Analysis of Spatial Empirical Results

Lesage (2008) [36] argued that the parameter values of point estimation are biased, and there are errors in the measurement of the influencing factors, so this paper adopts the solution of partial differential equations to solve this problem, and we can obtain the direct effect, indirect effect, and total effect. The direct effect reflects the influence of the explanatory variables of the spatial unit itself on the carbon sink of marine fisheries in the region, the indirect effect reflects the influence of the explanatory variables of the neighboring spatial units on the carbon sink of marine fisheries in the region, and the total effect is the sum of the direct effect and the indirect effect. The results from the partial differential decomposition are unbiased and reliable compared to the parameter results obtained from the point estimation.

Table 7. Decomposition of spatial effects of the spatial Durbin model based on fixed effects.

	Varieble	The Three Marine Economic Circles		Northern Marine Economic Circle		Eastern Marine Economic Circle		Southern Marine Economic Circle
		Modulus	z Value	Modulus	z Value	Modulus	z Value	Modulus	z Value
direct effect	cap	0.08 ***	−7.80	0.10 ***	5.12	0.08 ***	5.91	1.17 **	2.49
	lab	49.29 ***	−8.21	39.59 ***	3.14	16.97 **	1.74	42.24 ***	3.21
	inno	227.61 **	−3.72	278.5 **	2.07	−137.26	−3.41	275.19 ***	6.25
	env	−57.95	−0.07	498.63	0.35	84.17	0.09	3098.04 **	2.84
	mar	90.34 ***	−3.93	47.15 ***	3.57	17.79	0.11	47.49 ***	5.48
	open	10.17	−1.74	11.2 **	2.55	10.19 ***	2.34	10.07	0.49
indirect effect	cap	0.18 **	−2.48	−0.4 ***	−2.74	0.04	2.87	0.08	1.06
	lab	−48.57 ***	−4.35	−17.85 ***	−2.87	11.08	2.18	−77.48 ***	−4.07
	inno	141.12 **	−2.10	−104.86 *	−1.74	−147.12	−1.25	198.45	2.05
	env	−31.92	−0.09	−2687.14 *	−1.86	549.98	0.35	497.14 ***	2.15
	mar	58.47 **	−2.11	27.15 **	−2.68	13.57	0.05	28.92	1.64
	open	10.08	−1.32	10.57 **	−2.07	10.06	−0.31	10.11	0.25
overall effect	cap	0.12 ***	5.97	0.05 ***	6.58	0.16 ***	4.28	0.24 **	2.04
	lab	−0.23	−0.03	20.75 ***	3.51	14.74 **	1.84	−35.48	−1.61
	inno	207.84 **	3.56	101.25 **	2.41	−129.34	−1.35	114.25 **	2.04
	env	−84.59	−0.11	−2016.76 *	−2.64	628.49	0.48	8054.75 **	2.84
	mar	147.84 **	3.89	69.45 ***	4.49	30.94	0.48	71.29 **	2.05
	open	21.24 *	1.54	10.59 **	2.49	10.15	1.45	10.14	0.64
ρ		0.75 *** (3.28)		−0.59 *** (−5.49)		0.57 *** (6.90)		0.54 *** (3.21)
N/sample size		180		60		40		80
Sqrt/R		0.9477		0.8947		0.9954		0.9861
Log-L		−904.5874		−324.5978		−231.5774		−309.5481

Note: Figures in () are robust standard error; ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.

reports the regression results of the spatial econometric model, and the results show that the coefficients of the overall spatial posterior coefficients of the ocean fishery carbon sinks of the three marine economic circles have positive signs and pass the test at the 1% significance level, which proves that there are significant positive spatial correlations of the ocean fishery carbon sinks of the three marine economic circles. There are significant spatial spillover effects of marine fishery carbon sink among different economic circles, which is consistent with the previous spatial Moran’s I measurement results. This further validates the rationality of constructing the spatial econometric model.

From the regression results of the model, the direct effect coefficient of capital on the development level of marine fishery carbon sink is 0.08, and the indirect effect coefficient is 0.18, and passes the significance level test of 1% and 5%, respectively, which indicates that the input of capital has a positive role in promoting the development of marine fishery carbon sink. The direct effect coefficient of the labor force is 49.29, and the indirect effect coefficient is −48.53, and both passed the 1% significance level test. As a basic element of economic growth, the labor force is the first resource for the high-quality development of marine fisheries and plays a decisive role in the carbon sink of marine fisheries, while the negative indirect effect may be due to the fact that the fishery employment policy of the region will attract the inflow of labor force from the surrounding areas, forming a “siphon effect” on the surrounding areas, which has a negative impact on the development of the marine fisheries in the surrounding areas. The direct effect coefficient of innovation drive is 227.61, and the indirect effect coefficient is 141.12, which pass the significance level test of 1% and 5%, respectively, indicating that the enhancement of marine scientific and technological innovation ability has a positive role in promoting the development of the marine fishery carbon sink. The direct effect coefficient of environmental regulation is −57.95, and the indirect effect coefficient is −31.92, which do not pass the significance test. The possible explanation is that because the marine fishery industry is experiencing the pain period of low-carbon transition, the environmental regulation does not form a statistically significant effect on the carbon sink of the marine fishery industry in the short term, and therefore, we need to consider the influence of other factors on the carbon sink of the marine fishery industry. The direct effect coefficient of the degree of marketization is 90.34, and the indirect effect coefficient is 58.47, which pass the 1% and 5% significance level tests, respectively, indicating that the degree of marketization has a positive role in promoting the carbon sinks of marine fisheries, and that the market mechanism of the carbon sinks of marine fisheries needs to be further improved in order to promote the development of carbon sink trading. The direct effect coefficient of technical input is 10.17, and the indirect effect coefficient is 10.08, both of which are significantly positive, indicating that technical input still has a positive driving effect on marine fishery carbon sinks, and it is necessary to promote marine fishery technology research.

Based on the consideration of the spatial effect on the development of marine fishery carbon sinks in China’s three marine economic circles, this paper further discusses this issue by subregion.

The coefficients of the direct effect of capital on the carbon sinks of marine fisheries in the three marine economic circles passed the significance level tests of 1% and 5%, and only the indirect effect on the carbon sinks of marine fisheries in the southern marine economic circle did not pass the significance level test, which indicates that capital has a significant role in promoting the level of development of the carbon sinks of marine fisheries in the three marine economic circles, but the spatial feedback effect of the southern marine economic circle is not at a high level of influence. In terms of spatial spillover effect, capital only produces a more significant squeeze effect on the eastern marine economic circle, the reason is that the eastern capital market is the most active, Jiangsu, Zhejiang, Shanghai, and other Yangtze River Delta regions have a significant capital agglomeration, which effectively drives the level of marine fisheries and the fishery carbon sink capacity of the neighboring regions to improve. The spillover effect of capital on the northern marine economic circle is significantly negative, indicating that there is an obvious polarization effect of capital in the northern marine economic circle, which is susceptible to the negative influence of capital from neighboring provinces.

The direct and indirect effects of the labor force on the marine fishery carbon sinks in the three marine economic circles both pass the 1% and 10% significance level tests, and the labor force has a direct positive driving effect on the development of the marine fishery carbon sinks in the content provinces of the three marine economic circles. From the indirect effect, the labor force has a positive driving effect on the development of marine fisheries in the eastern ocean gold economic circle, indicating that there is a spatial “diffusion” effect of the labor force in the eastern ocean circle, indicating that the labor force promotes the spillover effect in the exchange of the carbon sinks in fisheries among coastal provinces. The labor force in the southern and northern marine economic circle shows a negative role, according to the “return effect” restraint, the southern and northern marine economic circle within the developed fishery areas because of its resource advantages and policy advantages will inevitably attract the inflow of laborers, which will lead to the loss of laborers in marine fisheries in the neighboring provinces, and then the progress of the carbon sink of marine fisheries.

The direct effect coefficients of the innovation drive on the southern and northern marine economic regions are significantly positive and pass the significance test of 1% and 5%, while the indirect effect coefficients only pass the significance test of the northern marine economic region and are significantly negative. Innovation drive can effectively promote the progress of fishery technology, help the development of marine fishery, and promote the transformation and upgrading of traditional marine fishery. With the improvement of innovation-driven capacity in the core provinces such as Shandong and Liaoning, the northern marine economic circle has increased the “siphoning” effect of innovation resources on the neighboring regions that are lagging behind in the development of the carbon sink fishery, thus further inhibiting the development of the carbon sink of the marine fishery in the neighboring regions.

The coefficient of indirect effect of environmental regulation on the fishery carbon sinks in the northern and southern marine economic circles passed the 10% significance level test, while the carbon sinks of marine fisheries in the eastern marine economic circle did not pass the significance level test, and the indirect effect only passed the 5% significance level test in the southern marine economic circle. The most significant effect of environmental regulation on the fishery carbon sinks in the southern marine economic circle is due to the fact that the southern marine economic circle represented by Guangdong, as an important window of China’s opening to the outside world and economic globalization, effectively reduces the unit cost of offshore environmental regulation by raising the standard of environmental regulation and improving the traditional industrial chain, promotes the green and low-carbon transformation of marine fisheries, and achieves the gradual improvement of the four-province fishery industry chain with carbon sinks, The scale of the carbon sink fishery industry chain in the four provinces has been gradually improved and continuously expanded.

The absolute value of the direct effect coefficient of opening up to the outside world is larger than that of the indirect effect coefficient of the three marine economic circles, but only the southern marine economic circle fails the significance test. In terms of spatial spillovers, technical inputs only show a significant negative effect on the fishery carbon sinks in the northern marine economic circle, which may be due to the fact that in the process of opening up to the outside world, Hebei, Liaoning, and Shandong, as an important region for the coordinated development of the Beijing–Tianjin–Hebei region and a shipping hub to serve the construction of the “One Belt, One Road”, have a significant negative effect on the development of the region, especially on the traditional industries such as marine fisheries, and the positioning of the industry in the region. As the scale of import and export trade expands, neighboring regions lower the standards for foreign investment in pursuit of economic development, generating the over-exploitation of marine resources, which negatively affects the carbon sink fishery and green development of the fishery industry in neighboring provinces.

The direct effect coefficients of the degree of marketization on the carbon sinks of marine fisheries in the three marine economic circles are positive, and the indirect effect coefficients on the northern marine economic circle are significantly negative, which may be due to the fact that with the continuous improvement of China’s market economic system, the market mechanism has taken a dominant position, and the regional mobility of the elements of the marine fishery activities has been continuously strengthened, which then guides the flow of the factors of production from the low-quality and low-efficiency areas to the high-quality and high-efficiency areas, and thus the development of resource resources in the developed areas of marine fisheries. The developed areas of marine fisheries have formed a resource agglomeration effect so that the increased marketization of the northern marine economic circle has to some extent inhibited the development of the marine fisheries economy in the surrounding areas.

5. Conclusions and Suggestions

5.1. Conclusions

Based on the refinement of the carbon sink calculation methods, this paper calculates the carbon sink capacity of fisheries in the three marine economic circles of China using data from nine coastal provinces. The kernel density estimation method is employed to reveal the regional differences and distribution evolution trends in marine fisheries’ carbon sink development across these circles. Spatial econometric models are utilized to identify the spatial spillover effects of marine fisheries’ carbon sink development. Additionally, partial differential equations are used to identify the direct and spillover effects of the factors influencing marine fisheries’ carbon sinks.

(1)

According to the analysis of the typical facts of the development of the marine fishery carbon sinks in the three marine economic circles, it can be seen that the carbon sinks of marine fisheries in the three marine economic circles show a rising trend year by year, and the annual growth rates of the eastern, southern and northern marine economic circles are 17.1%, 29.1%, and 17.8%, respectively; the development level of the carbon sinks of the eastern marine economic circle is higher than that of the north and the south as a whole, followed by the northern marine economic circle, and the southern marine economic circle is slightly higher than the northern marine economic circle. The level of development of marine fishery carbon sinks in the eastern marine economic circle is higher than that in the north and south, followed by the northern marine economic circle, and the southern marine economic circle is slightly lower than that in the north, but basically equal to it. These findings align with previous studies indicating regional differences in carbon sink capacities due to varying environmental and economic conditions [37,38].

(2)

The overall, northern, and southern marine economic circles have obvious trends of multilevel differentiation, while the distribution curve of the eastern marine economic circle has been transformed from a bimodal distribution to a unimodal distribution, and the polarization phenomenon has been alleviated. This transformation suggests a stabilization in the development of carbon sinks in the eastern circle, which is consistent with recent research highlighting the effects of targeted environmental policies and investments in these regions [39].

(3)

According to the regression results of the spatial econometric model, there is a significant positive spatial correlation between the development of marine fishery carbon sinks in the three marine economic circles. From the perspective of direct effect, except for environmental regulation, which is not significant, all the other influencing factors have a positive role in promoting the development of the marine fishery carbon sinks in the region; from the perspective of indirect effect, except for the environmental regulation and opening up to the outside world, which are not significant, the impact of the fishery industry’s labor force inputs on the surrounding area marine fishery carbon sinks is negative, while the impacts of other factors are negative. In terms of indirect effects, except for environmental regulation and opening up, the effect of labor input in the fishery industry on the carbon sink of marine fisheries in the neighboring regions is negative, while all the other influencing factors play a positive role in promoting the carbon sink of marine fisheries in neighboring regions. These factors include technological advancements, policy support, and economic investments, as noted by Li and Zhao (2020) [40]. From the perspective of indirect effect, except for environmental regulation and opening up to the outside world, which are not significant, the impact of the fishery industry’s labor force inputs on the surrounding area’s marine fishery carbon sinks is negative, while the impacts of other factors are positive. This reflects the complexity of regional economic interactions and is supported by findings from recent spatial economic studies [41,42].

5.2. Suggestion

Based on the above conclusions, the regional differences among and within the three marine economic circles (between provinces) in China will become an important constraint in China’s future implementation of the strategy of strong marine power and the promotion of high-quality development of fisheries. Therefore, in order to effectively and rationally narrow and improve the regional differences and divergent evolutionary trends in the development of marine fishery carbon sinks, it is necessary to start from the following three aspects:

Optimize the spatial development pattern of marine fisheries and promote the coordinated development of fisheries within the marine economic circles in an integrated manner: There is a problem of the uneven distribution of marine fishery resources within the three marine economic circles, which can be addressed by formulating reasonable fishery resource development plans, adjusting the fishery production layout, and promoting the rational allocation and utilization of resources. At the same time, due to the positive spatial correlation of fishery carbon sinks, it is necessary to promote synergistic development among the marine economic circles, strengthen the protection and management of marine fishery resources, and ensure the sustainable development of spatial fishery carbon sinks.

Enhance the input of carbon sink fisheries: Strengthen fishery resource surveys and assessments; promote the research, development, and application of carbon-sinking aquaculture technologies and carbon-sinking fishing tools; and improve the environmental friendliness of fisheries. Strengthen policy support and management, clarify the development goals and government support policies for carbon sink fisheries, strengthen regulation and law enforcement, promote the publicity and promotion of carbon sink fisheries, and promote cooperation between carbon sink fisheries and scientific research institutes, fishery enterprises, and environmental protection organizations so as to jointly promote the development of carbon sink fisheries.

Refine the design of the fishery industry chain: From the development of fishery resources, production, processing and manufacturing, distribution and sales to consumer services, improve the overall efficiency and added value of the fishery industry chain. Particular attention needs to be paid to upstream and downstream cooperation: in the upstream, focus on seedling cultivation, improve the collection and identification of seedling resources, expand and multiply the use of the mechanism, build a new system to build the whole industrial chain of seedling innovation system, strengthen the advantages of industrial integration, support the seedling enterprises to become stronger and bigger, build a research and development system adapted to the wisdom of the nursery, optimize the environment of the seedling market, and improve the incentive mechanism of the seedling innovation market, to achieve the aquatic green production of the “source of fresh water”.

Improve the specialization level of aquatic processing: It is necessary to change the current shellfish and algae products which are mainly primary processing, lack of finishing, low value-added products of the status quo, and take the path of “active ingredient mining—ingredient extraction—product cultivation—marketing promotion—industry shaping” which extends the shellfish and algae industry, realizing the transformation of the “industrialization” of aquatic products’ green production; at the same time, it is necessary to strengthen the linkage between the upstream and downstream industrial chains of the fishery, and to promote the blockage of the fishery industry chain and the construction of the market. It is also necessary to strengthen the market development and brand construction of the fishery circulation and sales link, and improve the competitiveness of the fishery products and their added value.