Measurement, Spatiotemporal Evolution, and Spatial Spillover Effects of Carbon Sinks and Emissions from Shellfish and Algae Mariculture in China

Abstract

Under carbon emission reduction constraints, accurately assessing the spatial–temporal patterns and drivers of mariculture carbon emissions and sinks is critical for promoting marine economic development and achieving carbon neutrality. This study reviews key components of China’s mariculture carbon and analyzes provincial data from 2008 to 2023 using econometric models to estimate emissions, sinks, and net carbon values. Spatial heterogeneity and spillover effects are examined through geographically weighted regression, Moran’s I, and spatial Durbin models. The findings indicate the following: (1) Both direct and indirect mariculture carbon emissions are rising, with indirect emissions growing faster, notably in Shandong, Fujian, and Guangdong. (2) Shellfish carbon sinks generally dominate; algal carbon sinks are growing rapidly, especially in Fujian, Zhejiang and Shandong. (3) Net carbon values vary by region—positive in Liaoning, Hebei, Shandong, and Hainan, and negative in Jiangsu, Zhejiang, Fujian, Guangdong, and Guangxi. (4) Energy intensity increases emissions; industrial upgrading reduces them. Technological innovation, energy intensity, and ecological constraints enhance sinks. (5) Emission spillovers are positive for energy and negative for structure; sink spillovers are positive for energy and negative for technology; ecological effects are insignificant. Overall, shellfish and algae mariculture play a key role in China’s marine carbon sequestration. Regionalized carbon governance is essential to balance emissions–sinks, and to advance low-carbon mariculture.

1. Introduction

With the intensification of the greenhouse effect and global warming, energy conservation, emission reduction, and low-carbon development have become essential goals for sustainable development [1]. According to the Global Carbon Budget 2024, published by the Global Carbon Project (GCP), China was projected to contribute 32% of global carbon dioxide emissions in 2024. As the world’s largest energy consumer and emitter [2,3,4,5,6,7], China is actively advancing its “3060 goal”—aiming to peak emissions by 2030 and achieve carbon neutrality by 2060—reflecting China’s leadership role in global carbon emission reduction. While industrial sectors remain the primary focus of carbon reduction, agriculture—which is responsible for the second-largest share of global greenhouse gas emissions—also plays a critical role [8]. Within agriculture, fisheries contribute significantly to both emissions and mitigation potential [9]. The FAO estimates global fisheries and aquaculture production will reach 201 million tons by 2030, increasing energy demand and associated environmental pressures. Thus, reducing emissions in fisheries through clean energy, feed optimization, and improved practices, alongside enhancing biological carbon sinks, is vital.

China’s coastal regions are key economic drivers and ecological buffers [10]. Mariculture in these areas had expanded rapidly by 2023, reaching over 2.21 million hectares and producing 488.5 billion yuan in output value, far exceeding capture fisheries. Among them, shellfish and algae dominate this growth. Shellfish sequester carbon via calcification and filtration [11], while algae contribute through photosynthesis and PH value enhancement [12], promoting the formation of particulate organic carbon (POC) and refractory dissolved organic carbon (RDOC), which significantly enhance the capacity of the marine carbon sink. However, greenhouse gas emissions from aquaculture energy use [13] may offset some of these benefits. Therefore, a comprehensive assessment of mariculture carbon sinks and emissions—along with their drivers—is essential to clarify their net environmental impact and to inform policies that align carbon neutrality goals with sustainable economic development.

Current research on marine carbon sinks and emissions primarily focuses on two dimensions. The first dimension emphasizes single-aspect quantitative analyses, examining either carbon sinks or carbon emissions in isolation. Among them, regarding carbon sinks, scholars typically quantify the carbon sink capacity of specific marine organisms or ecosystem components while assessing their environmental or socioeconomic impacts [14]. For instance, Luo et al. [15] evaluated the carbon sink function of global cultivated seaweeds from 2000 to 2009, revealing an annual carbon removal of approximately 4.48 × 10⁷ tons, with RDOC playing a critical role in seaweed-based carbon sinks. Similarly, Liu et al. [16] proposed an integrated marine carbon sink assessment framework and accounting methodology by incorporating shellfish and algae aquaculture, coastal wetlands, and nearshore carbon sinks, with the total ocean carbon sink in China estimated at 69.83–106.46 Tg C/year, of which the mariculture carbon sink accounted for 2.27–4.06 Tg C/year. In studies addressing carbon emissions, research prioritizes carbon footprint assessments of energy-intensive processes (e.g., energy consumption, feed production) within marine economic activities. These studies provide data-driven insights for enhancing carbon sink efficiency or reducing emissions through single-dimensional quantification. However, their limitations lie in the lack of holistic analysis, failing to elucidate the dynamic equilibrium between carbon sequestration and emissions.

The second dimension involves integrated studies of carbon sinks and emissions. These investigations typically address the dynamic equilibrium between carbon sinks and emissions [17,18]. For example, Wang et al. [19], in their assessment of carbon efficiency and carbon neutrality trends in Jiangsu Province from 2016 to 2020, defined carbon sinks as encompassing coastal ecosystems (e.g., salt marshes, tidal flats), marine aquaculture systems (shellfish and algae), and offshore wind power systems. From a marine industry perspective, carbon emissions were attributed to sectors such as marine transportation and fisheries (classified under the Ocean and Related Industries Classification Standard GB/T 20794-2006) [20], while the carbon sink of marine aquaculture systems was limited to biomass-based sinks, yielding an estimated carbon sequestration of 53,200 to 65,300 metric tons. Similarly, based on the carbon-neutral perspective, Guan et al. [21] used a trophic level approach. Carbon transfers through the food chains/webs of fish, crustaceans, and cephalopods were used as carbon sinks in marine fisheries, while carbon emissions included only those generated by motorized fishing vessels in marine capture fisheries (approximately 4,000,000 tons). The changes in the net carbon emissions, carbon emissions, and carbon sinks of marine fisheries were analyzed in eleven coastal provinces of China from 2010 to 2019, and the LMDI decomposition method was used to examine the driving factors. This type of study can reveal the interaction between carbon sinks and carbon emissions. However, existing studies have yet to establish a systematic framework to comprehensively characterize the spatiotemporal evolution of carbon sinks and emissions across different mariculture systems. In addition, insufficient attention has been paid to the spatial heterogeneity and spillover effects of influencing factors.

In summary, existing studies tend to focus on either carbon sinks or emissions in isolation, or explore their relationship from a dynamic perspective, often based on inconsistent accounting standards. However, the inherent complexity of mariculture systems leads to variations in the roles and mechanisms of carbon sinks and emissions across different components and stages. In marine aquaculture ecosystems, shellfish and algae play pivotal roles in maintaining the carbon balance. Shellfish contribute to carbon sequestration through calcification—absorbing bicarbonate to form shells—and through filtering suspended organic carbon via soft tissues, while also releasing POC. Algae sequester CO₂ via photosynthesis to form biomass-based sinks, while promoting the formation of RDOC and POC during their growth [22]. Nevertheless, mariculture processes also generate emissions, such as diesel combustion by mariculture vessels, and electricity consumption in pond and factory-based operations. (Figure 1). Therefore, comprehensively assessing the carbon emissions and shellfish-algae carbon sinks, revealing their dynamic interplay, is crucial. Moreover, insufficient attention has been paid to the spatial heterogeneity and spillover effects of the respective influencing factors, which are vital to understanding regional differences and formulating targeted carbon management strategies.

The innovation of this study lies in systematically and comprehensively assessing the carbon emissions and sinks from shellfish and algae in mariculture ecosystems, with particular emphasis on their spatiotemporal evolution characteristics, spatial heterogeneity of influencing factors, and spatial spillover effects. Specifically, this study goes beyond traditional biomass carbon sink estimates by incorporating multiple mechanisms, including POC from shellfish and algae and RDOC from algae. It also accurately quantifies carbon emissions from aquaculture fishing vessels, ponds, and industrialized farming processes. By integrating data on carbon sinks and emissions, this study provides a more comprehensive understanding of the net carbon balance of marine aquaculture in China. Furthermore, it identifies the spatial heterogeneity and spillover effects of influencing factors, offering reliable scientific evidence and data support for achieving carbon reduction and carbon neutrality goals, thereby contributing to the green and low-carbon transformation of the marine aquaculture industry.

The rest of this paper is organized as follows. Section 2 describes the study area, data sources, and methodology. Section 3 presents the results of the study and its discussion. Section 4 details the selection of influencing factor variables; the results and discussion of least squares and geographically weighted regressions; the process of selecting the spatial econometric model; and the results and discussion of the spatial Durbin model. Section 5 presents policy recommendations. Section 6 provides conclusions and outlook.

2. Methods

2.1. Study Area

China’s coastal areas, with their long coastlines and vast sea areas, provide unique natural conditions for the development of the mariculture industry. Among them, Liaoning, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, Guangxi, and Hainan together constitute the core area of mariculture in China (Figure 2). As shown in

Table 1. Area of mariculture in coastal regions of China.

Year	Liaoning	Hebei	Shandong	Jiangsu	Zhejiang	Fujian	Guangdong	Guangxi	Hainan	Total Area (ha)
2008	411,556	109,755	426,217	160,089	96,139	120,704	189,717	47,380	12,983	1,574,540
2009	630,700	121,013	441,403	172,754	94,514	133,942	194,766	50,670	15,247	1,855,009
2010	763,101	123,810	500,946	192,426	93,905	137,636	199,258	51,287	14,529	2,076,898
2011	751,387	134,264	512,126	201,073	90,839	142,315	203,410	52,212	14,646	2,102,272
2012	813,035	134,682	523,705	199,352	89,747	145,486	201,834	53,249	15,845	2,176,935
2013	942,050	117,928	546,814	193,807	89,358	154,453	197,198	54,001	16,791	2,312,400
2014	928,503	122,434	548,487	188,657	88,178	161,418	193,691	54,233	16,691	2,302,292
2015	933,068	117,533	563,198	181,829	85,881	166,075	194,861	55,015	17,138	2,314,598
2016	698,400	124,800	604,800	185,480	78,701	153,000	166,200	45,400	32,323	2,089,104
2017	698,400	107,583	610,377	192,390	75,954	155,739	161,690	47,022	31,715	2,080,870
2018	693,190	111,404	570,857	186,641	80,924	162,464	165,614	47,844	21,372	2,040,310
2019	661,817	107,041	561,501	179,951	82,019	163,713	164,990	49,822	20,510	1,991,364
2020	650,719	105,341	580,350	177,629	82,535	163,144	164,719	52,277	17,595	1,994,309
2021	647,606	104,185	608,376	171,106	81,466	164,641	166,805	64,255	15,881	2,024,321
2022	677,201	105,587	617,464	172,188	83,439	167,953	166,596	67,393	15,594	2,073,415
2023	773,974	105,022	646,026	171,452	84,255	177,321	172,133	69,075	14,673	2,213,931

, in terms of the time scale, from 2008 to 2015, the area of mariculture showed a trend of relatively steady growth, while from 2016 to 2019, it basically showed a slow decrease. As of 2023, the area of coastal mariculture in China reached 2,213,931 ha, a year-on-year increase of about 6.78%. In terms of spatial changes, from the development in 2008 to 2023, the area of mariculture in six places, namely Liaoning, Shandong, Jiangsu, Fujian, Guangxi, and Hainan, increased, which was mainly due to policy support, abundant sea resources, and leading aquaculture technology, while Hebei, Zhejiang, and Guangdong showed a decreasing trend, which was mainly due to industrial structural adjustment, strengthened ecological protection, and market factors. Overall, the development of mariculture in China is relatively stable and occupies a significant position in the economic structure.

2.2. Data Sources

Shellfish and algae carbon sinks in the ocean carbon sinks play an important role in mitigating global warming [23]. Carbon sink generation in shellfish mainly relies on their filtration, calcification, and biodeposition processes, as follows [11,24,25]: firstly, through calcification to absorb bicarbonate from seawater into calcium carbonate shells; secondly, through filtration of suspended organic carbon in the water (e.g., phytoplankton, microscopic zooplankton, and organic detritus) to synthesize their own material and increase the carbon content of their organisms. In addition, unused organic carbon is deposited through feces and pseudo-feces, facilitating vertical carbon transport and the formation of sedimentary carbon pools. However, recent studies have shown that dissolved organic carbon (DOC) released by shellfish is rapidly reabsorbed and utilized, and is difficult to accumulate in seawater, so this carbon contributes little to the seawater DOC pool [22]. Therefore, only biomass carbon sinks and POC carbon sinks are considered for shellfish carbon sinks. Algae, on the one hand, absorb carbon dioxide dissolved in seawater through photosynthesis, thus reducing the partial pressure of seawater carbon dioxide and further accelerating the entry of airborne carbon dioxide into seawater; on the other hand, algae absorb nutrients during the growth process, increase the pH value in seawater, and promote the diffusion of atmospheric carbon dioxide through the carbonate equilibrium system [26,27]. In addition, a large amount of organic carbon is deposited and exported during seaweed aquaculture [27]. Therefore, algae carbon sinks mainly include biomass carbon sinks, RDOC carbon sinks, and POC carbon sinks. However, diesel carbon emissions from the use of motorized fishing vessels and electricity carbon emissions from pond and factory farming also occur during shellfish and algae culture [28]. Therefore, this paper adopts the results of previous research on carbon sinks and emissions from shellfish and algae [16,29] to further refine the assessment methodology. The data used in the accounting are as follows:

(1)

Data on shellfish and algae production in mariculture, seaweed culture area, the power of mariculture fishing vessels, and pond and factory production in mariculture were obtained from the China Fisheries Statistical Yearbook (CFSY, 2008–2024).

(2)

Shellfish and algae biomass carbon sink correlation coefficients, including dry-to-wet coefficients for shellfish and algae, the specific gravity of shell and soft tissue in shellfish, the corresponding carbon content, and algal carbon content, were mainly derived from the research results of Sun et al. [30].

(3)

The ratio of carbon entering biological deposition to carbon removed by harvesting during shellfish aquaculture was obtained from Tang and Liu [31]. Conversion coefficients between net primary productivity (NPP) and gross primary productivity (GPP) for carbon sink accounting of RDOC carbon sinks in algae were obtained from Zhang et al. [32]. Conversion coefficients between GPP and DOC were obtained from Abdullah and Fredriksen [33] and Krause-Jensen and Duarte [34]. Conversion coefficients between DOC and RDOC [22] were obtained from previous studies. The average deposition rates for algae POC carbon sink accounting for seaweed farming areas were obtained from Zhang et al. [22] and Cai et al. [35]. The percentage of sedimentary organic carbon content in seaweed farming areas [36] was obtained from previous research results.

(4)

The conversion coefficient for diesel consumption by mariculture fishing vessels was obtained from the Reference Standard for Measuring Oil Consumption for Domestic Motorized Fishing Vessel Oil Price Subsidy of the Ministry of Agriculture and Rural Affairs. The carbon emission coefficient of diesel fuel was obtained from the General Rules for Calculating Comprehensive Energy Consumption (GB/T2589-2008) and the Guidelines for the Preparation of Provincial Greenhouse Gas Inventories (using raw coal as an example). The conversion coefficient of electric power consumption generated in the process of pond and factory aquaculture was obtained from Xu [28], and the carbon emission coefficient of electricity was obtained from the 2021 Electricity CO² Emission Factors.

2.3. Method

This paper first organizes and accounts for the specifics of mariculture carbon emissions and carbon sinks. Secondly, the spatial–temporal evolution of carbon emissions, carbon sinks, and net carbon values is analyzed. Finally, ordinary least squares linear regression and geographically weighted regression are used to analyze the influencing factors of carbon emissions and sinks. The spatial spillover effects are further analyzed by using Moran’s I and the spatial Durbin model. The specific research framework diagram is shown in Figure 3.

2.3.1. Net Carbon Value

$$N={CS}_s-{CS}_t$$

(1)

where $N$ denotes net carbon value (t/yr), ${CS}_s$ denotes total carbon emissions (t/yr); ${CS}_t$ denotes total carbon sinks (t/yr).

2.3.2. Total Carbon Emissions

$${CS}_s=S_d+S_i$$

(2)

$$S_d=P\times \alpha \times {\beta }_1$$

(3)

$$S_i=\sum M_t\times \gamma \times {\beta }_2$$

(4)

where $S_d$ denotes the direct carbon emissions from diesel consumption by aquaculture fishing vessels (t/yr); $S_i$ represents the indirect carbon emissions from electricity consumption in pond and factory aquaculture methods in mariculture (t/yr); $P$ denotes the power of mariculture fishing vessels (kW); $M_t$ is the production of pond aquaculture and factory aquaculture (t/yr); $\alpha$ and $\gamma$ denote the conversion coefficients (t/kW) of diesel and electricity energy consumption, respectively; ${\beta }_1$ and ${\beta }_2$ (t/kWh) correspond to the carbon emission coefficients, respectively.

2.3.3. Total Carbon Sinks

$${CS}_t={CS}_{t1}+{CS}_{t2}$$

(5)

where ${CS}_{t1}$ indicates the total carbon sinks from shellfish (t/yr); ${CS}_{t2}$ represents the total carbon sinks of algae (t/yr).

Shellfish Carbon Sinks

$${CS}_{t1}={CS}_{bio1}+{CS}_{P1}$$

(6)

where ${CS}_{bio1}$ denotes shellfish biomass carbon sinks (t/yr); ${CS}_{P1}$ represents POC carbon sinks in shellfish aquaculture (t/yr).

(1)

Shellfish biomass carbon sinks:

$${CS}_{bio1}={CS}_{shell}+{CS}_{tissue}$$

(7)

$${CS}_{shell}=\sum \left(Y_i\times r_i\times p_i\times l_i\right)$$

(8)

$${CS}_{tissue}=\sum \left(Y_i\times r_i\times p_i^1\times l_i^1\right)$$

(9)

where ${CS}_{shell}$ denotes shell carbon sinks (t/yr); ${CS}_{tissue}$ represents shellfish soft tissue carbon sinks (t/yr); $Y_i$ denotes the production of the $i$th shellfish (t/yr); $r_i$ shows the wet/dry coefficient of the $i$th shellfish (%); $p_i$ denotes the shell’s specific gravity of the $i$th shellfish (%); $l_i$ stands for the shell carbon content of the $i$th shellfish (%); $p_i^1$ denotes the soft tissue specific gravity of the $i$th shellfish (%); $l_i^1$ represents the soft tissue carbon content of the $i$th shellfish (%).

(2)

Shellfish POC carbon sinks:

$${CS}_{P1}={CS}_{bio1}\times k_1$$

(10)

where $k_1$ represents the ratio of carbon entering biological deposition to the carbon removed through harvesting.

Algal Carbon Sinks

$${CS}_{t2}={CS}_{bio2}+{CS}_{R1}+{CS}_{P2}$$

(11)

where ${CS}_{bio2}$ denotes the carbon sinks of algal biomass (t/yr); ${CS}_{R1}$ represents the carbon sinks of RDOC generated in the process of algal aquaculture (t/yr); ${CS}_{P2}$ denotes the POC carbon sinks generated during the process of seaweed cultivation (t/yr).

(1)

Algal biomass carbon sinks:

$${CS}_{bio2}=\sum \left(Y_i^1\times r_i^1\times l_i^2\right)$$

(12)

where $Y_i^1$ denotes the production of the $i$th algal species (t/yr), $r_i^1$ represents the wet/dry coefficient of the $i$th algal species (%), $l_i^2$ denotes the carbon content of the $i$th algal species (%).

(2)

Algal RDOC carbon sinks:

$${CS}_{R1}=\sum \left({CS}_{bio2}\times k_2\times k_3\times k_4\right)$$

(13)

where $k_2$ is the conversion coefficient between NPP and GPP; $k_3$ represents the conversion coefficient between GPP and DOC; $k_4$ denotes the conversion coefficient between DOC and RDOC.

(3)

Algal POC carbon sinks:

$${CS}_{P2}=\sum v\times 365\times S_i\times m$$

(14)

where $v$ is the average deposition rate (g/m²/day) in the seaweed culture area; $365$ is the number of days in a year (day/yr); $S_i$ denotes the area of the $i$th seaweed culture (m²); $m$ represents the percentage of the deposited organic carbon content (%) in the seaweed culture area.

2.3.4. Geographically Weighted Regression (GWR)

Geographically weighted regression (GWR), an extension of ordinary least squares (OLS) linear regression, focuses on the geospatial variability and variation of regression coefficients [37,38]. The method incorporates the spatial characteristics of the data into the model, aiming to explore the spatial variability characteristics and patterns of each influencing factor in different geographic locations [39,40,41]. When analyzing the influencing factors of carbon emissions and carbon sinks in this paper, firstly, the selected factors are subjected to OLS linear regression to analyze the statistical significance and multicollinearity between the respective variables and dependent variables. Secondly, the screened factors are subjected to GWR, which can more accurately reflect the influence of geographic location factors on carbon emissions and carbon sinks, and make the regression analysis more relevant to the actual situation. Since carbon emissions and sinks are driven by different factors, exhibit different spatial characteristics, and require their own targeted emission reduction or sink enhancement strategies, we analyze their influencing factors separately.

2.3.5. Spatial Durbin Model (SDM)

The spatial Durbin model (SDM) is employed in this study to analyze spatial spillover effects. When examining such effects, preliminary diagnostic tests, including the Lagrange multiplier (LM), likelihood ratio (LR), and Wald tests, were typically conducted to identify the most suitable spatial econometric model for the empirical data. The theoretical foundation stems from Anselin’s (1988) [42] seminal work proposing two fundamental spatial autocorrelation models, namely, the spatial lag model (SLM), which examines variable diffusion effects within regions, and the spatial error model (SEM), which analyzes error term influences across neighboring regions. Building upon this framework, Lesage and Pace (2009) [43] subsequently developed the more comprehensive spatial Durbin model (SDM). Our research specifically applies the SDM framework to investigate the spatial spillover effects of various drivers on both carbon emissions and carbon sink capacity. This methodological approach enables simultaneous examination of both dependent variable spatial dependence and independent variable spatial spillovers, providing a robust analytical tool for assessing regional environmental interactions.

3. Results and Discussion

3.1. Spatial–Temporal Evolution Characterization of the National Perspective

Figure 4 comprehensively illustrates the dynamic characteristics of carbon emissions, carbon sinks from shellfish and algae, and net carbon values in China’s mariculture from 2008 to 2023. Temporally, total direct and indirect carbon emissions showed continuous growth, which is consistent with the findings of Li et al. [17] and Wu and Li [29]. This indicates that with the expansion of the scale of the mariculture industry and the increase in production intensity, the demand for diesel and electricity grows synchronously. Among them, the direct emissions were relatively stable, likely due to improved diesel efficiency in fishing vessels through fuel optimization and energy-saving technologies. However, indirect emissions grew rapidly, closely linked to the expansion of pond and industrialized aquaculture, heightened electricity dependency per unit output, and insufficient application of clean energy sources.

Meanwhile, carbon sinks from shellfish and algae also exhibited a steady growth trend, aligning with studies by Li et al. [17] and Wu and Li [29]. Shellfish carbon sinks have long dominated, attributable not only to their superior carbon sequestration capacity but also to stable farming scales and technological advancements, such as efficient water management and the adoption of eco-friendly aquaculture practices [44,45]. Furthermore, seaweed carbon sinks showed improvement, likely due to enhanced carbon absorption efficiency through species breeding innovations and the expansion of coastal seaweed farming areas, which collectively boosted total carbon sequestration.

From 2008 to 2021, China’s mariculture carbon sinks consistently exceeded carbon emissions, aligning with findings from Li et al. [17], highlighting its ecological advantages in biological carbon sequestration. However, during 2022–2023, carbon emissions surpassed sinks. This reversal may stem from two factors. On the one hand, the rapid expansion of energy-intensive factory aquaculture heightened electricity dependence, driving indirect emissions; on the other hand, a surge in diesel-powered fishing vessels, delayed clean energy adoption, and slowed carbon sink growth (e.g., saturated farming densities, bottlenecks in mariculture carrying capacity) collectively exacerbated the “high emissions-low absorption” imbalance.

3.2. Spatial–Temporal Evolution Characterization at the Provincial Scale

3.2.1. Spatiotemporal Evolution Analysis of Carbon Emissions from China’s Mariculture

Figure 5 illustrates the spatiotemporal evolution of direct and indirect carbon emissions from China’s mariculture between 2008 and 2023. Over the 16-year period, both emission types have shown a sustained upward trend, with indirect carbon emissions increasing at a significantly faster rate than direct ones. This divergence highlights the growing influence of electrification and intensive farming models on emission dynamics.

At the provincial level, emissions and their growth rates vary considerably, reflecting differences in aquaculture models, energy-use structures, and industrial development trajectories. Direct emissions—mainly originating from diesel consumption by aquaculture vessels—have increased steadily but modestly across provinces. This pattern suggests that in traditional diesel-powered aquaculture segments, technological upgrading or scale expansion has a limited impact on reducing carbon intensity, indicating a plateau in efficiency gains. In contrast, indirect emissions—primarily from electricity consumption in aquaculture infrastructure—have surged, especially since 2016. This reflects the widespread adoption of electricity-intensive practices such as factory and pond farming, which require substantial power for water circulation, aeration, and temperature control. A notable case is Shandong Province, where indirect emissions surged dramatically after 2016, reaching 760,000 tons in 2017, likely due to the province’s aggressive expansion of industrialized aquaculture and upstream–downstream fishery integration.

Spatially, provinces display distinct emission structures. Liaoning, Hebei, and Jiangsu exhibit synchronous growth in both direct and indirect emissions, suggesting parallel development of traditional and modern aquaculture modes. Among them, Hebei stands out for its unexpectedly high total carbon emissions despite its relatively modest coastline. This anomaly is likely driven by its concentration of energy-intensive aquaculture operations near urban-industrial clusters such as Tangshan and Qinhuangdao, where the integration of aquaculture with secondary industry leads to higher diesel and electricity consumption. Additionally, the low adoption rate of clean-energy technologies and a reliance on older, inefficient equipment further elevate Hebei’s emission levels. In contrast, Shandong, Fujian, and Guangdong maintain relatively stable direct emissions, with their total emissions growth largely driven by increased indirect emissions. Provinces like Guangxi and Hainan, although having lower total emissions due to their smaller mariculture base, are experiencing rapid emission growth, suggesting that they are still in the early stages of aquaculture industrialization.

3.2.2. Spatiotemporal Evolution Analysis of Carbon Sinks in China’s Mariculture

Figure 6 depicts the spatiotemporal evolution of shellfish and algae carbon sinks from mariculture in nine coastal provinces between 2008 and 2023. Overall, the total carbon sink from mariculture has grown steadily, with shellfish remaining the dominant contributor throughout the period. This is primarily due to the large-scale development of shellfish farming and the strong carbon sequestration capacity of shellfish species, such as oysters and mussels.

Among the provinces, Liaoning, Shandong, and Fujian have consistently maintained high levels of shellfish carbon sinks, with further increases observed in recent years. This trend reflects the continued expansion of aquaculture areas and technological improvements, such as species selection, disease control, and nutrient management, which enhance biological productivity and carbon fixation efficiency. While shellfish sinks remain the primary component, algae carbon sinks have grown more rapidly, especially in Fujian, Zhejiang, and Shandong. The accelerated growth is likely a result of optimized algal strains, a better farm layout design, and scale expansion, particularly in integrated multi-trophic aquaculture systems that enhance overall ecosystem efficiency.

In terms of spatial heterogeneity, Liaoning and Fujian specialize in shellfish aquaculture, while Shandong exhibits strong performance in both shellfish and algae carbon sinks. These outcomes are closely linked to better infrastructure, sustained policy support, and technological innovation. Conversely, Guangdong, Guangxi, and Hainan show relatively low carbon sink levels, with Hainan experiencing a sharp decline in algae carbon sinks after 2017, possibly due to aquaculture area contraction, environmental regulation pressure, or industrial restructuring. Hebei, meanwhile, consistently reports low carbon sink values, which correlate with its limited mariculture scale and its focus on energy-intensive production systems rather than large-scale shellfish or algae farming.

3.2.3. Spatiotemporal Evolution Analysis of Net Carbon Values in China’s Mariculture

Figure 7 illustrates the spatiotemporal trends in the net carbon value (i.e., emissions minus sinks) from mariculture in nine coastal provinces between 2008 and 2023. The analysis reveals distinct provincial trajectories in achieving carbon neutrality in mariculture. Provinces such as Liaoning, Hebei, Shandong, and Hainan generally exhibit positive net carbon values, meaning that their carbon emissions exceed carbon sinks. In Liaoning and Hebei, this is largely attributed to the dominance of diesel-powered vessels and outdated aquaculture systems, as well as the limited penetration of clean energy technologies. Particularly in Hebei, the high net carbon value is due not only to low carbon sink capacity, but also to the province’s heavily industrialized aquaculture setup, which relies on fossil fuel-driven equipment and non-optimized infrastructure. The combination of low carbon sequestration potential and high energy intensity makes Hebei’s aquaculture sector one of the most environmentally inefficient in the coastal region.

Shandong’s positive net carbon value is primarily a consequence of its rapid industrialization of aquaculture, particularly through factory farming, which has increased its indirect electricity-related emissions. While it does maintain substantial carbon sinks, the emission growth outpaces sequestration gains. In Hainan, emissions have been further amplified by energy demand spillovers from the tourism industry, which supports a shared infrastructure that increases overall power consumption. In contrast, provinces such as Jiangsu, Zhejiang, Fujian, Guangdong, and Guangxi show negative net carbon values, indicating that carbon sinks surpass emissions. This suggests successful efforts in decarbonizing mariculture through both technology and structural optimization. For instance, Zhejiang and Fujian have proactively promoted clean-energy fishing vessels, energy-efficient aquaculture equipment, and environmentally friendly farming practices. These efforts, combined with a strategic shift toward shellfish and algae farming, have significantly reduced emissions while enhancing carbon sinks.

Notably, Guangxi has advanced energy restructuring in fisheries, integrating eco-farming and renewable energy systems, which has enabled it to achieve a net carbon surplus despite being in the growth phase of aquaculture development. Moreover, policy support in these provinces—including the promotion of recirculating aquaculture systems (RAS), optimization of energy-use efficiency, and regulatory incentives for green aquaculture—has played a critical role in steering the sector toward low-carbon and sustainable development.

4. Analysis of Influencing Factors on Carbon Emissions and Carbon Sinks from Mariculture in China

4.1. Variable Selection

Carbon emissions and carbon sinks in China’s Mariculture, which is mainly based on shellfish and algae, are closely related to elements of regional marine economic development. Relevant studies have shown that carbon emissions and carbon sinks in coastal areas are affected by the combined effects of many factors [46]. In this paper, we refer to the existing research results [47,48,49,50,51,52] and select the influencing factors of carbon sinks and carbon emissions according to the actual development of China’s coastal mariculture. Among them, the influencing factors of carbon emissions are as follows: energy intensity is an important indicator used to measure the efficiency of fishery carbon emissions and energy utilization, which is expressed by the carbon emissions generated from aquaculture, fishery fishing, and aquatic product processing [47]. Upgrading the industrial structure mainly reflects the degree of development of the marine fishery economy toward the high-value-added tertiary industry, which is expressed by the ratio of the total output value of the tertiary fishery industry to the total output value of the secondary fishery industry [48]. Factors affecting carbon sinks include the technical level, which refers to the level of technological innovation in the fishery industry and is characterized by the proportion of fishery technology promotion expenditure to the total economic output value of the fishery industry [49]. The industrial structure is characterized by the proportion of the output value of the tertiary industry in the fishery industry to the total economic output value of the fishery industry [49,50]. Energy intensity [47] is the same as the above. The level of ecological resource constraints represents the carrying capacity of marine ecological resources in the region, which is calculated by the following method: the farmed area of the sea area/farmable area of the sea area [51,52]. Additionally, to address instances of missing data, the study applied mean and interpolation methods to derive the corresponding values.

4.2. Spatial Heterogeneity of Factors Influencing Carbon Emissions and Sinks

4.2.1. OLS Model and Results

In this study, carbon emissions were selected as the explanatory variables, and energy intensity and upgrading industrial structure were taken as the explanatory variables. Meanwhile, carbon sinks were taken as the explanatory variables, and the technological level, industrial structure, energy intensity, and the level of ecological resource constraints were taken as the explanatory variables; regression models were constructed respectively. The OLS linear regression model was used to observe the degree of influence of the explanatory variables on the explained variables, significance levels, and other elements. The results are shown in

Table 2. OLS regression results on influencing factors of carbon emissions.

Parameters	Coefficient	T Value	p Value	Standard Deviation	VIF
Energy intensity	0.790	9.091	0.000 *	0.087	1.053
Upgrading of industrial structure	−0.150	−2.399	0.000 *	0.063	1.053
R2				0.421
Adjusted R2				0.411
Join F(P)				0.000 *
Jarque-Bera Test				8.916
AICc				253.188

* p < 0.1.

and

Table 3. OLS regression results on influencing factors of carbon sinks.

Parameters	Coefficient	T Value	p Value	Standard Deviation	VIF
Technical level	2.157	6.139	0.000 *	0.351	1.370
Industrial structure	0.597	0.559	0.480	1.068	2.899
Energy intensity	1.402	8.204	0.000 *	0.171	3.395
Level of ecological resource constraints	2.426	4.344	0.002 *	0.559	1.150
R2				0.691
Adjusted R2				0.680
Join F(P)				0.000 *
Jarque-Bera Test				2.216
AICc				276.735

* p < 0.1.

.

The results show that the variance inflation factors (VIFs) are all much lower than 5, indicating that there is no multicollinearity problem among the variables in the equation. The goodness of fit (GoF) values of the equations with carbon emissions and carbon sinks as explanatory variables are 0.411 and 0.680, respectively. The joint F passes the statistical significance test at the 0.05 level, indicating that there is no heteroscedasticity in the modeling equations. By observing the p-value in the regression results, it can be seen that the influencing factors with carbon emissions as the explanatory variables have passed the significance test at the 0.05 level, indicating that energy intensity and upgrading industrial structure have a significant effect on carbon emissions. Among the influencing factors with carbon sinks as explanatory variables, the technology level, energy intensity, and level of ecological resource constraints passed the significance test at the 0.05 level, indicating that the above three variables have a significant effect on carbon sinks, while the industrial structure did not pass the significance test (Table 2 and Table 3).

4.2.2. GWR Model and Results

Considering the limitations of the OLS linear regression model, which only focuses on global characteristics, this study further constructs a GWR model to further analyze the local effects of the factors affecting carbon emissions and carbon sinks in Chinese mariculture. The geographically weighted regression analysis was performed with the help of the GWR tool in ArcMap 10.8. The results are shown in

Table 4. GWR regression results on influencing factors of carbon emissions.

Parameters	Bandwidth	Residual Squares	Sigma	AICc	R2	Adjusted R2
Value	0.115	2.242	0.138	−92.491	0.977	0.976

and

Table 5. GWR regression results on influencing factors of carbon sinks.

Parameters	Bandwidth	Residual Squares	Sigma	AICc	R2	Adjusted R2
Value	0.433	47.058	0.634	242.818	0.780	0.774

. The GoF values of the GWR model for the equations with carbon emissions and carbon sinks as explanatory variables are 0.976 and 0.774, and the AICc values are −92.492 and 242.818, respectively. By comparing Table 2 and Table 4, and Table 3 and Table 5, respectively, the results show that the fitting effect of the GWR model is significantly better than that of the OLS model. According to the existing research results, if the AICc difference between the GWR model and the OLS model is greater than 3, it indicates that the GWR model fits better [53]. The AICc value of the GWR is much lower than that of the OLS model, which further reflects the rationality of the GWR model.

4.2.3. Analysis and Discussion of Spatial Heterogeneity of Factors Influencing Carbon Emissions and Sinks

Carbon emissions and sinks from mariculture in China are influenced by a variety of factors and show obvious spatial differences in different regions (Figure 8). Among them, energy intensity and industrial structure upgrading have important effects on carbon emissions. From the spatial distribution of the regression coefficients, energy intensity has a significant positive effect on carbon emissions, especially in Jiangsu and Zhejiang, where the regression coefficient is as high as 1.668, which reflects the strong coupling relationship between energy consumption and carbon emission costs. It also indicates that mariculture enterprises are more inclined to exchange energy inputs for output growth, especially in pond farming and factory-long aquaculture, where the lack of green energy substitution technology leads to high energy consumption and carbon emissions per unit of output value. At the same time, in terms of fishing vessel operations, small fishing vessels have been upgraded to larger sizes to increase production capacity, and this pursuit of “economies of scale” does improve productivity in the short term, but ignores the increase in marginal carbon costs due to the increase in engine power. Thus, the current increase in energy intensity has not yet led to improvements in energy efficiency through technological advances, but rather to capacity expansion at the expense of increased energy consumption.

The impact of industrial upgrading on carbon emissions, on the other hand, presents a more complex situation. The upgrading of industrial structures in some regions, such as Shandong, Jiangsu, Zhejiang, Fujian, and Hainan, presents an inhibition of release, reflecting structural effects. High value-added, high-technology farming modes (e.g., recirculating water aquaculture, deep-sea aquaculture) are gradually replacing traditional crude aquaculture, resulting in a decline in carbon emissions per unit of output value, and reflecting a trend toward synergizing production efficiency and environmental protection. In other regions, such as Liaoning, Hebei, Guangdong, and Guangxi, structural upgrading has been accompanied by an increase in carbon emissions. This may be due to the “rebound effect” or “initial structural upgrading effect”, i.e., the introduction of new technologies and equipment that require more energy supply, such as higher-specification oxygenation systems, water quality monitoring equipment, etc., which in turn increase energy consumption in the short term. Although these investments have long-term green benefits, their short-term increase in energy intensity may offset the emission benefits of structural optimization.

Carbon sinks are strongly influenced by the level of technology, energy intensity, and the degree of ecological resource constraints (Figure 8). Among them, there is an obvious positive correlation between technological progress and carbon sinks, especially in the Pearl River Delta region, where advanced aquaculture technology not only improves the unit aquaculture efficiency of shellfish and algae, but also improves the aquaculture environment and reduces the mortality rate, thus enhancing the accumulation of biomass and the total amount of carbon sinks. This positive correlation indicates that technological progress not only improves output but also enhances the carbon fixation function of the ecosystem, which can be a win–win situation for both ecological and economic benefits. The positive effect of the degree of ecological resource constraints on the growth of carbon sinks suggests that the ecological functions of the shellfish carbon sink system can be effectively activated under strengthened institutional arrangements and ecological management. Strict ecological management (e.g., red line, habitat protection, germplasm resource management) constitutes a regulatory constraint on aquaculture behaviors and creates an “ecological output incentive” within the ecosystem. The higher the level of management, the greater the recovery of shellfish and algae populations, and the more carbon sinks are released. At the same time, this constraint also improves the efficiency of resource allocation, allowing high-value ecological zones to shift to carbon sink-oriented farming modes, such as marine pastures aimed at ecological restoration, and facilitating the transformation of farming activities from a single output to a diversified supply of ecological services.

The positive effect of energy intensity on carbon sinks suggests that carbon emissions and carbon sinks may increase simultaneously under the expansion of the farming scale. On the one hand, higher energy inputs tend to be used to expand the culture area, enhance unit density, or increase facility equipment, thus promoting the growth of the number of carbon sink carriers such as shellfish and algae. Thus, even as carbon emissions rise, carbon sinks grow accordingly. On the other hand, in aquaculture systems, carbon emissions and carbon sinks are not one-way opposites, but coexist in the same process. For example, electricity-driven water circulation systems and oxygenation systems increase energy consumption (increased emissions) but also optimize the ecosystem and promote algal photosynthesis (increased carbon sinks). This reflects the environmentally coupled nature of production systems, i.e., the dual nature of the environmental impacts of output activities, which may cause pollution as well as create ecological value. From a cost–benefit perspective, farmers may still choose to expand the scale of farming as long as the marginal benefits of carbon sink growth are higher than the marginal environmental costs associated with carbon emissions.

4.3. Spatial Spillover Effects of Factors Influencing Carbon Emissions and Sinks

4.3.1. Autocorrelation Test Results

Figure 9 depicts the autocorrelation coefficients of carbon emissions and carbon sinks, respectively. It can be seen that from 2008 to 2020, most of the Moran indexes of carbon emissions were negative and basically passed the significance level test of 0.05; and most of the Moran indexes of carbon sinks were positive and basically passed the significance level test of 0.1. This indicates that carbon emissions have a negative spatial correlation among coastal provinces, while carbon sinks have a positive spatial correlation.

4.3.2. Model Applicability Tests

The spatial correlation analysis of carbon emissions and carbon sinks in China’s coastal provinces indicates that spatial econometric models are more suitable for studying the driving factors. To select an appropriate spatial econometric model for investigating the spatial effects between energy intensity, industrial structure upgrading, and carbon emissions, as well as between the technological level, energy intensity, ecological resource constraints, and carbon sinks, this study conducted a series of model selection tests, with the results presented in

Table 6. Results of the applicability test of the spatial measurement model (carbon emissions).

Test Item	Test Value	p-Value
LM (SAR)	96.484	0.000
Robust LM (SAR)	19.519	0.000
LM (SEM)	79.725	0.000
Robust LM (SEM)	2.760	0.097
Hausman (Fixed Effect)	8.23	0.016
Wald (SAR)	29.25	0.000
Wald (SEM)	8.17	0.017
LR (SAR)	27.01	0.000
LR (SEM)	21.98	0.000

and

Table 7. Results of the applicability test of the spatial measurement model (carbon sinks).

Test Item	Test Value	p-Value
LM (SAR)	70.222	0.000
Robust LM (SAR)	5.153	0.000
LM (SEM)	82.310	0.000
Robust LM (SEM)	17.241	0.097
Hausman (Fixed Effect)	123.26	0.016
Wald (SAR)	25.87	0.000
Wald (SEM)	27.53	0.017
LR (SAR)	61.29	0.000
LR (SEM)	97.39	0.000

.

After autocorrelation testing, the Lagrange multiplier (LM) test was used to determine whether an OLS model or a spatial econometric model should be employed. The LM test results for carbon emissions and carbon sinks were significant at the 1% level, leading to the rejection of the OLS model in favor of a spatial econometric model. Among the spatial econometric models, the spatial Durbin model (SDM) was hypothesized to be the optimal choice for analyzing spatial effects, as it not only accounts for the spatial lag effect of the dependent variable but also captures the spatial spillover effects of explanatory variables [43]. In contrast, the spatial lag model (SLM) can only handle the spatial dependence of the dependent variable while ignoring the spillover effects of explanatory variables. The spatial error model (SEM), on the other hand, merely addresses the spatial autocorrelation of error terms and cannot directly capture spatial spillovers between variables. Therefore, the SDM provides a more comprehensive reflection of the complex spatial transmission mechanisms of regional carbon emissions and carbon sinks. Additionally, we employed the likelihood ratio (LR) test to evaluate whether the SDM could degenerate into either SEM or SLM. The results of the LR test, as presented in Table 6 and Table 7, allowed us to reject the null hypothesis at the 1% significance level, confirming that the SDM does not degenerate into SEM or SLM. Both the Wald and LR test results (Table 6 and Table 7) rejected the null hypothesis at the 1% level, thereby supporting the validity of SDM as the final model.

In summary, the SDM demonstrates significant advantages over SLM and SEM by more accurately capturing spatial dependence and spillover effects in the research context. Having successfully passed a series of model diagnostic tests, the Spatial Durbin Model proves to be the most appropriate choice for meeting the research objectives.

4.3.3. Analysis and Discussion of Spatial Spillover Effects of Factors Affecting Carbon Emissions and Sinks

When spatial spillover effects exist, not only are a region’s carbon emissions and carbon sinks influenced by certain factors, but neighboring regions are also affected by these factors. Therefore, the impacts of various factors on carbon emissions and carbon sinks are decomposed based on pathways and directions into direct and indirect effects. The direct effect refers to the total impact of a factor’s change on a region’s own carbon emissions/carbon sinks, which includes spatial feedback effects—meaning that when a factor in one region changes, it affects the carbon emissions/carbon sinks of neighboring regions, which in turn influence the original region’s emissions/sinks in a cyclical process. The indirect effect captures the impact of a factor’s change on the carbon emissions/carbon sinks of neighboring regions, representing the spatial spillover effect of the influencing factors. Both direct and indirect effects can be decomposed using the partial differential method, and their sum constitutes the total effect [54]. The calculation results are presented in

Table 8. Decomposition of spatial spillover effects of SDM model coefficients (carbon emissions).

Variables	Local Effect	Neighborhood Effect	Total Effect
Energy intensity	0.671 ***	0.581 ***	1.252 ***
	(6.95)	(2.84)	(7.03)
Upgrading of industrial structure	−0.280 *** (−4.22)	−0.460 *** (−4.92)	−0.740 *** (−5.95)
rho		−0.551 ***
		(−3.81)
sigma2_e		0.339 ***
		(−7.31)
Observations		117
R-squared		0.403

*** p < 0.01.

and

Table 9. Decomposition of spatial spillover effects of SDM model coefficients (carbon sinks).

Variables	Local Effect	Neighborhood Effect	Total Effect
Technical level	7.834 ***	−4.893 ***	2.941
	(7.38)	(−2.93)	(1.36)
Energy intensity	1.432 ***	1.203 ***	2.635 ***
	(16.09)	(5.66)	(11.75)
Level of ecological resource constraints	3.399 *** (8.86)	0.209 (0.27)	3.608 *** (3.91)
rho		−0.484 ***
		(−3.54)
sigma2_e		0.195 ***
		(−8.07)
Observations		117
R-squared		0.781

*** p < 0.01.

.

Regarding carbon emissions (Table 8), the direct effect, indirect effect, and total effect coefficients of energy intensity all passed significance tests. This shows that the increase in energy intensity will make the carbon emissions in the region and neighboring areas increase. It also shows that energy intensity has a positive impact on the increase in carbon emissions in the region, and that increased energy intensity leads to increased carbon emissions in the region and neighboring areas. When the energy intensity of fishery production in coastal provinces rises, the increase in diesel consumption of aquaculture fishing vessels and the expansion of electricity demand for pond and factory farming directly push up the level of carbon emissions in the region. At the same time, this effect will be transmitted to neighboring provinces through spatial spillover effects. On the one hand, industrial transfers in high-energy-intensity regions may spread energy-intensive production segments to neighboring regions; on the other hand, interregional interconnections of energy infrastructures will increase the pressure on electricity supply in regions with lower energy efficiency. In addition, increased energy intensity is often accompanied by the continuation of traditional production technologies, inhibiting the regional diffusion of clean energy technologies and further reinforcing this spatial correlation.

The advanced industrial structure passed the significance test under direct, indirect, and total effects, proving that the advanced industrial structure in the region has a negative effect on carbon emissions, and the carbon emissions of the neighboring regions are also reduced. When the regional fishery economy is transformed into a tertiary industry, high-value-added, service-oriented industries (e.g., aquatic logistics, leisure fishery, etc.) gradually replace energy-intensive traditional aquaculture and processing industries, directly reducing energy consumption intensity and, thus, reducing local carbon emissions. Moreover, this transformation will produce significant spatial spillover effects. On the one hand, the technology and management experiences of tertiary industries in advanced regions will spread to neighboring provinces through the demonstration effect, leading to the upgrading of the overall regional industry; on the other hand, the reconstruction of the regional industrial chain can lead to the centralization and optimization of high-energy-consuming segments, helping to avoid the energy waste caused by repetitive construction. In addition, the advanced industrial structure is often accompanied by the application of clean technology and the improvement of environmental standards. This trend in green transformation will foster benign competition among regions, further amplifying the effect of emission reduction. Overall, industrial structure upgrading can not only directly curb local carbon emissions, but also drive neighboring regions to achieve low-carbon development through spatial linkage mechanisms.

In terms of carbon sinks (Table 9), the direct effect coefficient of the technology level is 7.834, indicating that every unit increase in fishery technology innovation within the province can directly promote local carbon sinks by 7.834 units, which is mainly due to the improvement in aquaculture efficiency and the increase in the production of sink organisms (e.g., shellfish) brought about by the popularization of technology. The indirect effect coefficient of −4.893 shows that there is a negative spatial spillover effect of technological innovation; this may be due to the “siphon effect” formed by the technology-leading provinces, which attracts the inflow of resource elements from neighboring provinces and inhibits the enhancement of the carbon sink capacity of the neighboring regions; or there is a time lag in the diffusion of technology, which results in the imbalance of regional development in the short term. Although both the direct promotion and indirect inhibition effects passed the significance test, the total effect was not statistically significant due to the opposite direction of the two effects. This reveals that the impact of fishery technology innovation on carbon sinks has significant spatial heterogeneity, and that the technology spillover path needs to be optimized through regional synergistic mechanisms.

The effect of energy intensity on carbon sinks demonstrates a significant positive spatial effect. The results of the direct effect show that every 1-unit increase in energy intensity in this province can promote the increase of local carbon sinks by 1.432 units, which may be due to the increase in energy inputs leading to the expansion of the scale of aquaculture, which in turn improves the biomass of shellfish and their carbon sequestration capacity. The indirect effect coefficient of 1.203 reflects the positive spatial spillover of energy intensity, i.e., changes in energy efficiency in the province will lead to the simultaneous enhancement of carbon sink capacity in neighboring provinces through the channels of industrial chain linkage (e.g., feed supply, seedling transportation, etc.) and technology diffusion. The total effect also passes the significance test, confirming that energy intensity improvement has an overall promotion effect on regional carbon sink growth. This suggests that at the current stage of development, a moderate increase in fishery energy inputs may promote the accumulation of carbon sinks through the scale effect, but attention should be paid to balancing this relationship with the growth of carbon emissions.

The influence of ecological resource binding on carbon sinks is mainly manifested as a local effect. The direct effect coefficient of 3.399 shows that when the degree of aquaculture development (farmed area/farmable area) increases by 1 unit in the sea area of the province, it could promote the increase of local carbon sinks by 3.399 units. This is mainly attributed to the full utilization of aquaculture space, which enhances the production of carbon-sequestering organisms of shellfish and algae. It is worth noting that the total effect coefficient of 3.608 is similar and significant to the direct effect, while the indirect effect does not pass the significance test, suggesting that the impact of ecological resource binding has obvious geographic limitations and has not yet formed an effective regional synergistic effect. This phenomenon may be due to two reasons, as follows: first, the development of farming resources in coastal provinces is relatively independent, with weak spatial linkage; second, the influence of ecological carrying capacity is mainly confined to the local area, and it is difficult to produce significant radiation to neighboring provinces through channels such as industrial linkage or technology diffusion. Unlike the influence mechanisms of energy intensity and technology level, the promotion effect of ecological resource binding on carbon sinks focuses more on the development and utilization efficiency of local resource endowments.

5. Policy Recommendations

According to the analysis of dynamic changes in carbon emissions and carbon sinks from shellfish and algae in China’s mariculture from 2008 to 2023, it can be clearly seen that the balance between carbon emissions and carbon sinks has been changing dynamically throughout the process of large-scale and intensive development. In terms of temporal changes, direct carbon emissions have remained relatively stable, but the rapid growth of indirect carbon emissions indicates that the current aquaculture model’s dependence on energy, especially electricity, is gradually increasing. Meanwhile, the overall carbon sinks from shellfish and algae, although growing steadily, have not increased enough to fully offset the increase in carbon emissions. In terms of spatial distribution, shellfish carbon sinks in Liaoning, Shandong, Fujian, and Guangdong are outstanding, while the growth of algae carbon sinks in Zhejiang, Fujian, and Shandong has accelerated significantly, indicating that technology optimization has a significant role in enhancing carbon sink capacity. However, some regions, such as Hebei, have relatively high carbon emissions due to the high energy consumption of traditional aquaculture modes. In response to the above problems, the following recommendations are made in conjunction with the analysis of the factors affecting carbon emissions and carbon sinks in this study: (1) Liaoning and Hebei exhibit relatively high carbon emissions, with Hebei showing strong dependence on traditional energy-intensive aquaculture. It is recommended to prioritize energy restructuring, accelerate the retirement of outdated equipment, and pilot solar- or wind-powered farming technologies. Moreover, incentivizing the transition to less carbon-intensive farming systems, such as integrated multi-trophic aquaculture (IMTA), could help curb emissions. (2) Shandong shows a dual trend of strong shellfish and algae carbon sinks, along with varied industrial transformation effects. Zhejiang’s rapid algae sink growth reflects successful technological upgrades, but also rising electricity use. These regions should strengthen clean energy applications in intensive farming, continue upgrading industrial structures toward high-value-added services (e.g., marine biopharma, eco-tourism), and promote green standards in equipment renewal to reduce the energy rebound effect. (3) With high energy intensity and relatively mixed industrial transformation impacts, Jiangsu and Fujian should focus on enhancing energy efficiency, monitoring energy use in pond/factory operations, and promoting digital aquaculture platforms for precision energy and resource use. Fujian, with strong algae sink growth, can serve as a model for technology-driven carbon sink enhancement. (4) Guangdong and Guangxi face increasing emissions due to larger fishing vessels and industrial upgrading processes that may introduce new energy demands. Recommendations include guiding vessel renewal through clean engine standards, supporting low-carbon logistics infrastructure, and developing ecological compensation mechanisms to account for simultaneous increases in emissions and sinks. (5) Hainan should be encouraged to further develop low-carbon aquaculture tourism and high-end services, leveraging its clean energy advantage. Moreover, knowledge and best practices can be shared with neighboring regions to generate positive spillovers.

In order to ensure the operability and effectiveness of the regional recommendations, we propose an implementation pathway that involves tripartite collaboration among the government, industry, and research institutions. Among them, the government should play a leading role in formulating locally adapted low-carbon aquaculture development strategies, including piloting clean energy technologies such as solar energy in key mariculture parks, providing incentives such as financial subsidies and tax incentives, incorporating carbon sink capacity into the marine spatial planning and eco-compensation mechanisms, and promoting the incorporation of green GDP and low-carbon performance indicators into the evaluation system of local governments. In terms of industry, aquaculture enterprises should focus on promoting equipment renewal and energy structure optimization, adopting digital management platforms to improve energy use efficiency, and transforming to low-carbon models such as integrated multi-trophic aquaculture (IMTA). In addition, through the formation of regional green farming alliances or cooperatives, resource sharing and collaborative carbon reduction can be realized. Participation in carbon market trading and green certification can also help improve the sustainability image and market competitiveness of enterprises. Scientific research institutions should establish accounting and monitoring systems for carbon emissions and sinks applicable to different regions, carry out research and development and promotion of shellfish and algae aquaculture technologies with high carbon sinks, and assess the effectiveness of the implementation of various policy tools. In addition, academic institutions can build an interregional knowledge exchange platform and promote the replication of typical cases and capacity-building initiatives, so as to jointly advance the mariculture industry toward low-carbon and high-quality development.

6. Conclusions and Prospects

6.1. Conclusions

This study focuses on the mariculture of shellfish and algae in China’s coastal regions. A carbon emission accounting model was applied to quantify emissions from diesel and electricity consumption during aquaculture processes, while a carbon sink accounting model evaluated biological carbon sequestration, POC, and algae-derived RDOC. Net carbon values were calculated as the difference between emissions and sinks, followed by analysis of their spatiotemporal evolution characteristics at national and provincial scales. Additionally, OLS and GWR models were applied to identify the spatial heterogeneity of carbon emissions and sinks influencing factors, and Moran’s I and SDM were used to further analyze the spatial spillover effects. The main conclusions are as follows: (1) Both direct and indirect carbon emissions from mariculture show an increasing trend, with indirect carbon emissions increasing more significantly, reflecting the promotion of factory and power-intensive aquaculture modes. In Liaoning, Hebei, and Jiangsu, direct and indirect carbon emissions are basically growing at the same pace, while indirect carbon emissions in Shandong, Fujian, and Guangdong are growing more rapidly. (2) From 2008 to 2023, carbon sinks from shellfish and algae in the nine coastal provinces of China as a whole showed a steady growth trend, and shellfish carbon sinks dominated in each province, with Liaoning, Shandong, and Fujian contributing more. The accelerated growth of algae carbon sinks, especially in Fujian, Zhejiang, and Shandong, is closely related to the expansion of aquaculture scale, technology improvement, and species optimization. (3) The net carbon value of mariculture in some regions was positive (carbon emissions exceeded carbon sinks), such as Liaoning and Hebei, while the net carbon value in Jiangsu, Zhejiang, and Fujian was negative (carbon sinks exceeded carbon emissions). There was an upward trend in net carbon values in most regions. (4) The level of technology and the ecological resource constraints are conducive to the increase in carbon sinks in mariculture, and the advanced industrial structure has a certain binding effect on carbon emissions. Energy intensity has a certain promotion effect on carbon emissions and carbon sinks, which indicates that energy consumption produces carbon emissions while promoting the increase in carbon sinks. (5) Energy intensity has a significant positive spillover effect on carbon emissions, while industrial structure upgrading shows a negative dampening effect. For carbon sinks, the technology level shows negative spillovers, while energy intensity generates positive spillovers. In addition, the spatial spillover effect of ecological resource binding did not pass the significance test.

Taking into account the research results, we found that to effectively realize the low-carbon transition of mariculture in China’s coastal areas, policymakers need to recognize and address the reality of the imbalance of carbon emissions and sink capacity among regions. Instead of adopting a “one-size-fits-all” approach to policymaking, differentiated spatial policy tools should be developed based on local energy intensity, carbon sink potential, and industrial transformation. This requires combining marine spatial planning with clean energy deployment, scientifically incorporating regional carbon sink capacity, improving ecological compensation mechanisms, and promoting the incorporation of low-carbon development indicators into local government performance appraisal systems. Only through precise governance based on local evidence can China realize strategic synergy between the “dual-carbon” goals while safeguarding the development of the marine economy.

6.2. Limitations and Future Research

Most of the previous studies have focused on the evaluation of carbon sinks or carbon emissions in a single location, as well as studies combining the two. In studies combining carbon sinks and carbon emissions, carbon sinks are mostly based on shellfish and algae biomass carbon sinks or shellfish and algae POC and algae RDOC assessment, and carbon emissions are mostly industry-based or dominated by diesel carbon emissions from mariculture vessels, and electricity carbon emissions from pond and factory farming. However, carbon sinks and carbon emissions are important parts of carbon neutrality; their specific and perfect accounting is crucial. It is important to sort out the mechanisms of each link and accurately assess them. This study focuses on improving the understanding of carbon sinks and carbon emissions associated with mariculture. However, this study does not consider the rest of the ocean carbon sinks and emissions (for example, coastal wetland carbon sinks and offshore water carbon sinks under marine carbon sinks, and marine fishing carbon emissions under marine carbon emissions). Therefore, future research should combine the multiple sources of marine carbon sinks and carbon emissions, with a view to establishing a more complete and accurate accounting system for carbon sinks and carbon emissions, and providing more accurate data support for the realization of carbon neutrality.