In the context of addressing climate change and advancing “carbon neutrality,” enhancing marine carbon sink capacity has become a national strategy for many countries. Scientifically evaluating marine carbon sink performance and its driving mechanisms is crucial for optimizing marine environmental governance. This paper employs the Super-SBM model to measure the marine carbon sink performance of 11 coastal provinces (municipalities and autonomous regions) in China from 2008 to 2022, and empirically examines the nonlinear impact of heterogeneous environmental regulations, as well as the mediating mechanisms of technological innovation and industrial upgrading. The results show that: Firstly, China’s marine carbon sink performance has generally improved, but regional development is uneven. Secondly, command-and-control and market-incentive environmental regulations exhibit an “inverted U-shaped” relationship with performance, while social-supervised environmental regulations show a “U-shaped” relationship, indicating that there is an optimal intensity range for regulatory effects. Finally, technological innovation and industrial upgrading are important transmission pathways through which environmental regulations affect carbon sink performance. The research findings provide theoretical references and empirical evidence for governments to develop differentiated and diversified environmental regulation policy portfolios aimed at enhancing marine carbon sink capacity.
heterogeneous environmental regulationsindustrial upgradingmarine carbon sinkmarine carbon sink performancetechnological innovationThe author(s) declared that financial support was not received for this work and/or its publication.section-at-acceptanceSolutions for Ocean and Coastal SystemsIntroduction
Against the dual backdrop of intensifying global climate change and the advancement of “carbon neutrality” goals, the ocean—as the largest and most active carbon sink in the Earth’s system—has seen its carbon sequestration potential and ecological service functions become core issues in international climate governance and marine ecological conservation. The IPCC Sixth Assessment Report highlights that the ocean absorbs approximately 23%–33% of annual anthropogenic CO2 emissions (Wei and Wang, 2024), playing an irreplaceable and critical role in regulating the global carbon cycle and mitigating global warming (Gruber et al., 2023; Zhou et al., 2024). Enhancing marine carbon sink capacity is not only a crucial pathway for achieving long-term temperature control targets but also essential for safeguarding marine ecological integrity, conserving biodiversity, and advancing sustainable development in coastal regions (Rickels et al., 2024).
“Marine carbon sink performance,” as a core indicator for measuring the efficiency of human policy interventions and management efforts in enhancing ocean carbon sequestration, comprehensively reflects the effectiveness of a region or country in marine ecological protection, low-carbon industrial transformation, and blue governance capacity building. However, improving marine carbon sink performance does not solely rely on the natural carbon sequestration capacity of ecosystems but is also profoundly influenced by the approaches of human policy interventions. Environmental regulation, as a key policy tool for governments to address environmental externalities (Mbanyele and Wang, 2022) and guide the environmental behavior of economic entities, directly determines the development efficiency and sustainability of marine carbon sink functions through its type selection, intensity setting, and implementation pathways. Therefore, systematically analyzing the driving mechanisms of heterogeneous environmental regulations on marine carbon sink performance and clarifying the operational patterns of different types of policy tools is of great theoretical value and practical significance for optimizing marine environmental governance systems and supporting the achievement of the “dual carbon” goals (Zhang and Lin, 2022).
China, as a major maritime nation, possesses a long coastline and vast jurisdictional waters, abundant marine carbon sink resources, and a robust endowment. Steadily enhancing marine carbon sink capacity and improving the ocean’s ability to respond and adapt to climate change have become crucial components of the national strategy. In recent years, the Chinese government has been progressively establishing a diversified institutional system for marine ecological civilization, comprehensively utilizing various types of environmental regulation tools to strengthen marine environmental protection and ecological restoration (Zhang and Lin, 2022). In terms of command-and-control regulations, a strict marine ecological redline system has been implemented, total pollutant discharge control in key sea areas has been strengthened, and financial investment in marine ecological restoration has been continuously increased. Regarding market-incentive regulations, tools such as differentiated collection of sea area use fees, marine ecological compensation mechanisms, and blue bonds are being explored to guide marine-related entities towards green transformation through economic leverage. For social-supervised environmental regulations, the marine environmental information disclosure system has been improved, public participation channels and public interest litigation pathways have been unblocked, enhancing the transparency and level of multi-stakeholder co-governance in marine environmental governance. These policy practices have significantly improved the quality of marine ecosystems (Xu, 2022). However, challenges persist, including imbalanced regulatory intensity across regions, insufficient policy coordination, and incomplete long-term mechanisms. The actual contribution of these measures to marine carbon sink performance still requires further clarification through scientific assessment.
Academic research on the relationship between environmental regulation and carbon performance has yielded numerous findings, but most focus on the impact of marine environmental regulation on the marine economy or the assessment of carbon performance in the industrial sector. Literature specifically targeting marine carbon sink performance and delving into its impact mechanisms remains relatively scarce. On one hand, most studies concentrate on the macro-level effects of marine environmental regulation on technological innovation, industrial structure upgrading, or marine economic performance. For instance, research by Wang et al. (2023) found a “U-shaped” relationship between marine environmental regulation and marine carbon efficiency, suggesting that marine environmental regulation can indirectly promote marine carbon efficiency through transmission mechanisms like resource allocation efficiency and optimization of the marine industrial structure. Cheng and Chen (2021), using an evolutionary game model, proposed that marine carbon sinks could promote economic growth through green technology and carbon taxes by 2030. Sun et al. (2023) argued that heterogeneous environmental regulations can affect the green development of the marine economy through marine industrial structure adjustment and technological innovation changes. Ren and Ji (2021) suggested that environmental regulations impact the sustainable development of the marine economy through technological innovation, and whether the “compensation effect” or the “offset effect” dominates depends on the technological grade. Other scholars have focused on the impact mechanism of environmental regulation on carbon neutrality performance. Research by Zheng et al. (2025) found that the impact of environmental regulation on carbon neutrality performance initially inhibits and then promotes, with green technological innovation playing a partial mediating role between environmental regulation and carbon neutrality performance. On the other hand, research on carbon performance mostly unfolds at the provincial or industry level and primarily focuses on carbon emission performance rather than carbon sink performance. For example, Chen et al. (2022) analyzed the impact of urban density on spatial carbon performance using Shanghai as a case study. Liu et al. (2023) explored the enhancing effect of the digital economy on urban carbon performance. Yu and Guo (2025) studied the impact of industrial integration on the carbon performance of manufacturing enterprises. Wang et al. (2025) argued that carbon emission reduction targets enhance carbon performance by promoting two capabilities: resource reconfiguration and organizational learning. Yu et al. (2021) used the synthetic control method to evaluate the impact effect of the carbon trading mechanism on the carbon performance level of pilot provinces and cities. These studies provide valuable references for understanding the economic and environmental effects of environmental regulations, but they still have three main limitations: First, there is a lack of research specifically targeting the carbon sink performance of the ocean as a special ecosystem, casting doubt on the applicability of relevant conclusions in the marine context. Second, most literature builds models based on linear assumptions, neglecting the potential “threshold effects” or “nonlinear impacts” of environmental regulations, making it difficult to capture the complex relationship between policy intensity and performance. Third, the “black box” of how environmental regulations affect marine carbon sink performance through mediating mechanisms has not been fully opened, lacking a systematic theoretical framework and empirical testing.
Based on the aforementioned practical and theoretical background, this paper utilizes panel data from 11 coastal provinces (municipalities, autonomous regions) from 2008 to 2022. It comprehensively employs panel regression, U-test, and mediation effect models to systematically examine the nonlinear impact mechanism of heterogeneous environmental regulations on marine carbon sink performance. From the dual perspectives of “technological innovation” and “industrial structure upgrading,” it reveals the mediating roles and practical pathways. The marginal contributions of this paper are mainly reflected in three aspects: First, in terms of assessment methodology, it uses the Super-SBM model to scientifically measure the marine carbon sink performance of China’s coastal areas from 2008 to 2022, overcoming the limitation of traditional DEA models being unable to distinguish the efficiency levels of effective units, thus providing a more refined measurement basis for marine carbon sink efficiency research. Second, in terms of theoretical mechanism, it breaks through the linear relationship assumption of existing studies, empirically testing the nonlinear relationship between three types of environmental regulation tools—”command-and-control,” “market-incentive,” and “public-participation”—and marine carbon sink performance, thereby more comprehensively revealing the complex correlation between policy intensity and performance. Third, regarding transmission mechanisms, by constructing a mediation effect model, it identifies the transmission role of technological innovation and industrial upgrading in the process where heterogeneous environmental regulations affect marine carbon sink performance, thus clarifying the pathway of “policy tools → mediating mechanisms → carbon sink performance,” providing a theoretical basis and empirical support for the government to design differentiated and refined marine environmental policies.
The structure of the paper is as follows: The second part conducts theoretical analysis and proposes research hypotheses; the third part is the research design, including model specification, variable selection, and data source description; the fourth part presents the calculation results of marine carbon sink performance and its spatiotemporal evolution characteristics, as well as the baseline regression results of heterogeneous environmental regulations and marine carbon sink performance, heterogeneity analysis, and robustness checks; the fifth part further discusses the mediating effects of technological innovation and industrial upgrading; finally, the research conclusions and discussion are provided.
Theoretical analysis and research hypothesesThe nonlinear impact of heterogeneous environmental regulations on marine carbon sink performance
As a core policy tool for governments to correct environmental externalities and address market failures, environmental regulations profoundly influence marine carbon sink performance through multiple pathways. Based on externality theory, marine carbon sinks possess typical public good attributes and positive externality characteristics. The ecological value generated by their carbon sequestration function cannot be fully quantified or compensated through market mechanisms, leading to a lack of motivation among marine-related entities to proactively enhance marine carbon sink capacity. This results in market failure in the allocation of resources for marine carbon sinks (Yang and Shen, 2024). Environmental regulations internalize the external effects of marine carbon sinks through mandatory constraints, economic incentives, and social guidance measures, optimizing the environmental behavior decisions of marine-related entities and providing institutional guarantees for improving marine carbon sink performance.
Depending on the mechanisms and implementation pathways of regulatory tools, environmental regulations can be categorized into three types: command-and-control, market-based incentives, and social-supervised environmental regulation (Ye et al., 2022). Due to significant differences in the constraint intensity, operational carriers, and incentive logic of these three types of regulatory tools, their impact on marine carbon sink performance is not linear but exhibits a nonlinear characteristic of “intensity threshold dependence.”
The relationship between command-and-control environmental regulations and marine carbon sink performance
Command-and-control environmental regulations, centered on government administrative authority, directly constrain the environmental behavior of marine-related entities through measures such as establishing mandatory standards, implementing administrative permits (e.g., marine discharge permits), and issuing bans (e.g., fishing moratoriums) (Hu et al., 2024). During the phase of moderate regulatory intensity, the “constraint effect” dominates: by setting clear compliance baselines, these regulations quickly curb highly polluting activities of marine enterprises (such as direct discharge of aquaculture wastewater and ship oil spills), reduce human interference in marine ecosystems, and create foundational conditions for the restoration of carbon sink ecosystems like mangroves and seagrass beds, thereby promoting the improvement of marine carbon sink performance.
However, according to the “law of diminishing marginal utility” and “compliance cost theory,” when the intensity of command-and-control regulations exceeds the optimal threshold, their “rigidity” drawbacks gradually become apparent and produce negative effects. On one hand, excessively high compliance costs (e.g., marine enterprises purchasing pollution treatment equipment) may divert resources away from research and development in marine environmental protection technologies (Zhao and Luo, 2024). On the other hand, overly detailed administrative directives may rigidify the production models of marine-related entities, suppressing the market’s efficiency in optimizing the allocation of marine carbon sink resources (Li et al., 2025), ultimately hindering marine carbon sink performance. Based on this, the following hypothesis is proposed:
H1a: Command-and-control environmental regulations exhibit an inverted U-shaped relationship with marine carbon sink performance.
The relationship between market-based incentive environmental regulations and marine carbon sink performance
Market-based incentive environmental regulations utilize market mechanisms and economic instruments (such as differentiated marine area usage fees, marine carbon sink subsidies, and blue bonds) to internalize environmental costs, guiding marine-related entities to voluntarily adopt environmentally friendly behaviors (Sun et al., 2024; Li et al., 2024). In the initial stage of low regulatory intensity, the “price signal effect” and “innovation compensation effect” are significant: appropriate economic incentives effectively motivate marine-related entities to seek low-cost emission reduction pathways, promoting the development of low-carbon technologies (such as marine carbon capture technology and carbon sequestration technologies in ecological aquaculture) and directing resource flows toward the marine carbon sink industry. This directly enhances the carbon sequestration capacity of marine ecosystems, thereby improving marine carbon sink performance.
However, according to the theories of “policy arbitrage” and “resource misallocation,” when the intensity of market-based incentive regulations exceeds a reasonable range, negative “crowding-out effects” begin to emerge. Excessive subsidies may induce “policy arbitrage” behaviors among marine-related entities (such as fabricating the scale of marine carbon sink projects to obtain subsidies), leading to the diversion of incentive resources to non-substantive carbon sink activities. On the other hand, excessively high environmental taxes (such as surcharges on marine discharge taxes) may distort market signals, increase operational costs for marine enterprises, and divert funding from marine carbon sink projects (such as coral reef ecological restoration), ultimately suppressing the improvement of marine carbon sink performance (Zhang and Qiao, 2021). Based on this, the following hypothesis is proposed:
H1b: Market-based incentive environmental regulations exhibit an inverted U-shaped relationship with marine carbon sink performance.
The relationship between social-supervised environmental regulations and marine carbon sink performance
Social-supervised environmental regulations rely on public participation, media, environmental organizations, and other social forces to constrain marine-related entities through informal means such as environmental information disclosure, public opinion supervision, and public interest litigation (Xing et al., 2022). In the initial stage, due to information asymmetry, limited public awareness of marine environmental protection, and corporate “greenwashing” practices, the “reputation mechanism” of social supervision struggles to function effectively. It may even slightly inhibit marine carbon sink performance by increasing the costs for marine-related enterprises to respond to public pressure, exhibiting characteristics of “low-efficiency initiation.”
As societal environmental awareness grows, transparency of marine environmental information improves, and supervision systems mature (e.g., amplified by new internet media), social-supervised environmental regulations enter an effective phase: the “reputation mechanism” and “self-monitoring effect” are significantly enhanced. To maintain social legitimacy and brand reputation, marine-related entities proactively fulfill their marine environmental responsibilities, implement substantive marine carbon sink actions (Ye et al., 2021), and drive continuous improvement in marine carbon sink performance, demonstrating characteristics of “late-stage accelerated enhancement.” Based on this, the following hypothesis is proposed:
H1c: Social-supervised environmental regulations exhibit a U-shaped relationship with marine carbon sink performance.
Mediating effects of technological innovation and industrial upgrading
The “Porter Hypothesis” provides a core theoretical foundation for understanding the medium-to-long-term economic impacts of environmental regulations, positing that well-designed environmental regulations can stimulate an “innovation compensation” effect that not only offsets compliance costs but may even enhance the competitiveness of enterprises or industries (Wang et al., 2021). This paper extends this theoretical framework to the marine carbon sink domain, examining the crucial mediating roles of technological innovation and industrial structure upgrading in the process through which heterogeneous environmental regulations influence marine carbon sink performance.
The mediating role of technological innovation
Environmental regulations drive technological innovation by altering the cost-benefit expectations of enterprises. Firstly, the mandatory standards of command-and-control regulations directly compel coastal enterprises to invest resources in the research and development of pollution control technologies and clean production technologies to meet compliance requirements, thereby reducing the discharge of substandard wastewater into the ocean. Secondly, market-based incentive regulations change the relative prices of production factors (e.g., by increasing marine area usage costs or providing green technology subsidies), offering clear economic incentives for coastal enterprises to engage in low-carbon technological innovation. This enables them to reduce environmental compliance costs and seek new market opportunities through independent technological innovation (Zhang and Yao, 2018). Social-supervised environmental regulations shape the social image and reputation capital of coastal enterprises, stimulating their initiative for green technological innovation from the demand side in response to consumer and public environmental concerns.
However, this incentive effect is not linear. According to resource constraint theory, excessively high regulatory intensity may inhibit innovation vitality by diverting corporate R&D funds and increasing uncertainty (Zhang, 2019). Simultaneously, the impact of different regulatory tools on technological innovation varies: command-and-control regulations may tend to promote “end-of-pipe treatment technology” innovation, while market-based incentive regulations are more likely to stimulate “clean production process” innovation (Dong et al., 2019). These technological innovations ultimately contribute to the improvement of marine carbon sink performance by enhancing resource utilization efficiency and reducing pollutant discharge into the ocean. Therefore, heterogeneous environmental regulations are likely to exert a nonlinear influence on technological innovation, which in turn nonlinearly affects marine carbon sink performance. Based on this, the following hypothesis is proposed:
H2: Technological innovation plays a mediating role between heterogeneous environmental regulations and marine carbon sink performance, and this mediating pathway is subject to nonlinear influences.
The mediating role of industrial structure upgrading
Industrial structure upgrading serves as another crucial pathway through which environmental regulations affect ecological performance. According to industrial structure theory, environmental regulations promote the transition of industrial structure towards greening and advanced development by altering the relative returns and factor allocation across different industries (Liu and Chen, 2022). Command-and-control regulations directly phase out backward production capacities with high pollution and energy consumption (such as traditional extensive fishing and coastal polluting industries), thereby creating development space for modern marine industries characterized by high technological content and low environmental impact (e.g., marine renewable energy, high-end tourism, and ecological aquaculture). This drives a “passive upgrading” of the industrial structure (Ye et al., 2021; Wang et al., 2023).
Market-based incentive regulations, by changing the relative profitability of different industries, guide production factors to spontaneously flow from traditional sectors to green sectors, inducing an “active upgrading” of the industrial structure (Zheng et al., 2021). The differentiated collection of sea area usage fees can encourage efficient and sustainable utilization of marine spatial resources. Social-supervised environmental regulations, through influencing consumers’ green preferences and investors’ ESG (environmental, social, and governance) assessments, promote the green transformation of marine industries from both the demand side and the capital supply side.
Simultaneously, the process of marine industrial structure upgrading exhibits significant “structural inertia” and “transformation thresholds.” According to path dependence theory, adjustments in industrial structure require overcoming existing technological trajectories and institutional arrangements, leading to a nonlinear impact of environmental regulations (Zhu et al., 2024). Only when regulatory intensity reaches a certain threshold can fundamentally changes in marine industrial structure be triggered. The advancement and greening of the marine industrial structure signify an overall improvement in marine resource utilization efficiency and ecological benefits, thereby directly enhancing marine carbon sink performance.
Research designModel specification
To examine the nonlinear impact of heterogeneous environmental regulations on marine carbon sink performance, this study tests Hypothesis 1 by incorporating the quadratic term of the environmental regulation variable, constructing the regression model shown in Equation 1:
MCSPit=α0+α1ERt+α2ERt2+α2Controlsit+λt+δt+ϵit
In the equation, MCSEit represents marine carbon sink performance, ERit denotes heterogeneous environmental regulations (specifically command-and-control, market-based incentive, and social-supervised environmental regulations), ERit2 is the quadratic term of heterogeneous environmental regulations, Controlsit Controls it represents the control variables,( λi)、( δt) denote province fixed effects and year fixed effects respectively, subscripts i and t indicate province and year, and ϵit is the random error term.
To test the mediating effects of marine technological innovation and industrial structure upgrading, this study constructs the following mediation effect models to examine Hypotheses 2 and 3:
In the equations, MTIit and MIUit represent technological innovation and industrial upgrading, respectively. The meanings of the remaining variables are the same as above.
Variable selection
(1) Dependent Variable
Marine Carbon Sink Performance (MCSP). This study employs the Super-SBM model to evaluate the marine carbon sink performance values of 11 coastal provinces in China from 2008 to 2022. The Super-SBM model is based on the SBM model proposed by Tone in 2001. The SBM model is a Data Envelopment Analysis (DEA) method that is independent of orientation and direction, utilizing slack variables to assess efficiency and accounting for the disparities between input and output terms (Tone, 2001). The Super-SBM model, an improvement made by Tone in 2002 on the SBM model, allows efficiency values to be equal to or greater than 1 (Lee, 2022). This characteristic enables the study to avoid being limited to the Tobit model, which only handles censored data, in the regression analysis.
The indicator system for MCSP includes the following input indicators: land (sea area), capital investment, labor input, and marine capital stock. The output indicator is the marine carbon sink volume. This indicator is calculated based on the industry standard “Marine Carbon Sink Accounting Method” (HY/T 0349-2022), which estimates China’s overall marine carbon sink capacity by accounting for the carbon sinks of marine ecosystems represented by mangroves, salt marshes, seagrass beds, phytoplankton, macroalgae, and shellfish. Specific indicators are shown in Table 1.
Marine carbon sink performance indicator system.
Indicator type
Indicator name
Indicator definition
Input Indicators
Land Area
Sum of the area of marine nature reserves and the aquaculture area of shellfish and algae.
Capital Investment
Financial expenditure on marine environmental protection.
Labor Input
Number of personnel at all levels in the marine environmental protection system.
Marine Capital Stock
Marine capital stock for the current year calculated using the perpetual inventory method.
Output Indicator
Marine Carbon Sink Volume
Annual marine carbon sink volume.
(2) Explanatory Variables
Command-and-control environmental regulation (CCER): Measured by the investment in marine pollution control. This refers to the total funds allocated for activities such as marine pollution treatment, marine ecosystem restoration, and marine environment monitoring and regulation.
Market-based incentive environmental regulation (MBER): Measured by the total collection of sea area usage fees. This indicator encourages marine resource users to incorporate environmental costs into their own cost considerations through resource usage charges, thereby helping to reduce extensive utilization of marine resources.
Social-supervised environmental regulations (SSER): Measured by the number of news disclosures on marine environmental information. Media coverage shapes public oversight through information dissemination. The frequency of reporting not only reflects public attention to environmental issues but also helps overcome information insularity that may result from the singularization of government regulation.
(3) Mediating Variables
Marine technological innovation (MTI): Measured by the number of marine technology patent grants per capita. Compared to patent applications, the number of granted patents better reflects both the quality of technological innovation and its actual output outcomes.
Marine industrial upgrading (MIU): Measured by the ratio of the output value of the tertiary marine industry to the total marine production value. Industrial structure upgrading typically involves a shift from primary and secondary industries to the tertiary sector. An increase in the proportion of the tertiary industry indicates a transition of the marine economy from traditional resource exploitation and processing manufacturing to high-value-added industries.
(4) Control Variables
Urbanization Rate (UR): Measured by the proportion of urban population to total population in each province. Population Density (POP): Measured by the ratio of total population to land area in each province. Marine Economic Development (MED): As a crucial supporting factor for the development of the marine carbon sink industry, this is measured by the ratio of gross ocean product to gross regional product. Foreign Investment Proportion (FIP): Measured by the ratio of total foreign investment to gross regional product. Openness Level (OPEN): Measured by the ratio of total import and export value to gross ocean product.
Data sources and description
The research sample of this paper covers data from 11 coastal provinces and municipalities in China from 2008 to 2022, yielding a total of 165 observations. The data sources include the China Statistical Yearbook, China Trade and External Economic Statistical Yearbook, China Marine Economy Statistical Yearbook, China Fishery Statistical Yearbook, China Environmental Statistical Yearbook, as well as the China Ecological and Environmental Statistics Annual Report and the China Marine Ecological and Environmental Status Bulletin, the specific sources of the data are shown in Table 2. To mitigate heteroscedasticity issues in the data, logarithmic transformation was applied to the selected non-ratio variables. The screening, preprocessing, and analysis of the selected data were conducted using Excel and Stata 17 software to ensure the accuracy of data processing and the reliability of analytical results. The descriptive results are presented in Table 3.
Data source description.
Variables
Data source
Marine Carbon Sink Performance (MCSP)
China Marine Statistical Yearbook, China Marine Economy Statistical Yearbook, China Fishery Statistical Yearbook, Industry Standard for Accounting Methods of Economic Value of Marine Carbon Sink (HY/T 0349-2022)
China Statistical Yearbook, China Marine Statistical Yearbook
Marine industrial upgrading (MIU)
China Marine Statistical Yearbook
Urbanization Rate (UR)
China Statistical Yearbook
Population Density (POP)
Statistical Yearbooks of Provinces
Marine Economic Development (MED)
China Statistical Yearbook, China Marine Economy Statistical Yearbook
Foreign Investment Proportion (FIP)
China City Statistical Yearbook, China Statistical Yearbook
Openness Level (OPEN)
China Statistical Yearbook
Results of descriptive statistics of variables.
Variable
N
Mean
SD
Min
Max
OCSP
165
0.6474
0.5745
0.0364
2.3523
CCER
165
5.9260
0.9306
1.3080
8.1200
MBER
165
8.3476
1.2852
4.0641
10.2445
SSER
165
3.6589
1.0690
0.6931
5.6021
MTI
165
0.7034
0.1290
0.0380
0.8000
MIU
165
0.3230
0.1450
0.0050
0.6846
UR
165
0.6525
0.1304
0.3796
0.9110
POP
165
7.7372
0.3832
6.0355
8.5204
MED
165
0.1794
0.0874
0.0520
0.3740
FIP
165
0.8795
0.9837
0.1200
7.0480
OPEN
165
0.0298
0.0195
0.0055
0.1206
Based on the China Marine Statistical Yearbook, the 11 coastal provinces and municipalities are categorized into the Northern Marine Economic Circle (Liaoning Province, Hebei Province, Tianjin Municipality, Shandong Province), the Eastern Marine Economic Circle (Jiangsu Province, Shanghai Municipality, Zhejiang Province), and the Southern Marine Economic Circle (Fujian Province, Guangdong Province, Guangxi Zhuang Autonomous Region, and Hainan Province), the map of the study area is shown in Figure 1.
Map of the coastal study area.
Map highlighting various regions along China's eastern coast, including Liaoning, Tianjin, Hebei, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Guangxi, and Hainan. Includes latitude and longitude markers, and a small inset map on the lower right.Results and analysisFactual characteristics
Overall, China’s MCSP showed an upward trend from 2008 to 2022. In terms of the three major marine economic circles, the northern and eastern marine economic circles exhibited an upward trend, while the southern marine economic circle gradually declined. The MCSP value of the northern marine economic circle increased from 0.3018 to 0.8087 in Table 4, while that of the eastern marine economic circle rose from 0.6636 to 1.0047. In contrast, the MCSP of the southern marine economic circle decreased from 0.8221 to 0.6513.
MCSP values in China, 2008-2022.
Region
Northern marine economic circle
Eastern marine economic circle
Southern marine economic circle
Mean
Tianjin
Hebei
Liaoning
Shandong
Jiangsu
Shanghai
Zhejiang
Fujian
Guangdong
Guangxi
Hainan
2008
0.0645
0.2604
0.4419
0.4402
0.5966
1.0544
0.3397
1.0409
0.3285
1.5367
0.3821
0.4324
2009
0.1487
0.3781
1.0985
0.6188
0.6153
1.0308
0.4287
1.0173
0.3349
1.4997
0.3590
0.5020
2010
0.0677
0.2996
1.1305
0.4565
0.6202
0.3520
0.3306
0.7193
0.2134
1.4815
0.3354
0.4004
2011
0.0965
0.3256
1.0588
0.4686
0.6126
0.4668
0.4407
0.6804
0.2292
1.6112
0.2571
0.4165
2012
0.0990
0.3231
1.1026
0.4750
0.6823
0.5901
0.4923
1.0304
0.2709
1.4365
0.2970
0.4533
2013
0.0507
0.2080
1.0449
0.3263
0.4784
0.3804
0.3200
0.4740
0.1825
2.1562
0.2321
0.3902
2014
0.0518
0.2109
1.1918
0.3832
0.5097
0.4494
0.3567
0.5037
0.1880
2.0294
0.2623
0.4091
2015
0.0553
0.2084
1.3015
0.3775
0.4568
0.4747
0.3816
0.4634
0.1927
2.1839
0.2349
0.4220
2016
0.0667
0.2516
1.9879
0.3742
0.5418
0.5264
0.3366
0.4167
0.2036
1.8804
0.1835
0.4513
2017
0.0844
0.3015
2.1524
0.4325
0.5860
0.6677
0.4021
0.4823
0.2097
1.8320
0.1889
0.4893
2018
0.0639
0.2389
2.3523
0.3910
0.5832
1.2052
0.4335
0.4496
0.2015
1.8060
0.1467
0.5248
2019
0.0364
0.2271
2.1905
0.4396
0.6265
1.3983
0.4271
0.5260
0.2313
1.6602
0.1907
0.5302
2020
0.0653
0.2639
2.2924
0.4558
0.6154
1.3834
0.4221
0.5815
0.2439
1.6495
0.1803
0.5436
2021
0.0694
0.3043
2.2498
0.5380
0.7349
1.4881
0.4653
0.6922
0.2826
1.4363
0.2225
0.5656
2022
0.0799
0.3517
2.1684
0.6346
1.0462
1.4693
0.4987
0.8641
0.3575
1.1462
0.2372
0.5903
Mean
0.0733
0.2769
1.5842
0.4541
0.6204
0.8625
0.4050
0.6628
0.2447
1.6897
0.2473
Due to space limitations, only partial calculation results are listed.
At the provincial level, there were significant differences in the MCSP values of the 11 coastal provinces, municipalities, and autonomous regions. The development of MCSP among the provinces and cities in the northern marine economic circle was highly uneven. Liaoning achieved a notably high average MCSP value of 1.5842, while Tianjin and Hebei had relatively low averages of 0.0733 and 0.2769, respectively. Shandong recorded an average of 0.4541. Liaoning’s high performance value can be attributed to its abundant marine resources. In 2022, the mariculture area for shellfish and algae in Liaoning reached 466,433 hectares and 13,530 hectares, respectively (Yu et al., 2024). The shellfish mariculture area ranked first in the country, providing a crucial foundation for enhancing marine carbon sinks. In contrast, Tianjin’s MCSP consistently remained at a low level, with values mostly ranging between 0.05 and 0.1. This is primarily due to severe coastal seawater pollution and the moderate eutrophication of wetland waters in Tianjin, which have led to the fragility of the marine ecological environment and caused damage to coastal ecosystems and marine fisheries (Gao et al., 2022). The MCSP values of provinces and cities in the eastern marine economic circle were relatively stable, with minor disparities among them. Jiangsu and Zhejiang had average MCSP values of 0.6204 and 0.4050, respectively, while Shanghai achieved a higher average of 0.8625, showing an overall trend of initial decline followed by an increase. The southern marine economic circle exhibited significant overall disparities in MCSP, with Guangxi standing out for its exceptional performance level. Guangxi’s average MCSP value was 1.6897, as the gradual reduction in marine fishing, coupled with rapid growth in mariculture, effectively alleviated the ecological pressure on the marine environment caused by overfishing.
Heterogeneity test of environmental regulation tools
Table 5 presents the results of the heterogeneity test examining the impact of different environmental regulation tools on marine carbon sink performance. Detailed analysis indicates that CCER, MBER, and SSER types of environmental regulations all exhibit nonlinear relationships with MCSP. An inverted U-shaped relationship is observed in columns (1) and (2) for CCER and MBER with MCSP, respectively, which is graphically represented in Figures 2, 3. The test in Column (1) reveals that the linear coefficient of CCER on MCSP is 0.2952, and the quadratic coefficient is -0.0317, both statistically significant at the 1% level. This indicates that, in the initial stages of regulation, strengthening CCER can enhance MCSP. However, when a specific threshold is exceeded, its negative impact on MCSP gradually emerges, thereby inhibiting the growth of MCSP The test in Column (2) shows that the linear coefficient of MBER is 0.4684, and the quadratic coefficient is -0.0328, both statistically significant at the 5% level. The results suggest that, before reaching a certain threshold, MBER contribute to the improvement of MCSP. However, beyond this threshold, their impact on MCSP becomes inhibitory. The test in Column (3) indicates that the linear coefficient of SSER is -0.4464, and the quadratic coefficient is 0.0877, both statistically significant at the 1% level. This implies a U-shaped relationship between SSER and MCSP, with the specific U-shaped curve depicted in Figure 4. In other words, SSER initially suppresses and subsequently promotes MCSP, indicating that moderate SSER can enhance MCSP through environmental regulation. In summary, the research findings confirm hypothesis H1, demonstrating a nonlinear relationship between environmental regulation tools and MCSP.
Heterogeneity test results of environmental regulation tools.
Variable
MCSP
(1)
(2)
(3)
CCER
0.2952***(2.6401)
CCER2
-0.0317***(-2.6660)
MBER
0.4684**(2.3354)
MBER2
-0.0328**(-2.5588)
SSER
-0.4464***(-3.1283)
SSER2
0.0877***(3.7442)
Constant Term
4.5000***(2.6590)
2.8770(1.3211)
4.8666***(3.2598)
Control Variables
Yes
Yes
Yes
Year Fixed Effects
Yes
Yes
Yes
Province Fixed Effects
Yes
Yes
Yes
R2
0.8655
0.8675
0.8788
The values in parentheses are t-statistics; ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. The same applies below.
Inverted U-shaped relationship between command-and-control environmental regulation and marine carbon sink performance.
Graph showing a parabolic curve depicting marine carbon sink performance versus command-and-control environmental regulation. Performance increases, peaks around regulation level 4, then declines gradually.
The inverted U-shaped relationship between market-incentive environmental regulation and marine carbon sink performance.
Line graph showing the relationship between market-based incentive environmental regulation and marine carbon sink performance. The curve peaks around regulation level 7, with performance values ranging from 0.4 to 0.8.
The U-shaped relationship between social-supervised environmental regulation and marine carbon sink performance.
Graph depicting a U-shaped curve of Marine Carbon Sink Performance against Social–Supervised Environmental Regulation. Performance decreases from 1.2 to 0.3 as regulation increases from 1 to 3, then rises to 1.3 by 6.
After conducting preliminary regression analysis, this study further examines the relationship between heterogeneous environmental regulations and MCSP using the U-test method. The detailed test results are presented in Table 6. Regarding the relationship between CCER and MCSP, the U-test results show a composite p-value of 0.0235, leading to the rejection of the null hypothesis. Further analysis reveals that the slope of their relationship shifts from a positive value of 0.2123 to a negative value of -0.2196, and this change passes the significance test (p = 0.0074). This confirms an inverted U-shaped relationship between CCER and MCSP. Similarly, both MBER and SSER pass the U-test in their relationships with MCSP. The results indicate that MBER also exhibits an inverted U-shaped relationship with MCSP, while SSER demonstrates a U-shaped relationship with MCSP. These findings provide further support for the hypothesis proposed in this study, suggesting that heterogeneous environmental regulations have significantly different impacts on MCSP, and these influences exhibit nonlinear characteristics.
U-test results of environmental regulation tools and marine carbon sink performance.
Variable
CCER
MBER
SSER
Lower limit
Upper limit
Lower limit
Upper limit
Lower limit
Upper limit
Value range
1.308
8.12
4.0641
10.2445
0.6931
5.6021
Slope
0.2123
-0.2196
0.2018
-0.2036
-0.3249
0.5358
t-value
2.4705
-2.0040
2.0611
-3.0014
-2.9067
4.1028
P>|t|
0.0074
0.0235
0.0206
0.0016
0.0021
0.0000
Composite P-valueExtreme point
0.02354.6567
0.02067.1404
0.00212.5459
Regional heterogeneity test of environmental regulation
Environmental regulation tools exhibit distinct nonlinear relationships with MCSP across the three major marine economic zones. Table 7 presents the results of the regional heterogeneity test. In the northern marine economic circle, an inverted U-shaped relationship is observed between CCER and MCSP. The primary term coefficient of CCER is 1.7530, while the quadratic term coefficient is -0.1339, both statistically significant at the 1% level. This indicates that regulatory measures in the initial stages of environmental regulation implementation can significantly enhance MCSP. However, as the intensity of environmental regulation increases, diminishing marginal benefits in MCSP emerge, eventually leading to negative effects. This suggests that early-stage environmental regulation can attract substantial investments into marine environmental governance, incentivizing both enterprises and governments to adopt environmental protection measures. Nevertheless, further strengthening regulatory intensity may raise operational costs for enterprises and governments in environmental management, thereby negatively impacting marine carbon sink performance. In the eastern marine economic circle, a U-shaped relationship exists between SSER and MCSP. The primary term regression coefficient of SSER is -1.2273, and the quadratic term coefficient is 0.1498, both of which pass the significance test. In the short term, social media and public opinion may suppress MCSP in the eastern marine economic circle. However, by enhancing transparency in marine environmental information and ensuring effective supervision, governments and enterprises will rapidly engage in environmental protection initiatives and invest in marine environmental governance, thereby fostering the growth of MCSP in the long run. In the southern marine economic circle, none of the environmental regulations show a significant relationship with MCSP.
Regional heterogeneity test results.
Variable
Northern marine economic circle
Eastern marine economic circle
Southern marine economic circle
CCER
1.7530***(3.6947)
-0.3304(-0.6185)
0.1297(0.6408)
CCER2
-0.1339***(-3.2407)
-0.0009(-0.0163)
-0.0058(-0.2872)
MBER
0.4763(1.2651)
-0.1915(-0.5235)
0.1257(0.4501)
MBER2
-0.0264(-1.1435)
0.0182(0.7830)
-0.0097(-0.5242)
SSER
0.0316(0.1871)
-1.2273***(-3.4853)
0.0835(0.3973)
SSER2
0.0530**(2.1414)
0.1498**(2.4181)
0.0059(0.1769)
Constant Term
5.1005*(1.7101)
9.9512***(4.0176)
7.5576***(4.2213)
6.9114(0.8300)
22.3109(1.7127)
16.9968**(2.2564)
1.1311(0.3805)
-0.2815(-0.0859)
-1.2462(-0.4829)
Control Variables
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Province Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
R2
0.9542
0.9482
0.9723
0.9170
0.8409
0.9338
0.9542
0.9532
0.9621
The values in parentheses are t-statistics; ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Robustness test
This study employs two approaches to verify the robustness of the nonlinear relationship between environmental regulation tools and MCSP. First, a regression analysis was conducted using the one-period lagged term of heterogeneous environmental regulations and MCSP. The relevant results are presented in Table 8. Columns (1), (2), and (3) display the regression results of environmental regulation tools on the one-period lagged MCSP. In Column (1), the primary term coefficient of CCER is 0.4241, and the quadratic term coefficient is -0.0433, both statistically significant at the 1% level, confirming an inverted U-shaped relationship between the two. Similarly, the results in Columns (2) and (3) validate an inverted U-shaped relationship for MBER and a U-shaped relationship for SSER with MCSP, respectively. To address potential outliers in the data sample that may affect the accuracy of the regression analysis, the sample was winsorized at the 1% level before regression. Columns (4), (5), and (6) present the regression results of environmental regulation tools on MCSP after winsorization. The results in Columns (4) and (5) show that the quadratic term coefficients of CCER and MBER are -0.0544 and 0.0301, respectively, further confirming the inverted U-shaped relationship between these two types of environmental regulations and MCSP. In conclusion, the test results demonstrate that the core findings of this study remain robust.
Robustness test results.
Variable
MCSP
(1)
(2)
(3)
(4)
(5)
(6)
CCER
0.4241***(3.0784)
0.5597**(2.0721)
CCER2
-0.0433***(-3.2752)
-0.0544**(-2.4093)
MBER
0.5016**(2.5384)
0.4241*(1.8993)
MBER2
-0.0329**(-2.5336)
-0.0301**(-2.1276)
SSER
-0.3113*(-1.9548)
-0.4400***(-3.1623)
SSER2
0.0653**(2.5810)
0.0863***(3.7857)
Constant Term
4.8839***(4.6539)
2.8493*(1.7782)
6.1329***(6.2195)
4.4444***(2.6157)
3.9368*(1.6740)
5.5425***(3.5400)
Control Variables
Yes
Yes
Yes
Yes
Yes
Yes
Year Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Province Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
R2
0.8926
0.8891
0.8951
0.8708
0.8718
0.8833
The values in parentheses are t-statistics; ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Further analysisMediating effect of MTI
Based on the preceding regression analysis, this study further employs a mediating effect model (Equations 2, 3) to evaluate whether MTI exhibits a mediating effect. Table 9 presents the test results of the mediating effect of technological innovation.
Test results of the mediating effect of MTI.
Variable
MTI
MCSP
MTI
MCSP
MTI
MCSP
CCER
0.0633(1.5237)
0.2602**(2.3802)
CCER2
-0.0091**(-2.2637)
-0.0267**(-2.2237)
MBER
0.2174***(3.1844)
0.3412*(1.6988)
MBER2
-0.0133***(-3.0370)
-0.0250*(-1.9507)
SSER
-0.1154*(-1.8182)
-0.3873***(-2.6598)
SSER2
0.0184*(1.7843)
0.0782***(3.2549)
MTI
0.5532***(2.9161)
0.5850***(3.2971)
0.5124**(2.4939)
Constant Term
-0.0976(-0.3631)
4.5540***(2.6585)
-1.2696***(-2.6435)
3.6196(1.6548)
0.0652(0.2305)
4.8332***(3.2415)
Control Variables
Yes
Yes
Yes
Yes
Yes
Yes
Year Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Province Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
R2
0.7350
0.8696
0.7445
0.8719
0.7283
0.8824
The values in parentheses are t-statistics; ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
First, the primary term coefficient of command-and-control environmental regulation is 0.0633, which fails the significance test. This indicates that under strict command-and-control environmental regulation, its direct incentive effect on technological innovation may not be significant. However, the quadratic term of this regulation has a significant negative impact on technological innovation, suggesting a potential inverted U-shaped nonlinear relationship between command-and-control environmental regulation and technological innovation.
Second, market-incentive environmental regulation demonstrates a significant positive impact on technological innovation, with a negative quadratic regression coefficient. This implies an inverted U-shaped relationship between market-incentive environmental regulation and technological innovation. When market-incentive environmental regulation is at a relatively weak level, it may stimulate corporate innovation motivation by standardizing innovation pathways and addressing internal deficiencies within enterprises. Alternatively, it could accelerate innovation by narrowing the scope of advanced technology searches, thereby reducing search costs (Zhang and Yao, 2018). However, once the intensity of environmental regulation exceeds a certain threshold, resources of marine-related enterprises may be excessively diverted to pollution control departments, crowding out funding for other research and development activities and forcing relevant enterprises to abandon uncertain R&D innovation initiatives. When the quadratic term of market-incentive environmental regulation and technological innovation are incorporated into the regression analysis of marine carbon sink performance, the coefficients of all three remain significant, further confirming the mediating role of technological innovation between market-incentive environmental regulation and marine carbon sink performance.
Finally, the regression results of social-supervised environmental regulation and its quadratic term on OTI suggest a potential U-shaped relationship between social-supervised environmental regulation and technological innovation. This indicates that social-supervised environmental regulation may initially inhibit and subsequently promote technological innovation over a certain period. Social-supervised environmental regulations primarily relies on public participation or the implementation of national environmental policies, which may start on a small scale initially. However, as regulatory intensity surpasses a certain inflection point, the expansion of policy implementation scope generates positive effects. The regression coefficient for technological innovation is 0.5124, confirming the existence of a mediating effect of technological innovation between social-supervised environmental regulation and marine carbon sink performance.
Therefore, Hypothesis H2 is validated: heterogeneous environmental regulations have a nonlinear impact on technological innovation, and technological innovation plays a mediating role in the relationship between heterogeneous environmental regulations and marine carbon sink performance.
Mediating effect of MIU
This study further employs the mediation effect model specified in Equation 4 to evaluate the mediating effect of industrial upgrading. Table 10 presents the test results examining the mediating effect of MIU. The quadratic term coefficient of CCER is 0.0228, indicating a U-shaped relationship with MIU. When environmental regulation standards are relatively lenient, they may attract a certain number of pollution-intensive industries, leading to a reduction in the proportion of cleaner industries, particularly those dominated by services, thereby hindering the optimization and upgrading of the marine industrial structure. However, as the government subsequently tightens environmental regulation standards and increases investment in pollution control, the innovation compensation effect of environmental regulation tools is triggered, offsetting the earlier negative impacts and thereby promoting the optimization and upgrading of the marine industrial structure. These findings align with the study by Yang (2021), which used pollution control investment as the primary measurement indicator for CCER and verified its nonlinear relationship with the upgrading of the marine industrial structure. Building on this, the nonlinear impact of MBER on MIU is further confirmed. The quadratic term coefficient of MBER is -0.0109, indicating an inverted U-shaped relationship with MIU, i.e., a nonlinear effect that initially promotes and subsequently inhibits industrial upgrading.
Test results of the mediating effect of industrial upgrading.
Variable
MIU
MCSP
MIU
MCSP
MIU
MCSP
CCER
-0.2382***(-3.3854)
0.4691***(3.1911)
CCER2
0.0228***(3.4733)
-0.0483***(-3.4420)
MBER
0.1378*(1.8896)
0.4080*(1.9734)
MBER2
-0.0109**(-2.2241)
-0.0280**(-2.1087)
SSER
0.0114(0.1668)
-0.4504***(-3.0608)
SSER2
0.0084(0.7950)
0.0847***(3.5678)
MIU
0.7297***(3.2693)
0.4380**(2.0855)
0.3568*(1.9155)
Constant Term
1.4329***(3.3576)
3.4543**(2.2934)
0.5860(0.8276)
2.6203(1.2693)
1.1256***(3.0563)
4.4650***(3.1355)
Control Variables
YES
YES
YES
YES
YES
YES
Year Fixed Effects
YES
YES
YES
YES
YES
YES
Province Fixed Effects
YES
YES
YES
YES
YES
YES
R2
0.5971
0.8792
0.5826
0.8726
0.6090
0.8819
The values in parentheses are t-statistics; ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
The test results from the mediating effect model (Equation 5) show that after including the mediating variable of MIU, the regression coefficients are 0.7297 and 0.4380, respectively, and both are significant. This verifies the dual mediating role of MIU in the relationships between CCER and MCSP, and between MBER and MCSP. Therefore, Hypothesis H4 is partially validated, meaning that heterogeneous environmental regulations have a nonlinear impact on MIU, and MIU plays a mediating role in the relationship between heterogeneous environmental regulations and MCSP.
Conclusions and discussionConclusions
Based on panel data from 11 coastal provinces (municipalities and autonomous regions) in China from 2008 to 2022, this study employed the Super-SBM model to measure MCSP and constructed nonlinear regression and mediating effect models to systematically examine the nonlinear impact of heterogeneous environmental regulations on MCSP and its transmission mechanisms. The main findings are as follows:
First, China’s MCSP generally shows an upward trend but exhibits significant regional heterogeneity. The performance in the Northern and Eastern Marine Economic Circles has steadily improved, while the Southern Economic Circle has experienced an overall decline, with notable disparities among provinces. For instance, Liaoning and Guangxi demonstrated outstanding performance, whereas Tianjin and Hebei exhibited lower performance, reflecting differences in marine resource endowment, ecological environment quality, and policy implementation effectiveness.
Second, there is a significant nonlinear relationship between heterogeneous environmental regulations and MCSP. Both CCER and MIER exhibit an “inverted U-shaped” impact, meaning moderate regulation enhances performance, while excessive regulation suppresses it. In contrast, SSER demonstrates a “U-shaped” impact, initially inhibiting performance but significantly promoting it in the long run.
Third, MTI and MIU play significant mediating roles in the process through which environmental regulations affect MCSP. A U-shaped relationship exists between CCER and MIU. MIER shows a significant inverted U-shaped relationship with MTI and a U-shaped relationship with MIU. SSER exhibits a significant U-shaped relationship with MTI. MTI and MIU serve as mediators in the relationship between heterogeneous environmental regulations and MCSP.
Policy implications
The findings offer the following policy recommendations for studying the nonlinear impact of heterogeneous environmental regulations on MCSP:
First, establish precise and synergistic environmental regulation policy combinations. The study clearly indicates that CCER, MBER, and SSER tools have significantly different impacts on MCSP, all of which are nonlinear. This suggests that relying solely on a single policy or blindly increasing regulatory intensity may be counterproductive. Therefore, policy formulation must shift toward precision and synergy. Specifically, the government should scientifically combine the three types of tools and set their optimal intensity ranges based on the development stage, industrial structure, and resource endowment of different coastal regions. In the northern marine economic circle, which is densely industrialized and faces significant pollution pressure, CCER can be prioritized to set clear environmental baselines, supplemented by market incentives such as emissions trading. In the eastern marine economic circle, where public environmental awareness is high and information flows quickly, efforts should focus on improving environmental information disclosure systems and public participation channels to fully leverage the long-term promotional role of social supervision.
Second, strengthen the core transmission pathways of MTI and MIU. This study validates the critical mediating roles of MTI and MIU in the process through which environmental regulations affect MCSP. This suggests that policymakers should not only focus on end-of-pipe supervision but also aim to stimulate the endogenous motivation of micro-level entities. The government should transform the pressure from environmental regulations into drivers for green transition through targeted support policies. On the one hand, special funds for marine carbon sink technology and green innovation should be established to encourage enterprises to engage in the research, development, and application of technologies related to marine ecological restoration, low-carbon aquaculture, and carbon sink monitoring through R&D subsidies and tax incentives. On the other hand, regulatory policies should be actively used to guide industrial restructuring toward advanced and green transformation. Strict emission standards can phase out outdated production capacity, while economic instruments such as sea area usage fees and ecological compensation can guide the flow of production factors from traditional high-energy-consuming industries to low-carbon sectors, thereby strengthening the industrial foundation for enhancing marine carbon sink capacity.
Third, implement differentiated governance strategies based on regional heterogeneity. This study reveals significant regional disparities in China’s MCSP and the varying effectiveness of the same regulatory tools across different economic circles. This strongly calls for region-specific governance strategies. For the northern marine economic circle, while optimizing regulatory intensity, emphasis should be placed on strengthening the coordinated management of industrial pollution in coastal areas and the restoration and protection of typical ecosystems. For the southern marine economic circle, the decline in performance and the ineffectiveness of regulations serve as a warning that simply replicating policies may be ineffective. It is necessary to deeply analyze the underlying structural issues, such as economic structure, cross-border pollution, or governance deficiencies, and complement this with supportive policies such as robust ecological compensation, fiscal transfers, and technical assistance to help the region overcome development and transition barriers.
Research value
In terms of theoretical value, this study enriches the theoretical research on the relationship between environmental regulations and MCSP by extending the research perspective from industrial and terrestrial systems to marine ecosystems, thereby contributing to the blue carbon sink theoretical framework. Moreover, it breaks through the traditional assumption of linear relationships, empirically tests the nonlinear impact of heterogeneous environmental regulation tools on MCSP, and reveals two key mediating pathways—”MTI” and “MIU”—providing a more refined theoretical framework for understanding the complex “policy-behavior-performance” black box.
In terms of practical value, the findings provide a basis for governments to formulate differentiated and precise marine environmental policies. It is recommended to reasonably set regulatory intensity based on the current state of MCSP and the characteristics of regulatory tools in each region, avoiding one-size-fits-all policies. Simultaneously, attention should be paid to the mediating roles of MTI and MIU, with policy combinations designed to stimulate their positive transmission effects.
Limitations and future research
This study has several limitations: First, marine carbon sink accounting is still in its developmental stage, and some data rely on estimations, which may affect the accuracy of performance measurement. Second, although proxy variables for environmental regulations were selected from multiple dimensions, they may not fully capture their complexity and dynamism. Finally, the study did not consider the influence of external factors such as climate change and international policies.
Future research could further refine the marine carbon sink accounting system, incorporate more multidimensional indicators of environmental regulations, and extend to cross-country or long-time-series comparative analyses. Additionally, methods such as machine learning and spatial econometrics could be employed to more comprehensively reveal the complex relationship between environmental regulations and MCSP.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author/s.
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Edited by: Chao Liu, Ministry of Natural Resources, China
Reviewed by: ZhenLong Miao, Zhejiang University of Finance and Economics, China