The Driving Factors and Path Selection for the Development Level of China’s Mariculture—A Dynamic Analysis Based on the TOE Framework

Abstract

Mariculture is a key practice to promote the supply-side reform of fishery, and it is of strategic significance to explore the causes and paths of its high-level development. Based on the TOE (Technology-Organization-Environment) theoretical framework and the configuration methodology, this paper adopts the dynamic qualitative comparative analysis (QCA) method and panel data from 10 coastal provinces and cities in China from 2013 to 2021 to explore the configuration effects of six antecedents, namely, the intensity of technology promotion, investment in scientific research, personnel specialization, industry intensification, nearshore water quality, and offshore pollution discharge, along temporal and spatial dimensions, on the level of mariculture development. The results show that (1) individual driving factors do not constitute the necessary conditions for a high level of mariculture industry development, but the necessity of the three conditions—research funding, industry intensification and nearshore seawater quality—shows a general increasing trend; and (2) the results of the path analysis show that a total of seven configuration paths for a high level of development are generated, which can be further classified into “organization-led and technology synergistic”, “technology-organization-environment multiple-driven type”, and “technology-environment dual-driven type”. Based on the panel data, this study explores the impact of spatial and temporal changes in factor combinations on the development level of mariculture and provides a theoretical basis and practical insights for the development of locally adapted execution pathways.

1. Introduction

The development of urbanization and consumerization has resulted in the growing inelastic consumer demand for animal protein. However, the current sustainable development of the food system is facing many challenges: first, resource and environmental constraints. Both animal husbandry and marine fishing are hindered by the shortage of space and biological resources [1,2]. Second, the nutritional structure of diets is unreasonable, with an obvious shortage of aquatic products. This unhealthy dietary structure is prone to a high incidence of chronic diseases. In contrast, marine fisheries do not need to occupy land, have low farming costs, and have significant carbon sequestration functions [3]. The nutritional value of some seawater aquaculture products can replace or even exceed that of land-based protein [4,5]. Based on the above status quo, it is necessary to build a diversified food supply system and develop food sources in multiple ways. Farmed sea areas, recognized as “blue basic farmland”, can alleviate the pressure on food security brought about by the above problems.

However, the development of mariculture is constrained by various factors, the potential behind it has not been fully developed, and the domestic population’s demand for seafood needs to be filled urgently [6]. China is highly dependent on the import of aquatic products. In 2023, China’s cumulative imports of aquatic products amounted to 6,762,300 tons, which equated to 23.774 billion U.S. dollars, resulting in a trade deficit of 3311 million U.S. dollars, marking a 390% increase year-on-year compared to 2022. It is clear that China’s aquatic products supply capacity is still unable to meet domestic demand. More specifically, there is a gap in China’s fish demand market, coupled with the increasing depletion of fishery resources in recent years [7], which makes it particularly urgent to promote the further development of the fishery industry. Therefore, mariculture has become a critical industry in the construction of the “blue granary”.

In the past decade, the mariculture industry has made great breakthroughs; however, unbalanced and insufficient development of the mariculture industry in various regions persists. Many scholars have conducted exploratory research on this issue from various perspectives, such as government subsidies [8], corporate governance [9], consumer demand [10], public opinion [11], climatic factors [12,13], socio-economic factors [13], and resource constraints [14], etc. In recent years, an increasing number of scholars have begun to focus on green development and sustainable development in the fisheries sector [15,16,17,18,19,20]. In China, multiple bottlenecks, including technological breakthroughs and promotion, low levels of industrial specialization and scale, as well as environmental pollution, are hindering the further development of marine aquaculture. Previous studies, which mostly measured the production efficiency of marine fisheries in various regions from an input-output perspective [21], have reached broadly consistent conclusions. Namely, Shandong, Guangdong, Fujian, and other traditional fishery provinces have long coastlines, an extended history of fishery development and high volumes of raw material inputs and production outputs, while Tianjin, Liaoning, and other provinces and municipalities with smaller volumes of fishery and lack of commitment of resources have limited the development of mariculture. This study focuses on the coordinated drivers of marine aquaculture development by multiple factors, with a particular emphasis on reducing the industry’s dependence on resources. Although resource endowment plays a significant role in the marine aquaculture industry, if the relationships among technology, organization, and the environment are not well-coordinated, its advantages can turn into disadvantages. Furthermore, for provinces with poorer self-conditions, it is necessary to take an alternative approach to make up for the inherent shortcomings by utilizing highly efficient fishery production layouts and scientific and technological support. Therefore, each coastal area needs to accurately assess its own conditions and driving paths to achieve sustainable development of marine fisheries.

Taking this into consideration and based on the TOE framework, this paper analyzes the factors impacting the development of marine aquaculture from the perspectives of scientific and technological development, organizational systems, and ecological protection to inform the development pathway of marine aquaculture. The key questions to be addressed in this paper are as follows: Do the necessary conditions exist in China’s coastal provinces to generate a high level of mariculture development? What antecedent configurations exist to drive high levels of mariculture development? Are there substitution effects between different pathways? How can regions with low levels of development choose suitable paths to enhance their development levels according to their factor endowments? This paper attempts to explore the focus areas and appropriate paths for the development of mariculture across regions from both temporal and spatial dimensions and further analyzes and explains the conclusions through specific cases, thereby providing policy suggestions for developing mariculture according to local conditions, which is of great theoretical and practical significance.

2. Literature Review and Research Framework

2.1. Literature Review

Existing literature has conducted multifaceted discussions on the factors influencing the development of mariculture. This study will delve into the discussion from three aspects: technology, organization, and environment, based on the TOE theoretical framework.

Firstly, technological innovation and research and development (R&D), as well as the promotion of technological achievements, are crucial drivers for enhancing technical efficiency [22] and promoting sustainable development in mariculture. As a highly resource-dependent industry, the increase in fishery production has historically been achieved through expanding aquaculture areas and increasing aquatic seedling and feed, etc. The level of fishery science and technology is low, and there is a significant gap across regions [23]. Technological innovation and the promotion of its achievements have not only provided intelligent aquaculture equipment and technologies but also equipped digital management platforms, significantly reducing labor and capital costs. Additionally, these innovations leverage technologies such as recirculating aquaculture systems (RAS) [24,25] and integrated multi-trophic aquaculture (IMTA) [26] to effectively address the challenges in the green transformation of marine aquaculture, facilitating a shift from factor-driven to innovation-driven development in mariculture.

Secondly, organizational conditions such as optimizing the industrial structure, enhancing the benefits of large-scale aquaculture, and upgrading the quality of professionals are fundamental guarantees for advancing the development of marine aquaculture. Studies have shown that changes in the marine fisheries industrial structure indirectly impact the marine fisheries economy by influencing maritime spatial resources [27]. Industrial restructuring brings structural dividends to marine fisheries economic growth, and the upgrading and rationalization of the industrial structure, as well as the transformation and upgrading of production methods, provide endogenous driving forces for the marine aquaculture industry [28]. By optimizing aquaculture models through large-scale aquaculture and improving the quality of professionals [29], we can achieve rational allocation and efficient utilization of resources, eliminate backward production capacity, introduce efficient and environmentally friendly aquaculture methods and technologies, and promote industrial upgrading within the marine aquaculture industry.

Thirdly, favorable seawater quality and stable climatic factors are important prerequisites for advancing the development of marine aquaculture [12,13,30,31]. The external environment of the marine aquaculture industry broadly influences the stability of its development level. These factors encompass not only natural causes such as seawater quality, ocean current movements, temperature variations, and sudden disasters, but also social causes including government subsidies, the living standards of fishermen, and consumer preferences.

In summary, the development of mariculture is influenced by multidimensional factors, and the impacts of technological, organizational, and environmental conditions on the mariculture industry are not mutually independent. This perspective aligns with the views of configurational theory. However, most of the studies mentioned above focus on the “net effect” of individual factors and are less focused on the synergies behind a “configuration effect” of multiple factors on mariculture. Furthermore, analyzing and processing cross-sectional data from a single year fails to capture the patterns of individual behavior over time and is susceptible to sample selection bias. Therefore, this study abandons the traditional fsQCA method and instead opts for a dynamic QCA approach. Due to this, this paper analyzes the factors driving the development level of mariculture in different regions of China by utilizing the TOE framework through a configuration perspective. Utilizing the panel data of 10 coastal provinces and municipalities from 2013 to 2021, this paper adopts a dynamic QCA methodology to explore sustainable development pathways for mariculture across different temporal and spatial dimensions. The possible contributions of this paper are as follows: First, based on previous studies, we construct an analytical framework of the factors influencing the development level of marine aquaculture based on the three dimensions of technology, organization, and environment, which provides a reference for relevant empirical studies. Second, based on the configuration theory and the factors selected, we consider multifactor interactions and explore sustainable development paths. Third, we adopt dynamic QCA as the analysis method, which is conducive to facilitating the capturing of consistent trends of causal conditions and the development of mariculture, enabling the analysis of differentiated development pathways in coastal regions, thus rendering the results more robust, compared to the fsQCA method of analyzing cross-sectional data.

2.2. Research Framework

The TOE framework is a comprehensive analytical framework based on technology applications, first proposed by Tornatizky and Fleischer in 1990 [32]. The framework analyzes the impact of technology implementation in an enterprise or organization through studying the interaction of three factors: technology, organization, and environment. This framework was later given new theoretical connotations by academics and has been widely used in the research fields of enterprise economy, administrative science, and agricultural economy, etc., due to its comprehensiveness and applicability across fields [33]. In the TOE framework, technological factors focus on the matching relationship between technological conditions, technological application, and profitability; organizational factors focus on human resources, industrial structure, and government support, etc.; and environmental factors focus on internal and external synergies and economic fundamentals.

The technology dimension includes two variables: the level of industrial development and the intensity of technology diffusion. Science and technology are the fundamental driving forces of economic development, and the popularization and promotion of technological innovation and new technology can play an effective role in seedling cultivation, aquaculture management, processing and transportation, and other aspects [34], as well as to produce high-quality and diversified breeding, improve breeding efficiency, optimize the breeding environment, aid in disease prevention and green production, and labor cost reduction [35]. On the one hand, the investment in scientific research provides the necessary financial support for technological innovation and R&D in the field of mariculture; on the other hand, the increase in the intensity of technology promotion helps accelerate the commercialization of scientific research [22].

The organizational dimension includes two variables: the investment in scientific research and the specialization of personnel. The regional marine aquaculture industry faces problems of unoptimized production management and low professionalism and needs to improve organizational efficiency to clear the obstacles to development. Through optimizing resource allocation, increasing production scale and adopting advanced aquaculture technologies, industrial intensification can significantly improve the production efficiency of mariculture [36]. While promoting industrial intensification, it is also necessary to recruit personnel with rich aquaculture experience and technical knowledge so as to deal with non-routine and non-procedural problems flexibly.

The organizational dimension includes two variables: the water quality in nearshore waters and the offshore pollution discharge. Aquaculture organisms depend on seawater for their survival, yet one source of pollution in nearshore marine areas is wastewater discharges from human and industrial activities. Pollutants in the discharged sea wastewater, such as organic matter, heavy metals, and nutrients, etc., will pollute the marine ecosystem and aggravate the vulnerability of marine fisheries [37]. Therefore, it is crucial to have a marine environment with good water quality.

If we view the mariculture industry as a whole, there are interdependencies between related industrial technology, industrial organization, and industrial environment, and these three factors together affect the development level of the regional industry. Drawing on the application of TOE theory in other fields [38,39], and according to the characteristics of the mariculture industry, this paper selects technology promotion intensity and scientific research funding input as technical factors, personnel specialization and industrial intensification as organizational factors, and nearshore seawater quality and offshore pollution discharge as environmental factors. We model the factors influencing the development level of the mariculture industry based on the TOE framework below in Figure 1.

3. Research Methodology and Data Sources

3.1. Research Methodology

3.1.1. Reasons for Choosing QCA

QCA (qualitative comparative analysis) is a case-oriented nonparametric statistical analysis method for comparing multifactorial effects in social science phenomena. The reasons for selecting QCA as the data processing tool in this study are twofold: (1) A core concept of QCA is the asymmetry of causality, which implies that even if two cases are similar in terms of outcomes, the causes leading to these outcomes may be vastly different. The driving factors of marine aquaculture development are complex and variable. Regions with high levels of marine aquaculture development may have different development conditions yet achieve similar outcomes, aligning with the concept of QCA methodology. (2) QCA also takes into account the interactions between different causes. These interactions are nonlinear, meaning that the presence of one factor can influence the impact of another factor on the outcome. This complexity allows QCA to reveal richer information than traditional regression analysis. In the analysis of the TOE (Technology, Organization, and Environment) theoretical framework, technology, organization, and environment interact with each other, and together, they influence the development of marine aquaculture. Therefore, this nonlinear interaction is more suitably addressed using the QCA method. Based on the above analysis, compared to traditional linear regression methods, QCA can address the causal complexity inherent in social phenomena and is more suitable for this study.

3.1.2. Differences Between Traditional QCA and Dynamic QCA Methods

The traditional QCA method is constrained by the theoretical framework and tools, primarily focusing on cross-sectional data, with limited ability to mine the configuration effect across time. Since the development of the mariculture industry is a continuous dynamic process, research based solely on cross-sectional grouping cannot fully depict the continuity in the time dimension. To overcome the time blindness, this paper draws on the study by García-Castro and Ariño (2016) [40] where it cracked the barrier between panel data and QCA analysis using the R-4.3.2 software and further allowed them to explore configurational relationships under the influence of time effects. Compared to traditional fsQCA, the dynamic QCA approach measures three dimensions: between, within, and pooled in order to measure configuration consistency and test its stability across time and case dimensions by calculating consistency-adjusted distances. Compared to traditional QCA, it adds an analysis of the time dimension to capture the configuration consistency and logical relationships across different dimensions. The use of this method better informs and explains the factors influencing industrial development and provides a more targeted theoretical basis for regional strategy formulation.

3.1.3. Key Concepts of Dynamic QCA

Consistency: This includes between-group consistency, within-group consistency, and pooled consistency. Between-group consistency, disregarding individual differences, measures the cross-sectional consistency of panel data for each year. Within-group consistency, disregarding temporal differences, assesses the consistency of the causal relationship between condition variables and outcome variables for each case. Pooled consistency, disregarding both temporal and individual differences, evaluates the consistency of causal relationships across the entire sample.

Consistency-adjusted distances: These include between-group consistency-adjusted distances (BECONS-distance) and within-group consistency-adjusted distances (WICONS-distance). Consistency-adjusted distances are used to measure the stability of consistency over time and across individuals, with smaller values indicating greater stability.

Coverage: This includes between-coverage (BECOV), within-coverage (also typically referred to as within-group coverage, WICOV for clarity), and pooled-coverage (POCOV). Between-coverage calculates the coverage of specific configurations across years. Within-coverage (WICOV) measures the coverage of specific configurations within a set of cases.

3.2. Samples and Data

3.2.1. Variable Measurement and Data Calibration

1. Industry development level. Due to significant variations in economic scale, resource endowments, and other conditions among different regions, it is not appropriate to directly measure the level of industrial development in these regions based on the output value of mariculture. Drawing on the research of Mao (2014) [41], this paper uses the proportion of the mariculture output value in the regional gross domestic product (GDP). The larger the proportion is, the stronger the mariculture industry’s driving effect on the local economy will be.

2. Technological factors. First is the intensity of technical promotion. In this paper, the ratio of aquatic technology promotion funding to the number of technical sales personnel is used as a measure of aquaculture technology promotion intensity. Under the complex and changing conditions of the marine environment, the popularization and application of new technologies provide a well of foundational knowledge to guide professionals through their production activities. Second is scientific research funding. Since scientific research funding in the mariculture industry is not accessible, this paper draws on Qiu (2023) [42] and uses the proportion of internal expenditure on R&D funding and the value of mariculture output to the gross ocean product (GOP) to represent it. From an input-output point of view, the input of internal R&D funding directly supports technological R&D and innovation activities in the mariculture industry. These funds are used to introduce advanced aquaculture technologies, cultivate superior breeds, develop new aquaculture equipment, and optimize the aquaculture environment.

3. Organizational factors. First, personnel specialization. Professionals who have rich farming experience and management knowledge can scientifically and reasonably develop the breeding plan and manage the breeding costs, thereby improving breeding efficiency. At the same time, they can also effectively prevent and manage diseases and disasters in the process of farming, reduce the risk of farming, and guarantee the stable development of the farming industry. Drawing on the study by Zhu Aifang and Pingying (2023) [43], this paper uses the proportion of professional employees in mariculture to the fishery population as a proxy for personnel specialization. Second, industrial intensification. Intensification can realize the efficient development of the mariculture industry by concentrating competitive advantages, saving production costs, and then driving economies of scale. It is not only conducive to alleviating the environmental pressure of nearshore waters and breaking the constraints of the ecological environment and natural resources but it also promotes the optimization and upgrading of the aquaculture model and promotes a circular economy and the green aquaculture model. The application of deep-water net pens and factory farming is the main embodiment of aquaculture intensification and technological innovation. This paper draws on the research by Qiu (2023) [44] and uses the proportion of deep-water net pens and factory farming area to mariculture area as a proxy for industrial intensification.

4. Environmental factors. First, the water quality of the nearshore sea. Good water quality can provide a suitable living environment for marine organisms. The deterioration of water quality will lead to a sharp decrease in the diversity of marine organisms [45], restrict growth, and increase mortality, therefore reducing the yield and quality of mariculture [46], which, in turn, will directly affect the economic benefits of the marine aquaculture industry. As consumers’ awareness of food safety and environmental protection increases, the quality and safety requirements for mariculture products are becoming more stringent. Therefore, good water quality conditions are of crucial significance to enhancing the market competitiveness of mariculture products. In this paper, the proportion of the area with good (Class I and II) water quality in nearshore waters is used as a variable. Second, offshore pollution discharge. The interference of human activities on the nearshore marine environment cannot be ignored, and the existence of excessive discharge of industrial and domestic sewage and microplastics has caused marine ecological disasters, such as red tides and damage to the mariculture industry. Referring to the research by Han (2021) [47], we calculate the sewage volume over the mariculture area to reflect the level of pollution discharge, and the data are processed using the reverse normalization formula: Max − X/(Max − Min). A higher value of this indicator indicates less sewage volume per unit area of the sea. (See

Table 1. Variable naming.

Variable Type		Variable Name	Notation
Outcome variable		Level of industrial development	Y
Conditional variable	Technology	Intensity of technology diffusion	A
		Investment in scientific research	B
	Organization	Specialization of personnel	C
		Industrial intensification	D
	Environment	Water quality in nearshore waters	E
		Offshore pollution discharges	F

). The classification and names of the variables are shown in Table 1.

To facilitate comparisons from both temporal and spatial dimensions, data with large variations (variables Y, A, B, C, D, F) were max-min standardized according to the numerical characteristics of the variables involved in this paper and calibrated uniformly across all cases for subsequent analyses of consistency and coverage within, between, and across the groups. Calibration refers to the process of transforming raw data into data with clear properties and that conforms to the logic of QCA based on thresholds. For instance, if the poverty rate in region A is 20% and if we use the overall average poverty rate of 15% as the threshold for calibration, then region A would be categorized into the “high poverty” set. Conversely, if we use 30% as the threshold, the region will belong to the “low poverty” set. The specific positions of the three qualitative anchors should be determined based on both theoretical knowledge and empirical evidence. However, there are relatively few situations in social science where calibration can be directly conducted according to conventionally established standards. Furthermore, there is a lack of QCA research directly related to the outcomes and conditions focused on in this paper, and consequently, there is a dearth of empirical experience to draw upon. Therefore, this study extensively consults relevant literature and employs the approach of using objective quantile values to determine the positions of the three qualitative anchors. The 95% quartile, 50% quartile, and 5% quartile were calibrated [48,49,50] and set as the three anchor points for full affiliation, crossover point, and full non-affiliation. The specific calibration results are shown in

Table 2. Variable calibration protocols.

Variable Type	Variant	Full Affiliation	Intersection Point	Totally Unaffiliated
Outcome variable	Level of industrial development	0.720	0.180	0.009
Technology	Intensity of technology diffusion	0.595	0.147	0.020
	Investment in scientific research	0.673	0.100	0.002
Organization	Specialization of personnel	0.979	0.222	0.005
	Industrial intensification	0.609	0.211	0.037
Environment	Water quality in nearshore waters	1.000	0.830	0.208
	Offshore pollution discharges	0.997	0.930	0.531

.

3.2.2. Data Sources

In this paper, data from 10 Chinese coastal provinces were used, including Tianjin, Hebei, Liaoning, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Guangxi, and Hainan. Because of the small scale of mariculture in Shanghai and the severe lack of data, it was not included in the scope of this study. The data on the output value of marine aquaculture, funding for aquatic technology promotion, the number of technology promotion personnel, the number of professionals engaged in marine aquaculture, fishery population statistics, marine aquaculture area, and the areas of deep-sea cage and industrial aquaculture are sourced from the “China Fishery Statistical Yearbook” spanning from 2013 to 2021. The figures for internal expenditure on R&D, gross oceanic product, and regional gross domestic product are derived from the “China Marine Economic Statistical Yearbook” for the same period, from 2013 to 2021. The proportion of excellent (Grade I and II) water quality areas in coastal seas and the volume of wastewater directly discharged into the sea originate from the “China Marine Ecological Environment Status Bulletin” and the “China’s Offshore Water Quality Bulletin” for the years 2013 to 2021, with some missing data supplemented using multiple interpolation.

4. Data Analysis and Empirical Results

4.1. Necessity Analysis of Individual Conditions

Prior to the necessity condition analysis, in order to avoid the case of a set of fuzzy affiliation scores exactly at 0.5, for affiliation scores less than 1, we add 0.001 to the score to adjust [51]. Aggregate consistency, aggregate coverage, inter-group consistency, intergroup consistency adjustment distance, and intra-group consistency adjustment distance can be calculated with the help of R-4.3.2 software and the panel QCA program package, which serve as an important basis for conducting necessity analysis. Firstly, based on QCA theory, if a consistency level is higher than 0.9 [52], the conditional variable can be regarded as a necessary condition for the outcome variable. If a necessary condition exists, it means that the condition is a key factor in generating either high or non-high levels of development, i.e., either consistently occurring in all high grouping paths or consistently occurring in all non-high grouping paths. Secondly, to ensure the robustness of the results, an analysis of the distance for consistency adjustment should also be conducted. Inter- or intra-group consistency-adjusted distances were calculated using Garcia-Castro’s adjustment formula for consistent Euclidean distances, which was used to determine whether there were significant time or spatial effects. In the analysis of QCA panel data, an adjustment distance below 0.2 is considered as passing the test, indicating a high level of accuracy in aggregated consistency. Nevertheless, when the adjustment distance surpasses 0.2, a deeper analysis of the necessity of the condition variables is warranted [52].

Firstly, we examine the results of the presence and absence (with the “~” sign) of univariate variables. It can be found from

Table 3. Results of the analysis of the necessary conditions.

Conditional Variable	High Level of Industrial Development				Non-High Level of Industrial Development
	Aggregate Consistency	Aggregate Coverage	Intergroup Consistency Adjustment Distance	Intra-Group Consistency Adjustment Distance	Aggregate Consistency	Aggregate Coverage	Intergroup Consistency Adjustment Distance	Intra-Group Consistency Adjustment Distance
A	0.615	0.897	0.129	0.309	0.594	0.419	0.143	0.407
~A 1	0.602	0.754	0.119	0.363	0.854	0.518	0.056	0.163
B	0.635	0.946	0.227	0.538	0.554	0.399	0.259	0.534
~B	0.597	0.735	0.217	0.552	0.925	0.551	0.101	0.211
C	0.659	0.963	0.052	0.625	0.511	0.362	0.119	0.581
~C	0.563	0.704	0.049	0.650	0.948	0.573	0.073	0.240
D	0.511	0.775	0.203	0.687	0.885	0.649	0.122	0.254
~D	0.769	0.933	0.147	0.396	0.693	0.407	0.196	0.349
E	0.709	0.89	0.119	0.512	0.649	0.394	0.101	0.454
~E	0.517	0.753	0.213	0.585	0.818	0.576	0.126	0.287
F	0.686	0.848	0.077	0.570	0.660	0.395	0.150	0.469
~F	0.686	0.848	0.108	0.738	0.746	0.534	0.143	0.461

¹ “~” means the variable is at non-high level.

that the consistency of all the conditional variables is below 0.9, suggesting that none of these factors are necessary for the outcome variable. Then, we further analyze the consistency adjustment distances from the conditional variables A to F. According to the results in Table 3, the adjustment distance for within-group consistency for all condition variables is greater than 0.2, indicating that there are obvious regional effects between the six condition variables and the outcome variable. Since the ten coastal regions selected in this paper have large differences in resource bestowment, economic foundation, and location conditions, etc., the regions are affected by internal and external factors to different degrees, which leads to a larger intra-group consistency adjustment distance.

4.1.1. Analysis of Between-Group Effects

Since the consistency of all the conditional variables is below 0.9, a further analysis of the time effect on the level of consistency is conducted. Among the adjusted distances for intergroup consistency, the adjusted distances for technology diffusion intensity, personnel specialization, nearshore seawater quality, and offshore pollution discharge were all less than 0.2, and their aggregated consistency was reliable, suggesting that the four conditions do not constitute a necessary condition for the level of industrial development across time. Intergroup consistency and coverage for cases with large intergroup consistency adjustment distances were further examined (as shown in

Table 4. Data between groups with adjusted distances greater than 0.2.

Causal Combinations			Vintages
			2013	2014	2015	2016	2017	2018	2019	2020	2021
Scenario 1	B and Y	Intergroup consistency	0.468	0.563	0.526	0.564	0.525	0.789	0.793	0.769	0.726
		Intergroup coverage	0.981	1	0.997	0.995	0.99	0.934	0.923	0.896	0.882
Scenario 2	~B and Y	Intergroup consistency	0.747	0.64	0.713	0.652	0.702	0.494	0.508	0.465	0.443
		Intergroup coverage	0.744	0.639	0.824	0.631	0.812	0.752	0.776	0.718	0.733
Scenario 3	~B and ~Y	Intergroup consistency	0.465	0.359	0.613	0.365	0.588	0.676	0.715	0.639	0.623
		Intergroup coverage	0.469	0.36	0.456	0.386	0.439	0.401	0.427	0.376	0.324
Scenario 4	D and Y	Intergroup consistency	0.382	0.382	0.436	0.469	0.604	0.574	0.606	0.602	0.535
		Intergroup coverage	0.82	0.722	0.867	0.651	0.844	0.747	0.722	0.889	0.74
Scenario 5	~E and Y	Intergroup consistency	0.189	0.288	0.401	0.472	0.575	0.659	0.736	0.805	0.872
		Intergroup coverage	0.75	0.749	0.942	0.749	0.834	0.741	0.738	0.757	0.804

), and it was found that none of the antecedent conditions had a consistency greater than 0.9 in a single year in Cases 1 to 5, and thus did not constitute a necessary condition for a high level of industrial development under the time dimension. Therefore, it can be seen that when assessing the driving factors of marine aquaculture, the interactions and influences among multiple variable conditions need to be considered.

Figure 2 plots the consistency of the six elements over time. The following findings are summarized: From the overall development trend, several observations can be made. Firstly, the consistency levels of all conditional variables are below 0.9, with the consistency levels fluctuating within a small range over time. Secondly, around the year 2017, there were significant changes in the ranking of consistency levels among the groups for various factors, particularly highlighting the emerging importance of research funding, technology promotion intensity, and offshore water quality. Thirdly, the impact of sudden accidents on conditional variables cannot be overlooked. The COVID-19 pandemic in 2020 first struck the promotion of mariculture technology and then indirectly affected other conditional variables. Further analysis is provided below:

Technical aspects. The inter-group consistency of technology promotion intensity fluctuated at a lower level between 2013 and 2017. After the country proposed the fishery supply-side reform policy in 2016, the consistency level significantly improved between 2017 and 2019, but it fell sharply in 2020. This may be due to the significant hindrance posed by the global COVID-19 pandemic on technology promotion efforts in that year, resulting in the largest decline in the technology promotion intensity variable, which then began to rebound. The consistency between research funding investment and the development level of the marine aquaculture industry showed a clear upward trend, with an overall increase of 0.197. The consistency fluctuated at a lower level between 2013 and 2017, but it increased significantly from 2018 onwards. The reason may be the increasing emphasis placed by the country on innovative research and development, leading to increased investment in research funding. This has provided a solid material foundation for the transformation of scientific and technological achievements in marine fisheries, resulting in a higher consistency with the development level of the marine aquaculture industry. This study reveals the changing trend of research funding investment from a temporal dimension, addressing related deficiencies.

Organizational aspects. The level of inter-group consistency in personnel specialization remained relatively stable between 2013 and 2021, indicating that the degree of specialization of marine aquaculture personnel is consistent with the level of industrial development, showing a trend of steady increase. Overall, although the level of inter-group consistency in industrial intensification is relatively low, it has shown a significant increase with an overall growth of 0.153. This suggests that the degree of industrial intensification in China is still at a low level, but once developed, it will play an important role in promoting the marine aquaculture industry. Therefore, local governments need to pay attention to the benefits brought by industrial intensification and scale in future industrial planning. Additionally, the decline in the level of consistency in 2021 also reflects the time lag effect of the pandemic.

Environmental aspects. The inter-group consistency of offshore water quality has consistently been at a high level. It remained relatively stable between 2013 and 2016 and gradually rose to the top position from 2017 onwards, with an overall increase of 0.238. This indicates that China has made significant progress in offshore environmental governance during this period. The inter-group consistency of offshore pollution emissions stabilized at a level of 0.6–0.7 between 2017 and 2021, which is lower than before. This is due to the inevitable increase in direct sewage discharge resulting from accelerated urbanization and industrialization. However, due to the strengthened supervision of sewage outlets into the sea by ecological and environmental departments, as well as the gradual improvement of sewage treatment infrastructure, the proportion of excellent and good offshore water quality has increased instead of decreased. Additionally, the consistency of the two environmental variables both increased in 2021, reflecting the impact of the sudden pandemic—the pandemic slowed down the pace of industrialization, indirectly inhibiting sewage discharge and improving the quality of offshore water.

4.1.2. Necessity Analysis (NCA)

NCA was further used for the analysis of necessity factors (as shown in

Table 5. Necessity analysis (NCA) for individual conditions.

Prerequisite	Methodologies 1	Precision	Upper Bound	Realm	Effect Size (d)	p-Value 2
Intensity of technology diffusion	CR	97.8%	0.028	0.946	0.030	0.402
	CE	100%	0.034	0.946	0.036	0.025
Investment in scientific research	CR	96.7%	0.018	0.925	0.020	0.407
	CE	100%	0.028	0.925	0.030	0.047
Specialization of personnel	CR	96.7%	0.085	0.890	0.095	0.006
	CE	100%	0.063	0. 890	0.070	0.000
industrial intensification	CR	91.1%	0.009	0.952	0.010	0.733
	CE	100%	0.008	0.952	0.008	0.785
Water quality in nearshore waters	CR	87.8%	0.135	0.913	0.148	0.008
	CE	100%	0.119	0.913	0.130	0.000
Offshore pollution discharges	CR	74.4%	0.189	0.937	0.201	0.002
	CE	100%	0.089	0.937	0.095	0.000

¹ Calibrated fuzzy set affiliation values were used; ² permutation test was used with the number of repeated samples = 10,000.

). In NCA, the effect sizes take values ranging from 0 to 1; the closer the value is to 1, the greater the influence of antecedent conditions on the results, where it is low when 0 ≤ d < 0.10, medium when 0.10 ≤ d < 0.30, and high when 0.30 ≤ d < 0.50 [53]. In this paper, we adopt a more rigorous NCA necessary conditions screening method, which needs to fulfill three conditions [53,54]: (1) the effect size (d) is not less than 0.1; (2) the precision is not less than 95%; and (3) the Monte Carlo simulation replacement test shows that the effect size is significant (p ≤ 0.05).

The four variables of technology promotion intensity, scientific research funding, personnel specialization, and industrial intensification cannot meet conditions (1) and (2) at the same time; although nearshore marine water quality and nearshore pollution discharge satisfy conditions (1) and (2), but the precision is low, so there is no separate factor that constitutes a necessary condition for the development level of mariculture industry, confirming the necessity analysis result of dynamic QCA. This result reflects the complexity of the development of the mariculture industry and further suggests that multi-factor linkage should be considered when examining the development of the industry.

The NCA bottleneck level analysis method can clearly delineate the extent to which each antecedent variable should reach when a specific level of the outcome is achieved. This includes two methods: ceiling regression analysis (CR) and ceiling envelope analysis (CE). Given that the discrete variables in this study all exceed five in number, the data characteristics are more suitable for the CR method [55]. Therefore, this paper uses the CR method to analyze the results. As shown in

Table 6. Results of the NCA method bottleneck level (%) analysis under the CR method ¹.

Level of Industrial Development	Intensity of Technology Diffusion	Investment in Scientific Research	Specialization of Personnel	Industrial Intensification	Water Quality in Nearshore Waters	Offshore Pollution Discharges
0	NN	NN	NN	NN	NN	NN
10	NN	NN	NN	NN	NN	NN
20	NN	NN	NN	NN	NN	NN
30	NN	NN	NN	NN	NN	NN
40	NN	NN	NN	NN	NN	NN
50	NN	NN	NN	NN	NN	NN
60	NN	NN	NN	NN	NN	14.2
70	NN	NN	9.6	NN	13.0	31.5
80	NN	NN	23.6	NN	36.4	48.9
90	9.7	NN	37.6	4.7	59.7	66.2
100	46.8	99.5	51.5	11.7	83.0	83.6

¹ Bottleneck CR-FDH, NN indicates not necessary.

, in order to achieve a 90% development level of the mariculture industry, it requires a technology promotion intensity of 9.7%, personnel specialization of 37.6%, industry intensification of 4.7%, nearshore seawater quality of 59.7%, and offshore pollution discharge control of 66.2%.

4.2. Sufficiency Analysis of Conditional Grouping

According to relevant literature, conditional grouping sufficiency analysis attempts to examine the sufficiency of groupings formed by multiple antecedent conditions on the results [56]. In this paper, the consistency threshold was set at 0.8, the PRI threshold was set at 0.75, and the frequency threshold was set at 2. After completing the construction of the truth table, we conducted a strengthened criterion analysis to exclude contradictory assumptions. Due to China’s long coastline, coastal provinces have significant variations in climate, resource bestowment, and other conditions, and evaluating the impact of antecedent conditions in a uniform manner poses significant challenges, so we do not preset the direction, but choose “presence or absence” as indicators, to obtain the enhanced parsimonious solution, intermediate solution, and the complex solution. In this paper, we mainly report the intermediate solution and distinguish the core conditions and auxiliary conditions based on the nesting relationship between the intermediate solution and the parsimonious solution.

4.2.1. Summary Results

Table 7. Configuration paths for high mariculture development levels.

Conditional Variable (2013–2021)	High Industrial Development Level Path
	Organizationally Led and Technologically Synergistic	Technology-Organization-Environment Multi-Driven			Dual Technology-Environment Driven
	Configuration 1	Configuration 2	Configuration 3	Configuration 4	Configuration 5	Configuration 6	Configuration 7
Intensity of technology diffusion			★	★	★		★
Investment in scientific research	●	★			★	★	⊗
Specialization of personnel	★	★	★	★		⊗	⊗
industrial intensification	⊗			●		●
Water quality in nearshore waters	⊗		★	★	★	⊗	⊗
Offshore pollution discharges		★	⊗	★	★	★	★
Aggregate consistency	0.997	1	0.985	0.990	0.999	0.995	1
Aggregate PRI	0.992	1	0.967	0.966	0.997	0.956	0.998
Aggregate coverage	0.289	0.341	0.267	0.235	0.289	0.174	0.182
Unique coverage	0.031	0.044	0.077	0.045	0.019	0.021	0.019
Intergroup consistency adjustment distance	0.011	0.000	0.040	0.018	0.004	0.011	0.000
Intra-group consistency adjustment distance	0.070	0.003	0.077	0.063	0.003	0.049	0.003
Overall Consistency	0.989
Overall PRI	0.981
Overall coverage	0.615

★ indicates that the core condition exists, ● means that the auxiliary condition exists, indicates that the core condition is missing, ⊗ means that the marginal condition is missing, and a space denotes that the condition can either be present or missing. Consistency is used to determine whether a conditional configuration passes the consistency test based on fuzzy set theory. PRI serves as a supplement to consistency, where a higher PRI indicates a higher likelihood that the configuration is a subset of the outcome variable (Y) and a lower possibility of existing in a simultaneous subset relationship (i.e., being a subset of both Y and ~Y). Aggregated coverage refers to the proportion of outcome cases covered by given configurations, including the coverage of overlapping explanatory parts among configurations. Unique coverage indicates the degree to which a single configuration explains the outcome after excluding the common parts with other configurations. Overall coverage represents the proportion of outcome cases covered by all configurations. The consistency adjustment distance between (or within) groups is used to measure the stability of the consistency of each configuration over time (or across cases).

shows the results of the analysis of the configuration analysis: there are seven high industrial development level paths, the consistency coefficients of the seven paths are from 0.985–1, and the overall consistency coefficient is 0.989, which is higher than the acceptable minimum standard (0.75); the overall coverage of the group conditions is 0.615, i.e., it can explain 61.8% of the cases. The intra-group consistency-adjusted distance and inter-group consistency-adjusted distance for all configuration paths are less than 0.1, so none of the condition combinations have time effects or case effects, indicating strong explanatory strength for the outcome variables, which can be used as sufficient conditions for improving the level of industrial development.

Table 7 reports the results of the configuration analysis that have achieved a high level of mariculture industry development. An in-depth analysis of the seven high-configuration paths can be summarized into three types according to the distribution of core conditions in the results of the configuration analysis: Configuration 1 can be named “organization-led and technology synergistic”, which is characterized by the existence of core conditions in the organization only; Configurations 2–4 can be named “technology-organization-environment multidimensional driving type”, which is characterized by the core role of one indicator in each of the three dimensions of technology, organization, and environment; and Configuration 5–7 can be summarized as “technology-environment dual-dimensional driving type”, which is characterized by the existence of one or two core conditions in the two dimensions of technology and environment at the same time. Based on the configuration analysis, the next step is to further identify the differentiated fit of the six antecedent conditions under the three dimensions of technology, organization, and environment.

1. Organization-led and technology synergy type (Configuration 1). This type of path emphasizes the primary driving role of organizational factors in the development of mariculture, as well as the auxiliary and synergistic role of technological factors. Professional aquaculturists possess up-to-date breeding concepts and technologies, along with the ability to identify and solve problems. They can propose innovative and practical research programs, thereby attracting more investment in scientific and technological research funds, converting scientific research achievements into actual productivity, and achieving sustainable development in mariculture.

Configuration 1 indicates that a high level of industrial development can be achieved with personnel specialization as the core condition and research funding, ~industry intensification, and ~nearshore seawater quality as the marginal conditions. The coverage of this pathway is 0.289, and the typical cases are Fujian, Liaoning, and Shandong Provinces. Fujian is traditionally a major mariculture province, and the output value of mariculture has traditionally ranked among the top in the country. From 2013 to 2021, personnel specialization in Fujian Province ranked first in the country and increased steadily thanks to the gradual growth of the mariculture professional workforce and the optimization of the management of the overall fishery personnel—the number of professionals increased by 2878 and the overall fisheries personnel base was streamlined, with the total number of personnel decreasing by about 6% compared to the base period. To enhance the professionalism of mariculture personnel, Fujian Province has adopted a series of optimization measures. For example, by integrating “external attraction” and “internal cultivation”, it attracted professors, graduate degree holders, and other high-quality talent into the industry and constructed an innovative model of “talent + project + base”. Tapping into and cultivating local fishery talent, cultivating and attracting aquaculture experts, rural innovation, and entrepreneurship leaders, etc., in the region to form a diverse talent pool to meet the demands at different levels of management and areas within aquaculture. In addition, a variety of incentive mechanisms and policy support have been used to motivate aquaculturists to continuously improve their skills by evaluating and recruiting fishery technicians and recommending them to participate in specialized academic education for new types of vocational farmers.

2. Technology-Organization-Environment Multi-driven type (Configuration 2–4). This type of pathway places greater emphasis on the balanced state among technology, organization, and environment. Superior research conditions serve as the foundational guarantee for professionals to engage in scientific and technological innovation. A talented professional workforce can attract more research projects and produce more scientific research achievements, thus facilitating a mutually reinforcing relationship between organizational and technological factors. Pursuing a green and sustainable development path is an inherent requirement of the mariculture industry in response to the environment, and a damaged marine ecosystem poses an obstacle to the development of mariculture. Technological research and development, along with the promotion of its achievements, can reduce the ecological damage caused by aquaculture processes. Reasonable organizational structures and management mechanisms can optimize resource allocation within the mariculture industry and alleviate environmental pressure on coastal waters. Therefore, there is also a mutually promoting relationship among technological, organizational, and environmental factors. The synergistic interplay among technology, organization, and environment jointly enhances the level of sustainable development within the mariculture industry.

Configuration 2 takes research funding input, personnel specialization, offshore pollution discharge, and ~industry intensification as the core conditions and results in a coverage of 0.341; the typical cases are Shandong and Liaoning Provinces. Removing the influence of the economic scale factor, Shandong and Liaoning Provinces have the top three research funding inputs in the study sample. For example, Shandong Province has several provincial and ministerial-level key laboratories, engineering and technology research centers, and other scientific research platforms, and is supported by scientific research institutes, including the Ocean University of China, and has several national and provincial scientific research projects in the field of mariculture, with abundant scientific research personnel and funds. Shandong Province has less sewage per unit area and thus excellent nearshore water quality; this is due to the effective management of sea outfalls and the management of total nitrogen in the area where the river crosses into the sea. The key elements of Configuration 3 include technology promotion intensity, personnel specialization, nearshore seawater quality, and ~industry intensification, with a coverage of 0.267; a typical case is Hainan Province. Hainan strengthened its promotion of fishery technology through the joint efforts of government agencies and enterprises and provided financial subsidies for the promotion and application of research and development results. As a result, the intensity of technology promotion in Hainan increased steadily from 2017 to 2021. Due to the impact of recreational fisheries conversion policy, the number of mariculture professionals has been decreasing year by year; the number of people in 2021 is 17.3% lower compared to 2013. Even so, the specialization of personnel in Hainan has been maintained at a relatively high level. In addition, Hainan Province is surrounded by the sea and is less impacted by man-made pollution. The percentage of nearshore areas with good water quality has been higher than 98.4% in past years, so the water quality conditions are superior. The difference between Configuration 4 and Configuration 3 lies in the increase of the core condition offshore pollution discharge and the edge condition industrial intensification; the coverage of Configuration 4 is 0.235. The typical case is Guangxi Province. Mariculture is the main marine specialization for Guangxi Province; the total output for mariculture accounts for a much higher proportion of the gross marine product than when compared to other provinces. The intensity of technology promotion in 2021 has increased by 170% compared to that in 2013; the level of personnel specialization has increased steadily, and the industrial intensification has begun to bear fruit. So far, Guangxi has built a total of 21,300 land-based water-circulation aquaculture pools, ranking first in the country in terms of scale. In terms of environmental factors, due to the late start of industrialization, Guangxi’s direct discharge of sea wastewater is less and the water quality of coastal waters is less polluted.

3. Technology-environment dual-driven type (Configurations 5–7). This type of pathway emphasizes the joint driving force of technology and the environment in promoting the sustainable development of mariculture. It illustrates that even if the levels of personnel specialization and industrial intensification have not yet synchronized with the development level of mariculture, making it difficult to bring about improvements in organizational efficiency, technological efficiency, and environmental support are sufficient to facilitate the development of mariculture.

Configuration 5 takes technology promotion intensity, scientific research funding input, nearshore seawater quality, and offshore pollution discharge as core conditions and the presence of industrial intensification as the marginal condition. Typical cases are Guangdong and Shandong provinces, achieving a coverage of 0.289. Taking Guangdong as an example, similar to Shandong and Fujian provinces, it is also a traditional fishery province, with a strong industrial base. The number of technology promotion personnel has shown an increasing trend from 2014 to 2020. The funding for technical promotion has increased significantly since 2017 and the investment in scientific research has also doubled since 2018. Offshore pollution discharge was reduced by nearly 2.15 billion tons of wastewater discharge in 2019–2021; the water quality of nearshore waters has improved from 57.7% in 2017 to 90.2% in 202—water pollution control has achieved great results. Configuration 6 takes ~technology promotion intensity, scientific research funding investment, and offshore pollution discharge as core conditions, and industrial intensification as the edge condition; Configuration 7 takes technology promotion intensity, ~industry intensification, and offshore pollution discharge as core conditions, and ~investment in scientific research funds, ~specialization of personnel, and ~water quality of offshore waters as marginal conditions. The typical province for both configuration 6 and 7 is Jiangsu, where the intensity of technology diffusion was at the upper-middle level in 2015–2016 and then dropped abruptly; the investment in scientific research funding was in the middle level and increased in 2017–2018; wastewater discharge has been the lowest in the country.

Based on the comparison of the differences between the configuration paths, it can be concluded that there is a potential substitution relationship between different combinations of conditional variables, so that the different paths can obtain the same effect (a high level of industrial development). Therefore, different combinations of factors can achieve high levels of industrial development in different ways. This study suggests that, under the practical scenario of large differences in resources and historical development among provinces in China, localities should take into account their own conditions to determine the right pathway and utilize the synergies between factors to promote the development of the mariculture industry.

4.2.2. Intergroup Results

The adjusted distance of intergroup consistency for all 7 configuration paths is less than 0.1, and there is no significant time effect. By observing the changes in the consistency level of each path on the time axis, it is found that the consistency of all seven configuration paths is greater than 0.75, indicating that these seven configuration paths have better explanatory power between 2013–2021 [52], which compensates for the shortcomings of the cross-sectional path patterns of the development level of the past mariculture industry under the time span. Except for Configurations 3 and 5, the consistency levels of the other configurations are relatively stable, fluctuating between 0.96 and 1, which falls within a benign deviation. According to the intergroup adjustment distance and consistency results, it does not affect the overall explanatory power; this paper has strong explanatory power for the level of development of the mariculture industry in the sustainable development of each coastal region. (See Figure 3).

4.2.3. Within-Group Results

The intra-group consistency adjustment distance of the seven configuration paths is less than 0.1, indicating that the configuration has no obvious individual differences between cases. The effect of industrial intensification in the group state is not obvious in the cases with a high industrial development level, indicating that the distribution of mariculture in China is relatively scattered, and it is still necessary to vigorously develop advanced aquaculture methods such as factory aquaculture and net-pen aquaculture to improve the efficiency of scale. In addition, the analysis found that the explanatory power of the seven histogram pathways was limited in regions with a lower level of mariculture development, such as Tianjin, Hebei, and Zhejiang provinces, which may be related to the specificity of their environments and economic development statuses of the regions. Nevertheless, in other provinces, the explanatory power and consistency of the seven histogram pathways were higher. This also reveals the significant heterogeneity in the development process of mariculture in all coastal regions of China, which is the result of the combined effect of several factors.

Table 8. Configuration paths for low mariculture development levels.

Conditional Variable (2013–2021)	Low Industry Development Level Configuration Analysis Path
	Configuration 8
Intensity of technology diffusion
Investment in scientific research
Specialization of personnel	⊗
Industrial intensification	★
Water quality in nearshore waters
Offshore pollution discharges
Aggregate consistency	0.922
Aggregate PRI	0784
Aggregate coverage	0.533
Intergroup consistency adjustment distance	0.010
Intra-group consistency adjustment distance	0.073

★ indicates that the core condition exists, indicates that the core condition is missing, ⊗ means that the marginal condition is missing.

presents the results of the configurational analysis for low-development-level scenarios, with a consistency threshold greater than 0.8 and a PRI threshold greater than 0.75, both exceeding the preset values. The overall coverage rate is 53.3%. Both the inter-group and intra-group consistency adjustment distances are less than 0.1, indicating a relatively stable configurational consistency. Therefore, the time effect and case effect can be neglected.

Combined with Table 8, the obstacles and barriers that existed in areas with low levels of development included the following three main points:

First, scientific research is insufficient, and technology promotion is hindered. The lack of attention toward and funding of scientific research has limited the attraction and nurturing of scientific and technological leaders, resulting in serious constraints on the cultivation of new varieties, the innovation of breeding modes, and the transformation of practical technological achievements. The lack of breeding equipment and corresponding technical measures is the main reason for the continued sloppy industrial development.

Second, the building of a professional team needs to be strengthened. Traditional farming methods face problems such as low farming efficiency, production, and quality that can hardly meet growing consumer needs, are less environmentally sustainable, and lack brand advantages. Yet, improving the professionalism of personnel is not a one-day effort; we need to find candidates with the right potential and provide enough time and other training resources to equip them with the knowledge and skills of how to monitor the breeding environment, manage disease prevention and control, and conduct scientific feedings, among other skills.

Third, insufficient attention has been paid to the environment, and water quality management has been slow. The fundamental condition for the breeding of marine fish fry is having a stable growing environment, but land-based pollution such as urban sewage, industrial wastewater, and farmland leachate in Tianjin, Zhejiang, and other places have significantly impacted the offshore waters. The sudden deterioration of water quality may lead to marine ecological disasters such as algal blooms, which have led to a massive reduction in production on farms.

4.3. Robustness Tests

The dynamic QCA examines time and individual effects, yielding results that are more robust than those of traditional QCA and can even serve as a robustness check for traditional QCA [40]. Therefore, this paper follows the recommendation to conduct tests based on the principle that “results can be considered robust if they do not undergo substantial changes after minor adjustments to the QCA operation”. Robustness tests encompass methods such as adjusting the consistency threshold, adjusting the PRI threshold, altering the frequency of cases, and reducing the cases. Given the relatively small sample size in this study, we adopt the methods of adjusting the consistency threshold and the PRI threshold. Specifically, the consistency threshold is adjusted from 0.8 to 0.85, and the PRI threshold is adjusted from 0.75 to 0.8 (with other conditions remaining unchanged). Following these adjustments, the high-configuration pathways remain unchanged, and there are no alterations in the adjusted configuration pathways, their consistency, or their coverage, whose results can be considered robust. Due to space limitations, the robustness test results are shown in the Appendix A (

Table A1. Robustness Tests.

Conditional Variable (2013–2021)	High Industrial Development Level Path
	Organizationally Led and Technologically Synergistic	Technology-Organization-Environment Multi-Driven			Dual Technology-Environment Driven
	Configuration 1	Configuration 2	Configuration 3	Configuration 4	Configuration 5	Configuration 6	Configuration 7
Intensity of technology diffusion			★	★	★		★
Investment in scientific research	●	★			★	★	⊗
Specialization of personnel	★	★	★	★		⊗	⊗
industrial intensification	⊗			●		●
Water quality in nearshore waters	⊗		★	★	★	⊗	⊗
Offshore pollution discharges		★	⊗	★	★	★	★
Aggregate consistency	0.997	1	0.985	0.990	0.999	0.995	1
Aggregate PRI	0.992	1	0.967	0.966	0.997	0.956	0.998
Aggregate coverage	0.289	0.341	0.267	0.235	0.289	0.174	0.182
Unique coverage	0.031	0.044	0.077	0.045	0.019	0.021	0.019
Intergroup consistency adjustment distance	0.011	0.000	0.040	0.018	0.004	0.011	0.000
Intra-group consistency adjustment distance	0.070	0.003	0.077	0.063	0.003	0.049	0.003
Overall Consistency	0.989
Overall PRI	0.981
Overall coverage	0.615

★ indicates that the core condition exists, ● means that the auxiliary condition exists, indicates that the core condition is missing, ⊗ means that the marginal condition is missing, and a space denotes that the condition can either be present or missing.

).

5. Discussion

5.1. Level of Development of Mariculture in the Context of Synergistic Multidimensional Factors

Based on the TOE framework and using a dynamic QCA approach, this study investigated the impacts of six antecedent conditions in depth, namely, technology promotion intensity, investment in scientific research, personnel specialization, industry intensification, nearshore seawater quality, and offshore pollution discharge, on the development level of the mariculture industry in 10 coastal provinces and municipalities in China. The results show that each individual factor is not a necessary condition for a high level of mariculture industry development, but the synergistic effect of multiple factors jointly promotes industry development. This finding differs from previous studies based on the “net effect” of a single factor and emphasizes the complexity and multifaceted nature of mariculture development. Technology is one of the core factors driving the development of marine aquaculture. This study finds that the necessity of technology has shown an upward trend over time and is ubiquitous across all seven high-configuration pathways. It can be said that technological innovation is a powerful driving force for the transformation of the mariculture industry, which aligns with the findings of scholars [22]. However, relying solely on technological innovation to drive industrial upgrading is difficult to achieve. Technological innovation needs to be matched with a sound industrial system [28], specialized technology, and management talent to maximize the efficiency of scientific and technological achievements’ implementation and promotion, which is the “organization-led and technology-synergistic” pathway. The green and sustainable development of the mariculture industry serves as the impetus for technological innovation. Technology research and development should balance social needs and ecological benefits, fully leveraging marine ecological advantages to provide positive feedback for mariculture and establish a low-carbon, circular, and sustainable development model. This also aligns with the strategic concept of the “technology-environment dual-dimensional driving type”. Additionally, organizational factors such as top-down design, institutional arrangements, and technological advancements accelerate the formation of a modern marine industrial system, enabling the ocean to continuously provide marine resources and environmental services [31]. This forms a synergistic linkage among technology, organization, and environment, vividly illustrating how the “technology-organization-environment multidimensional driving type” promotes the substantial development of the marine aquaculture industry.

5.2. Existing Research Pathways Provide Examples for the Sustainable Development of Mariculture

This study reveals three strategic pathways characterized by high levels of development: “organization-led and technology-synergistic”, “technology-organization-environment multidimensional driving type”, and “technology-environment dual-dimensional driving type”. The proposal of these pathways provides theoretical support and practical insights for different regions to select suitable development paths based on their respective factor endowments and actual conditions. By formulating development strategies tailored to local conditions, regions can more effectively promote the development of marine aquaculture, enhancing economic efficiency and ecological benefits.

For instance, Tianjin is notably characterized by its short coastline and scarcity of resources. However, the local breeding and aquaculture of flatfish such as flounder and sole are relatively mature, and a number of enterprises engaged in industrialized recirculating aquaculture have emerged. Tianjin’s advantages lie in its small scale, high innovation efficiency, and strategic geographical location within the Beijing-Tianjin-Hebei Economic Circle. It can leverage national policies to promote regional cooperation, strengthen technological exchanges by hosting exchanges on modern agricultural industrial technology systems and establishing industry-academia-research technology cooperation platforms, and attract high-end talents and innovative teams in mariculture to Tianjin for work by relying on the Beijing-Tianjin-Hebei Economic Circle, providing intellectual support for the development of mariculture. Nevertheless, due to severe water pollution in Tianjin’s offshore areas, positive environmental feedback cannot be expected in the short term. Therefore, based on local conditions, Tianjin can adopt the “organization-led and technology-synergistic” pathway in the near term while gradually improving the marine environment and balancing the relationship between the industrial economy and the ecological environment. In the long term, it can pursue the “technology-organization-environment multidimensional driving type” pathway. Similar to Tianjin, Zhejiang also faces serious environmental pollution issues, with a large amount of sewage from Shanghai and pollutants carried by seven major river systems such as the Qiantang River and Cao’e River entering the offshore waters, further exacerbating water quality problems. Although the proportion of excellent offshore waters has increased from 18% in 2013 to 46.5% in 2021, efforts in environmental governance still need to be intensified. Therefore, Zhejiang’s development path is similar to Tianjin’s. Unlike Tianjin, Hebei boasts 100% excellent sea areas, with good water quality providing an environmental foundation for marine aquaculture. However, despite also culturing flatfish such as flounder and sole, Hebei has a low penetration rate of recirculating aquaculture technology, relatively backward aquaculture techniques, irregular management during the breeding process, and a low educational level among aquaculture practitioners. Many farms lack professional technicians. Therefore, Hebei should leverage the Beijing-Tianjin-Hebei Economic Circle to strengthen technological exchanges and scientific and technological innovation, adopting the “technology-environment dual-dimensional driving type” strategy in the near term. After a certain period of technological accumulation, it can gradually strengthen organizational factors and implement the “technology-organization-environment multidimensional driving type” strategy in the long term.

5.3. Research Findings Serve to Identify and Rectify Weaknesses in the Demonstration Area

Despite the urgent need to address the imbalance in the development of marine aquaculture, advantageous regions should not rest on their laurels. Table 7 summarizes the configurational pathways of high-level development areas, revealing that each region has weaknesses in technology, organization, and environment. All parties should learn from each other and complement each other’s strengths. It is noteworthy that among all configurations, the driving role of organizational factors in the development of marine aquaculture needs to be strengthened. Although the conditional variable of industrial intensification only plays an auxiliary role in Configurations 5 and 7, combined with the analysis of necessary conditions in Figure 2, there is a clear upward trend in the consistency of industrial intensification. During the stages of supply-side reform in the fishery industry and the transformation and upgrading of modern fishery industries, intensification contributes to optimal resource allocation. By advancing technology and improving management, it enhances the quality and efficiency of production factors, addresses issues of redundant inputs, and increases marine environmental carrying capacity by optimizing aquaculture layouts, thereby promoting the sustainable development of marine aquaculture.

6. Conclusions and Implications

Starting from a configurational perspective, this paper employs dynamic QCA combined with NCA to comprehensively discuss the multiple configurations of sustainable development in the mariculture industry. The following are the main conclusions obtained.

The study finds that (1) individual factors do not constitute the necessary conditions for a high level of development of the mariculture industry; the necessity of the three conditions of research funding, industry intensification, and nearshore water quality showed an overall increasing trend. In addition, there are obvious differences in the level of factors in different regions (sea areas), which is due to the different resource endowments, economic levels, scientific research conditions and the focus of marine fishery policies in different regions. Thirdly, the impact of sudden accidents on conditional variables cannot be overlooked. The COVID-19 pandemic in 2020 first struck the promotion of mariculture technology and then indirectly affected other conditional variables. (2) From the analysis of the sufficiency of condition combinations, three types of strategies can be distilled. The first one is “organization-led and technology synergy” (Configuration 1), which emphasizes the improvement of industrial personnel specialization level and the support of scientific research. The second is the “technology-organization-environment multidimensional driving type” (Configurations 2–4), which emphasizes the promotion of technology research and development and the management of the marine environment while guaranteeing the specialization of personnel, and the third is the “technology-environment double-dimensional driving type” (Configurations 5–7), which is characterized by taking technological empowerment and environmental protection as core development elements. (3) The high-cohort path has limited explanatory power for some provinces. The uneven development situation still exists in Tianjin, Hebei, Zhejiang, and other regions, necessitating the formulation of short-term and long-term development plans based on local realities.

The theoretical contributions of this paper are as follows: Firstly, based on previous research, it constructs an analytical framework for the factors influencing the development level of mariculture using the TOE framework, which encompasses three dimensions: technology, organization, and environment. This framework is applied to the field of marine fisheries economics for the first time. Secondly, grounded in configurational theory, it considers the synergistic effects of multiple factors and explores possible high-configuration pathways based on the selected influencing factors, providing suitable pathways for the development of mariculture in coastal areas with different conditions. Thirdly, by adopting the dynamic QCA analysis method, compared to the traditional fsQCA method, it is more conducive to capturing the consistent trends between antecedent conditions and mariculture development, analyzing the differentiated development pathways of coastal areas, and yielding more robust results.

Based on the aforementioned research, this paper proposes the following recommendations: (1) Emphasize collaboration among multiple stakeholders. Given the significant variations in provincial resources and developmental foundations, regions can leverage their unique circumstances, maximize their geographical advantages, grasp key factors, achieve factor interconnectedness, and thereby promote the development of marine aquaculture. For instance, Tianjin and Zhejiang have short coastlines and relatively poor seawater quality, but they possess a foundation for industrialized, recirculating seawater aquaculture of superior species. These regions can continue to harness their industrial intensification advantages while leveraging the Beijing-Tianjin-Hebei Economic Circle to increase technological investment. In the near term, they should adopt an “organization-led and technology synergy” approach. For long-term development, they should continue to improve the marine environment and transition to a “technology-organization-environment multidimensional driving type” strategy. Hebei boasts superior offshore water quality but lacks experience in large-scale aquaculture. Therefore, in the short term, it is suitable for Hebei to adopt a “technology-environment double-dimensional driving type” strategy, transitioning to a “technology-organization-environment multidimensional driving type” strategy in the long term. (2) Attach importance to technological innovation and facilitate the commercialization of research and development achievements. Technological factors consistently appear in the seven high-level development pathways. Regardless of whether the marine environment is suboptimal or organizational support is inadequate, technological factors can always foster sustainable development of the marine aquaculture industry through beneficial coupling with other variables. (3) Explore the driving force of organizational factors while fostering a long-term positive feedback loop from the environment to the mariculture industry. Research has found that organizational factors are increasingly coupled with the development of mariculture and will become a powerful driving force for the industry’s green transformation and upgrading. Therefore, it is essential to introduce large-scale aquaculture enterprises, strengthen infrastructure, and attract and cultivate professionals and managers with technical and organizational skills. Additionally, creating a healthy marine environment is not a quick fix; it requires persistent efforts to strengthen water quality supervision, properly treat sewage directly discharged into the sea and aquaculture wastewater, and enhance the carrying capacity of the marine environment, thereby fostering an optimal environment for mariculture.

Although this study has achieved certain results, it still has some limitations. Firstly, despite adopting a relatively systematic TOE theoretical framework, this paper inevitably omits some influencing factors. Future research can attempt to adopt different analytical perspectives or methods to incorporate more conditional variables, thereby enriching the research conclusions of this paper and enhancing the universality and accuracy of the research results. Secondly, the application of the dynamic QCA method in the field of marine fisheries research is a novel exploration, and this method has its own limitations. For example, in selecting calibration anchor points, due to the lack of guidance from relevant field experience, only objective quantile values can be used for calibration, inevitably leading to certain deviations. Future research can combine this method with other quantitative methods to improve the research design of this paper. Lastly, regarding sample selection, this study uses provincial-level secondary public data and only discusses the issue from a macro perspective, lacking sufficient depth in data mining. Future research can further explore this topic from a micro perspective by obtaining sub-provincial regional data or first-hand data through field research.