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Article

Has Finance Promoted High-Quality Development in China’s Fishery Economy?—A Perspective on Formal and Informal Finance

by
Shengchao Ye
1,
Qian Zhang
2,*,
Xiao Li
3,
Jianli Yu
4 and
Haohan Wang
1,*
1
Donghai Institute of Ningbo University, Business School of Ningbo University, Ningbo 315211, China
2
Institute of Digital Economy and Industrial Innovation, Ningbo University of Finance and Economics, Ningbo 315175, China
3
Quzhou Longhuan Environmental Protection Technology, Quzhou 324000, China
4
College of Science & Technology, Ningbo University, Ningbo 315300, China
*
Authors to whom correspondence should be addressed.
Fishes 2025, 10(2), 87; https://doi.org/10.3390/fishes10020087
Submission received: 13 December 2024 / Revised: 3 February 2025 / Accepted: 14 February 2025 / Published: 19 February 2025
(This article belongs to the Section Fishery Economics, Policy, and Management)
Versions Notes

Abstract

:
The high-quality development of China’s fishery economy serves as its core objective, with robust financial support playing a pivotal role. This study employs provincial panel data spanning 2005 to 2020 and utilizes the entropy method to evaluate the level of high-quality development in China’s fishery economy across three dimensions: fundamental security, sustainability, and comprehensive efficiency. From the perspectives of formal and informal finance, it compares their support effects on different aspects of high-quality development in China’s fishery economy, while also exploring the mechanisms underlying these effects by considering factors such as industrial uncertainty and economic scale. The findings indicate that, overall, the support provided by both formal and informal finance for high-quality development in the fishery economy is insufficient. Further analysis reveals a significant threshold effect of fishery economic scale, with turning points at 108.44 billion CNY and 232.98 billion CNY for formal and informal finance, respectively. For higher-level indicators, such as sustainability and comprehensive efficiency, formal and informal financial systems demonstrate complementary roles, depending on the scale of the regional fishery economy. Furthermore, industrial uncertainty serves as a significant mediating factor only for formal financial support, with the levels of sustainability and comprehensive efficiency most affected.
Keywords:
fishery economy; high-quality development; financial support; formal finance; informal finance
Key Contribution: We believe that our research makes several important contributions. First, it provides a comprehensive analysis of the role of different financial systems in promoting the high-quality development of China’s fishery economy. Second, it reveals the threshold effect of fishery economic scale and the complementary roles of formal and informal finance. Finally, it identifies industrial uncertainty as a mediating factor for formal financial support.

1. Introduction

The fishery economy plays a vital role in ensuring national food security and improving the living standards of the population. In the new era, the core objective of China’s fishery economy is to achieve high-quality development [1]. Since the Reform and Opening-up, China’s fishery economy has made significant strides. Key indicators, such as aquatic product output and total fishery economic output, have ranked first in the world for consecutive years. However, the industry still encounters the challenge of being “large but not strong”, with a formidable task lying ahead in terms of upgrading and optimizing its structure [2]. Official data indicate that in 2023, China’s total fishery output reached 71.16 million tons, with the gross output value of the fishery economy amounting to 3267 billion CNY. However, the industrial structure of the fishery remains suboptimal, with the primary industry accounting for the highest proportion, at 48.8%. Furthermore, the scale of the fishery population remains substantial, totaling 16.99 million individuals, with a per capita income of merely 25,777.21 CNY. After achieving significant quantitative growth, the next critical challenge for China’s fishery economy lies in advancing qualitative development. Attaining higher-level objectives, such as sustainability and comprehensive efficiency, while ensuring steady enhancement of fundamental capabilities, requires robust financial support. However, due to the unique production processes and product characteristics of the fishery sector, stakeholders often face substantial practical difficulties in accessing adequate financing. This issue has garnered considerable attention in academic research.
In response to this context and guided by the practical demands of high-quality development in China’s fishery economy, this study systematically constructs an evaluation framework and investigates the financial support mechanisms from the dual perspectives of formal and informal finance. The analysis contributes to the theoretical understanding of financial support in the fishery sector while offering valuable insights with substantial practical implications.

2. Literature Review

2.1. Relevant Theoretical Foundations

As a vital component of the national macroeconomic system, the relationship between finance and economic development has always been a significant research topic in the field of economics. Since the mid-20th century, economists, through long-term research on the economic growth of developing countries, have formed a body of representative theoretical achievements around concepts such as financial deepening, financial structure, and financial constraints. These studies posit that the deepening of financial markets and the optimization of financial structures can promote the effective allocation of capital, thereby enhancing firms’ financing and innovation capabilities. A sound and well-functioning financial market system is a critical condition for fostering rapid economic and industrial development. In many late-developing economies, whether industries can secure sufficient financial support after their initial growth phase becomes a key determinant of their long-term development prospects [3,4,5]. The “New Structural Economics”, born out of China’s economic reform and development practices, introduces the concept of an “optimal financial structure”, suggesting that the optimal financial structure, corresponding to different stages of economic development, varies. Whether the financial system can provide sufficient support for industrial development depends on whether its structure aligns with the current stage of economic development and the trend of industrial structural evolution [6,7,8].
Applying these theoretical perspectives to China’s fishery economy suggests that during its extensive growth phase, a financial structure predominantly reliant on operators’ capital accumulation, government policy support, and small-scale fundraising can support rapid development. However, as China’s fishery economy enters the stage of diminishing returns to scale [9], the intensified demand for capital driven by economic transformation makes effective financial support a critical issue for the sector’s high-quality development.

2.2. Key Issues in Financial Support for China’s Fishery Economy

Globally, fishery development is highly uneven. Developing countries, including China, have not yet achieved the level of industrialization and specialization seen in developed countries’ fisheries. Despite this, the critical role of financial support in exploring suitable fishery development paths has gained widespread attention. Research on the flexibility of small-scale fishery units in production and economic resilience, the role of blue finance in promoting marine ecological sustainability and fishery development, and the financing barriers faced by small- and medium-sized fishery enterprises has generated significant reference value [10,11,12,13].
Researchers generally recognize that the key issue lies in the inherent disadvantages of fisheries when seeking financial support, due to high production uncertainty, perishable products, long payback cycles, and small-scale operations [14,15,16]. As a late-developing industry, early research on financial support for Chinese fisheries primarily took a theoretical perspective, focusing on industry needs and policy support to explore feasible solutions to financing difficulties. Among these, the limited financial support from formal sources, such as banks and capital markets, has garnered the most attention. Researchers argue that a moderately relaxed policy environment could help to form a favorable financial support structure for fisheries [17] and that strengthening financial risk management is a crucial guarantee for rationally guiding the optimal allocation of fishery financial resources [18]. Structural inefficiencies, institutional shortcomings, and limited financial products in China’s financial markets have been cited as major unfavorable factors hindering fisheries’ access to financial support [19].

2.3. The Financial Support System for China’s Fishery Economy

In light of the unfavorable financing situation, researchers have identified the long-standing, relatively flexible, and pragmatic financial support paths as an important factor behind the sustained and rapid development of China’s fishery economy. While there is considerable research on formal financial instruments, like fishery loans, capital markets, and insurance, the findings are inconsistent. For instance, some researchers found that fishery loans significantly promote marine fishery economic growth, but that the opposite is not true [20]. Others argue that the situation is more complex, with studies showing that overall financial development significantly boosts fishery per capita GDP and labor productivity, but that changes in the overall financial structure negatively affect these indicators. While the development of credit and insurance markets has significantly promoted the growth of fishery per capita GDP, the insurance market’s contribution is relatively small; the development of credit and insurance markets does not significantly affect labor productivity. Conversely, stock market development shows a significant negative impact on both per capita GDP and labor productivity in the fishery economy [21]. Some researchers even found that financial development in China has not promoted the upgrading of the fishery industrial structure and may have had a suppressing effect, possibly due to the higher risk premiums associated with the primary sector of the fishery industry [22]. Su et al. (2021) discovered that, from a regional perspective, financial development only significantly impacts fishery economic growth in China’s eastern marine economic zones, with little effect in non-coastal regions [23].
The inconsistency of formal finance in supporting fishery development has implications for other agricultural economic sectors as well. Researchers have begun to explore other financial factors contributing to the development of China’s fishery economy, with widespread attention being drawn to informal finance. Lu and Yang (2008) proposed a financial path selection model in Chinese fisheries, in which informal finance serves self-employed entities, rural credit cooperatives support operational entities, commercial banks cater to market-oriented entities, and policy finance assists support-oriented entities [24]. Various researchers have highlighted the relationship between informal finance and agricultural or overall economic development, such as the effect of informal finance on farmers’ income growth. Li and Shan (2014) found that informal finance is efficient in promoting rural economic growth and has the potential to become an engine driving rural development, although regional disparities are evident [25]. With respect to the sector heterogeneity of the role of informal finance in agriculture, Wang et al. (2017) discovered that rural informal finance boosts crop planting but has a minimal influence on fishery, and likewise has little impact on forestry and animal husbandry [26]. Informal finance also plays a role in promoting agricultural scaling, with Chen and Zhang (2022) discovering that informal finance promotes overall agricultural growth through scaling [27] and that its effect on forestry is more pronounced than formal finance [28]. Informal finance also contributes to high-quality economic development, with Liu and Cao (2024) finding that informal finance fosters high-quality economic development by transforming growth modes, optimizing economic structure, and shifting growth drivers, with spillover effects across regions [29].
Moreover, most previous studies have employed single-dimension indicators, such as per capita income, total output, and innovation [30,31,32], when analyzing the impact of financial development on fisheries or other agricultural sectors. In the current context, where China’s fishery economy emphasizes high-quality development, the representativeness and reference value of these studies are somewhat limited.
In conclusion, there are reasons to hypothesize that appropriate financial supply may positively impact multiple aspects of fishery economic development, with both formal and informal finance playing crucial roles. While existing research provides some support, it has limitations, including: (1) insufficient alignment between research designs and the current high-quality development goals of China’s fishery economy, limiting the reference value; (2) the difficulty in generalizing conclusions based on single-indicator studies to the entire fishery economy, necessitating a scientific measurement of high-quality development levels based on overall goals; and (3) the unclear roles of formal and informal finance in supporting the high-quality development of fisheries, considering regional disparities in China. This paper investigates the primary objectives of high-quality fishery development using a scientific framework across three dimensions: fundamental security, sustainability, and comprehensive efficiency. It analyzes the effects of formal and informal financing on the high-quality development of China’s fishery economy, explores underlying mechanisms, and provides policy recommendations to enhance financial support.

3. Methods and Data

3.1. Model Setting

3.1.1. Baseline Model

To investigate the impact of formal and informal finance on the high-quality development of China’s fishery economy, this paper specifies the following baseline model, expressed in Equation (1), as follows:
F i s h e r y i t = α 0 + α 1 F i n a n c e i t + α 2 C o n t r o l i t + ε i t
where i denotes the provincial administrative region and t denotes the year, Fishery represents the level of high-quality development of China’s fishery economy, Finance refers to the level of development of different types of financial systems, Control is a vector of control variables, and ε is the error term.

3.1.2. Mediation Effect Model

Building on the baseline model, this paper draws on the mediation effect testing model proposed by Wen et al. (2004) [33] and constructs a recursive mediation model with the economic uncertainty of fisheries as the mediating variable. This approach aims to investigate the intrinsic mechanisms and differences of various types of financial support for the high-quality development of fisheries, detailed in Equations (2)–(4), as follows:
F i s h e r y i t = β 0 + β 1 F i n a n c e i t + β 2 C o n t r o l i t + ε i t
R i s k i t = δ 0 + δ 1 F i n a n c e i t + δ 2 C o n t r o l i t + ε i t
F i s h e r y i t = γ 0 + γ 1 F i n a n c e i t + γ 2 R i s k i t + γ 3 C o n t r o l i t + ε i t
where i denotes the provincial administrative region and t denotes the year, Risk is the mediating variable representing fishery economic uncertainty, Fishery denotes the level of high−quality development of China’s fishery economy, Finance refers to the development level of various types of financial systems, Control represents a series of control variables, and ε is the error term.

3.1.3. Threshold Model

To examine the impact of economic scale, this study constructs a panel threshold model, drawing on the methodology from related research [34], illustrated in Equation (5), as follows:
F i s h e r y i t = μ 0 + μ 1 F i n a n c e i t × I D F ≤ θ 1 + μ 2 F i n a n c e i t × I θ 2 < D F ≤ θ 3 + ⋯ + μ n F i n a n c e i t × I θ n − 1 < D F ≤ θ n + μ n + 1 F i n a n c e i t × I D F > θ n + μ ∅ C o n t r o l i t + ε i t
where i denotes the region and t denotes the year, DF is the threshold variable which captures the scale of the fishery economy, θ is the threshold value to be estimated, I (∙) is the indicator function, where I = 1 if the condition holds; otherwise, I = 0, μ represents the coefficients in different threshold regimes, capturing the impact of financial support on fishery development across those regimes.

3.2. Variables

3.2.1. Dependent Variables

The Ministry of Agriculture’s guidelines on the high-quality development of China’s fishery economy (Notice on Implementing Fishery Development Support Policies to Promote the High-Quality Development of the Fishery Industry (https://www.gov.cn/zhengce/zhengceku/2021-07/05/content_5622531.htm, accessed on 23 September 2024)), encompasses three key aspects: Stable Fundamental Security, Strong Sustainability, and High Comprehensive Efficiency. Stable Fundamental Security refers to positive trends in fishery output, production value and growth, underscoring the sector’s capacity to maintain a consistent supply of aquatic products while supporting employment. Strong Sustainability focuses on metrics, such as unit output, average resource consumption and technological intensity, reflecting the sector’s potential for sustained long-term development and ecological compatibility. High Comprehensive Efficiency encompasses key dimensions, including industrial structure, economic contributions, and per capita income, providing an integrated assessment of the sector’s overall economic performance and competitive strength. This study applies the entropy method to calculate the dependent variables based on these criteria.
The High-Quality Development Index (YT) measures the overall level of high-quality development in China’s fishery economy, with higher values indicating a more advanced state of development. The Fundamental Security Index (YS) evaluates the overall scale of the fishery economy and its capacity to ensure foundational security, where higher values signify stronger foundational stability. The Sustainability Index (YQ) reflects the sustainability of fishery development, with higher values indicating greater long-term development potential and environmental compatibility, serving as a key indicator for assessing the high-quality development of China’s fishery economy. The Comprehensive Efficiency Index (YE) assesses the overall efficiency and economic benefits of fishery development, where higher values represent greater efficiency and economic gains, providing an additional advanced metric for evaluating the high-quality development of China’s fishery economy.

3.2.2. Independent Variables

Formal Financial Development (XN): This refers to the allocation of capital through formal financial intermediaries, including state-owned banks, policy banks, joint-stock commercial banks, and financial markets. Consistent with prior research [35,36], the level of formal financial development is quantified using the ratio of total loan balances of financial institutions to regional GDP. A higher ratio signifies a more advanced level of formal financial development.
Informal Financial Development (XU): Informal financial development encompasses financial activities conducted outside formal regulatory frameworks, often operating at the margins of regulation. It serves as a complement to formal finance, particularly in mitigating capital shortages [37]. Following established methodologies [38,39], this study employs the ratio of microloan balances to regional GDP as a measure of informal financial development. A higher ratio reflects a higher degree of development in the informal financial sector.

3.2.3. Mediating Variable

Fishery Industry Uncertainty (DF): The inherent production processes and characteristics of the fishery sector result in a higher degree of uncertainty compared to other agricultural industries, particularly in financial contexts where investment risks are pronounced. The risk assessment criteria employed by financial institutions play a critical role in shaping their investment decisions [40,41]. Such uncertainty may limit access to formal financial support, whereas informal financial systems, with a higher tolerance for risk, may provide differentiated financial backing [42]. In this study, fishery industry uncertainty is quantified as the ratio of fishery product losses to total production, with higher values indicating greater levels of uncertainty.

3.2.4. Threshold Variable

Fishery Economic Scale (XM): The substantial regional disparities in fishery development conditions across China lead to significant variations in the economic scale of the fishery sector. In general, a larger economic scale is associated with higher levels of specialization, refinement, and standardization, which in turn enhance the region’s ability to attract financial resources [29]. In this study, the fishery economic scale is measured by the total output value of the regional fishery economy.

3.2.5. Control Variables

This study incorporates several control variables based on prior research [43,44], including the following:
Regional Economic Development (LnGDP): represented by the natural logarithm of per capita GDP, reflecting the overall economic performance of a region; Natural Endowments (Lnwater): measured by per capita water resource availability, capturing the region’s natural resource base; Industrial Structure Optimization (Ind3): quantified as the share of the tertiary sector in total economic output, indicating the level of industrial structure optimization; Market Development (Urbr): proxied by the urbanization rate, serving as an indicator of market expansion and modernization; Infrastructure Development (Railp): represented by per capita railway mileage, reflecting the level of regional infrastructure development; and Human Capital Quality (Lnedu): measured by per capita fiscal expenditure on education, serving as a proxy for the quality of human capital.

3.3. Datas

Due to the impact of the COVID-19 pandemic, statistical data from 2021 onward are somewhat incomplete. Accordingly, this study primarily relies on statistical indicators from 2006 to 2020 for China’s provincial administrative regions. To ensure data integrity and continuity, data from Hong Kong, Macau, Taiwan, Xizang, and Qinghai are excluded. Data sources include successive editions of the China Fisheries Statistical Yearbook, China Statistical Yearbook, China Financial Yearbook, and regional statistical yearbooks. Missing values were addressed through interpolation methods. To objectively assess high-quality development in China’s fishery economy based on historical data and reduce potential biases from expert evaluations, this study employs the entropy method [31]. The resulting indicators are used as the dependent variable, with the indicator system presented in Table 1. Descriptive statistics for all study variables are provided in Table 2. Unit root tests confirm that all data have the property of stationarity; multicollinearity tests indicate that all VIF values are below 10, suggesting an absence of multicollinearity. These results meet the conditions required for model estimation.

4. Results

4.1. Baseline Estimations

Using the baseline model, this study estimates the effects of formal and informal financial development on the overall and specific dimensions of high-quality development within China’s fishery economy, accounting for fixed time and regional effects. The estimation results are shown in Table 3. Models (1) to (4) present the estimated effects of formal finance on the high-quality development, fundamental security, sustainability, and comprehensive efficiency levels of China’s fishery economy. Models (5) to (8) show the estimated effects of informal finance on these same dimensions.
The estimation results presented in Table 4 indicate that both formal and informal financial development exert limited influence on enhancing the overall level of high-quality development in China’s fishery economy, as well as on its secondary sub-indicators. Except for the statistically insignificant effect on sustainability, the estimated coefficients for both types of financial development that reach statistical significance are predominantly negative. This suggests that formal and informal financial development may, to some extent, hinder the process of high-quality development in China’s fishery economy.
Furthermore, in the regression model analyzing the impact of formal financial development on high-quality growth in the fishery sector, regional economic development, industrial structure optimization, and infrastructure development are found to have a significant negative correlation with high-quality development in fisheries. This indicates that advancements in overall regional economic performance may create competitive or crowding-out effects from other industries, thereby further limiting the fishery sector’s access to formal financial support [45]. Given the generally constrained availability of formal financial resources, such competitive dynamics may exacerbate the challenges faced by the fishery sector in securing adequate financial support.
In the regression model assessing the impact of informal finance on high-quality development in the fishery sector, regional economic development and natural endowments exhibit a significant positive correlation with high-quality fishery development. This finding reflects a pattern distinct from that observed with formal finance but aligns with trends reported in the existing literature. In contrast, infrastructure quality and per capita educational attainment show a significant negative correlation, likely driven by crowding-out and competitive effects. Notably, the positive relationship between regional economic development and the advancement of fishery economic and financial development has been corroborated by numerous prior studies, which contrasts with the findings of the initial regression results.

4.2. Robustness Test

4.2.1. Substituting the Independent Variables

To address potential issues of spurious significance resulting from the arbitrary selection of explanatory variables, and to mitigate endogeneity concerns between explanatory and dependent variables, this study adopts an approach commonly used in the literature by employing alternative proxy variables [46,47]. Specifically, the density of regional bank branches (measured as the number of bank branches per 10,000 people) and the density of microcredit institutions (measured as the number of microcredit institutions per 10,000 people) are utilized as proxies for formal and informal financial development, respectively. The model parameters are re-estimated using these alternative proxies. Descriptive statistics for the proxy variables are presented in Table 4; the re-estimation results are summarized in Table 5.
In Table 5, Models (9) and (11) present estimation results without control variables, while Models (10) and (12) include control variables. From these results, it can be observed that, after replacing the original independent variables with alternative proxy variables, the new model estimates exhibit a high degree of consistency with the original results. The effects of formal and informal finance on high-quality development in the fishery economy remain minimal, with most estimated coefficients either significantly negative or statistically insignificant. Only formal finance shows a positive effect on sustainability at the 10% significance level.

4.2.2. Changing the Estimation Method of the Dependent Variables

To mitigate potential measurement errors that may arise from using a single method to assess high-quality development in the fishery economy, this study remeasures it using the entropy–TOPSIS method applied to the original data. The newly derived series of high-quality fishery development indices (R-YT/R-YS/R-YQ/R-YE) are introduced as dependent variables in the original model for re-estimation. Descriptive statistics for the recalculated indicators are presented in Table 6; the model estimation results after replacing the dependent variables are presented in Table 7.
In Table 7, Models (13) and (15) present estimation results without control variables, while Models (14) and (16) include control variables. It can be observed that, after reintroducing the fisheries high-quality development index recalculated using the Entropy–TOPSIS method as the dependent variable, the overall trend in the estimation results remains largely consistent with the original model. The influence of formal and informal financial development on various dimensions of high-quality fishery development remains largely negative across the estimations.
In summary, the estimation results have passed the robustness tests conducted by substituting independent or dependent variables. The new estimation outcomes reveal a strong consistency with the initial findings, showing that both formal and informal finance exhibit limited support across various dimensions of high-quality development in China’s fishery economy. This suggests that the preliminary findings of this study possess a degree of reliability, supporting further analysis of underlying mechanisms.

5. Further Analysis

The empirical findings above indicate that both formal and informal finance seem to play a negative role in promoting high-quality development in China’s fishery economy. Given the strong financing demand associated with the development of the fisheries industry, it is unlikely that China’s fisheries could have achieved such rapid growth and maintained a leading global position without necessary financial support. Thus, it is challenging to draw highly credible conclusions based solely on these results. This study proposes two possible underlying mechanisms for these findings.
First, the impact of economic scale on the fishery economy’s access to financial support should be considered. Numerous studies have shown that the relationship between financial development and industrial growth is nonlinear [48,49,50]. The scale of an industry can influence the ease of obtaining financial support for enterprises by affecting factors such as credit ratings, repayment capacity, and risk resilience. In the specific context of the fishery economy, the expansion of economic scale enables relevant enterprises to secure a greater share of production factors and market resources, thereby enhancing their capacity to manage, bear, and mitigate risks. This increased scale makes banks and other financial institutions more willing to extend loans and other forms of financial support to these enterprises. Furthermore, economies of scale can reduce production and sales costs, thereby boosting profitability, strengthening cash flows, and lowering financing costs. The regional disparities within China’s fishery economy are substantial, with variations in the scale of fishery economies across regions leading to qualitative differences in their economic structures. This diversity is expected to be reflected in the distinct characteristics of financial support within each region. These observations suggest that economic scale may introduce nonlinear characteristics in the impact of financial support on the high-quality development of the fishery economy.
Second, the impact of fisheries-related economic uncertainty on access to financial support should be considered. High uncertainty is a key characteristic of fisheries that has been noted by many researchers [51,52,53] and significantly affects their ability to secure financial support. Affected by factors, such as the natural environment, climate, resource availability and production methods, the fishery economy is characterized by high uncertainty, which typically increases investor caution in providing financial support to fishery enterprises, thereby raising their associated financing costs. Additionally, due to this inherent uncertainty, fishery enterprises often face challenges in providing adequate collateral or guarantees, which may limit financial institutions’ lending willingness and the volume of funds provided. Based on these observations, it can be inferred that China’s regional disparities in fishery economies, coupled with the varying capacities of formal and informal finance to accommodate industry uncertainty, may lead each to play distinct roles in promoting high-quality development within the fishery economy.
Based on the above analysis, this section focuses on exploring the mechanisms behind financial support for high-quality fishery development in China, primarily by examining the threshold effect of economic scale and the mediating effect of industry uncertainty.

5.1. Threshold Effect of Economic Scale

Traditionally, discussions of heterogeneity have often divided regions based on China’s economic geography, conducting analyses through comparisons of the eastern, central, and western regions [54]. In the context of China’s fishery economy, however, traditional regional classifications may not fully apply. For example, several provinces in central and western regions, such as Hubei and Hunan, demonstrate higher levels of fishery economic development than certain eastern provinces. Therefore, a classification based on the scale of the fishery economy is more appropriate. This study adopts the approach of Zhang and Weng (2015) [55], applying a panel threshold model with fishery economic scale as the threshold variable used to examine the underlying impact mechanisms.
Before modeling the threshold effect, this study applies the F-test, with 300 rounds of Bootstrap resampling, to examine the threshold nature of the economic scale of fisheries. The test results are presented in Table 8.
Based on the test results in Table 8, it can be observed that the F-statistics for both the single threshold and the triple threshold of the economic scale of fisheries exceed the critical values at the 0.01 and 0.05 significance levels, respectively. This indicates a triple-threshold effect of the economic scale of fisheries on the impact of financial support for high-quality fishery development, with threshold values of 108.34 billion CNY, 232.98 billion CNY and 340.68 billion CNY. Therefore, a threshold regression model was constructed; the estimation results are presented in Table 9 and Table 10.

5.1.1. Threshold Effect to Formal Finance

The estimation results for the threshold effect model of economic scale in the support of formal finance for the high-quality development of China’s fishery economy are shown in Table 9. Models (17) to (20), respectively, present the threshold effect estimates of formal finance on high-quality development, fundamental security, sustainability, and comprehensive efficiency in China’s fishery economy.
According to the estimation results, in regions where the economic scale is below 108.34 billion CNY, the overall effect of formal finance on high-quality fishery development is negative. However, in regions where the economic scale exceeds 108.34 billion CNY, the overall effect not only becomes positive but also exhibits a significant increase in magnitude as the economic scale rises. This trend is similarly observed across the individual indicators, with 108.34 billion CNY serving as the tipping point between negative and positive effects. For sustainability support, however, the economic scale of fisheries needs to exceed 232.98 billion CNY before the effect turns positive. Thus, 108.34 billion CNY serves as an important indicator for effectively leveraging formal financial support in high-quality fishery development across China’s regions. When the economic scale of a province’s fisheries surpasses this threshold, the impact of formal finance on high-quality fishery development undergoes a qualitative shift and continues to strengthen.

5.1.2. Threshold Effect to Informal Finance

The estimation results for the threshold effect model of economic scale in the support of informal finance for high-quality development of China’s fishery economy are shown in Table 10. Models (21) to (24), respectively, present the threshold effect estimates of informal finance on high-quality development, fundamental security, sustainability and comprehensive efficiency in China’s fishery economy.
The results indicate that informal finance also exhibits a significant threshold effect in supporting high-quality development in China’s fishery economy and its required economic scale for regional fisheries is higher compared to formal finance which contrasts with the commonly perceived role and characteristics of informal finance [56]. For overall high-quality fishery development and fundamental security levels, informal finance only begins to provide substantial support when the economic scale of the fisheries reaches 232.98 billion CNY; this support effect becomes increasingly pronounced as the scale rises. For the sustainability of fisheries, informal finance exerts a positive effect, primarily in regions where the economic scale is between 108.34 billion and 232.98 billion CNY, which complements the role of formal finance, the main influence of which occurs in regions where the economic output of fisheries exceeds 232.98 billion CNY. The highest economic scale requirement for informal finance is observed in supporting comprehensive efficiency, where a positive effect only becomes significant when the output exceeds 340.68 billion CNY.

5.2. The Mediating Effect of Industrial Uncertainty

We continued our analysis of the mediating role of uncertainty in the fisheries industry. First, following the mediation effect testing steps proposed by Wen et al. (2004) [33], we conducted a mediation effect test for uncertainty in the fisheries industry. The results revealed that it only plays a significant mediating role between formal finance and the high-quality development of the fishery economy. Specifically, the direction and strength of this effect vary across different sub-indicators of high-quality fishery development. Detailed testing results are presented in Table 11.
In Table 11, c represents the regression coefficient in the formal finance model without the inclusion of mediating variables, indicating the total effect; a denotes the regression coefficient of formal finance on uncertainty in the fisheries industry; while b indicates the regression coefficient of uncertainty in the fisheries industry on the level of high-quality fishery development. The product a × b represents the mediating effect. The 95% BootCI denotes the 95% confidence interval calculated through Bootstrap sampling; if the interval does not include 0, it indicates significance. c’ represents the regression coefficient of formal finance on the level of high-quality fishery development when the mediating variable is included, indicating the direct effect. It can be concluded that industrial uncertainty plays a partial mediating role in the overall support of formal finance for the high-quality development of fisheries. Specifically, it acts as a partial mediator in supporting the foundational security of fisheries, an attenuating factor in fostering sustainability, and a full mediator in enhancing the comprehensive efficiency level within the fishery economy.
The estimation results of the mediating effect model are presented in Table 12. From this, it is evident that industrial uncertainty significantly diminishes the support of formal finance for the development level of the fisheries industry across various dimensions. The mediating effects on the overall level of high-quality fishery development, foundational security level, sustainability level, and comprehensive efficiency level are 26.336%, 16.636%, 47.49% and 100%, respectively. Notably, for the two higher-order indicators of sustainability and comprehensive efficiency, which are particularly critical for the high-quality development of the fishery economy in China at this stage, the mediating effect of industrial uncertainty is especially vital, representing an urgent issue that needs to be addressed in the pursuit of high-quality development in China’s fishery economy.

6. Discussion

6.1. Support of Formal Finance for High-Quality Development in China’s Fishery Economy

Overall, the support of formal finance for the high-quality development of China’s fishery economy is less than satisfactory, as evidenced by negative estimated coefficients. This phenomenon can largely be attributed to significant regional disparities within China’s fishery economy, which can be partially explained by the threshold effect of the economic scale of the fisheries. When the total output value of regional fisheries’ economics is below 108.34 billion CNY, the support effect of formal finance on the overall level of high-quality fishery development is negative. Conversely, once the total output value exceeds 108.34 billion CNY, the support effect becomes positive and intensifies rapidly, with further increases in the scale of fisheries’ economics. Given that only eight of the 29 provincial-level administrative regions selected for this study had a total fisheries’ economic output exceeding 100 billion CNY in 2020, effective support from formal finance for high-quality fishery development is not easily achievable in most regions of China. Furthermore, industrial uncertainty plays a crucial role, especially regarding the higher-order indicators of sustainability and comprehensive efficiency, where its mediating effect is particularly pronounced. This provides valuable insights into the importance of financial support for high-quality fishery development. Since the scale of fisheries’ economics is often constrained by natural endowments, focusing on industrial uncertainty is especially pertinent, as it typically signifies higher risk in the financial market, which can be a decisive factor for risk-averse investors in their investment decisions. Grassroots fishery organizations have accumulated a wealth of risk management strategies [14]. Adapting and refining these strategies based on the current development characteristics and needs of the fishery economy would be significant in mitigating risks within the industry.

6.2. Support of Informal Finance for High-Quality Development in China’s Fishery Economy

The support effect of informal finance for the overall level of high-quality fishery development is similarly unsatisfactory. By introducing the scale of the fisheries’ economics as a threshold variable, this study finds that informal finance requires a higher economic scale to support high-quality development. Specifically, the estimated coefficients only become positive when the total economic output of fisheries reaches at least 232.98 billion CNY, indicating that only a few provincial regions meet this condition. In supporting high-quality development within China’s fishery economy, informal finance demonstrates a distinct preference for wealthier regions, diverging from the general consensus in existing studies in the literature. Many studies suggest that informal finance, as a crucial supplement to formal finance, plays an essential role in promoting industrial growth, especially during the early stages of regional economic development [29]. However, this complementary role does not appear to hold in the context of China’s fishery economy. On the other hand, the insignificant mediating effect of industrial uncertainty on the relationship between informal finance and high-quality fishery development suggests a higher tolerance for risks induced by industrial uncertainty. Considering the implications for high-quality development, this characteristic of informal finance provides a referential framework for promoting industrial upgrading and structural optimization in more advanced fishery regions, while also offering strategies to support more balanced growth in less developed fishery areas.

6.3. Complementary Effects of Formal and Informal Finance

One of the most insightful findings of this study is the complementary effect observed between formal and informal finance in supporting higher-level indicators of high-quality development in the fishery economy, contingent upon economic scale. On the one hand, neither formal nor informal finance show a significant effect in supporting the fundamental security level, indicating that following prolonged rapid expansion, China’s fishery economy is facing increasing difficulty in securing effective financial support through extensive growth models, rendering this approach largely unsustainable. On the other hand, in the threshold model based on economic scale, formal and informal finance exhibit a clear “complementary” effect in supporting sustainability. Specifically, informal finance demonstrates a more substantial impact in regions where fishery economic output ranges from 108.34 billion to 232.98 billion CNY, while formal finance shows a stronger support effect in regions with fishery economic output exceeding 232.98 billion CNY. This insight provides a valuable framework for different regions to explore tailored pathways toward high-quality fishery development. Furthermore, considering the markedly different sensitivities of formal and informal finance to the uncertainties within the fishery industry, this complementary relationship offers deeper explanatory value. Given the diverse segments within the fishery industry chain, entities within different chain links can form an objective understanding of their unique risk characteristics, thereby enabling them to devise more targeted financing strategies.

7. Conclusions

This paper constructs an evaluation system for the high-quality development level of China’s fishery economy based on three indicators: fundamental security level, sustainability level, and comprehensive efficiency level. It examines the supportive effects of formal finance and informal finance on both the overall high-quality development of China’s fishery economy and its various dimensions. Building on this, the study further analyzes the intrinsic mechanisms of financial support for high-quality fishery development by establishing a panel threshold regression model based on fishery economic scale and a mediating effect model based on fishery industry uncertainty. The conclusions drawn from the research findings provide valuable insights for both managers and operators.
The multilayered findings presented in this study offer valuable insights for exploring regionally tailored financial support strategies for the fishery economy in a country, like China, where fishery development is highly uneven across regions. Recognizing the critical roles of industry uncertainty and economic scale is essential for advancing China’s fishery economy towards genuine, high-quality development. Given the sensitivity of formal finance to industry uncertainty, establishing a robust risk-sharing mechanism, and creating a standardized production supervision system for aquatic products are of significant practical importance. Considering the complementarity between formal and informal finance, the distinct types of financial products represented in the underlying financial markets, and the varied financial needs of different fishery operators, developing a differentiated multi-level financial support system will serve as a strong guarantee for the high-quality development of China’s fishery economy. Achieving this objective requires the establishment of a comprehensive financial service and cooperation network that includes financial institutions, fishery enterprises, government agencies, and other relevant organizations. This network will facilitate the efficient circulation of information among enterprises, financial institutions, and policymakers, thereby enabling the introduction of practical and targeted financial products and services.

Author Contributions

Conceptualization: S.Y. and Q.Z. methodology: H.W. and X.L. draft writing: S.Y., review and editing: S.Y., J.Y., Q.Z., S.Y. and Q.Z. have made equal contributions to this paper. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by grant 72373078 from the National Natural Science Foundation of China in 2023.

Data Availability Statement

All data used in this research are publicly available and details are provided in the paper. Further inquiries can be directed to the corresponding authors.

Acknowledgments

We appreciate all the subjects for giving their consent to participate in this project. The authors gratefully thank the editor and anonymous reviewer for their review.

Conflicts of Interest

Author Xiao Li was employed by the company of Quzhou Longhuan Environmental Protection Technology. Xiao Li contributed to the research by investigation and visualization. The remaining authors declare that the research was conducted ivn the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Yang, Z.Y.; Zhang, D. The high-quality development of Chinese fisheries from the perspective of the all-encompassing approach to food: Current situation, problems and improvement strategies. Chin. Fish. Econ. 2023, 41, 1–13. [Google Scholar] [CrossRef]
  2. Lu, Q.; Chen, X.J. Development of Chinese fishery industry in 40 years of reform and opening up and production forecast in the 14th five-year plan. J. Shanghai Ocean. Univ. 2021, 30, 339–347. [Google Scholar] [CrossRef]
  3. Shaw, E.S. Financial Deepening in Economic Development; Oxford University Press: New York, NY, USA, 1973. [Google Scholar]
  4. McKinnon, R.I. The Order of Economic Liberalization: Financial Control in the Transition to a Market Economy; Johns Hopkins University Press: Baltimore, MD, USA, 1993. [Google Scholar]
  5. Shaw, P.E.; Newby, L. Sustainable Wealth Creation at the Local Level in an Age of Globalization. Reg. Stud. 1998, 32, 863–871. [Google Scholar] [CrossRef]
  6. Lin, J.Y.; Sun, X.F.; Jiang, Y. Toward a theory of optimal financial structure in economic development. Econ. Res. J. 2009, 21, 45–49. Available online: https://ssrn.com/abstract=1471136 (accessed on 12 September 2024).
  7. Lin, J.Y.; Zhang, Q.; Liu, M.X. Financial structure and economic growth: The case of manufacturing industry. J. World Econ. 2003, 1, 3–21. Available online: http://www.escience.cn/system/download/64000 (accessed on 12 September 2024).
  8. Lin, J.Y.; Sun, X. Banking structure and economic growth: Evidence from China. Front. Econ. China 2009, 4, 479–504. [Google Scholar] [CrossRef]
  9. Liang, P.P.; Yu, L.P. Empirical study on the fishery performance in China: Based on 1999–2010 panel data. Sci.-Technol. Manag. 2014, 16, 21–26. [Google Scholar] [CrossRef]
  10. Pomeroy, R.; Arango, C.; Lomboy, C.G.; Box, S. Financial Inclusion to Build Economic Resilience in Small-scale Fisheries. Mar. Policy 2020, 118, 103982. [Google Scholar] [CrossRef]
  11. Shiiba, N.; Wu, H.H.; Huang, M.C.; Tanaka, H. How Blue Financing can Sustain Ocean Conservation and Development: A Proposed Conceptual Framework for Blue Financing Mechanism. Mar. Policy 2022, 139, 104575. [Google Scholar] [CrossRef]
  12. Badriyah, S.M.; Mahmudah, S.; Soemarmi, A.; Kamello, T. Leasing Agreement on Financing SMEs in Fisheries Industry. J. Leg. Ethical Regul. Issues 2019, 22, 1–7. Available online: https://www.xueshufan.com/publication/2962967357 (accessed on 12 September 2024).
  13. Nazir, I.; Chauhan, R.S.; Khati, A.; Arya, P. Role of Credit for the Upliftment of the Fisheries Sector. Int. J. Fish. Aquat. Stud. 2018, 6, 1–4. Available online: https://www.fisheriesjournal.com/archives/2018/vol6issue2/PartA/6-1-15-444.pdf (accessed on 12 September 2024).
  14. Zheng, H.; Li, J.; Zhao, X. How Does Financial Policy Support the Development of China’s Fishery? Characteristics, experience and prospects. Mar. Policy 2021, 132, 104678. [Google Scholar] [CrossRef]
  15. Lu, C. The Fishery Industrial Structure in China Based on the Application of Shift—Share Analysis. Asian Agric. Res. 2016, 8, 8. [Google Scholar] [CrossRef]
  16. Cao, L.; Chen, Y.; Dong, S.; Hanson, A.; Huang, B.O.; Leadbitter, D.; Little, D.C.; Pikitch, E.K.; Qiu, Y.; Sadovy de Mitcheson, Y.; et al. Opportunity for Marine Fisheries Reform in China. Proc. Natl. Acad. Sci. USA 2017, 114, 435–442. [Google Scholar] [CrossRef]
  17. Qiao, X. Financial support policy for fishery development in China. Chin. Fish. Econ. 2009, 01, 59–63. [Google Scholar] [CrossRef]
  18. Wang, D.C.; Wu, X.; Wang, L.L. Risk Control of Marine Fishery Based on Financial Instrument Innovation. Ocean. Dev. Manag. 2017, 34, 50–54. [Google Scholar] [CrossRef]
  19. Zhang, F.; Yu, T.; Chen, Z.H. On the Support of Capital Market to Marine Economy. J. Shenyang Agric. Univ. (Soc. Sci. Ed.) 2018, 20, 184–189. Available online: https://www.ncpssd.cn/cccd9af4-161a-4277-820c-0a89d2094d4f (accessed on 12 September 2024).
  20. An, Q.Q.; Xiao, Y. Research on the relationship between fishery loans and the growth of marine fishery economy—An empirical analysis based on VAR model. Ocean. Dev. Manag. 2015, 32, 75–78. [Google Scholar] [CrossRef]
  21. Xiong, D.P.; Sun, J. The fishery economic growth effect of China’s financial development—Provincial panel data GMM estimation from the perspectives of per capita GDP and labor productivity. J. Agrotech. Econ. 2014, 03, 97–107. Available online: https://www.ncpssd.cn/ab4ab6c9-37dd-42ca-8f81-800c56af5729 (accessed on 15 September 2024).
  22. Yu, T.; Zhao, W.L. An empirical analysis of the impact of financial development on the upgrading of China’s fishery industrial structure. J. Dalian Marit. Univ. (Soc. Sci. Ed.) 2015, 14, 20–23. Available online: https://www.ncpssd.cn/fa3a108e-362b-47fa-9da4-7dcd196035b8 (accessed on 15 September 2024).
  23. Su, C.-W.; Song, Y.; Umar, M. Financial Aspects of Marine Economic Growth: From the Perspective of Coastal Provinces and Regions in China. Ocean Coast. Manag. 2021, 204, 105550. [Google Scholar] [CrossRef]
  24. Lu, M.Q.; Yang, L. Study on supply-path of fishery finance based on requirement. J. Financ. Dev. Res. 2008, 02, 68–69. [Google Scholar] [CrossRef]
  25. Li, H.S.; Shan, M.J. Analysis of informal financial efficiency and its regional differences in China’s rural economy. Seeker 2014, 63–67. Available online: https://www.ncpssd.cn/1f13a787-898d-4c83-921f-7cccced7b266 (accessed on 15 September 2024).
  26. Wang, L.; Song, K.; Chen, X. Research on the impact of rural informal finance on farming, forestry, animal husbandry and fishery. World Agric. 2017, 10, 210–215. [Google Scholar] [CrossRef]
  27. Chen, Z.G.; Zhang, Y. Has rural finance promoted the agriculture large-scale operation in China: Based on the survey data of two national family farm demonstration bases. Issues Agric. Econ. 2022, 5, 83–97. [Google Scholar] [CrossRef]
  28. Zhao, H.H.; Huang, A.S. Influence of Rural Informal Finance on the Development of Forestry Industry—Empirical Analysis Based on 30 Chinese Provincial Panel Data. For. Econ. 2022, 44, 50–60. [Google Scholar] [CrossRef]
  29. Liu, X.Z.; Cao, P.Q. Informal Finance, Industrial Transformation and High-quality Economic Development. J. Stat. Inf. 2024, 39, 16–28. Available online: http://www.cnki.com.cn/Article/CJFDTotal-TJLT202408002.htm (accessed on 10 September 2024).
  30. Fu, X.-M.; Ku, H.-L.; Wu, W.-Y.; Wang, L.-X.; Chen, H.-X.; Lin, C.-Y.; Liu, Y. Spatial-temporal Differentiation and Convergence Analysis of Marine Fishery Innovation Ability in China. Fish. Res. 2022, 254, 106393. [Google Scholar] [CrossRef]
  31. Wang, S.; Lu, B.; Yin, K. Financial Development, Productivity and High-quality Development of the Marine Economy. Mar. Policy 2021, 130, 104553. [Google Scholar] [CrossRef]
  32. Wang, L.; Li, T. Marine Fisheries governance in China: Low-efficacy Policies and Future Adjustments. Coast. Manag. 2021, 49, 313–333. [Google Scholar] [CrossRef]
  33. Wen, Z.; Chang, L.; Hau, K.T.; Liu, H. Testing and application of the mediating effects. Acta Psychol. Sin. 2004, 36, 614–620. Available online: https://journal.psych.ac.cn/acps/EN/Y2004/V36/I05/614 (accessed on 10 September 2024).
  34. Hansen, B.E. Threshold Effects in Non-dynamic Panels: Estimation, Testing, and Inference. J. Econom. 1999, 93, 345–368. [Google Scholar] [CrossRef]
  35. Xie, W.M.; Fang, H.X. Financial development, financing constrains and firms’ R&D investment. J. Financ. Res. 2011, 5, 171–183. Available online: https://www.ncpssd.cn/a818ea1d-7f82-42ad-b920-cf809790dca3 (accessed on 13 September 2024).
  36. Yao, Y.J.; Dong, G.F. Financial Development Level vs. Financial Structure: Which Is More Important for Easing Financing Constraints of SMEs? Evidence from China’s Companies Listed on SME Board. Financ. Res. 2015, 4, 148–161. Available online: https://www.ncpssd.cn/cb080a30-8680-41bb-a379-86cea38c1d3c (accessed on 11 September 2024).
  37. Wang, Y.C.; Lin, B. Financial Intermediary, Informal Finance and Cash Value. Financ. Res. 2008, 03, 177–199. Available online: https://www.ncpssd.cn/55bc8f1c-acd5-4d59-9d59-718bbad0c1bb (accessed on 23 September 2024).
  38. Sun, Y.K.; Feng, Q. Research on the relationship between rural financial development and the income gap of farmers in China—From the perspective of rural formal finance and informal finance as a whole. J. Agrotech. Econ. 2014, 11, 65–74. [Google Scholar] [CrossRef]
  39. Nie, G.H.; Qiu, Y.D.; Long, W.Q. Informal finance, technological innovation and upgrading of industrial structure. Stud. Sci. Sci. 2018, 36, 1404–1413. [Google Scholar] [CrossRef]
  40. Ha, G.R.; Choi, S.B. The Impact of Technology Innovation Activity on Managerial Efficiency: An Inverted U shaped Model. J. Korean Soc. Qual. Manag. 2018, 46, 551–568. [Google Scholar] [CrossRef]
  41. Ma, S.; Kang, B.; Li, J.; Sun, P.; Liu, Y.; Ye, Z.; Tian, Y. Climate Risks to Fishing Species and Fisheries in the China Seas. Sci. Total. Environ. 2023, 857, 159325. [Google Scholar] [CrossRef]
  42. Lei, W.; Jing, C.; Jing, H. Local Financial Supervision Reform and the Return of Entity Enterprises to Their Basics: Based on the Quasi-natural Experiment of Local Financial “Office-Promoted-to-Bureau”. J. Financ. Econ. 2024, 50, 19–33. [Google Scholar] [CrossRef]
  43. Wang, S.; Chen, Y.B. How Does Digital Inclusive Finance Promote the High-quality Development of Agriculture? on Intermediary and Threshold Mechanism. J. Manag. 2022, 35, 72–87. [Google Scholar] [CrossRef]
  44. Zhang, Y.L.; Lin, Y.F.; Gong, Q. Firm size, bank size and optimal banking structure: From the perspective of new structural economics. Manag. World 2019, 35, 31–47+206. [Google Scholar] [CrossRef]
  45. Peng, Y.C.; Han, X.; Li, J.J. Economic Policy Uncertainty and Corporate Financialization. China Ind. Econ. 2018, 01, 137–155. [Google Scholar] [CrossRef]
  46. Ding, Z.G.; Tan, L.L.; Zhao, J. The effect of rural financial development on poverty reduction. Issues Agric. Econ. 2011, 32, 72–77+112. Available online: https://www.ncpssd.cn/1c532439-ff2e-4c18-8133-8239380a4687 (accessed on 30 September 2024).
  47. Zhou, Y.S.; Li, Y. Analysis of the impact of rural microfinance companies on formal credit rationing for rural small and micro enterprises—Based on a survey of rural small and micro enterprises in northern Jiangsu. Chin. Rural. Econ. 2013, 01, 85–96. Available online: https://www.ncpssd.cn/4e685cca-230d-4524-8391-a6c8d2750e22 (accessed on 13 September 2024).
  48. Zhang, L.; Zhang, W.K. Study on the Efficiency and Influencing Factors of Finance Serves the Growth of Real Economy. J. Macro-Qual. Res. 2017, 5, 47–60. [Google Scholar] [CrossRef]
  49. Kuang, X.; Liu, H.; Guo, G.; Cheng, H. The Nonlinear Effect of Financial and Fiscal Policies on Poverty Alleviation in China—An Empirical Analysis of Chinese 382 Impoverished Counties with PSTR Models. PLoS ONE 2019, 14, e0224375. [Google Scholar] [CrossRef]
  50. Chien, M.-S.; Cheng, C.-Y.; Kurniawati, M.A. The Non-linear Relationship Between ICT Diffusion and Financial Development. Telecommun. Policy 2020, 44, 102023. [Google Scholar] [CrossRef]
  51. Rangeley, R.W.; Davies, R.W. Raising the “Sunken Billions”: Financing the Transition to Sustainable Fisheries. Mar. Policy 2012, 36, 1044–1046. [Google Scholar] [CrossRef]
  52. Zhao, K.; Gaines, S.D.; Molinos, J.G.; Zhang, M.; Xu, J. Climate Change and Fishing are Pulling the Functional Diversity of the World’s Largest Marine Fisheries to Opposite Extremes. Glob. Ecol. Biogeogr. 2022, 31, 1616–1629. [Google Scholar] [CrossRef]
  53. Xue, Y.; Ding, L.; Lai, K.-H. How does Index-based Insurance Support Marine Disaster Risk Reduction in China? Evolution, Challenges and Policy Responses. Ocean Coast. Manag. 2023, 235, 106477. [Google Scholar] [CrossRef]
  54. Tian, P.; Li, J.L.; Cao, L.D.; Liu, Y.C.; Zhang, H.T. Evaluation of fishery economic efficiency and development trend forecast in China. Chin. J. Agric. Resour. Reg. Plan. 2023, 44, 160–172. Available online: https://www.ncpssd.cn/3f477d89-511e-4bda-bd02-ebd95767fb3e (accessed on 17 September 2024).
  55. Zhang, B.; Weng, C. The poverty reduction effect of rural financial development—Spatial spillover and threshold characteristics. J. Agrotech. Econ. 2015, 9, 37–47. [Google Scholar] [CrossRef]
  56. Chen, F.; Xue, Y. Economic Growth Effect of Private Finance: Optimal Scale and Space-Time Differences. Mod. Econ. Sci. 2022, 44, 76–87. Available online: https://www.ncpssd.cn/87c36def-def0-4c8e-a898-e5d8f84f898f (accessed on 19 September 2024).
Table 1. Evaluation indicator system for high-quality development of China’s fishery economy.
Table 1. Evaluation indicator system for high-quality development of China’s fishery economy.
IndicatorDescriptionAttributeIUVWeight
The Fishery Fundamental Security Index (YS)Total Fishery Economic Output (10,000 CNY)+0.10274.27%
Aquatic Product Output (tons)+0.07213.00%
Fishery Employment (people)+0.06262.60%
Total Aquatic Products Used for Processing (tons)+0.11464.77%
Fishery Value Added (10,000 CNY)+0.09023.75%
Motorized Fishing Vessels (gross tonnage)+0.05632.34%
Aquaculture Area (hectares)+0.04041.68%
Released Fish Species (tons)+0.06342.63%
Fishery Growth Rate (%)+0.07753.22%
The Fishery Sustainability Index (YQ)Proportion of Fishery Circulation and Service Industry Output (%)+0.09573.98%
Proportion of Fishery Industry and Construction Industry Output (%)+0.11194.65%
Proportion of Aquaculture Output (%)+0.07573.15%
Aquatic Product Output per Fishery Law Enforcement Vessel (tons/vessel)+0.04681.95%
Aquaculture Output per Aquaculture Area (tons/hectare)+0.08993.74%
Proportion of Intermediate Consumption in Fisheries (%)−0.0251.04%
Funding for Aquatic Technology Promotion Institutions per Fishery Population (10,000 CNY)+0.1315.45%
Proportion of Aquatic Product Losses (%)−0.05772.40%
Per Capita Aquatic Product Output (tons)+0.08433.50%
The Fishery Comprehensive Efficiency Index (YE)Average Aquatic Product Processing Volume per Enterprise (tons)+0.09954.14%
Per Capita Fishery Output Value (10,000 CNY)+0.11814.91%
Per Capita Net Income of the Fishery Population (CNY)+0.13635.67%
Aquatic Product Export Value (10,000 CNY)+0.11674.85%
Composition of Fishery Value Added (%)+0.08183.40%
Recreational Fishery Output (10,000 CNY)+0.1215.03%
Per Capita Aquatic Product Consumption in Rural Areas (kilograms)+0.1536.36%
Urban Residents’ Net Income relative to Fishery Population Net Income−0.13215.49%
Total Fishery Economic Output as a Percentage of Regional GDP (%)+0.04872.02%
Table 2. Descriptive statistics of all variables.
Table 2. Descriptive statistics of all variables.
VariableObservationMinMaxMeanStd. DevMed
YT4353.31860.48616.6313.20610.812
YS4350.03633.3086.9238.1052.952
YQ4351.37810.5963.5991.3573.245
YE4350.15622.0615.0454.7153.064
XN4350.5522.6481.2380.4341.162
XU4350.2567.5251.2961.0171.025
DF4351.4884147.588624.329936.483166.598
XM4350.0020.1440.0180.0220.011
LnGDP4353.7625.2174.5920.2684.611
Lnwater4351.7153.7513.0190.5183.197
Ind34350.2860.8390.4480.0990.427
Urbr4350.2750.8960.560.1380.543
Railp4350.1365.9050.9690.8210.696
Lnedu4352.3483.7163.1170.2813.188
Table 3. Estimation effects of formal and informal finance on high-quality development of China’s fishery economy.
Table 3. Estimation effects of formal and informal finance on high-quality development of China’s fishery economy.
XNXU
(1)(2)(3)(4)(5)(6)(7)(8)
XN−4.201 ***
(1.015)		−2.625 ***
(0.636)		0.043
(0.246)		−1.635 ***
(0.432)
XU −1.695 ***
(0.651)		−0.915 **
(0.383)		−0.086
(0.078)		−0.696 ***
(0.250)
LnGDP−12.70 ***
(4.157)		−8.606 ***
(2.603)		1.771 *
(1.008)		−5.903 ***
(1.771)		93.299 ***
(9.315)		63.751 ***
(5.479)		4.897 ***
(1.110)		24.745 ***
(3.580)
Lnwater−1.064
(1.137)		−0.876
(0.712)		0.261
(0.276)		−0.443
(0.484)		7.201 ***
(1.443)		4.088 ***
(0.849)		0.074
(0.172)		3.044 ***
(0.555)
Ind3−11.666 **
(5.529)		−3.717
(3.462)		−2.257 *
(1.340)		−5.708 **
(2.355)		4.482
(13.362)		4.562
(7.859)		2.324
(1.592)		−2.357
(5.135)
Urbr6.742
(7.927)		17.527 ***
(4.964)		−13.517 ***
(1.921)		2.799
(3.376)		−49.10 ***
(15.954)		−42.864 ***
(9.384)		−3.039 *
(1.901)		−3.274
(6.132)
Railp−1.717 **
(0.647)		−1.433 ***
(0.405)		0.474 ***
(0.157)		−0.752 ***
(0.276)		−4.047 ***
(0.790)		−2.413 ***
(0.465)		−0.140
(0.094)		−1.496 ***
(0.304)
Lnedu5.203
(3.756)		4.319 *
(2.352)		−1.369
(0.910)		2.265
(1.580)		−61.279 ***
(9.179)		−46.043 ***
(5.399)		−0.551
(1.094)		−14.762 ***
(3.528)
Constant69.825 ***
(16.623)		32.029 ***
(10.409)		6.860 *
(4.029)		29.974 ***
(7.080)		−208.781 ***
(35.731)		−127.053 ***
(21.016)		−16.761 ***
(4.258)		−66.208 ***
(13.733)
YEARYESYESYESYESYESYESYESYES
REGIONYESYESYESYESYESYESYESYES
N435435435435435435435435
Adj R20.9730.9730.8470.9630.9480.9590.8270.948
Note: The standard errors in parentheses; ***, ** and * after numbers indicate that significance levels of 1%, 5%, and 10% passed the significance test.
Table 4. Descriptive statistics of new independent variables.
Table 4. Descriptive statistics of new independent variables.
VariableObservationMinMaxMeanStd. DevMed
P-XN4350.0520.4540.2100.0930.198
P-XU4350.0110.1990.0600.0320.054
Table 5. Estimation results after replacing independent variables.
Table 5. Estimation results after replacing independent variables.
XNXU
(9)(10)(11)(12)
YT−10.133 (9. 837)−11.002 * (10.135)−7.687 (7.920)−8.148 * (6.651)
YS−6.334 (5.569)−7.585 * (6.461)−10.427 (10.553)−11.558 * (9.441)
YQ−0.722 (0.954)0.694 * (0.373)−6.097 (6.248)−9.110 * (6.166)
YE−14.548 ** (9.536)−15.772 ** (10.285)−17.476 ** (9.479)−16.104 ** (10.546)
ControlsNOYESNOYES
Constant66.278 ** (40.235)71.588 ** (47.226)−36.454 ** (9.299)−39.774 ** (12.049)
YEARYESYESYESYES
REGIONYESYESYESYES
N435435435435
Adj R20.7430.7510.5890.661
Note: The standard errors in parentheses; ** and * after numbers indicate that significance levels of 1%, 5%, and 10% passed the significance test.
Table 6. Descriptive statistics of new dependent variables.
Table 6. Descriptive statistics of new dependent variables.
VariableObservationMinMaxMeanStd. DevMed
R-YT4353.72162.34718.45514.37512.132
R-YS4350.05437.3188.3199.1914.256
R-YQ4352.55813.4225.7311.4744.85
R-YE4350.65625.187.1145.3653.717
Table 7. Estimation results after replacing dependent variables.
Table 7. Estimation results after replacing dependent variables.
XNXU
(13)(14)(15)(16)
R-YT−5.533 *** (1.205)−5.745 *** (1.315)−1.221 * (0.831)−1.147 ** (0.752)
R-YS−1.332 ** (0.718)−2.168 *** (0.915)−1.356 * (0.974)−1.117 ** (0.745)
R-YQ−0.102 (0.257)−0.148 * (0.116)−0.154 ** (0.077)−0.214 ** (0.088)
R-YE−0.997 *** (0.413)−0.889 ** (0.438)−0.512 ** (0.279)−0.731 ** (0.330)
ControlsNOYESNOYES
Constant9.885 *** (2.577)12.891 *** (5.155)−3.136 *** (0.819)6.647 *** (4.866)
YEARYESYESYESYES
REGIONYESYESYESYES
N435435435435
Adj R20.8320.9030.7850.876
Note: The standard errors in parentheses; ***, ** and * after numbers indicate that significance levels of 1%, 5%, and 10% passed the significance test.
Table 8. Threshold effect test of the economic scale of fisheries.
Table 8. Threshold effect test of the economic scale of fisheries.
Threshold NumberFP10% CV5% CV1% CV
152.270.010 ***32.43038.53252.267
234.860.12755.8097.015151.613
3123.760.023 **60.87877.969132.639
Note: *** and ** after numbers indicate that significance levels of 1% and 5% passed the significance test.
Table 9. Threshold effect of economic scale on formal finance.
Table 9. Threshold effect of economic scale on formal finance.
(17) YT(18) YS(19) YQ(20) YE
DF ≤ 1083.4−1.397 ** (0.638)−1.355 *** (0.383)0.356 (0.248)−0.411 (0.280)
1083.4 < DF ≤ 2329.82.903 *** (0.721)1.306 *** (0.432)0.326 (0.280)1.262 *** (0.316)
2329.8 < DF ≤ 3406.87.306 *** (0.774)4.006 *** (0.465)0.653 * (0.301)3.036 *** (0.339)
DF > 3406.811.310 *** (0.943)5.989 *** (0.566)0.819 ** (0.366)5.324 *** (0.413)
ControlsYESYESYESYES
Constant−15.074 ** (5.950)1.160 (3.573)−10.348 *** (2.312)−6.951 *** (2.606)
N435435435435
Adj R20.7960.7550.3390.768
Note: The standard errors in parentheses; ***, ** and * after numbers indicate that significance levels of 1%, 5%, and 10% passed the significance test.
Table 10. Threshold effect of economic scale on informal finance.
Table 10. Threshold effect of economic scale on informal finance.
(21) YT(22) YS(23) YQ(24) YE
DF ≤ 1083.4−0.05 (0.187)0.124 (0.091)−0.146 (0.095)−0.027 (0.108)
1083.4 < DF ≤ 2329.8−0.954 * (0.540)−0.755 *** (0.264)0.598 ** (0.276)−0.786 ** (0.314)
2329.8 < DF ≤ 3406.81.319 ** (0.635)1.056 *** (0.310)0.199 (0.324)0.073 (0.369)
DF > 3406.87.302 *** (1.060)4.416 *** (0.517)−0.431 (0.541)3.588 *** (0.615)
ControlsYESYESYESYES
Constant−53.812 *** (7.986)−9.559 **
(3.896)		−32.274 ***
(4.076)		−13.268 ***
(4.635)
N435435435435
Adj R20.6950.5660.4350.603
Note: The standard errors in parentheses; ***, ** and * after numbers indicate that significance levels of 1%, 5%, and 10% passed the significance test.
Table 11. Mediation effect test of uncertainty in the fisheries industry.
Table 11. Mediation effect test of uncertainty in the fisheries industry.
Pathcaba × bBoot SEz95% BootCIc’Type
XN => XM => YT−7.255 ***−0.013 ***146.973 ***−1.9680.018−108.634−0.104~−0.033−5.286 **partial
XN => XM => YS−6.319 ***−0.013 ***80.864 ***−1.0830.017−63.986−0.095~−0.029−5.236 ***partial
XN => XM => YQ0.388−0.013 ***14.174 ***−0.190.019−10.041−0.105~−0.0310.578 **masking
XN => XM => YE−1.35−0.013 ***51.963 ***−0.6960.02−35.683−0.107~−0.030−0.655full
Note: *** and ** after numbers indicate that significance levels of 1%, 5%, and 10% passed the significance test.
Table 12. Threshold effect of economic scale on informal finance.
Table 12. Threshold effect of economic scale on informal finance.
(21) YT(22) YS(23) YQ(24) YE
XM146.973 *** (−5.362)80.864 *** (−4.839)14.174 *** (−4.455)51.963 *** (−5.208)
XN−5.286 ** (−2.550)−5.236 *** (−4.142)0.578 ** (−2.401)−0.655 (−0.867)
ControlsYESYESYESYES
Effect size26.366%16.636%47.49%100%
N435435435435
Note: The standard errors in parentheses; *** and ** after numbers indicate that significance levels of 1%, 5%, and 10% passed the significance test.
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MDPI and ACS Style

Ye, S.; Zhang, Q.; Li, X.; Yu, J.; Wang, H. Has Finance Promoted High-Quality Development in China’s Fishery Economy?—A Perspective on Formal and Informal Finance. Fishes 2025, 10, 87. https://doi.org/10.3390/fishes10020087

AMA Style

Ye S, Zhang Q, Li X, Yu J, Wang H. Has Finance Promoted High-Quality Development in China’s Fishery Economy?—A Perspective on Formal and Informal Finance. Fishes. 2025; 10(2):87. https://doi.org/10.3390/fishes10020087

Chicago/Turabian Style

Ye, Shengchao, Qian Zhang, Xiao Li, Jianli Yu, and Haohan Wang. 2025. "Has Finance Promoted High-Quality Development in China’s Fishery Economy?—A Perspective on Formal and Informal Finance" Fishes 10, no. 2: 87. https://doi.org/10.3390/fishes10020087

APA Style

Ye, S., Zhang, Q., Li, X., Yu, J., & Wang, H. (2025). Has Finance Promoted High-Quality Development in China’s Fishery Economy?—A Perspective on Formal and Informal Finance. Fishes, 10(2), 87. https://doi.org/10.3390/fishes10020087

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