High-Quality Development of China’s Marine Economy: Green Finance Perspectives (2010–2021)

Abstract

The explosive growth in marine economy has the capacity to not only revolutionize the marine economic development model but also produce a transition from a marine powerhouse to a marine superpower. China’s 11 coastal provinces and municipalities, capitalizing on their geographic advantages and distinct resource endowments, have emerged as principal locations propelling maritime economic growth. In this report, we employ a green finance (GF) framework and analyze panel data from 11 coastal provinces and municipalities in China as obtained over the period from 2010 to 2021. Such an analysis has the capacity to elucidate the driving mechanisms and extent of GF’s influence on the high-quality growth of the marine sector (EQUS). Our results reveal that GF substantially promotes the EQUS, a finding that is consist with that from several robust tests involved with evaluating this relationship. When analyzing the mediating impact of GF, it appears that GF may indirectly enhance the quality and efficiency of the maritime economy by stimulating technical innovations. Results from threshold effects research indicate that the promotional impact of GF is limited by the extent of maritime technical innovation, with levels above a certain threshold markedly increasing the influence of GF. When evaluating the role of heterogeneity, the impact of green money on promotion demonstrates regional and temporal diversity, exhibiting nonlinear traits across various locations and phases of development. In areas with robust economic foundations and developed maritime sectors, the marginal impacts of green financing are significantly enhanced. Based upon these findings, it is recommended that any courses which advance the EQUS should be promoted. Specifically, the augmentation of marine-related innovation skills, cultivation of green technology innovation (TEC), and the optimization of innovative resource distribution represents critical measures to achieve this goal.

1. Introduction

With the increasing depletion of land resources and constrained availability for future terrestrial development, coastal countries around the world are gradually shifting their focus to the ocean. Accordingly, marine economies are playing an increasingly significant role in driving economic growth within these land-depleted regions. It is projected that by 2030, the global Gross Ocean Product (GOP) will exceed 3 trillion US dollars, which represents twice the level as reported in 2010. In China, despite its relatively limited coastline, vast maritime areas with abundant marine resources remain accessible for economic development. A strategic utilization of marine resources can effectively alleviate pressures on terrestrial development while boosting sustained national economic growth. Simultaneously, these resources would elevate the quality of population-wide diets through enhancing nutritional sources as found in aquatic systems. However, utilization of these resources necessitates that increasing attention be directed to the escalating problem of marine environmental pollution as can be achieved with marine economy.

The report from the 20th National Congress of the Communist Party of China explicitly delineated a strategic initiative of “developing the marine economy, safeguarding the marine ecological environment, and expediting the establishment of a maritime power.” In the 21st century, a new era of marine resource development and use of maritime strategic space has emerged. This emphasis on utilizing ocean resources and actively advancing a blue economy has achieved a consensus among several governmental agencies. Since the recommendations of the 18th National Congress, China’s marine sector has significantly expanded, with marine GDP increasing from CNY 384.39 billion in 2010 to CNY 10.5438 trillion in 2024, reflecting an average annual growth rate of 7.4%. China’s ambition for achieving a superpower maritime status faces persistent constraints in marine economic development. Specifically, three critical challenges remain: (1) an inadequate development of technological innovation, (2) a suboptimal utilization of marine resources, and (3) pollutant emissions which exceed environmental thresholds. These interconnected issues collectively impede sustainable ocean-based growth. Attaining superior development in the maritime sector is intricately associated with the overarching objectives of socialist modernization. An important component in this process is green finance (GF). GF, a crucial element of financial supply-side structural reform, can optimize resource allocations, direct capital flow toward environmental protection and low-carbon sectors, improve resource utilization efficiency, and foster green technology innovation (TEC). In this way, GF can serve as a pivotal catalyst for the low-carbon transformation of the marine economy and adjustment of industrial structures.

Results of studies from other countries that have been involved with evaluating the high-quality growth of their marine economy have primarily emphasized the essential role of marine economic development [1,2], marine policy management [3,4], and sustainable marine ecological development [5]. Initial studies as conducted in China have primarily focused on establishing the basic foundation required for marine economic development. Subsequent research shifted toward quantitative assessment frameworks, particularly through those involving gross marine production metrics [6,7], total factor productivity analyses [8], and sustainable industry development evaluations [9]. With increasing interest in the concept and connotation of high-quality economic development, research focus has shifted and concentrates on measurements and evaluations of the implications of EQUS and comprehensive indicators of the system. For example, Ye and Xiao assessed the influence of factors affecting the EQUS in the Guangdong Province by constructing a framework of evaluation indicators of EQUS [10]. By constructing an integrated evaluation index system, Li analyzed the dynamic mechanisms connecting digital transformation, marine industrial structure optimization, and high-standard marine economy development in coastal China [11]. Another group, Sun et al., constructed a catalog of development levels for new marine productivity in terms of four dimensions consisting of the development of drive, structure, mode, and outcome. An evaluation index system incorporating four aspects of development dynamics, structure, mode, and results was employed to study the spatial and temporal evolution, regional gap, and sources of marine new productivity in China’s coastal provinces [12].

GF research has been in progress for over 50 years in other nations. In the 1970s, industrialized Western nations prompted commercial banks to finance environmental pollution management and ecological conservation initiatives, signifying the beginning of GF. In 1987, the United Nations World Commission on Environment and Development published “Our Common Future” (the Brundtland Report). This landmark document articulated the concept of initiating sustainable development, defining it as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” This concept gained worldwide awareness during the 1992 United Nations Conference on Environment and Growth in Rio de Janeiro, and officially integrated the notion of merging economic growth with environmental conservation into the international agenda. GF then attracted the attention of scholarly research initiatives with investigators both in China and abroad forming three main types of views. In the first type, GF is regarded as a means to effectively combine financial and environmental protection of industries which focus on the safeguarding of the ecological environment while realizing economic growth [13]. The second type considers GF as a financial instrument focusing on environmental protection and a reform for the direction of finance as required for sustainable development, as discussed in Ma et al. [14], Lv and Guo [15], Alahmadi [16]. Shen et al. [17] proposed that strategic capital allocation toward eco-friendly initiatives effectively mitigates environmental hazards while enhancing project standards, thus facilitating a balanced progression between ecological conservation and economic growth within sustainable financial frameworks. In the third category, GF is suggested to involve a combination of financial products, market systems, and government supervision that compensates for the failures of the government and the market on the environmental protection of industry. Such an effect then strengthens the social responsibility of these enterprises Wang et al. [18], Reboredo [19], Suki et al. [20], Liu and Li [21]. Based on the theory that green credit affects the adjustment of industrial structure, Chunlan and Keru analyzed the development of green credit on the optimization of marine industrial structure and provided some suggestions of relevance to this topic [22]. Mosnier noted that the inefficiency of marine protected areas constitutes a major obstacle to achieving a sustainable blue economy. He then went on to investigate the means through which green bonds could provide funding for marine conservation, which in turn protects marine ecosystems and drives the development of the marine economy [23].

To summarize, although existing studies have touched upon either GF or EQUS alone, systematic investigations directed toward understanding the integration of EQUS with its financial interrelationships remain relatively scarce. Studies which are available are predominantly qualitative, with a notable deficiency in applying an empirical approach to their research [24,25]. To address such deficiencies in the present study, we raise the following questions: (1) Does GF promote EQUS? (2) If GF does exert an impact on EQUS, what are the specific mechanisms of this action? (3) Is this impact of GF heterogeneous? and (4) Does TEC play an intermediary role in this process? Addressing these questions holds significant theoretical value for clarifying the relationship between GF and EQUS. Guided by these research questions, we construct a theoretical framework as presented in Figure 1.

As based on this background, panel data from 11 coastal provinces in China, as obtained over the period from 2010–2021, were used to investigate the impact of GF on the EQUS. We focused on the following objectives: (1) to verify the driving effect of GF on the EQUS; (2) to examine the intermediary pathway through which TEC operates; (3) to identify the threshold constraint law of the GF effect; and (4) to identify the characteristics of spatio-temporal heterogeneity.

2. Theoretical Analysis and Research Hypothesis

2.1. Direct Effects of GF on the EQUS

GF, as one of the key paths involved with promoting green development, has created a new type of financial support system for the sustainable development of the marine economy as achieved with its capacity for unique market-oriented operations. The mechanisms of GF mainly involve three dimensions. The first is the capital pooling effect. With this dimension, the risk control and resource allocation functions of the modern financial system provide key support for sea-related enterprises [26]. Through the establishment of special green credit products, issuance of blue bonds, and other financial incentive tools, the financing challenges faced by environmentally friendly marine enterprises can be effectively addressed. This in turn can inject a sustained impetus into their technological research and development as well as in their capacity for expansion. The second dimension involves optimizing factor allocations. Green financial policies can guide the flow of factors through differentiated credit standards. Such an effect implements financing restrictions on marine industries with high energy consumption and pollution while simultaneously incentivizing enterprises to generate clean technology innovation through preferential policies such as subsidized loans. As a result, the green transformation process of the entire industrial chain is promoted. The final dimension is policy signal transmission. Here, GF plays a role in the implementation of processes involved in the transmission of the national green development signal, implementation of regulations and the guidelines needed to promote the continuous transition of enterprises to green development [27]. The receipt of green financial incentives for marine enterprises can then obtain sufficient funds to (1) achieve innovative green technology upgrades and expansion of production scales, (2) promote their own business development to guide the efficient development of green financial practices, thereby promoting the development of a green marine industry, (3) generate the formation of economies of scale, and (4) ultimately realize the greening of the entire industry chain. Collectively, these effects will lead to an efficient development of green financial practices, thus promoting the development of green marine industry, forming economies of scale, and ultimately realizing the EQUS. Based on this background information, the following hypothesis is proposed:

H1: 

GF directly contributes to the EQUS.

2.2. Science, Technology, and Innovation Play an Intermediary Role in GF and High-Quality Development of the Ocean Economy

Innovations in science and technology are crucial for China to surmount its middle-income trap and provide the primary catalyst for achieving high-quality growth in its maritime economy [28,29]. The GF system facilitates marine research and technology innovation via a dual process, as shown below. Due to high expenditures, extended durations and the significant risks associated with marine science and technology research and development, the capacity for a single enterprise’s capital to satisfy the innovation requirements represents a challenging endeavor. GF offers targeted financial assistance to environmentally sustainable maritime firms by establishing varied financing avenues, thereby enhancing the intensity of research and development investment and technological innovations. With regard to risk management, enhancement of the ESG information disclosure framework and implementation of the green rating system can progressively augment market information transparency. Financial institutions can enhance monetary allocations according to environmental performance metrics, direct social capital towards the concentration of TEC and their ability to efficiently amplify the scale impact of marine scientific and technological innovations [6]. These innovations in science and technology are considered the principal catalyst for progress. Each scientific and technical revolution in human civilization is catalyzed by substantial economic and social advancements. With the application of TEC, a partial or complete substitution of conventional resources can be achieved. Such effects can then optimize the resource combination strategy, thereby enhancing resource utilization efficiency and improving various aspects of marine productivity. This in turn enhances the overall productivity of marine aspects and significantly elevates the quality of marine economic growth and benefits. Additionally, it fosters the development of new competitive advantages through innovative technologies, products, and services, thereby promoting a comprehensive growth in the marine economy. TEC also has the potential to establish new competitive advantages via innovative technologies, goods, and services to augment the dynamism of maritime economic development and the market’s competitive strength in all dimensions. Combining marine science and TEC may resolve the significant conflict that exists among resources, environment, and development and in this way facilitate the advancement of marine industry and the high-quality growth of marine economies. Consequently, the following hypothesis is proposed:

H2: 

GF promotes EQUS through TEC.

2.3. Nonlinear Relationship Between GF and EQUS

Sustainable finance enables an intrinsic connection with marine sector development, serving as both a catalyst for industrial restructuring and a critical driver of productivity enhancement in maritime economic activities [30,31]. During the initial stages of GF development, the traditional marine industry is subject to a credit access threshold enhancement, which leads to the narrowing of financing channels. The additional cost burden of equipment renewal and pollution control brought about by the increase in environmental protection compliance requirements is not conducive to the transformation and upgrading of the traditional marine industry, which in turn restricts enhancement of the high-quality level of marine economy development. However, as the scale of green financial development expands and the concept of green development continues to grow, the transformation and upgrading of projects involving traditional marine industries and the development of new marine industries will receive large-scale financial support. As a result, the structure of the marine industry will be transformed to rationalization and advancement, thus promoting improvements in the level of EQUS. Regional disparities in the advancement of sustainable finance are particularly noteworthy. This difference stems from both imbalances in local financial capacities and economic foundations, and is also subject to differences in policy implementation strength and degree of marketization. Such factors ultimately lead to the phenomenon of regional differentiation in the effects of green financial policies. Based on this information, we propose the following hypothesis:

H3: 

A nonlinear relationship exists between GF and EQUS.

3. Research Methodology

3.1. Entropy Value Method

To measure the comprehensive level of the research subjects, the original indicators are first standardized via the entropy—value method, aiming to eliminate the impacts arising from different dimensions and orders of magnitude.

$$\mathrm{Positive}\mathrm{indicators}:y_{ij}={\displaystyle \frac{x_{ij}-min{x}_{ij}}{max{x}_{ij}-min{x}_{ij}}}$$

(1)

$$\mathrm{Negative} \mathrm{indicators}:y_{ij}={\displaystyle \frac{max{x}_{ij}-x_{ij}}{max{x}_{ij}-min{x}_{ij}}}$$

(2)

In Equations (1) and (2), $x_{ij}$, $y_{ij}$ denote the specific data and standardized values of the $j$th indicator of the $i$th year, respectively, $max{x}_{ij}$ represents the maximum value of the jth indicator, and $min{x}_{ij}$ represents the minimum value.

$$y_{xj}^{\prime }=y_{ij}+0.00001$$

(3)

Next, the share of indicator $j$ in year $i$ is calculated:

$$P_{ij}={\displaystyle \frac{y_{ij}^{\prime }}{{\sum }_{i=1}^m{y}_{ij}^{\prime }}}$$

(4)

Then, the entropy value of the $\mathrm{j}$th indicator is calculated according to the definition of information entropy:

$$e_j=-k\sum _{i=1}^m{P}_{ij}ln{P}_{ij}, k>0 ,0\le e_j\le 1$$

$$g_j=1-e_j$$

(5)

Finally, the weights of the indicators are calculated:

$$W_j={\displaystyle \frac{g_j}{{\sum }_{j=1}^n{g}_j}}$$

(6)

3.2. Benchmark Regression Model

To empirically investigate the direct effect of green finance on the high-quality development of the marine economy, a benchmark regression model is formulated to account for regional and temporal fixed effects.

$${EQUS}_{it}={\alpha }_0+{\alpha }_{1}G{F}_{it}+\sum {\alpha }_m{x}_{it}+u_i+{\delta }_t+{\epsilon }_{it}$$

(7)

where EQUS denotes the level of EQUS; GF denotes the level of green financial development; $x$ denotes the control variable; $u_i$ denotes the fixed effect of region; ${\delta }_t$ denotes the fixed effect of time; ${\epsilon }_{it}$ denotes the random error term; $i$ denotes the region; and $t$ represents the period.

3.3. XGBoost Model

XGBoost is an efficient, flexible, and scalable machine learning algorithm based on the gradient boosting framework. With this model, which is widely used in the classification, regression and ranking of tasks, it is possible to gradually optimize prediction performance by integrating multiple decision trees. It incorporates several improvements based on traditional gradient boosting algorithms, including the weighing of data distribution, regularization to prevent overfitting, support for parallel computing to accelerate training, and the ability to automatically handle missing values. In this study, the XGBoost model was used to predict the impact of digital financial inclusion on food production resilience. This XGBoost model consists of an integrated model of k CART regression trees, with x being the ith feature, y_i as the actual value of the ith sample, and the value of the ith sample then being predicted by this sample as set at the tth base learner as

$${\widehat{y}}_i^{(t)}=\sum _{k=1}^t{f}_k\left(x_i\right)={\widehat{y}}_i^{(t-1)}+f_{(t)}\left(x_i\right)$$

(8)

where ${\widehat{y}}_i^{(t)}$ is the predicted value after the tth iteration, ${\widehat{y}}_i^{(t-1)}$ is the predicted value of the (t − 1)st tree, and $f_{(t)}\left(x_i\right)$ is the tth tree model. The objective function for the tth base learner of the model is derived based on ${\widehat{y}}_i^{(t)}$ as follows:

$$X^t={\sum }_{i=1}^n l(y_i,{\widehat{y}}_i)+{\sum }_{i=1}^t \Omega (f_i)$$

(9)

$$\Omega (f_i)=\gamma T+\lambda {\displaystyle \frac{1}{2}}\sum _{J=1}^T {\omega }_j^2$$

(10)

where $l(y_i,{\widehat{y}}_i)$ is the loss function, ${\sum }_{i=1}^n l(y_i,{\widehat{y}}_i)$ is the training error, ${\sum }_{i=1}^t \Omega (f_i)$ is the regularity term to prevent model overfitting, T is the number of leaf nodes of the tth base learner, γ and λ are artificially adjustable hyperparameters for controlling the punishment strength, where λ is the control leaf node fraction and ω is the leaf node fraction. The objective function values can be obtained by solving the objective function as follows:

$$X_{obj}=\sum _{j=1}^T [(\sum _{iϵI_j} g_i){\omega }_j+{\displaystyle \frac{1}{2}}(\sum _{iϵI_j} h_i+\lambda ){\omega }_j^2]+\lambda T$$

(11)

where gi and hi are the first- and second-order derivatives of the loss function, respectively, and Ij is the set of samples under the jth node.

3.4. SHAP Values

To clarify the XGBoost regression model and better understand the impact of digital financial inclusion on food production resilience, the SHAP framework, as proposed by Lundberg et al. [32], was used in this study. Their approach provides a more consistent and locally accurate interpretation in terms of imputation values and significantly reduces computational complexity by tracking the proportion of all possible subsets reaching each tree leaf decision through a polynomial time algorithm. The SHAP calculation is based on Shapley values, which is an important method in game theory for calculating how features contribute to explained variables. The formula for calculating Shapley values is as follows:

$${\varphi }_i=\sum _{S\subseteq N\setminus \left\{i\right\}} {\displaystyle \frac{\left|S\right|!\left(\left|N\right|-\left|S\right|-1\right)!}{\left|N\right|!}}(v(S\cup \{i\})-v(S))$$

(12)

where N denotes the set of all features; S is any subset of features that does not contain feature i;$\left|S\right|$ is the number of features in the set S; v(S) is the contribution of the feature set S to the predicted output of the model; and $v(S\cup \{i\})$ is the contribution of the feature set $S\cup \left\{i\right\}$ that contains feature i to the predicted output of the model.

3.5. Mediated Effects Model

In order to evaluate whether there is a mediating effect of marine TEC in the process of GF as affecting the EQUS, the marine TEC was introduced into the model as a mediating variable. The model is set as follows:

$$\begin{array}{c}{TEC}_{it}={\beta }_0+{\beta }_{1}G{F}_{it}+\sum {\beta }_m{x}_{it}+u_i+{\delta }_t+{\epsilon }_{it}\\ EQU{S}_{it}={\gamma }_0+{\gamma }_{1}G{F}_{it}+{\gamma }_{2}te{c}_{it}+\sum {\gamma }_m{x}_{it}+u_i+{\delta }_t+{\epsilon }_{it}\end{array}$$

(13)

where ${\alpha }_1$ represents the total effect of GF on the level of EQUS, ${\gamma }_1$ represents the direct effect of GF on the level of EQUS, and ${\beta }_1{\gamma }_2$ represents the indirect effect, i.e., examining the mediating role of the marine TEC in promoting EQUS through influencing mechanism variables.

Under the condition that the regression coefficients are significant, the coefficients ${\beta }_1$ and ${\gamma }_2$ were tested in turn. If ${\beta }_1$ and ${\gamma }_2$ were significant, then the coefficient ${\gamma }_1$ was tested. If ${\gamma }_1$ was significant, the mediator variable plays a partially mediating role; if ${\gamma }_1$ was not significant, this mediator variable plays a fully mediating role. If at least ${\beta }_1$ or ${\gamma }_2$ were not significant, the mediator variable does not play a mediating role.

3.6. Threshold Effect Model

In order to test for the nonlinear impact of GF on the EQUS, with reference to the method of Hansen’s threshold regression model, a threshold model was constructed to test for any nonlinear relationships between the explanatory variables by using GF and TEC as the threshold variables. Models using GF and TEC as threshold variables can be categorized into single- or double-threshold models:

$$\begin{array}{cc}\hfill EQU{S}_{it}& ={\phi }_0+{\phi }_{1}G{F}_{it}•I\left(M_{it}\le {\theta }_1\right)+{\phi }_{2}G{F}_{it}•I\left(M_{it}>{\theta }_1\right)\hfill \\ & +\sum {\phi }_m{x}_{it}+u_i+{\delta }_t+{\epsilon }_{it}\hfill \\ \hfill EQU{S}_{it}& ={\pi }_0+{\pi }_{1}G{F}_{it}•I\left(M_{it}\ge {\theta }_1\right)+{\pi }_{2}G{F}_{it}•I\left({{\theta }_1<M}_{it}\le {\theta }_2\right)\hfill \\ & +{\pi }_{3}G{F}_{it}•I\left(M_{it}\ge {\theta }_2\right)+\sum {\pi }_m{x}_{it}+u_i+{\delta }_t+{\epsilon }_{it}\hfill \end{array}$$

(14)

where $M_{it}$(GF, tec) are threshold variables and $I$($•$) is an indicative function that takes the value of 1 when the expression in parentheses holds and 0 otherwise.

4. Variable Definitions and Data Descriptions

4.1. Explained Variables

Regarding the level of EQUS, the new development concepts of innovation, coordination, greenness, openness, and sharing are not only highly compatible with the actual situation in China but also conform with the requirements of the times and guides of the high-quality economic development. Consequently, numerous investigations that have been launched into assessing the essence of premium economic growth employ innovation, coordination, sustainability, openness, and inclusiveness as key evaluative dimensions. Based on this notion, in this paper, we incorporate a new development concept as described in previous reports [33,34]. With this new development concept, a total of 35 indicators from the five dimensions of TEC, coordination and stability, green development, openness and inclusiveness, and sharing of people’s livelihood were selected in order to construct the indicator system of EQUS (

Table 1. Evaluation Index System for EQUS.

Target Level	Secondary Indicators	Tertiary Indicators	Indicator Properties
EQUS	Science, technology, and innovation	Number of marine research and development institutions	+
		Practitioners in marine research and development institutions	+
		R&D staff in marine and development agencies	+
		Internal expenditures on R&D funding for marine institutions and development agencies	+
		Number of R&D projects in marine institutions and development agencies	+
		Number of scientific and technical papers published by marine research and development institutions	+
		Number of patents granted by marine research and development organizations	+
	Coordination and stability	Gross value of marine industry	+
		Gross Maritime Product (GMP)	+
		Gross Maritime Product (GMP) as a percentage of Gross Regional Product (GRP)	+
		Gross marine product per capita	+
		Coastal mariculture area	+
		Share of marine tertiary sector	+
		GDP growth rate	+
	Green development	Comprehensive industrial solid waste utilization rate	+
		Sulphur dioxide emissions	−
		Total wastewater discharge	−
		Industrial solid waste generation	−
		Number of marine type protected areas	+
		Amount of maritime royalties payable	+
	Open and inclusive	Marine cargo turnover	+
		Marine passenger turnover	+
		Throughput of goods	+
		Passenger throughput	+
		International standard container throughput at coastal ports	+
		Number of travel agencies in the coastal region	+
		Total exports and imports	+
	People’s livelihood	Number of students enrolled in marine programs in general higher education	+
		Number of students enrolled in adult higher education marine programs	+
		Number of institutions of higher education specializing in marine subjects	+
		Mariculture production	+
		Marine capture production	+
		Pelagic fisheries production	+

).

4.2. Core Explanatory Variables

In 2016, the People’s Bank of China (PBOC), along with other entities, published the Guiding Recommendations on Building a Green Financial System. The recommendations resulting from this report enhance and refine the green financial system, asserting that green finance is a national strategic policy that advances sustainable development through green credits, bonds, insurance, and carbon finance. Therefore, in this paper, an analysis of the above guiding recommendations was included. Based upon the study of Muganyi and Liu [35,36], it is possible to derive a comprehensive index of GF according to the entropy value method from nine indicators in five dimensions: green credits, green bonds, green insurance, green investments, and carbon finance. According to the above analysis, the GF set indices are presented in

Table 2. GF Development Evaluation Indicator System.

Level 1 Indicators	Secondary Indicators	Tertiary Indicators	Meaning of the Indicator	Indicator Properties
Level of development of GF	Green credit	Loan size of environmentally friendly listed companies	Loan amount of A-share environmental protection listed companies/Loan amount of A-share listed companies	+
		Percentage of interest expenses in energy-intensive industries	Interest Expenditure of the Six Major Energy-Consuming Industries/Interest Expenditure of Industrial Industries	−
	Green bond	Market share of environmental companies	Total market capitalization of environmental companies/Total A-share market capitalization	+
		Market share of energy-intensive industries	Total market capitalization of the six major energy-intensive industries/total A-share market capitalization	−
	Green insurance	Agricultural insurance scale share	Agricultural insurance expenditure/total insurance expenditure	+
		Agricultural insurance payout ratio	Agricultural insurance expenditure/income from agricultural insurance	+
	Green investment	Percentage of investment in environmental pollution control	Investment in environmental pollution control/GDP	−
		Percentage of public expenditure on energy conservation and environmental protection	Fiscal expenditure on energy-saving and environmental protection industries/total fiscal expenditure	+
	carbon finance	Share of carbon dioxide emissions	CO2 emissions/GDP	−

.

4.3. Intermediate Variables

Regarding Green Technology Innovation (TEC), based on previous research [37,38], we used the procedure of taking the number of green patents granted in each region to the total number of patents granted in that region as a measure of TEC.

4.4. Control Variable

Many factors can affect EQUS. Based upon criteria used in existing studies [39,40], the following were selected as control variables for use in this study: (1) Regional economic development level (DEV)—the logarithm of per capita gross regional products, (2) Marketization rate (MAR)—the marketization index in this paper, (3) Human capital level (HC)—rate of general higher education graduates, and (4) Openness to the outside world (OPE)—proportion of total regional imports and exports to GDP.

4.5. Sample Selection and Data Sources

Panel data of 11 coastal provinces and cities were obtained over the period from 2010–2021 along with data from the China Statistical Yearbook, China Science and Technology Statistical Yearbook, China Marine Statistical Yearbook, China Financial Statistical Yearbook, China Urban Statistical Yearbook, China Environmental Statistical Yearbook, and statistical yearbooks of the 11 coastal provinces were included for analyses. Missing data were filled in by linear interpolation.

5. Analysis of Measurement Results

With 2010 and 2021 used as representative years, ArcMap 10.8 software was employed to visualize the EQUS and GF index results from China’s coastal areas as presented in Figure 2, while relevant spatial distribution characteristics are presented in Figure 3.

5.1. EQUS Index Results

The EQUS index in China showed a steady increase from 2010 to 2021, with the overall average increasing from 0.183 to 0.279, thus demonstrating an average annual growth rate of 4.3%. In 2010, Shandong topped the list with 0.332, while Guangxi was at the bottom with 0.063. In 2021, the ranking of coastal provinces in the EQUS index was slightly adjusted, with the top three provinces being Guangdong (0.631), Shandong (0.524), and Zhejiang (0.300). Provinces with intermediate levels of EQUS indices included Jiangsu, Fujian, and Shanghai, which comprised the second tier. While the marine economies of Tianjin and Liaoning fluctuated between the second and third echelons, those within the Hebei and Guangxi provinces were consistently low, remaining in the third echelon. Overall, these EQUS data, as obtained over the period from 2010–2021, demonstrate obvious spatial imbalances and a trend for multipolarization. Such findings may be related to differences in the structure of the marine industry, level of land–sea economic synergy and marine resource endowments present in the different coastal regions. In particular, Guangdong, Shandong, and Zhejiang all show an accelerated enhancement of marine independent innovation capacity as achieved by relying on policy dividends, location advantages, and financial support.

5.2. GF Index Results

The GF Index for the coastal regions generally shows an upward trend over the period between 2010 and 2021, with the mean value increasing from 0.178 to 0.278 and an average annual growth rate of 4.6%. It is worth noting that the average annual GF growth rate was greater than that of the EQUS over this study period, indicating that GF represents a key driver of the marine economy. Salient regional differences in the development of GF within coastal areas were present in 2010, with high levels of GF observed in Guangdong and Shandong provinces and Tianjin showing the highest levels. Jiangsu and Zhejiang were in the second echelon, while the provinces of Hebei, Fujian, and Guangxi were in the third tier, demonstrating the lowest levels compared to the other coastal regions. In 2021, the top three coastal provinces included Guangdong (0.537), Zhejiang (0.408), and Hainan (0.325), while those at the bottom of the rankings consisted of Liaoning (0.173), Hebei (0.174), and Tianjin (0.194). These ranking results were very consistent with the actual effects of policies in each of these provinces, which to some extent reflects the rationality of the GF evaluation index system and measurement method constructed in this study.

6. Analysis of Empirical Results

6.1. Linear Analysis of the Long-Run Equilibrium Effect of GF on EQUS

6.1.1. Benchmark Regression

Table 3. Benchmark Regression Results.

Variant	(1)	(2)	(3)	(4)	(5)	(6)
	EQUS	EQUS	EQUS	EQUS	EQUS	EQUS
GF	0.995 *** (9.3276)	0.489 *** (5.1964)	0.451 *** (4.8482)	0.453 *** (4.9486)	0.455 *** (5.0568)	0.454 *** (5.0897)
DEV			0.110 *** (4.1057)	0.106 *** (3.7209)	0.098 *** (3.5771)	0.098 *** (3.5822)
MAR				0.013 *** (2.6982)	0.015 ** (2.8269)	0.015 ** (2.9549)
HC					−0.029 ** (−2.0012)	−0.026 * (−1.6879)
OPE						−0.086 *** (−3.0009)
Constant	0.039 * (1.7572)	0.020 (0.9234)	−1.190 *** (−4.0312)	−1.271 *** (−4.1787)	−0.172 *** (−3.9603)	−0.177 *** (−3.9754)
Year FE	No	Yes	Yes	Yes	Yes	Yes
Industry FE	No	Yes	Yes	Yes	Yes	Yes
N	132	132	132	132	132	132
R2	0.401	0.953	0.959	0.960	0.962	0.963
Adj_R2	0.396	0.944	0.950	0.951	0.953	0.953

Note: ***, ** and * are significance levels of 1 per cent, 5 per cent, and 10 per cent, respectively; data in parentheses are robust standard errors.

contains the test results of the baseline regression. All empirical analyses in this section were performed with the use of StataMP 18. The following statistical procedures were included in these analyses. Results presented in the first column indicate that the univariate regression coefficient of GF on EQUS was 0.995, which is statistically significant at the 1% level. In the second column, which includes two-way fixed effects of province and time, a statistically significant (1% level) regression coefficient of GF of 0.489 was obtained. These results suggest that the core relationship remains robust after controlling for individual and time-dependent differences. After including all control variables (DEV, MAR, HC, OPE), as shown in the sixth column, the GF coefficient was stable at 0.454 and significant at the 1% level. In order to verify the reliability of the model, the VIF test was conducted for the full-variable model, with the results showing that VIF values of all variables were between 1.17 and 4.24. The finding that these values were much lower than the critical value of 10 indicates that no serious multicollinearity was present. The DEV and MAR control variables showed significant positive correlations, while HC and OPE showed significant negative correlations. These results may reflect the factor allocation efficiency and openness risk at this stage. Overall, the core role of GF remains robust, a finding consistent with Hypothesis 1.

6.1.2. Robustness Test

To evaluate test robustness, we first replaced explanatory variables and re-regressed the explanatory variable GF with a one period lag as a means to consider the lag and persistence of the impact effect, as shown in

Table 4. Robustness Test for Endogeneity within Lagged Periods of Explanatory Variables.

Variant	(1)	(2)
	EQUS	EQUS
L.GF	0.437 *** (4.6393)
L2.GF		0.309 *** (3.2244)
DEV	0.125 *** (4.0225)	0.135 *** (4.2342)
MAR	0.010 *** (1.9616)	0.006 (1.2442)
HC	−0.032 ** (−2.4056)	−0.034 *** (−3.1972)
OPE	−0.049 *** (−1.8523)	−0.058 * (−1.8827)
Constant	−1.357 *** (−3.5071)	−1.392 *** (−3.8257)
Year FE	Yes	Yes
Industry FE	Yes	Yes
N	121	121
R2	0.965	0.966
Adj_R2	0.956	0.956

Note: ***, ** and * are significance levels of 1 per cent, 5 per cent, and 10 per cent, respectively; data in parentheses are robust standard errors.

. Second, the sample capacity was altered by shortening the sample to rerun the regression, as shown in

Table 5. Robustness Tests for Shortened Time Samples.

Variant	(1)	(2)	(3)	(4)	(5)	(6)
	EQUS	EQUS	EQUS	EQUS	EQUS	EQUS
GF	1.188 *** (8.9396)	0.305 *** (2.7373)	0.292 *** (2.6533)	0.293 *** (2.6489)	0.299 *** (2.7399)	0.299 *** (2.7385)
DEV			0.053 ** (2.1824)	0.049 * (1.9864)	0.049 ** (2.0197)	0.048 * (1.9902)
MAR				0.010 ** (2.0726)	0.012 ** (2.3001)	0.013 ** (2.4020)
HC					−0.025 * (−1.8803)	−0.022 (−1.750)
OPE						−0.070 ** (−2.2783)
Constant	0.028 (1.0721)	0.059 ** (2.2762)	−0.525 * (−1.9638)	−0.586 ** (−2.2109)	−0.573 ** (−2.2275)	−0.575 * (−2.2201)
Year FE	No	Yes	Yes	Yes	Yes	Yes
Industry FE	No	Yes	Yes	Yes	Yes	Yes
N	99	99	99	99	99	99
R2	0.445	0.965	0.966	0.967	0.968	0.968
Adj_R2	0.439	0.956	0.957	0.958	0.958	0.959

Note: ***, ** and * are significance levels of 1 per cent, 5 per cent, and 10 per cent, respectively; data in parentheses are robust standard errors.

. Third, the measurement model was changed in order to eliminate autocorrelation and heteroskedasticity effects, with the least squares method used for re-regression, as shown in

Table 6. Least Squares Test.

Variant	(1)
	EQUS
GF	0.4541 *** (7.0688)
DEV	0.0984 *** (3.5158)
MAR	0.015 ** (2.4887)
HC	−0.0258 * (−1.7306)
OPE	−0.0858 (−1.4953)
Constant	−1.1765 *** (−3.7730)
N	132
Adj_R2	0.953

Note: ***, ** and * are significance levels of 1 per cent, 5 per cent, and 10 per cent, respectively; data in parentheses are robust standard errors.

. Finally, a shrinking of the tails was performed in order to avoid any interference resulting from outliers, as shown in

Table 7. Results of Benchmark Regression for Shrinkage Treatment.

Variant	(1)	(2)	(3)	(4)	(5)	(6)
	EQUS	EQUS	EQUS	EQUS	EQUS	EQUS
GF	1.006 *** (9.2817)	0.496 *** (5.0943)	0.457 *** (4.7769)	0.458 *** (4.8789)	0.453 *** (4.8278)	0.438 *** (5.0028)
DEV			0.108 *** (3.9980)	0.104 *** (3.6636)	0.098 *** (3.5182)	0.098 *** (3.5959)
MAR				0.014 *** (2.7749)	0.015 *** (2.8530)	0.016 *** (3.1006)
HC					−0.029 (−1.3843)	−0.023 (−1.0462)
OPE						−0.474 * (−1.8562)
Constant	0.037 (1.6483)	0.020 (2.2762)	−1.174 *** (−3.9100)	−1.266 *** (−4.1182)	−1.173 *** (−3.9012)	−1.145 *** (−3.9176)
Year FE	No	Yes	Yes	Yes	Yes	Yes
Industry FE	No	Yes	Yes	Yes	Yes	Yes
N	132	132	132	132	132	132
R2	0.401	0.953	0.958	0.960	0.961	0.962
Adj_R2	0.396	0.943	0.949	0.951	0.952	0.953

Note: *** and * are significance levels of 1 per cent and 10 per cent, respectively; data in parentheses are robust standard errors.

. The sample data were regressed after shrinking the tails by 1% in both directions. Table 4 contains a summary of these results and shows that all GF coefficients were positive and statistically significant. These findings were consistent with those of the benchmark regression results, indicating that the results obtained in this report were robust.

6.2. Nonlinear Resolution of GF on EQUS Long-Run Equilibrium Effects

6.2.1. Ranking of Characteristic Importance of Full-Sample Estimates

Python3.8.10 programming language was used to perform an in-depth analysis of the influence of nonlinear mechanisms of five key variables, GF, MAR, DEV, HC, and OPE, on EQUS as based on the stochastic optimal SHAP (Shapley Additive Explanations) model.

MAR ranked the highest, followed by GF, highlighting the central role of both in promoting EQUS (Figure 4). In the context of the “dual-carbon” strategy and sustainable development, GF is not only a tool for optimizing capital allocations, but also a key factor in guiding the structural transformation of the marine industry and achieving green growth. GF promotes the development of green industries such as clean energy, marine ecological restoration, and energy-saving and emission reduction fisheries by lowering the financial costs of green projects and improving the environmental benefits of capital. In contrast, the SHAP values of DEV and OPE were significantly lower, indicating a limited or marginal contribution to EQUS. These findings may be attributable to the fact that differences in economic development and openness between the sample regions were not large, or that the magnitude of changes in these two factors during the study period was small. Such effects would produce a relative weakening of their impact on the EQUS. HC exerted the lowest level of impact on EQUS, which may indicate that the supply and demand of marine economy-related talents have yet to become a bottleneck, or that its potential role has yet to be effectively stimulated through GF and market-oriented reform mechanisms.

6.2.2. Characteristic Contribution Analysis of Full Sample Estimates

The SHAP Bees Plot in Figure 5 illustrates the importance of each variable in affecting EQUS as well as their distribution characteristics. MAR ranks first in the order of importance of features and its SHAP value presents a markedly distinct and obvious positive contribution, indicating that the market-oriented environment is a key factor in promoting the EQUS. GF ranks second among these variables and the distribution of its SHAP values was relatively concentrated. Despite this concentration, GF continues to exert a significant contribution in the positive interval, indicating that it plays an important role in promoting EQUS by optimizing capital allocations and guiding the upgrading of green industries. In contrast, the SHAP values of DEV and OPE were mostly concentrated in the near-zero interval, indicating low marginal contributions to EQUS. This may be related to the small differences in economic development and openness between the samples and the limited changes as obtained over the study period. HC exhibited the densest distribution of SHAP values, with most clustering near zero, suggesting that its role in EQUS has yet to emerge. Such effects may potentially result from a failure of supply and demand of talents to have become a constraining bottleneck or a lack of EQUS to have been activated by GF and market mechanisms.

According to the SHAP thermal scatter shown in Figure 4, the role of GF in driving EQUS is significantly represented. This figure illustrates that an obvious nonlinear relationship is present between the SHAP value of GF and EQUS, and its contribution to EQUS gradually increases as the GF SHAP value increases. Specifically, the horizontal axis of the scatterplot represents the GF SHAP value, which reflects the strength of the impact of GF on the model’s predicted results. The vertical axis represents the importance of GF characteristics. The findings that 74% of the samples have SHAP values concentrated between −0.05 and 0.05 indicates that the impact of GF on these samples is relatively weak. However, the impact of GF is progressively strengthened as the SHAP value increases, with some of the samples having SHAP values close to 0.25, indicating a significant boost of GF in these samples. The color gradient in Figure 5 ranges from blue to red, representing low to high impact intensities, respectively. The blue area indicates that GF has a low impact on the EQUS, while the red area indicates a high contribution. This color mapping effectively demonstrates the key role of GF in promoting green industries, optimizing resource allocations and facilitating industrial structure upgrading.

6.3. Further Analysis

6.3.1. Test of Intermediary Effects

The TEC results as presented in

Table 8. Mediation Effect Test of GF Driving EQUS.

Variant	(1)	(2)	(3)
	EQUS	tec	EQUS
GF	0.797 *** (0.103)	0.336 *** (0.036)	0.532 *** (0.129)
TEC			0.789 ** (0.024)
Sobel’s statistic			0.265 (0.087) **
Control variable	YES	YES	YES
Constant	0.348 (0.311)	−0.091 (0.109)	0.420 (0.300)
N	132	132	132
R2	0.577	0.492	0.610

Note: *** and ** are significance levels of 1 per cent and 5 per cent, respectively; data in parentheses are robust standard errors.

serve as a means to test the mediating effect of GF in promoting the EQUS. In this table, Column 1 shows the direct effects of the benchmark regression, while Columns 2 and 3 show the mediating effects of TEC. The effect of GF on TEC (Column 2) is positive and statistically significant at the 1% level, indicating that GF can enhance the level of marine TEC. The regression coefficients of GF and TEC are positive (Column 3) with both being statistically significant, indicating that TEC plays a significant mediating effect. Calculations of these results reveal that the TEC coefficient effect is 0.789, its mediating effect is 33.29%, and the direct effect of GF is 66.71%, which provides support for the validity of Hypothesis 2.

6.3.2. Threshold Effect Analysis

In order to further clarify the specific relationships among GF, TEC, and EQUS, the panel threshold regression model, as proposed by Hansen [41], was used for empirical analysis. A threshold existence test was initially performed with the results of GF and TEC after 300 Bootstrap self-help method sampling presented [42] in

Table 9. Threshold Effect Tests.

Tecshold	RSS	MSE	Fstat	Prob	Crit10	Crit5	Crit1
Single	0.0794	0.0007	31.19	0.003	10.809	13.418	18.222
Double	0.0684	0.0006	19.49	0.007	10.594	12.534	17.499
Triple	0.1210	0.0004	23.29	0.587	42.230	48.363	65.800

. As shown in Table 9, the double threshold effect of the core explanatory variable GF on the explanatory variable EQUS was statistically significant at the 1% level. These results indicate that a threshold effect of GF on the promotion of EQUS is present, providing support for the validation of Hypothesis 3.

On the basis of determining the existence of a double threshold, threshold values were estimated with the results presented in

Table 10. Results of Threshold Estimations.

Threshold Variables	Threshold Model	Estimated Value	Confidence Interval (Math.)
tec	Unitary	0.0071	(0.0068, 0.0072)
	Double	0.0269	(0.0267, 0.0269)

. These results indicate that the unitary threshold value of the impact of the threshold variable TEC on the relationship between the core explanatory variable GF and the explanatory variable EQUS was 0.0071. The corresponding 95% confidence interval was [0.0068,0.0072], the double threshold value was 0.0269, and the corresponding 95% confidence interval was [0.0267, 0.0269]. The threshold values show significant disparity between models, with the double threshold being approximately 3.8 times higher than the single threshold. Both models exhibit narrow confidence intervals, indicating high estimation precision.

Based on the above threshold effect test and analysis, for this study, we adopted the threshold regression model to serve as a means for an in-depth analysis (

Table 11. Threshold Regression Results.

Variant	Standard Factor	Standard Error
tec < 0.0071	0.0602	0.0788
0.0071 ≤ tec < 0.0269	0.204 ***	0.0616
0.0269 ≤ tec	0.395 ***	0.0450
Dev	0.0632 ***	0.0196
Mar	0.00277	0.00864
Hc	−0.0315	0.0179
Ope	−0.134	0.220
Constant	−0.486 **	0.165
N	132
R2	0.762

Note: *** and ** are significance levels of 1 per cent and 5 per cent, respectively; data in parentheses are robust standard errors.

). We found that a TEC value was smaller than the single threshold value of 0.0071, and the influence coefficient of GF on EQUS was only 0.0602. This result fails to achieve statistical significance at the 10% level, which indicates that at a stage when the foundation of regional TEC is relatively weak, the resource allocation function of GF has yet to be effectively transformed into the kinetic energy of high-quality development. When TEC crosses a single threshold value of 0.0071 ≤ TEC < 0.0269, a marginal effect of GF rises significantly, with its impact coefficient reaching 0.204. Such results indicate that an improvement in the technological innovation level can activate the dividends of green finance. When the threshold variable TEC was ≥ 0.0269, the facilitating effect of GF showed a nonlinear acceleration, and the influence coefficient surged to 0.0395, which is 93.63% greater than that of the previous stage. These findings verify an important role and synergistic development mechanisms of “science and technology innovations upon green finance”.

6.3.3. Heterogeneity Analysis

According to the State Council’s division standard, the 11 provinces and cities along the coast of China are divided into three major ocean economic circles: (1) northern ocean economic circle (Tianjin, Hebei, Liaoning, Shandong), (2) eastern ocean economic circle (Shanghai, Jiangsu, Zhejiang), and (3) southern ocean economic circle (Fujian, Guangdong, Guangxi, Hainan). These three major regions were used to assess the factor of heterogeneity based on regression analyses (

Table 12. Results of Heterogeneity Test.

Variant	(1) East	(2) North	(3) South	(4) 2010–2015	(5) 2016–2021
GF	0.3507 **	0.1745	0.2638 **	0.0370	0.2433 **
	(0.1354)	(0.1398)	(0.1142)	(0.0745)	(0.1001)
Constant term (math.)	−0.6242 ***	−1.7624 ***	−0.6146	−0.9053 ***	0.0465
	(0.1751)	(0.4108)	(0.2678)	(0.2499)	(0.3482)
Control variable	Yes	Yes	Yes	Yes	Yes
Province fixed	Yes	Yes	Yes	Yes	Yes
Fixed time	Yes	Yes	Yes	Yes	Yes
N	36	48	38	66	66
R2	0.9577	0.9742	0.9904	0.98	0.9886

Note: *** and ** are significance levels of 1 per cent and 5 per cent, respectively; data in parentheses are robust standard errors.

). The results show that the promoting effect of GF on EQUS exhibits significant regional heterogeneity: the promoting effect in the eastern marine economic circle is the strongest, being significant at the 5% level; the southern marine economic circle comes next; while the northern marine economic circle shows no statistical significance. A likely explanation for these results is that the east relies on the integration strategy of the Yangtze River Delta, which possesses high financial market maturity and a significant green technology agglomeration effect that can efficiently transform green financial resources. While the south has an active marine economy, the financial constraints of small- and medium-sized enterprises and the unequal distribution of regional resources may weaken the effectiveness of GF in this region. The north is affected by dependence on a traditional path of heavy industry and a lagging response to green policies, factors which preclude synergistic mechanisms between GF and the marine economy. The fact that the constant term is significantly negative for the north circle provides further evidence of the weakness in basic conditions that exist in this region.

Sample intervals were divided into two periods, 2010–2015 and 2016–2021, with regressions then being separately conducted to assess temporal heterogeneity. The results are shown in Columns (4) and (5) of Table 12. Our results indicate that the driving role of GF significantly increased over time, with a GF coefficient of 0.2433 ** in 2016–2021, which was 5.58 times higher than obtained in 2010–2015 (0.0370). A reasonable explanation for this finding lies in the Introduction of the 2016 “Guiding Opinions on Building a Green Financial System.” Improvements in green credit standards, environmental information disclosures, and other systems have the capacity to subsequently release policy dividends, which, when superimposed on the marine economic transformation under the “Thirteenth Five-Year Plan,” enhance pressures that can drive green investment. In contrast, this policy effect had yet to be introduced within this early stage (2010–2015) due to the single green financial instrument and insufficient cognition regarding market subjects. In addition, the negative constant term obtained in the 2010–2015 period transformed to a positive value in 2016–2021. Although this change was not statistically significant, it does demonstrate a positive trend, reflecting improvements in the overall levels of development of the marine economy and increasing influence of the green transformation.

7. Conclusions and Policy Recommendations

7.1. Conclusion of the Study

Three notable findings/conclusions can be derived from this report. First, results from the robustness test and benchmark regression both show that the regression coefficients of GF on EQUS are consistently and significantly positive. These findings provide compelling evidence that GF can directly interact with EQUS. This conclusion is in line with the central assertion of Fu and Zhao [43] that “GF promotes the new quality productivity enhancement of the enterprises.” By extending its application to the marine industry, here, we show that GF has emerged as a new development engine to support EQUS and offer sustained financial and power support for a green transformation of the marine economy, thus substantiating Hypothesis H1. A second major finding, as obtained from the mechanism analysis, is the demonstration that GF influences EQUS via a dual pathway of “direct promotion + intermediary of TEC.” Within the context of marine economies, TEC not only acts as an intermediary variable, but also exerts a synergistic effect with GF. This synergy not only directly reduces financial barriers that would prevent a green transformation of the marine industry, but also incentivizes the development of technologies related to low-carbon shipping and marine ecological restoration. Assuming that H2 is valid, GF not only directly reduces the financial barriers to the marine industry’s green transition, but it also indirectly enhances EQUS by providing incentives for advancements in low-carbon shipping and marine biological restoration technologies. Third, the stochastic optimal SHAP model’s analysis of data, as obtained over the period from 2010 to 2021, reveals that GF’s impact on EQUS exhibits a dynamic marginal effect, and that this effect varies as a function of GF level and regional changes in the bases of science and technology. This supports the idea that a nonlinear correlation is present between GF and EQUS, a conclusion which supports Hypothesis 3. With regard to the factor of heterogeneity, our results reveal that the impact of GF differs significantly among regions, within regional science and technology levels, maritime infrastructure, as well as with other factors that may play a role in producing these differences. The conclusion of Qin et al. [44] that “GF effects are constrained by the basis of regional development” is in line with this finding. These findings, which align with those reported in other studies, support the overall contribution of GF to EQUS. They also enhance our understanding of the mechanisms and heterogeneity as related to specific issues involving marine economies while providing novel empirical evidence for this and related fields of study.

7.2. Research Recommendations and Future Research Directions

Based on the above conclusions, the following policy recommendations are presented.

First, it is critical to establish a standardized green financial system and improve service mechanisms for the marine economy. To achieve this goal, it is necessary to promote the standardization of the green financial system, unify product classification standards, clarify the rules for identifying marine projects, and expand the coverage of scenarios to seawater desalination and low-carbon port upgrading. In addition, the matrix of multi-level products needs to be improved, as can be achieved with the development of green credit, bonds, insurance, and futures tools. Moreover, efforts must be made to address the financial challenges of marine engineering equipment and the low-carbon transformation of ships, the establishment of a special project database and provisions for financial subsidized interest support to resolve the issue of long-term fund matching.

Second, an implementation of the “talent-science and technology” dual-wheel drive strategy is required to create a marine innovation ecosystem. The construction of talents and scientific and technological support must be strengthened and the marine TEC chain opened up. Talent is both a key and foundation for independent innovation. It is only by fully unleashing the vitality of innovation and creativity in the marine field and continuously converting talent advantages into innovation advantages that a competitive and developmental foundation edge can be generated. This, in turn, will strengthen the driving force behind EQUS.

Third, a differentiated regional policy system should be established to activate the momentum of graded synergistic development. To implement policies based on regional endowments, we provide the following suggestions. The Yangtze River Delta, Guangdong, Hong Kong, and Macao pilot carbon financial derivatives and blue bond centers should be utilized to attract international capital. The Bohai Rim region can serve to promote upgrades in traditional industries through the Green Technology Reform Fund and provide incentives for supporting carbon accounting. Finally, the Hainan Free Trade Port could be used to develop a cross-border platform for ESG and establish a system accounting for the value of the marine ecology to form a graded development pattern of “Innovation Leadership–Transformation Demonstration–Opening and Synergy”. In this way, the possibility for forming a development pattern of “innovation leadership, transformation demonstration, openness and synergy” can be achieved.

The core scope of this study focused on the industrial and economic dimensions of the marine economy and its environmental technology spillover effects. However, it has not fully integrated the concept of the blue economy and its core implications—an increasingly prominent framework in international research on sustainable development. Given the rising relevance of the blue economy approach in recent literature concerning the sustainable development of marine and coastal ecosystems, future research should pursue a conceptual modernization that incorporates this perspective.