A Study on the Degree of Coordination Between Regional Marine Innovation Capacity and Marine Economic Resilience in China

Abstract

Marine innovation, as a fundamental driving force behind the development of the marine economy, is crucial for the realization of the maritime power strategy. The reports from the 19th and 20th National Congresses of the Communist Party of China explicitly advocate for the acceleration of maritime power construction and emphasize the innovation-driven development strategy. Marine innovation and the resilience of the marine economy dynamically interact and mutually reinforce one another. Investigating the coordination between marine innovation and the resilience of the marine economy can provide theoretical support for regional marine technological innovation and sustainable economic development, thereby facilitating the achievement of innovation-driven development goals. This article establishes an evaluation index system for regional marine innovation capacity, considering two perspectives: marine innovation input and output. Additionally, it constructs an evaluation index system for marine economic resilience, which is based on three dimensions: resistance, robustness, and recovery. The entropy weight TOPSIS method is employed to calculate the sub-indices for China’s regional marine technological innovation capacity and marine economic resilience. Furthermore, a coordination degree and coordinated development degree model is developed to assess the coordination and development of marine innovation capacity and economic resilience across 11 coastal provinces (municipalities and autonomous regions) in China from 2013 to 2022. The research results indicate that from 2013 to 2022, the coordination degree of marine innovation capacity and economic resilience in the 11 coastal provinces (municipalities and autonomous regions) of China has exhibited a positive development trend. The southern and eastern economic circles display synchronized development patterns, with the southern economic circle experiencing the fastest improvements, while the northern economic circle shows slight regression. The marginal contribution of this study lies in the integration of marine innovation capacity and economic resilience for the first time, further exploring the degree of coordinated development based on coordination degree and providing a systematic analysis of the coordinated development of regional marine innovation and economic resilience from the perspectives of individual provinces and economic circles.

1. Introduction

Marine innovation, as a vital component of national innovation, is not only the fundamental driving force behind the development of the marine economy but also a critical support for achieving the strategic goal of becoming a maritime power. The report from the 19th National Congress of the Communist Party of China proposed several policies, including “implementing the regional coordinated development strategy”, “innovation as the primary driving force for development”, and “adhering to the coordinated planning of land and sea to accelerate the construction of a maritime power”, emphasizing the interactive relationship between innovation and the economy. Marine innovation not only stimulates the vitality of the marine economy but also plays a crucial role in promoting technological progress, industrial upgrading, and the sustainable utilization of marine resources. With the rapid development of the global marine economy, countries have increasingly recognized marine innovation as a critical strategy for enhancing national competitiveness. As China accelerates the construction of a maritime power, it has continuously strengthened innovation in marine science and technology as well as marine industries.

The relationship between the marine economy and innovation is mutually complementary. The interaction between innovation and the economy is bidirectional, where innovation drives economic development, and economic growth provides feedback that fosters further innovation. However, the coordinated development of these two factors is not a simple linear relationship; it is often influenced by multiple factors, such as regional development levels, resource conditions, and policy environments. The report from the 20th National Congress of the Communist Party of China explicitly proposed policies for “developing the marine economy, protecting the marine ecological environment, and accelerating the construction of a maritime power”, offering strategic guidance and a theoretical framework for the coordinated development of marine innovation and marine economic resilience.

Economic resilience, as a critical strategic goal, becomes especially significant when facing global competition, natural disasters, and other external shocks, emphasizing the capacity of regional economies to withstand pressure and recover. General Secretary Xi Jinping has consistently emphasized that “China’s economy has strong resilience”, which not only affirms the overall development of China’s economy but also instills confidence in the sustainable development of regional economies. In the marine economy sector, the key issue is how to enhance marine innovation capacity to improve marine economic resilience, and how marine economic resilience, in turn, supports marine innovation capacity.

Therefore, clarifying the coordination between marine innovation capacity and marine economic resilience is not only essential for understanding the concepts of “innovation-driven development” and “economic resilience” but also crucial for promoting regional marine technological innovation and the sustainable development of the marine economy. This study aims to analyze the coordinated development between regional marine innovation and economic resilience, explore their mutual interactions and constraints, and provide theoretical support for regional marine technological innovation while offering scientific evidence for the sustainable and dynamic development of the marine economy.

2. Literature Review

Research on marine innovation can generally be categorized into three main types: capacity, efficiency, and policies. Scholars have measured the first type, marine innovation capacity, using various methods, including Liu et al. (2015) [1], who established a national marine innovation index system from four aspects—marine innovation environment, marine innovation input, marine innovation output, and marine innovation performance; Gao and Liu (2023) [2], who constructed a marine economic science and technology innovation evaluation index system based on structural equations; Kou (2023) [3], who employed the entropy weight TOPSIS method to construct a comprehensive evaluation system for marine science and technology innovation capacity in Fujian Province; and Fu et al. (2022) [4], who employed a combined approach—using both a static evaluation model based on the Gini criterion and a dynamic evaluation model incorporating velocity characteristics—in order to assess the innovation capacity of marine fisheries. The second type of research is the evaluation of marine innovation efficiency. Tao and Zhou (2023) [5] constructed an associative two-stage DEA model based on the innovation value chain perspective. Min and Ping (2023) [6], as well as Shi and Chen (2023) [7], built upon this by using a three-stage DEA model to measure the efficiency of marine science and technology innovation in coastal regions. Additionally, other scholars have employed the super-efficient SBM model and Malmquist index to measure and compare China’s marine innovation efficiency from both static and dynamic perspectives (Wang et al., 2022) [8]. The third type of research is the evaluation of marine innovation policies. Lin (2021) [9] analyzed the evolution and characteristics of China’s marine science and technology innovation development policies, including their implementation effects and associated challenges. Ning et al. (2021) [10] studied the marine science and technology innovation policy of Shandong Province using the perspective of matching supply and demand. Li (2022) [11] examined 228 local marine science and technology policies, introduced in China since 1997, employing three quantitative analysis methods: multidimensional scaling, cluster analysis, and social network analysis.

The study of marine economic resilience begins with the term “resilience”, a concept originally derived from physics, which refers to the ability to withstand or recover from external shocks. Today, the concept of “resilience” is widely adopted across disciplines such as economics, psychology, and others. Studies on the resilience of the marine economy can be categorized into four primary types: the first being resilience measurement methods. Several studies have employed various models to measure marine economic resilience, such as the entropy value–TOPSIS model, the super-efficient SBM model (Han et al., 2022) [12], the virtual worst solution TOPSIS model, gray correlation (Wang et al., 2022) [13], entropy efficiency model (Sun et al., 2020) [14], and the CRITIC–TOPSIS model (Wei et al., 2025) [15]. The second type of research is spatial and temporal differentiation characteristics. Existing studies have highlighted that the resilience of China’s coastal marine economy exhibits significant spatial and temporal differentiation. Han et al. (2022) [12] noted that the synergistic evolution of economic resilience and marine fisheries efficiency shows notable spatial–temporal variations and highlighted the lack of regional cooperation. Zhao et al. (2021) [16] observed that marine economic resilience in China’s coastal provinces and cities exhibits a fluctuating trend, making the marine economic system highly vulnerable to external influences. Li and Qu (2023) [17] emphasized that regional industrial path dependence and breakthroughs significantly impact regional economic resilience, with considerable differences in resilience across regions at various periods. Zhai et al. (2023) [18] observed that the overall spatial characteristics of China’s marine economic resilience are relatively stable, though there are variations in the development speed of resilience across three sub-indices. The third type of research includes influencing factors. Several studies have examined the macro-level factors influencing marine economic resilience. Wang et al. (2022) [19] noted that the investment base, financing environment, and transportation infrastructure positively influence the resilience of the marine shipping industry, while openness has a negative effect. Li and Cao (2022) [20] highlighted that the hierarchy, matching, and transmission within the marine economic network structure play a crucial role in enhancing economic resilience. Shen and Guo (2024) [21] found that digital financial inclusion significantly strengthens the economic resilience of marine cities by boosting entrepreneurial activity and consumption capacity. Yu et al. (2024) [22] investigated the impact of the synergy between market-based and government-driven environmental regulations on marine economic resilience. Li et al. (2024) [23] suggested that diversified development and collaborative cooperation can enhance the endogenous drivers of marine economic resilience. Zhang et al. (2024) [24] concluded that environmental regulations significantly bolster marine economic resilience. Wei et al. (2025) [15] argued that spatial imbalances and a lack of coordination impede the overall improvement of marine economic resilience. The fourth type of research is the relationship between resilience and economic efficiency. The synergistic development of marine economic resilience and efficiency is essential for achieving sustainable development. Zhao et al. (2021) [16] identified a positive correlation between China’s marine economic resilience and its economic efficiency. Han et al. (2022) [12] noted that marine fishery economic efficiency, as an ordinal covariate in synergistic evolution, plays a dominant role in the sustainable development of China’s marine fishery economy. Additionally, Li and Qu (2023) [17] emphasized that breakthroughs in industrial evolution paths play a more prominent role in enhancing regional economic resilience, with different regions exhibiting varying levels of resilience during the financial crisis and the onset of the new normal.

Common methods for studying the degree of coordination include the EDA efficiency model, entropy weight TOPSIS, and coupling models. The EDA efficiency model is primarily used to analyze the relationship between resource allocation and efficiency improvements by calculating the correlations and efficiencies of various factors to evaluate the overall system coordination. The entropy weight TOPSIS method is a multi-attribute decision-making approach that evaluates the quality of decision alternatives based on their relative distances to the ideal and negative ideal solutions. By integrating the entropy weight method to determine the weights of indicators, the entropy weight TOPSIS method can reasonably assign weights based on the uncertainty and information content of each indicator, thus offering a more objective and impartial evaluation in coordination degree analysis. Coupling models are typically used to analyze the interactions and influences among different systems or subsystems. By calculating the coupling degree, these models can reveal the synergistic effects and coordination levels among system components. However, this approach often neglects the independent development status of each subsystem, potentially leading to an incomplete assessment of the overall system’s development potential. For example, even if certain subsystems exhibit high degrees of coordination, their limited development levels may constrain the system’s overall potential. Conversely, subsystems with better development, despite having lower coordination degrees, may contribute more to the overall system’s potential. Therefore, coordination development models address this issue by not only focusing on the coordination among subsystems but also by incorporating a comprehensive evaluation of their development status. This allows the coupling development model to more comprehensively and dynamically reflect both the coordination and potential of the system.

According to the literature reviewed above, most existing studies on marine science and technology innovation and marine economic resilience focus on assessing the current status and efficiency of marine innovation capacity and economic resilience. However, they fail to explore the relationship of coordinated development between marine innovation capacity and economic resilience, neglecting the consideration of their mutual coordination. Furthermore, most research samples are based on data from a single province or city, failing to conduct a systematic analysis of the differences between various economic regions. Additionally, there is a lack of integrated and comparative analysis of coordination between marine innovation and resilience across multiple economic regions in China. Wang et al. (2020) [25] and Fang et al. (2019) [26] have examined the coordinated relationship between marine innovation capacity and the marine economy at the national and regional levels, respectively. In contrast to studies focusing on marine economic development, this paper assesses the marine economic resilience. This concept not only examines the performance of the marine economy under normal conditions but also its ability to adapt and recover in the face of shocks. Furthermore, this assessment extends beyond mere economic development, considering the marine economy’s sustainable development potential and vitality.

This paper adopts the entropy weight TOPSIS method to calculate the marine innovation capacity and marine economic resilience scores of 11 coastal provinces (municipalities and autonomous regions) in China. It further constructs the coordination degree and coordinated development models, systematically analyzing the coordinated development of marine innovation capacity and marine economic resilience across these regions. The provinces are classified into the northern, eastern, and southern economic circles for heterogeneity analysis (Wang et al., 2022) [27]. The Northern Marine Economic Circle is an economic region consisting of the coastal areas of the Liaodong Peninsula, Bohai Bay, and Shandong Peninsula. It primarily encompasses the maritime and terrestrial areas of Liaoning Province, Hebei Province, Tianjin Municipality, and Shandong Province. The Eastern Marine Economic Circle consists of the coastal areas of the Yangtze River Delta, primarily covering the maritime and terrestrial areas of Jiangsu Province, Shanghai Municipality, and Zhejiang Province. The Southern Marine Economic Circle comprises the coastal areas of Fujian, the Pearl River Estuary and its two wing regions, the Beibu Gulf, and Hainan Island, primarily covering the maritime and terrestrial areas of Fujian Province, Guangdong Province, Guangxi Zhuang Autonomous Region, and Hainan Province. The contributions of this paper lie in the following: (1) By analyzing the existing literature, this study enriches the indicator construction system, developing indicators to measure China’s regional marine innovation capacity in terms of innovation inputs and outputs, as well as marine economic resilience in terms of resistance, robustness, and recovery; (2) A comparative analysis of marine innovation capacity and economic resilience across different coastal provinces and economic circles is conducted, examining the heterogeneity among various subsystems. The study systematically analyzes the differences in the coordinated development of marine innovation capacity and economic resilience across these provinces and economic circles. (3) A comprehensive coordination degree model is constructed, combining marine innovation capacity with marine economic resilience, thereby filling the gap in the theoretical analysis of the coordinated development of marine innovation.

3. Indicator System and Model Construction

3.1. Indicator System

The selection of the index system should meet the requirements of being systematic, scientific, dynamic, and operational [28]. Science and technology innovation input, as the foundation of research and development, plays a crucial role in enhancing innovation capacity, while the output of innovation is the most direct manifestation of this capacity. Therefore, this paper adopts two sub-indices—marine innovation inputs and marine innovation outputs—to assess marine innovation capacity [29]. Based on Liu et al. (2015) [1], considering the availability of regional data, eight positive indicators are constructed (as shown in Table 1) to measure China’s regional marine innovation capacity. Regarding marine innovation inputs, the intensity of funding for marine research and development measures the level of government and business investment in marine R&D, reflecting the importance a region places on marine science and technology innovation. The intensity of marine R&D manpower investment reflects the number of personnel engaged in marine research, representing the human resources invested in marine innovation. The per capita GNP of the coastal area reflects the region’s economic foundation and development level, which are essential conditions for promoting marine innovation. The number of projects per capita of marine scientific researchers reflects the level of scientific research activities and innovation capacity, serving as a measure of researchers’ scientific output and innovation capacity. Regarding marine innovation output, the number of effective invention patents per capita represents the direct results of technological innovation in the marine field, effectively measuring innovation capacity. Furthermore, the economic output per unit of energy consumption in the marine field reflects the efficiency of economic output and energy consumption, indicating the sustainability of economic activities. The productivity of marine science and technology represents the output of scientific research institutions, used to measure the ratio of research input to output. The number of papers per capita reflects researchers’ academic output and the publicity of research results, serving as a visible indicator of scientific research strength.

Academics have not yet standardized the definition of marine economic resilience, with various evaluation systems proposed. Martin (2015) [30] summarizes resilience into four dimensions: resistance, robustness, adaptability, and renewal. This paper considers the availability of regional economic data and the overlap between adaptability and renewal, which makes it difficult to distinguish between the two, leading to some index duplication or calculation challenges. Therefore, the paper combines adaptability and renewal into a single “recovery” sub-index, constructing three sub-indexes: resistance, robustness, and recovery. Resistance refers to the extent of disturbance to the state or dynamics of the regional economy following a shock. Robustness refers to a system’s ability to maintain or restore core functions or performance by adjusting and adapting its structure and components in response to both external and internal disturbances. Recovery refers to the extent and speed with which the regional economy recovers from a shock. Six indicators, all positive, were constructed to measure the resilience of China’s regional marine economy, including the proportion of marine GDP to regional GDP (see Table 1). Regarding resistance, the share of marine GDP reflects the importance of the marine economy within the regional economy and measures its resilience to economic risks. Marine GDP per capita of sea-related personnel reflects their economic contribution, serving as a key indicator for measuring the health of the marine economy and its resilience to risks. Regarding robustness, the number of scientific and technical papers reflects the level of scientific research activities and the degree of knowledge accumulation in the marine economy, which helps measure economic resilience. The proportion of government funding for marine scientific research institutions reflects governmental support, serving as a measure of the stability of these institutions and ensuring their sustainable development. Regarding recovery, the growth rate of marine GDP reflects the vitality and growth of the marine economy, serving as an important indicator of economic resilience. The per capita output of scientific researchers reflects the efficiency and outcomes of their work, serving as a key factor in measuring the recovery of an innovation-driven economy.

Table 1. Regional marine innovation capacity and marine economic resilience indicator system.

Subsystems	Sub-Index	Norm	Characteristic	Weights
Marine innovation capacity A	$\mathrm{Marine} \mathrm{innovation} \mathrm{inputs} A_1$	Intensity of funding for marine research and development	+	0.189556
		Intensity of human investment in marine research and development	+	0.077294
		GNP per capita in coastal areas	+	0.087720
		Number of projects per marine researcher	+	0.125294
	$\mathrm{Marine} \mathrm{innovation} \mathrm{outputs} A_2$	Effective patents per capita	+	0.139762
		Economic output per unit of energy consumed in the oceans (Wang et al., 2022) [31]	+	0.095838
		Productivity of marine science and technology	+	0.174764
		Number of papers per researcher	+	0.109774
Marine economic resilience B	$\mathrm{Resistance} B_1$	Percentage of Gross Maritime Product	+	0.155802
		Marine GDP per capita of sea-related personnel	+	0.140430
	$\mathrm{Robustness} B_2$	Number of scientific and technical papers	+	0.293571
		Percentage of government funding for marine scientific research institutions	+	0.044715
	$\mathrm{Recovery} B_3$	Growth rate of gross domestic product (GDP)	+	0.253087
		Output per scientific researcher	+	0.112395

Table 1. Regional marine innovation capacity and marine economic resilience indicator system.

Subsystems	Sub-Index	Norm	Characteristic	Weights
Marine innovation capacity A	$\mathrm{Marine} \mathrm{innovation} \mathrm{inputs} A_1$	Intensity of funding for marine research and development	+	0.189556
		Intensity of human investment in marine research and development	+	0.077294
		GNP per capita in coastal areas	+	0.087720
		Number of projects per marine researcher	+	0.125294
	$\mathrm{Marine} \mathrm{innovation} \mathrm{outputs} A_2$	Effective patents per capita	+	0.139762
		Economic output per unit of energy consumed in the oceans (Wang et al., 2022) [31]	+	0.095838
		Productivity of marine science and technology	+	0.174764
		Number of papers per researcher	+	0.109774
Marine economic resilience B	$\mathrm{Resistance} B_1$	Percentage of Gross Maritime Product	+	0.155802
		Marine GDP per capita of sea-related personnel	+	0.140430
	$\mathrm{Robustness} B_2$	Number of scientific and technical papers	+	0.293571
		Percentage of government funding for marine scientific research institutions	+	0.044715
	$\mathrm{Recovery} B_3$	Growth rate of gross domestic product (GDP)	+	0.253087
		Output per scientific researcher	+	0.112395

In order to fully utilize the information of the original data and avoid the subjectivity of the data, this paper adopts the entropy weight TOPSIS method to assign the indexes (as in Table 1) and calculate their scores. The entropy weight TOPSIS method is a multi-attribute assignment method that combines the entropy value method of the objective assignment with the method of approximation of the ideal point ordering, and the specific steps are as follows:

(1) Construction of the data matrices for raw indicators:

$$X={\left(x_{ij}\right)}_{m\times n}=\left[\begin{array}{ccc}{x}_{11}& \cdots & x_{1n}\\ ⋮& \ddots & ⋮\\ x_{m1}& \cdots & x_{mn}\end{array}\right]$$

(1)

In the formula: $X$ represents the original evaluation indicator data matrix; there are a total of m evaluation objects and n evaluation indicators for each evaluation object; $x_{ij}$ is the first $i.$ The raw data for the first evaluation object of the $j.$ The raw data of the first evaluation object, the $i=\mathrm{1,2},\dots ,m;j=\mathrm{1,2},\dots ,n$, is the raw data of the first indicator of the first evaluation object.

(2) Standardization of the data:

According to the different characteristics of the indicators, the polar method was used to standardize the indicators according to the direction of the different indicators.

Positive indicators:

$$r_{ij}={\displaystyle \frac{x_{ij}-\min \left(x_{ij}\right)}{\mathrm{m}\mathrm{a}\mathrm{x}\left(x_{ij}\right)-{\mathrm{m}\mathrm{i}\mathrm{n}(x}_{ij})}}$$

(2)

Negative indicators:

$$r_{ij}={\displaystyle \frac{\max \left(x_{ij}\right)-x_{ij}}{\mathrm{m}\mathrm{a}\mathrm{x}\left(x_{ij}\right)-{\mathrm{m}\mathrm{i}\mathrm{n}(x}_{ij})}}$$

(3)

(3) Calculation of the share of each indicator:

$$P_{ij}={\displaystyle \frac{r_{ij}}{\sum r_{ij}}}$$

(4)

(4) Calculation of the entropy value of the $j$ entropy value of the first indicator:

$$e_j=-k\sum P_{i}ln{P}_i$$

(5)

(5) Calculation of the redundancy of the indicators:

$$d_j=1-e_j$$

(6)

(6) Calculation of the weights of the indicators:

$$w_j={\displaystyle \frac{d_j}{\sum d_j}}$$

(7)

(7) Construction of a weighted decision matrix:

$$R={\left(r_{ij}^{\prime }\right)}_{mn}={\left(w_j\times r_{ij}\right)}_{mn}$$

(8)

(8) Determination of the positive and negative ideal values:

$$R^{+}=\max \left(r_{1j},r_{2j}\dots r_{1j}\right)$$

(9)

$$R^{-}=\min \left(r_{1j},r_{2j}\dots r_{1j}\right)$$

(10)

(9) Calculation of the Euclidean distance:

$$D_i^{+}=\sqrt{\sum {\left(r_{ij}-R_j^{+}\right)}^2}$$

(11)

$$D_i^{-}=\sqrt{\sum {\left(r_{ij}-R_j^{-}\right)}^2}$$

(12)

(10) Calculation of the progress of proximity:

$$C_i={\displaystyle \frac{D_i^{-}}{D_i^{+}{+D}_i^{-}}}$$

(13)

The entropy weight TOPSIS method was used to assign weights to the indicators (as shown in Table 1), in which the weights of the marine innovation input sub-index $A_1$ and the marine innovation output sub-index $A_2$ of the marine innovation capacity subsystem are 0.479863 and 0.520137, respectively, and the weights of the body resistance sub-index $B_1$, robustness sub-index $B_2$ and recovery sub-index $B_3$ of the marine economic resilience subsystem are 0.296232, 0.338286, and 0.365482, respectively.

3.2. Coordination Degree Model Construction

The composite scores for the two subsystems of marine innovation capacity and marine economic resilience are derived, respectively using $S_c$ and $S_r$. After obtaining the comprehensive scores of marine innovation capacity and marine economic resilience, this paper calculates the degree of coordination between the development of marine innovation capacity and marine economic resilience by referring to the standardized formula pointed out by Wang et al. (2021) [32]:

$$C={\left[{\displaystyle \frac{S_{ci}\times S_{ri}}{{\left(\frac{S_{ci}+S_{ri}}{2}\right)}^2}}\right]}^{{\displaystyle \frac{1}{2}}}$$

(14)

Of these, the $S_{ci}$ and $S_{ri}$ denote the first $i$ marine innovation capacity and marine economic resilience subsystem scores of the evaluation object, and $C$ represents the degree of coordination, whose value interval is $\left[\mathrm{0,1}\right]$, which reflects the coordination between marine innovation capacity and marine economic resilience. The closer to 1, the better the coordination is between marine innovation capacity and marine economic resilience; on the contrary, the worse it is.

However, the degree of coordination $C$ can only reflect the degree of interaction between the two subsystems of marine innovation capacity and marine economic resilience, and it fails to show the development of the interaction between marine innovation capacity and marine economic resilience; therefore, it has certain limitations. In order to be able to further reflect the development trend of the interaction between marine innovation capacity and marine economic resilience, this paper introduces the coordinated development degree model:

$$D=\sqrt{C\times T},T=\left(\alpha S_{ci}+\beta S_{ri}\right)$$

(15)

where D is the degree of coordinated development; T is the combined score of marine innovation capacity and marine economic resilience;$\alpha$ and $\beta$ are coefficients to be determined; and $\alpha +\beta =1$. This paper considers that marine innovation capacity and marine economic resilience are equally important, thus taking $\alpha =1/2$ and $\beta =1/2$.

According to the size of the coordinated development degree D, this paper categorizes the coordinated development status of marine innovation capacity and marine economic resilience into five categories (Wang et al., 2020) [25], as shown in

Table 2. Harmonized development degree classification.

D	Typology	First Level	Second Level
0.8~1.0	Well-coordinated development	$S_{ci}<S_{ri}$	Well-coordinated development of marine innovation lags behind
		$S_{ci}=S_{ri}$	Well-coordinated development of marine innovation and synchronization of marine economic resilience
		$S_{ci}>S_{ri}$	Well-coordinated development of marine economic resilience lags
0.6~0.8	Medium coordinated development	$S_{ci}<S_{ri}$	Medium coordinated development of marine innovation lags
		$S_{ci}=S_{ri}$	Medium coordinated development of marine innovation and synchronization of marine economic resilience
		$S_{ci}>S_{ri}$	Medium coordinated development of the ocean economy lags in resilience
0.4~0.6	Barely coordinated development	$S_{ci}<S_{ri}$	Barely coordinated development of marine innovation lags behind
		$S_{ci}=S_{ri}$	Barely synchronized development of marine innovation and marine economic resilience
		$S_{ci}>S_{ri}$	Barely coordinated development of the ocean economy lags in resilience
0.2~0.4	Moderately dysfunctional recession	$S_{ci}<S_{ri}$	Moderately dysfunctional recession; marine innovation lags
		$S_{ci}=S_{ri}$	Synchronization of marine innovation and marine economic resilience in a moderately dysfunctional recession
		$S_{ci}>S_{ri}$	Moderately dysfunctional recession; marine economic resilience lags
0~0.2	Severely dislocated recession	$S_{ci}<S_{ri}$	Severely dislocated recession; marine innovation lags
		$S_{ci}=S_{ri}$	Severe dysregulation of synchronization of marine innovation and marine economic resilience
		$S_{ci}>S_{ri}$	Severely dislocated recession; marine economic resilience lags

.

4. Empirical Analysis

In this paper, the data for the indicators of marine innovation capacity and marine economic resilience are sourced from the Ministry of Science and Technology’s marine science and technology statistics and the China Marine Statistics Yearbook. This study utilized Stata version 16 software to analyze the data. First, the data were standardized, followed by the use of the entropy weight TOPSIS method to calculate the weights of the indicators for marine innovation capacity and marine economic resilience. The scores for the two subsystems were then calculated, and coordination and coordinated development models were constructed, resulting in the coordinated development of the 11 coastal provinces (municipalities and autonomous regions) of China. Additionally, from the perspective of economic circles, the coordinated development of different economic regions was analyzed.

4.1. Analysis of Regional Marine Innovation Subsystems

From 2013 to 2022, the marine innovation capacity of the 11 coastal provinces (municipalities and autonomous regions) in China generally increased (Figure 1), with an average annual growth rate of 4.31%. This suggests that the 11 coastal provinces (municipalities and autonomous regions) have actively pursued the goal of “ocean power” and have enhanced their marine innovation capacity. Among these, Fujian has the highest average annual growth rate at 11.07%, while Hebei has the lowest at −5.92%. Hebei’s economic structure is relatively traditional, dominated by heavy industry and manufacturing, particularly in sectors such as steel and coal, which have limited demand for marine innovation. Additionally, Hebei has a relatively short coastline, and compared with coastal provinces like Fujian, its foundation for marine resource development and marine technology research is weaker.

In terms of the average marine innovation capacity score, the provinces (municipalities and autonomous regions) that exceed the average score are Jiangsu, Guangdong, Hebei, and Fujian. Among these, Jiangsu has the strongest marine innovation capacity, with an average score of 0.395, which is 1.68 times higher than the overall average, due to its highest average marine innovation inputs and outputs. In contrast, Hainan and Tianjin have weaker marine innovation capacity, with average scores of 0.173 and 0.160, respectively, due to their lower average marine innovation outputs. Jiangsu has consistently promoted marine technological innovation. Currently, the shipbuilding and offshore engineering industries in Nantong account for one-tenth and one-fourth of the national total, respectively. Its high-tech shipbuilding and offshore equipment clusters have also become nationally recognized as part of advanced manufacturing clusters. (See Figure 2).

In terms of economic circles, both the Eastern Economic Circle and the Southern Economic Circle have shown a steady upward trend, while the Northern Economic Circle has stagnated. In 2013, the Eastern Economic Circle and the Northern Economic Circle were relatively similar in terms of development, while the Southern Economic Circle lagged behind. Over time, the Eastern Economic Circle has gradually distanced itself from the Northern Economic Circle, while the Southern Economic Circle has gradually caught up with the Northern Economic Circle. The Eastern Economic Circle has relatively abundant research institutions and high-end talent resources, with cities like Shanghai hosting a large concentration of marine science and technology professionals, top universities, and research institutes. Additionally, government policy support in the Eastern Economic Circle is well developed, particularly in terms of technological innovation and high-tech industries. In contrast, the Northern Economic Circle has a weaker industrial foundation, especially in marine science and technology, relying more on traditional manufacturing and lacking large-scale innovation-driven development. (See Figure 3).

4.2. Analysis of the Ocean Economic Resilience Subsystem

From 2013 to 2022, the marine economic resilience of the 11 coastal provinces (municipalities and autonomous regions) in China generally increased (Figure 4), with an average annual growth rate of 0.81%. Among these, Guangdong has the highest average annual growth rate at 4.76%, while Hebei has the lowest at −3.25%. Guangdong has a relatively complete industrial chain and efficiently integrates resources. Leveraging the economic clusters of the Pearl River Delta, abundant marine resources, and advanced port facilities, Guangdong’s marine economy includes not only traditional industries such as shipping and fisheries, but also high-tech sectors like marine engineering, marine energy, and marine biomedicine. This diversification strongly supports Guangdong’s marine economy and enhances its resilience.

In terms of the average marine economic resilience score, the provinces (municipalities and autonomous regions) exceeding the average include Guangdong, Shandong, Shanghai, Tianjin, and Hainan. Among these, Guangdong has the highest marine economic resilience score, with an average of 0.406, which is 1.58 times higher than the overall average, due to its highest score in the robustness sub-index. Shandong follows with an average score of 0.328, which is 1.27 times the overall average, due to its higher robustness. (See Figure 5).

In terms of economic circles, the development trends of China’s Eastern, Northern, and Southern Economic Circles were similar from 2013 to 2022, all peaking in 2017. Meanwhile, the Dynamic Economic Circle lagged behind in 2013, but by 2022, the Southern Economic Circle had the highest score, followed by the Eastern Economic Circle, with the Northern Economic Circle ranked last. The optimization of the industrial structure plays an important role in enhancing economic resilience. Relying on advanced manufacturing and high-tech industries, the Eastern and Southern Economic Circles have continued to optimize the structure of the marine industry, promoting the transformation of the traditional marine industry into one with high value-added and technological content. These changes have not only improved economic resilience to risks but also facilitated the transformation and application of regional innovations. However, parts of the Northern Economic Circle are still dominated by traditional marine fisheries and low value-added industries, and industrial upgrading has been slow, limiting the enhancement of economic resilience. (See Figure 6).

4.3. Analysis of the Coordinated Relationship Between Regional Marine Innovation Capacity and Marine Economic Resilience

The relationship between regional marine innovation capacity and marine economic resilience is dynamic and complementary. Marine innovation capacity is the key driving force behind enhancing marine economic resilience. Through technological research and development, industrial upgrading, and efficient resource utilization, the industrial structure can be optimized, strengthening the risk resilience of the industrial chain and enabling the region to respond more effectively to external shocks. Simultaneously, marine economic resilience provides a stable environment and resource support for innovation activities, ensuring continued investment in innovation even amid economic fluctuations, while stimulating additional demands for innovation, such as the development of post-disaster recovery technologies and green low-carbon technologies. In this process, enhancing innovation capacity not only directly improves marine economic resilience but also gradually internalizes economic resilience. Through continuous enhancement of innovation capacity, the marine economic system gains the ability for self-regulation and self-recovery. This internalizes the ability to cope with external shocks as an inherent characteristic of the system, achieving proactive crisis avoidance and long-term stable development through innovation. The relationship between the two shows a distinct stage-wise progression. In the early stages of economic development, marine economic resilience provides a foundation for innovation. However, as innovation capacity gradually increases, its role in promoting economic resilience becomes more significant. Ultimately, the continuous and cyclical strengthening of both capabilities leads to their synergistic development, promoting the high-quality and sustainable development of regional marine economies. (See Figure 7).

Overall, the coordinated development of marine innovation capacity and marine economic resilience in the 11 coastal provinces (municipalities and autonomous regions) of China exhibits an upward trend. In 2013, the ranking of the coordinated development of marine innovation capacity and marine economic resilience in these regions was as follows: Guangdong, Hebei, Shanghai, Shandong, Tianjin, Jiangsu, Fujian, Hainan, Liaoning, Zhejiang, and Guangxi. By 2022, the ranking had changed to: Guangdong, Jiangsu, Shanghai, Fujian, Shandong, Hainan, Tianjin, Zhejiang, Liaoning, Guangxi, and Hebei. (See Figure 8).

The coordinated development of marine innovation capacity and marine economic resilience in coastal provinces exhibits significant regional differences and temporal evolution trends. Guangdong and Jiangsu exhibited strong coordinated development capacity throughout the period. Guangdong’s coordinated development degree steadily rose from 0.501 in 2013 to 0.637 in 2022, reflecting significant progress in marine innovation and economic resilience. Jiangsu peaked at 0.683 in 2017 and, despite fluctuations, remained high at 0.565 in 2022. This suggests that these two provinces have made significant strides in promoting marine science and technology innovation and enhancing economic resilience. In contrast, the coordinated development of the provinces of Shanghai, Shandong, and Fujian has also maintained steady upward momentum. Shanghai increased from 0.486 in 2013 to 0.557 in 2022, Shandong peaked at 0.555 in 2021, and Fujian improved from 0.428 in 2013 to 0.550 in 2022. In contrast, the coordinated development of the provinces of Hebei, Guangxi, and Tianjin remains relatively low. Hebei declined from 0.486 in 2013 to 0.394 in 2022, and the coordinated development degrees of Guangxi and Tianjin also remained low throughout the period, indicating that these provinces face greater challenges in promoting the coordinated development of marine innovation and economic resilience. Guangdong and Jiangsu are strategically located near major marine trade routes. The ports of Guangzhou and Shenzhen in Guangdong are among the busiest globally, while Jiangsu’s Lianyungang port also has a solid foundation in the marine economy. The presence of these ports provides substantial support for the development of the marine economy and fosters marine innovation and technological advancement. Although ports in Guangxi, such as Beihai and Fangcheng, possess some marine economic functions, their scale and level of internationalization remain relatively limited. Additionally, the economic structures of both Guangxi and Tianjin are more reliant on traditional industries, lagging behind in emerging fields such as high technology and the efficient utilization of marine resources. (See Figure 9).

From the perspective of economic circles, the coordinated development of the Northern and Southern Economic Circles has generally shown an upward trend, albeit with some differences. The coordinated development of the Southern Economic Circle has steadily increased during this period, with an average annual growth rate of 2.45%. It increased from 0.4243 in 2013 to 0.527 in 2022, indicating that the coordination between marine innovation capacity and marine economic resilience in this region has been consistently enhanced. In the Eastern Economic Circle, the coordinated development degree was 0.443 in 2013, with an average annual growth rate of 1.81%, reaching 0.521 by 2022. This indicates that the coordination degree in this region has gradually improved. Although both the Eastern and Southern Economic Circles have made progress in coordinated development, the Northern Economic Circle has slightly declined and requires further improvement in the coordination between marine innovation capacity and marine economic resilience. The Southern Economic Circle has vigorously developed marine science, technology, and the marine economy. For example, the Hainan Free Trade Port policy interpretation press conferences have emphasized accelerating the development of marine economic tasks, enhancing marine technological innovation capacity, and promoting the upgrading of marine industries. Government policies and substantial resource investment have led to a continuous increase in the coordinated development degree of the South. In contrast, the marine economy in the Eastern Economic Circle started earlier, with relatively well-developed infrastructure and industrial systems, resulting in stable development and a natural slowdown in growth.

5. Conclusions and Recommendations

5.1. Conclusion

This study systematically evaluates the coordinated development of marine innovation capacity and marine economic resilience in 11 coastal provinces (municipalities and autonomous regions) of China through the entropy weight TOPSIS method and the coordination degree model. The main conclusions are as follows:

(1)

The overall improvement in the marine innovation capacity subsystem in China’s coastal regions from 2013 to 2022 is remarkable, with an average annual growth rate of 4.31%. Fujian has the highest growth rate at 11.07%, while Hebei has a negative average annual growth rate of −5.92%. The efforts of the state and local governments to promote marine science and technology innovation have yielded significant results, and the importance attached to and investment in marine innovation continue to increase across all regions. There are significant regional differences, with the Eastern and Southern Economic Circles rising steadily, while the Northern Economic Circle is developing slowly and lagging behind, resulting in a gradually widening gap.

(2)

The overall trend of the marine economic resilience subsystem in China’s coastal regions from 2013 to 2022 is positive, with an average annual growth rate of 0.81%. Guangdong has the highest growth rate at 4.76%, while Hebei has the lowest at −3.25%. Regional differences are not significant, with the Southern Economic Circle showing better marine economic resilience than the Eastern and Northern Economic Circles, while the Northern Economic Circle is slightly underdeveloped, lagging behind both the Eastern and Southern Economic Circles.

(3)

From 2013 to 2022, the overall coordination between marine innovation capacity and marine economic resilience in China’s coastal areas showed an upward trend, indicating significant progress in enhancing both marine technological innovation capacity and economic resilience across regions. The coordination development degree in the Southern Economic Circle experienced the fastest improvement, with an average annual growth rate of 2.45%, demonstrating a positive interaction between marine innovation capacity and economic resilience. In contrast, the Northern Economic Circle saw a decline, with an average annual growth rate of −0.26%, indicating the need for further strengthening of coordination between innovation and economic resilience. The Southern and Eastern Economic Circles displayed similar development patterns with synchronous progress, while the Northern Economic Circle remains relatively underdeveloped.

5.2. Recommendations for Countermeasures

The synergistic development of marine innovation capacity and marine economic resilience is conducive to the overall positive progression of the region, whereas if only a single subsystem develops, it may further widen the development gap between subsystems or limit the progress of the other subsystem. To promote further synergistic and high-quality development across all regions, this paper proposes the following countermeasures and recommendations:

(1)

Strengthening policy support is essential. Governments at all levels should continue to strengthen policy support for marine technological innovation. They should establish special funds, such as the “Marine Technological Innovation Fund”, to increase support for regions with relatively weak technological innovation, thereby promoting coordinated regional development. Additionally, the government can incentivize enterprises and research institutions to increase investment in marine technology through measures such as tax reductions and R&D subsidies.

(2)

Optimizing the industrial structure is essential. Each region should optimize the structure of marine industries based on its own advantages and promote the transformation and upgrading of traditional marine industries, such as the digital and green transformation of sectors like fisheries and marine transportation, while vigorously developing emerging marine industries to improve overall economic resilience. In particular, they should support the development of high value-added and technology-intensive industries, such as marine biomedicine, marine engineering equipment, and marine new energy, in order to enhance the risk-resistant capacity of the industrial chain.

(3)

Upgrading investment in science and technology is crucial. Financial and human investment in marine scientific research should be increased, particularly in basic and applied research, and promote the transformation and industrialization of research outcomes. The government should incentivize enterprises to increase R&D investment through tax incentives and subsidies, build a platform for cooperation among industries, universities, and research institutes, and promote technological innovation and its application. Additionally, in conjunction with the “Technology Innovation 2030” plan, encourage collaboration between universities and enterprises to undertake joint R&D projects.

(4)

Strengthening regional cooperation is essential. Cooperation among coastal provinces (municipalities and autonomous regions) should be promoted in order to achieve common development through shared resources and experiences. A cross-regional collaborative innovation mechanism should be established to encourage research institutions and enterprises to conduct cooperative research and development and jointly address market and technological challenges. Drawing on the integrated development model of the Yangtze River Delta, deeper cooperation among coastal provinces in marine research, industrial development, and other sectors should be promoted.

(5)

Focusing on environmental protection is crucial. While enhancing the resilience of the marine economy, attention must also be given to the protection of the marine ecological environment, promoting green development, and achieving a win–win situation for both economic and environmental benefits. Regions should formulate stringent environmental protection regulations, strengthen the sustainable use of marine resources, and promote marine ecological restoration and protection. A “Smart Ocean Environmental Monitoring System” should be built in order to monitor environmental data such as water quality and air quality in real time, ensuring the effective implementation of environmental protection measures.

(6)

Promoting digital development is essential. The digital transformation of the marine economy should be accelerated by utilizing digital financial inclusion and big data technology to enhance economic resilience and innovation capacity, thus promoting the high-quality development of the marine economy. Vigorous efforts should be made to develop marine information technology, establish a “Marine Big Data Service Platform”, improve the intelligence and informatization of the marine economy, and enhance the ability to respond quickly to market changes and risks.