Spatial-Temporal Evolution of Coupling Coordination between Green Transformation and the Quality of Economic Development

Abstract

Taking “blue granary” as the research object, this study focuses on the mechanism and evolution of coupling coordination relationship between green transformation and the quality of economic development, to explore the path of sustainable development. Firstly, the theoretical framework of coupling relationship between green transformation and the quality of development is constructed. Secondly, an evaluation index system is established to measure green transformation and the quality of economic development. Thirdly, the entropy approach and coupled coordination degree model are used to calculate the coordination of green transformation and the quality of economic development in different provinces in China from 2009 to 2018. The results show that: (1) Green transformation affects the quality of economic development through resource effect, social effect, and technological effect; the quality of economic development affects green transformation through new growth momentum effect, income distribution effect, scale effect, and opening up effect. (2) Both the quality of economic development and the level of green transformation continue to improve, but the growth rate of green transformation is relatively slow. (3) The overall coupled coordination relationship improves from a barely balanced stage to a favorably balanced stage, but it has not reached the ideal state of superiorly balanced, and there is significant regional heterogeneity. It will help to clarify the difference in coordinated development levels in different regions and provide a reference value for the precise implementation of eco-economic coordinated development.

1. Introduction

In the past decades, food security has been facing unprecedented pressures, such as ecosystem degradation, climate change, population growth, and the reduction of arable land. Food systems have been central in the debate on sustainable development [1]. As a result, almost all the coastal countries attach great importance to the development of marine food resources [2]. Seafood can provide people with high-quality protein and a variety of nutrition, which become a beneficial supplement to land food [3]. In this context, the sustainable development of marine food plays a key role in alleviating food security [4].

The development research center of the state council of the People’s Republic of China predicted that the peak of China’s food demand is expected to come around 2030. However, after a long period of extensive growth, China’s terrestrial agriculture is confronted with the shortage of land and water resources, serious pollution of chemical fertilizers and pesticides, and low efficiency of scattered small-scale production modes, which makes it difficult to further improve the grain output. The shortage of grain will also limit the production of animal food. Against the above background, China is trying to utilize marine resources to produce marine food to fill the food gap and put forward the “blue granary” strategy. The “blue granary” mainly refers to marine agriculture, which includes mariculture, marine fishing, marine aquatic product processing industry, etc. It’s a sector of the blue economy, but there are also obvious differences between them. Blue economy emphasizes the role of marine resources in the development of the national economy, it’s a hot topic, and scholars have carried out large numbers of research on it. By comparison, the "blue granary" underlines the complementary role of marine food to land food from the perspective of food security. Food security is of vital importance, but the studies on “blue granary” remain limited. Meanwhile, compared with other sectors of the blue economy, the marine space and fishery resources are the typical public goods, and food safety and quality are more vulnerable to the marine environment, so the "blue granary" puts more emphasis on cleaner production. In a word, “blue granary” emerges as a new-type marine food production system, intending to use the marine environment and biological resources to provide high-quality marine food, which is characterized by innovation, industrial upgrading, and green transformation [5]. In practice, seafood has been the fastest-growing area in the food production sector in the past 30 years, which is conducive to ensuring China’s food security and meeting the needs for balanced diets [6].

However, the growth of marine aquatic products was achieved at the cost of resource depletion and environmental degradation. From the perspective of marine resources, marine fishing and mariculture are the basic industries of “blue granary”, and both of them rely heavily on the input of resources such as maritime space and tidal flats. It is worth noting that almost all the sea areas and tidal flats within the 15 m isobaths in China have been developed. Additionally, the extensive development also brought about the degradation of fishery resources and led to the change in population structure and the decline of biodiversity [7,8,9]. Therefore, the input of factors cannot be the driving force of long-term output growth, and the construction of “blue granary” faces serious resource constraints [10]. From the perspective of the marine ecological environment. “Blue granary” is not cleaner production. To begin with, the wastewater of mariculture contains significant amounts of nutrients, organics, heavy metals, and antibiotics, which may lead to eutrophication. Moreover, some studies have indicated that residual bait will also release nitrogen, phosphorus, COD, and other nutrients, damaging the marine environment [11,12,13,14]. Besides this, feces and other excrements of cultured organisms will also cause pollution [15,16]. From above statement, the constraints from maritime space, the degradation of fishery resources, and the deterioration of the marine environment not only affect the marine ecosystem function, but also threaten the ecological security of the industry, and even restrict the sustainable development of “blue granary” [17]. The green transformation has been precisely the critical factor affecting resource conservation and environmental protection.

Through the above analysis, it becomes obvious that the quality of economic development and the ecological environment are interrelated. The worst situation is that extensive development mode leads to ecological destruction and then restricts long-term growth. The coordination between the quality of economic development and green transformation is another case. Specifically, high-quality development takes innovation as the growth momentum, reducing the dependence of output growth on production factors. Meanwhile, the adjustment of industrial scale and upgrading of the industrial structure are conducive to the highly efficient and healthy development of the fishery economy, thereby reducing the pressure of output growth on ecological carrying capacity. In short, the coupling and coordination between green transformation and the quality of economic development is of vital importance.

According to economic theory, the driving force of economic development comes from factor input and technological progress, of which only technological progress determines the sustainability of economic growth. On this basis, many studies regard total factor productivity (TFP) as an indicator of the quality of economic development, and they are convinced that TFP is a major determinant of coordinated development [18]. Some scholars calculated the TFP of “blue granary”. For example: Francisco (2004) used the distance function to measure the TFP of shrimp farming in Mexico, and the results showed that technological progress was conducive to improving TFP [19]. Vassdal and Holst (2011) used the Malmquist index to measure the total factor productivity of the mariculture industry in Norway and pointed out that TFP had a downward trend [20]. Ji et al. (2020) used the DEA model to measure and analyze the total factor productivity of China’s mariculture industry [21]. Further, some scholars analyzed the influencing factors of TFP in the mariculture industry. Cultivation mode [22], technological progress [23], cultivation scale [24], and marine management level [25] are the core factors affecting TFP. Nevertheless, TFP only explores the coordinated development of ecology and economy from the perspective of production, and it is not systematic and comprehensive.

Other researchers explore coordinated development from the perspective of decoupling. Li et al. (2016) analyzed the decoupling relationship between fishery carbon emissions and industrial development, and the result shows that there is a negative decoupling relationship between them [26]. Zhang and Ji (2020) quantified the eco-economy decoupling relationship in the mariculture industry, and the results indicate that the decoupling relationship has improved, and scale, structure, and technology are the main driving forces [27]. However, these studies use a single indicator to measure economic development or ecological protection, which is not accurate.

Compared with previous studies, the marginal contribution of this research is as follows: Firstly, previous literature mainly focused on empirical analysis and lacked theoretical analysis on the coupling mechanism between green transformation and the quality of economic development of “blue granary”. Secondly, after an in-depth analysis of the connotation of green transformation and the quality of economic development, an index system for evaluating the quality of economic development and the green transformation of “blue granary” is constructed. Finally, the spatial-temporal differences in the coupling coordination relationship between green transformation and the quality of economic development is analyzed. This study is instrumental in identifying the mechanism and evolution of coupling coordination and providing reference value for the precise implementation of eco-economic coordinated development.

2. Theoretical Mechanism

Based on the coupling theory, the quality of economic development and green transformation are not dependent on one another, but are closely related and integrated. The coupling mechanism is embodied in the operation of the eco-economic system of “blue granary” (Figure 1). Green transformation contributes to resource conservation and environmental protection, which supplies the material base for economic development. In terms of the quality of economic development, the goal is to achieve high-quality development, which mainly emphasizes the transformation of growth mode, that is, from extensive growth to intensive growth, such as improving the efficiency of resource utilization, optimizing the industrial structure, and transforming the driving force of economic growth. These changes will also have an impact on green transformation.

2.1. The Impact of Green Transformation on the Quality of Economic Development

The green transformation has the following impacts on the improvement of the quality of economic development:

(1)

Resource effect. “Blue granary” is a typical case of resource-dependent industry, the destruction of the ecosystem will inhibit the improvement of economic efficiency. Firstly, the deterioration of the marine environment, the degradation of fishery resources, and the decline of ecological functions will restrict the improvement of resource utilization efficiency [28]. Secondly, environmental pollution will lead to eutrophication, red tide, and other natural disasters, which will not only cause huge economic losses, but also increase the risks faced by economic development. Thirdly, ecological remediation will also lead to an increase in production costs and a decrease in productivity, which is the paradox between ecology and economy [29]. Therefore, green transformation can promote the quality of economic development by optimizing the quality of input factors, improving economic efficiency, reducing environmental costs and disaster losses.

(2)

Social effect. Marine resources have the property of public goods and externality, which make private costs different from those of society, and the social cost of the “blue granary” is seriously underestimated. It may lead to overexploitation of fishery resources, and unwillingness to carry out green production [30]. The classical Gordon–Schaefer model also gives a similar conclusion (Figure 2). Under the condition of unclear property rights, the maximization of personal profits can only be realized when the total income is equal to the total cost, the output of marine aquatic products is Q3, which far exceeds the output of maximizing social benefits Q1 and also exceeds the maximum output of sustainable development Q2 [31,32,33]. Thus, the green transformation has aroused the concern of society on environmental costs, which helps to reduce the negative externalities and realize the equitable allocation of resources.

(3)

Technical effect. Green transformation contributes to the diffusion of environmental knowledge and green technology. On the one hand, with the enhancement of environmental regulation, pollutant discharge fees will increase, which will force fishery enterprises to increase R&D investment in green technology [34]. Green technology is regarded as the key to realize the coordinated development of ecological economy [35]. On the other hand, green development has changed the traditional mode of development, so that residents pay more attention to environmental protection than output growth, and then, they are motivated to learn more about environmental protection. For this reason, the change of development concept, the accumulation of environmental knowledge, and the diffusion of green technology help to achieve high-quality development. Based on the above analysis, we propose the following research hypotheses:

Hypotheses 1 (H1).

Green transformation improved the quality of economic development through resource effect, social effect, and technological effect.

2.2. The Impact of the Quality of Economic Development on Green Transformation

The improvement of the quality of economic growth will also have an impact on the green transformation, and the mechanism is as follows:

(1)

New momentum of economic growth. The improvement of the quality of economic development intends to take innovation as the main driving force of the output growth of “blue granary”, which helps to reduce the factor input of per unit product and improve efficiency. On this basis, new technologies, new industries, and new business forms generated by innovation will be beneficial to decouple economic development from environment pollution and resource exhaustion [27]. Meanwhile, technical advancement can also improve total factor productivity and provide new methods for green transformation.

(2)

Income distribution effect. The improvement of the quality of economic development emphasizes the reasonable distributions of social income. In fact, “blue granary” can generate huge economic benefits, such as job creation and the growth of social income [36]. According to the Kuznets curve, with the continuous increase of fishermen’s income, the extensive growth mode will be abandoned and replaced by cleaner production [37]. Consequently, the improvement of the quality of economic development is the economic basis of green transformation.

(3)

Scale and structure effect. In terms of the economic scale, the improvement of the quality of economic development will transform the scattered small-scale production into specialized large-scale production. On the one hand, large-scale production reduces the risk of green technology research and solves the problem of insufficient funds [34]. On the other hand, large-scale production promotes the integration of production factors and facilitates the conservation of natural resources [27]. In terms of economic structure, the improvement of the quality of economic development requires increasing the proportion of high-value-added industries, reducing the proportion of industries with excess capacity, decreasing the dependence on coastal resources, and enhancing the proportion of offshore fisheries, that is, achieving coordinated development, realizing industrial transformation and upgrading [38]. The advanced industrial structure is conducive to forming a win–win situation of economy and ecology [39].

(4)

Opening up effect. The improvement of the quality of economic development also underlines the importance of opening up to the outside world. Opening up can provide experience and reference for green transformation. Japan, the United States, Norway, and other marine countries have rich experiences in green technology, marine ecological management, and sustainable development, which can provide a reference for the green transformation of “blue granary”. Based on the above analysis, we propose the following research hypotheses:

Hypotheses 2 (H2).

The quality of economic development affects green transformation through new momentum of economic growth, income distribution effect, scale effect, and opening up effect.

Hypotheses 3 (H3).

There is a coupling coordination between green transformation and the quality of economic development.

3. Materials and Methods

3.1. The Construction of the Evaluation Index System

3.1.1. Index System for the Quality of Economic Development

Based on theoretical analysis, the present study measures the quality of economic development from the perspective of economic efficiency and scale, industrial upgrading, and industrial diversity. Referring to previous studies [18,40], specific indicators are selected as follows (

Table 1. Evaluation index system for the quality of economic development.

Subsystem	Basic Indexes	Evaluation Indexes
The Quality of Economic Development	Economic efficiency and scale	Per capita output of fishermen
		Average annual net income of fishermen
		Productivity of aquaculture areas
		Fishery output
	Industrial upgrading	The proportion of recreational fishing output in fishery output
		Output of distant fishing
		Industrial upgrading index
		Average selling price of export products
		Proportion of export of aquatic products in fishery output
		Proportion of product processing industry in fishery output
		Output of distant fishing/fishery output
		Number of aquatic technology promotion institutions
	Industrial diversity	Diversity of mariculture
		Diversity of marine fishing

).

(1)

Economic efficiency and scale. Improving profits and efficiency are important goals of “blue granary”, which is also the standard to measure the improvement of the quality of economic development. Among them, economic efficiency is measured by the per capita output of fishermen and the productivity of aquaculture areas. Economic benefits are calculated by the average annual net income of fishermen. The economic scale is reflected by the fishery output value.

(2)

Industrial upgrading. Firstly, the rational industrial structure is the inherent requirement of the improvement of the quality of economic development, which is measured by the development of recreational fishing, distant fishing, the aquatic product processing industry, and the industrial upgrading index. Secondly, innovation is an important driving force for industrial upgrading, which is measured by the number of aquatic technology promotion institutions. Thirdly, industrial upgrading is also reflected in the improvement of international competitiveness, which is calculated by the proportion of export of aquatic products in fishery output, and the average selling price of export products.

(3)

Industrial diversity. Industrial diversity is mainly reflected in the diversity of marine aquatic products, measured by the diversity of mariculture products and the diversity of mariculture products.

3.1.2. Index System for Green Transformation

The European Environment Bureau first proposed the DPSIR (Driving-Pressure-State-Impacts-Response) model to evaluate the status of the ecosystem, which combines the advantages of the PSR model and the DSR model. Since both the driving force and pressure derive from economic growth, they are combined to form the PSIR model. Green transformation concentrates on resource conservation and environmental protection. The index system is constructed based on the PSIR model (

Table 2. Evaluation index system for green transformation.

Subsystem	Basic Indexes	Evaluation Indexes
Green Transformation	Pollution discharge	Nitrogen emissions
		Phosphorus emission
		COD emission
		Land pollution discharge
		Fishing vessel pollution (number of fishing vessels)
	Water quality status	Water quality of nearshore waters
	Human positive response	Number of aquatic seed-multiplication farm
	Ecological impact	Red tide area
		Fishery disaster losses

).

Firstly, the pressure on the marine ecological environment mainly comes from the discharge of pollution wastes, including the excessive discharge of nitrogen, phosphorus, COD, land sewage, and pollution from fishing boats. Secondly, the accumulation of pollutants will cause changes in the state of the marine environment, which is manifested as changes in water quality. Thirdly, pollution impacts will result in natural disasters, such as red tides, which will cause huge losses to the fishery industry. To avoid economic losses, people will make positive responses. For example, China emphasizes the construction of aquatic seed-multiplication farms.

3.2. Coupling and Coordination Model

3.2.1. Indicator Normalization

In order to eliminate the influence of index dimension, the original data were standardized. The positive indicators are calculated as follows:

$$y_{ij}=\frac{x_{ij}-\min x_{ij}}{\max x_{ij}-\min x_{ij}}$$

Then, the negative indicators are calculated as follows:

$$y_{ij}=\frac{\max x_{ij}-x_{ij}}{\max x_{ij}-\min x_{ij}}$$

Among them, $y_{ij}$ represents the standardized value of indicator i for evaluation object j, $x_{ij}$ is the original data, $\max x_{ij}$ and $\min x_{ij}$ represent the maximum and minimum values, respectively.

3.2.2. Entropy Weight

The entropy weight method (EWM) calculates the weight of each index by using information entropy, which is widely used in the comprehensive evaluation of multiple indexes [41]. The information entropy is calculated by the following equations:

$$E_j=-\frac{1}{\ln m}{\displaystyle \sum _{i=1}^m{p}_{ij}\ln }{p}_{ij}$$

$$p_{ij}=y_{ij}/{\displaystyle \sum _{i=1}^m{y}_{ij}}$$

The weight value was calculated as follows:

$${\mathrm{w}}_{\mathrm{j}}=(1-E_j)/(n-{\displaystyle \sum _{j=1}^m{E}_j)}$$

3.2.3. Coupling Coordination Degree Model

To study the degree of coordinated development of high-quality development and industrial green transformation, the research uses the coupling coordination degree model to calculate. The specific calculation process is as follows:

$$\{\begin{array}{l}C=2\times \left\{{\left[\raisebox{1ex}{$\left(U_1\times U_2\right)$}\!\left/ \!\raisebox{-1ex}{${\displaystyle \prod \left(U_1+U_2\right)}$}\right.\right]}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}\right\}\\ T=\alpha \times U_1+\beta \times U_2\\ D=\sqrt{C\times T}\end{array}$$

$U_1$ and $U_2$ represent the level of industrial green transformation and the quality of economic development, respectively. C is the coupling degree of industrial green transformation and the quality of economic development. D is the coordination degree of industrial green transformation and the quality of economic development. Since industrial green transformation and the quality of economic development are equally important, both coefficients $\alpha$ and $\beta$ are taken as 0.5.

In reference to previous studies [42,43], the coupling degree model has classified the results into four categories, as shown in

Table 3. Classification of Coupling Degree.

	Classification of Coupling Degree
Results	[0, 0.4]	(0.4, 0.6]	(0.6, 0.8]	(0.8, 1.0]
Grade	Primary Coupling	Middle Coupling	Well Coupling	High Coupling

.

In addition, the coordination degree is classified the results into five categories, as shown in

Table 4. Classification of Coordination Degree.

	Classification of Coordination Degree
Results	[0, 0.2]	(0.2, 0.4]	(0.4, 0.6]	(0.6, 0.8]	(0.8, 1.0]
Grade	Seriously Unbalanced (SU)	Moderately Unbalanced (MU)	Barely Balanced (BB)	Favorably Balanced (FB)	Superiorly Balanced (SB)

.

3.3. Data Sources

We used the data of 10 major “blue granary” construction provinces in China, including Tianjin, Hebei, Liaoning, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Guangxi, and Hainan. Considering the data availability, Shanghai, Taiwan, Macau, and Hong Kong were not included.

4. Results

4.1. Comprehensive Evaluation Results of the Quality of Economic Development of “Blue Granary”

The comprehensive evaluation results of the quality of economic development level of “blue granary” are shown in

Table 5. Evaluation results of the quality of economic development level of “blue granary”.

Province	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	Mean
Tianjin	0.22	0.25	0.27	0.29	0.24	0.25	0.25	0.24	0.26	0.28	0.26
Hebei	0.11	0.13	0.14	0.14	0.14	0.17	0.17	0.17	0.19	0.21	0.16
Liaoning	0.34	0.35	0.37	0.36	0.37	0.42	0.41	0.41	0.47	0.45	0.39
Jiangsu	0.28	0.26	0.30	0.32	0.33	0.35	0.37	0.39	0.43	0.45	0.35
Zhejiang	0.27	0.33	0.41	0.39	0.41	0.50	0.52	0.49	0.53	0.55	0.44
Fujian	0.31	0.35	0.39	0.41	0.42	0.45	0.48	0.47	0.53	0.56	0.44
Shandong	0.30	0.36	0.39	0.40	0.38	0.49	0.55	0.64	0.62	0.62	0.48
Guangdong	0.30	0.31	0.32	0.31	0.31	0.32	0.32	0.33	0.35	0.44	0.33
Guangxi	0.16	0.19	0.21	0.23	0.22	0.24	0.26	0.28	0.30	0.24	0.23
Hainan	0.14	0.15	0.17	0.18	0.17	0.19	0.19	0.20	0.17	0.18	0.17
Mean	0.24	0.27	0.30	0.30	0.30	0.34	0.35	0.36	0.38	0.40

. In terms of the mean score of each province from 2009 to 2018, the mean score of Zhejiang, Fujian, and Shandong exceeded 0.4, higher than that of other provinces, belonging to the first class. The mean scores of Liaoning, Jiangsu, and Guangdong exceeded 0.3, belonging to the medium level. It is not difficult to see that all these provinces are rich in marine resources, which has laid a good foundation for improving the quality of economic development. The average scores of Tianjin, Guangxi, Hebei, Guangxi, and Hainan are below 0.3, which are relatively low. Thus, the quality of economic development of “blue granary” in China shows great regional heterogeneity, and the main reason is that China has a vast coastline of 18,400 km, including the Bohai Sea, the Yellow Sea, the East China Sea, and the South China Sea, the climate conditions range from the temperate zone to subtropical zone. Therefore, different provinces face different constraints and have different resource endowments, resulting in different patterns of economic development.

In terms of the time trend, the mean score of the quality of economic development of the “blue granary” in 2018 was 0.4, higher than that of 0.24 in 2009, showing a continuation of the upward trend. We also calculated the average annual growth rate of the quality of economic development and found that Shandong and Zhejiang have the highest growth rates, with an average annual growth rate of 9.24% and 9.28%, respectively. In line with previous studies, an explanation is that the rapid improvement of the quality of economic development in Shandong province is driven by the progress of fishery technology and the improvement of fishermen’s income [44], and the progress of Zhejiang stems from the country’s emphasis on the East China Sea [27]. The scores of Hebei, Jiangsu, Fujian, Guangdong, and Guangxi also showed an increasing trend, with an average annual growth rate of more than 5%, belonging to the medium level. However, the average annual growth rates of Tianjin, Liaoning, and Hainan are relatively low, all below 5%. We speculate that this might be due to the above provinces paying more attention to growth rate than to development quality. The time trend of the quality of economic development of “blue granary” in different regions is shown in Figure 3a.

4.2. Comprehensive Evaluation Results of the Green Transformation of “Blue Granary”

We describe the comprehensive evaluation results of the green transformation of “blue granary” in

Table 6. Evaluation results of the green transformation of “blue granary”.

Province	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	Mean
Tianjin	0.36	0.35	0.38	0.33	0.32	0.31	0.33	0.41	0.37	0.42	0.36
Hebei	0.43	0.39	0.43	0.40	0.41	0.39	0.40	0.40	0.39	0.47	0.41
Liaoning	0.40	0.43	0.37	0.43	0.43	0.43	0.43	0.46	0.46	0.45	0.43
Jiangsu	0.61	0.61	0.66	0.61	0.61	0.65	0.65	0.65	0.62	0.65	0.63
Zhejiang	0.66	0.67	0.60	0.60	0.63	0.67	0.67	0.70	0.70	0.70	0.66
Fujian	0.40	0.40	0.40	0.43	0.46	0.46	0.49	0.49	0.49	0.48	0.45
Shandong	0.74	0.74	0.73	0.70	0.81	0.85	0.82	0.89	0.89	0.89	0.81
Guangdong	0.51	0.49	0.49	0.52	0.51	0.62	0.60	0.60	0.57	0.60	0.55
Guangxi	0.51	0.51	0.51	0.52	0.53	0.51	0.55	0.55	0.55	0.55	0.53
Hainan	0.51	0.49	0.52	0.52	0.51	0.50	0.53	0.52	0.52	0.52	0.52
Mean	0.51	0.51	0.51	0.51	0.52	0.54	0.55	0.57	0.56	0.57

, which hint that: the mean score of Shandong is 0.81, higher than that of other coastal provinces in China, belonging to the first echelon. The average scores of Jiangsu and Zhejiang are 0.63 and 0.66, respectively, which belong to the medium level. The average scores of other coastal provinces are in the range of 0.36 to 0.55, which is relatively low. We confirm that there exist statistically significant differences in the level of green transformation.

The average annual growth rate of green transformation is shown in Figure 3b. We showed that: on the one hand, the overall level of green transformation has been improved, but the extent of improvement was not significant. The main reason for such a problem is that China’s coastal resources have been fully exploited and are facing serious environmental pollution, unsustainability has gradually emerged, and the intensity of marine environmental regulation in all provinces has been enhanced. However, marine environmental regulation represented by the charge of sea area utilization has yet to be developed, there is still a lack of supervision system, and the effect of marine environmental regulation is greatly reduced. On the other hand, the growth rate of each province varies greatly. Shandong and Fujian have the highest growth rates, with an average annual growth rate of 2.28% and 2.41%, respectively. The scores of Tianjin, Hebei, Liaoning, and Guangdong also showed an increasing trend, with an average annual growth rate of below 2%, belonging to the medium level. Moreover, the average annual growth rates of Jiangsu, Zhejiang, Guangxi, and Hainan are all below 1%. The results imply that the state of the marine ecological environment in all regions has great potential for improvement.

4.3. Coupling and Coordination of Green Transformation and the Quality of Economic Development

The results of the coupling degree of “blue granary” are shown in

Table 7. The results of the coupling degree of China’s “blue granary”.

Province	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018
Tianjin	0.97	0.99	0.99	1.00	0.99	0.99	0.99	0.97	0.98	0.98
Hebei	0.81	0.86	0.86	0.88	0.87	0.91	0.91	0.91	0.93	0.93
Liaoning	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
Jiangsu	0.93	0.91	0.93	0.95	0.96	0.96	0.96	0.97	0.98	0.98
Zhejiang	0.91	0.94	0.98	0.98	0.98	0.99	0.99	0.98	0.99	0.99
Fujian	0.99	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
Shandong	0.91	0.94	0.95	0.96	0.93	0.96	0.98	0.99	0.98	0.98
Guangdong	0.96	0.98	0.98	0.97	0.97	0.95	0.96	0.96	0.97	0.99
Guangxi	0.86	0.89	0.91	0.92	0.92	0.94	0.93	0.94	0.95	0.92
Hainan	0.82	0.85	0.86	0.87	0.87	0.89	0.88	0.89	0.87	0.88

. The relationship between green transformation and the quality of economic development of the “blue granaries” shows a high-level coupling state, which infers that there is a strong interaction between green transformation and the quality of economic development. This result ties well with the theoretical analysis above.

The results of the coordination degree of “blue granaries” are shown in

Table 8. The results of the coordination degree of “blue granaries”.

Province	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	Mean
Tianjin	0.5	0.5	0.6	0.6	0.5	0.5	0.5	0.6	0.6	0.6	0.6
Hebei	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.6	0.5
Liaoning	0.6	0.6	0.6	0.6	0.6	0.7	0.6	0.7	0.7	0.7	0.6
Jiangsu	0.6	0.6	0.7	0.7	0.7	0.7	0.7	0.7	0.7	0.7	0.7
Zhejiang	0.7	0.7	0.7	0.7	0.7	0.8	0.8	0.8	0.8	0.8	0.8
Fujian	0.6	0.6	0.6	0.7	0.7	0.7	0.7	0.7	0.7	0.7	0.7
Shandong	0.7	0.7	0.7	0.7	0.7	0.8	0.8	0.9	0.9	0.9	0.8
Guangdong	0.6	0.6	0.6	0.6	0.6	0.7	0.7	0.7	0.7	0.7	0.7
Guangxi	0.5	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6
Hainan	0.5	0.5	0.5	0.6	0.5	0.6	0.6	0.6	0.5	0.6	0.6
Mean	0.6	0.6	0.6	0.6	0.6	0.7	0.7	0.7	0.7	0.7

. From the perspective of time trends, it can be roughly divided into two stages. The first stage was from 2009 to 2014, and the mean value of the coupling coordination degree was 0.6, which belonged to the barely balanced stage, indicating that the green transformation and the quality of economic development coexist and mutually promote each other, but the degree is insufficient. The second stage was from 2015 to 2018. The mean coordination degree is 0.7, which belonged to the favorably balanced stage. It indicates that the quality of economic development drives green transformation, and vice versa. Furthermore, the coordination degree is significantly improved. Simply put, the coordination of “blue granary” has steadily improved, but it has not reached the ideal state of superiorly balanced.

From the perspective of the mean value of 2009–2018, Tianjin, Hebei, and Hainan are on the verge of imbalance. It indicates that we should maintain a cautious attitude and take measures to improve the coordination of development; otherwise, it may face a serious imbalance in eco-economic development. Liaoning and Guangxi are in a state of bare coordination, while Jiangsu, Zhejiang, Fujian, and Guangdong are in a stage of primary coordination. Although the above-mentioned provinces have got rid of the imbalance, there is still much room for improvement in the coordination of the eco-economic system. Shandong is at the favorably balanced stage, which is better than other provinces; it indicates that Shandong has taken into account the quality of economic development and green transformation in its development, but it has not yet formed an ideal state of being superiorly balanced.

5. Discussion

Taking “blue granary” as the research object, the present study theoretically analyzes the coupling relationship and driving mechanism between green transformation and the quality of economic development. The quality of economic development is evaluated from three dimensions of economic efficiency and scale, industrial upgrading, and industrial diversity. The index system of green transformation is constructed based on the PSIR model. On this basis, the research studies the spatial-temporal evolution of coupling coordination between green transformation and the quality of economic development. Under the contradiction between ecological constraints and the goal of ensuring food security, our research is crucial to exploring the path of sustainable development.

Firstly, there are few studies on the coupling relationship between green transformation and the quality of economic development of “blue granary”, and only a few are focused on theoretical analysis. Given this, starting from the concept of green transformation and the quality of economic development, analyzing the operating mechanism of the eco-economic system of “blue granary”, and studies the interaction between green transformation and the quality of economic development. It is found that: on the one hand, green transformation affects the quality of economic development through resource effect, social effect, and technological effect; on the other hand, the quality of economic development affects green transformation through the new momentum of economic growth, the income distribution effect, scale effect, and opening up effect. It is worth noting that the combination of theoretical analysis and quantitative analysis increases the reliability of conclusions.

Secondly, through the research, we also find that the existing literature mostly uses a single dimension or single indicator to measure the quality of economic development, such as total factor productivity [19,20,21,22,23,24,25], but the total factor productivity only analyzes from the perspective of the input–output of the production, ignoring the momentum of development and economic structure. Similarly, the original research also used pollution emissions to measure green transformation, which is a stock indicator, and the index system based on the PSIR model is more comprehensive and systematic. Notably, some articles have also built the evaluation index system to analyze the quality of economic development of “blue granary”, but the indicators are selected mainly based on the definition of high-quality development in China’s policy documents, namely, “innovation, coordination, greenness, openness and sharing” [40]. In this paper, the definition of the quality of economic development is following the economic theory, and indicators are selected from the perspective of economic efficiency and scale, industrial upgrading, and industrial diversity. Thus, it enriches and completes the research on the evaluation of green transformation and the quality of economic development.

Thirdly, previous studies often used the decoupling elasticity index and green total factor productivity to study the relationship between ecology and economy. These methods regard ecology and economy as two independent things and neglect the mutual integration between ecological protection and economic development [26,27]. For example, green total factor productivity adds unexpected output to reflect the impact of environmental pollution on economic efficiency, but the emphasis is still on the economic production process [2]. In this research, the coupling coordination model is used to further study the interactions between them. The implication of the coupling and coordination relationship is that we should jointly consider ecology and economy during policy development [45].

Finally, it should be pointed out that the paper still has many defects and deficiencies. On the one hand, due to the availability of data, this paper uses provincial panel data of 10 coastal provinces in China from 2009 to 2018. If micro-data is used, the conclusions may be more abundant and practical. On the other hand, scholars have not reached a consensus on the calculation method of pollution emissions from the “blue granary”; we will further improve the measurement of the ecological environment impact of “blue granary” in the future, to make the results more accurate.

6. Conclusions

This research constructs the theoretical framework of the coupling mechanism between green transformation and the quality of economic development. On this basis, the quality of economic development is evaluated from three dimensions of economic efficiency and scale, industrial upgrading, and industrial diversity, and the index system of green transformation is constructed based on the PSIR model. Using data of 10 major “blue granary” construction provinces in China, the entropy approach and coupled coordination degree model are adopted to explore the spatial-temporal evolution of the coordination between green transformation and the quality of economic development. The main conclusions are as follows:

(1)

There is a coupling relationship between green transformation and the quality of economic development. Concretely, green transformation and the quality of economic development are interrelated; green transformation affects the quality of economic development through resource effect, social effect, and technological effect, while the quality of economic development affects green transformation through the new growth momentum effect, income distribution effect, scale effect, and opening up effect. We suggest that the integration of high-quality development and green transformation is the path to sustainable development.

(2)

The overall level of green transformation and the quality of economic development continues to improve. In comparison, the improvement in the quality of economic development level is significant, but the improvement in green transformation is relatively low, reflecting that the administration still puts more emphasis on the economy compared to ecological protection. In addition, there is regional heterogeneity in the quality of economic development and ecological protection in different provinces. In terms of the quality of economic development, Zhejiang, Fujian, and Shandong are at a high level; Liaoning, Jiangsu, and Guangdong take second place; and Tianjin, Guangxi, Hebei, Guangxi, and Hainan are relatively low. In terms of green transformation, Shandong belongs to the first class, Jiangsu and Zhejiang belong to the medium level, and other coastal provinces belong to the third level. A direct policy implication is that the government should continue to pay more attention to environmental protection and treat ecology and economy equally, so as to better promote the integrated development of green transformation and high-quality development

(3)

The coupling coordination level of “blue granary” can be divided into two stages. It belongs to the barely coupling coordination stage from 2009 to 2014, and to the primary coupling coordination stage from 2015 to 2018. The coordination level has steadily improved, but it has not reached the ideal state of being superiorly balanced. The coupling coordination level of different regions varies considerably. Tianjin, Hebei, and Hainan are on the verge of imbalance. Liaoning and Guangxi are barely coordinated; Jiangsu, Zhejiang, Fujian, and Guangdong are primarily coordinated; and Shandong is favorably balanced. The results identify the difference in coordination levels in different regions and provide a reference value for the precise implementation of high-quality development and green transformation.