Aquaculture industry under the blue transformation in Jiangsu, China: Structure evolution and spatial agglomeration

Shun Wu; Tao Xiong; Chen Sun

doi:10.1515/geo-2022-0531

Artikel Open Access

Aquaculture industry under the blue transformation in Jiangsu, China: Structure evolution and spatial agglomeration

Shun Wu , Tao Xiong und Chen Sun

Veröffentlicht/Copyright: 6. November 2023

Veröffentlicht von

Veröffentlichen auch Sie bei De Gruyter Brill

Manuskript einreichen Informationen für Autor*innen

Aus der Zeitschrift Open Geosciences Band 15 Heft 1

Abstract

This article focuses on studying the spatial distribution and evolution of the aquaculture industry in Jiangsu, a significant coastal province in China, within the context of the blue transformation. By collecting spatial data on the aquaculture industry and using ArcGIS software, this article analyses the characteristics of spatial changes and the driving forces behind them in Jiangsu’s aquaculture industry while providing predictions for future pattern evolution. The findings reveal that the overall distribution of the aquaculture industry in Jiangsu Province exhibits strong directionality but weak density, primarily oriented in the northwest–southeast direction. There is an evident trend of the industry shifting from the northern to the western regions and from the central to the northwest areas of the province. This indicates that initially, the aquaculture industry was primarily concentrated in the inland regions, while marine aquaculture gradually influenced the industry structure after 2010. Although the aquaculture industry shows signs of diversification, industry agglomeration is only observed in approximately 30.8% of the cities that exhibit a positive spatial correlation, which is a relatively small proportion compared to the total number of cities. However, despite the overall negative correlation in spatial distribution, the absolute difference between Moran’s I of all cities and zero is less than 0.4. This suggests that the spatial differences are not significant, regardless of the spatial autocorrelation characteristics of the overall distribution of a city. Based on the findings, this article recommends the urgent need for the development of policies that promote industry agglomeration in order to achieve effective regulation and pollution control in aquaculture.

Keywords: Moran’s I; aquaculture industry; ArcGIS; spatial changes; pattern evolution

1 Introduction

Fisheries refer to the industry involved in the commercial harvesting of aquatic organisms such as fish, shellfish, and algae through fishing or aquaculture. China, with the tenth longest coastline in the world, has a rich history in fisheries and plays a significant role in its economy [1]. Since the introduction of economic reforms and liberalization policies, China’s tertiary industry has witnessed a continuous increase in its contribution to the overall economy. As a result, related industries, including fisheries, have become highly developed, enabling the large-scale production of various aquatic products across multiple regions. In 2021, China’s marine production value reached 9.0385 trillion yuan, marking an 8.3% increase compared to 2020 and emphasizing the economic importance of marine fisheries [2]. Over the years, the fisheries sector has received government support, leading to the formulation of favourable policies and regulations by coastal cities and relevant authorities to foster its growth. However, it is important to note that most of these policies have primarily focused on resource consumption without considering the potential damage caused by overfishing. In recognition of the finite nature of marine resources, both individuals and governments have come to realize that unregulated development can lead to unsustainable economic practices, resulting in long-term and irreversible losses for the nation and humanity as a whole. Consequently, various cities in China have placed great importance on the concept of “blue transformation” as a means to achieve sustainable development of marine resources.

The concept of “blue transformation” encompasses a series of measures aimed at addressing ecological degradation caused by overfishing, pollution, inadequate resource management, low value-added utilization, and ensuring equitable access to food for residents while preserving biodiversity within ecosystems. These measures seek to promote innovative practices that increase the output and utilization rates of aquatic products, while simultaneously safeguarding the well-being of both human and natural systems. In order to steer marine fisheries towards a sustainable path, relevant government bodies have implemented efforts to promote new knowledge, tools, and practices conducive to the transition. These initiatives include regulating existing industries and providing policy support and subsidies for emerging sectors such as recreational fishing. However, it is important to acknowledge that while these policies have accelerated the progress towards sustainability, they have also had unintended consequences. For instance, the rise of new industries has led to the dispersal of the industrial structure and a decrease in economies of scale. Additionally, research has found that the increasing demand for environmental protection standards has led industries to relocate to areas with less stringent regulations [3].

To provide insights into the current state of the industry and future prospects, this article examines and analyses the spatial distribution and evolution of the aquatic industry at the municipal level, using Jiangsu Province as a case study within the context of the “blue transformation”. Prior to the development of information technology, spatial distribution and evolution studies of spatial units relied heavily on qualitative analysis methods, such as on-site visits and questionnaire surveys, which were limited by data availability and time constraints [3]. However, with advancements in information technology and Geographic Information System (GIS), the use of big data combined with mathematical models has become more popular for studying the properties of spatial units [4]. For example, studies have used geospatial methods to analyse the spatial relationships between different industries and variables, such as price elasticity in the selection of Airbnb locations [5], and the impact of technological progress and financial support policies on vegetable production layout [6]. Additionally, the fairness of the service industry has been examined through spatial accessibility analysis of healthcare facilities in different areas [7].

2 Literature review

In previous studies, scholars have used various qualitative analysis methods to investigate the spatial distribution characteristics and influencing factors of fishery enterprises, such as questionnaire surveys and in-depth interviews [8,9,10]. These studies mainly focus on the reasons for site selection of fishery enterprises, locational preferences, and the extent of their engagement with supply chains and markets. For instance, St. Martin [8] proposed a qualitative framework that combines expert opinion and the analytic hierarchy process to analyse alternative spatial management options in coastal fisheries. The framework considered multiple objectives inherent in the geographic location choices of fishery enterprises and fishery resources.

With the abundance of economic and statistical data, spatial economic analysis methods have gradually emerged in the research on the spatial distribution of fishery enterprises, providing new paradigms for macroeconomic analysis. Daw [11] studied the lobster fisheries of the Corn Islands, Nicaragua. The study combined spatial models and qualitative interviews that can effectively analyse the relationship between the spatial distribution of fisheries and influencing factors. These models combined approaches from economics and geography, considering spatial correlation and spatial dependence [12–16]. For example, Graziano et al. [13] used a spatial regression model to examine the application of the Blue Economy paradigm in the Great Lakes basin. The research results demonstrate significant impacts of marine environmental conditions and resource availability on the spatial performance of fishery economy.

With the richness of spatiotemporal data at the regional level, quantitative analysis methods incorporating GIS technology have been widely applied in the micro-level spatial distribution research of fisheries. GIS is regarded as a powerful tool for processing and analysing spatial data of fisheries [17,18]. Through GIS analysis, scholars have discovered the association between the spatial distribution of fishery enterprises and geographic factors such as terrain, water bodies, and population, revealing the site selection and distribution patterns of fisheries [19]. Subsequently, researchers have used abundant spatial data and mathematical models to analyse the distribution patterns, agglomeration levels, and regional differences of fisheries. For instance, Panthi and Hodar [20] conducted a study on how to use GIS technology to understand the spatial data of fisheries sector. The study also found distinct spatial agglomeration characteristics of fishery enterprises, influenced by geographical location, resources, and market factors.

To investigate the spatial distribution patterns and agglomeration levels of fisheries, Moran et al. [21] introduced the Moran’s I index as a commonly used indicator for assessing spatial correlation in fishery enterprise studies. By calculating the Moran’s I index, it is possible to understand the degree of spatial association, i.e. agglomeration or dispersion, among fishery enterprises. Although the growth of spatial research in the fishery industry can be observed, there is currently a relatively limited study on regional performance in China. Yang et al. [22] studied the spatial distribution characteristics and agglomeration of fishery enterprises in Shanghai using the Moran’s I index and spatial autocorrelation models. The study revealed a significant spatial agglomeration phenomenon of fishery enterprises in Shanghai, closely related to factors such as environmental governance, market demand, and transportation networks. Additionally, Wu et al. [23] conducted a spatial distribution study on recreational fisheries (LF). The study took Zhejiang Province, an important coastal province in China, as a case and used ArcGIS spatial analysis tools to calculate indicators such as nearest neighbour index, outlier analysis, kernel density analysis, clustering, and coefficient of variation to analyse the spatial distribution characteristics and influencing factors of LF units in Zhejiang Province. The research results showed significant variations in the distribution density of LF units among cities in Zhejiang Province. The location of transportation, markets, and scenic areas had a significant impact on the distribution of LF units in Zhejiang Province. Additionally, population and economic factors also played a role in the spatial distribution of LF units. These two studies provide important references for our research, as we select the aquaculture industry as the research subject for spatial analysis for the first time.

3 Methods

3.1 Research area

Jiangsu Province, situated on China’s eastern coast, holds historical significance and serves as a major hub for the country’s economy and trade. With extensive water systems covering a total inland area of 17,300 km², a coastline spanning 954 km, and an approximate marine area of 37,500 km², the province boasts abundant natural resources [24]. As one of the pioneering provinces in China’s blue transformation, Jiangsu has implemented several measures to ensure the aquaculture industry’s sustainable development. To investigate the aquaculture industry in Jiangsu, this study collected data from 2,745 enterprises and used various methods and mathematical models, using software packages such as ArcGIS and Python for spatial analysis. The research adopted a comprehensive approach, analysing the overall spatial distribution of aquaculture using techniques such as standard deviation ellipses (SDEs) and kernel density analysis. Furthermore, spatial clustering and autocorrelation analyses were conducted at the city level in Jiangsu, using measures such as the nearest neighbour index, spatial Gini index, and Moran’s I. These analyses provide valuable insights into the spatial distribution trends and patterns of the aquaculture industry in Jiangsu Province, contributing to the promotion of its sustainable development.

3.2 SDE

The SDE is a widely used geospatial method that allows for the visualization of data distribution characteristics by transforming a series of spatial coordinates into an ellipse parameter [25]. Using the analysis of the SDE, researchers focus on four important parameters: the major axis, minor axis, orientation angle, and centroid [26]. The major and minor axes provide information about the directionality and density of the data, while the orientation angle and centroid represent the centre of the data distribution and the overall preferred direction. The calculation method for determining the SDE involves several steps, including the calculation of the covariance matrix, eigenvalue decomposition, and the utilization of statistical formulas. These calculations enable the derivation of the ellipse’s parameters, allowing for a comprehensive understanding of the spatial distribution characteristics of the data. The specific formulas are as follows:

(1) tan ⁡ θ = ∑ n = 1 k ( x n − x ̅ ) 2 − ∑ n = 1 k ( y n − y ̅ ) 2 + ∑ n = 1 k ( x n − x ̅ ) 2 − ∑ n = 1 k ( y n − y ̅ ) 2 2 + 4 ∑ n = 1 k ( x n − x ̅ ) ∑ n = 1 k ( y n − y ̅ ) 1 / 2 × 2 ∑ n = 1 k ∑ n = 1 k ( x n − x ̅ ) ∑ n = 1 k ( y n − y ̅ ) σ x = ∑ n = 1 k [ ( x n − x ̅ ) cos ⁡ θ − ( y n − y ̅ ) sin ⁡ θ ] 2 / k σ y = ∑ n = 1 n [ ( x n − x ̅ ) sin ⁡ θ − ( y n − y ̅ ) cos ⁡ θ ] 2 / k , .

where ( x n , n ) represents the nth coordinate in the set of coordinates, θ represents the orientation angle of the SDE, and y ̅ and x ̅ represent the weighted average centre.

3.3 Kernel density estimation (KDE)

Kernel density analysis, also known as KDE, is a geospatial method commonly used to identify the distribution pattern of a series of coordinate points. Unlike SDEs, kernel density analysis allows for the visualization of the distribution pattern of a coordinate set at a more granular level [27]. The calculation involves selecting a kernel function, such as the Gaussian kernel, and determining the bandwidth parameter to control the scale of neighbouring points considered. The resulting density surface helps identify hotspots, clusters, and areas with sparse data. Kernel density analysis is valuable for resource allocation, planning, and risk assessment in fields such as ecology, criminology, and urban planning. It can be combined with other spatial analysis techniques for a comprehensive understanding of spatial dynamics.

(2) f ( x ) = 1 nm ∑ i = 1 n F x − x i m ,

where “m” represents the search radius, “n” represents the number of study units within the search radius, and “F” represents the kernel density function.

3.4 Nearest neighbour index

The nearest neighbour index is a powerful tool for studying the spatial characteristics of the fisheries industry. By applying the nearest neighbour index to the analysis of fishery data, researchers can gain valuable insights into the clustering patterns and spatial distribution of fishery-related activities. The nearest neighbour index is a metric used to assess the clustering tendency of a set of coordinate points by measuring the average distance between each point and its nearest neighbour and comparing it to the expected distance in a random distribution. It provides insights into the spatial pattern and degree of clustering within the dataset. The formula for calculating the nearest neighbour index is as follows:

(3) ANN = d ̅ A 4 n ,

where d ̅ = 1 / n ∑ i = 1 n d i , in which d i is the distance between the i coordinate point and its nearest neighbour coordinates.

3.5 Spatial Gini index

The spatial Gini coefficient is a data analysis method that combines the Lorenz curve with the traditional, economically significant Gini coefficient to measure spatial agglomeration [28]. Additionally, the spatial Gini coefficient can be used to track changes in the spatial distribution of aquaculture farms over time. By comparing the spatial Gini coefficient values across different time periods, researchers can identify temporal shifts in the concentration or dispersion patterns of aquaculture activities [29]. This information is valuable for monitoring the dynamics of the aquaculture industry, evaluating the impact of policies and interventions on spatial distribution, and guiding decision-making processes related to site selection and resource allocation for aquaculture development. The spatial Gini coefficient and the economic Gini coefficient share similar characteristics, such as a range of values between 0 and 1. The calculation method of the spatial Gini coefficient is as follows:

(4) G = ∑ i = 1 n ( K i − X i ) 2 ,

where K i represents the ratio of the employment number of the i th enterprise in the Jiangsu aquaculture industry to the total employment number in Jiangsu Province, while X i represents the ratio of the total employment number in Jiangsu Province to the total employment number in China. The value of the spatial Gini coefficient indicates the degree of spatial agglomeration, with a higher value indicating a more significant agglomeration phenomenon.

3.6 Moran’s index

Moran’s I is a powerful tool for studying the spatial autocorrelation and clustering of geographic units within a set of coordinates, making it an essential method in spatial analysis, including the study of aquaculture industry distribution. One advantage of using Moran’s I in the analysis of aquaculture industry spatial distribution is its ability to quantify the degree of spatial autocorrelation. By calculating Moran’s I, researchers can determine whether the observed distribution of aquaculture farms exhibits clustering (positive autocorrelation) or dispersion (negative autocorrelation) patterns [30]. This information provides insights into the spatial dependence and arrangement of aquaculture activities, highlighting areas with similar or dissimilar characteristics in terms of aquaculture presence and production. The specific formula is as follows:

(5) I = n W ∑ i = 1 n ∑ j = 1 n w ij ( x i − x ̅ ) ( x j − x ̅ ) ∑ i = 1 n ( x i − x ̅ ) 2 ,

where n is the total number of geographic units in the dataset; W is the spatial weights matrix, which defines the spatial relationship between each pair of geographic units; x i and x j are the attribute values of the i th and j th geographic units, respectively; x ̅ is the mean attribute value of all geographic units; and w ij is the spatial weight between the i th and j th geographic units, indicating the strength of their spatial relationship.

4 Results

4.1 Distribution of aquaculture industry in Jiangsu Province

4.1.1 SDE

Using the collection of enterprise data from the aquaculture industry in Jiangsu Province during the years 2000, 2010, and 2021, SDEs were generated to analyse the spatial distribution of industry (refer to Figure 1). Examination of these ellipses revealed notable characteristics in the overall distribution of the aquaculture industry in Jiangsu Province. Specifically, the industry exhibited a distinct directionality with weak density. The distribution centre was primarily concentrated in the central region of Jiangsu Province and the Yangzhou area. The general distribution trend of Jiangsu Province showed a northwest–southeast orientation. Comparing the distribution centres across the years, it was observed that the centre of the aquaculture industry in Jiangsu Province displayed a tendency to shift towards the northwest. Furthermore, significant changes in the distribution pattern were identified between 2000, 2010, and 2021, particularly in the long-axis direction, which shifted approximately 25° northward. The period from 2000 to 2010 marked the Chinese government’s increased emphasis on sustainable development, coinciding with the full implementation of the Yangtze River fishing ban system in 2003. Thus, a correlation can be inferred between sustainable development policies and the evolving pattern of the aquaculture industry in Jiangsu Province.

Figure 1

SDE evolution in Jiangsu Province.

4.1.2 KDE

Figure 2 presents a comprehensive visualization of the kernel density function (KDF) for Jiangsu’s aquaculture industry in 2021, using a 40 km grid resolution. The geographical layout of Jiangsu Province exhibits a distinct concentration of the coastline in the northern and eastern regions, while the western and southern areas are adjacent to the inland. By closely examining the distribution pattern of the aquaculture industry from a micro-perspective, Figure 2 offers valuable insights into its spatial characteristics.

Figure 2

Kernel density map of aquatic farming industry in Jiangsu Province.

The KDF plot reveals that the aquaculture industry in Jiangsu Province primarily manifests in the inland regions, with notable clusters and intensities observed. Among them, Nanjing city emerges as a significant hotspot, exhibiting the highest KDF value. This concentration suggests a pronounced presence and strong development of the aquaculture industry in this particular area. In contrast, the KDF values along the eastern coastline generally remain below 0.011, indicating a relatively sparse distribution of aquaculture activities and an overall underdeveloped state in the coastal regions.

It is noteworthy that freshwater aquaculture in Jiangsu Province predominantly takes the form of pond culture, accounting for a substantial portion of the industry. In 2020, the pond area encompassed a significant 316,000 ha, representing approximately 75.2% of the total freshwater aquaculture area. This highlights the significance of pond culture as the prevailing method for freshwater aquaculture production in the province.

To fully leverage the advantages of coastal cities in Jiangsu Province, it is imperative to explore the potential of marine aquaculture. While the inland areas have shown a robust development in the aquaculture industry, the coastal regions present an opportunity for further growth and diversification. Developing marine aquaculture in these areas can enhance the overall productivity and economic potential of the industry.

Furthermore, in order to enhance the vitality and competitiveness of the aquaculture market, it is crucial to foster efficient competition among industry players and promote the development of differentiated products. By encouraging innovation and product diversification, the aquaculture industry can cater to diverse market demands and sustain long-term growth.

In conclusion, Figure 2 and the analysis of the aquaculture industry in Jiangsu Province shed light on its spatial distribution and development patterns. While the inland regions exhibit concentrated clusters of aquaculture activities, there is a notable opportunity for growth and development in the coastal areas through the exploration of marine aquaculture. By fostering competition and product differentiation, the industry can further enhance its market vitality and contribute to the sustainable development of Jiangsu’s aquaculture sector.

4.2 Clustering of Jiangsu Province’s aquaculture industry

4.2.1 Nearest neighbour index analysis

Table 1 presents the nearest neighbour index statistics, which offer valuable insights into the spatial clustering characteristics of the aquaculture industry in various cities of Jiangsu Province. The average nearest neighbour ratio (ANN) for all cities in Jiangsu Province is observed to be less than 1, indicating a prevalent state of spatial clustering. A value of ANN below 1 signifies that the average observed distance between aquaculture enterprises is smaller than the expected average distance in a random distribution. This suggests that the aquaculture industry in Jiangsu Province tends to exhibit a clustered pattern, with enterprises located in close proximity to one another. Among the cities in Jiangsu Province, Nanjing and Yancheng exhibit the most pronounced agglomeration effects. This implies that the aquaculture enterprises in these cities are densely concentrated within specific geographic areas, fostering strong interconnections and potential synergies. On the other hand, Nantong stands out as having the most sparse distribution of aquatic farming, with aquaculture enterprises scattered over larger distances. In particular, Nantong’s average enterprise distance of 5,182 m and a nearest neighbour ratio of 0.85 reflect a relatively lower degree of spatial clustering compared to other cities in Jiangsu Province. This indicates that the aquaculture industry in Nantong experiences a more dispersed distribution, with enterprises being relatively farther apart.

Table 1

Nearest neighbour indices for cities in Jiangsu Province

City	Mean inter-observation distance	Expected mean distance	Nearest-neighbour ratio	Z-score	P-value
Zhenjiang	2866.934072	3509.399228	0.81693	−3.431494	0.0006
Yangzhou	3777.628575	5087.471163	0.742536	−4.150278	0.000033
Yancheng	2996.013474	5512.68091	0.543477	−10.516661	0
Xuzhou	2289.507634	3855.224672	0.593871	−13.299285	0
Wuxi	2561.664642	3717.853869	0.689017	−6.211275	0
Taizhou	3725.840105	4706.283062	0.791674	−3.188344	0.001431
Suzhou	2232.674779	3770.908797	0.592079	−10.642992	0
Suqian	3354.74657	5027.4657	0.667284	−7.05923	0
Nantong	5182.373452	6124.241438	0.846207	−2.564929	0.01032
Nanjing	1323.823603	2688.85932	0.492337	−19.179581	0
Lianyungang	2035.057977	3493.514517	0.582525	−11.322957	0
Huaian	3166.447143	4471.154358	0.708195	−7.170778	0
Changzhou	2394.204231	3887.151212	0.615928	−7.347572	0

These findings highlight the spatial variations in the clustering tendencies and distribution patterns of the aquaculture industry across different cities in Jiangsu Province. Understanding such spatial characteristics is crucial for policymakers and industry stakeholders to effectively plan and implement targeted interventions for sustainable development and resource optimization in the aquaculture sector.

4.2.2 Spatial Gini coefficient

Table 2 provides an overview of the spatial Gini coefficient analysis, which offers insights into the agglomeration characteristics of the aquaculture industry in different cities of Jiangsu Province. The Gini coefficient (G) measures the degree of industry concentration, with higher values indicating a greater tendency for clustering. In Table 2, Nanjing, Xuzhou, and Nantong have the highest G values, indicating significant agglomeration effects in these cities.

Table 2

Spatial Gini coefficient of cities in Jiangsu Province

City	Gini coefficient
Changzhou	0.7796
Huaian	0.82666667
Lianyungang	0.797951
Nanjing	0.875468
Nantong	0.855263
Suqian	0.851543531
Suzhou	0.74955384
Taizhou	0.74365234
Wuxi	0.742025
Xuzhou	0.860651
Yancheng	0.85736
Yangzhou	0.802618528
Zhenjiang	0.71006944

The finding of a substantial agglomeration effect in Nanjing aligns with the results obtained from the spatial autocorrelation analysis. However, there is a discrepancy regarding the agglomeration effect in Nantong between the two statistical methods. This discrepancy may arise from the limitations of the spatial Gini coefficient, as it can yield a high coefficient if a large enterprise exists in an area, even without an actual clustering phenomenon. Thus, while the aquaculture industry in Nantong demonstrates notable economic agglomeration, there is no evident spatial clustering phenomenon.

4.2.3 Spatial autocorrelation

Moving on to Table 3, it presents the Moran’s I index and related statistics for each city in Jiangsu Province. The Moran’s I index measures spatial autocorrelation, reflecting the similarity or dissimilarity of aquaculture enterprises across geographic units. Zhenjiang, Xuzhou, Nantong, and Huai’an exhibit a positive spatial autocorrelation, indicating a tendency for similar enterprises to cluster together in these cities. Conversely, the remaining cities in the aquaculture industry show a negative spatial autocorrelation, suggesting a dispersion of dissimilar enterprises.

Table 3

Moran’s I in each city of Jiangsu Province

City	Moran’s I	Expected index	Variance	Z-Score	P-value
Changzhou	−0.037044	−0.2	0.064336	0.642458	0.520576
Huaian	0.207846	−0.166667	0.108088	1.13914	0.254645
Lianyungang	−0.324239	−0.2	0.068155	−0.475892	0.634151
Nanjing	−0.019873	−0.1	0.015133	0.651348	0.514822
Nantong	0.076023	−0.142857	0.062567	0.875055	0.381544
Suqian	−0.17086	−0.25	0.067946	0.303611	0.761424
Suzhou	−0.197988	−0.125	0.04926	−0.328854	0.742266
Taizhou	−0.352692	−0.2	0.02716	−0.926515	0.354179
Wuxi	−0.137475	−0.166667	0.00552	0.392908	0.694387
Xuzhou	0.012063	−0.111111	0.038912	0.624424	0.532349
Yancheng	−0.1453	−0.125	0.111566	−0.060774	0.951539
Yangzhou	−0.154736	−0.2	0.036422	0.237175	0.812521
Zhenjiang	0.164487	−0.2	0.045714	1.704731	0.088245

Among the cities, Nanjing stands out with a Moran’s I value of −0.019 and a z-score of 0.65. This indicates an overall negative spatial autocorrelation in Nanjing, but with a limited spatial variation among the aquaculture enterprises. These findings provide valuable insights into the spatial agglomeration and autocorrelation patterns of the aquaculture industry in Jiangsu Province. Understanding these patterns can guide policymakers and industry stakeholders in making informed decisions to promote sustainable development, enhance collaboration, and optimize resource allocation in the aquaculture sector.

5 Discussion

The findings of this study offer valuable insights into the spatial distribution of the aquaculture industry in Jiangsu Province and provide important implications for policy and industry stakeholders. The results reveal that the industry exhibits distinct directional characteristics and moderate density, with the distribution centre predominantly located near the centre of Jiangsu Province and the Yangzhou area. Furthermore, there is a noticeable north-westward shift in the distribution centre, which can be attributed to the sustainable development policies implemented by the Chinese government during the period from 2000 to 2010. These policies have played a role in shaping the spatial pattern of the aquaculture industry in the province.

Additionally, the study underscores the significance of efficient competition and product differentiation in enhancing the industry’s overall vitality. To achieve this, coastal cities in Jiangsu Province should capitalize on their regional advantages and prioritize the development of marine aquaculture. This strategic shift can help stimulate growth, optimize resource utilization, and foster a more competitive and resilient aquaculture sector.

The analysis of nearest neighbour statistics and spatial autocorrelation confirms the presence of spatial clustering effects across all cities in Jiangsu Province, with Nanjing and Yancheng exhibiting the strongest agglomeration tendencies. The spatial Gini coefficient analysis supports the conclusion that the aquaculture industry in Nanjing experiences significant agglomeration. However, it also highlights the limitations of the spatial Gini coefficient as a standalone measure, particularly in cases where a high coefficient may arise due to the presence of a few large enterprises, without indicating actual clustering phenomena.

Based on the study’s findings, several policy recommendations emerge to foster the sustainable development of the aquaculture industry in Jiangsu Province. Firstly, there is a need to encourage and support the development of marine aquaculture, leveraging the untapped potential of coastal areas. Secondly, promoting efficient competition and differentiated products should be a priority to enhance industry competitiveness. Thirdly, targeted support should be provided to Nantong, where aquaculture exhibits sparse distribution, in order to stimulate growth and bridge the development gap. Moreover, the government should continue to implement and strengthen sustainable development policies, which have shown a positive correlation with changes in the industry’s spatial pattern. Lastly, supporting the clustering of the aquaculture industry, particularly in cities such as Nanjing and Yancheng, can create synergies, facilitate knowledge sharing, and promote industry collaboration.

In summary, to achieve sustainable development in the aquaculture industry, the government should actively engage in providing policy support, promoting the development of marine aquaculture, enhancing competitiveness, encouraging industry clustering, and maintaining a favourable business environment. By adopting these recommendations, Jiangsu Province can further unlock the potential of its aquaculture industry and contribute to its long-term growth and prosperity.

6 Conclusions

Against the backdrop of the “blue transformation”, this study aimed to examine the impact of sustainable development policies on the efficiency of fisheries enterprises in Jiangsu Province. Data on enterprise locations in the aquaculture industry were collected for the years 2000, 2010, and 2021. The study focused on four aspects: overall spatial distribution trends, local distribution characteristics, clustering features, and spatial autocorrelation, to gain insights into the current status and development process of the fishery industry in the province. The following conclusions were drawn:

The overall distribution of the aquaculture industry in Jiangsu Province exhibits strong directionality and weak density characteristics. The distribution centre is predominantly concentrated in the central area of the province and near Yangzhou city. The industry’s overall distribution shows a northwest–southeast orientation. Additionally, there is an observable trend of the industry shifting from north to west and from the centre to the northwest within Jiangsu Province.

Aquaculture activities are primarily concentrated in inland areas, with Nanjing city exhibiting the highest concentration of enterprises. Coastal cities in Jiangsu Province, on the other hand, have a sparse distribution of aquaculture activities. It is recommended that relevant governments encourage the development of the marine aquaculture industry and foster competition among enterprises to enhance the vitality of the market.

The aquaculture industry in Jiangsu Province demonstrates a significant agglomeration effect, as evidenced by the ANN index of all cities being below 1. Nanjing and Yancheng exhibit the most pronounced agglomeration effects. In contrast, Nantong shows a high concentration of aquaculture enterprises from an economic perspective but lacks clear spatial clustering, indicating the presence of a few dominant enterprises in the region.

The overall spatial autocorrelation of the aquaculture industry in Jiangsu Province is negative, with spatially positive correlated cities accounting for only 30.8% of the total cities. However, regardless of the overall spatial distribution being negative, the absolute difference between Moran’s I and 0 for all cities is less than 0.4. This suggests that there is little variation in spatial differentiation among enterprises, irrespective of the spatial autocorrelation characteristics of the cities as a whole.

In conclusion, the spatial distribution of the aquaculture industry in Jiangsu Province is significantly influenced by the “blue transformation” and predominantly concentrated in inland areas, exhibiting distinct agglomeration effects and minimal differences among enterprises. Relevance governments should strive to ensure sustainable development while considering enterprise efficiency. Achieving sustainable development is a long-term goal, while addressing local economic development needs is also essential. Balancing and making informed decisions between long-term planning and short-term interests requires careful attention from relevant authorities. It is important to acknowledge that this study has certain limitations, such as constraints in terms of time and funding, which prevented the inclusion of additional influencing factors such as policy formulation, economic development, and population changes in the analysis. Future research endeavours will aim to build more accurate models and explore from multiple perspectives as recent industrial studies [31 32 33 34].

# These authors contributed equally to this work and should be considered first co-authors.

Acknowledgments

We would like to express our gratitude to the reviewers for their valuable comments and suggestions, as well as to Miss. Meng Wu from YourS Education (Shanghai) for her language editing assistance.

Author contributions: S.W., T.X., and S.C. conceived and planned all the workflow of the article. S.W. and S.C contributed to the interpretation of the results. T.X. contributed to the thin section description. S.W. took the lead in writing the manuscript.
Conflict of interest: Authors state no conflict of interest.
Data availability statement: The data will be available under reasonable request.

References

[1] Huang S, He Y. Management of China’s capture fisheries: Review and prospect. Aquac Fish. 2019;4(5):173–82.10.1016/j.aaf.2019.05.004Suche in Google Scholar

[2] Lin Y, Xue X, Hu W, Chen Q, Zhang Y, Huang F, et al. Implementing regional sea use planning system in China: Evolution, drivers and experiences. Mar Policy. 2022;146:105298.10.1016/j.marpol.2022.105298Suche in Google Scholar

[3] Bibri SE, Krogstie J, Kärrholm M. Compact city planning and development: Emerging practices and strategies for achieving the goals of sustainability. Dev Built Environ. 2020;4:100021.10.1016/j.dibe.2020.100021Suche in Google Scholar

[4] Höpken W, Eberle T, Fuchs M, Lexhagen M. Improving tourist arrival prediction: A big data and artificial neural network approach. J Travel Res. 2021;60(5):998–1017.10.1177/0047287520921244Suche in Google Scholar

[5] Eugenio-Martin JL, Cazorla-Artiles JM, González-Martel C. On the determinants of Airbnb location and its spatial distribution. Tour Econ. 2019;25(8):1224–44.10.1177/1354816618825415Suche in Google Scholar

[6] Wang H, He J, Aziz N, Wang Y. Spatial distribution and driving forces of the vegetable industry in China. Land. 2022;11(7):981.10.3390/land11070981Suche in Google Scholar

[7] Zafri NM, Sameen I, Jahangir A, Tabassum N, Hasan M, Uddin M. A multi-criteria decision-making approach for quantification of accessibility to market facilities in rural areas: an application in Bangladesh. GeoJournal. 2021;86(4):1863–79.10.1007/s10708-020-10161-zSuche in Google Scholar

[8] St. Martin K. Making space for community resource management in fisheries. Ann Assoc Am Geogr. 2001;91(1):122–42.10.1111/0004-5608.00236Suche in Google Scholar

[9] Pascoe S, Bustamante R, Wilcox C, Gibbs M. Spatial fisheries management: a framework for multi-objective qualitative assessment. Ocean Coast Manag. 2009;52(2):130–8.10.1016/j.ocecoaman.2008.10.009Suche in Google Scholar

[10] Fletcher WJ. The application of qualitative risk assessment methodology to prioritize issues for fisheries management. ICES J Mar Sci. 2005;62(8):1576–87.10.1016/j.icesjms.2005.06.005Suche in Google Scholar

[11] Daw TM. Spatial distribution of effort by artisanal fishers: Exploring economic factors affecting the lobster fisheries of the Corn Islands, Nicaragua. Fish Res. 2008;90(1–3):17–25.10.1016/j.fishres.2007.09.027Suche in Google Scholar

[12] Mueller JM, Loomis JB. Spatial dependence in hedonic property models: Do different corrections for spatial dependence result in economically significant differences in estimated implicit prices?. J Agric Resour Econ. 2008;33(2):212–31.Suche in Google Scholar

[13] Graziano M, Alexander KA, Liesch M, Lema E, Torres JA. Understanding an emerging economic discourse through regional analysis: Blue economy clusters in the US Great Lakes basin. Appl Geogr. 2019;105:111–23.10.1016/j.apgeog.2019.02.013Suche in Google Scholar

[14] Wang H, Dai J, Wei H, Lu Q. Understanding technological innovation and evolution of energy storage in China: Spatial differentiation of innovations in lithium-ion battery industry. J Energy Storage. 2023;30(66):107307.10.1016/j.est.2023.107307Suche in Google Scholar

[15] Xu J, Han L, Yin W. Research on the ecologicalization efficiency of mariculture industry in China and its influencing factors. Mar Policy. 2022;137:104935.10.1016/j.marpol.2021.104935Suche in Google Scholar

[16] Liu X, Pan Y, Zhang W, Ying L, Huang W. Achieve sustainable development of rivers with water resource management-economic model of river chief system in China. Sci Total Environ. 2020;15(708):134657.10.1016/j.scitotenv.2019.134657Suche in Google Scholar PubMed

[17] Lokman ED, Yaban M, Arshad S, Man N. Fish farming and off-farm activities in Sungai Siput, Perak using geographic information system (GIS) and remote sensing. Glob Bus Manag Res. 2022;14(1).Suche in Google Scholar

[18] Teniwut WA, Hasyim CL, Pentury F. Towards smart government for sustainable fisheries and marine development: An intelligent web-based support system approach in small islands. Mar Policy. 2022;143:105158.10.1016/j.marpol.2022.105158Suche in Google Scholar

[19] Teniwut WA, Hamid SK, Makailipessy MM. Using spatial analysis with weighted overlay on selecting area for fisheries agroindustry in Southeast Maluku, Indonesia. J Phys: Conf Ser. 2019;1424(1):012016.10.1088/1742-6596/1424/1/012016Suche in Google Scholar

[20] Panthi MF, Hodar A. GIS technology and its application in fisheries sector. Agric Environ. 2021;2(4):22–5.Suche in Google Scholar

[21] Moran P, Tuttle VJ, Bishop S, LaVoy L. Compositional forecasting of Chinook Salmon Evolutionarily Significant Units in bycatch for Pacific Hake fisheries. bioRxiv. 2021-11.10.1101/2021.11.29.470462Suche in Google Scholar

[22] Yang D, Geng H, Fu YK, Zhu T. Sustainability assessment of marine economy in China: Spatial distributions of marine environmental governance entities in Shanghai. Front Environ Sci. 2022;10:1083.10.3389/fenvs.2022.960464Suche in Google Scholar

[23] Wu J, Zheng S, Wang Y. Spatial distribution and influencing factors of leisure fishery in Zhejiang Province, China. Mar Freshw Res. 2023;74(10):796–806.10.1071/MF22194Suche in Google Scholar

[24] Jin X, Fu X, Lu W, Wang H. The effects of riverside cities on microplastics in river water: A case study on the Southern Jiangsu Canal, China. Sci Total Environ. 2022;858:159783.10.1016/j.scitotenv.2022.159783Suche in Google Scholar PubMed

[25] Rogerson PA. Historical change in the large-scale population distribution of the United States. Appl Geogr. 2021;136:102563.10.1016/j.apgeog.2021.102563Suche in Google Scholar

[26] Mariam A, Tariq S, Ul‐Haq Z, Mehmood U. Spatio‐temporal variations in fine particulate matter and evaluation of associated health risk over Pakistan. Integr Environ Assess Manag. 2021;17(6):1243–54.10.1002/ieam.4446Suche in Google Scholar PubMed

[27] Gu B, Zhang T, Meng H, Zhang J. Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation. Renew Energy. 2021;164:687–708.10.1016/j.renene.2020.09.087Suche in Google Scholar

[28] Shu H, Xiong P. The Gini coefficient structure and its application for the evaluation of regional balance development in China. J Clean Prod. 2018;199:668–86.10.1016/j.jclepro.2018.07.224Suche in Google Scholar

[29] Liu B, Xu M, Wang J, Xie S. Regional disparities in China’s marine economy. Mar Policy. 2017;82:1–7.10.1016/j.marpol.2017.04.015Suche in Google Scholar

[30] Coca O, Ricaurte-Villota C. Regional patterns of coastal erosion and sedimentation derived from spatial autocorrelation analysis: Pacific and Colombian Caribbean. Coasts. 2022;2(3):125–51.10.3390/coasts2030008Suche in Google Scholar

[31] Wang Y, Wei H, Wang Y, Peng C, Dai J. Chinese industrial water pollution and the prevention trends: An assessment based on environmental complaint reporting system (ECRS). Alex Eng J. 2021;60(6):5803–12.10.1016/j.aej.2021.04.015Suche in Google Scholar

[32] Chou L, Dai J, Qian X, Karimipour A, Zheng X. Achieving sustainable soil and water protection: The perspective of agricultural water price regulation on environmental protection. Agric Water Manag. 2021;28(245):106583.10.1016/j.agwat.2020.106583Suche in Google Scholar PubMed PubMed Central

[33] Bai Y, Dai J, Huang W, Tan T, Zhang Y. Water conservation policy and agricultural economic growth: Evidence of grain to green project in China. Urban Clim. 2021;40:100994.10.1016/j.uclim.2021.100994Suche in Google Scholar

[34] Chen CY, Lin SH, Chou LC, Chen KD. A comparative study of production efficiency in coastal region and non-coastal region in Mainland China: An application of metafrontier model. J Int Trade Econ Dev. 2018;27(8):901–16.10.1080/09638199.2018.1472292Suche in Google Scholar

Received: 2023-04-30

Revised: 2023-06-11

Accepted: 2023-06-30

Published Online: 2023-11-06

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artikel in diesem Heft

https://doi.org/10.1515/geo-2022-0531

Schlagwörter für diesen Artikel

Moran’s I; aquaculture industry; ArcGIS; spatial changes; pattern evolution

Creative Commons

BY 4.0