Governance structure and performance of mariculture Sci-Tech parks: Evidence from Zhejiang Province, China

Marine Policy 109 (2019) 103670

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Governance structure and performance of mariculture Sci-Tech parks: Evidence from Zhejiang Province, China

T

Sining Zhenga, Qiao Liangb, Qiang Liuc,∗ a

College of Public Administration, Fujian Agriculture and Forestry University, 15 Shangxiadian Road, Fuzhou, 350002, China China Academy for Rural Development, School of Public Affairs, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China c School of Economics and Management, Zhejiang A&F University, 252 Yijin Road, Ling'an District, Hangzhou, 311300, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Mariculture Sci-Tech park Performance Stochastic frontier analysis (SFA) Governance structure

Mariculture Sci-Tech parks promote modern mariculture and help improve China's mariculture industry. A park consists of a core area and a radiation area. The core area includes a farm, a cooperative, or a company, and the radiation area consists of many small farmers. Stochastic frontier analysis is employed to analyze the performance and its influencing factors of different governance structures in mariculture Sci-Tech parks. Results indicated the following: 1) Core areas show diminishing returns of scale, indicating that the increase in the scale of mariculture production can decrease the output. 2) The average technical efficiency of the core area is 0.4728, with the large family farms having the highest (0.4907), followed by the mariculture companies (0.4777) and the fish farmer cooperatives (0.4499). Several reasons can explain the difference in technical efficiency. First, family farms can better apply the new technology and new varieties to the mariculture production through moderate scale management. Second, companies have a higher operating cost, and the construction of mariculture Sci-Tech park has little effect on companies' performance. Third, cooperatives' advantage is not obvious in the organization management and the repeated construction of the infrastructure decreases utilization. 3) When the radiation area is considered, the input of large family farms has the most significant impact on the performance of the entire Sci-Tech park, with the highest driving efficiency. However, the cooperatives' driving efficiency is very poor, and the company performance is only reflected in the capital investment.

1. Introduction Due to the large population and limited natural fishery resources, China has considered aquaculture as a priority industry since the 1980s, resulting in the rapid growth of this industry over the years. In 2015, China's total output of aquatic products reached 793, 894, 000 tons, 39.75% of the global production, ranking it first worldwide [2,13,14]. China is also a major trader of aquatic products, generating $28.18 billion sales in 2015, once again ranking China first worldwide [3,10]. However, mariculture production models in China reflect the tendency of fish farmers and fishermen to work independently from each other, which has contributed to several related problems, such as excessive consumption of resources, low production efficiency, environmental pollution, and food safety issues [5,36]. To solve these problems, the Chinese government has supported new modern operation models, such as modern family farms, fish farmer cooperatives, and fish companies, to drive traditional fish farms through the establishment of mariculture Sci-Tech parks.

∗

In recent years, many studies have proposed methods to estimate economic performance, economic efficiency, and capacity in fisheries, including capture and aquaculture. In these studies, the standardized indicators [4], Stochastic Frontier Analysis (SFA) [12,15,29,44,46], and Data Envelopment Analysis (DEA) [8,9,37,40,48,54,56,61] are useful methods to estimate the performance and its overall influence on the economy. The aquatic products in most of the developed mainly come from capture, so most of these literatures focus on the economic performance and economic efficiency of capture [4,8,9,12,15,29,37,40,44,46,48,54,56] and ocean fisheries management [11]. And the researches on the economic performance and economic efficiency mainly come from developing countries with high population density, such as China, Philippines and Bangladesh [1,28,47,61,62,64]. Most of the results show that moderate scale of aquaculture can improve the economic efficiency [61,64], and freshwater farms are substantially more efficient than their brackish water counterparts [28]. In addition, Dey et al. (2005) [7] compared the economic efficiency of freshwater pond polyculture among China,

Corresponding author. E-mail addresses: [email protected], [email protected] (S. Zheng), [email protected] (Q. Liang), [email protected] (Q. Liu).

https://doi.org/10.1016/j.marpol.2019.103670 Received 26 October 2018; Received in revised form 2 August 2019; Accepted 26 August 2019 Available online 26 September 2019 0308-597X/ © 2019 Elsevier Ltd. All rights reserved.

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India, Thailand and Vietnam, the results showed that technical efficiency (TE) in China is much higher than three other countries and the education plays a significant role in improving farm efficiency. However, few studies focused on the effects of different governance structures on the fishery industry's performance. For example, Brinson and Thunber (2016) [4] and Madau et al. [38] investigated the performance of small-scale fishery cooperatives (catch shares) in the United States and Sardinia, respectively. Yin et al. [62] analyzed the influence of farming performance on the performance of large yellow croaker fish farmers. Zheng et al. [64] discussed the efficiency and the influencing factors of aquaculture in China. Most of these studies have reported that fisheries with a higher level of vertical integration, including capture and aquaculture operation subjects, have shown better performance. Cluster-based economic development is an advanced form of organization. For over three decades, researchers have studied cluster-based economic development, and most of them considered that cluster-based development strategies have employment, performance, and competitive benefits [32,33,43,49]. Clusters are a network of economic relationships that create a competitive advantage for the related firms in a particular region. This characteristic is the same with agriculture. As a large agricultural country, China has the largest farmer population worldwide. To improve the competitiveness of its agricultural products, the Chinese government has begun promoting the construction of agricultural Sci-Tech parks since the 1990s. Thus, some Chinese scholars have begun engaging in the research of agricultural Sci-Tech parks [50,57,63]. Most of them found that the core subject supported by government agencies can play a leading role through demonstration. However, only a few studies have investigated the performance of the mariculture Sci-Tech parks. In China, Zhejiang Province is rich in fishery resources and is known for its high-quality mariculture environment. The Zhoushan archipelago in this province is China's largest archipelago, and the Zhoushan fishing ground is also the largest fishing ground in China. The curved coastline provides a unique natural environment for the development of the mariculture industry. Therefore, this paper takes Zhejiang Province as an example and discusses the performance of mariculture Sci-Tech parks, aiming to provide several references for the development of the world's mariculture. The current study aims to present the model of mariculture Sci-Tech parks in China. Moreover, it focuses on the performance of different governance structures in the core subject of such parks and how different governance structures influence the input to output performance of these parks. The rest of this article is organized as follows: Section 2 delineates the different mariculture governance structures and mariculture Sci-Tech parks in China. Section 3 discussed the research methods and data of this empirical study and presents the brief descriptive statistics. Section 4 details and discusses the empirical analysis results. The final section presents some conclusions and introduces some discussions to improve the performance of the mariculture SciTech parks.

Fig. 1. Composition of mariculture parks in China.

leading industries, spreading modern mariculture science and technology. The core area (including land and coastal waters) is not requisitioned by the government within 10 years or more, with the measure of more than 66.67 ha. The governance structure in the core area of one Sci-Tech park include one modern family farm, one fish farmer cooperative, or one mariculture company, whose annual sales volume should be more than 5 million RMB,1,2. There is only one new modern operation model in the core area in one Sci-Tech park. The radial area (including land and coastal waters) is more than 66.67 ha, and traditional fishing areas and islands can be properly reduced according to the geographical condition. The governance structure in the radial area is dominated by traditional family farmers.

2.2. Governance structures of operation models in the mariculture Sci-Tech parks Rural China has implemented a household contract responsibility system (HRS) since 1978. China has shifted from collective to household-based farming since then [58]. In this system, agricultural land operation rights are assigned to farmer households, whereas ownership rights remain with the state. This condition also applies to the land (or coastal waters) suitable to mariculture, including the intertidal zone. As a result of the HRS, fish farmers began to have ownership rights over the yields of their lands, and the aquaculture industry grew rapidly thereafter. However, the system also requires fish farmers to market their products themselves. Thus, smallholders faced difficulties entering large markets and obtaining reasonable benefits. Fish farmers are also faced with other challenges, e.g., small size and low marginal profits, due to industrialization, specialization, informatization, and globalization by other participants in the aquacultural supply chain. Similar to other product farmers, traditional fish farmers are incapable of negotiating effectively with other supply chain participants; moreover, they rarely benefit from the products' value added [30]. Hence, numerous institutional innovations were implemented to help smallholders access the market. Currently, among new modern operation models, a diverse range of governance structures, including family farms, farmer cooperatives, aquacultural companies, and others, govern the aquaculture production in China [27].

2. Mariculture Sci-Tech parks and various governance structures of operation models in China 2.1. Composition and scale of mariculture Sci-Tech parks in China A modern mariculture Sci-Tech park is a mariculture products intensive production base and composed of a modern operation model and many small farmers. The basic composition of a park is displayed in Fig. 1. Parks are initiated by local governments and usually supported by scientific research institution. Local governments who intend to initiate parks try to identify relatively well-performed mariculture operation models and facilitate the foundation of parks by infrastructure as well as subsidies. The goal of these parks is to improve the production efficiency through taking the advantage of local resources and

1 The internal statistical report of Zhejiang Oceanic and Fisheries Bureau, China. 2 1 RMB roughly equals 0.1505 US dollars during 2016.

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1) Mariculture family farms Family farms are owned and managed by family labor and their control is often transferred to the next generation in the family [16]. Pollak [42] observes that family farms are managed jointly by couples and their children or by relatives of the family. Farms are grouped into family farms, intermediate farms, and non-family farms, depending on the composition of the labor input [22]. Family farms are characterized by having more than 95% of labor provided by family members. A farm is considered intermediate if half to 95% of the labor is supplemented with hired labor, and it is a non-family farm if hired labor accounts for more than half of all labor [65]. Therefore, family farms are typically small farms because of the limited supply of family labor. The same is true for the mariculture family farm structure.

farming [51]. First, companies may provide inputs and technical assistance to contracted farmers to control quality. Second, companies can help increase the value added and provide farmers with protective prices [31]. Third, companies can generate benefits from stable product supplies and reduced transaction costs by offering farmers protective prices, which are specified in purchasing contracts [16,27]. Such stable relationship between companies and fish farmers exist in export-oriented aquatic production, such as the eel industry in China. 3. Methods, data, and descriptive statistics 3.1. Research methods and choice of variables Considering the fact that the mariculture Sci-Tech parks are affected by external random factors [52], such as market fluctuations and weather changes, also, to improve the accuracy of the estimation results, to better explain the factors affecting the performance, in the study, a stochastic frontier analysis (SFA) method is employed to estimate the performance of different governance structures in the core of mariculture parks and their impact on the output of such parks. The general model of SFA can be expressed as

The HRS reform divided China's family farms into traditional and modern family farms, including mariculture family farms, according to the production intensification level. The former pertains to the farms with the traditional agricultural production model, such as small farmers lacking capital, technology, and labor, as mentioned above. The modern family farms are economic enterprises engaged in agricultural production, processing, and marketing, which are operated by family members and feature large-scale market and profit orientations [16]. Modern family farms have advantages over traditional farms because they operate on a larger scale, employ standardized production and marketing, and invest in product verification and branding [16]. Based on these, the Chinese government began developing family farms in 2008, and by the end of 2012, 877,000 family farms were already operating in China. Government support for family farms is multidimensional, including rural land transformation, financial support, and machinery subsidies, among others.

Yit = f (x it ; β )exp(νit − μit )

(1)

where i = 1, 2, …, N; t = 1, 2, …, T; Xit is the input; Yit is the output; and β is the parameter to be estimated. In addition, f (·) is the production function. The stochastic frontier function is characterized by a composed error structure; εit = νit - μit. νit is assumed to be identically and independently distributed as N (0, σν2) and captures the statistical noise, interpreted as a measure of inefficiency; μit, is assumed to be a one-sided component error and is independent from νit, satisfying cov (μit, νit) = 0. The technical efficiency (TE) can be calculated from the estimated values of εit and μit; thus,

2) Fish farmer cooperatives Competing with other supply chain participants is very difficult for the traditional family farm. Fortunately, increasing farmers' production scales could alleviate it. Thus, collective action via farmer cooperatives is often recognized as an effective response to market competition and market failure [19,24,25,34,53].

TE = E[exp(−μit ) εit ]

(2)

In the empirical model, environmental factors were grouped together with input variables that affect production frontiers into production functions, referring to the study of O'Donnell (2016) [39]. To facilitate estimation, we used the commonly used C-D production function form to construct the empirical model and take logarithmic form for all variables. When constructing empirical model, with reference to other studies [1,28,47,61,62,64], we used the output value as the output variable; the area, capital investment, and labors as input variables; and the number of roads and canals, occupation ratio of high standard fish pond, occupation ratio of fining breed, number of environmental monitoring equipment, number of new varieties, and new technologies introduced as environment variables. Moreover, regional dummy variables were set to control the regional differences. The specific model can be expressed as

Farmer cooperatives, including fish farmer cooperatives, began their rapid growth in the 2000s [6,59]. Following the promulgation of the National Farmer Cooperative Law (referred to as “Law” afterwards) in 2007, farmer cooperatives developed even more rapidly. By the end of July, 2017, 1,933,000 farmer cooperatives with more than 100 million members in China were in operation. Cooperatives are expected to address emerging problems, such as small-scale production challenges; collectively provide technical trainings; and supervise product production, processing, and marketing for members [23]. Thus, most farmer cooperatives in China improved their performance in terms of uniform input supply, technique training, marketing, packing, and branding.

M

ln Y1it = α 0 + α1 t +

m=1

3) Fish companies Agricultural companies (including fish or mariculture companies) are more efficient in terms of value added efficiency than farmer cooperatives because cooperatives collectively make decisions and target members' benefits, whereas companies are focused on profit and efficiency [35]. Moreover, they are more adaptable to the changing market and fierce competition than family farms because they have sufficient capital and technology [45]. Agricultural companies play an important role in the fields of technical innovation and extension, capital aggregation and utilization, and scale efficiency [41].

J

∑ βm ln Xmit + ∑ δj ln Zjit + νit − μit j=1

(3)

where Ylit is the net profits of different governance structures in the core of a mariculture Sci-Tech park; Xit is the input variables for the governance structures, including the size of the core area (X1), capital investment (X2), labors in the core area (X3), Zit is other environmental variables, including number of roads and canals (Z1), occupation ratio of high-standard fish pond (Z2),3 occupation ratio of fining breed (Z3),4 number of environmental monitoring equipment (Z4), number of new 3 High-standard fish pond: The mariculture wastewater needs a certain kind of purification treatment according to the DB33/453–2006 standards [66]. 4 Fining breed area: Facilities for aquaculture, pond aquaculture, and breeding areas should be equipped with reservoirs, purification facilities, and wastewater treatment systems, which account for approximately 5%–10% of the total mariculture area.

Fish companies emerged in China in the early 1990s, and their development increased rapidly in the 2000s. Three types of links between farmers and fish companies are observed: loose linking based on random purchasing, payment based on protective prices, and contract 3

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companies and modern family farms with 5478.32 and 5391.69 thousand RMB, respectively. The average output value of mariculture Sci-Tech parks is 10,896.21 thousand Yuan. The output value of a Sci-Tech park, in which cooperatives in the core are the highest, reaches 12,090.01 thousand RMB, followed by parks with modern family farms at the core (11,716.00 thousand RMB) and parks with fishery companies at the core (9840.40 thousand RMB). Input in the core area The average area of a mariculture Sci-Tech park is 74.63 ha, capital investment is 8847.1 thousand RMB, and the number of labor force is 273.50. Significant differences among various governance structures exist in terms of land, capital, and labor inputs. In particular, the cooperatives have the largest land (124.72 ha) and labor inputs (308.09), while companies have the largest capital input (7115.82 thousand RMB). In China, the members of the cooperative are divided into the core and ordinary members. In the input and output statistics, the core and ordinary members are all counted, thus indicating that the land and labor inputs are significant. Infrastructure in the core area The average number of roads and canals in the core areas is 273.50. Among the different governance structures, cooperatives have the highest number of roads and canals, reaching a total distance of 6.58 km. Technical application in the core area The average occupation ratio of high-standard fish ponds in the core area is 45.24%, whereas that in modern family farms is the largest among all governance structures, reaching 50.81%. The average occupation ratio of the fining breed in the core area is 48.99%, and that in companies is the largest among governance structures, reaching 60.28%. The average number of environmental monitoring equipment in the core area is 126.60, and that in modern family farms is the highest among governance structures, reaching 301.94. The average number of new varieties and technologies introduced in the core area is 2.35, and that introduced in cooperatives is the highest among governance structures, reaching 2.88.

varieties and new technologies introduced (Z5), and regional virtual variables. In addition, α, β, and δ are the parameters to be estimated; among them, β is output elasticity of input variables, εit is residual error, and

cov(εit , ln Zjit ) = cov(εit , lnXmit) = cov(εit , ln Yjit )

(4)

We also analyzed the impact of the core area input on the performance of a mariculture Sci-Tech park. The model can be expressed as M

ln Y2it = α 0 + α1 t +

J

∑ βm ln Xmit + ∑ δj ln Zjit + νit − μit m=1

j=1

(5)

where Y2it is the total output value of the mariculture Sci-Tech park. Other variables are defined as previously stated. 3.2. Data The data used in this study was collected from 325 mariculture parks in 49 counties of Zhejiang Province under the governance of the Zhejiang Oceanic and Fisheries Bureau from 2010 to 2016. Statistical indicators include the following: 1) output value, including output value in the core area and output value in the mariculture Sci-Tech park, combined with the core and radial areas; 2) input in the core area, including the size of the core area, capital investment, and number of labor force; and 3) technical environment in the core area, such as number of roads and canals, occupation ratio of high-standard fish pond, occupation ratio of fining breed, number of environmental monitoring equipment, and number of new varieties and technologies introduced. Notably, a total of 325 modern marine mariculture parks in Zhejiang Province passed the inspection from 2010 to 2016, and the sample data of modern mariculture Sci-Tech parks were collected during the inspection year in this study. Thus, the data source for this study is mixed cross-sectional data. A total of 159 maricultural companies, 85 farmer cooperatives, and 81 modern family farms in the core areas of these 325 mariculture Sci-Tech parks, accounted for 49%, 26%, and 25% of the total, respectively. In data processing, to eliminate the price changes, the value variables, including output value and capital input value, were reduced using the Zhejiang production price index with 2010 as the base period.

4. Empirical results and discussion 4.1. Performance in the core area To prevent the multicollinearity effect on model estimation, Table 2 presents the correlation of variables entering the empirical model. As can be seen, the multicollinearity of variables is not serious; thus, they can be used for model estimation. Table 3 shows the estimation results of the factors affecting the performance of governance structures in the core of mariculture SciTech parks. The interpreted variable is the output value of governance structures. Model 1 is the results for all the three governance structures, and Models 2, 3, and 4 are the results for modern family farms, fish farmer cooperatives, and fishery companies, respectively. The chisquare values of Models 1 to 4 are 224.85, 239.17, 99.19, and 65.65, respectively, and are all significant at the 1% level, indicating good

3.3. Descriptive statistics Table 1 provides the descriptive statistics of the variables of different governance structures in the mariculture Sci-Tech park, including modern family farms, fish farmer cooperatives, and fishery companies. Output value of different mariculture Sci-Tech parks The average output value of operation models in core areas for mariculture parks is 5958.03 thousand RMB. The output values of different governance structures are different. The output value of cooperatives is the highest, reaching 7395.07 thousand Yuan, followed by fishery

Table 1 Variables of different governance structures at the core of the mariculture Sci-Tech park. Variable name

Variable description

Modern family farms

Cooperatives

Fishery companies

Average

Y1 Y2 X1 X2 X3 Z1 Z2 Z3 Z4 Z5

Output value in the core area (thousand RMB) Output value in mariculture park (core area + radial area) (thousandRMB) Size of core area (Ha) Capital investment (thousand Yuan) Number of labor force in the core area (No.) Number of roads and canals (Km) Occupation ratio of high-standard fish pond (%) Occupation ratio of fining breed (%) Number of environmental monitoring equipment (No.) Number of new varieties and technologies introduced (No.)

5391.69 11,716.00 47.43 5704.24 280.77 5.56 50.81 7.23 301.94 2.44

7395.07 12,090.01 124.72 7115.82 308.09 6.58 44.71 43.61 83.56 2.88

5478.32 9840.40 61.72 11,373.62 251.31 4.76 42.48 73.15 60.28 2.02

5958.03 10,896.21 74.63 8847.06 273.50 5.44 45.14 48.99 126.60 2.35

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Table 2 Correlation matrix of variables.

ln_Y1 ln_Y2 ln_X1 ln_X2 ln_X3 ln_Z1 ln_Z2 ln_Z3 ln_Z4 ln_Z5

ln_y1

ln_y2

ln_x1

ln_x2

ln_x3

ln_z1

ln_z2

ln_z3

ln_z4

ln_z5

1 0.211 0.302 0.255 0.302 0.269 0.079 0.101 0.215 0.111

1 0.142 0.026 0.089 0.265 0.240 0.076 0.230 0.089

1 0.469 0.355 0.457 0.001 −0.138 0.162 0.099

1 0.268 0.437 0.059 0.126 0.279 0.148

1 0.254 −0.059 −0.058 0.258 0.124

1 0.335 −0.003 0.353 0.271

1 0.121 0.277 0.145

1 0.246 0.002

1 0.138

1

performance of the models. In addition, the λ values of Models 1 to 4 are 51.40, 64.95, 55.78, and 61.29, respectively. These results are significantly greater than 1 and are significant at the 1% level, indicating that compared with the general production function estimation, the result of the SFA estimation is better, and the actual output of the mariculture zone deviates from the frontier output.

the profit of mariculture Sci-Tech parks. Some authors found that a slightly positive relationship exists between output performance and farm size in crop farming [20,21,55]. However, the situation of aquaculture is different. A negative relationship exists between farm size and output performance in aquaculture, contradicting the institutional arrangement of farm size in recent years [61]. The current study came to the same conclusion, that is, the increase in scale will lead to the decrease in output. This finding is explained by the following: compared with other agricultural activities, mariculture has higher technical requirements and higher product value. Thus, intensive mariculture can improve the quality of aquatic products, forming a brand of seafood with local characteristics.

4.1.1. Factors predicting the performance of all governance structures From all the governance structure (total sample) estimation results (Model 1), area (X1), capital (X2), and labor (X3), input have a significant positive effect on the output performance (profit) mariculture Sci-Tech parks. Moreover, the estimation coefficients of area, capital, and labor force are 0.3860, 0.2248, and 0.0998, respectively. So the sum of the elastic coefficients of them is 0.7106, which is less than 1. There is a decline in the returns of scale in mariculture Sci-Tech parks, whereas the increase in scale leads to a decrease in output. In addition, the occupation ratio of fining breed (Z3) and the number of new varieties and technologies introduced (Z5) also have a positive impact on the output performance of the mariculture Sci-Tech parks, suggesting the two can significantly increase the annual profit of the parks. The estimated coefficient of time (t) is positive and significant, indicating that the progress of technology has also increased

4.1.2. Factors affecting the performance of modern family farms From the regression results of Model 2, three variables (X1, X2 and X3) have significant positive effects on the annual profit of modern family farms and the sum of elastic coefficients of the variables is 0.9642, indicating a decline in the returns of scale in family farms. In addition, the number of roads and canals (Z1), occupation ratio of fining breed (Z3), and the number of new varieties and technologies introduced (Z5) have a significant positive impact on the annual profit of family farms. Thus, the mariculture-related infrastructure, the

Table 3 Estimation result of the performance of governance structures in the core of Sci-Tech parks. Variable

Size of core area (Ha)

X1

Capital investment (thousand Yuan)

X2

Number of labor force in the core area (No.)

X3

Number of roads and canals (Km)

Z1

Occupation ratio of high-standard fish pond (%)

Z2

Occupation ratio of fining breed (%)

Z3

Number of environmental monitoring equipment (No.)

Z4

Number of new varieties and technologies introduced (No.)

Z5

t Regional dummies Constant sigma_u sigma_v lambda Loglikelihood Wald chi2

Model 1 All samples

Model 2 Modern family farms

Model 3 Fish farmer cooperatives

Model 4 Fishery companies

0.3860*** (0.0547) 0.2248*** (0.0531) 0.0998** (0.0442) 0.0405 (0.0714) 0.0113 (0.0322) 0.0618* (0.0321) −0.0102 (0.0258) 0.1367* (0.0733) 0.0686* (0.0369) Control 1.0823*** (0.1389) 32.12 0.62 51.40 −437.55 224.85

0.4835*** (0.0233) 0.3418*** (0.0253) 0.1389*** (0.0165) 0.4455*** (0.0341) 0.0121 (0.0118) 0.1656*** (0.0114) −0.0580*** (0.0074) 0.0739*** (0.0271) 0.2786*** (0.0001) Control 1.8414*** (0.2987) 22.19 0.34 64.95 −73.22 239.17

0.7594*** (0.0001) 0.2093*** (0.0001) 0.2286*** (0.0000) −0.2196*** (0.0001) 0.0653*** (0.0000) 0.2044*** (0.0000) −0.1542*** (0.0000) 0.2514*** (0.0001) 0.1097*** (0.0000) Control 1.2433*** (0.1117) 31.03 0.56 55.78 −107.31 99.19

0.2970*** (0.0778) 0.1703** (0.0685) 0.0560 (0.0651) −0.0346 (0.1036) 0.0037 (0.0469) 0.0131 (0.0463) −0.0034 (0.0409) 0.1223 (0.1225) 0.0075 (0.0556) Control 1.2010*** (0.2246) 37.37 0.61 61.29 −224.36 65.65

Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1. 5

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improvement of mariculture level, and the introduction of new varieties and new technologies have effectively promoted the improvement of the output performance of family farms. However, the number of environmental monitoring equipment (Z4) has a negative impact on profit, and the impact coefficient is −0.0580. Catering to the government's requirements for environmental protection results in the following outcome: the more the environmental monitoring equipment, the greater the cost and the lower the profit. In addition, the highstandard fish pond ratio (Z2) has no significant impact on the output performance because the high-standard fish pond in the farm is 50.81%, which is the highest value for all governance structures. The high ratio of high-standard fish pond reduces the marginal profit of farms.

Table 4 Estimation results of the technical efficiency (TE) of governance structures in the core of Sci-Tech parks. Governance structures

Technical efficiency

Modern family farms Fish farmer cooperatives Fishery companies Average

0.4907 0.4499 0.4777 0.4728

of the modern family farms is obviously higher than those of the company and the cooperative. Probably, the family farms are extra intensive in the factor input, the production of which is also specialized and standardized. Moreover, the new technology and new varieties can be better applied to the production. Different from family farms, the cooperative is the union of many fish farmers. The advantage of organization management is not obvious; thus, its efficiency value is relatively low. In addition, the average TE is 0.4728, lower than freshwater aquaculture [61]. The main reason may be that pond culture is the main way of freshwater aquaculture, which is more controllable and has higher feed utilization rate. The main production mode of mariculture is cage and beach farming, which is less controllable and has low feed utilization rate. Even in factory farming, the cost is too high. This same result is validated in Irz and Mckenzie (2008) [28].

4.1.3. Factors affecting the performance of fish farmer cooperatives From the regression results of Model 3, six variables in the production input of cooperatives have a significantly positive impact on their profits. The coefficient of scale reward is 1.1973, which is greater than 1, indicating an increase in returns of scale in the family farms. The main reason is that the cooperatives are the conglomerates of farmers, and the investments of land and labor are relatively large. Moreover, the compatibility and expansibility of the cooperatives increase the efficiency of the scale. Meanwhile, the efficiency of the cooperative is promoted by pond construction, pond facilities, and the level of new technology and new varieties, because Z2, Z3 and Z5 have significant positive effects on their annual profits. However, the number of roads and canals (Z1) has a significant negative impact (impact coefficient is −0.2196). The finding is explained by the fact that the cooperative is a loose union of farmers, and the construction of infrastructure is not as intensive and efficient as the family farms [26]. Thus, the increase in cooperatives' profit is hindered by the decline in the utilization rate resulting from repeated infrastructure construction. The number of environmental monitoring equipment (Z4) also plays a significantly negative role on the cooperatives' profit (coefficient is −0.1542). The reason for the latter is the same as that cited in the case of family farms.

4.2. Core area input impact on the mariculture Sci-Tech parks' output performance The mariculture parks have a strong driving effect (i.e., positive externality). To take this external effect into consideration, the next analysis will integrate the driving effect (i.e., the newly added mariculture output value of the surrounding farmers) into the total output of the mariculture parks. Due to the consideration of the driving effect, some of the input investements are external to the mariculture parks. For ease of description, the frontier function should only be estimated and the TEs of different mariculture parks are not discussed. Table 5 provides an estimate of the impact of the core area input on the park's output performance. The explained variable in the model is the total profit in the mariculture Sci-Tech park (core area profit + radial area profit). Models 5, 6, 7, and 8 are the estimates of all samples, modern family farms, cooperatives, and company estimates, respectively. The chi2 values of Models 5 to 8 are 141.90, 69.17, 92.88, and 50.10, respectively, all at the 1% level; their λ values are 35.49, 11.94, 9.12, and 48.88, respectively, which are significantly greater than 1 and are significant at the 1% levels. Thus, compared with the general production function estimation, the result of SFA estimation is better. Considering the driving effect of Sci-Tech park, a loss of efficiency also occurs. From the estimation results of Model 5, five variables in the input variables of the fishery park have a positive effect on the output performance of the park. The scale reward coefficient is 0.5591, showing the phenomenon of diminishing returns of scale. In addition, the occupation ratio of fining breed (Z3) has a significantly positive effect on the output performance of the mariculture Sci-Tech parks. The influence coefficient is 0.0945. However, the influence of the number of environmental monitoring equipment (Z4) is significantly negative. The influence coefficient is −0.0444, and its influence mechanism is the same as that without considering the driving effect.

4.1.4. Factors affecting the performance of fishery companies From the regression results of Model 4, only two variables in the company's production input have significant positive effects on their output performance, namely, area (X1) and capital (X2). And the sum of the coefficients is 0.5433, indicating a decline in the returns of scale in fishery companies. The output elasticity of land and capital input is significantly smaller than that of farms and cooperatives, which shows that a big gap remains in the return on factor input compared with the other two governance structures. Moreover, the number of roads and canals (Z1), occupation ratio of high standard fish pond (Z2), occupation ratio of fining breed (Z3), the number of environmental monitoring equipment (Z4), and the number of new varieties and technologies introduced (Z5) have no significant influence on the annual profit of companies. The possible reasons are as follows. First, the company itself has established a modern enterprise management model; thus, the construction and investment in Sci-Tech park have little impact on its profit. Second, the relatively perfect management model and more perfect market supervision also increase the operation cost than other governance structures. Finally, the profit of China's fishery company comes from the sales end and not the production side. 4.1.5. Technical efficiency (TE) of different governance structures According to the estimation results presented above, following formula (2), we can calculate the technical efficiency (TE) of different mariculture parks. Table 4 presents the TE estimations of the governance structures in the mariculture Sci-Tech parks. From the table, the average value of TE in Sci-Tech parks is 0.4728. Differences exist among the different governance structures. The TE values from high to low are the modern family farms, companies, and cooperatives. The TE

4.2.1. Modern family farms' input impact on mariculture Sci-Tech parks' output performance From the regression results of Model 6, seven variables in the core area's production input play a significant positive role on the output performance of Sci-Tech park, in which the core area comprises modern 6

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Table 5 Estimate results of the impact of the core area input on the parks' output performance. Variable

Size of core area (Ha)

X1

Capital investment (thousand Yuan)

X2

Number of labor force in the core area (No.)

X3

Number of roads and canals (Km)

Z1

Occupation ratio of high-standard fish pond (%)

Z2

Occupation ratio of fining breed (%)

Z3

Number of environmental monitoring equipment (No.)

Z4

Number of new varieties and technologies introduced (No.)

Z5

t Regional dummies Constant sigma_u sigma_v lambda Loglikelihood Wald chi2

Model 5 All samples

Model 6 Modern family farms

Model 7 Fish farmer cooperatives

Model 8 Fishery companies

0.2784*** (0.0547) 0.1706*** (0.0609) 0.1101** (0.0477) 0.0314 (0.0723) 0.0302 (0.0314) 0.0945*** (0.0352) −0.0444* (0.0268) 0.1185 (0.0730) 0.0287 (0.0429) Control 6.5628*** (1.1600) 26.01 0.73 35.49 −434.11 141.90

0.6587*** (0.0884) 0.1794** (0.0771) 0.2571** (0.1094) 0.4872*** (0.1234) 0.1250* (0.0655) 0.1272*** (0.0438) −0.0953* (0.0574) 0.0386 (0.0982) 0.1362*** (0.0005) Control 1.3246*** (0.3177) 9.59 0.80 11.94 −93.55 69.17

0.2400** (0.1132) 0.3686*** (0.0172) −0.1691*** (0.0527) 0.4665*** (0.0561) −0.0292 (0.0190) 0.0773* (0.0421) −0.0623*** (0.0121) 0.0335** (0.0169) 0.0438*** (0.0001) Control 0.8265*** (0.3185) 6.46 0.71 9.12 −91.98 92.88

0.0186 (0.0857) 0.5645*** (0.0606) 0.0601 (0.0697) −0.0656 (0.0422) 0.0407 (0.0441) −0.0924*** (0.0178) −0.0317 (0.0362) 0.0285 (0.0394) 0.1533*** (0.0002) Control 2.8280*** (0.2680) 33.19 0.68 48.88 −222.47 50.10

Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

absorbing excessively large labor force from the radiation areas. This scenario causes the core area to play a weak driving role in the radiation area. In addition, the impact coefficients of the number of roads and canals (Z1), occupation ratio of fining breed (Z3), and the number of new varieties and technologies introduced (Z5) are 0.4665, 0.0773, and 0.0335, respectively. These values indicate that the infrastructure, fining breed, and new variety and technology promoted the output performance of the entire mariculture Sci-Tech park. Notably, cooperatives play a stronger role in the promotion new varieties and technologies (Z5) than large farmers, mainly because cooperatives are made up of small-scale fish farmers, whose technical levels and technical requirements are closer to traditional farmers in the radial area. Similarly, the impact of the number of environmental monitoring equipment (Z4) is significantly negative, with a coefficient of −0.0623. This result is slightly larger than that without considering the driving effect.

family farms. The coefficient of scale reward is 1.0952, which presents a phenomenon of increasing returns to scale, indicates that the scale return effect is more obvious than that without considering the driving effect. In addition, the number of roads and canals (Z1), occupation ratio of high-standard fish pond (Z2), and occupation ratio of fining breed (Z3) in family farms also have a significant positive effect on the output performance of the park. Thus, roads and canals, high-standard fish pond, and fining breed have effectively promoted the performance of the entire aquaculture Sci-Tech parks. However, the impact of the number of environmental monitoring equipment (Z4) is significantly negative, which is smaller than that without considering the driving effect. This finding is explained by the fact that the impact of environmental monitoring requirements on the performance of traditional farmers is greater than that of modern farmers because of the low level of mariculture technology. Moreover, different from the case of not considering the driving effect (Model 4), the influence of the number of new varieties and technologies introduced (Z5) is not significant. The possible reason is that the educational level of traditional farmers is not highly enough, resulting in lower science and culture quality than modern farmers, as well as poor technology level. Thus, their abilities to accept, absorb, and apply new varieties and technologies are poor. Education is the key to improving farm efficiency is validated in Dey et al. (2005) [7].

4.2.3. Fishery companies' input impact on mariculture Sci-Tech parks' output performance From the regression results of Model 8, only two variables in the core area's production input have a significant positive impact on the output performance of mariculture Sci-Tech park, namely, capital investment (X2) and occupation ratio of the fining breed (Z3), in which the core areas consist of fishery companies. Compared with the driving effect excluded from consideration (Model 4), the output elasticity of capital (X2) for companies (coefficient is 0.5645) obviously increased, indicating that the company promoted the development of the surrounding fishery culture through capital. However, the occupation ratio of fining breed (Z3) decreased the performance of the entire Sci-Tech park. Two reasons can be cited: first, there exists a high level of fining breed in the company because of sufficient capital, but small family farms in the radial area lack capital, resulting in an undetectable driving effect; second, the occupation ratio of the fining breed (Z3) in companies is up to 73.15% (Table 1); thus, the quality of its aquatic products is obviously higher than that of surrounding small family farms, which lowers the price of the products of small family farms. In

4.2.2. Fish farmer cooperatives' input impact on mariculture Sci-Tech parks' output performance From the regression results of Model 7, seven variables in the core area's production input have a significant positive impact on the output performance of Sci-Tech park, in which the core area comprises fish farmer cooperatives. Among them, the coefficient of labor input (X3) is significantly negative. The cooperatives have labor input redundancy when considering the driving effect because the labor excessive input leads to a decrease in output, which is fixed in the situation of land and capital investment. This finding is related to the organizational structure of the cooperatives. The cooperative members in the core areas are from the farmers in the radiation areas, which lead to the core areas 7

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addition, roads and canals (Z1), high-standard fish pond (Z2), and new varieties and technologies (Z5) have no significant impact. The influence of the number of environmental monitoring equipment (Z4) is negative and insignificant. This finding is roughly the same as when the driving effect is not considered (Model 4).

to modern operation subject and guide them to properly control the breeding scale. Specific measures include: firstly, increasing investment in R&D of mariculture research, especially the optimal scale of different mariculture species; secondly, establishing an efficient setups for popularizing latest mariculture techniques to the mariculture operation models; thirdly, according to the different mariculture varieties, constructing the corresponding mariculture Sci-Tech park, and leading the core body produce a marked driving effect to promote mariculture technology and moderate scale to traditional family farmers.

5. Conclusions and policy recommendations 5.1. Conclusions The following conclusions can be drawn from the previous study: First, with respect to the bodies in the core areas of mariculture SciTech parks, the output elasticity of the bodies in mariculture Sci-Tech parks is 0.7106. This finding shows the characteristics of diminishing returns of scale, which indicate that the increase in the scale of culture can lead to a decreased output. This result is the same as that reported by Yin et al. (2014) [61]. In addition, the occupation ratio of the fining breed and the number of new varieties and technologies introduced have significant positive effects on the output performance of mariculture Sci-Tech parks, with coefficients of 0.0618 and 0.1367, respectively. Moreover, the estimated coefficient of time t is positive and significant, indicating that technology input has a significant positive effect on the bodies in the core areas of mariculture Sci-Tech parks (coefficient is 0.0686). Second, with respect to different governance structures in the core areas, the technical efficiency (TE) values from high to low are those from the modern family farms, fish companies, and fish farmer cooperatives, which are 0.4907, 0.4777, and 0.4499, respectively. Through proper scale operation and intensive management, family farms can better apply new technologies and new varieties to fishery production. However, the advantage of farmer cooperatives in organization and management is not obvious, and the repeated construction of infrastructure leads to a decline in the utilization and a rise in profits. Further, most of the mariculture companies have established a modern enterprise management mode, and the cost of operation is relatively high. The construction of mariculture Sci-Tech parks has little effect on the output performance of companies. Third, from the aspect of core area input impact on the mariculture Sci-Tech parks' output performance, the driving effect of family farms on the entire mariculture Sci-Tech parks is the best. Moreover, the output elasticity of land, capital, and labor elements is 1.0952, showing a progressive increase in scale returns. However, as a loose organization, the cooperatives' driving effect is very poor. Nevertheless, they are conducive to the promotion of new varieties and technologies in the radial area. The driving effect of companies is reflected in the capital; companies promote the development of the entire mariculture Sci-Tech parks through capital.

5.2.2. Support modern family farms to moderate scale operation From the aspect of different governance structures, the TE of modern family farms is the highest due to proper scale operation and intensive management. The cooperative is a loose union composed of fish farmers, and the production method is not as intensive and efficient as the family farms. The company has a high operating cost; thus, its degree of intensification is not as good as family farms. Considering the high technical requirements, moderate scale management is suitable for mariculture. Thus, the governance structure of family farms is fit for mariculture. On the basis of this finding, the government should support the transformation of modern family farms to moderate scale operation to enhance the competitiveness of the mariculture industry. 5.2.3. Support the modern family farms as the core of mariculture Sci-Tech parks and standardize the management of mariculture cooperatives With respect to different governance structures' input impact on mariculture Sci-Tech parks' output performance, the driving effect of family farm is the best; the driving effect of a company is reflected in the capital; and the driving effect of a cooperative is poor. Accordingly, the government should encourage the modern family farms as the core of each mariculture Sci-Tech park to promote the output performance of the entire park. In addition, China's mariculture cooperatives face problems of loose organizational structure and non-standard management [25]. Therefore, the government should further standardize the management of mariculture cooperatives in order to improve their profit and driving effect. This study considers that establishing mariculture Sci-Tech park is an effective way to promote mariculture technology, moderate the scale of mariculture and control the pollution in mariculture. The mode of large family farm in the core area has the highest technical efficiency and the best driving role. These provide some experiences for the development of mariculture in other countries. However different countries should explore mariculture developing model suitable for their own national conditions, for example, the technological level of mariculture and the educational level of fish farmers [7,61]. Declarations of interest

5.2. Policy recommendations

None.

Mariculture Sci-Tech parks are an important way to improve the output performance of mariculture industry. This study focuses on governance structure and performance of the mariculture Sci-Tech park in China. Thus, the following sections will comprehensively outline the three policy recommendations.

Acknowledgements The study was supported by the National Natural Science Foundation of China (Grant No. 71703023; 71573227; 71361140369), the Fujian Provincial Social Science Planning Project (Grant No. FJ2017C020), the Zhejiang Science and Technology Department Soft Science Project (Grant No. 2018C3504), and the Zhejiang Philosophy and Social Science (Grant No. 17NDJC193YB).

5.2.1. Popularize mariculture technology and properly control the breeding scale The research results show that technology input has a positive effect on mariculture output performance. In addition, the production of mariculture shows the characteristics of diminishing returns of scale, indicating that when production reaches a certain scale, mariculture presents the characteristics of diminishing returns of scale. Moreover, moderate scale management is the key to improve the profit of the mariculture industry. Thus, the government should encourage scientific research institutions to further popularize new mariculture techniques

Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.marpol.2019.103670. 8

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