Mitig Adapt Strateg Glob Change DOI 10.1007/s11027-016-9719-7 O R I G I N A L A RT I C L E

Evaluating the technical efficiencies of fishing vessels to achieve effective management of overexploited fisheries Chenxing Yang 1 & Xiaobo Lou 1 & Takahiro Matsui 2 & Junbo Zhang 3

Received: 31 January 2016 / Accepted: 12 July 2016 # Springer Science+Business Media Dordrecht 2016

Abstract Global marine capture fisheries are undergoing serious stress, with overfishing as one of the major problems. In order to mitigate the overexploitation of capture fisheries, government regulation or fisheries management is necessary. Among various management approaches, vessel quantity control is being widely employed. To achieve effective governance of fisheries, the technical efficiency (TE) issue needs to be considered in the implementation of vessel quantity control. Using the Pacific saury (Cololabis saira) stick-held dip net fishery in Japan as a case study, this paper estimated the TE of sampled fishing vessels and explored the possible factors affecting the gap in efficiency. This paper aims to provide suggestions for a better implementation of vessel quantity control in global Pacific saury fishery, and also to serve as an empirical example of integrating TE analysis into management of overexploited fisheries for achieving satisfactory effects. Results show the TE score of the sampled fishery averaged around 0.7 from 2009 to 2014, and factors concerning owners/skippers’ motivation such as vessel ownership and specialization, vessel tonnage as well as skippers’ age show positive effects on the TE. Our findings in the present work provide important strategies for mitigating overexploitation in fisheries. Conducting technical efficiency analysis of targeted fisheries is a vital issue to be considered for designing and realizing an effective implementation of fisheries management approaches. The large vessels and the enthusiasm of vessel owners/skippers need to be particularly addressed when vessel quantity limit is considered to mitigate the problem of overfishing. Keywords Input control . Overfishing . Pacific saury . Stick-held dip net fishery . Stochastic frontier analysis . Technical efficiency

* Junbo Zhang [email protected]; [email protected]

1

Tokyo University of Marine Science and Technology, Tokyo, Japan

2

Mie University, Tsu, Japan

3

University of Tokyo, Tokyo, Japan

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1 Introduction According to the United Nations Food and Agriculture Organization (FAO), global marine fisheries expanded rapidly with population growth, rising incomes, and urbanization in the last five decades. The production of global marine fisheries peaked in 1996 and has shown a declining trend since then (FAO 2014). Besides the annual fishery production, the status of global marine fishery resources is also monitored. In 2011, the fraction of fish stocks utilized at an unsustainable level was 29 % (FAO 2014), indicating that the situation of global marine fishery resources is in a serious state (Beddington et al. 2007). A variety of management approaches has been developed and employed worldwide to mitigate the depletion of fish stocks and the unsustainability of marine fisheries (Caddy and Cochrane 2001; Makino 2011; McPhee 2008; Cunningham and Bostock 2005; Årland and Bjørndal 2002; Shen and Heino 2014; Charles 1997). Among these regulatory tools, input controls are considered more easily enforced than output measures (Beddington et al. 2007). As one case of input restrictions, vessel quantity control is widely applied in global fishery management. In the enforcement of this regulatory tool, which vessel should be controlled is vital and therefore affects the effectiveness of vessel quantity limit. This requires a comprehensive grasp of vessels’ characteristics, where the technical efficiency (TE) is a significant component. The TE gap of different fishing vessels should be taken into consideration when fishery managing authority decides to regulate fisheries through vessel quantity control; otherwise, the projected results may not be fully achieved. Therefore, integrating technical efficiency analysis in the design and implementation of management approaches is supposed to facilitate effective governance of fisheries; thus, better mitigation of overfishing problem could be achieved. Pacific saury (Cololabis saira), a highly migratory pelagic marine fish species, is distributed widely over the North Pacific Ocean and generally harvested by Japan, Russia, Korea, Taiwan, and China (Watanabe et al. 1988; NPFC 2015). In recent decades, the economic structure of global Pacific saury fishery has undergone enormous changes. Figure 1 reveals that Japan had kept the top one position in saury catch until 2012 with a declining trend, while Taiwan showed a continuous increase in saury harvest and exceeded Japan in 2013. In recent years, China is devoting great effort to develop distant Pacific saury fishery, and the catch was estimated to be around 77,000 t in 2014 (Yantai Daily Newspaper 2015). Concerns over the potential depletion of saury stocks have been raised. The global economic structural change of Pacific saury fishery has promoted the call for a multilateral management of Pacific saury fishery in order to realize sustainable utilization rather than the overexploitation of fisheries stock. For achieving longterm conservation and sustainable use of fisheries resources and protecting the marine ecosystems of the North Pacific Ocean (NPFC 2015), the Convention on the Conservation and Management of High Seas Fisheries Resources (hereinafter referred to as Convention) was negotiated and adopted in 2012 by Japan, Canada, Russia, Korea, China, the United States (USA) and others. The Convention entered into force in 2015 with a target of all the marine species caught in the Convention area, which also covers Pacific saury. An intergovernmental organization named the North Pacific Fisheries Commission (NPFC) was therefore established. The first meeting of NPFC was held in Japan during September of 2015, with the focus on the stock status of Pacific saury and the establishment of management approaches. As

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summarized by the Japanese Fisheries Agency (JFA), vessel quantity control and vessel registration are put forward to be considered in future management frameworks (JFA 2015). Vessel quantity limit is also considered as an appropriate way to sustainably manage the Pacific saury fishery in scientific view (Huang and Huang 2015). In this paper, the stochastic frontier analysis (SFA) approach is applied to the Pacific saury stick-held dip net (SHDN) fishery in Habomai region, one of the major saury producing areas in Japan. From 2003 to 2012, the total catch of Pacific saury in Japan remained relatively stable between 200,000 and 360,000 t reported by the Ministry of Agriculture, Forest and

Fig. 1 Production of Pacific saury in Russia (a), Korea (b), Japan (c), Taiwan (d), and the aggregated production (e) (source: from the report Stock assessment of the Northwest subpopulation of Pacific saury in 2014 published by Fisheries Research Agency of Japan excluding China)

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Fisheries (MAFF), more than 96 % of which was caught by an effective fishing gear called stick-held dip net (MAFF 2012). The production process, TE scores, and possible determinants of technical inefficiency are examined for exploring the production characteristics and understanding potential factors for efficiency differences of the targeted fishery in a quantitative basis. This study aims to provide an example of integrating technical efficiency analysis into fisheries management by taking the Pacific saury fishery in Japan as the case study. The related results are also expected to provide useful implications for the construction of the international Pacific saury management framework in the near future.

2 Methodology 2.1 Concept of technical efficiency The technical efficiency measures the capacity of one decision-making unit (DMU) (i.e., fishing vessel in the context of capture fisheries) to maximize its output given the inputs (output-oriented) or minimize its inputs given the output (input-oriented) (Farrell 1957). To be more specific, in the output-oriented case, the ratio of actual output by one fishing vessel to its potential output frontier is the TE value of this vessel (between 0 and 1). If the TE value is 1, the vessel operates on its production frontier, implying that it is fully efficient, while the TE close to 0 indicates that the vessel operates in an extremely poor efficiency.

2.2 Methodologies of technical efficiency evaluation Generally, the approaches for studying the TE can be classified into two categories, parametric and nonparametric methods. The typical representatives of these two types of approaches are SFA and data envelopment analysis (DEA), respectively. SFA is based on estimating a specified function with a composite error term, consisting of a random error and a term representing firms’ technical inefficiency, while DEA applies linear programming to construct a frontier with only one error term as technical inefficiency (Coelli et al. 2005). Hence, SFA takes noise into consideration but may result in misspecification error, while DEA can avoid the misspecification problem but contributes all the deviation from the frontier exclusively to technical inefficiency (Felthoven 2002). In the fisheries context where the random nature is fundamental, noise should be incorporated into the empirical model, which may suggest that SFA tends to be an appropriate approach to conduct TE analysis (Kirkley et al. 1995). The SFA approach has rapidly developed since 1970s and was applied in numerous empirical studies covering an extensive range of industries, such as banking, agriculture, manufacturing, and tourism (Battese 1992; Battese et al. 2000; Anderson et al. 1999; Kalirajan and Tse 1989). In the capture fisheries context, Kirkley et al. (1995) were believed to be the pioneers to employ a stochastic production frontier in estimating the TE of the MidAtlantic sea scallop fishery (Fousekis and Klonaris 2003). Overall, the empirical studies of the SFA in the context of capture fisheries can be mainly classified into three categories according to their objectives and data availability: (1) estimating the TE scores of each decision-making unit and conducting preliminary economic analysis (Kirkley et al. 1995; del Hoyo et al. 2004; Kim et al. 2011; Sakai et al. 2012); (2) conducting the same analysis as the first category and further finding the underlying factors resulting in technical inefficiency when sufficient data are available (Kirkley et al. 1998; Campbell and Hand 1998; Sharma and Leung 1998; Pascoe

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and Coglan 2002; Fousekis and Klonaris 2003; Tingley et al. 2005; Esmaeili 2006); and 3) evaluating the effects of some management approaches on the TE based on the same analysis with the second category (Pascoe et al. 2001; Kompas et al. 2004).

2.3 Model of the SFA approach The SFA model is composed of two parts. One part estimates the parameters of production functions and TE scores of each DMU, and the other evaluates the possible factors influencing TE scores. As the first component of the SFA model, the stochastic frontier production function was independently constructed by Aigner et al. (1977) and Meeusen and van den Broeck (1977). In the SFA, the production model for panel data is specified as follows: Y it ¼ f ðX it ; β ÞexpðV it −U it Þ

ð1Þ

where Yit represents the production by the ith DMU (i = 1, 2,..., n) in the tth time (t = 1,2,..., p), Xit denotes a 1 × k vector of input quantity applied by the ith DMU in the tth time, and β is a k × 1 vector of parameters to be estimated. Vit is a random error term which is assumed to be independently and identically distributed, attributed to factors beyond the control of DMUs, while Uit is used to describe the error term caused by technical inefficient performance of DMUs, which is nonnegative and usually takes four types of distributions, i.e., half-normal, truncated normal, exponential, and gamma. Among the literatures of the SFA in the aspect of production, the functional forms of production usually adopted are Cobb-Douglas and translog production functions. For the technical inefficiency model, it was specified by Battese and Coelli (1995) as follows: U it ¼ Z it δ þ W it

ð2Þ

where Uit designates technical inefficiency of the ith DMU in tth time, Zit represents the DMUspecific variables which are considered to exert their influences on the inefficient performance, δ is a vector of unknown parameters, and Wit is random error. TE scores of production for each DMU can be defined as follows: T E it ¼ expð−U it Þ ¼ Y it=f ðX it ;βÞexpðV it Þ

ð3Þ

2.4 Analytical data and model specification 2.4.1 Analytical data As the empirical example of our research, the Pacific saury fishery in Japan is studied, which is mainly operated using an effective fishing method called stick-held dip netting. Data from MAFF in Japan shows that more than 96 % of Japan’s Pacific saury was caught by SHDN (MAFF 2012). Among the SHDN saury fishing vessels, those larger than 10 GRT are managed by the national government (MAFF), contributing the most to Japanese saury production. When further considering these vessels managed by MAFF, vessels of 19–29 GRT account for over 60 % with respect to vessel quantity (JFA 2016). This implies the important position of SHDN 19–29 GRT vessels in Japanese saury fishery production.

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In this study, an unbalanced panel dataset of 12 sampled fishing vessels in Habomai region of Japan employing SHDN to catch the Pacific saury from 2009 to 2014 are acquired from the Habomai Fisheries Cooperative Association (FCA). This sample covers nearly 60 % of the Pacific saury SHDN fishing vessels in Habomai region composed of both 19 GRT and 29 GRT vessels, which can be considered as representative. The dataset includes one output, five inputs, and other vessel/skipper specific information which are necessary to conduct TE analysis. The dependent variable is the monthly landings of Pacific saury by each sampled fishing vessel measured in tonne. It is known that one limitation of the SFA approach is that it can usually only deal with a single output. This becomes a problem when applying the SFA in studying the efficiency of fisheries, because many fisheries are multi-species or produce multi-output in other words. A composite output index and the adoption of production value are two ways to solve this problem. However, this will be simply dealt with in the Pacific saury SHDN fishery in Habomai as more than 95 % of the catch by this fishing method is Pacific saury (MAFF 2013), which is a very special characteristic. Our dataset includes vessel gross registered tonnage (GRT), vessel length, number of fishing days per month, crew size (including the skipper) per month, and yearly stock biomass, with the first four categories obtained from the Habomai FCA while the last was acquired from the annual report by the Fisheries Research Agency in Japan (FRA 2014). As shown in Table 1, for the sampled fishing vessels, GRT ranged from 18 to 29 GRT, with a sample mean of 22.1 GRT; fishing days per month changed from 0 to 21 days, with the mean value of 10.5 days; monthly crew size ranged from 5 to 10 persons, with the sample mean of 7.4 persons; skippers’ fishing years were from 10 to 45 years, with a relatively large value of 31.9 years; and skippers’ ages were from 35 to 67 years old in 2016, with its mean value of 53.3 years, indicating an aging trend of the skippers. One of the fishing vessels in the sample is specialized in Pacific saury fishery while the remaining vessels also catch salmon (Oncorhynchus keta) and trout (Oncorhynchus mykiss) from May to July, or harvest cod (Gadus macrocephalus) from December to March.

2.4.2 Model specification In fisheries, production functions are generally described as the relationship between production and fishing effort as well as stock biomass (Cunningham and Whitmarsh 1980;

Table 1 Summary statistics of the sampled fishing vessels Variables

Description

Mean

Max

Min

Output (tonne)

Monthly saury landings

190.9

608.2

Ton (GRT)

Vessel tonnage

22.1

29

18

4.7

Day (day)

Monthly fishing days

10.5

21

0

5.1

Man (person)

Monthly crew size

7.4

10

5

Stock (1000 t)

Yearly saury stock biomass

3756

1920

rso

Dummy variable for the skipper-owner relationship

0.7

1

0

0.5

sis vt

Dummy variable for specialization Dummy variable for large tonnage vessel

0.1 0.3

1 1

0 0

0.3 0.5

sfy (years)

Years of skipper engaging in fishery

31.9

45

10

9.7

sa (years)

Skipper’s age

53.3

67

35

8.9

2573

4.3

SD 144.7

1.3 585

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Hannesson 1983; Fousekis and Klonaris 2003). And the empirical model in this study takes the form of Cobb-Douglas instead of the translog production function following Sakai et al. (2012). The specified production model is described as follows: lnY it ¼ β 0 þ β 1 ilnTonit þ β 2 ln Dayit þ β 3 lnManit þ β 4 ln Stock t β 5 Dum9 þ β 6 Dum10 þ β 7 Dum11 þ V it −U it

ð4Þ

where lnYit represents the natural logarithm of monthly landings of the Pacific saury by the ith vessel (i=1, 2,…, 12) in the tth month (t = 1, 2,…, 24). As some vessels did not operate in several months, the unbalanced panel dataset finally includes 277 observations. The input variables selected for this empirical model consist of vessel GRT, number of days fishing per month, crew size per month, and yearly stock biomass. Although the overall length is also available, it is excluded from the input list as it is proved to be highly correlative to vessel tonnage, which is considered as an important vessel physical factor for deciding the landings of Pacific saury fishery. Monthly dummy variables, representing September, October, and November, are also considered to include seasonal variations in stock as we could only get access to yearly stock biomass data. For the technical inefficiency model, it is specified in the form following Battese and Coelli (1995), U it ¼ δ0 þ

9 X

β k Z it þ W it

ð5Þ

K¼1

where Uit designates technical inefficiency of the ith vessel in the tth month, Zit represents the vessel- and skipper-specific variables which are considered to exert their influences on the inefficient performance of vessels, and Wit is the error term to explain random differences. In this study, nine variables are considered in the inefficiency model, i.e., dummy variable for the relationship between skipper and vessel owner (Z_rso) (1 if the skipper is also the vessel owner while 0 if the skipper is employed), dummy variable for specialization in Pacific saury fishery (Z_sis) (1 if the vessel is specialized in saury fishery while 0 if it also operates other fisheries), dummy variable for large vessel tonnage (Z_vt) (1 if the vessel is 29 GRT while 0 if it is 19 or 18 GRT), years for skippers engaging in fishing (Z_sfy), skipper age (Z_sa), and fishing operation month dummy variables (from Z_dum8 to Z_dum11). The inclusion of monthly dummy variables in the inefficiency model is expected to evaluate whether different months affect the TE of saury vessels. The choice of variables included in the SFA model is based on data availability and review of related literatures (Pascoe and Coglan 2002; Kirkley et al. 1995, 1998; Sakai et al. 2012).

3 Results and discussion 3.1 Parameter estimates of the stochastic production frontier model Based on the maximum likelihood estimation approach, the estimated results of stochastic production frontier are presented in Table 2. The selected inputs in the production frontier are found to be positively associated with Pacific saury productions. Coefficients of the natural

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logarithm of vessel tonnage, monthly fishing days, monthly crew size, and yearly stock abundance are 0.4, 1.03, 0.87, and 0.37, respectively. These above four independent variables are significant at a 1 % level. This result implies that vessel tonnage, monthly fishing days, monthly crew size, and stock abundance are essential determinants of the Pacific saury output in the sampled fishing vessels from 2009 to 2014. As the empirical model takes the form of Cobb-Douglas production function, the parameter of input designates its elasticity. In this case, when vessel tonnage, fishing days, crew size, or stock abundance increase by one unit, the output of Pacific saury will be theoretically raised by 0.4, 1.03, 0.87, and 0.37 %, respectively. These four inputs show a positive correlation with the Pacific saury output. Furthermore, the empirical results in this study prove that vessels with larger tonnage or taking more time fishing are supposed to catch a larger quantity of the Pacific saury, when other inputs are constrained to be constant. And when the stock abundance is higher, fishing vessels will theoretically catch more Pacific saury. The increase in crew size is proved to theoretically contribute to a larger amount of saury production. A possible reason is that this is related with the vessel tonnage as a larger crew size often corresponds with a large vessel size. Meanwhile, three monthly

Table 2 Parameter estimates of the stochastic production frontier and technical inefficiency model for the sampled saury fishing vessels in Habomai region, Japan Variables

Parameter

Coefficient

Production frontier Constant

β0

−2.45***

ln(ton)

β1

0.40***

ln(day)

β2

1.03***

ln(man)

β3

0.87***

ln(stock)

β4

0.37***

Dum9 Dum10

β5 β6

0.44*** 0.72***

Dum11

β7

0.83***

Inefficiency model Constant

δ0

0.79

Z_rso

δ1

−0.34*

Z_sis

δ2

−0.70**

Z_vt

δ3

−0.37*

Z_sfy Z_sa

δ4 δ5

0.08*** −0.07***

Z_dum8

δ6

1.12**

Z_dum9

δ7

−1.64**

Z_dum10

δ8

−0.01

Z_dum11

δ9

Sigma-squared

σ2

0.44***

Gamma

γ

0.97***

Log-likelihood

1.32**

−50.11

*Statistically significant at 10 % level or less; **statistically significant at 5 % level or less; and ***statistically significant at 1 % level or less

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dummy variables for September, October, and November are all found to be significantly positive compared with August. The variance of the one-sided component γ is 0.97, and it could be used to calculate the relative contribution of technical inefficiency effects to the total variance term. The corrected relative contribution of technical inefficiency is equal to 88 % (Coelli 1995), implying that technical inefficiency plays a major part in explaining the deviation of actual output from potential output. The remaining portion of 12 % can be attributed to the random factors out of the control, such as weather and measurement errors.

3.2 Parameter estimates of the technical inefficiency model Results of the technical inefficiency model are also listed in Table 2. Among the selected explanatory variables for the inefficiency model, all of them except for the dummy variable for October show a highly significant relationship with technical inefficiency. It is important to remember that the dependent variable of the inefficiency model is technical inefficiency. A negative coefficient of one explanatory variable therefore means that it will facilitate the increase in the TE, and vice versa. The coefficients of Z_rso, Z_sis, Z_vt, Z_sa, and the dummy variable for September are negative; therefore, they are supposed to show a positive influence on the TE. With respect to Z_sfy and dummy variables for August and November, their coefficients are positive demonstrating a negative effect on the TE. The vessel ownership of the skipper shows positive influence on the TE in our sampled saury vessels. To be specific, if the skipper is also the owner of one vessel, this vessel seems to operate more efficiently than those vessels where the skipper is hired. The association between vessel ownership and the TE was also evaluated in the work of Squires et al. (2003). They studied the TE score and influencing factors of the TE in the Malaysian gill net artisanal fishery, where the non-owner operator dummy variable was considered in the technical inefficiency model. Although their results showed that vessel ownership was not significant in explaining differences in technical inefficiency, they mentioned that owning and operating a vessel may influence incentives which can be explained by the Marshallian inefficiency concept. This economic concept was originally applied in agriculture and states that the efficiency of owner-operated land is higher than rent land of the same household (Holden and Bezabih 2008). The findings also reveal that specialization in Pacific saury fishery may be another factor affecting the technical inefficiency differences. In this study, the fishing vessel specialized in Pacific saury production (vessel 4) exhibited higher technical efficiency than most of those operating several types of fisheries in the same tonnage group (vessels 5, 6, 8, 10, 11, and 12), consistent with the results noted by Pascoe and Coglan (2002). To be more specific, with the inputs being the same, the vessel which operates during saury season only is supposed to catch more fish than those vessels operating other fisheries when the Pacific saury season ends. This could possibly be explained by the fact that a fishing vessel specialized in Pacific saury fishery will catch for only 4 months during the whole year, indicating fishery income from catching saury may be insufficient for this vessel. In this situation, the skipper (also owner in vessel 4) may exhibit a high motivation in operation as he may exclusively rely on the income of Pacific saury landings. Therefore, it is the skipper/owner’s motivation underlying the specialization indicator that may positively affect the efficiency of Pacific saury fishing vessels. Incentives have been proved to possibly affect the efficiency of a fishing vessel mentioned in explaining the positive influence of vessel ownership.

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In the case of vessel tonnage, a positive effect on the TE can be observed, meaning that larger vessels tend to be more technically efficient than small ones. This result corresponds with the study conducted by Esmaeili (2006), in which the TE of larger vessels was 0.85 and that of smaller ones was 0.6 on an average. The same result was also proved by Waldo (2006), who examined the capacity and efficiency in Swedish pelagic fisheries and concluded that larger vessels seemed to be preferred than small vessels in the perspective of efficiency. Based on our study result, fishing vessels with larger tonnage are proved to be more technically efficient that those with smaller tonnage. Although the skipper’s age and his experience in fishery are both significantly related with the TE in our sample, the effects are contradictory. As one vessel with an older skipper operates more technically inefficient than that with a younger skipper, the longer years in fishery operation negatively influence the TE of the sampled vessel. The coefficients of monthly dummy variables reflect that the TE in September is higher than that in August and November, which may be explained by the differences in Pacific saury stock biomass.

Fig. 2 Technical efficiencies of the 12 sampled saury fishing vessels from 2009 to 2014

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3.3 Technical efficiency estimates In Fig. 2, results show that the mean TE of the 12 sampled vessels catching Pacific saury in Habomai was 0.7 from 2009 to 2014, ranging from 0.59 (vessel 12) to 0.79 (vessel 9). Among the 12 fishing vessels, the average TE for the four 29 GRT fishing vessels was 0.72, while it was 0.69 for the 19 GRT category vessels. In terms of the monthly TE, the maximum value was 0.97 (vessel 4 in Oct. 2012) while the minimum value was 0.09 (vessel 4 in Nov. 2009). The average TE for the sampled Pacific saury SHDN fishery in Habomai region is estimated to be about 0.7, indicating that there exists an appreciable potential for the sampled fishing vessels to improve their Pacific saury catches. They could increase the Pacific saury production by 30 % at the present state of technology without adding any variable or fixed inputs. The efficiency score of the sampled fishery suggests that there is still scope to increase the saury output through approaching fully technically efficient operation, which should be addressed when management approaches are directed at controlling and limiting further expansion.

3.4 Implications for mitigating overfishing problem in fisheries Technical efficiency was calculated and potential factors affecting inefficiency were investigated with respect to the Pacific saury fishery in our study. The main findings suggest that vessels with large tonnage or enthusiastic owners/skippers tend to be more technically efficient than others. This provides important information for policy makers to construct multi-lateral management framework for global Pacific saury fishery in the near future. Pacific saury fishery around the world is undergoing great changes as more countries or regions participate in and vigorously develop this fishery. Increasing concerns over the potential depletion of saury resources are raised and an intergovernmental management framework is considered as urgent, where vessel quantity control is put forward as an appropriate way. Nevertheless, whether this control can sustainably manage international saury fishery is affected by its effective removal of excessive catching capacity. Relevant results also offer important implications for mitigating the overexploitation issue in global fisheries. Conducting technical efficiency analysis will be beneficial in guiding policy makers to design effective regulatory tools for fisheries. For example, if policy makers plan to use vessel quantity restriction as an approach to manage an over-exploited fishery, technical efficiency study of this fishery is desired to be carried out for understanding the differences in real catching capacity of various vessels. Controlling vessel quantity can therefore achieve the objective of removing overabundant catching ability in a more effective manner. Which vessel needs to be controlled becomes a key point in the application of quantity limit. Based on our study results, vessels with large vessels or those with enthusiastic owners/skippers are proved to be more efficient. Policy makers therefore shall pay particular attention to these vessels when designing management strategies.

4 Conclusions Marine capture fisheries are facing the stress of overexploitation in a global scope, indicating that fisheries resources are being utilized at an unsustainable level. To mitigate the change in fisheries, government regulatory tools are vital and being applied worldwide. Nevertheless, the anticipated effects of fisheries management approaches may not be fully achieved due to their

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limitations. Vessel quantity control, as one of these regulatory tools, is being widely employed but may not function well if the gap in technical efficiency of different vessels is not taken into account. To achieve effective management of fisheries, technical efficiency of fishing vessels operating in the same fishery needs to be evaluated in the implementation of vessel quantity control. Pacific saury fishery around the world is undergoing great changes since more countries or regions embark on participating in and vigorously developing saury fishery. The rapid increase in saury fishing vessel quantities and global saury catch bring about disputes among the saury fishing countries or regions. Meanwhile, increased concerns over the potential depletion of Pacific saury fisheries resources have also been raised and an inter-governmental management framework is considered as urgent. Among the potential management approaches, vessel quantity control is put forward to be considered (JFA 2015) and is regarded as an appropriate way to sustainably manage the Pacific saury by scientific scholars (Huang and Huang 2015). Therefore, this study selected one of the typical saury producing regions in Japan and estimated TE scores as well as possible influencing factors by means of the SFA approach. Results show TE scores of the sampled fishery averaged around 0.7 from 2009 to 2014, and ownership of vessels, specialization, vessel tonnage, and skipper’s age show positive effects on the TE. The technical efficiency score of Japanese Pacific saury SHDN fishery in the sampled region is less than 1, indicating that it still has a considerable potential to increase the output, which should be paid particular attention. When constraining the development of fishery or conserving the fish stock becomes the priority of fishery management, a clear view of each vessel’s potential capacity and current status would help the policy makers understand the differences in vessels’ real catching ability. Those vessels with high efficiencies therefore should be particularly addressed. Meanwhile, when the vessel quantity control or vessel buyback program is considered, the efficiency evaluation approach can promote the effectiveness of these tools. The vessels with low efficiency should also be tracked and monitored as they may possibly improve efficiency in the future. In this case, the analysis of factors affecting efficiency will provide important information for policy makers. Results of our study show that skippers or owners’ incentives and large vessels can bring about a higher technical efficiency of vessels. Therefore, vessels with low efficiencies at present may increase their production when the above factors are addressed and improved. In the establishment of management strategies, the possibility of vessels to enlarge their size and to increase incentives for aggressive fishery operation should also be taken into account. Based on the results of our study, important conclusions could be drawn which inform mitigation strategies for global fisheries. Firstly, technical efficiency analysis is a vital issue to be considered for achieving effective design of management approaches regarding overexploited fisheries. The analytical framework in the present study could be extended to other fisheries in different countries or regions, where overfishing continues to be a problem and regulatory approaches are required. This method can be carried out to evaluate the efficiency of fisheries and potential factors causing gap in efficiency. The acquired results are supposed to offer important insights into the real catching capacity of fishing vessels and facilitate an appropriate and effective design of management controls such as input restrictions, which consequently results in a better mitigation of overfishing problem in fisheries. Secondly, vessels with large tonnage or enthusiastic owners/ skippers should be particularly addressed when vessel quantity control is considered in intergovernmental management framework of global Pacific saury fishery, because our

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study implies that vessels with these two characteristics tend to be more technically efficient than others. These strategies could facilitate effective management of fisheries and therefore better mitigate the overfishing problem in a global scope. Acknowledgments The authors express their sincere thanks to the staff of the Habomai Fisheries Cooperative Association for providing the valuable data and information. We would like to thank the China Scholarship Council (CSC) for providing the scholarship to the first author.

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