Effects of Incentive Policy on Maritime Stakeholders in

Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019

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Effects of Incentive Policy on Maritime Stakeholders in Japanese Local

Ports

Tomoya KAWASAKI a, Hoshi TAGAWAb, Takumi TAMANEc,

Shinya HANAOKAd, Toshihiro WATANABEe

a,b,d,eDepartment of Transdisciplinary Science and Engineering, Tokyo Institute of Technology

aE-mail:[email protected]

bE-mail: [email protected]

cE-mail: [email protected]

dE-mail: [email protected]

eFaculty of Business Administration,Ishinomaki Senshu University

eE-mail: [email protected]

Abstract: This study simulates the effects of “incentive policy” on maritime stakeholders in

Japanese local ports. In incentive policy,local government (owner of the port) provides

monetary support for shippers and/or shipping company in order to obtain more containers.

We employ a multi-agent simulation model to express interactions among stakeholders, such

as port manager, shipping company, and shipper. The model is applied to the case study for

local ports in Kyushu region, Japan where three local ports (i.e. Miike, Kumamoto, and

Yatsushiro ports) plus one major port (i.e. Hakata port) are available. We find that incentive

policy for shipper is not effective in terms of total surplus in spite of slight increase in

container volume are observed. On the other hand, incentives for shipping company is able to

increase both container volume and total surplus only if shipping company could increase

their port of calls (i.e. frequency) by the monetary support given by local government.

Keywords:Incentive Policy, Subsidy, Local Port, Multi-agent Simulation Model, Japan

1.INTRODUCTION

There are more than 80 ports dealing with containers in Japan (MOF, 2018). The most of the

ports deal with small volume of containers (e.g. less than 20,000 TEU per year) comparing to

Japanese major ports (i.e. Tokyo, Yokohama, Kobe, etc.). These small-scale local ports highly

compete with one another. In this situation, local ports support a part of port charges as a

subsidy or exempt port charges for shippers and/or shipping company,which is an ocean

carrier, in orderto obtain more containers from other rival ports in proximity. In Japan, this

policy is called as “incentive policy”, which 65 Japanese local ports have been enforced since

1990s. Since similar incentive policy in terms of amount of incentives among rival ports,

effectiveness of incentive policy is questioned (Kayano and Ishiguro, 2014). Several local

governments are discussing change in incentive policy for more effective implementation in

terms of providing more amount of incentives, stopping incentive policy, and changing

incentive beneficiaries. Several studies (e.g. Brooks(2004); Helling and Poister(2000);

Haralambides,(2014)) argue effectiveness of port subsidy; however, its effect on each

stakeholder are not addressed. Thus, it is necessary to explore how incentive policy can be

more effective in terms of container volume and total surplus of all stakeholders in local

Corresponding author.


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maritime industry. For each type of incentive policy, effect on each stakeholder is expected to

be different. For example, increase in the amount of incentives would be resulted in increase

in cargo volume and consumer surplus while profit of port manger would be decreased. In this

way, several stakeholders would be differently affected by each incentive policy. Therefore,

the objective of this study is set as; (i) to develop a multi-agent simulation model to evaluate

the effect of incentive policy on each stakeholder and (ii) to explore the effective incentive

policies in terms of container volume and total surplus of the region.

The effects of incentive policy are examined by using indicators such as container cargo

volume, total surplus, profit of shipping lines, consumer surplus, etc. Port management is

fairly complex due to the involvement of several stakeholders and interaction with one

another. Bonabeau (2002) states that the benefit of the multi-agent simulation model, which is

defined as a simulation model for simulating the actions and interactions of agents to assess

their effects on the whole system, is the provision of a natural description of a complex

system in which behavior of stakeholders interact with each other. Thus, in this study,

multi-agent simulation model is developed to describe the port management and operation

systems. The model is applied to case study for local ports in Kyushu region, Japan where

three local ports (i.e. Miike, Kumamoto, Yatsushiro ports) plus one major port (i.e. Hakata

port) are compete one another.

The rest of this paper is structured as follows. Section 2 conducts extensive reviews of

the existing literature regarding port competition and subsidy policy. Section 3 explains the

target of the study and actual situation of target ports and incentive policies. In Section 4, a

simulation model is developed form multi-agent perspective considering interaction among

the stakeholders. Subsequently, as a case study, the developed model is applied to Kyushu

region and the effects of incentive policies are examined in Section 5. Finally, conclusions

and directions for further researches are given in Section 6.

2.LITERATURE REVIEWS

Main purpose of incentive policy is to make their ports superior to rival ports. Thus, papers

related to port competition are firstly reviewed. As Slack (1985) suggests, port charges and

level of service are fundamental factors for port competitiveness. Heaver (1995) finds

appropriate public port policies and strategies of port managements are needed to be

competitive. Cullinane et al. (2004) and Yap et al. (2006) analyze port competition in East

Asia where Hong Kong and Busan port are the distinctive beneficiaries from inter-port

competition in the region for the past three decades. Ishii et al. (2013) examines the effect of

inter-port competition between two ports by game theoretical approach. Since Kobe port

missed to make port charges lower at proper timing, large portion of container cargoes were

shifted to Busan port. Luo et al. (2012) analyses port competition considering both pricing

and capacity expansion strategy. From the result, smaller ports with elastic demand and lower

operation/investment cost are more preferable to expand in an increasing market. Wang et al.

(2014) discuss the competition among various carriers using game theory to set the optimum

price to maximize profits. With increasing container demand in the market, expanding ship

capacity setting is preferable due to its low marginal cost. Other several studies explore port

competition (e.g. Anderson et al. 2008, Hoshino 2010, Agarwal and Ergun, 2010, and

Alverez-San et al. 2015) and find that port competitiveness are highly dependent on strategies

taken by port manager.

Papers related to port subsidy can be found as many as port competition. Heaver(1995)

describesthat inefficiencies are likely to arise if subsidies or other institutional interventions


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distort the competitive relationship between the ports. Regarding the US ports, Brooks(1992)

argues unfair subsidies such as government loans, tax-exempt revenue bonds and dredging by

US Army Corps of Engineers have been denounced and promoted an uneven playing field

between ports both nationally and internationally. Helling and Poister(2000) argue subsidized

port competition in the US may have causedexcessive port development; in particular, subsidy

distorts the price formation and overcapacity. Terada (2002) also points out investment and

subsidy by Japanese port authority are excessed. Regarding the subsidy from port

authority,Qu et al. (2017) divide maritime stakeholders into three (mainline carriers, feeder

carriers, and shippers) and analyze the effect of subsidies to each stakeholder. The result

shows that port subsidies change the market equilibrium. Subsidies to mainline carriers

increase the profit of the entire mainline-feeder liner while those to shippers decrease the

equilibrium of freight rates. As for subsidies to feeder carriers, it reduces operational costs and

increase profitability. Xu et al. (2015) simulate the case where forwarders are provided

subsidy and concluded that repositioning problems can be solved by the subsidy. As for

subsidies to maritime passengers, Jimenez et al. (2018) analyze the efficiency of subsidies in

European maritime passenger routes and concludes subsidized routes make price per

kilometer around 40% up and nonresident passengers suffer higher prices. Yan et al. (2014)

mention that a favorable port charge system and subsidies are also needed to increase the use

of domestic container carrier shifted from land transportation in Taiwanese case. Seo and Ha

(2010) reveals that the port size and incentives play an important role for user’s port selection

by using analytic network process. Almen and Hernandez(2014) argue that promoting port

efficiency in EU’s short sea shipping might be a more suitable target to increase the modal

split of short sea shipping, rather than subsidizing firms to transfer cargo from road to sea.In

Japanese context, study on port subsidy is fairly limited. Only Kayano and Ishiguro (2014)

analyze the effect of incentive policy in Japanese local ports. From the result, effectiveness of

Japanese incentive policy is fairly small due to the implementation of almost identical

incentives with rival ports. Thus, one needs to change the amount or beneficiaries of

incentives so that its effectiveness is improved. In accordance with economic

principle,appropriate price of port charge can lead a port to prosperity and growth; however,

the inappropriate priceand subsidy would guide to inefficiency (Haralambides,2014).As seen

in several papers regarding port subsidies, there is no study regarding the evaluation of

subsidy for shipper and shipping company and its effect on each port stakeholder.

3.STUDYAREAAND INCENTIVE POLICY

The target ports of this study are three local ports in Kyushu region, Japan such as Miike,

Kumamoto and Yatsushiro ports plus one major port such as Hakata port as shown in Figure 1.

Three local ports are located in proximity one another, while Hakata port is located

geographically far from Kumamoto city (i.e. 120km). However, Hakata port is also one of the

competitors of three local ports for hinterland containers of local ports due to its high level of

service (e.g. frequency of trunk lines). Figure 2 shows the share of final port loaded (first port

unloaded) for international shipping for Kumamoto and Fukuoka prefecture. Note that

Kumamoto and Yatsushiro ports belong to Kumamoto Prefecture and Hakata and Miike ports

belong to Fukuoka prefecture. In both prefectures, more than 60% of cargoes choose Hakata

port as final/first port for international shipping in 2013. Interestingly,Hakata port deals with

large portion of containers attracted/generated at Kumamoto prefecture even though ports in

Kumamoto prefecture are geographically much superior to Hakata port. According to

statistics from each prefecture website, the majority of main destination of these containers


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isChina. Therefore, this study considers only Chinese route for the purpose of simplicity of

the model.

There is also competition among three local ports, which are owned and operated by

each local government. In order to obtain more containers from rival ports, each local

government had started “incentive policy”which is defined as subsidy provided by local

government (i.e. port manager) to shipper and/or shipping company in order to support

shipping cost for shipper and operating costs for shipping company. The contents of current

incentive policy of three local ports are shown in Table 1. In these ports, incentive policy is

implemented by almost same timing (i.e. early 2010s) and its beneficiary is only shipper; in

other words, shipping company is out of support. Note that some of the ports in Japan (e.g.

Yokohama, Shimizu ports, etc.) support shipping company as beneficiary of incentive policy.

As shown in Table 1, shippers newly use the port are given twice amount of incentives than

existing shippers. This is because each port intends to obtain shippers from other ports.

Figure 1. Port position of target port

(a) Kumamoto Prefecture

(Hinterland of Kumamoto and Yatsushiro Port) (b) Fukuoka Prefecture

(Hinterland of Miike port)

Figure 2. Final port loaded/first port unloaded for international shipping of each prefecture

(Source: MLIT, 2014)

Table 1.Current incentive policy of three local ports

(Source: website of each port) Port Miike port Kumamoto port Yatsushiro port

Start year 2010 2011 2012

Beneficiary Shipper Shipper Shipper

Amount of incentive

(JPY/TEU)

10,000 (New user)

5,000 (existing user)

20,000 (New user)


20,000 (New user)



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4.SIMULATION MODEL

4.1Interrelationships among Stakeholders

Port management and operation is complex and several stakeholders are involved and interact

one another. Bonabeau (2002) states that the benefit of the multi-agent simulation model is to

provide a natural description of a complex system in which stakeholders interact with one

another and no stakeholder has centralized administrative power. Therefore, in this study, the

multi-agent simulation model is applied to describe the port management systems and three

stakeholders such as port manager, shipping company and shipper are considered. The

relationships among stakeholders (details will be addressed in following sections) are shown

in Figure 3 while notations for simulation model are shown as followings. In this study,

Japanese Yen (JPY) is used for the currency (1USD=110JPY as of February 2018).

Figure 3. Interrelationships among stakeholders

Notations:

𝑜 Origin of cargo (Hinterland of Port i)

𝑖 Origin port

𝑘 Transhipment port

𝑑 Destination of cargo

𝛱𝑖 Profit of port manager of port i [JPY]

𝐶𝑆𝑜 Consumer surplus at origin o [JPY]

𝑇𝑆𝑜 Total surplus at origin o [JPY]

𝑃𝑠𝑘 Profit of shipping company using port k [JPY]

𝐺𝐶𝑜𝑖𝑘𝑑 Generalized cost of shipper from origin o to destination d using port i and k [JPY/TEU]

𝐺𝐶𝑜𝑖𝑘𝑑𝑡

Generalized cost of shipper from origin o to destination d using port i and k at year t

[JPY/TEU]

𝑒𝑖 Port charge of port i [JPY/gross-ton]

𝑤𝑖 Handling charge of port i [JPY/TEU]

𝜏𝑖𝑘 Freight rate from port i to port k [JPY/TEU]

𝑓𝑖𝑘 Frequency of vessel from port i to port k [times/week]

𝑄𝑖 Total container cargo using port i [TEU]

𝑞𝑜𝑖𝑘𝑑𝑡 Total container cargo from origin o to destination d using port i and port k at year t [TEU]

𝑞𝑖𝑘 Total container cargo from port i to port k [TEU]

𝑄𝑂𝑜𝑑 Total container cargo from origin o to destination d [TEU]

𝐾𝑖 Annual handling capacity at port i [TEU/year]

𝑚𝑐𝑖 Maintenance cost at port i [JPY/TEU]


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𝑂𝐶𝑘 Operation cost of shipping company using port k [JPY-week/times]

𝑁𝑘𝑑 Navigation distance from port k to port d [km]

𝑉𝑘𝑑 Navigation velocity from port k to port d [knot/hour]

𝑆𝑘𝑑 Capacity of vessel from port k to port d [TEU/vessel]

𝑦𝑖𝑘 Fuel surcharge from port i to port k [JPY/hour-vessel]

𝑛𝑖𝑘 Navigation distance from port i to port k [km]

𝑣𝑖𝑘 Navigation velocity from port i to port k (feeder) [knot/hour]

𝑠𝑖𝑘 Capacity of vessel from port i to port k (feeder) [TEU/vessel]

𝑔𝑡𝑖𝑘 Gross tonnage of vessel from port i to port k [gross-ton/vessel]

𝑙𝑜𝑖 Land distance from origin o to port i [km]

𝑧 Unit cost of land transport [JPY/km-TEU]

𝑇𝑖𝑘𝑑 Navigation time from Port i to Port dtranshipped at port k [hour]

W(𝑓𝑖𝑘) Waiting time for a loading/unloading at port i [hour/times-TEU]

𝜉𝑜 Sum of incentives provided by port manager [JPY]

𝛿𝑜𝑖 Incentives to shipper [JPY/TEU]

𝜇𝑜𝑘 Incentives to shipping company [JPY-times/week]

𝛼 Value of time [JPY/hour-TEU]

𝛾 Converter to monetary value (unknown parameter) [JPY/TEU]

𝜃, 𝛽, 휀 Unknown parameter

4.2Port Manager

In Japan, local ports are generally managed by local government as a port manager. The role

of port manager is not only port owner but also port operatorto handle cargos at the port. Port

manager has discretionary power to determine port charge (𝑒𝑖) and handling charge of cargos

(𝑤𝑖) levied on the shipping company. In general, public bodies such as local government aim

to maximize total surplus as mentioned in Matsushima and Takauchi (2014) which show that

total surplus equals to domestic benefit that is calculated as the sum of all stakeholders’ profit

including shipper’s consumer surplus. As shown in Equation 1, consumer surplus (𝐶𝑆𝑜) and

profit of port manager (𝛱𝑖) are included as elements of total surplus while profit of shipping

company is not included. This is because public bodies aim to maximize total surplus of their

region while shipping companies calling for local ports of Kyushu are South Korean

companies but no Japanese companies. Therefore, it is appropriate that profit of South Korean

shipping company is not considered as total surplus as an effect of incentive policy provided

by local governments of Japan.Equation 2 shows the profit of port manager (𝛱𝑖). Port charge

(𝑒𝑖) and handling charge (𝑤𝑖) are incorporated as revenue whereas maintenance cost of berth

(𝑚𝑐𝑖) is considered as expenditure. Frequency (𝑓𝑖𝑘) represents the number offeeder lines to

Busan port per week. In order to calculate yearly profit, frequency is multiplied by 52.The

amount of incentives (𝜉𝑜) are included as cost of port manager. Equation 3 indicates amount

of incentives. Incentives are given by each port manager and thus, considered as cost of port

manager as shown in Equation 1. The consumer surplus (𝐶𝑆𝑜) shown in Equation 4 is

calculated by the rule of half, as Winkler (2015) applies. Consumer surplus is determined by a

linear relationship between generalized cost (𝐺𝐶𝑜𝑖𝑘𝑑𝑡 ) and container cargo volume (𝑞𝑜𝑖𝑘𝑑

𝑡 ).

𝑞𝑜𝑖𝑘𝑑0 and 𝐺𝐶𝑜𝑖𝑘𝑑

0 indicate total container cargo and generalized cost, respectively, from

origin o to destination d using port iandk at base year 2015.The values for the base year 2015

use the actual values. 𝜃 is a scale parameter that is estimated to minimize the gap between

actual and estimated container volume of all target Japanese ports in 2015.


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max𝑒𝑖,𝑤𝑖

𝑇𝑆𝑜 = 𝐶𝑆𝑜 +∑𝛱𝑖

𝑖∈𝑜

(1)

𝛱𝑖 = ∑(52 ∙ 𝑓𝑖𝑘 ∙ 𝑒𝑖𝑔𝑡𝑖𝑘 + 𝑤𝑖𝑞𝑖𝑘)

𝑘

−𝑚𝑐𝑖𝐾𝑖 − 𝜉𝑜 (2)

𝜉𝑜 = ∑∑∑𝛿𝑜𝑖 ∙

𝑑

𝑞𝑜𝑖𝑘𝑑𝑘𝑖∈𝑜

+∑∑𝜇𝑜𝑘 ∙ 52 ∙ 𝑓𝑖𝑘𝑘𝑖∈𝑜

(3)

𝐶𝑆𝑜 =∑∑∑1

2(𝑞𝑜𝑖𝑘𝑑

0 + 𝑞𝑜𝑖𝑘𝑑𝑡 )(𝐺𝐶𝑜𝑖𝑘𝑑

0 − 𝐺𝐶𝑜𝑖𝑘𝑑𝑡 )

𝑑𝑘𝑖

(4)

𝑞𝑜𝑖𝑘𝑑𝑡 = 𝑄𝑂𝑜𝑑

exp(−𝜃 ∙ 𝐺𝐶𝑜𝑖𝑘𝑑𝑡 )

∑ ∑ exp(−𝜃 ∙ 𝐺𝐶𝑖𝑘𝑡 )𝑘 𝑖

(5)

4.3Shipping Company

Shipping company is a private enterprise and determines freight rate (𝜏𝑖𝑘) and frequency (𝑓𝑖𝑘)

to call for each port in order to maximize own profit, as shown in Equation 6. As Sheng et al.

(2017) and Yin et al. (2014) discuss, the following costs are considered for the shipping

company. The fuel cost (𝑦𝑖𝑘) is proportionally changed with navigation distance (𝑛𝑖𝑘) as

Notteboom (2009) assumes. The loading/unloading cost (𝑤𝑖) proportionally changes with

container cargo volumes. Operation cost (𝑂𝐶𝑘), which includes the cost of crews, insurance

and so on, is included in Equation 6. Port charge (𝑒𝑖) set by port manager is considered as

expenditure of the shipping company. Finally, incentive to shipping company (𝜇𝑜𝑘 ) is

considered asrevenue and is exogenously given as scenario. There are several ways to

alleviate the burden of shipping company, such as tax exemption or reduction of the port

charge. Either way is able to reduce shipper’s generalized cost of haulage. In case shipping

company is supported by incentives, shipping company might discount freight rate to collect

more cargoes. In this study, subsidies in the scheme of incentive policy for the case of

shipping company is provided for one port of call.

𝑃𝑠𝑘 =∑(𝜏𝑖𝑘 − 𝑤𝑖)𝑞𝑖𝑘 − 52 ∙ 𝑓𝑖𝑘 {휀 ∙𝑦𝑖𝑘𝑛𝑖𝑘𝑣𝑖𝑘

+ 𝑒𝑖𝑔𝑡𝑖𝑘 + 𝑂𝐶𝑘}

𝑖

+ 52 ∙ 𝑓𝑖𝑘𝜇𝑜𝑘 (6)

4.4 Shipper

The shipper chooses a route by logit model based on generalized cost (𝐺𝐶𝑜𝑖𝑘𝑑) of haulage, as

shown in Equation 7, which indicates total container cargos from origin o to destination d

through port i and k. Note that freight forwarders sometimes conducta route, port, and mode

choice instead of shippersin practical; however, their behavior is also minimization of

generalized cost of shipper since they are the agents of shippers. Therefore, in this study,

shipper and freight forwarders are not explicitly separated as independent players; in other

words, shipper is assumed to be a player to choose a route, port, and mode of the

haulage.Equation 8 indicates total container cargos at port i. Generalized cost is calculated by

Equation 9 and is changed as a result of the behavior of other stakeholders. It consists of lead

time, port congestion cost, and freight rate, which are identified as important cost factors for

shippers in several studies (e.g., Kawasaki and Matsuda 2015; Kavirathna, et al.,2018). The


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congestion cost is incorporated as 𝑄𝑖/𝐾𝑖 on the basis of De Borger and Van Dender (2006)

and Basso and Zhang (2008). The freight rate (𝜏𝑖𝑘) is also components of generalized cost.

Land transportation cost (𝐿𝑜𝑖) can be calculated as shown in Equation 12. In this study, unit

land transport cost (𝑧 ) is set as 151 yen/km-TEU and 8,915 is an intercept derived

byMatsukura and Seta (2016). Following Tran and Takebayashi (2018), navigation time and

waiting time are calculated by Equations 10 and 11, respectively. Incentive to shipper (𝛿𝑜)

reduces shipper’s generalized cost.There are several ways to alleviate the burden of shippers,

such as tax exemption or reduction of port charge. Either way is able to reduce shipper’s

generalized cost of haulage. In this study, subsidies in the scheme of incentive policy are

provided for one container for the purpose of simplicity. To consider the unit of generalized

cost (JPY/TEU), incentives of shipper can be calculated in Equation 9.

𝑞𝑖𝑘 =∑∑𝑄𝑂𝑜𝑑exp(−𝜃 ∙ 𝐺𝐶𝑜𝑖𝑘𝑑)

∑ ∑ exp(−𝜃 ∙ 𝐺𝐶𝑜𝑖𝑘𝑑)𝑖𝑘𝑑𝑜

(7)

𝑄𝑖 =∑𝑞𝑖𝑘𝑘

(8)

𝐺𝐶𝑜𝑖𝑘𝑑(𝜏𝑖𝑘,𝑊(𝑓𝑖𝑘)) = 𝛼[𝑇𝑖𝑘𝑑 + 𝛽 ∙ 𝑊(𝑓𝑖𝑘)] + 𝛾 ∙𝑄𝑖𝐾𝑖

+ 𝜏𝑖𝑘+𝐿𝑜𝑖 − 𝛿𝑜𝑖 (9)

𝑇𝑖𝑘𝑑 = 𝑛𝑖𝑘𝑣𝑖𝑘

+𝑁𝑘𝑑

𝑉𝑘𝑑 (10)

𝑊(𝑓𝑖𝑘) = 7 ∙ 24

𝑓𝑖𝑘 (11)

𝐿𝑜𝑖 = 𝑧 ∙ 𝑙𝑜𝑖 + 8,915 (12)

4.5Solution Algorithm

The calculation is performed for every year from 2015 until 2030. In order to solve the

optimization problem, the Hooke–Jeeves pattern search (Hooke and Jeeves, 1961) is applied.

Pattern search is a widely used solution algorithm for solving the multi-agent optimization

problem (e.g., Liedtke, 2009). The algorithm is shown in Figure 4.

Figure 4. Solution algorithm


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Port manager first sets port charge (𝑒𝑖) and handling charge (𝑤𝑖). Thereafter, shipping

company sets the frequency of vessel (𝑓𝑖𝑘) and freight rate (𝜏𝑖𝑘) based on Equation 6.

Generalized cost of shipper can be calculated after decision making of shipping company.

Based on this, shipper makes a decision to choose a route based on Equation 6. It makes

possible to calculate profit of port manager (𝛱𝑖), profit of shipping company (𝑃𝑠𝑘) and total

surplus (𝑇𝑆𝑜 ). Finally, total surplus is repeatedly calculated until maximum values are

obtained, so that optimum 𝑒𝑖 and 𝑤𝑖 are obtained. In order to avoid divergence and to

reduce computational complexity, the range of value is set for each variable (i.e., 1.0 ≤𝑒𝑖≤ 3.0,

250 ≤𝑤𝑖≤ 350, 550 ≤𝜏𝑖𝑘≤ 750, and 0 ≤𝑓𝑖𝑘≤ 7).As for 𝑓𝑖𝑘, minimum value is set as zero, which

means shipping company is allowed to skip local port of Japan.

5.NUMERICAL ANALYSIS

5.1 Input Values and Assumptions

In order to simulate the effect of incentives, input values are prepared, as shown in Table 2.

Those values are differed depending on origins. Navigation velocity from port k to port d

(𝑉𝑘𝑑=17.2;Notteboom, 2009) and value of time (α=2,300; WAVE, 2011) are common values

at all routes and ports. The values such as 𝑚𝑐𝑖 and 𝑂𝐶𝑘 cannot be acquired as real data. For

such values,identical value in threelocal ports based on interviews with port managerssuch as

𝑚𝑐𝑖=110 and 𝑂𝐶𝑘=88,000 are set.As one of the input data, future container cargo volume is

needed. In this study, future container cargo volume at each year, which is forecasted on the

basis of future macroeconomic conditions by MLIT (2011), is used. As for port charges in

Kumamoto and Yatsushiro ports, which is determined based on gross-tonnage of the vessels,

they are zero as real situation. In Japan, several local ports set port charge as zero (MLIT,

2017) to attract shipping companies and shippers. Thus, we set input value of port charge of

Kumamoto and Yatsushiro port as zero as shown in Table 2.

Table 2. Input values Input values Miike Kumamoto Yatsushiro Hakata Source

Port charge of port iin 2015 [JPY/gross-ton] (𝑒𝑖)

2.7 0 0 n/a Website of each port manager

Terminal handling charge of porti in 2015 [JPY/TEU] (𝑤𝑖)

113 124 124 n/a Website of each port manager

Freight rate from port i to port k in 2015 [JPY/TEU] (𝜏𝑖𝑘)

330 300 280 500 Searates.com

Frequency of vessel from port i to port k in 2015 [times/week] (𝑓𝑖𝑘)

2 2 3 7 Website of each port manager

Annual handling capacity at port i [TEU/year] (𝐾𝑖)

1,200 720 770 n/a Website of each port manager

Fuel surcharge from port i to port k [JPY/hour-vessel] (𝑦𝑖𝑘)

650.1 658.6 677.5 n/a Notteboom (2009)

Navigation velocity from port i to port k (feeder) [knot/hour] (𝑣𝑖𝑘)

9.7 14.7 12.1 15.3 Marine Traffic.com

Capacity of vessel from port i to port k (feeder) [TEU/vessel] (𝑠𝑖𝑘)

600 360 385 2,000 Marine Traffic.com

Gross tonnage of vessel from port i to port k [gross-ton/vessel] (𝑔𝑡𝑖𝑘)

3,809 3,866 3,825 9,600 Marine Traffic.com


2269

Figure 5. Conceptual network of the study

In this study, there are several assumptions. The first assumption is that destination port

is assumed to be Shanghai port only for the purpose of simplicity as shown in Figure 5 while

cargo volume is used for all containers between target cities and China.The trade volume

from/to Kyushu region is mostly with China as mentioned in section 2. By assuming

destination port as Shanghai port, navigation distances (𝑁𝑘𝑑) between port k and d can be

obtained. Besides, target three ports currently have Busan route only (i.e. no domestic route

and Shanghai route from three local ports). Thus, transshipment port k denotes Busan port

only. The second assumption is that,according to interview surveys with several shipping lines,

Hakata port is not very likely to well consider other three local ports as competitors owing to

small scale of three local ports. Thus, this simulation model does not include Hakata port as

one of the stakeholders, which means service level of Hakata port is constant over the

year.From this reason, freight rate (𝜏𝑖𝑘, 500 JPY/TEU) and frequency (𝑓𝑖𝑘, 7 times/week) of

vessels to call for Hakata port are constant over the years from 2015 (base year) to 2030.

Consequently, four input values (port charge (ei), terminal handling charge (wi), annual

handling capacity(Ki), and fuel surcharge (yik)) of Hakata port, which affect service level of

shipping company to call for Hakata port, are not needed in this simulation model. From this

reason, these four input values are not applicable (n/a) as shown in Table 2.The third

assumption is that the capacity of vessel (𝑠𝑖𝑘,𝑆𝑘𝑑) is determined as the average size of actually

operated vessels in 2018 and is fixed over the year. By determining vessel size, it becomes

possible to calculate and determine navigation speed, gross tonnage of vessels, and fuel

surcharge by referring to Notteboom (2009).

5.2Simulation Results and Discussion

5.2.1 Reproducibility of developed model (Base case)

In order to simulate several scenarios related to incentive policies, first of all, current situation

is reproduced for the year 2015, where incentives for shippers at Miike, Kumamoto, and

Yatsushiro ports are currently being implemented as shown in Table 1. Current amount of

incentives for shippers are differed between new and existing users. For the simplicity of the

model calculation, current situation is reproduced for existing user case such as 5,000

JPY/TEU for Miike port and15,000 JPY/TEU for Kumamoto and Yatsushiro ports.Parameter

estimation is done as minimizingthe gap between actual and estimated container volumes in

2015. As a result, parameters are estimated as 𝛽=10, 𝛾=40,휀=0.01 and 𝜃=0.00119. Figure6

shows actual and estimated container volume at three local ports and Hakata port in 2015. In

overall, these values are fit well in terms of ratio of port choice although slight difference

between actual and estimated values are observed. Using simulation model developed above,

several scenarios related to incentive policies are conducted in following sections.

Hinterland of

Miike port

Kumamoto port

Yatsushiro port

Kumamoto Port

Miike Port

Yatsushiro Port

Hakata Port

Busan

Port

Origin o Port i

Transshipment

Destination d

Land transport International maritime shipping

Port k

Shanghai

Port


2270

Figure 6. Actual and estimated container volume ateach port

5.2.2 No incentives in all ports (Scenario 1)

As scenario 1, the case where all port manager stops incentive policy is analyzed. From this

scenario, it can be compared thatthe impact of with- and without-incentive policy. Simulation

results of the year 2030 are shown in Table 3. Note that simulation results other than the year

2030 (between 2015 and 2029)were also obtained; however, they are not shown in the paper

because any notable different trends from 2030 can be obtained.

Table 3. Simulation results of without incentive case for all local ports in 2030 case Miike Kumamoto Yatsushiro Hakata

Container cargo volume

[TEU]

Base case 22,599 18,678 27,454 1,309,587

Without

incentive

21,505

(-4.8 %)

16,595

(-11.2 %)

23,514

(-14.4 %)

1,316,704

(0.5%)

Consumer surplus

[thousand JPY]

Base case 12,015 10,244 28,787 -

Without

incentive

11,503

(-4.3 %)

9,098

(-11.2 %)

24,909

(-13.5%) -

Profit of port manager

[thousand JPY]

Base case 504,843 429,544 487,971 -

Without

incentive

687,432

(36.2 %)

628,918

(46.4 %)

745,032

(52.7 %)

-

Total surplus

[thousand JPY]

Base case 516,858 439,787 516,757 -

Without

incentive

698,935

(35.2 %)

638,130

(45.1 %)

769,941

(49.0 %) -

*Values in parenthesis indicate percent change from base case

From the results, container volume of all local ports would be decreased while that of

Hakata port is expected to be increased. Accordingly, consumer surplus is decreased while

profit of port manager is increased due to stopping incentive policy as expenditure. Finally,

total surplus of all three local ports is increased.Almost no shift between local ports and some

shift from local port to Hakata ports are observed if incentive policy is stopped in three local

ports. Thus, generalized cost for all route through local ports is equally changed since almost

identical incentive policy is simultaneously conducted in local ports. Consequently, in case

incentives are stopped in all local ports, containers are just shifted to Hakata port from local

ports.

From this result, we can understand that obtaining one containers from Hakata port

needs much higher cost than cost of incentives. Consequently, incentive policy for all three

ports currently being conducted can increase container volume; however, total surplus would

be decreased since benefit from increase in one container is smaller than the cost (i.e.

incentive) of obtaining one container from Hakata port.


2271

5.2.3Combination of with and without incentive cases (Scenario 2)

As second scenario, the case of different combinations of with and without incentive policy

amongst three local ports is discussed. The contents of current incentive policy being

implemented at three local ports are similar one another. As seen in the results of scenario 1,

stopping incentive policy is beneficial in terms of total surplus although container volume and

consumer surplus are decreased. Therefore, some of the port manager would possibly stop the

incentive policy to maximize total surplus of the region. In scenario 2, we assume that two

ports (i.e. Miike and Kumamoto ports) stop incentive policy while one port (i.e. Yatsushiro

port) continues current incentive policy. The simulation results are shown in Table 4. As

expected, Yatsushiro port which continues incentive policy increases containervolumeby 7.3%

comparing to base case in 2030. On the other hand, unexpected positive results are obtained

for total surplus. As shown in Table 4, total surplus is increased in all three ports. As for Miike

and Kumamoto ports, profit of port manager is increased due to stopping incentive policy

which is expenditure of port manager. On the other hand, profit of port manager in Yatsushiro

is increased since container cargo volume is increased. Although different reasons contribute

on increase in profit of port manger, these are the main reason to increase total surplus of each

port.

Table 4. Simulation results of with incentivesfor only one port (Yatsushiro port) in 2030 case Miike Kumamoto Yatsushiro Hakata

Container cargo volume

[TEU]

Base case 22,599 18,678 27,454 1,309,587

Only

Yatsushiro

20,187

(-11.9%)

15,697

(-19.0%)

29,626

(7.3%)

1,312,808

(0.2%)

Consumer surplus

[thousand JPY]

Base case 12,015 10,244 28,787 -

Only

Yatsushiro

10,832

(-9.8 %)

8,613

(-15.9 %)

31,343

(8.9 %) -

Profit of port manager

[thousand JPY]

Base case 504,843 429,544 487,971 -

Only

Yatsushiro

643,803

(27.5 %)

595,261

(38.6 %)

547,503

(12.2 %) -

Total surplus

[thousand JPY]

Base case 516,858 439,787 516,757 -

Only

Yatsushiro

657,636

(26.7 %)

603,874

(37.3 %)

578,846

(12.0 %) -

*Values in parenthesis indicate percent change from base case

Although we identify that incentive for only one port is better than currentincentive

policy in terms of total surplus, one needs to explore how indicators including total surplus

will be changed when the amount of incentives changed from current amount. Thus,we

assume additional cases where the incentive amountsof Yatsushiro port is changed while other

two ports are still no incentives. The results are shown in Figure 7. As incentive amount of

Yatsushiro port increases from zero, container volume, consumer surplus, and profit of port

manager increase due to obtaining containers from other ports including Hakata port. On the

contrary, total surplus of all ports decreases especially Yatsushiro port. This is because

obtaining one container needs higher cost than obtaining profit from one container for port

managers. Thus, profit of port manager in Yatsushiro is dramatically decreased as incentives

increase.


2272

(a) Container cargo volume(b) Total surplus

(c) Consumer surplus in target ports (d) Profit of port manager

Figure 7. Impact of change in incentive amount of Yatsushiro port oneach indicator

*No incentives are given at Miike and Kumamoto ports

Table 5. Comparison of total surplus for combinations of with and without incentive policies

in 2030

Incentive policy Total surplus [thousand JPY]

Miike port

Kumamoto

port

Yatsushiro

port Miikeport

Kumamoto

port

Yatsushiro

port

Case 1 (current) with with with 516,858 439,787 516,757

Case 2 with with without 550,909 465,731 695,331

Case 3 with without with 551,626 586,024 553,466

Case 4 with without without 591,908 617,506 739,068

Case 5 without with with 616,621 454,097 538,226

Case 6 without with without 654,410 482,484 721,587

Case 7 without without with 654,635 603,874 578,846

Case 8 without without without 698,935 638,016 769,941

Other combinations of with and without incentive policy among three local ports are

simulatedin order to explore appropriate combinations of incentive policies.Table 5 shows

total surplus of each case. Since incentive policies are implemented by public entity who

maximizes total surplus in their own region’s total surplus is observed for each case. Note that

incentive amount is same as base case in case port(s) implement incentive policy (with

case).From Table 5, total surplusof all regions is the smallest when all ports are implementing

incentive policy (i.e. current case) while the highest total surplus can be received when no

incentive conducted. The simulation results reveal that, if Kumamoto prefecture (Kumamoto

and Yatsushiro ports) implement incentive policy, it is better to focus on either port to increase

sum of the total surplus of Kumamoto prefecture.

10,000

15,000

20,000

25,000

30,000

35,000

0 5,000 10,000 15,000 20,000

TEU

Incentive amount at Yatsushiro port (JPY/TEU)(Base case)

400,000

500,000

600,000

700,000

800,000

0 5,000 10,000 15,000 20,000

Tho

usa

nd

JPY

Incentive amount at Yatsushiro port (JPY/TEU)

Miike

Kumamoto

Yatsushiro

(Base case)

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

0 5,000 10,000 15,000 20,000

Tho

usa

nd

JPY

Incentive amount at Yatsushiro port (JPY/TEU)(Base case)

400,000

500,000

600,000

700,000

800,000

0 5000 10000 15000 20000

Tho

usa

nd

JPY

Incentive amount at Yatsushiro port (JPY/TEU)

Miike

Kumamoto

Yatsushiro

(Base case)


2273

5.2.4Incentives for shipping company(Scenario 3)

In scenario 3, the case where subsidies as incentive policy is provided for shipping

company calling for local port is discussed. Note that incentives in three portsare currently

provided for shippers onlywhile some of the Japanese ports (e.g. Shimizu port)currently

implement incentive policy for shipping company as exempting port charges. In general, port

charge is levied on the basis of gross tonnage of vessel called for. The ratio of port charge in

total cost of shipping company is fairly small.Thus, shipping company does not so much care

about port charge compared to other operational costs such as fuel cost. Therefore, in this

study, the amount of incentive for shipping company is increased to observe the change in

behavior of shipping company and shipper. As an example, shipping company that calls for

Yatsushiro port is assumed to be given incentives by 500, 750, 800, and 1,000 thousand JPY

per a port of call per a vessel.The simulation results are shown in Figure 8.

Compared to base case (no incentive for shipping company), total surplus of hinterland

in Yatsushiro is slightly decreased until the incentive is 750 thousand yen. Nevertheless,

incentives are increased up to 800 thousand JPY, total surplus turns to increase. This is due to

increase in frequency of shipping company thanks to incentives.Monetary support more than

800 thousand JPY enable shipping company to increase frequency.Increase in frequency

induces more containers at the port and consequently, consumer surplus and total surplus

increases.Note that profit of port manager is decreased due to providing incentives for

shipping company.Compared to incentive policy for shipper, incentive for shipping company

would contribute more on strengthening competitiveness of the port and total surplus due to

increase in frequency of the vessels. Therefore, it can be concluded that incentives for

shipping company is more effective than that for shipper in case incentives are given to

shipping company till increasing frequency to call for the port.

(a) Container volume oftarget ports (b) Total surplus in target ports

(c) Consumer surplus in target ports (d) Profit of port manager

Figure 8.Impact of incentives for shipping company oneach indicator in 2030

0

1

2

3

4

5

6

7

16,000

18,000

20,000

22,000

24,000

26,000

28,000

30,000

No incentive(Base case)

500 750 800 1,000

freq

uen

cy (t

imes

/wee

k)

Co

nta

iner

Vo

lum

e (T

EU)

Incentive amount at Yatsushiro port (Thousand JPY)

0

1

2

3

4

5

6

7

400,000

420,000

440,000

460,000

480,000

500,000

520,000

540,000


500 750 800 1,000

freq

uen

cy (t

imes

/wee

k)

Tota

l su

rplu

s (t

ho

usa

nd

JPY)


frequency

Miike

Kumamoto

Yatsushiro

0

1

2

3

4

5

6

7

4,000

8,000

12,000

16,000

20,000

24,000

28,000

32,000


500 750 800 1,000

fre

qu

en

cy [t

ime

s/w

ee

k]

Co

nsu

mer

Su

rplu

s (t

ho

usa

nd

JPY)



2274

6.CONCLUSION

In this study, amulti-agent simulation model was applied to simulate the effects of several

incentive policies on container volume, each stakeholder, and total surplus. In the model,

interaction among stakeholders, such as the port manager, shipping company, and shipper are

considered. As incentive policies, subsidies are provided for shipper or shipping company.

Throughout of this study, the following are mainly found.

First of all, currentincentive policy seems to be not effective from the perspective of

total surplus; however, container volume can be increased. Therefore, current incentive policy

does not dramatically change shipper’s port choice and makes port manager’s cost higher. In

case the amount of incentives are increased, total surplus is dramatically decreased even

though container volume can be obtained from other rival ports. On the other hand, in case

incentive policy is not implemented at all in three ports, container cargo volume of three ports

is decreased (i.e. flowing out to Hakata port). However, total surplus in all ports can be

increased comparing to current practice due to no expenditure for incentive policy.

Second, incentive policy for shipper is not effective in terms of increase in total surplus

of the region regardless of any combination of with/without incentives and amount of

incentives.Total surplus is less than the case of no incentives. This is mainly because

obtaining one container by incentive needs higher cost comparing to benefit (e.g. port charge,

handling charge, etc.) gained from one container.

Finally, incentives for shipping company is able to increase frequency of port of calls.

Increase in frequency makes container volume increase and total surplus increase. However,

incentives for shipping company is not effective if amount of incentive is not sufficient to

increase frequency of shipping company; in this case, total surplus equally decreases as

incentive provided for shipping company. Consequently, incentives for shipping company is

more effective than for shipper only if shipping company could increase frequency of the port

of calls with sufficient incentive amounts.

Several issues remain for further research. Target ports of this simulation are only small

ports, which trade partner is mostly China. Thus, transshipment port is Busan port only and

there is no competitiveness between transshipment ports. For example, other large port such

as Kobe port have more routes; in this case, competitiveness between transshipment ports and

domestic and international shipping company would occurred. This remains for future work.


2275

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Documents

Effects of Incentive Policy on Maritime Stakeholders in