sustainability

Article Tourism as a Key for Regional Revitalization?: A Quantitative Evaluation of Tourism Zone Development in

Hyunjung Kim 1 and Eun Jung Kim 2,*

1 Institute of Construction and Environmental Engineering, Seoul National University, Seoul 08826, Korea; [email protected] 2 Department of Urban Planning, Keimyung University, Daegu 42601, Korea * Correspondence: [email protected]; Tel.: +82-53-580-5247

Abstract: Since the dawn of the 21st century, Japan has switched its national industry strategy from traditional industries—manufacturing and trading—toward tourism. Regional revitalization is a particularly important issue in Japan, and by uniting regions as an integrated tourism zone, the government expects an increase in visits to tourism zones. This study quantitatively evaluates whether the regions that contain a tourism zone experience a significant increase in visitors by using a quasi-experimental pretest–posttest control group design. Additionally, it examines the effects of subsidies through regression modeling. The results indicated that the tourism zones that were comprised of a narrow region in the same prefectures experienced a significant increase in visitors. The subsidy on information transmission, measures for the secondary traffic, and space formation had a significant positive impact on the increase in visitors to these tourism zones. Implications on tourism policies, urban and regional development, and community development can be obtained through this study.   Keywords: tourism zone development; regional revitalization; tourism nation; Japanese tourism Citation: Kim, H.; Kim, E.J. Tourism policy; tourism policy evaluation; pretest and posttest control group design as a Key for Regional Revitalization?: A Quantitative Evaluation of Tourism Zone Development in Japan. Sustainability 2021, 13, 7478. https:// 1. Introduction doi.org/10.3390/su13137478 According to the World Travel and Tourism Council’s report from 2019 [1], the tourism

Academic Editor: Carola Hein industry is the second-fastest growing sector in the world. The tourism sector contributed to 4.4% of the GDP, 6.9% of the employment, and 21.5% of the service-related exports of OECD Received: 22 May 2021 countries in 2020 [2]. Since the tourism sector’s contribution to GDP and employment Accepted: 29 June 2021 has been increasing, many countries have been promoting tourism as a key industry and Published: 5 July 2021 have revised their tourism law and suggested new tourism policies with the expectation of overcoming the economic recession [3]. Regardless of the fluctuating characteristics of the Publisher’s Note: MDPI stays neutral tourism industry [4], many countries consider it an agile and accessible solution for the with regard to jurisdictional claims in new service economy, given the weakening of many other aspects of the economy [5]. published maps and institutional affil- Japan is plagued by a significant population decline, aging society, and long-term iations. national debt that was equivalent to 236.57% of its GDP in 2018 [6]. In its search for countermeasures, the Japanese government identified tourism as one of the keys to solving the nation’s economic issues and opted to enhance its importance as a national strategy [7]. A “tourism nation” is one that seeks to strengthen its economy through tourism [8–11]. By Copyright: © 2021 by the authors. labeling itself as a “tourism nation,” Japan has displayed its intent to reorient its economy Licensee MDPI, Basel, Switzerland. toward tourism from manufacturing and trading, which have been the critical basis of This article is an open access article its economy for last decades. The Tourism Nation Promotion Basic Law was completely distributed under the terms and revised in 43 years, as the Japanese government positioned tourism as one of the pillars conditions of the Creative Commons of its national policy [10–12]. Among the various proposed policies, the Tourism Zone Attribution (CC BY) license (https:// Development Act served as a comprehensive and systemic tool for enabling the realization creativecommons.org/licenses/by/ of Japan’s aim of becoming a tourism nation [10]. 4.0/).

Sustainability 2021, 13, 7478. https://doi.org/10.3390/su13137478 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 7478 2 of 24

The purpose of this study is to measure the effectiveness of the tourism zone de- velopment policy, which was promoted to facilitate regional revitalization. Through a quasi-experimental pretest–posttest control group design and regression modeling, this study identifies and examines the impact of the tourism zone development policy and identifies the characteristics of the tourism zones that were most significantly affected by it. This study will conduct an empirical examination based on nation-wide quantitative data.

2. Background Study 2.1. Tourism Nation and Tourism Zone Development 2.1.1. Tourism Nation and Regional Revitalization The tourism industry is known to have the potential to expand economic opportuni- ties within local communities [13]. In the Tourism Nation Promotion Basic Law, region-led development through regional communities was described as essential for economic devel- opment. The main goals of this law appear to be the improvement of the attractiveness of regions and the facilitation of economic development [10]. “Regional development” is now inseparable from tourism planning, and “region” has become an important key word in the new tourism law. Figure1 shows the main policies under the Tourism Nation Promotion Basic Law. There are five ongoing policies under this law—creating tourism destinations, international tourism, tourism industry, human resource development, and utilization and sports tourism. Creating tourism destinations originally focused on domestic tourism. Making a region attractive involves providing a good place to live for residents and pro- viding a good place to visit for tourists. The inbound tourism policy was promoted by Sustainability 2021, 13, x FOR PEER REVIEW 3 of 28 the Japan Tourism Agency (JTA). Simultaneously, inbound tourism was mostly promoted through the Japan National Tourism Organization’s Visit Japan campaign.

FigureFigure 1. 1. MainMain policies policies under under the the Tourism Tourism Nation Nation Promotion Promotion Basic Basic Law. Law.

2.1.2. TargetThe main Policy: policy Tourism oriented Zone toward Development regional revitalization is “creating tourism desti- nations”.In 2008, This thepolicy Tourism is promoted Zone Development under the TourismAct (Act Zoneno. 39 Development of 2008) was Act.introduced This act underoriginally the Tourism focused onNation domestic Promotion tourism. Basic However, Law. itBased was revisedon this to Act, target the inbound tourism tourists zone developmentas well. plan has been carried out since 2008. The tourism zone development plan seeks to promote the arrangement of tourism zones for stay-oriented tourism, which includes the provision of lodging facilities, with the exemption of specific regulations, in order to enable Japan to become a tourism nation [14]. The plan aims to increase the number of tourists that visit Japan and to extend the length of their visits. This plan enables diverse groups such as local governments, tourism-related organizations, agriculture and fishing associations, and non-profit organizations to cooperate and function as an integrated union for regional revitalization (see Figure 2). They create a system of supporting subsidies for tourism zone development so that it systematically assists the formation of multiple broad tourism zones. A tourism zone is an area consisting of tourism sites that are closely linked in terms of nature, history, culture, or otherwise. A tourism zone is designed to enable longer stay during travel through cooperation among its tourism sites and aims to enhance the attractiveness of these sites [15].

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2.1.2. Target Policy: Tourism Zone Development In 2008, the Tourism Zone Development Act (Act no. 39 of 2008) was introduced under the Tourism Nation Promotion Basic Law. Based on this Act, the tourism zone development plan has been carried out since 2008. The tourism zone development plan seeks to promote the arrangement of tourism zones for stay-oriented tourism, which includes the provision of lodging facilities, with the exemption of specific regulations, in order to enable Japan to become a tourism nation [14]. The plan aims to increase the number of tourists that visit Japan and to extend the length of their visits. This plan enables diverse groups such as local governments, tourism-related organizations, agriculture and fishing associations, and non-profit organizations to cooperate and function as an integrated union for regional revitalization (see Figure2). They create a system of supporting subsidies for tourism zone development so that it systematically assists the formation of multiple broad tourism zones. A tourism zone is an area consisting of tourism sites that are closely linked in terms of nature, history, culture, or otherwise. A tourism zone is designed to enable longer Sustainability 2021, 13, x FOR PEER REVIEW 4 of 28 stay during travel through cooperation among its tourism sites and aims to enhance the attractiveness of these sites [15].

Figure 2. TourismFigure zone 2. development Tourism zone plandevelopment (modified plan from (modified [15]). from [15]).

Due to the introductionDue to the introduction of the Tourism of the Nation Tourism Promotion Nation Promotion Basic Law, Basic regional Law, regional re- vitalization wasrevitalization expected was to be expected crystallized to be crystallize via the developmentd via the development of possible of possible landmarks landmarks and tourism zonesand tourism that possess zones that enough possess national enough powernational to power attract toa attract number a number of people. of people. It It unites regions as an integrated tourism zone––generating numerous associations between unites regions as an integrated tourism zone—-generating numerous associations between tourist attractions. Through strategic tourism zone development, any related regions may tourist attractions.encourage Through tourists strategic to stay tourismfor longer zone periods development, and revisit its any areas. related In accordance regions may with the encourage touristsapproval to stayof the for tourism longer periodszone development and revisit plan, its areas. totalIn comprehensive accordance with supports the for approval of thetourism tourism zone zone development development could plan, be total implem comprehensiveented. These supports include: for(1) tourismsupport and zone developmentsubsidies could to promote be implemented. a return visit These or include:projects (1)expected support to andfacilitate subsidies tourism to zone promote a returndevelopment visit or projects (up to 40%), expected (2) exceptions to facilitate for tourism the travel zone business development regulation (up regarding to 40%), (2) exceptionslanding fortravel the package travel business sales, and regulation (3) easing regardingthe transportation-related landing travel packageprocedures by sales, and (3) easingadopting the a gas transportation-related coupon. procedures by adopting a gas coupon. The MinistryThe of Land,Ministry Infrastructure, of Land, Infrastructure, Transport Transport and Tourism and Tourism approved approved the tourism the tourism zone developmentzone development plan for 16 plan zones for on16 zones 1 October on 1 October 2008, and 2008, 14 and zones 14 zones on 22on April22 April 2009 2009 (30 (30 zones in total)zones [16in ].total) Figure [16].3 showsFigure 3 the shows list ofthe certified list of certified tourism tourism zones, andzones, detailed and detailed information regarding every tourism zone and their constituent municipalities is listed in information regarding every tourism zone and their constituent municipalities is listed Appendix A. In this study, a policy impact evaluation will be conducted in order to in AppendixA. In this study, a policy impact evaluation will be conducted in order to accurately measure the policy’s effectiveness on the first stage tourism zones that were accurately measureestablished the policy’sin 2008 and effectiveness 2009. Owing on to thethe firstrevision stage of tourismthe Tourism zones Zone that Development were established inAct 2008 in and2012, 2009. some Owingof these to early the tourism revision zones of the were Tourism duplicated Zone or Development reassigned, which presents a risk of bias in measuring the policy’s effectiveness if the tourism zones that were constructed after 2012 are included. Through a quantitative summative evaluation of the tourism zone development policy during its early stages, this study seeks to obtain guidance for the implementation of the ongoing tourism zone development policy.

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Act in 2012, some of these early tourism zones were duplicated or reassigned, which presents a risk of bias in measuring the policy’s effectiveness if the tourism zones that were constructed after 2012 are included. Through a quantitative summative evaluation Sustainability 2021, 13, x FOR PEER REVIEWof the tourism zone development policy during its early stages, this study seeks5 of 28 to obtain guidance for the implementation of the ongoing tourism zone development policy.

Figure 3. A list of approved tourism zones. (Adapted from: white paper on tourism in Japan, 2009, Figure 3. A list of approved tourism zones. (Adapted from: white paper on tourism in Japan, 2009, p. 60 [17]). p. 60 [17]). 2.2. Tourism Policy Evaluation 2.2. Tourism Policy Evaluation 2.2.1. Policy Evaluation Method 2.2.1. Policy Evaluation Method A policy evaluation is conducted to observe the impact of a policy in terms of necessity,A policy efficiency, evaluation validity, is conducted etc., in order to observeto improve the its impact planning of a and policy implementation in terms of necessity, efficiency,process [18,19]. validity, The etc.,types in of order policy to evaluation improve vary its planning according and to its implementation timing, stage, purpose, process [18,19]. Theetc. typesThere ofare policy several evaluation ways to classify vary accordingthe types toof itspolicy timing, evaluation, stage, purpose,but Scriven’s etc. There areclassification several ways is generally to classify accepted. the types According of policy to Scriven’s evaluation, study, but policy Scriven’s evaluations classification can is generallybe divided accepted. into: (1) formative According evaluations to Scriven’s and (2) study, summative policy evaluati evaluationsons [20]. can A formative be divided into: (1)evaluation formative (also evaluations known as process and (2) evaluation summative) aims evaluations for policy [performance20]. A formative improvement. evaluation (also knownIt identifies as process what kind evaluation) of program aims works for policy properly performance and what is improvement. required to improve It identifies the what kindprogram. of program Generally, works a formative properly evaluation and what is is conducted required to before improve or during the program. the policy Generally, aimplementation. formative evaluation A summative is conducted evaluation, before on orthe during other hand, the policy is outcome implementation. focused. A A sum- mativesummative evaluation, evaluation on theis otheralso known hand, isas outcome a policy focused. impact Aeval summativeuation, an evaluationoutcome is also evaluation, or an effectiveness evaluation. Since they are the most widely used type, policy known as a policy impact evaluation, an outcome evaluation, or an effectiveness evaluation. evaluations are generally considered to be policy impact evaluations (summative Since they are the most widely used type, policy evaluations are generally considered to be evaluations). Additionally, summative evaluations entail more objective, quantitative methods of data collection [21].

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policy impact evaluations (summative evaluations). Additionally, summative evaluations entail more objective, quantitative methods of data collection [21]. The policy evaluation methodology was developed particularly from the perspectives of social psychology, sociology, and economics. Social psychologists and sociologists determined the effectiveness of a certain program or project by using an experimental design. On the other hand, economists measured the efficiency of a program or project by applying a cost-benefit analysis or cost-effectiveness analysis [22]. The evaluation designs and methodology were developed with a focus on policy impact evaluation. Thus, in this study, basic evaluation designs for summative evaluation (policy impact evaluation) will be introduced as an analytical design. A policy impact evaluation enables the identification of the difference between the conditions before and after the policy implementation. Therefore, the precise measurement of the direct impact of the policy is a critical element of the evaluation. Experimental research aims to determine whether a specific treatment or interven- tion influences an outcome [23]. As an analytical design for policy impact evaluation, true-experimental design is the most powerful in terms of controlling spurious or con- founding factors that threaten internal validity [23,24]. Additionally, this design is widely known as the most convincing method of evaluation [25]. However, during the imple- mentation of policies, it is occasionally not possible to randomly assign the groups among which the policy is implemented. This means that the treatment or intervention group (policy implemented group) and control group may not be assigned randomly. A quasi- experiment has all of the features of an experiment except random assignment [24]. For policy evaluation, the quasi-experimental design is often adapted to evaluate the impact of the treatment/intervention [24]. A non-experimental design is utilized when experimental or quasi-experimental designs cannot be easily applied. A typical non-experimental design method is statistical analysis. The type of information available to the researcher, the under- lying model, and the parameter of interest are the factors that affect the appropriateness of non-experimental data [25]. In sum, experimental designs (i.e., quasi-experiment and true ex- periment) are a powerful method for measuring whether the treatment/intervention (policy) had an impact. On the other hand, regression modeling as a typical non-experimental design method serves as a tool to measure parameters, i.e., the degree of the treatment’s impact.

2.2.2. Tourism Policy Evaluation in Japan This study reviews on the tourism policy evaluation in Japan as two aspects; empirical tourism studies and studies regarding tourism zone development. Isikawa and Fukeshige analyzed the correlation between local government and the socioeconomic status of residents of Amami Oshima Island by applying a probit model (bi- nary choice). Their study suggested that tourism policies and industrial development should be promoted, municipalities should undertake greater responsibility, and the tourism sector and each of the three administrative levels—-municipalities, the prefectures, and the central government—-should examine industrial policies [26]. They also inves- tigated the impact of tourism and fiscal expenditure in Amami Island. They estimated the long-run fiscal and tourism multiplier with applying Schwartz’s Bayesian Information Criteria (SBIC), with survey of statements/taxation of accounts of cities, towns and villages, account books, and statistics bureau data [27]. Romão et al. conducted a multinomial logistic regression to determine tourists’ choice to visit the Shiretoko Peninsula [28]. Their analysis enabled them to figure out the visitors’ probability choice. However, a limitation of their research was that tourists’ choice with respect to all areas in Japan could not be obtained. Ohe and Kurihara evaluated the relationship between local brand farm products and rural tourism using an estimation model, which calculated the direct and indirect economic impact of rural tourism [29]. They resultantly indicated that a local partnership between agriculture and tourism is important and that wider, longstanding perspectives on the management of local resources are necessary [29]. Ohe also evaluated the household leisure behavior of rural tourism Sustainability 2021, 13, 7478 6 of 24

by applying a binominal logit model [30]. The rural preference function was estimated through this research, and it was significant in comparison to other previous research because it investigated all areas in Japan. However, it only examined rural tourism. Previous studies have utilized both qualitative and quantitative approaches to in- vestigating tourism zone development. Seki demonstrated that the public and private partnership in regional tourism management, especially with respect to tourism zone de- velopment, is the challenge facing the social and economic development of Japan [10]. The paper provided a background and idiographic explanation for tourism zone development. Patandianan and Shibusawa quantitatively evaluated the tourism demand in Prefecture and considered its spatial spillover effects [11]. They were able to estimate the economic spillover effects of tourist demand in each municipality. However, they only focused on . Chi et al. quantitatively evaluated the impact of tourism nation promotion project using a difference-in-differences methodology; however, they were mainly focused on inbound tourist in Japan [31]. Japanese regional planning is mainly based on a bottom-up approach, known as Machizukuri (community planning) [32–35]. However, once the government identified tourism as being the driving force behind regional revitalization, the tourism zone devel- opment policy was promoted as a nation-wide project. Considering the importance of this policy, it is paramount to investigate it using a comprehensive nation-wide approach that quantitatively estimates its effectiveness by examining the subsidies provided, features of tourism zones, etc.

3. Research Framework 3.1. Theoretical Background and Methodology Scientific experiments usually can usually divide their subjects into a control group (comparison group) and a treatment group through a randomized distribution. On the other hand, this method is not feasible for social science, particularly for policy evaluation, due to ethical and practical reasons. For example, policies cannot be applied to a randomized group. Thus, a quasi-experimental design is usually adopted for policy evaluation [23,24]. Table1 shows certain basic types of quasi-experimental design. The pretest–posttest nonequivalent control group design is a popular approach to quasi-experiments [23]. In it, both groups (group A and group B) take a pretest and a posttest, but only the intervention (experimental) group receives the treatment. On the other hand, in the single- group interrupted time-series design, the researcher measures the results only for a single group both before and after treatment. A control group interrupted time-series design is a modification of the single-group interrupted time-series design. In this study, the pretest–posttest nonequivalent control group design was chosen as the methodology for conducting a summative evaluation of tourism zones.

Table 1. Quasi-experimental designs [23].

Quasi-Experimental Designs Schema Non-equivalent (Pretest and Posttest) Group A O———X———O Control Group Design Group B O——————–O Single-Group Interrupted Times-Series Design Group A O–O–O–O–X–O–O–O–O Group A O–O–O–O–X–O–O–O—O Control Group Interrupted Time-Series Design Group B O–O–O–O–O–O–O–O–O

Estimating impact is important because of the need to know whether policy inter- ventions actually have the desired beneficial effects [24]. As seen in Section 2.1.1 (Policy Evaluation Method), to figure out the actual impact of a policy, the measurement should be based on whether the policy was implemented or not. For example, to observe the impact of tourism zone development, all variables other than the tourism zone development policy should be controlled. Considering merely simple statistics, a problem is observed for which the main confounders cannot be controlled. For example, even if the number of visitors Sustainability 2021, 13, x FOR PEER REVIEW 8 of 28

should be based on whether the policy was implemented or not. For example, to observe Sustainability 2021, 13, 7478 the impact of tourism zone development, all variables other than the tourism7 ofzone 24 development policy should be controlled. Considering merely simple statistics, a problem is observed for which the main confounders cannot be controlled. For example, even if the number of visitors increased drastically in a certain area, it is challenging to explain increasedwhether drasticallythe increased in anumber certain of area, visitors it is challenging was a result to of explain the policy whether implementation the increased or numberother external of visitors effects. was a result of the policy implementation or other external effects. ToTo estimate estimate the the effectiveness effectiveness of of the the tourism tourism zone zone development development policy, policy, control control group group cities/townscities/towns (before(before thethe policypolicy implementation)implementation) that that were were similar similar to to the the tourism tourism zones zones in in termsterms of of their their population, population, economic economic situation, situation, nature, nature, industry, industry, etc., etc., were were selected selected in in order order toto observe observe their their conditions conditions before before and and after after the the policy policy implementation. implementation. If If it it is is possible possible to to controlcontrol other other factors factors except except policy policy implementation, implementation, the the results results of of a a comparative comparative analysis analysis betweenbetween the the tourism tourism zone zone area area (treatment/intervention (treatment/intervention group) group) and and control control group group area area can can bebe regarded regarded as as the the impact impact of of the the policy policy (treatment/intervention (treatment/intervention effect) effect) (see (see Figure Figure4 ).4).

FigureFigure 4. 4.Policy Policy impact impact measurement measurement (pretest–posttest (pretest–posttest control control group group design). design).

3.2.3.2. Data Data •• NationalNational urban urban traffic traffic characteristic characteristic survey survey (person (person trip trip data) data) ToTo observe observe the the before before and and after after changes changes uponupon thethe implementationimplementation ofof thisthis policy,policy, informationinformation regarding regarding travelers’ travelers’ trips trips is is necessary. necessary. This This data data should should include include trip trip records records thatthat cover cover all all departure departure and and arrival arrival information informatio ofn travelersof travelers from from all areasall areas within within Japan. Japan. In addition,In addition, for morefor more up-to-date up-to-date information information pertaining pertaining to the to period the period after theafter policy, the policy, the data the shoulddata should be checked be checked periodically periodically to update to update the travel the travel related related information. information. TheThe national national urban urban traffic traffic characteristic characteristic survey survey (herein, (herein, person person trip data)trip data) [36] was[36] first was conductedfirst conducted in 1987. in It1987. has It since has beensince continuallybeen continually conducted conducted every 5every years. 5 years. Person Person trip data trip isdata comprised is comprised of a variety of a variety of information, of information, including including occupation, occupation, age, gender, age, gender, trip mode, trip tripmode, purpose, trip purpose, time travelled, time travelled, etc. Most etc. of Most all, itof isall, an it excellentis an excellent resource resource that enables that enables the identificationthe identification of trips of thattrips were that solely were forsolely the purposesfor the purposes of tourism. of tourism. In addition, In addition, the detailed the individualdetailed individual characteristics characteristics that it offers that can it be of usedfers can to determine be used theto determine visitors’ attributes. the visitors’ •attributes.Regional data Since the smallest unit area of tourism zone is the municipality, the regional data to be used for the factor analysis and regression model should also pertain to the municipal level.

In order to set control groups, detailed regional data regarding the pretest year (in this study: 2005) that represents each city/town’s characteristics, such as population, economic Sustainability 2021, 13, 7478 8 of 24

situation, etc., are required. To conduct the regression model, since the purpose of the estimation is to identify significant differences in conditions before and after the policy implication, regional data for the pretest and posttest years are required (in this study: 2005 and 2010, respectively). Municipal level data related to population and households, natural environment, economic base, administrative base, labor, culture and sports, etc., can be obtained from the Portal Site of Official Statistics of Japan [37]. However, some data could not be obtained due to the change of the data collection method. For example, economic base data were collected as Commercial Statistics in 2004; however, the data were collected as Economic Census in 2011 due to the revision of the Statistics Act.

3.3. Research Procedures Since the goal of the policy was to increase the number of tourists, it is necessary to ascertain whether each tourism zone received more tourists after the implementation of the policy. Furthermore, the effectiveness of subsidies offered within each tourism zone area should be calculated. Based on the background and the purpose of this study, the research questions to be addressed are sequentially presented as follows: (1) Did the tourism zone development policy have any impact? (2) Based on the configuration type of each tourism zone, how distinctively did the impact vary? Accordingly, the measurement of the impact will be divided into two parts. First, by applying the pretest–posttest control group design, an analysis will be carried out in order to ascertain whether the policy implementation had an impact. Second, the effect of each subsidy can be measured through regression modeling. Based on the configuration of each tourism zone, the tourism zones will be categorized. Then, based on each category, the quasi-experiment and regression model will be utilized to observe the different results across each category. Figure5 shows the analytical flowchart of the study. • Did the development of tourism zones have an impact? (Method: quasi-experiment) The measurement of the policy’s impact will be divided into two parts. First, by applying the pretest–posttest control group design, an analysis will be carried out in order to ascertain whether the policy implementation had an impact. To conduct this quasi-experiment, a representative control group needs to be assigned for the test [23,24]. The cities/towns that have similar social, economic, and physical characteristics to the tourism zone regions prior to the policy implementation are assigned as the control group. A quasi-experiment analysis can be divided in to four phases: (1) categorizing tourism zone, (2) factor analysis, (3) cluster analysis, and (4) t-test. Since the features of the areas that comprise a tourism zone may vary, these zones are categorized based on their characteristics. To assign control group, a factor analysis is conducted to extract the main characteristics of each area: population, economic situations, physical information, etc. Using these main characteristics, a cluster analysis is conducted to identify the control group city/town. Lastly, whether policy implementation resulted in significant differences between the tourism zone and control group can be assessed through a t-test. • How will the effects vary across tourism zones? (Method: regression modeling) The tourism zones vary in terms of size, administrative division, and regional charac- teristics. Some tourism zones consist of two municipalities (e.g., Lake Hamana Tourism Zone and Holy Place Kumano Healing and Reconstruction Tourism Zone), whereas other tourism zones are composed of twenty-five municipalities (e.g., Sanin Culture Tourism Zone). In addition, there are some zones that consist of two or more prefectures, while some tourism zone areas are within the same prefecture. Some tourism zones also contain nearby islands. The impact of the policy will differ based on each tourism zone’s features. Using regression modeling and the subsidy expenditure information, the policy’s impact on each tourism zone is calculated. An empirical estimation of the relationship between tourism demand and its explanatory variables across all areas and categories is conducted. Sustainability 2021, 13, x FOR PEER REVIEW 10 of 28

prefectures, while some tourism zone areas are within the same prefecture. Some tourism zones also contain nearby islands. The impact of the policy will differ based on each tourism zone’s features. Using regression modeling and the subsidy expenditure Sustainability 2021, 13, 7478 information, the policy’s impact on each tourism zone is calculated. An empirical9 of 24 estimation of the relationship between tourism demand and its explanatory variables across all areas and categories is conducted.

FigureFigure 5. Analytical flowchart. flowchart.

4.4. Results 4.1.4.1. Data Processing Processing •• Data cleaning To examine the the national national traffic traffic situation, situation, the the national national urban urban traffic traffic characteristics characteristics surveysurvey (person trip trip data) data) investigated investigated 70 70 cities cities and and 60 60towns towns and and villages villages that that consist consist of of 500500 householdshouseholds andand received received responses responses from from about about 38,000 38,000 households. households. The surveyThe survey obtained 52,005obtained and 52,005 33,542 and responses 33,542 inresponses 2010 and in 2005, 2010 respectively. and 2005, Thisrespectively. data is composed This data of is three categories:composed of household three categories: characteristics, household individual characteristics, characteristics, individual and characteristics, traffic characteristics. and Therefore,traffic characteristics. this study buildsTherefore, a dataset this st thatudy consists builds ofa thesedataset characteristics that consists basedof these on the touristcharacteristics destination. based Tourism-related on the tourist destination. trips were Tourism-related identified from amongsttrips were 17 identified different from trip pur- poses.amongst After 17 eliminatingdifferent trip the purposes. outliers, 18,347 After sampleseliminating remained the outliers, for 2010 and18,347 10,540 samples samples remainedremained for 2010 2005. and 10,540 samples remained for 2005. • • Data matching Each region was described as their regional code. This regional code consists of five letters. The first two letters denote the prefecture, and the remaining three letters denote the city/town. For example, “01229” represents Furano-shi (“01” stands for Hokkaido prefecture and “229” indicates Furano-shi). Since the dataset was gathered from the data based on the tourist destination, the regional data (independent variable) and person trip data (dependent variable) should correspond to the same standard. Therefore, the data-matching process was conducted for the 2010 and the 2005 data based on these five-letter codes. • Data adjusting Sustainability 2021, 13, 7478 10 of 24

While matching the data, some areas were combined or experienced a change in their code number. This was due to “the great Heisei mergers.” The mergers were performed in order to strengthen local governments by allotting them certain regulatory functions from the central government. The largest mergers were done in 2005 [38–40]. Therefore, it was necessary to adjust person trip data as well as regional data based on the new codes.

4.2. Policy Impact Measurement: Quasi-Experiment 4.2.1. Categorizing Tourism Zones Before the tourism zones were grouped, numbers were assigned to represent the 30 tourism zones in order to facilitate the analysis. The characteristics of each zone are listed in Table2.

Table 2. Characteristics of tourism zones.

Municipalities Island Num Tourism Zone Name Prefectures (City/Town) (Dummy) 1 Furano Biei Wide Tourism Zone 6 1 0 2 Elegant Wide Tourism Zone 11 3 0 3 Aizu and Yonezawa Region Tourism Zone 9 2 0 4 Tenderness and Natural Warmth 4 1 0 5 Fukusima Tourism Zone 13 1 0 6 Mito Hitachi Tourism Zone; Your Sky and Earth 4 1 0 7 Southern Boso Region Tourism Zone 6 1 0 8 Mt. Fuji and Fuji Five Lakes Tourism Zone 4 1 0 9 Ise Shima Region Tourism Zone 5 1 0 10 Kyoto Prefecture Tango Tourism Zone 25 2 1 11 Sanin Cultural Tourism Zone 12 2 1 12 , Miyajima, 4 1 0 13 and Iwakuni Region Tourism Zone 7 2 0 14 Nishi Awa Tourism Zone 10 3 0 15 New East Kyushu Tourism Zone 4 1 0 16 Aso Kuju Tourism Zone 8 1 0 17 Shiretoko Tourism Zone 8 1 0 18 Sapporo Wide Tourism Zone 10 3 1 19 New Travel in , 1 1 0 20 Wide Tourism Zone 7 3 0 21 Kira Kira Uetsu Tourism Zone 5 1 0 22 Nikko Tourism Zone 9 1 0 23 Snow Country Tourism Zone 6 1 0 24 Toyama Bay, Kurobe Canyon, 2 1 0 25 Etchu Niikawa Tourism Zone 10 1 0 26 Noto Peninsula Tourism Zone 3 1 1 27 Fukui Sakai Wide Tourism Zone 2 2 0 28 Lake Hamana Tourism Zone 6 1 0 29 , Omiji Tourism Zone 3 1 1 30 Awaji Island Tourism Zone 7 2 1

The tourism zones are significantly varied in terms of size. Tourism zone 1 (Furano Biei Wide Tourism Zone) consists of six municipalities in the same prefecture and no nearby island. Tourism Zone 29 and Tourism Zone 10 contain a nearby island (see Figure6), which Sustainability 2021, 13, x FOR PEER REVIEW 12 of 28

24 Toyama Bay, Kurobe Canyon, 2 1 0 25 Etchu Niikawa Tourism Zone 10 1 0 26 Noto Peninsula Tourism Zone 3 1 1 27 Fukui Sakai Wide Tourism Zone 2 2 0 28 Lake Hamana Tourism Zone 6 1 0 29 Lake Biwa, Omiji Tourism Zone 3 1 1 30 Awaji Island Tourism Zone 7 2 1 Sustainability 2021, 13, 7478 11 of 24 The tourism zones are significantly varied in terms of size. Tourism zone 1 (Furano Biei Wide Tourism Zone) consists of six municipalities in the same prefecture and no nearby island. Tourism Zone 29 and Tourism Zone 10 contain a nearby island (see Figure signifies that each of their territories contains a small island that is not one of the four main 6), which signifies that each of their territories contains a small island that is not one of the Japanese islands (Hokkaido, , , and Kyushu). four main Japanese islands (Hokkaido, Honshu, Shikoku, and Kyushu).

FigureFigure 6. 6.Examples Examples ofof thethe tourismtourism zoneszones that that contain contain a a nearby nearby island. island.

PerceptualPerceptual mapsmaps are are usually usually utilized utilized in in business business management management as as a a tool tool for for grouping grouping targets,targets, but but they they have have been been positively positively adopted adopt ined tourism in tourism studies studies as well as [ 41well]. In [41]. this In study, this usingstudy, the using different the different characteristics characteristics shown show in Tablen in2 ,Table groups 2, groups for the tourismfor the tourism zones were zones constructedwere constructed (see Figure (see Figure7 and Table 7 and3). Table 3). EachEach tourism tourism zone zone is is placed placed in in category category A, A, B, B, or or C dependingC depending on on whether whether it consists it consists of twoof two or more or more prefectures prefectures (category (category A), is withinA), is within a single a prefecture single prefecture (category (category B), or includes B), or aincludes nearby islanda nearby (category island C). (category Additionally, C). Additionally, the tourism zonesthe tourism are further zones differentiated are further baseddifferentiated on whether based they on cover whether a wide they region cover (category a wide region 1) or a narrow(category region 1) or (category a narrow 2). region For example,(category category 2). For example, A1 consists category of tourism A1 consists zones thatof tourism contain zones two orthat more contain prefectures two or more and coverprefectures a wide and region cover (seven a wide or more region municipalities). (seven or more Tourism municipalities). zone 2 (Elegant Tourism Wide Tourism zone 2 Zone)(Elegant would Wide fall Tourism within categoryZone) would A1, as fall it iswithin comprised category of three A1, as different it is comprised prefectures of three and coversdifferent 11 municipalities.prefectures and covers 11 municipalities.

4.2.2. Exploratory Factor Analysis Ahead of conducting the cluster analysis, problems such as the correlation of some variables with each other should be considered. For example, total population and labor force of a region have some correlation each other. To resolve such problems, an exploratory factor analysis is conducted in this study to reduce the numbers of the variables and construct a model that exclusively consists of major factors. Factor analysis seeks to analyze the correlation among the variables so that underlying factors in mutual actions can be extracted to reduce the numbers of some variables that represent all the data. Therefore, by implementing a factor analysis, data comprised of different forms of variables can be narrowed down to the main inherent factors, which makes the comprehension and analysis of data more convenient. In other words, factor analysis solves any problems of information flood, so data characteristics are easily analyz- able. Exploratory factor analysis is generally applied to factor analysis [42]. In this study, an exploratory factor analysis will be used to extract meaningful data. The variables from regional data used for exploratory factor analysis are from 2005—the pretest year. Sustainability 2021, 13, 7478 12 of 24

The Kaiser–Meyer–Olkin Measure of Sampling Adequacy (herein, KMO) is utilized to assess if this data is suitable for conducting factor analysis. Additionally, Bartlett’s Test is employed to check if the variables are sufficiently correlated. Principle component analysis was used as the extraction method because it diminishes the loss of information is less. The extractions were all over 0.4 (min value: 0.590, max value: 0.991). The Varimax rotation method was used. As shown in Table4, all results indicate that the data is suitable for factor analysis. The KMO was 0.094, and Bartlett’s test was significant in the level of significance 0.000. A

Sustainabilitycumulative 2021, 13, x FOR percentage PEER REVIEW that is 84.251 of total variance explained implies that can 84% of the13 of 28 information can be obtained using five components (see Table5).

Figure 7. Perceptual map of tourism zones. Figure 7. Perceptual map of tourism zones. Table 3. Categories of tourism zones. Table 3. Categories of tourism zones. Category Tourism Zone Characteristics Examples Category Tourism ZoneConsists Characteristics of two or more prefectures. Examples Tourism zone ConsistsA1 of twoCovers or more a prefectures.wide region (seven or more Tourism zone A1 2, 3, 13, 14, 20 Covers a wide region (seven or moremunicipalities). municipalities). 2, 3, 13, 14, 20 Consists of two or more prefectures. Consists of two or more prefectures. Covers a TourismTourism zone zone A2 A2 Covers a narrow region (less than narrow region (less than seven municipalities). 27 27 seven municipalities). Tourism zone B1 Covers a wide regionCovers within thea wide same region prefecture. within the same Tourism zone B1 5, 16, 17, 22, 25 prefecture. 5, 16, 17, 22, 25 Tourism zone B2 Covers a narrow region within the same prefecture. Tourism zone Covers a narrow region within the1, 4, 6, 7, 8, 9, 12, 15, 19, 21, 23, 24, 28 B2 1, 4, 6, 7, 8, 9, 12, 15, 19, 21, 23, Includes a nearby island and consistssame of prefecture. two or more Tourism zone24, 28 C1 prefectures. Covers a wide region (seven or more 10, 11, 18, 30 municipalities).Includes a nearby island and consists of two or more prefectures. Covers a Tourism zone IncludesC1 a nearby island and wide region (seven or more Tourism zone C2 Covers a narrow region within the same prefecture 10, 11, 18, 30 26, 29 (less than seven municipalities).municipalities). Includes a nearby island and Covers a narrow region within the Tourism zone C2 Table 4. KMO and Bartlett’s Test. same prefecture (less than seven 26, 29 municipalities). Kaiser–Meyer–Olkin Measure of Sampling Adequacy. 0.904 4.2.2. Exploratory Factor AnalysisApprox. Chi-Square 147,477.977 Bartlett’s Test of SphericityAhead of conducting the clusterdf analysis, problems such as the 406 correlation of some variables with each other should be considered. For example, total population and labor Sig. 0.000 force of a region have some correlation each other. To resolve such problems, an exploratory factor analysis is conducted in this study to reduce the numbers of the variables and construct a model that exclusively consists of major factors. Factor analysis seeks to analyze the correlation among the variables so that underlying factors in mutual actions can be extracted to reduce the numbers of some

Sustainability 2021, 13, 7478 13 of 24

Table 5. Total variance explained (rotation method: Varimax with Kaiser Normalization).

Extraction Sums Rotation Sums of Squared Loadings Component of Squared Loadings Cumulative% Total % of Variance Cumulative% 1 62.043 17.394 59.979 59.979 2 70.774 2.558 8.819 68.798 3 76.836 1.947 6.715 75.513 4 80.756 1.377 4.750 80.263 5 84.251 1.156 3.987 84.251 ......

Through the factor analysis, 29 variables were organized into five components: pop- ulation and labor force, primary industry, economic welfare, secondary industry, and unemployment rate. Table6 shows each variable and derived components expressed with same color. A cluster analysis is then conducted using these components (Section 4.2.3).

4.2.3. Cluster Analysis A cluster analysis was conducted to identify the control city/town for each tourism zone. Using the five components obtained from the factor analysis, a cluster analysis was carried out with the help of SPSS software. Ward’s linkage that applied the Euclidean distance and Z standardization was used as the cluster method. Ward’s linkage and Euclidean distance are commonly used for clustering analysis, and since the unit of each component is different, the Z standardization was chosen for the analysis. These pairs will be used in the t-test to observe the difference between the control group and the tourism zone.

4.2.4. t-Test: Tourism Zone vs. Control Group This is the final phase of pretest-posttest control group quasi-experimental design. The paired t-test was conducted to observe the significant differences between the tourism zones and control group cities/towns across each category with the trip change from 2010 to 2005 (purpose: tour) of person trip data. The results revealed there was no case in which the trip change of a tourism zone decreased significantly. There were eight zones in which the trip change increased significantly and eight zones in which the increase was not significant.

Table 6. Rotated component matrix.

Component Variables 1 2 3 4 5 Total households 0.993 0.048 0.043 0.016 0.007 Persons employed in the tertiary industry (person) 0.989 0.066 0.055 0.026 0.005 Population 15 to 64 years old (person) 0.989 0.076 0.064 0.044 0.009 Total population (person) 0.988 0.088 0.061 0.052 0.012 Labor force (person) 0.988 0.094 0.063 0.054 0.008 Immigrants from other prefectures (person) 0.987 −0.008 0.037 −0.019 −0.006 Daytime population (person) 0.986 0.055 0.044 −0.002 −0.003 Total welfare expenditure (thousand yen) 0.985 0.029 0.007 0.043 0.025 Taxable income (thousand yen) 0.981 0.025 0.064 −0.050 −0.022 Total dwellings 0.963 −0.100 0.061 −0.017 0.008 Commercial and neighboring commercial area (ha) 0.959 0.104 0.018 0.122 0.019 Persons employed in the secondary industry (person) 0.953 0.160 0.106 0.142 0.003 Manufacturing establishments 0.930 0.083 0.042 0.067 0.011 Sustainability 2021, 13, 7478 14 of 24

Table 6. Cont.

Component Variables 1 2 3 4 5 Local taxes (thousand yen) 0.928 0.090 0.085 0.241 0.005 DIDs area (km2) 0.926 −0.015 0.117 0.189 0.026 Public works expenditure (thousand yen) 0.883 0.116 0.025 0.315 0.035 Industrial and quasi-industrial area (ha) 0.861 0.214 0.083 0.277 0.042 Persons at work in manufacturing establishments (person) 0.829 0.240 0.164 0.255 −0.009 Commerce and manufacturing expenditure (thousand yen) 0.673 0.090 −0.066 0.435 0.055 Labor expenditure (thousand yen) 0.666 0.227 0.130 −0.009 0.082 Persons employed in the primary industry (person) 0.200 0.872 −0.048 0.091 0.026 Agriculture forestry and fishery expenditure (thousand yen) 0.157 0.852 −0.143 0.144 0.012 Cultivated land area (ha) 0.000 0.816 −0.074 −0.045 −0.096 Financial strength index 0.013 −0.099 0.751 0.329 −0.059 Taxable income per tax debtor (thousand yen) 0.238 −0.080 0.703 −0.052 −0.269 Ratio of owned houses 0.044 −0.167 0.674 0.031 0.191 Public halls per 1,000,000 persons −0.050 −0.238 −0.510 0.216 −0.473 Exclusive industrial area (ha) 0.217 0.138 0.160 0.776 0.037 Unemployment rate 0.053 −0.124 −0.042 0.089 0.891

The paired t-test indicated that Tourism Zone 8 (Ise Shima Region Tourism Zone), Tourism Zone 19 (Nikko Tourism Zone), and the corresponding control groups displayed a significant difference (p < 0.01), and the number of visitors increased in the regions where the policy was implemented (tourism zone). Tourism Zone 1 (Furano Biei Wide Tourism Zone), Tourism Zone 12 (Nishi Awa Tourism Zone), Tourism Zone 15 (Shiretoko Tourism Zone), and their control groups also displayed a significant difference (p < 0.05), and the number of visitors increased in the regions where the policy was implemented. Tourism Zone 3 (Aizu and Yonezawa Region Tourism Zone), Tourism Zone 4 (Tenderness and Natural Warmth Fukusima Tourism Zone), and the corresponding control groups displayed a significant difference (p < 0.1), and the number of visitors increased in the regions where the policy was implemented (See Table7).

Table 7. T-test result: increased significantly.

Group Mean Std. Deviation t-Value Sig. Control Group1 0.7143 1.20439 −2.233 0.044 ** Tourism Zone 1 2.0714 2.26900 Control Group 3 −0.0714 1.60851 −1.842 0.077 * Tourism Zone 3 0.0000 2.50924 Control Group 4 1.1154 4.79439 −1.758 0.091 * Tourism Zone 4 2.3462 5.80994 Control Group 7 −0.1667 2.47890 −3.669 0.001 *** Tourism Zone 7 2.0417 2.47561 Control Group 8 0.4138 1.11858 −4.332 0.000 *** Tourism Zone 8 2.6207 2.65133 Control Group 12 1.3889 2.27877 −2.887 0.010 *** Tourism Zone 12 3.0556 2.85888 Control Group 15 0.2857 0.95119 −3.873 0.008 *** Tourism Zone 15 1.7143 0.48795 Control Group 19 0.1579 1.64192 −6.470 0.000 *** Tourism Zone 19 3.7368 2.18180 Significant at the level of * p < 0.1, ** p < 0.05, *** p < 0.01. Sustainability 2021, 13, 7478 15 of 24

The number of visitors to Tourism Zone 5 (Mito Hitachi Tourism Zone; Your Sky and Earth), Tourism Zone 6 (Southern Boso Region Tourism Zone), Tourism Zone 20 (Snow Country Tourism Zone), Tourism Zone 23 (Fukui Sakai Wide Tourism Zone), Tourism Zone 24 (Lake Hamana Tourism Zone), Tourism Zone 27 (Holy Place Kumano Healing and Reconstruction Tourism Zone), Tourism Zone 28 (Shimanto and Ashizuri Area Tourism Zone), and Tourism Zone 30 (Unzen Amakusa Tourism Zone) increased in comparison to that of their control groups. However, they did not display a significant difference (See Table8).

Table 8. T-test result: increased not significantly.

Group Mean Std. Deviation t-Value Sig. Control Group 5 0.9714 1.88626 −1.128 0.267 Tourism Zone 5 1.4571 1.97548 Control Group 6 0.6667 1.21106 −0.896 0.411 Tourism Zone 6 3.0000 6.06630 Control Group 20 1.1613 2.60892 −0.299 .0767 Tourism Zone 20 1.3548 2.07442 Tourism Zone 23 0.9655 2.94573 −1.438 0.161 Control Group 23 2.3448 3.91221 Control Group 24 1.6875 2.27211 −0.92 0.928 Tourism Zone 24 1.7500 0.85635 Control Group 27 0.8710 1.89283 −0.867 0.393 Tourism Zone 27 1.1613 1.00322 Control Group 28 1.6875 2.27211 −1.165 0.262 Tourism Zone 28 2.2500 1.80739 Control Group 30 0.5238 2.42114 −0.249 0.806 Tourism Zone 30 0.7143 3.88771

The visitors to Tourism Zone 11 (Hiroshima, Miyajima, and Iwakuni Region Tourism Zone), Tourism Zone 13 (New East Kyushu Tourism Zone), Tourism Zone 18 (Kira Kira Uetsu Tourism Zone), Tourism Zone 21 (Toyama Bay, Kurobe Canyon, Etchu Niikawa Tourism Zone), Tourism Zone 26 (Awaji Island Tourism Zone), and Tourism Zone 29 (Hirato, Sasebo, Saikai Long Stay Tourism Zone), with no significance, the visitors decreased in comparision to that of their control groups (see Table9).

Table 9. T-test result: decreased not significantly.

Group Mean Std. Deviation t-Value Sig. Control Group 11 2.2143 8.50393 0.50 0.960 Tourism Zone 11 2.1607 8.93568 Control Group 13 0.6842 2.32618 0.612 0.545 Tourism Zone 13 −0.0526 6.28575 Control Group 18 0.7059 3.72159 0.227 0.822 Tourism Zone 18 0.5588 1.58001 Control Group 21 6.7778 18.93579 0.458 0.650 Tourism Zone 21 5.2963 12.47093 Control Group 26 0.4211 1.57465 0.297 0.770 Tourism Zone 26 0.2632 1.75885 Control Group 29 2.1875 1.64190 0.889 0.388 Tourism Zone 29 1.6875 1.07819

Except for Tourism Zone 3, the areas where the trip changes increased significantly were the tourism zones in category B2, which are those that cover a narrow region within the same prefecture. This result echoes those of existing policy impact studies, which indicate that the impact of policy is large in regions where significant subsidies are provided [43]. Sustainability 2021, 13, 7478 16 of 24

4.3. Policy Impact Measurement: Regression Modeling 4.3.1. Variable Setting The tourism demand is generally measured based on the number of visiting tourists or tourist expenditure [44]. In this study, tourism demand (dependent variable) is measured by the increase in the number of visiting tourists (∆ trip 2010–2005). There are three types of projects that received subsidies from the government. Using regression modeling, whether a project was successful or not can be ascertained (see Table 10).

Table 10. Projects of the tourism zone development program.

Project Project Details Improving stay program, marketing, development of human Project 1 resource/awareness enlightenment Project 2 Information transmission, measures for the secondary traffic, space formation Project 3 Inbound support, quality management

The explanatory variables are vitality, commerce, attractions, transportation, and policy. Except for the variables related to the tourism zone development policy, the variables are the difference in the values from 2010 and 2005 (see Table 11).

Table 11. Variables used in regression modeling.

Arrivals Change for Tour Dependent Variable Source [36] (∆2010–2005) Daytime population (person) Vitality Rate of day to night population (%) Commerce and manufacturing expenditure (thousand yen) Commerce Persons employed in the tertiary industry (person) Source [37] Commercial and neighboring commercial area (ha) Number of public parks Number of zoos Attractions Number of art museums Number of golf clubs Total real length of roads (km) Explanatory Variables Total real length of major roads (km) Transportation Total real length of local roads (km) Source [45] Total real length of major paved roads (km) Total real length of local paved roads (km) Dummy_project1 Dummy_project2 Dummy_project3 Tourism Zone Project 1 subsidy(yen) Development Source: JTA Project 2 subsidy(yen) Policy Project 3 subsidy(yen) Total project subsidy (Total yen) Number of cities/towns

4.3.2. Regression Model for All Tourism Zones This is the empirical result of a regression model for all tourism zones. Project 1 had an adverse impact on the arrival change for tourism. On the other hand, Project 2 and Project 3 had no impact overall (see Table 12).

4.3.3. Regression Model for Most vs. Least Effective Case Regression models were conducted to identify the most effective case and least ef- fective case across all categories (see Tables 13 and 14). The most effective case would be the case where the subsidy had a positive impact at the most significant level across all the possible models. The least effective case would be the case where the subsidy had a negative impact at the most significant level across all possible models. Sustainability 2021, 13, 7478 17 of 24

Table 12. Result of multiple regression model: all tourism zones.

Independent Variables Beta t-Value Sig. Tolerance VIF (Constant) 3.316 0.001 *** Commerce and 0.476 8.097 0.000 *** 0.967 1.035 manufacturing expenditure Number of public parks 0.382 6.317 0.000 *** 0.916 1.092 Project 1 subsidy −0.102 −1.671 0.097 * 0.898 1.113 Project 2 subsidy −0.019 −0.322 0.748 0.965 1.037 Project 3 subsidy 0.007 0.121 0.904 0.926 1.08 R2 = 0.422 (adjusted R2 = 0.405), Durbin–Watson = 1.914. Significant at the level of * p < 0.1, *** p < 0.01.

Table 13. Result of multiple regression model: most effective case.

Independent Variables Beta t-Value Sig. Tolerance VIF (Constant) −3.313 0.002 *** Commerce and manufacturing expenditure 0.635 10.066 0.000 *** 0.969 1.032 (thousand yen) Number of public parks 0.427 6.606 0.000 *** 0.922 1.084 Project 1 subsidy 0.307 3.579 0.001 *** 0.522 1.914 Project 2 subsidy 0.185 2.729 0.008 *** 0.843 1.186 Project 3 subsidy 0.267 3.329 0.002 *** 0.601 1.664 R2 = 0.776 (adjusted R2 = 0.757), Durbin–Watson = 1.791. Significant at the level of *** p < 0.01.

The selection criteria of finding these model is from each category, where the subsidy is significantly positive or negative. The explanatory variables were selected equally in both the most effective and least effective cases. The beta sign for the size of commerce and manufacturing expenditure and the number of public parks was almost the same in both the most effective and least effective cases. The subsidy effect was different in the most effective and least effective case. The subsidy for Project 1 (improving stay program, marketing, development of human resource/awareness enlightenment) had a positive impact in the most effective case, but it had a negative impact in the least effective case. The subsidies for Project 2 and Project 3 had a positive impact in the most effective case but demonstrated no significant impact in the least effective case. The tourism zones where the policy was most effective were mostly in category B (zones within a single prefecture), whereas the tourism zones where the policy was the least effective were mostly in category C (zones that include a nearby island).

Table 14. Result of multiple regression model: least effective case.

Independent Variables Beta t-Value Sig. Tolerance VIF (Constant) 3.001 0.004 *** Commerce and manufacturing expenditure 0.706 7.619 0.000 *** 0.919 1.088 (thousand yen) Number of public parks 0.417 4.380 0.000 *** 0.873 1.146 Project 1 subsidy −0.254 −2.678 0.010 *** 0.879 1.138 Project 2 subsidy −0.077 −0.787 0.435 0.820 1.220 Project 3 subsidy −0.015 −0.151 0.880 0.858 1.166 R2 = 0.589 (adjusted R2 = 0.550), Durbin–Watson = 1.791. Significant at the level of *** p < 0.01.

4.3.4. Regression Model for Each Category Regression models for each category were conducted in order to observe the differ- ences between each category. For the zones within category A (zones that consist of two or more prefectures), Project 1 had an adverse impact on the arrival change for tourism. On the other hand, Project 2 and Project 3 had no impact overall. For the zones within category B, Project 2 had a positive impact on the arrival change for tour. On the other hand, Project 1 and Project 3 had no impact overall. For the zones within category C, all projects had no impact overall (see Tables 15–17). Sustainability 2021, 13, 7478 18 of 24

Table 15. Result of multiple regression model: category A.

Independent Variables Beta t-Value Sig. Tolerance VIF (Constant) 1.252 0.22 Number of public parks 0.451 3.012 0.005 *** 0.94 1.064 Project 1 subsidy −0.419 −2.459 0.019 ** 0.725 1.38 Project 2 subsidy 0.104 0.618 0.541 0.736 1.359 Project 3 subsidy −0.026 −0.168 0.867 0.913 1.096 R2 = 0.306 (adjusted R2 = 0.222), Durbin–Watson = 2.318. Significant at the level of ** p < 0.05, *** p < 0.01.

Table 16. Result of multiple regression model: category B.

Independent Variables Beta t-Value Sig. Tolerance VIF (Constant) 3.653 0 Commerce and 0.679 9.282 0.000 *** 0.92 1.087 manufacturing expenditure Dummy_project1 −0.273 −2.582 0.011 ** 0.439 2.275 Project 1 subsidy 0.077 0.758 0.451 0.472 2.119 Project 2 subsidy 0.162 1.954 0.054 * 0.718 1.392 Project 3 subsidy 0.138 1.62 0.108 0.683 1.465 R2 = 0.512 (adjusted R2 = 0.488), Durbin–Watson = 1.804. Significant at the level of * p < 0.1, ** p < 0.05, *** p < 0.01. This result was similar to the quasi-experiment result. Overall, the policy resulted in an increase in the number of tourists visiting the zones in category B. The data analysis suggests that the policy impact was large on the zones that consist of regions within the same prefecture.

4.3.5. Synthesis of Regression Models Table 18 shows the synthesis of the regression results. From this table, it can be ob- served that only the subsidy effect of Project 1 had a negative impact (all areas, category A, least effective case), while Project 2 had a positive impact. It is inferred that the characteris- tics of Project 1 are based on the software program (improving stay program, marketing, development of human resource/awareness enlightenment), whereas those of Project 2 are based on the hardware program (information transmission, measures for the secondary traffic, space formation). The effectiveness of software programs is generally demonstrated over the long-term, which is why Project 1 appeared to presently have a negative impact. The development of human resources especially needs sufficient time for a genuine impact to be observed.

Table 17. Result of multiple regression model: category C.

Independent Variables Beta t-Value Sig. Tolerance VIF (Constant) 0.251 0.803 Daytime population 0.759 6.023 0.000 *** 0.704 1.42 (person) Number of public parks 0.512 4.228 0.000 *** 0.761 1.313 Project 1 subsidy 0.065 0.191 0.85 0.096 10.433 Project 2 subsidy 0.035 0.101 0.92 0.09 11.054 Project 3 subsidy 0.1 0.872 0.388 0.852 1.174 R2 = 0.52 (adjusted R2 = 0.464), Durbin–Watson = 1.604. Significant at the level of *** p < 0.01.

Table 18. Synthesis of regression results.

All Category Category Category Most Least Areas A B C Effective Effective Project 1 - - + - Subsidy Project 2 + + Subsidy Project 3 + Subsidy Sustainability 2021, 13, 7478 19 of 24

4.4. Comprehensive Results In order to measure the impact of the tourism zone development policy, a comprehen- sive measurement was conducted through a quasi-experiment and regression modeling. The zones where the number of visitors increased as a result of the policy intervention Sustainability 2021, 13, x FOR PEER REVIEWwere those that were located within a single prefecture. In addition, the subsidy effect22 of 28 was positive in the tourism zones that consisted of areas within the same prefecture covering a narrow range (less than seven municipalities). TableTable 19. Tourism 19 lists zones: the tourism increased zones visitors, where positive the subsidy number effect. of visitors increased as a result of the policy intervention (result from quasi-experiment) and had positive subsidy effects (resultTourism from regressionIncrease model Visitors of most effectiveMost case). Effective Except forCase Tourism Zone 3, these Category areasZone all fell within(Quasi-Experiment) category B2 (in the same prefecture,(Regression narrow Model) range). Tourism zone 3 is in category A1, but this zone is actually positioned next to Tourism Zone 4, which is in category1 B2 (see Figure8). ○ ○ B2 3 ○ ○ A1 Table 19. Tourism zones: increased visitors, positive subsidy effect. 4 ○ B2 Increase Visitors Most Effective Case Tourism Zone Category 5 (Quasi-Experiment) (Regression○ Model) B1 1 B2 7 ○ ○ B2 3 ## A1 8 4 ○ ## B2B2 5 # B1 9 ○ B2 7 # B2 11 8 ## ○ B2C1 # 12 9 ○ ○ B2B2 11 # C1 14 ○ A1 12 # B2 15 14 ○ ## ○ A1B2 15 # B2 19 ○ B2 19 ## B2 24 24 # ○ B2B2 # 25 25 ○ B1B1 #

Figure 8. Tourism Zone 3 and Tourism Zone 4. Figure 8. Tourism Zone 3 and Tourism Zone 4. The constituent areas of the tourism zones that experienced a positive policy impact and subsidy effects are originally famous tourist spots (e.g., Furano Biei, Mt. Fuji), whereas those that experienced negative subsidy effects are not relatively famous tourist areas. Tourism Zone 18 (Kira Kira Uetsu Tourism Zone) consists of agricultural rural areas. One of the purposes of tourism zone development is the balancing of regional

Sustainability 2021, 13, 7478 20 of 24

The constituent areas of the tourism zones that experienced a positive policy impact and subsidy effects are originally famous tourist spots (e.g., Furano Biei, Mt. Fuji), whereas those that experienced negative subsidy effects are not relatively famous tourist areas. Tourism Zone 18 (Kira Kira Uetsu Tourism Zone) consists of agricultural rural areas. One of the purposes of tourism zone development is the balancing of regional disparities, so these areas experienced negative subsidy effects. In addition, the least effective cases were the tourism zones that consisted of a significant number of municipalities, especially Tourism Zone 10 (Sanin Cultural Tourism Zone).

5. Discussion This study proposed two research problems related to the evaluation of the tourism zone development policy. Based on these verifications, it analyzed the effectiveness of tourism zone development. The primary content and results of this research are as follows. First, empirical analyses were carried out, with the targets being tourism zones nation- wide. By utilizing person trip data and regional data, a quasi-experiment was conducted to ascertain whether the policy implementation had an impact, and regression modeling was utilized to observe how effectively the policy worked. The results revealed that overall, the tourism zones that cover a narrow region within the same prefecture experienced a positive impact. Among the three types of subsidies, the subsidy of Project 2 (information transmis- sion, measures for the secondary traffic, space formation) had an impact on increasing the number of visiting tourists. Second, the tourism zones were categorized into three broad categories (each of which had two sub-categories) and empirical regression models that included the subsidy expenditures were conducted. Results of the analysis indicated that the zones in category B1 (zones consisting of regions in the same prefecture and covering a narrow area) experienced the impact of the policy. The subsidy of Project 2 (information transmission, measures for the secondary traffic, space formation) had a positive impact on the zones in category B (zones consisting of regions in the same prefecture). The main policy implications of tourism zone development that were derived through the interpretation of the main findings of the study are as follows. First, the impact of the implementation of the tourism zone development policy may have been lower than expected. The empirical results revealed that overall, the tourism zone areas did not experi- ence a significant increase in the number of visiting tourists after the implementation of the policy. Additionally, a crowding-out effect on the subsidy investment was demonstrated. The presence of this effect can be observed due to the following facts: (1) policies related to infrastructure formation take a long time to display the expected results, (2) the tourism industry largely experiences an indirect effect, and 3) one of the policy objectives may be the balancing of regional disparities. Second, instead of including a wide area within a single tourism zone, the inclusion of moderately sized regions within a single zone results in a stronger policy impact. If a tourism zone consists of regions within multiple prefectures, a system for fluent cooperation and communication between prefectures would be required. This study is significant because it empirically evaluated a contemporary tourism policy (tourism zone development) in Japan. Even though Japan aims to become a tourism nation, very few previous studies have conducted nation-wide tourism research. Moreover, this study synthetically examined two different aspects of the implementation of the tourism zone development policy: (1) whether the policy implementation had an impact and (2) the degree of this impact. The main limitations of this study are the following. First, this study could not consider the factors such as tourists’ expenditure data, which might have a great influence on the tourism policy’s impact. Detailed tourism data, such as information regarding the number of accommodations or tourists’ expenditure, could not be acquired because of the data collection year (accommodation statistics in municipality were only collected from 2007). Second, this study could not include data regarding inbound tourism, because the data used for the measurement (person trip data) only pertained to the tourism of Japanese locals. However, considering the prominent role of domestic tourism in Japan (domestic travel spending generated around 81% of the direct Sustainability 2021, 13, 7478 21 of 24

travel and tourism GDP, whereas foreign visitor spending constituted 19% in 2019 [46]), this study still provides enlightening information regarding the Japanese tourism sector. The limitations related to analysis are mainly the result of limitations related to data acquisition. Most of the data provided concerns the prefectural level, whereas the tourism zone areas are at the municipal level. Thus, there was a limit in terms of useable data. If it is possible to conduct a materialized analysis across each region with detailed data, more useful suggestions on tourism policy could be provided. Japan has begun generating various tourism-related data since the establishment of the JTA. If follow-up studies are conducted using these data, analyses that reflect the economic impact of expenditure costs can be expected.

6. Conclusions Under the continuing global recession from the subprime mortgage crisis, many countries are struggling with overcoming this long-lasting recession. The tourism industry usually had been perceived as a major industry in developing countries. However, in the countries that had previously placed a greater focus on other sectors, the tourism industry is emerging as a strategy to overcome the economic recession. Japan, as a major global manufacturer and trader, had been leading the world economy and has tremendously influenced the global economy. Nevertheless, Japan continues to struggle with the economic recession. The Japanese government identified the tourism industry as a key strategy for economic revival as well as a key for regional revitalization. Given this global trend toward tourism, this paper explores the tourism-related policies being promoted by the Japanese government and their effectiveness. The tourism industry is affected by many aspects such as seasonal fluctuation, his- torical events, the global economy, disasters, etc. A recent global pandemic situation (i.e., COVID-19) affected the tourism sector due to restricted mobility and social distancing [47]. However, tourism is one of the key industries that can also reduce disaster risks, and fundamental changes in tourism can be observed as recovering and learning after disas- ters [48,49]. Therefore, it is important to conduct a summative evaluation of a certain event that can significantly affect tourism sector. In this study we empirically evaluated one of the key tourism policies in Japan. We hope that this study can contribute sustainable tourism policies and regional planning in urban studies.

Author Contributions: Conceptualization, H.K.; methodology, H.K. and E.J.K.; software, H.K.; validation, H.K. and E.J.K.; formal analysis, H.K.; investigation, H.K.; resources, E.J.K.; data cu- ration, H.K.; writing—original draft preparation, H.K.; writing—review and editing, H.K. and E.J.K.; visualization, H.K.; supervision, H.K. and E.J.K.; project administration, H.K. and E.J.K.; funding acquisition, H.K. and E.J.K. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: The authors would like to express their gratitude to the Japan Tourism Agency (JTA) for their support with respect to interviews and materials. Conflicts of Interest: The authors declare no conflict of interest.

Appendix A. List of Tourism Zones

No. Name Constituent Cities/Towns Furano Biei Wide Hokkaido Furano-shi, Biei-cho, Kamifurano-cho, Nakafurano-cho, 1 Tourism Zone Minamifurano-cho, Shimukappu-mura Sustainability 2021, 13, 7478 22 of 24

No. Name Constituent Cities/Towns Iwate-ken Ichinoseki-shi, Oshu-shi, Hiraizumi-cho, Miyagi-ken Elegant Wide 2 Sendai-shi, Kesennuma-shi, Tome-shi, Osaki-shi, Matsushima-machi, Tourism Zone Rifu-cho, Minamisanriku-cho, Yamagata-ken Mogami-machi Fukushima-ken Aizuwakamatsu-shi, Kitakata-shi, Shimogo-machi, Aizu and Yonezawa 3 Minamiaizu-machi, Kitashiobara-mura, Nishiaizu-machi, Region Tourism Zone Bandai-machi, Inawashiro-machi Tenderness and Natural 4 Warmth Fukusima Fukushima-ken Fukushima-shi, Soma-shi, Nihonmatsu-shi, Date-shi Tourism Zone Mito Hitachi Tourism Ibaraki-ken Mito-shi, Hitachi-shi, Hitachiota-shi, Takahagi-shi, 5 Zone; Your Sky Kitaibaraki-shi, Kasama-shi, Hitachinaka-shi, Hitachiomiya-shi, and Earth Naka-shi, Oarai-machi, Shirosato-machi, Tokai-mura, Daigo-machi Southern Boso Region Chiba-ken Tateyama-shi, Kamogawa-shi, Minamiboso-shi, 6 Tourism Zone Kyonan-machi Mt. Fuji and Fuji Five Yamanashi-ken Fujiyoshida-shi, Nishikatsura-cho, Oshino-mura, 7 Lakes Tourism Zone Yamanakako-mura, Narusawa-mura, Fujikawaguchiko-machi Ise Shima Region 8 Mie-ken Ise-shi, Toba-shi, Shima-shi, Minamiise-cho Tourism Zone Kyoto Prefecture Tango Kyoto-fu Maizuru-shi, Miyazu-shi, Kyotango-shi, Ine-cho, 9 Tourism Zone Yosano-cho Tottori-ken Yonago-shi, Kurayoshi-shi, Sakaiminato-shi, Misasa-cho, Yurihama-cho, Kotoura-cho, Hokuei-cho, Hiezu-son, Daisen-cho, Sanin Cultural Nambu-cho, Hoki-cho, Nichinan-cho, Hino-cho, Kofu-cho, 10 Tourism Zone Shimane-ken Matsue-shi, Izumo-shi, Oda-shi, Yasugi-shi, Unnan-shi, Okuizumo-cho, Iinan-cho, Ama-cho, Nishinoshima-cho, Chibu-mura, Okinoshima-cho Hiroshima, Miyajima, Hiroshima-ken Hiroshima-shi, Kure-shi, Otake-shi, Hatsukaichi-shi, 11 and Iwakuni Region -shi, Kaita-cho, Kumano-cho, Saka-cho, Yamaguchi-ken Tourism Zone Iwakuni-shi, Yanai-shi, Suooshima-cho, Waki-cho Nishi Awa Tokushima-ken Mima-shi, Miyoshi-shi, Tsurugi-cho, 12 Tourism Zone Higashimiyoshi-cho New East Kyushu Oita-ken Oita-shi, Beppu-shi, Saiki-shi, Usuki-shi, Tsukumi-shi, 13 Tourism Zone Yufu-shi, Miyazaki-ken Nobeoka-shi Kumamoto-ken Aso-shi, Minamioguni-machi, Oguni-machi, Aso Kuju 14 Ubuyama-mura, Takamori-machi, Nishihara-mura, Minamiaso-mura, Tourism Zone Yamato-cho, Oita-ken Taketa-shi, Miyazaki-ken Takachiho-cho Shiretoko 15 Hokkaido Shari-cho, Kiyosato-cho, Shibetsu-cho, Rausu-cho Tourism Zone Sapporo Wide Hokkaido Sapporo-shi, Ebetsu-shi, Chitose-shi, Eniwa-shi, 16 Tourism Zone Kitahiroshima-shi, Ishikari-shi, Tobetsu-cho, Shinshinotsu-mura New Travel in Aomori, Aomori-ken Aomori-shi, Hachinohe-shi, Towada-shi, Misawa-shi, 17 Lake Towada Wide Shichinohe-machi, Rokunohe-machi, Tohoku-machi, Oirase-cho Tourism Zone Akita-ken Nikaho-shi, Yamagata-ken Tsuruoka-shi, Sakata-shi, Kira Kira Uetsu 18 Tozawa-mura, Mikawa-machi, Shonai-machi, Yuza-machi, Tourism Zone -ken Murakami-shi, -mura, Awashimaura-mura 19 Nikko Tourism Zone Tochigi-ken Nikko-shi Niigata-ken -shi, -shi, Yuzawa-machi, Snow Country 20 Tokamachi-shi, -machi, Gumma-ken Minakami-machi, Tourism Zone -ken Sakae-mura Toyama Bay, Kurobe Toyama-ken Uozu-shi, Namerikawa-shi, Kurobe-shi, Nyuzen-machi, 21 Canyon, Etchu Niikawa Asahi-machi Tourism Zone Ishikawa-ken Nanao-shi, Wajima-shi, Suzu-shi, Hakui-shi, Noto Peninsula 22 Shika-machi, Hodatsushimizu-cho Nakanoto-machi, Anamizu-machi, Tourism Zone Noto-cho Sustainability 2021, 13, 7478 23 of 24

No. Name Constituent Cities/Towns Fukui Sakai Wide Fukui-ken Fukui-shi, Awara-shi, Sakai-shi, Eiheiji-cho, Ono-shi, 23 Tourism Zone Katsuyama-shi Lake Hamana 24 Shizuoka-ken -shi, Kosai-shi Tourism Zone Lake Biwa, Omiji Shiga-ken Hikone-shi, Nagahama-shi, Higashiomi-shi, Maibara-shi, 25 Tourism Zone Hino-cho, Ryuo-cho, Aisho-cho, Toyosato-cho, Kora-cho, Taga-cho Awaji Island 26 Hyogo-ken Sumoto-shi, Minamiawaji-shi, Awaji-shi Tourism Zone Holy Place Kumano Healing and 27 Nara-ken Totsukawa-mura, Wakayama-ken Tanabe-shi Reconstruction Tourism Zone Shimanto and Ashizuri Kochi-ken Sukumo-shi, Tosashimizu-shi, Shimanto-shi, Otsuki-cho, 28 Area (Hata District) Mihara-mura, Kuroshio-cho Tourism Zone Hirato, Sasebo, Saikai 29 Long Stay Nagasaki-ken Sasebo-shi, Hirado-shi, Saikai-shi Tourism Zone Nagasaki-ken Shimabara-shi, Unzen-shi, Minamishimabara-shi, Unzen Amakusa 30 Kumamoto-ken Kamiamakusa-shi, Uki-shi, Amakusa-shi, Tourism Zone Reihoku-machi

References 1. WTTC World Tavel and Tourism Council. Available online: https://wttc.org/Research/Economic-Impact (accessed on 16 April 2021). 2. Direct Contribution of Tourism to OECD Countries. Available online: http://dx.doi.org/10.1787/888934076134 (accessed on 16 April 2021). 3. OECD. OECD Tourism Trends and Policies 2012; OECD Tourism Trends and Policies; OECD Publishing: Paris, France, 2012; ISBN 9789264177550. 4. Darabi, H.; Ansari-Moqadam, A.; Saidi, A.; Rouzrokh, H. Economic Fluctuation and Its Effects on Tourism in Kish Island, Iran. J. Tour. Hosp. Sports 2014, 2, 1–16. 5. Gunn, C.A. Turgut var Tourism Planning: Basics Concepts Cases, 4th ed.; Routledge: London, UK, 2002; ISBN 0-415-93269-6. 6. Long, A.; Ascent, D. World Economic Outlook. Int. Monet. Fund 2020.[CrossRef] 7. JTA about JTA|Japan Tourism Agency. Available online: http://www.mlit.go.jp/kankocho/en/about/index.html (accessed on 15 April 2021). 8. Tourism Nation Council Report: Creating a Country Where You Can Live and Visit. Available online: https://www.kantei.go.jp/ jp/singi/kanko/kettei/030424/houkoku.html (accessed on 16 April 2021). 9. JTA Tourism Nation Promotion Basic Law. Available online: https://www.mlit.go.jp/kankocho/en/kankorikkoku/index.html (accessed on 16 April 2021). 10. Seki, K. A study on the process of regional tourism management in collaboration between public and private sectors. WIT Trans. Ecol. Environ. 2013, 179, 339–349. 11. Patandianan, M.V.; Shibusawa, H. Evaluating the spatial spillover effects of tourism demand in Shizuoka Prefecture, Japan: An inter-regional input–output model. Asia Pac. J. Reg. Sci. 2020, 4, 73–90. [CrossRef] 12. Funck, C.; Cooper, M. Japanese Tourism: Spaces, Places and Structures; Berghahn Books: New York, NY, USA; Oxford, UK, 2013; Volume 5, ISBN 1782380760. 13. Ashley, C.; De Brine, P.; Lehr, A.; Wilde, H. The Role of the Tourism Sector in Expanding Economic Opportunity; John F. Kennedy School of Government, Harvard University: Cambridge, MA, USA, 2007; Available online: https://www.hks.harvard.edu/sites/ default/files/centers/mrcbg/programs/cri/files/report_23_EO+Tourism+Final.pdf (accessed on 16 April 2021). 14. MLIT. Tourism Nation Promotion Basic Plan(Provisional Translation); MLIT: Tokyo, Japan, 2012. 15. JTA Tourism Zone Development Act|Creating Tourism Destinations|About Policy|Japan Tourism Agency. Available online: http://www.mlit.go.jp/kankocho/en/shisaku/kankochi/seibi.html (accessed on 16 April 2021). 16. Tourism Zone Development Act. Available online: https://www.mlit.go.jp/kankocho/en/shisaku/kankochi/seibi.html (ac- cessed on 16 April 2021). 17. White Paper on Tourism in Japan. 2009. Available online: https://www.mlit.go.jp/common/000221174.pdf (accessed on 16 April 2021). 18. Poland, O.F.; Horst, P.; Nay, J.N.; Scanlon, J.W.; Wholey, J.S.; Lewis, F.L.; Zarb, F.G.; Brown, R.; Pethtel, R.D.; Marvin, K.E.; et al. Program Evaluation. Public Adm. Rev. 1974, 34, 299–338. [CrossRef] 19. Hall, C.M. A typology of governance and its implications for tourism policy analysis. J. Sustain. Tour. 2011, 19, 437–457. [CrossRef] Sustainability 2021, 13, 7478 24 of 24

20. Scriven, M. The Methodology of Evaluation (Vol.1); American Education Research Association: Washington, DC, USA, 1967. 21. Wholey, J.S. Formative and summative evaluation: Related issues in performance measurement. Eval. Pract. 1996, 17, 145–149. [CrossRef] 22. Poland, O.F. Program evaluation and administrative theory. Public Adm. Rev. 1974, 34, 333–338. [CrossRef] 23. Creswell, J.W.; Creswell, J.D. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches; Sage Publications: London, UK, 2017; ISBN 1506386717. 24. Reichardt, C.S. Quasi-experimental design. SAGE Handb. Quant. Methods Psychol. 2009, 46, 490–500. 25. Blundell, R.; Costa Dias, M. Evaluation methods for non-experimental data. Fisc. Stud. 2000, 21, 427–468. [CrossRef] 26. Ishikawa, N.; Fukushige, M. Who expects the municipalities to take the initiative in tourism development? Residents’ attitudes of Amami Oshima Island in Japan. Tour. Manag. 2007, 28, 461–475. [CrossRef] 27. Ishikawa, N.; Fukushige, M. Impacts of tourism and fiscal expenditure to remote islands: The case of the Amami islands in Japan. Appl. Econ. Lett. 2007, 14, 661–666. [CrossRef] 28. Romão, J.; Neuts, B.; Nijkamp, P.; Shikida, A. Determinants of trip choice, satisfaction and loyalty in an eco-tourism destination: A modelling study on the Shiretoko Peninsula, Japan. Ecol. Econ. 2014, 107, 195–205. [CrossRef] 29. Ohe, Y.; Kurihara, S. Evaluating the complementary relationship between local brand farm products and rural tourism: Evidence from Japan. Tour. Manag. 2013, 35, 278–283. [CrossRef] 30. Ohe, Y. Evaluating Household Leisure Behaviour of Rural Tourism in Japan; Exploring Diversity in the European Agri-Food System: Zaragoza, Spain, 2002; Available online: https://ageconsearch.umn.edu/record/24932/files/cp02oh15.pdf (accessed on 16 April 2021). 31. Chi, P.-Y.; Chang, T.; Takahashi, D.; Chang, K.-I. Evaluation of the impact of the tourism nation promotion project on inbound tourists in Japan: A difference-in-differences approach. Asia Pac. J. Tour. Res. 2019, 24, 31–55. [CrossRef] 32. Okyere, S.A.; Diko, S.K.; Abunyewah, M.; Kita, M. Toward citizen-led planning for climate change adaptation in Urban Ghana: Hints from Japanese ‘Machizukuri’activities. In The Geography of Climate Change Adaptation in Urban Africa; Springer: Berlin/Heidelberg, Germany, 2019; pp. 391–419. 33. Sorensen, A.; Koizumi, H.; Miyamoto, A. Machizukuri, civil society, and community space in Japan. Polit. Civ. Sp. Asia Build. Urban Communities 2008.[CrossRef] 34. Hein, C. Toshikeikaku and machizukuri in Japanese urban planning: The reconstruction of inner city neighborhoods in Kobe.¯ Japanstudien 2002, 13, 221–252. [CrossRef] 35. Kusakabe, E. Advancing sustainable development at the local level: The case of machizukuri in Japanese cities. Prog. Plann. 2013, 80, 1–65. [CrossRef] 36. National Urban Traffic Chracteristics Survey. Available online: https://www.mlit.go.jp/toshi/tosiko/toshi_tosiko_tk_000033 .html (accessed on 16 April 2021). 37. e-Stat, Portal Site of Official Statistics of Japan. Available online: https://www.e-stat.go.jp/en/regional-statistics/ssdsview (accessed on 16 April 2021). 38. Takaharu, K. The great Heisei consolidation: A critical review. Soc. Sci. Jpn. 2007, 37, 7–11. 39. Rausch, A. The Heisei Dai Gappei: A case study for understanding the municipal mergers of the Heisei era. Jpn. Forum 2006, 18, 133–156. [CrossRef] 40. Shimizu, N. Effects of Municipal Mergers in Japan; Canadian Political Science Association, Annual Conference: Victoria, BC, Canada, 2013. 41. Kuo, R.J.; Akbaria, K.; Subroto, B. Application of particle swarm optimization and perceptual map to tourist market segmentation. Expert Syst. Appl. 2012, 39, 8726–8735. [CrossRef] 42. Habing, B. Exploratory factor analysis. Univ. S. C. Oct. 2003, 15, 2003. 43. Galster, G.; Walker, C.; Hayes, C.; Boxall, P.; Johnson, J. Measuring the impact of community development block grant spending on urban neighborhoods. Hous. Policy Debate 2004, 15, 903–934. [CrossRef] 44. Martin, C.A.; Witt, S.F. Tourism demand forecasting models: Choice of appropriate variable to represent tourists’ cost of living. Tour. Manag. 1987, 8, 233–246. [CrossRef] 45. National Land Numerical Information Download Service, MLIT. Available online: http://nlftp.mlit.go.jp/ksj/ (accessed on 16 April 2021). 46. WTTC Economic Impact Report. Available online: https://wttc.org/Research/Economic-Impact/country-analysis/country-data (accessed on 16 April 2021). 47. Gössling, S.; Scott, D.; Hall, C.M. Pandemics, tourism and global change: A rapid assessment of COVID-19. J. Sustain. Tour. 2021, 29, 1–20. [CrossRef] 48. UNISDR. From Shared Risk to Shared Value-The Business Case for Disaster Risk Reduction: Global Assessment Report on Disaster Risk Reduction; United Nations Office for Disaster Risk Reduction: Geneva, Switzerland, 2013. 49. Chan, C.-S.; Nozu, K.; Cheung, T.O.L. Tourism and natural disaster management process: Perception of tourism stakeholders in the case of Kumamoto earthquake in Japan. Curr. Issues Tour. 2020, 23, 1864–1885. [CrossRef]