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“Invisible Cities” – Jeopardized International Tourism of Chinese Cities under the Impact of Air Pollution
Student Name: Zhaoting Wang
Student Number: 342670
Specialization: MSc Urban, Port and Transport Economics
Supervisor: Erwin van Tuijl
Second reader: Jan-Jelle Witte
August 2014
Abstract
China has been increasing its attractiveness as a tourist destination after implementing “Open Door” policies
in 1978. Since the issue of air pollution in China has been increasingly attracting public attention in recent
years, this dissertation has investigated the impact of poor air quality on international tourism demand of
Chinese cities. First, literature reviews of evolution of international tourism industry demonstrated a great
expansion, and status quo of air pollution reflected a lack of sustainable approach for this development. The
case studies are also a part of method, in order to set up hypotheses for quantitative analysis of empirical
study, which is air pollution in this research. Due to a scarce of scholars, three similar cases are selected
because they share some common characteristics with air pollution. Based on the experiences learnt from
the case studies, two main hypotheses are set up, which both air pollution and media publicity of air
pollution will produce negative impacts on international tourism in Chinese cities.
With the assists of econometrical models, two main hypotheses are tested. The statistical results manifested
that the negative impacts on international tourism from air pollution and media publicity, indicating that
hypotheses are not rejected. Tourism demand of international tourists will decline if the air quality
decreases, as well as if media publicity of air pollution increases.
As a consequence, it can be concluded that poor air quality has negatively influenced demand of
international tourists in Chinese cities. Therefore, it calls for policy makers to seek for solutions of mitigating
air pollution, in order to acquire a sustainable development of international tourism in Chinese cities.
Key words: international tourism; air pollution; air quality; China
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Acknowledgements
First and foremost, I would like to express my sincerest appreciation to my supervisor, Erwin van Tuijl, for all
the efforts you made, both academically and non-academically, for helping me complete this research in time.
It could not be done without your smartness and patience. I also would like to thank Jan-Jelle Witte, for
inspiring and helping me with quantitative analysis, and your knowledge about China has also made this
research can be conducted so efficiently.
Next, I want to give my gratitude to all my friends who have supported me during this 4-year time study in
the Netherlands, you are all precious in my life. I want to thank in particular Danshu Wang, my housemate, 4-
year colleague in Erasmus School of Economics, for those thoughtful chats we shared in the raining
midnights; Di Lei, my best travel companion, the boss of authentic Italian gelaterias in future, for the
impulsive trips we made, famous football players we met, and matches we watched together; Suphakarn
Varinpramote, my excellent group member of master seminars, for all the considerations and exquisite
birthday gifts; Yi Cao, my personal fiction reading advisor, for all the discussions and laughter triggered by
those charming characters; Zaiyue Yu, my idealism fighter, for inviting me to your parties; and importantly,
for all of you, I want to give a special gratitude for your amazing cooking skills, which have saved a kitchen
disaster from starving; but by exception, I still want to thank Yixin Rong, my expert of fitness, for being my
Chinese colleague in this master specialization; Jia Qu, for those concerts and dinner nights; Xin Yin, for the
jersey from Munich and all the good memories in San Marino; and Shirui Pan, for that strange tour in
Juventus Stadium.
Moreover, my middle school classmates have been supporting me remotely as well. I want to thank Yiqing
Liu, for all those words, and visiting me two years ago; Yu Wang, for the tacit meeting in front of Louver
Musee without any cellphone connection, and the delicious food you brought from our hometown; Simeng
Song, for the packages of Japanese delights; and Yihong Jiang, for the cool postcard from Palestine.
Last but not least, I want to thank my parents for everything, hope that you are proud. Especially for my
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mother, it’s been not easy for looking after such a demanding child, but you have given me the best you can
offer.
Table of Content
Abstract..................................................................................................................................2
Acknowledgements............................................................................................................3
Chapter 1 Introduction.....................................................................................................7
1.1 Relevance.........................................................................................................................................................................................7
1.2 Research Question....................................................................................................................................................................... 9
1.3 Aim.................................................................................................................................................................................................. 10
1.4 Methodology................................................................................................................................................................................10
1.5 Structure....................................................................................................................................................................................... 11
Chapter 2 Theoretical Review......................................................................................12
2.1 Introduction to Tourism.........................................................................................................................................................12
2.2 Evolution in Tourism Industry in China..........................................................................................................................15
2.2.1 First phase: a diplomatic channel to establish friendships (1949 – 1978)....................................................15
2.2.2 Second phase: a gate opened to the world (1979 – 2000)....................................................................................16
2.2.3 Third phase: a platform to show the image (2001 – present)............................................................................20
2.3 Conclusion on tourism............................................................................................................................................................25
2.4 Air pollution and its status quo in China........................................................................................................................27
2.4.1 Introduction to air pollution............................................................................................................................................. 27
2.4.2 the status quo in China........................................................................................................................................................ 28
2.5 The sources of air pollutants and mitigating solutions............................................................................................29
2.5.1 Retrieving the sources.......................................................................................................................................................... 29
2.5.2 Mitigation of air pollution.................................................................................................................................................. 31
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2.6 Conclusion on air quality.......................................................................................................................................................33
2.7 Geographical comparisons between tourism and air pollution...........................................................................33
Chapter 3 Factors Influencing International Tourism Demand........................35
3.1 Introduction.................................................................................................................................................................................35
3.2 Determinants of attractiveness...........................................................................................................................................37
3.3 Impacts of media publication on tourism demand....................................................................................................39
3.4 Typology of incidents.............................................................................................................................................................. 40
3.5 Case studies................................................................................................................................................................................. 42
3.5.1 Case 1: SARS in Singapore.................................................................................................................................................. 43
3.5.2 Case 2: Street Crime in New Orleans.............................................................................................................................. 45
3.5.3 Case 3: Street Crime in Johannesburg........................................................................................................................... 48
3.5.4 Conclusions on case studies................................................................................................................................................ 49
3.6 Hypotheses................................................................................................................................................................... 51
Chapter 4 Empirical Models..........................................................................................53
4.1 Method........................................................................................................................................................................................... 53
4.2 Data................................................................................................................................................................................................. 54
4.2.1 Description................................................................................................................................................................................ 54
4.2.2 The dependent variable....................................................................................................................................................... 54
4.2.3 The independent variables................................................................................................................................................. 55
4.3 Model Specification..................................................................................................................................................................65
Chapter 5 Results..............................................................................................................68
5.1 Models............................................................................................................................................................................................ 68
5.1.1 Results in general................................................................................................................................................................... 68
5.1.2 Comparisons between different regions....................................................................................................................... 73
5.2 Goodness-of-fit...........................................................................................................................................................................75
Chapter 6 Discussion.......................................................................................................80
6.1 Effects of air pollution.............................................................................................................................................................80
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6.2 Effects of media publicity...................................................................................................................................................... 81
Chapter 7 Conclusion and recommendations.........................................................83
7.1 Conclusion.................................................................................................................................................................................... 83
7.2 Recommendations....................................................................................................................................................................84
Bibliography.......................................................................................................................85
Appendix.............................................................................................................................97
1. Result of Hausman test..............................................................................................................................................................97
2. Regression result excluding Lhasa.......................................................................................................................................97
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Chapter 1 Introduction
1.1 Relevance
“The Invisible Cities” – a novel written by Calvino, has narrated the trips in many mysterious cities in Asia in
Yuan Dynasty through the conversation between a famous traveler in the history – Marco Polo and a great
emperor – Kublai Khan. Those cities have not only gripped Khan’s attention deeply, but also triggered
discussions of normal people over the years because of the poetical descriptions. Of course, those
magnificent oriental cities have to be left with those flowery metaphors in the stories forever because they
were completely imaginative, but Khan finally never got a chance to reach the land in the east he was always
curious about because of the poor mobility at that time – almost 1000 years ago.
Luckily, different from the ancient time, the remote destinations can be more easily reached today. People are
able to witness the magnificent cities in the eastern continent through their own eyes, although the “visible”
cities cannot be as exaggerate as those stories told by the romantic Italian author, the trip to Asia countries
will still be very interesting and worthy to experience, because it will definitely bring some fresh and
unforgettable memories of the exotic cultures.
International tourism has kept expanding in the recent years. The reasons behind it are various, except for
satisfying the curiosity. The most appreciated one is the trend of globalization connects the world more
tightly. Economically, higher disposal income, especially in the western countries, has made long-distance
travelling more affordable; technically, the prosperity of aviation industry increased the accessibility of the
remote destinations greatly (Azarya, 2004). Consequently, tourist inflow in Asian and Pacific region has
grown fastest, which gained 14 million more international tourists in 2013, a 6% increase than it in 2012
(UNWTO, 2014).
As one of the biggest countries in this region, China has contributed enormously to this growth. It gained the
popularity as a destination, because several big events ha remarkably improved its awareness and
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reputation, such as the Beijing Olympics in 2008 and the Shanghai Expo in 2010 (Travel China Guide, 2014).
For example, in a survey that Nielson Company conducted across 16 countries, 7 out of 10 international
respondents recognized that Beijing appeared a more modern and hi-tech image than what they expected. 51
percent of international respondents expressed their intension of visiting China in the future after watching
the Beijing Olympics remotely, while a quarter of the rest had already been to China during the Olympic
games, and part of them are willing to visit China again (Xin, 2008).
However, the trips to China now seem to be exploring “the invisible cities” nowadays, but it is not because of
any romantic reasons – many Chinese cities hazed by the smog of particular matters heavily. In fact, the issue
of air pollution in China has been grasped public attention because it has already been frequently disclosed
to the media. Sometimes the value of Air Quality Index (AQI) in Beijing can reach above 500 (Fisher, 2013),
meaning that every cubic meter of air contains more than 500 micro grams of particular matter with a
diameter smaller than 2.5 micrometer, and thus any human activities are suggested being avoided according
to the Air Quality Guideline published by World Health Organization (World Health Organization, 2006).
Such substance is a danger for human’s health, because it enlarges the potential of serious health problems
such as heart attacks, by going deep into lungs and bloodstreams (United States Environmental Protection
Agency, 2014).
Will the poor air quality prevent international tourists’ visits? The decision of visiting a place depends on
many factors, from the demand side, tourist are longing to destinations with enjoyable attractions. Social
factors, historical factors, recreational facilities and infrastructure can all influence destination
attractiveness, and thus influence tourists’ demand (Gearing, Swart, & Var, 1974). The air quality can be seen
as a component of weather, which is one of the natural factors of influencing the attractiveness of the
destination (Maddison, 2001). Besides, media also plays a role in travel decisions, while online tourism
domain has increased importance, it is often used as a guidance of travelling, and communications via social
media changes tourists’ perception on a destination (Litvin, Goldsmith, & Pan, 2008; Xiang & Gretzel, 2010).
Success of media publicity will promote positive images of a destination, while a failure will probably become
a disaster of tourism industry.
While the natural factors, in other words, pleasant environment and scenery in the tourist destination will be
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a motivation for travelling (Scott & Lemieux, 2010), people will turn away from the places if they perceived
pollution exists (Mihalic, 2000). Most of the evidence of negative influence of pollution is collected in the
places where tourism is closely tied to natural resources, and there have been rare investigation between
pollution and urban tourism. Intuitively, air quality may not matter as strong as the area intensively relying
on the natural scenery, because the characteristics of tourism in the urban areas are different from the
destinations based on rich natural resources, and the cities have higher complexity (Pearce, 2001). But still, if
the air pollution has became a serious issue in some destinations, there will be a reason to suspect that the
tourists will avoid travelling there, in other words, the tourism demand might decrease due to air pollution.
1.2 Research Question
Chinese cities are reported suffering from air pollution. Since air quality is one of important natural factors
that influencing tourism, it leads to the following research question:
How has air quality influenced international tourists for the Chinese cities?
Sub Questions
In addition, various associated sub questions have been raised to facilitate answering the main question.
They will also accomplish a more integrated method of this dissertation through widening the coverage of
existing studies and deepening the available information.
Therefore, some sub questions are raised as follows:
1. What are the characteristics of the inbound tourists of China?
1.1 What is tourism?
1.2 How have foreign tourism and relevant industry developed in China in the recent decades?
2. What is the current situation of air pollution in China?
2.1 What is air pollution?
2.2 What are the causes of air pollution?
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3. How to measure the attractiveness of a city as a tourist destination?
4. How has media publicity of air pollution influenced the tourism demand?
1.3 Aim
This dissertation aims to look deep into the issue of air quality, to examine its significance of the inbound
tourism of China. Poor air quality, in other terms, air pollution, seems to be a potential threat of the
speculation of tourism demand, because pollution will stain the destination attractiveness and eventually
decrease in tourist demand. However, China as a newly raised market, although many works has been done
on its tourism, there is nearly no research focusing on air pollution, to confirm or dismiss its impact.
Therefore, it is necessary to investigate what this effect can be, and also contributes to fill in the research
blank that takes the factor of air quality into account.
1.4 Methodology
The method of this study is divided into two fragments, theoretical reviews and a quantitative analysis. In the
part of theoretical review, relevant theory will be presented first, following by a series of cases that the
destinations had coped with the impact of similar factors, in order to give an indication for this topic, in other
words, the expectation of the influence of air pollution in China. After reviewing the literatures and cases,
some hypothesis will be formulated, which will be tested by the quantitative model, which constitutes the
other fragment of the method of this research.
The modeling of tourism demand depends on the secondary data (Song & Li, 2008), as a consequence, desk
research will be adopted, in order to select appropriate independent variables for this study. As mentioned,
the tourist demand will be identified as tourist arrivals. The air quality will be illustrated by the variable
representing yearly emission of Sulfur Dioxide (ton/year), which is expected to show a change on the
demand of tourism.
Furthermore, the tourist demand can also be influenced by the year lag. Therefore, a long-term impact will
also be investigated.
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1.5 Structure
The rest of the dissertation will be organized as follows. Chapter 2 will look into a series of scientific
literatures to discuss tourism in general and characteristics of tourism in China, which also provide answers
to the first and second sub question. Chapter 3 will dig deeper of the factors influencing inbound tourism to
destinations, in order to formulate a hypothesis based on the expectations of how other negative incidences
drive the tourism demand in the case studies. Chapter 4 is going to introduce the quantitative method,
empirical model, and selection of the independent variables. Chapter 5 will contain the test of the hypothesis,
and present the primary results of the regression. Chapter 6 will further discuss the hypothesis and provide
the answer to the fourth sub question. Chapter 7 will summarize the findings from both theoretical and
empirical studies, and finally reveal the answer to the main research question; in other words, draw the main
conclusion of this dissertation.
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Chapter 2 Theoretical Review
Tourism has been taken into account by the policy makers of China as a channel of promoting modern image
in the global stage, and a way of increasing foreign exchange receipts and enhancing the mutual
understanding since 1978, the 3rd Plenary Session of 11th Central Committee, the moment that China opens
its gate to the world again (Uysal, Wei, & Reid, 1986). For over 30 years, there have been substantial policies
supporting tourism industry implemented to boost its foreign tourism, for example, enhancing the
conversation and environmental protection, investing in its infrastructure and tourism facilities, intensifying
tourism education, training on human resources, and regulating based on the market (Zhang, Chong, & Ap,
1999; Xiao, 2006). The other policies are running effectively due to the strong performance of Chinese
economy in the recent decades, however, the first and the most primary policy, the environmental protection,
seems to be shadowed its light in the public attention because of the issues of pollution. Therefore, it is
meaningful to investigate both tourism industry and the issue of air pollution in China.
This chapter will be arranged as follows: Section 2.1 is going to introduce the definition of tourism in general;
Section 2.2 will look into the details of tourism in China, to present the overview of tourism industry based
on the timeline; Section 2.3 will draw a conclusion of international tourism in China, thus answer the sub-
question 1.2. Section 2.4 begins with presenting relevant knowledge of air quality, will discuss the status quo
of air pollution issue in China; Section 2.5 will analyze the sources of air pollution based on a backward
tracking of composition of pollutant, and the discuss the methods of mitigation, thus the second sub-question
and its sub-question 2.1 will be answered; and Section 2.6 will summarize briefly from the analysis of air
pollution, so the answer to the sub-question 2.2 will come out. Finally, in order to link international tourism
to the air pollution issue in China, Section 2.7 will compare the differences of geographical distribution of
these two topics, and make connection to the next chapter.
2.1 Introduction to Tourism
There can be hard to find out a uniform version about the definition of the term “tourism”, some scientists
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even claimed that there is no commonly accepted one (Ritchie, Carr, & Cooper, 2003). One of the reasons
resulting in this chaotic circumstance could be complexity of tourism activity, which leads to a lot of
diversified elaborations, because they concerning different aspects regarding different interests (van Harssel,
1994).
Therefore, although there have been myriad discussions dedicated to conceptualize the jargon “tourism”,
many various valid explanations still exists. Nevertheless, it is imperative to seek for a dominant concept to
draw a border of this research.
In the academic field, three approaches were identified to define tourism – “economic”, “technical” and
“holistic” (Leiper, 1979), covered most of the typologies of the definitions. Thus, a framework of tourism
definitions can be established based on these approaches.
The holistic approach attempted to cover all the aspects of this subject. Jafari (1977) suggested it is
necessary to cooperate tourism into many affiliated subjects, such as sociology, psychology, anthropology and
so on. The recent definition from UNWTO inherited this idea, and defined tourism as “ a social, cultural and
economic phenomenon, which entails the movement of people to countries or places outside their usual
environment for personal or business/professional purposes”. This explanation also manifested that a
unique definition of tourism is hard to obtain due to its complexity, which encompasses the efforts from
different fields.
The early articles about tourism usually only noticed its economic meaning, so majority of them focused on
the supply side, believed that tourism is a type of business that provides to the customers, traveling for any
reasons, transportation, accommodation, catering facilities and related services (Australian Department of
Tourism an Recreation, 1975). Later on, some other scientists argued that the provision of qualified services,
“gracious hospitality”, in other words, is also critical for this industry. Thus by adding that into the concept,
tourism had been developed to be more completed, became a kind of science and arts of satisfying visitors’
needs and wants (McIntosh, Goeldner, & Rotchie, 1995). Thus the cooperation and coordination across these
related industries are needed.
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The technical approach is used for develop an appropriate measurement for research purposes (Leiper,
1979); it also attempted to draw a precise boundary for “tourists”, in order to get suitable data for the
analysis. Thus the technical definition is standing on the tourists’ point of view – the demand side of tourism.
This flipside also contains huge numbers of definitions, but currently there is a prevalent and widely
accepted one, amended by World Tourism Organization (WTO) in 1993, is rather succinct:
“The temporary visitors staying in a place outside their usual place of residence, for a continuous period of at
least 24 hours but less than one year, for leisure, business or other purposes” (World Tourism Organization,
1993)
Leaving the residential places indicates that tourism must contain a spatial movement of people, and
temporary staying indicates it is not a one-direction flow. So in the spatial point of view, tourism consists
three components: origin, route and destination (Zhang L. , 2008).
The motivations or purposes of travelling are also noticed, most of the time it is related to spending holidays.
Business is another significant part composing the travelling behavior, but there are also some other reasons,
such as education, visiting friends/relatives, and in transit (Toh, Khan, & Lim, 2004). One or more of them
triggers the demand of tourism.
However, the above definition has not mention the term “tourists”, instead it uses temporal visitors to refer to
the individuals participating the tourism activities. To clarify, Leiper (1979) distinguished the term “tourists”
from “visitors” in his paper, pointing out that tourists is a fraction of the visitors, which also includes
excursionists. Both of these two types of visitors are temporarily living in the countries or regions other than
their residential places for either leisure or business reasons. The main difference between these two visitor
groups is the time length of stay. The visitors who have spent longer than 24 hours and less than one year, are
classified into tourists, while the others, who perches less than 24 hours are excursionists.
To clarify, in this research, the term of tourism indicates the tourists spending between 24 hours and 1 year
in the places differing from their usual places of residence, for leisure, business or other purposes. In
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essence, it is temporal demand of consumption, which is also the most important feature comparing with
other economic activities.
2.2 Evolution in Tourism Industry in China
It is a truism that the transmission of leadership influences not only the development of tourism industry,
but also the entire economy, thus the evolution tracks for both economy and politics are synchronous, also
mutually interacted. When overviewing the development of tourism industry in China, the most impressive
feature is the inevitable influence from the leadership on the change of tourism policy. Consequentially, the
whole process of evolution can be divided into three phases, regarding to different policies implemented in
different time periods.
2.2.1 First phase: a diplomatic channel to establish friendships (1949 – 1978)
Both tourism supply and demand in PR China in this period of time were scarcely explored, while the whole
country initiated to recover from the ruin of the war. But the ideology of socialism expounded by the
Chairman Mao and his absolute position in the leadership had essential effect on resulting in that
circumstance.
Researchers have drawn their conclusion very clearly that both domestic and international tourism had been
suppressed under Mao’s era of the leadership (Sofield & Li, 1998) (Gao & Zhang, 1983), and it was used as a
diplomatic channel solitarily for connecting China to the foreign countries (Yan & Bramwell, Cultural
Tourism, Ceremony and the State in China, 2008). The destinations opened to the tourism were strictly
limited, which less than 12 cities or regions had permission of receiving foreign tourists until 1978 (Richter,
1989).
Furthermore, Uysal, Wei and Reid (1986) had confirmed that fact that under the central-planned economy
system, all the tourism businesses were national owned and operated, and certainly served only for political
purposes. They also further analyzed foreign tourism in details by marking off a more precise timeline, based
on the changes in both internal and external political environment. The tourism industry from 1949 to 1956
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was solely directed to be ready for the guests from USSR and Eastern Europe, who shared the same socialism
ideology in their countries. But in the early 1960s, with a serious disagreement between the leaders, the
collision against USSR almost utterly destroyed the foreign tourism in China, dropped more than 2/3 in the
total number of tourists. Then an adjustment of diplomatic policy came out, aiming to connect China to the
rest of the world, resulted in a radical increase of visitors from other western countries, where became the
majority of the origins of inflow tourists in 1964 – 65. The real catastrophe had been brewed for the decade
from 1966 to 1976 – the Great Cultural Revolution caused a massive chaos in China in every aspect, any
development of tourism was vanished as well.
2.2.2 Second phase: a gate opened to the world (1979 – 2000)
As mentioned in the last chapter, the advent of a significant milestone of modern PR China was in 1978, the
Third Plenary Session of 11th Central Committee, ensured the leadership position of Deng Xiaoping and
revised the ideology of socialism economy, shifting the main task of China from political struggle to economic
modernization (Airey & Chong, 2010), appeared an obvious division of the economic forms before and after
1978.
It should be noticed that Deng’s “Open Gate” policies did not changed the dominant position of Communist
Party, but adjusted the control of government in the economy, to some extent, liberalized this market (Yan &
Bramwell, Cultural tourism, ceremony and the state in China, 2008). Tourism, as the tertiary industry,
relative to the first (agriculture) and second (manufacture) industry, became a contributor of GDP – the first
and foremost indicator of measuring a industry’s economic contribution in China – at the first time.
However, all the industries were enlightened of market freedom thanks for the implementation of “Open
Gate”. This phenomena has reflected that one of the political-economic feature of contradictory tourism
development in China till now, which is, on one hand, authorities desire to arrange tourism as the first pillar
in order to increase GDP, but on the other hand, the first and second industries are inclined to get more
supportive policies while tourism is left aside (Feng, 2011).
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Still, tourism attracted much more public attention with the deepening economic reform, 24 provinces made
tourism industry one of their leading industries in their administrative areas (Zhang, Pine, & Zhang, 2000).
As it has become an acceptable commercial activity under the socialist economic system, the policies of
international tourism changed towards the aim that maximizes the foreign tourists in all possible ways
(Choy, Guan, & Zhang, 1986). The restrictions for foreign tourists were largely relaxed, lead a boost of visiting
in the following years, for various purposes of trip, covering business, recreational, and etc. China opened its
gate gradually to their international guests, 200 destination allowed entrance of foreign tourism in 1984, and
it became 888 in 1992 (Richter, 1989).
As a result, the benefits gained from foreign tourism were concerned to gravitate from political agreements
to economic revenues, which mostly contributed by the remarkable increased foreign exchange income, but
it does not mean that the political or cultural impact of tourism has disappeared, on the contrary, still
contributed to strengthen the mutual understanding in every aspects (Tisdell & Wen, 1991).
The most convincing evidence of this growth comes from statistical numbers. The total international tourists
arrived in 2000 was nearly 83.5 million, while it was only 1.8 million in 1978 (China Statistical Information
and Consultancy Service Center, 2000). Furthermore, the foreign receipt increased with a growth rate closed
to 20% per year from 1979 to 1989 (Tisdell & Wen, 1991). It generated more than US $16 billion of foreign
exchange receipt in 2000, which were only 14.1 in 1978 (China Statistical Information and Consultancy
Service Center, 2000). In 2000, 1.64% of GDP depended on international tourism. Though it was a relatively
small number at that time, it initiated high added-value activities in the modern service industries
(Oosterhaven & Fan, 2006).
Tourism–related industries have experienced a rapid expansion during this time period. For the
accommodation, new equity joint ventures with foreign capitals were made for hotel businesses, contributed
a real stock. The most famous example is the luxury “Great Wall Hotel” opened in June 1984; China provided
land, labor and basic construction, while the American partner developed architectural planning and interior
work, also bearing half of the financial cost (approx. U.S. $36 million) (Oudiette, 1990).
Regarding to the transport sector, new airports started to construct continuously in many cities. The number
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of travel agencies specialized international tourism also significantly increased, except for traditional
commissioned travel products (transport, accommodation, sightseeing and catering) their services became
more comprehensive and customized, e.g. interpret and translation (Uysal, Wei, & Reid, 1986).
In addition, the human resource of tourism industry has been improved. The quality of service came to be an
issue, and the reputation began to play a role, while it was a non–existed topic in the last phase, Mao’s era
(Uysal, Wei, & Reid, 1986). The first higher education institution of tourism studies, Shanghai Institute of
Tourism, was founded in 1979. Since 1980, China National Tourism Administration started a study fund for
several universities to help them set up tourism-related program. Till 2000, there were 251 programs was
founded in higher education institutions with 73,586 students (Zhang & Fan, 2005).
The richness of Chinese culture had been recognized, and heritages already been commercialized as a
tourism asset to promote more financial incomes of the localities, since the main attractiveness of
international tourists were, and still are, those destinations (Sofield & Li, 1998). Some festivals related to
these culture heritages attracted tourists as well, for example, the celebration of Xiaolan Chrysanthemum
Festival in Southern China, attracted 140,000 tourists came from overseas (mostly Hong Kong and Macau) in
1979 (Siu, 1990). This festival continued in 1994, it even triggered real estate marketing in tourism, which
was forbidden in the last decades (Sofield & Li, 1998), for example, the avenue of displaying chrysanthemum
was sold by $20 million (Siu, 1990).
However, integrating the cultural heritages into the business exploration made the visits to those places
inclined for the purpose of entertainment, while the educational meaning, which is another important
function of heritage visiting besides of economic benefits, was lost (Sofield & Li, 1998) (Li, Wu, & Cai, 2008).
At the same time, the historical and intrinsic value in the heritages had also been put in danger, since the
legislation of heritage protection was not enough completed, or even been ignored, because the localities
urged to increase income receiving by opening of heritages visiting (Hultsman, 1995). Furthermore, linking
heritages to commercial world also has created a worrying situation for both cultural and natural heritages,
since tourism has been criticized as a source of overexploitation (Saenz-de-Miera & Rossello, 2013). In China,
the issue of sustainability of heritages tourism only caught public’s attention from 1996 (the year of first
academic paper of this issue published), which was almost 20 years later than its first being explored (Li, Wu,
18
& Cai, 2008).
Tourism demand is vulnerable to the instability of political situation. The political events, such as terrorist
attacks, riots or international war, will significantly lead to an adverse effect on tourism demand, since it is
very sensitive to the safety in the destination, and tourism is usually closely related to peace and enjoyment
(Arana & Leon, 2008) (Breda & Costa, 2006). The fear of this kind of event is what tourist always trying to
avoid, and moreover, tourists can also become easy targets, at least will perceive increased risk (Sonmez &
Graefe, 1998; Lepp & Gibson, 2003). It does not matter of victims’ identity, or perpetrator’s motivation, if it
would harm people’s body and life, the image of destination will defect and tourism demand will decline
(Pizam, 1999). Furthermore, mass media is usually involved in reporting political turmoil or terrorism
attacks, which disseminate immediately to a broader level of international attention (Sonmez & Graefe,
1998).
This was the case happened to China. The political conflict in Tiananmen Square (Beijing) in 1989, slumped
hotel occupancy rate in Beijing to under 30 percent, while more than 300 groups cancelled their trips
(Sonmez, 1998). In general, Chinese international tourism sustained a U.S. $420 million loss of foreign
tourism receipts, 18.92% decrease comparing with 1988 (Tisdell & Wen, 1991). Later, a survey in U.S market
shows that the interest of visiting China was significantly declined due to this event and its damage of
attractiveness to tourists (Gartner & Shen, 1992).
However, the damage of terrorism is recoverable. The tourism inflow in 1990 rebounded, and kept growing
for the following years, with a 20% increase per year (Lim & Pan, Inbound tourism developments and
patterns in China, 2005). This might resulted from the remaining of “Open Gate” policy, in spite of that tragic
political turbulence occurred.
Finally, comparing with the radical change in 1978, the end point of time for the second phase is rather
obscure. China had experienced another general election of the Central Committee, but fortunately, there was
no drama of completely changing the fate of this country again.
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2.2.3 Third phase: a platform to show the image (2001 – present)
The year 2001 was chosen for several reasons. The most predominating reason is that China joint WTO in
2001 caused another flourish of policy adjustments of economy in the international level, promising that will
eliminate restrictions on, such as discretion, foreign ownership, and entry barriers (Mattoo, 2003), thus the
number of visiting for business purpose significantly higher than the past phases. Another reason is that
Beijing won the bid of 2008 Summer Olympic Games in 2001, urged Chinese government to make substantial
efforts on openness and modernization, including building up tourism infrastructure and improving service
quality, in order to show a favorable image to all the visitors during the event (Longman, 2001). Furthermore,
the flexibility of entry has grown by permitting individual visiting (Oudiette, 1990). In general, it has been a
time period of accelerated development of tourism industry ever in the history.
There is also prosperity of academic researches, especially conducted by Chinese scientists. Comparing with
the past decades, the numbers of studies in smaller geographical scales (i.e. Xuan, Lu, Zhang, & Yang, 2002)
and for specific events (i.e. Wang, Lu, & Xia, 2012) have been increasing. This phase keeps a continuation of
expansion of growing and opening for international tourism, and with more scientific articles published,
more facts have been revealed.
Firstly, Ma et al., (2008) discovered the uneven spatial distribution of tourist visiting in the regional level. The
causation of this difference between the regions can be categorized to two reasons; the first one is heritage
resource (Oudiette, 1990), while another is inequality of economic development (Jackson, 2006).
The rich reservoir of heritages resources is the most important factor of initiating a favorable outlook for
international visitors (Oudiette, 1990), thus the major tourism assets for Chinese tourism industry. Figure 1
depicted the heritages on the map of China. The tourist visits for the heritages in some destinations are
always popular, for example, Xi’an in Yellow River Basin Group, Chengdu (Sichuan Province) and Kunming
(Yungui Plateau) in Southwestern China Group.
Figure 1: World Heritages Sites in China
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Source: (Li, Wu, & Cai, 2008)
The formulation of these heritages are linked to geography and historical causation. Taking the southwestern
heritage group for an example of geographical reason, the special climate that has an average of 25-Celsius
degree throughout the year and the ethnic diversity because of the concentrated residence of minorities in
this region, composed its attractiveness (Oudiette, 1990). The historical reason of those attractive
destinations is far more than obvious, since the ancient civilization has bequeathed numerous inheritances
to China, e.g. Terra-Cotta Warriors in Xi’an, a miracle equivalent to pyramid in Egypt.
Figure 2: East-West division of wealth distribution
Source: (Jackson, 2006)
21
Regarding to business visits, the eastern China has been reached more. It can be explained by the amount of
foreign direct investments. The white provinces on the map of Figure 2 absorbed 86% of FDI in 2002
(National Bureau of Statistics, 2003).
Albeit there is not data showing how many tourists visited eastern provinces for business reasons, the total
numbers of visiting still give a comparable result. Again, the white regions received 80% of international
tourists and 90% of foreign exchange earnings in 2002 (National Tourism Administration of the People's
Republic of China, 2002).
Therefore, the geographical concentration exists. Ma et al., (2008) identified three hotspots for international
tourists, which are the same as the economic centers (the center city is shown in the bracelet):
Circum–Bohai Sea Region (Beijing)
Yangtze River Delta (Shanghai)
Pearl River Delta (Guangzhou, including Guilin)
The prosperity of tourism in these three hotspots is resulted from the richness of all kinds of tourist
products, e.g. histories, cultures, economics, social and natural resources. These tourist products are
complementary to each other, and competitiveness, or attractiveness of these regions is strengthened since
they are adjacent geographically, which is the most crucial reason of the formulation of these hotspots
covering several cities (Zhang, Gu, Gu, & Zhang, 2011). Another finding is that these three hotspots fall in the
eastern part, shows economic development has larger impact on international tourism in China, since Figure
2 depicted that the eastern China has more wealth distributed.
Furthermore, all the popular tourist destinations for both heritages and business have reflected three main
categories of visiting purposes for international tourists. The first two purposes, can be classified as leisure
(recreational) purposes, and the last is business purposes:
Cultural relic and heritages: Beijing, Xi’an
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Spectacular natural scenery: Guilin, Kumming
Transferring knot: Shanghai, Guangzhou
In addition, with the flourish of the total tourist inflow, new routes of travelling start to absorb it as well, e.g.
Tianjin, Hangzhou, Shenzhen (Ma, Zhang, Bai, Li, Cheng, & Liu, 2008).
Combining with the distribution of heritage attractions, tourists tend to explore in the eastern part than the
western part of China, in line with the regional economic development level. The policy support of regional
development is critical for this prosperity, and in this phase, policy makers came up with more integrated
approach to promote international tourism. For example, provincial level of conferences, like ‘Regional
Annual Conference of Economics Cooperation and Development’ in 2001, ‘Regional Periodical Political
Conference’ in 2004, were organized in YRD region, aiming to strengthen the cooperation of tourism industry
as the prominent activity across the cities in this region (Feng, 2011).
In the previous phase, leisure had dominated the purpose of visiting of the international tourists; however, it
has fell below 40% of the total number of the visitors. Tourists come for business increase fast, was slightly
less than 30% (Ma, Zhang, Bai, Li, Cheng, & Liu, 2008).
It is not surprising that leisure and business visitors are two big market segment of international tourism
industry in China. But niche markets also worth to be investigated. For example, adventurers are dreaming to
embrace the mountains in Tibet, or hike in the Gobi desert in Xinjiang; devout believers of Buddhism are
looking forward to embarking on a pilgrimage to the Potala Palace. It is difficult to exhaust the demand of
every niche market, because the diversity of tourism products that China offered cover a really wide range of
tourist attractions, e.g. event, sport, industrial, medical and adventure (Feng, 2011).
However, there has been initiated a unique niche market appeared in this phase, without any precedents
beforehand, because of being the host of mega events. The mega events of global range and worldwide
significance are frequently associated to a boost on tourism and economic development (Fayos-Sola, 1998).
Beijing was expected this increase brought by Olympic Games 2008, as well as Shanghai and its Expo 2010.
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However, it does not show a favorable result of international tourism in this event. It was expected to receive
half million of international tourists flock into Beijing for Olympics, but only 389,000 was achieved,
decreased 7% comparing with August 2007 (Dyer, 2009). This is contradictory to the research result that
mega sport event like summer Olympics would increase 8% of tourist arrivals in the same year (Fourie &
Santana-Gallego, 2011).
Several reasons can explain this decline, for example, China tightened its visa application early in April, so the
business visitors dropped significantly. Also the terrorist attack happened in Tibet in March, somehow pin a
negative impression of risk on tourists (London Evening Standard, 2008).
Another Olympic-equivalent mega event, Shanghai Expo 2010, might have positive effect on international
tourism. During this half-year event, about 4.2 million foreign visitors devoted themselves in the feast of
interesting exhibitions and performances (Barboza, 2010). In general, inbound tourist arrivals in Shanghai
grew significantly, with a 30% increase per month from May to October 2010, comparing with the same time
period in 2009 (Shanghai Statistical Bureau, 2011).
These results of numbers may give us a brief that the mega event in Shanghai is slightly more successful than
Beijing, which is also true in the practice. A tourist behavioral intention analysis has supporting evidences of
the success of Shanghai Expo, by showing an increase of perceived value of Shanghai (Wang, Lu, & Xia, 2012).
Feng (2010) summarized projects to improve the quality of tourism experience for Expo 2010. Firstly,
Shanghai City Tour Card was issued, mainly used for public transport, and was accepted by more than 100
local stores, tourist attractions and etc. to give discounted prices for the cardholders at the same time. It is
also integrated tourism in Shanghai into the whole YRD region, in another five cities (Suzhou, Hangzhou,
Ningbo, Jiaxing, Shaoxing), cardholders still are able to get discounted prices in certain touristic places.
Secondly, the municipality introduced the ‘High Quality Regional Tour Packages’ including 96 qualified
regional tourist attractions officially, later 55 another tour packages in Zhejiang and Jiangsu Provinces also
joined this package, created an integrated and holistic tourism image for the entire YRD region (Feng, 2011).
Several infrastructure construction projects were conducted to guarantee the satisfying experience for the
visitors coming for the big events. Since air is always the main transport mode for international tourists,
Terminal 3 of Beijing Capital International Airport was founded in 2000 and started to operate in February
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2008, just before the Olympics. The same for Shanghai Expo, Terminal 2 of Hongqiao International Airport in
March 2010, to meet the capacity requirement of large inflow of tourists for Expo (Travel China Guide, 2013).
Moreover, some flagship projects turned to be new landmarks. The national stadium in Beijing and
permanent architectures of Expo, already became a popular place of interest, implying a sustainable success
of the mega events (Shanghai Municipal Tourism Adiministration, 2010).
Other facts of international tourism are also discovered. Briefly, regarding to accommodation, hotels with 5
and 4 -star qualification are still preferable. For catering, tourists are willing to taste local flavors, but the
food service of their origins is also indispensable. In addition, souvenirs easy to take away are more
favorable, e.g. special local handicrafts, silk clothing and accessories, and Chinese tea (Ma, Zhang, Bai, Li,
Cheng, & Liu, 2008).
The available data shows total number of international arrivals increased from 89 million (2001), to 262
million (2013), which reached the peak of 271 million in 2012. Foreign exchange receipt increased from U.S.
$1.7 billion (2001) to $4.7 billion (2013).
Finally, the State Council has taken a long-term vision of development for tourism industry. As an important
composition of modernization, the reform of tourism industry is aiming to be more deeply implemented, and
service optimization and sustainability will be the new issue of focus. By expectation, in 2020, tourism
industry will contribute 5% of Chinese GDP (China National Tourism Administration, 2014).
2.3 Conclusion on tourism
From above analysis, it is clear that international tourism has experienced three phases of evolution. The
tourists in each phase have their own characteristics, and the similarities exist as well.
In the first phase began from 1949, Mao’s era, the international tourism is solely used as a diplomatic
channel. Under the extreme central-planned economic system, the market of tourism did not exist, or
seriously under-developed, which is the unique feature in this time period. The origins of tourists differed by
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the point of broken-up with USSR, where were Eastern European countries dominated ex ante, and western
countries ex post. In the end, the disaster of Cultural Revolution disrupted economic development in all the
sectors, so the expansion of tourism was a non-existed issue during 1967 – 77.
Thanks to the “Open Door” policies from 1978, tourism industry experienced a rapid expansion in the second
phase. Tourism was liberalized to the commercial world for the first time by becoming a contributor of GDP
in China, comprehensive developing projects carried out, covering throughout heritage sightseeing, festivals,
transport infrastructure, accommodation, and human resource. However, the sustainability of tourism was
an ignored issue in this phase. As a consequence, international tourists coming for all over the world, can be
segmented according to two major purposes: business and leisure.
The third phase started from 2001. It inherited the trend of growth from the last phase. Most importantly, the
uneven geographical distribution is discovered, that the majority of international tourism falls on the east
than the west parts, in line with level of economic development. Three hot spots of international visits are
Circum–Bohai Sea Region, Yangtze River Delta, and Pearl River Delta. Besides leisure and business, other
niche markets are also inevitable, which educational, medical, sports, events, adventures are all components
of Chinese tourism market.
The most remarkable impact on tourism came from the mega events, Beijing Olympics and Shanghai Expo.
The result from both statistical numbers and image perception researches showed that Shanghai achieved a
bigger success than Beijing. The decline of international visits during August 2008 can be explained by
stricter visa requirement for business applicants due to the terrorism attack happened in Tibet early in
March.
From the positive side, both cities conducted projects to promote international tourism, similarly, new
terminals of airports started to operate just before the two events. New landmarks were built, and have
become tourist attractions during and after the event. Furthermore, the success of Shanghai radiated the
entire YRD region. Integrated tourism projects provided convenience of visits and thus boosted tourism
inflow.
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2.4 Air pollution and its status quo in China
2.4.1 Introduction to air pollution
Air pollution indicates that the Earth’s atmosphere contains abnormal concentration of particular matters,
biological molecules or other harmful materials. Air pollution is a threat to human’s health, proved by the
evidences that the toxic composition in the particular matters has a causal relationship with cardiovascular
and respiratory diseases (Peng, et al., 2009).
To measure the quality of air, one frequently adopted indicator is the concentration of particular matter,
named PM 2.5. As introduced in the first chapter, the concentration of PM 2.5 in some Chinese cities is
several times higher than the standard international requirements set up by WHO.
Before going in depth into the tourism, the influence of air quality on the benefit of urban development as a
whole has been investigated. The most frequent studied topic is housing prices, that willingness to pay for a
dwelling house is very sensitive to the marginal improvement or damage of air quality, and therefore
indicated a demand of clean air (Harrison Jr. & Rubinfeld, 1978). Certainly, better air quality is always
favorable, especially in the urban area. A severe contamination issue has no added value to a city
endeavoring to pursue a positive image to its visitors. The weather of severe haze frequently occurred has
reflected an environmental problem in Chinese cities now. The intensive news coverage and research focus,
have undoubtedly created a worrying picture for the public, and will probably tarnish the image of Chinese
cities as tourism destinations. Although there is a paucity of researches shown the impact on tourism in
China of this issue, it can be conceived that it moves to the negative side, since weather and environment
have great importance for tourists’ experience at the destinations (Brunt, Mawby, & Hambly, Tourist
victimisation and the fear of crime on holiday, 2000).
In fact, the issue of air quality is not a new topic in China. It has been recognized after 1978, since it was
worsened significantly because of the rapid industrialization (Li, Zhou, Li, & Chen, 1995). A lot of the
scientific researches about pollutant components in the air (Kleeman & Cass, 1998; Bell, et al., 2009; Peng, et
al., 2009), or air quality in general, published after 1990s analyzed the result of experiments that captured
27
and measured data for a period of time in certain spots.
In addition, some comparisons are also made with other cities in the international level. For example, for the
mass concentration of PM 2.5, Beijing had a highest average value of 102 gm-3 in 2001 to 2002 (Duan et al.,μ
2006), while only 16.8 in Amsterdam (Vallius et al., 2005), 32.85 in Sao Paulo (Degobbi, Lopes, Carvalho-
Oliveira, Munoz, & Saldiva, 2011), and around 14.2 in several cities in the U.S. (Thurston, Ito, & Lall, 2011).
2.4.2 the status quo in China
It has become a public attention, because frequent occurrence of haze weather in the eastern part of China,
and the danger of air pollution is aware by publication of mass media. Wang et al., (2014) presented a map of
geographical distribution in eastern China based on PM 2.5 concentration. It is the most detailed description
at the moment for the whole picture of air pollution in China. The observation was made from January 12 to
February 1, 2013, found that the northern regions suffered from PM 2.5 more than the southern regions,
which can be explained by the time period of this experiment, dropped in the winter, which northern regions
had heating system working on. It is also noticeable that high concentration of PM 2.5 occurred in Shandong
peninsula and North China Plain (Hebei Province), which can be explained by not only heating reasons, but
also industrial production. The influence of topographical reason is also inevitable, which Taihang Mountain
sheltered the wind from the sea, thus air pollutants are easy to stuck in this region.
Note that there is no observation in the western China because of the limitation of this research. But the air
quality in the west will be taken into account in the later analysis of this dissertation.
Figure 3: Spatial distribution of PM2.5 ( gμ m-3) in China for days: (a) January 12, (b) 13, (c) 14, (d) 15, (e)
16, (f) 29, (g) 30, (h) 31, and (i) February 1. Grids in blue mean no data provided.
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Source: Wang et al., (2014)
2.5 The sources of air pollutants and mitigating solutions
2.5.1 Retrieving the sources
Since PM 2.5 is a critical air pollutant and also an indicator of air quality, it is logical to track the sources of
pollution through its composition.
Bergin et al (2001) studied the data over 7 days of mid- June 1999 in Beijing, observed the main chemical
components of PM 2.5, which are organic compounds, sulfate and nitrate. Ye et al (2003) operated a one-year
experiment in Shanghai from March 1999 to March 2000, manifested the similar results about the
compositions. Buczynska et al (2014) also overviewed the composition of PM 2.5, and added crustal matter,
sea salt, smoke and other elements (including heavy metals) into the analysis as well.
Based on these components, several sources that related to PM 2.5 can be reasonably traced. Both natural
and anthropogenic reasons play a role, and they also influence each other mutually. The natural factors
29
increase and dilute PM 2.5 in both ways, while the anthropogenic factors contribute to the emission.
The natural factors are related to terrain of the focused place, and the most important three factors are
temperature, humidity and atmospheric movements (wind). Many studies found that there is a strong
seasonal variation of PM 2.5 concentration that takes place in the late autumn and early winter. Thus, to
some extent, lower temperature increases the emission of PM 2.5, and the heating started due to the cold
weather is the most accepted causation (Ye et al., 2003; Buczynska, et al., 2014). The sample of air frog
collected in Shanghai showed that sulfate and nitrite compounds are significantly lower in the locations close
to the sea because of the higher humidity. Furthermore, the content of sea salt increases with the proximity
of the sea, which facilitates the generation of PM 2.5 (Li, Li, Yang, Wang, Chen, & Collett Jr., 2011). Finally, all
the above-mentioned researches conducted a back trajectory analysis, concluded that strong wind can bring
in and out particles. The wind force also matters, weak wind force usually causes poor air quality for Chinese
cities, since it cannot blow the pollutants in time – Beijing, located in a U-shape terrain, is a distinguish
example for this case (Chen, Schleicher, Chen, Chai, & Norra, 2014).
Comparing with natural factors, the anthropogenic factors are much more complex to cover, but are mostly
related to combustion and waste incineration. As it has been agreed in many studies, transportation in the
modern society because of human movement is the most crucial causation of the air pollution of PM 2.5. For
example, the air frog sample collected from Shanghai reflected several sources of air pollutant; found that the
emissions of sulfate and nitrate are increased along the traffic routes between the mega cities.
Figure 4 shows the main compositions of PM 2.5 using a sample collected in the U.S city Los Angeles, which
can be generalized to Chinese cities as well. Organic carbon, nitrates and sulfates proved that human
transportation is the main contributor of the generation of PM 2.5 (U.S Environmental Protection Agency,
2004).
Figure 4: Compositions of PM 2.5 in Los Angeles
30
Source: (U.S Environmental Protection Agency, 2004)
Subsequently, which is a unique characteristic in China, another equally important reason is rapid
urbanization and industrialization since 1978. It is mentioned in almost all the articles for this topic in China
(e.g. Ye et al., 2003; Kleeman & Cass, 1998). Indeed, in order to achieve the economic reform, mass scale of
infrastructure construction was triggered in every city, and inevitably sacrificed some natural resources.
Other type of chemical pollutants, for example, calcium ion solubilized in the fog water, was mainly generated
from construction. The smelting plants of iron and steel, or other metal are responsible as well, evidenced by
ions of heavy metals in captured samples. The observation in Foshan (a city in Guangdong Province, south
China) discovered the high concentration of Zn, Pb, V, Mn, Cu, As and Cd in their sample of air particles (Tan
et al., 2014). In addition, the metal ions are also manifested that could result from the intensive agricultural
emission from proximate regions.
2.5.2 Mitigation of air pollution
First it should be noticed that air is a pure public good by its attribute, implying that market will produce
inefficient results if only private benefit/cost is concerned (Olson & Olson, 2009). The solution to such a
problem, the most common method is calling for the government intervention, in order to take social
benefit/cost into account, by setting up relevant regulations, or imposing taxation (La Porta, Lopez-de-
Silanes, Shleifer, & Vishny, 1999). Therefore, the frequent occurrences of haze weather, to some extent,
reflected the failure of institutional mechanism and lack of administrative forces in China.
But it is still a task to complete for the government. Winning the bid of Olympic Games in 2008, gave Beijing a
great platform to show the great achievements of modernization in the past half century, but more
importantly, urged government to solve air pollution problem in order to prove the sustainability of its
development. Thus, such a mega event, and the event tourism generated by it, is calling for the Chinese
31
government to pay attention to it. As a result, the government implemented a series of strategies to ensure a
better air quality during the Summer Olympic Games in 2008. Due to the intervention, a significant reduction
of mass concentration of air pollutants was achieved in Beijing at that time.
Mega event is a tool used by the government to solve air pollution, because it is reasonable to implement
special policies or carry out certain projects to mitigate pollution in order to improve perception of
destination image for event tourism. Looking it into the details of Beijing Olympics, there are two main
methods they adopted to improve air quality. The primary is traffic restriction, including improving emission
standards, odd-even restriction of private car license, and 1/5 reduction of weekly passenger car flow. With a
32.3% decrease of traffic flow, almost a half of reduction of concentration for traffic-related elements were
collected in the sample of Olympic time period, comparing with the sample of December 2007 (Wang & Xie,
2009). Another is implementing the project of relocating the Capital Steel Company, moving from Beijing to
Hebei gradually. As a result, the concentration of heavy metal ions in the air sample also had a significant
drop (Chen, Schleicher, Chen, Chai, & Norra, 2014). Overall, through these efforts contributed to mitigate
contamination, the air quality during the Olympics met the WHO guideline.
Nevertheless, as pointed out before, the recent published researches presented of a large scale of severe haze
weather in 2013, unmasked a failure of sustainability of the intervention of Beijing in rooting out the sources
air pollutants. It is not surprising that the move of Capital Steel Company worsened the situation in Hebei
Province, which became the most polluted region in the eastern China. Therefore, in order to really mitigate
the contamination up in the air, government in Chinese cities need to come out with more effective and
sustainable solutions.
Akbari, Pomerantz, & Taha (2001) discussed some methods for reducing the concentration of air pollutants.
They suggested that cool surfaces (cool roofs and pavements) and urban trees will significantly reduced the
smog generated by energy consuming, in other words, inceneration activities. It was manifested in Chicago,
that green roofs largely removed the air pollutants (Yang, Yu, & Gong, 2008), and also many other cities in the
U.S. that urban trees considerably improved air quality (Nowak, Crane, & Stevens, 2006).
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2.6 Conclusion on air quality
In the previous two sections, we have reviewed literatures about air quality and air pollution in China. The
current situation of air quality in Chinese cities is not pleasant. The healthy standard of WHO cannot be
satisfied in many cities, and actually the air quality is far below, since the concentration of PM 2.5 is much
more higher than the standard requirement. The comparison in the international level also presented that
the air in Chinese cities are more contaminated than the others. Furthermore, there is also a spatial
distribution of air pollutants, showing that the northern China has suffered more than the south, and the
most severe pollution are in Hebei and Shandong Peninsula.
Tracking the composition of PM 2.5, it can be concluded that natural and anthropogenic reasons together
caused the air pollution, and the latter plays a more important role than the former. Likewise, natural causes
are easier to trace than the other, since it only related to temperature, humidity and wind force.
Anthropogenic causes are related to combustion. Transportation, construction and iron/steel smelting are all
responsible. Furthermore, the attribute of air quality as a public good reflected the shortage of legislative
forces of government. In the end, the projects carried out for the Olympic 2008 lost its effects after the event,
thus more sustainable solutions are needed for mitigating air pollution.
2.7 Geographical comparisons between tourism and air pollution
Putting the map of air pollution on top of the map of international tourism, some interesting overlaps are
discovered.
In general, both international tourism and air pollution have a trend that eastern China inclines to gain more
attention than the west, because the inequality of economic development. However, when look into the
details, there are still some differences in geographical distribution cause by various reasons.
The overlap of tourism and pollution falls in the surrounding regions of Beijing. Several reasons can explain
this phenomenon. First, Circum-Bohai Sea Region, representatively, the city of Beijing is both economic and
cultural center of China, which has rich reserves of tourist attractiveness and business potentials, increases
33
tourists’ willingness to visit. Second, as one of the economic centers in China, thus a large proportion of
manufacture industries, including a heavily polluted one – Capital Steel Company, is located in this region,
together with intensive transport activities, composed the basis of pollution. Third, the geographical location
requires heating in the winter, and topography that the shelter of Taihang Mountain locked the pollutants in
this region.
Subsequently, the distribution of extreme points differs from each other’s, most polluted regions are Hebei
and Shandong Province, covered in the Circum-Bohai Sea Region, while the other two most favorable
destinations of international tourists are Yangtze River Delta (YRD) and Pearl River Delta (PRD). While YRD
and PRD are also economic centers, intensive production is still the source of generating the air pollutants,
which is a fair-play with Circum-Bohai Sea. Therefore, the natural characteristics might be the major reasons
of less pollution in YRD and PRD. One reason is still related to heating, that there is an absence of heating
system in the southern China, which YRD and PRD are both included. The second reason might be the
topography, that monsoon blowing in YRD and PRD diluted the pollutants more easily. Additionally, tourism
industry in Hebei and Shandong are relatively under developed, while YRD and PRD already have some
integrated strategies of tourism development, especially for mega events tourism in YRD.
In conclusion, albeit the development of tourism industry is going on and the number of inbound
international tourists keeps increasing, the serious air pollution currently is truly undermining the potential
of growth since it might damage the image of a destination.
In the next chapter, an overview of a series cases will be presented, in order to elaborate more deeply and
give an expectation that how the influence of the unfavorable factor, similar to the characteristics of air
pollution, in the touristic destinations will be imposed on the tourism demand.
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Chapter 3 Factors Influencing International Tourism Demand
3.1 Introduction
The last chapter illustrated that the international tourism had experienced an expansion in China in the past
decades, and government is also willing to keep the pace of growth, since they started to promote event
tourism recently. Regarding the air pollution problem, which government has been trying to mitigate,
although there are few researches done about its impact on tourism, it can be seen as one of the negative, or
quasi-negative factors influencing tourism.
Air pollution is a kind of change of environment in the destination due to anthropogenic reasons. Likewise, a
downward change of environment caused by nature also leads to a negative impact on tourism, and
sometimes such an incident will trigger a chain reaction. For example, the drought in the southern Spain,
caused a water supply crisis in Benidorm because of the lack of infrastructure, resulted in a reduction of
tourism revenue in 1978, and undermined its reputation as a holiday destination (Martinez-Ibarra, 2013).
Once it happened, the media will very likely exaggerate it, and public would incline to put a negative tag on
this destination, because image in the media affects people’s decisions, and tourism is one of them (Avraham,
2000; Murphy & Bayley, 1989). Certainly, there have been following actions conducted to solve the water
crisis, but the image of the city have already been damaged through the communication of media in public,
which makes tourists to perceive risk in that destination (Lepp & Gibson, 2003). No matter what reasons
behind the cases, in general, such an incident will trap a destination into a negative image, which changes
behavioral intentions and satisfaction, leading to a reduction in tourism demand is the consequence (Chen &
Tsai, 2007). Satisfaction also links to tourists’ loyalty to a destination, so the image might have continuous
impact on tourism demand (Yoon & Uysal, 2005).
Meanwhile, tourism demand backward influences destination image. Tourism demand is higher in more
popular destinations, which facilitates growth of tourism industry, and a better-developed tourism industry
will improve destination image. This chain reaction is shown in Figure 5.
35
Negative Incident Media Publicity Destination Image Tourism Demand
Figure 5: Chain reaction of negative incidents
Source: own elaboration
Note that media publicity itself influences destination image directly, but in this chain reaction, it is put in a
different shape because media publicity has a moderating function. Successful public relation in the media
publicity might reduce negative impacts the incident brought, while a poor strategy of public relation may
enlarge the negative impacts on destination image.
Supposing this can also be true for our case – the poor air quality in Chinese cities, it is reasonable to focus
on the similar cases that have experienced a decline in their tourism industry because of certain (negative)
incidences. Meanwhile, in order to get a comprehensive analysis of the tourism demand in Chinese cities,
besides of incident itself and media publicity, the other factors also have to be taken into account. For
example, destination attractiveness is an important factor influencing tourism demand, and it is complicated
to measure. Therefore, this chapter will also go in depth of those factors and identify their impacts
respectively. It aims to help the hypothesis formulation based on the expectation gained from the results of
the cases, and some of the factors will be adapted to quantitative model for our case in the next chapter.
The rest of this chapter will be arranged as follows: In Section 3.2, factors influencing tourism demand will
be identified, and the third sub-question, which is factors influencing destination attractiveness will be
answered since they share some common factors. Section 3.3 will elaborate another critical variable in the
chain reaction – media publicity and its impact on tourism. Then, a typology will be set up in Section 3.4, in
order to select cases relevant and similar to this research, and such type of cases will be focused in the
following sections. Three cases will be studied in Section 3.5, in order to give empirical support that how
36
negative incidents have resulted in tourism demand in the reality. The lessons learnt from precedents will
give an expectation to the case of Chinese cities, thus a hypothesis of the main research question will be
formulated in Section 3.6.
3.2 Determinants of attractiveness
Although there are negative incidents happening, tourism industry is still keeping growing, because tourism
demand also depends on many other factors. In Section 2.2, the evolution of tourism in China was discussed;
it gives us some cues about how China has made itself become a more attractive destination. As a decisive
factor that influences tourism demand, destination attractiveness needs to be carefully evaluated. Therefore,
this section will conceptualize destination attractiveness by present an overview of its determinants used in
various precedent studies, in order to pick up appropriate factors for the case studies in Section 3.5, and
some of them will also be selected as variables to construct quantitative model in Chapter 4.
In general, travel decision is a result that two groups of factors contribute together, physical attributes and
perceived image (Kim & Perdue, 2011). Physical attributes are the tangible tourism products a place is able
to offer, which will compose cognitive image, while tourists’ opinions of their experience and s how their
demand is satisfied consists affective image (Hu & Ritchie, 1993).
There is a long tradition of destination attractiveness study, and a lot of them summarized a list of
determinants of destination attractiveness (Gearing, Swart, & Var, 1974) (Hu & Ritchie, 1993) (Crouch &
Ritchie, 1999). Table 1 summarized the factors they have been using for measurement, some of them
overlapped quite frequently, they are culture & history, landscape, special events & activities, services, and
safety & security (Lee, Ou, & Huang, 2009).
Table 1: Determinants of destination attractiveness
Authors Place conducted Method Determinants
Gearing,
Swart and
Var (1974)
Turkey Evaluation by
experts
Natural factors; Social factors; Historical factors;
Recreational & shopping facilities; Infrastructure &
food & shelter
37
Hu and
Ritchie
(1993)
Western Canada Telephone
survey
Climate; Availability/quality of accommodations;
Sports/recreational opportunities; Scenery; Food;
Entertainment; Uniqueness of local people’s life;
Historical attractions; Communication difficulty due to
language barriers; Festivals, special events;
Accessibility; Shopping; Attitude toward tourists;
Availability/quality of local transportation; Price levels
Crouch and
Ritchie
(1999)
Worldwide Interview and
discussion
Physiography; Culture & history; Market ties;
Activities; Special events; Infrastructure, facility
resources, enterprises
Source: own elaboration
Since this research is relevant to Chinese cities, some unique attributes of urban areas have to be added in.
For example, the landscape also covers interesting architecture and famous landmarks; special activities
include nightlife, music, performances and museums (Jansen-Verbeke, 1986).
Furthermore, since Chinese economy has been experiencing rapid expansion, and business visitor share a
large proportion of inbound tourists, it is reasonable to pay extra attention to the factors related to business.
The measurement for an attractive destination for this group of tourists has been briefly summarized by
Enright and Newton (2004), namely inputs, industrial & consumer demand, inter-firm competition &
cooperation, industrial & regional clustering, internal organization & strategy of firms, and institutions, social
structure and agendas.
Combining above-mentioned factors together and taking into account of the analysis of Section 2.2, in the
following cases studies, an attractiveness of a destination will be evaluated from the aspects shown in Table
2. Note that another determinant “security and safety” is not shown in the table, because it is a criterion of
case selection, only the destinations with negative security and safety issue will be analyzed.
Table 2: Determinants of destination attractiveness
38
Determinants Measurement
Cultural and natural heritages # of famous tourist attractions
Special activities, events or ways of life Experience of mega event(s)
Existence exotic culture of minor people
Types of Nightlife places
Accessibility International passenger arrivals
Accommodation Qualified hotels and room stock
Market ties GDP
Source: own elaboration
3.3 Impacts of media publication on tourism demand
Media publicity is a variable playing a role on destination image formation (Beerli & Martin, 2004). It usually
has an impact on secondary image, which is perception of a destination before visiting it, while the primary
image is formulate during the actual trip, but secondary image is agreed that to be sufficient to choose a
destination (Phelps, 1986; Mansfeld, 1992). Information provided by induced, autonomous and organic
agents, including broadcasting news, documentaries, film, TV programs, acquired in the daily life, will be
sources of learning the knowledge of destinations and finally will influence travel decisions. In this case,
media publicity of air pollution will enable tourists living in the foreign countries get better known the air
pollution problem in the Chinese cities, which make it to be an important indication in the estimation model.
Positive publicity generated from destinations’ peace and safety guides a growth of tourism demand, while
negative publicity no matter what factors ascribed, usually lead to a decline of tourism demand (Sonmez &
Graefe, 1998). It is understandable, because tourists are looking forward to enjoyable experience during
their trips, so naturally they prone to places with more positive media publicity.
The above discussion demonstrated that there is a direct impact of media publicity on destination image.
However, it can also be a moderating variable in the chain reaction. The main reason is that media publicity
might bring unstable effect due to the exaggeration. It is common sense that the actual situation or intensity
is easy to be exaggerated in the media, which results in an enlarged effect. Air pollution in Chinese cities is
39
frequently reported by media with a picture that buildings hidden behind heavy smog. It is a kind of
exaggeration because this photo might be taken in a certain day under an extreme weather in certain place,
but not an everyday situation. It is not necessarily to be an intended reporting, however, it will automatically
deliver suggestions that Chinese cities look like what they have seen in the picture now. Air quality is a part
of natural environment of destination, regarding environment in general, a negative exaggeration in will lead
to a larger decline in numbers of tourists (Mihalic, 2000).
Therefore, the impact of media publicity on tourism demand is ubiquitous, and probably has different
extents brought by exaggeration. The exact impact of media publication needs to be examined.
3.4 Typology of incidents
Millions of cases of tourism have occurred, covering a wide range, but not every of them are relevant to this
research. As mentioned before, air pollution is an incident a destination trying to avoid, so it is logical to
predetermine it as a quasi- negative factor that influences tourism demand. Another reason to set up this
typology is that the scarce of literature studying about direct of impact of air pollution on tourism compels us
to seek for the similar cases, in order to have expectation to formulate the hypothesis. Consequently, the
cases with an obvious positive factor will be excluded in the following analysis.
In the literatures studied management of negative incidents (Faulkner, 2001) (Ritchie W. , 2004) (Hystad &
Keller, 2008), two concepts – “disaster” and crisis” are distinguished. “Crisis” is agreed to refer that the cases
caused by the failure of management or practice in an organization for adapting the changes, while “disaster”
is conceptualized as an unpredictable catastrophe and the individuals or organizations can hardly control
(Ritchie W. , 2004). The principle of differentiating these two concepts, is that the organizations where
human beings act in, is the origin of the root of causes of the incident or not (Faulkner, 2001). In order to
adapt to the analysis of this dissertation, they can be simplified to the terms “natural” and “anthropogenic”, in
line with the causes of air pollution – disasters and crises are caused by natural and anthropogenic reasons,
respectively.
Along with the direction of disaster and crises management, Burnett (1998) identified the level of threat also
40
has critical relevance, because it leads to different strategies to confront with the incidents. Therefore the
same method is inherited in this analysis, the cases will be categorized according to their intensity, which are
“high” and “low”. A natural catastrophe, directly caused hundreds of deaths, surely has higher intensity than a
robbery happened on the street, which might just resulted a loss of some property or non-vital bodily harm.
In addition, the frequency of occurrence is also important. Some incidents rarely occur, especially natural
catastrophe, while some incidents like crimes always exist. Various frequency influences the time pressure
and strategies of response (Burnett, 1998), so it is set up as another dimension of typology, again to simply,
they are named “once” and “continue”.
As a result, a matrix of case classification can be built up. Some examples are also filled in in order to serve a
better understanding of this method of categorization.
Table 3: typology of incidents
Frequency Causation
IntensityNatural Anthropogenic
OnceHigh
Earthquake
Hurricane
Tsunami
Terrorism attack
Breakout of an
epidemic disease
Low Heavy rain Unorganized event
Continue
High Extreme climate in
the desert
Religious conflict
Incurable disease
Low Cold winter Street crime
Pollution
Source: own elaboration
Back to the topic of air pollution in Chinese cities, it is easier to identify the category it belongs to. Several
characteristics help – It is caused mainly by fuel incineration and construction activities by human beings,
threats health but not causes death directly, and is criticized many times. It is a type of incident that
continuously occurs, caused by anthropogenic reasons with low intensity, falling into the same category with
“street crime”. Due to the lack of literature about the impact of pollution on tourism directly, the other health
crises will be focused alternatively, since air pollution has a close linkage with health problem of human
41
beings. This kind of concern has been manifested to affect the evaluation of the traveling experience, that an
adverse health condition for tourists will significantly maculate the satisfaction of a destination (Lawton &
Page, 1997). Therefore, the destinations with two types incidents in Table 3 – the breakout of an epidemic
disease and the street crime – will be continued to focus in the next section.
3.5 Case studies
In Section 3.4, it has been remarked that two types of cases initiated by negative factors will be investigated:
break out of epidemic disease and high crime rate. Therefore, three representative cases are selected in order
to expound how those negative factors have driven changes of tourism demand. For measuring the tourism
demand, due to the availability of data access, certain quantitative information will be presented in Case 1 –
SARS in Singapore, such as tourism arrivals and hotel occupancy rate. When there is an absence of causal
relationship between tourist demand and the negative factor, in Case 2 – Street Crime in New Orleans,
perception of destination image will attempt to fill in this gap, since it has been demonstrated in the above
sections that negative perception of destination will lead to a deduction of tourism demand. Case 3 – Street
Crime in Johannesburg, some research results will be presented in both perception and actual number
perspective.
Singapore, New Orleans and Johannesburg were selected for some common reasons. They are popular
tourism destinations as attractive metropolitan cities, and SARS and street crime in these places are
respectively well known by the public. The case studies in these two cities may give guidance to Chinese
cities, especially for the three hotspots, since the central cities – Beijing, Shanghai and Guangzhou – are
similar metropolitans to these three cities.
3.5.1 Case 1: SARS in Singapore
Description
Severe acute respiratory syndrome (SARS), a fatal epidemic disease first discovered in Guangdong (China) in
late 2002, and occurred a rampant outbreak in Asia in 2003. It spread by close person-to-person contact;
42
respiratory droplets can transmit the virus when infected person coughs or sneeze, and airborne spread is
another way to infect larger population (Department of Health and Human Services, 2004). The last post of
global alert and response by WHO was in April 2004, indicated that the disappearance of the pervasion. At
early time (spring 2003) it caused a fear in the public because of a high fatal rate of 14 to 15 percent,
(Henderson, 2004), as controlled gradually, it has an average fatal rate of 9.6 percent. As Table 4 displayed,
238 Singapore citizens were infected, with 34 dead.
Table 4: Accumulative numbers of SARS cases in the main affected regions
Areas Female Male Total Number of deaths
Canada 151 100 251 44
China 2674 2607 5327 350
China, Hong Kong SAR 977 778 1755 300
China, Taiwan 218 128 346 38
Singapore 161 77 238 34
United States 13 14 27 1
Vietnam 39 24 63 6
Source: WHO (2004)
Singapore as a tourism destination
Singapore is famous for its cosmopolitan atmosphere for its international visitors. Although there is limited
land area, it has great prosperity of both natural landscape and cultural life. The combination of gorgeous
gardens and spectacular skyscrapers offers great living environment. For tourists, more than 10 million hotel
rooms were available in 2005, and this number increased to 11 million by the year 2010, over 80% of them
were occupied (Singapore Tourism Board, 2005; Singapore Tourism Board, 2010). Changi airport serves 42
million of inbound tourists with more than 100 airlines flying to over 300 destinations (Changi Airport
Group, 2014).
The strategic location in Southeast Asia and long historical relation with civilization of ancient China has
brought it mixed cultures with both Chinese and Malay characteristics. While it has a history of being a
colony, the self-defense heritage site and Eurasian heritages are also worth to explore for tourists. In
43
addition, both Chinese and Malay heritages exist and traditional festivals are celebrated.
Regarding to market ties, Singapore has always been attractive to business travellers. Its GDP reached more
than 360 million dollars in 2010, and has been keeping growth for 2 to 5 per cent on average in each year
(Department of Statistics Singapore, 2013).
Impact of SARS on tourism
SARS made a devastation of inbound tourism in Singapore in the year 2003. This dangerous diseases called
an alert from WHO, that people are warned to avoid visiting the rampant regions. Many events were
cancelled in order to prevent spreading the disease. SARS significantly discouraged consumers’ confidence
and international tourism, especially during the first half year of 2003. Tourism industry in Singapore did not
obviate this depression as well. The tourist arrivals dropped by 61.7 and 70.7 percent in April and May
comparing with the same months of the previous year, resulting in the hotel room occupancy rate declined
below 30 percent in these two months (Henderson, 2004). Furthermore, there are statistical significant
results, confirmed that one additional infected person in Singapore would lead to 580 less inbound tourists,
and one additional death caused by SARS would drive down inbound tourists by 8,942 (McAleer, Huang, Kuo,
Chen, & Chang, 2010).
This drop is mostly due to the plunge of outbound tourists from the main origins of tourists to Singapore,
because they were also struggling with preventing the spread of SARS. For example, the tourist arrivals from
Hong Kong decreased more than 90 percent in April (Pine & McKercher, 2004). Tourism industry got
recovered primarily until the last quarter of 2003, back to more than 70 percent of room occupancy rate,
almost the same as the level of pre-SARS time (Henderson & Ng, 2004).
3.5.2 Case 2: Street Crime in New Orleans
Description
New Orleans has been always blamed by its high crime rate. The street crime harms on person and property,
Table 5 shows the statistical number of crime rate in New Orleans. These violence on the streets includes
murders, rapes, robberies, assaults, burglaries, auto thefts, and thefts, resulting in all levels of severity – loss
44
of property, bodily harm, loss of life and mass destruction of life and property (Pizam, 1999). It was found
that in the past decade, the crime rate has been two to three folds higher comparing with the average
number in the U.S. The data was missing in 2005, because a wave of crimes generated after Hurricane
Katrina, caused a mass disorder and chaos in this city. Now the situation keeps still, since New Orleans has
ranked top three of the deadliest city, with a high probability of being murdered – 53.5 murders for every
100,000 citizens (Galik, 2013), this is 20% more than Detroit and 9 times more than other normal
metropolitans like New York and San Francisco (Christie, 2013).
Table 5: Crime Rate in New Orleans
Year 2000 2001 2002 2003 2004 2006 2008 2009 2010
Crime Rate 628.7 655.6 565.5 572.1 560.4 319.5 584.5 442.7 418.9
US
Average
299.7
Source: City-data.com (2014)
New Orleans as a tourism destination
With a nickname “Big Easy”, one of the oldest cities in the U.S., New Orleans impressed its guests by
blossoming Creole culture and the original root of jazz music. It is an ideal place for people enjoying urban
life – famous music festival like “Mardi Graz”, abundant alcohol and delicious food, on the beautiful street of
architecture built in 18 and 19 centuries. There are 202 attractions showed on the official website1 of New
Orleans Tourism Marketing Cooperation, which will cram colorful travel experiences into their tourists’
mind.
Louis Armstrong International Airport guarantees the accessibility of New Orleans for international tourists.
They directly connect to destinations in Canada and Mexico by three international airline operating in the
airport, receiving more than 10,000 passenger arrivals. The international tourists also choose flights
transferring in the U.S., with 10 airline companies and 4 million passenger arrivals in 2010 (Airport data &
Statistics, 2011). Moreover, regarding to accommodation, according to Tourism Marketing Cooperation of
New Orleans, there are 209 hotels and B&B registered providing 31,888 rooms in stock (Sinclair & Wilson,
1 http://www.neworleansonline.com/neworleans/attractions/attractions.php
45
2008).
In the end, business opportunities, New Orleans is not in an optimistic situation. Due to the attack of
Hurricane Katrina, the real GDP has decreased 4.6 percent in 2005, and continued by 1.2 percent in 2006
(Malagon, Mclnerney, & Panek, 2008).
Impact of street crime on tourism
Under the normal situation, the local newspapers more frequently report street crimes, but since the issue of
street crimes in New Orleans is really problematic, it attracted the attention of international media, like The
Economist, exposed the dangerous cases happened in the city (Dimanche & Lepetic, 1999). However, before
the year of 2000, the local survey found that there was not a significant impact of crime on tourism, which
indicated that tourism industry in New Orleans was not affected by this negative publicity. To some extent, it
can be explained by the attribute, or the personality of the tourists themselves, evidenced by the research
showed that street crime influences local residence more than the tourists, who perceived less danger and
even some are totally not afraid of it, called risk-seekers (Peck, 2006).
However, tourism industry had destructed by hurricane and crimes together, reflected by over half deduction
of tourism employment in 2005 comparing with 2000 (Dolfman, Wasser, & Bergman, 2007). Many articles
investigated the natural disaster, and figured out a negative impact on both tourism demand and destination
image (Chacko & Marcell, 2008; Pearlman & Melnik, 2008; Ryu, Bordelon, & Pealman, 2013). Table 6 shows
that the tourist arrivals in New Orleans from 2003 to 2010, it can be discovered that Hurricane Katrina
devastated tourism deeply, a significant drop appeared in 2006, then it bounced back gradually. However, its
tarnished image as a holiday destination will take long time to recover, because as presented, the tourism
arrivals had not bounced back to level prior to hurricane even 5 years later.
Table 6: Tourist arrivals in New Orleans from 2003 to 2010
Year Annual tourist arrivals (in millions)
2003 8.5
2004 10.1
2005 (Jan to June) 5.3
46
2006 3.7
2007 7.1
2008 7.6
2009 7.5
2010 8.3
Source: (New Orleans Convention & Visitors Bureau, 2011)
For crime itself, a research of New Orleans’ destination image showed that it has made tourists perceive New
Orleans as city of danger, which jeopardized marketing promotion of its famous event “Mardi Graz” (Gotham,
2002). However, high crime rate has not raised a concern for either supply or demand side of tourism, and
there have been no specific actions tackling street crime carried out. Furthermore, the risk-taker tourists
might also be a reason for lack of evidences of the impact of crime. In fact, this is a paradox of tourism
marketing, called perverse place marketing by Medway and Warnaby (2008), which accentuating the
negative incidences sometimes increases tourism demand.
Since street crimes keep taking place, it leaves a doubt that whether tourist demand would increase when
there is a lower crime rate. Theoretically, assuming that there was less crime in New Orleans, which
promotes positive image such as safety and comfort, tourism demand would also increase as an expectation.
But at the moment it can only be a castle in the air, and its impacts would be known only after the crime rate
was reduced, maybe someday in the future.
3.5.3 Case 3: Street Crime in Johannesburg
Description
In general, the Republic of South Africa has been suffering from street crime. Crime rate increased by 9%
between 1990 and 1995 (Glanz, 1995), in fact, there were not crimes in large scales going on, this increased
crime rate could be mostly resulted by property-related street crime cases, and some are serious violent
cases, which is similar with what happened in New Orleans. Tourists are the easy targets of such type of
crimes, which Johannesburg is a typical example. The most popular destinations for international tourists
are the major crime areas (Ferreira S. , 1999). It has been pointed out that the socio-political structure, which
47
formed by income inequality, social segregation and racist policy implemented in 19 th century due to
historical reasons, is the vital causation of high crime rate in Johannesburg (Allen, 2002).
Johannesburg as a tourism destination
It is the powerhouse of African economy, as one of the most modern and prosperity city in Africa, therefore it
receives majority of business visitors because of its commercial potential. Naturally, the urban areas are not
known for a traditional sightseeing tourism destination, but it still absorbs large number of tourist inflows
because of O R Tembo International Airport, the gateway of entering South Africa, which makes it become a
leading bed-night destination for international tourists (Rogerson, 2002).
It is still a good destination or urban tourism because of its multi-cultural cosmopolitan life vibes, which is
composed by interesting museums, emotive monuments, high-class restaurants, and colorful nightlife places.
Meanwhile, famous heritages are not far away from the city, the Cradle of Humankind is only 25 kilometers to
the northwest, the Sterkfontein fossil, the richest hominid site, is also in Province Gauteng area. Moreover,
tourists have wide range of variety of accommodation to choose, there are 53 qualified hotels, 22 B&Bs, and
11 Lodges officially registered at the moment (Joburg Tourism, 2014). There also a well-known mega event,
World Cup 2010, was held in South Africa, which makes Johannesburg a more attractive place worth to go. It
was one of the most critical cities of matches, because of the opening matches in FNB Stadium, which was an
important flagship project specialized for World Cup upgrading of infrastructure.
Impact of street crime on Tourism
Firstly, the image of Johannesburg as a tourism destination has been adversely affected by perception of
being unsafe. In a research conducted by Ferreira and Harmse (2000), South Africa in general was evaluated
less safe than the other countries by tourists, which scored only 4.8 on average in a 7-scale of survey. Higher
score indicates a safer destination, while Holland was 5.9, UK was 6.1 and Germany was 6.4; and overall
average was 5.8. In particular, Gauteng in Johannesburg was even worse in the evaluation of personal safety
comparing with other major cities of South Africa, which was 3.4 in 1995, while Cape Town (western Cape)
was 6.8 and KwaZulu-Natal was 5.9 (Ferreira & Harmse, 2000).
48
International tourism have been proved for experienced a significant decline in Johannesburg because its
deep involvement of crime in recent years (Pizam, Tarlow, & Bloom, 1997). Media publicity has also
contributed for this decline, for widely reported crime cases against tourists (Brunt, 2001). About 2% of
international tourists were affected by this continuing high crime rate, in numbers, 20 thousands of
individuals in 1995 (Satour, 1996), 2.6 in 1998, and was estimated to double in future (Ferreira & Harmse,
2000).
Mega event is an opportunity of mitigating social problems like crime. The perception of tourists about
crimes changed since Johannesburg has showed a safe image to inbound tourists in 2010. In order to
guarantee an enjoyable experience of World Cup to international tourists, the government of South Africa
adopted a series of security plans for ensuring safety during the event (Donaldson & Ferreira, 2007). As a
response, a post-event survey demonstrated a positive perception of crime-safety and overall satisfactions of
World Cup 2010. Tourists are willing to recommend South Africa as a holiday destination to other people
(George & Swart, 2012). However, the sustainability of this control of crime has left out for further discussion
because special policies have been implemented for World Cup.
3.5.4 Conclusions on case studies
Singapore, New Orleans and Johannesburg are cities with large metropolitan area, with charming culture
offerings, and dynamic urban life and activities for tourists worthy to experience. Likewise, they got negative
publicity when the negative incidents took place.
Moreover, the influence of disease and crime has been investigated respectively. SARS was a sudden hit of
tourism, while crime is continuous problem. Regarding negative publicity, in Singapore, the negative
information was an explosion while the SARS broken out, and was completely disappeared one year after the
disease being controlled. In New Orleans, the media exposure of street crimes always exists, but there has
been a lack of evidence of impact on tourism. In Johannesburg, media publicity clearly scared international
tourists, supported by statistical evidence.
In the perspective of tourism, under the influence of the rampant disease, during the first half-year of 2003,
Singapore rarely received international tourists. Along with the vanishing of the disease, negative publicity
49
also decreased gradually, and finally tourism got primarily recovered in the last quarter. For New Orleans,
tourism has not been influenced by its high crime rate, because of the absence of statistical evidence.
Although there was a devastation of tourism in 2005, its major causation standing behind was natural
disaster. Tourism in New Orleans renounced one year later, indicated a primary recovery as well. Different
from non-impact of New Orleans’ experience, Johannesburg has always been criticized by high crime rate,
and international tourists declined because of the perception of unsafe and fear. But similarly, it is difficult for
Johannesburg to limit its crime, although there was a positive perception of safety-crime during World Cup
2010, the real recovery from high crime rate has not been realized yet.
As a consequence, as the majority of them (2 out of 3) showed negative impacts on international tourism, the
same influence of air pollution in Chinese cities can be expected in the following parts of this dissertation.
Table 7: Comparisons of cases in Singapore, New Orleans and Johannesburg
Destination Singapore New Orleans Johannesburg
Incident SARS Street crime Street Crime
Frequency Once Continue Continue
Impact on tourism A sharp drop in early 2003 Lack of significant impact Significant decline
Recovery time 3 quarters to 1 year N/A N/A
Source: own elaboration
3.6 Hypotheses
Considering the above analysis throughout the cases and the negative factors influenced them, gives a hint
for Chinese cities in the main research question. Therefore, the main hypothesis for this dissertation can be
formulated based on their results.
H1: Lower air quality will lead to a decline on inbound tourism of Chinese cities.
Furthermore, the cases also showed there is a time period of recovery of more or less one year, leaving the
necessity to detect if the recovery time also exist in the case of air pollution. Meanwhile, in line with the
50
similar cases, it is also logical to postulate similar time lag as those cases. Therefore, the time-lagged effect
will also be detected.
H1.1: Lower air quality in the previous year will result in a decline of inbound tourism
of Chinese cities in the current year.
Moreover, it leaves a curiosity whether the negative publicity will have an impact on tourism as well, because
it showed in the case of Singapore and Johannesburg a significant influence on tourism demand but not in
the case of New Orleans. So it is necessary to confirm or dismiss this impact for air quality. In the sense that
more media publicity available for the tourists, the more possible impact it will generate, and since air
pollution itself is seen as a negative incident, the second hypothesis will be logically formulated as follows:
H2: More media publicity of air pollution will lead to a decline on inbound tourism of
Chinese cities.
Similarly, since there might be a time-lagged impact of sulfur dioxide, it is also reasonable to make a
hypothesis about media publicity.
H2.1: More media publicity of air pollution in the previous year would lead to a
decline of tourism in the current year.
Furthermore, media publicity might be a moderating variable of air quality to enlarge influence on tourism
demand. Therefore, it is going to be examined as well.
The model building up in Chapter 4 will assist to test the hypothesis formed above, in order to find out an
answer to how the air quality will affect inbound tourism to Chinese cities. Thus, the next chapter will
elaborate how the empirical model will be constructed and what variables will be selected.
51
Chapter 4 Empirical Models
From the previous chapters, the theoretical review has indicated that air pollution might have an impact on
tourism demand, thus in order to test the hypothesis, a quantitative model will be constructed. The method
used to analyze data will be introduced in Section 4.1, the necessity and benefit of applying certain method
will be explained. The entire dataset will be used for the estimation will be described 4.2, in its sub section,
the definition of all the variables and the reasons of inclusion or exclusion of each control variable will be
elaborated. Finally, the empirical model for testing the main hypothesis is going to be formulated in 4.3.
4.1 Method
A time series data covering 31 Chinese cities from mainland of China and their annual statistics from 2005 to
2012 are collected into the database. Thus two dimensions are included in this panel data set – different
cities and years. Regarding to the research question, the tourism demand changes over years in the same
cities are going to be investigated, the within effect is meaningful for reaching the conclusion. Thus a fixed
effect transmission is necessary in order to acquire within estimators, and an OLS regression will be
specified in this panel dataset.
As mentioned previously, there are many factors influencing tourism of a city, and obviously they cannot be
all included in the estimation. The important factors of making estimation in a panel dataset are what have
(been) changed over the years during the observation. Meanwhile, a lot of characteristics of cities cannot be
observed, which can be constant existing but also temporary appealing, probably also give an impact on
international visitors, however, they are too complex to be exhausted exploring. A fixed effect estimation
itself can partly eliminate these unobserved characteristics, meanwhile, time-demeaning variables can
facilitate to control them, thus 9 year dummies will be added into this fixed effect estimation. Some variables
explicitly keeping constant over time will be excluded.
Additionally, in order to support that the fixed effect estimation is the more preferable method for this panel
52
dataset, a Hausman test will be conducted to seek for any significant difference it created between the fixed
and random effect estimations.
All the estimation equations and inference tests in this dissertation will run in the software named STATA.
4.2 Data
4.2.1 Description
CEIC China premium database is the major source of data for this dissertation, and the Internet searching
engine Bing.com is the other source used for data collection. As mentioned, the complete database included
31 Chinese cities and correspondent data in the city level. These cities are the capitals of the provinces of
China mainland, which play leading roles in the international tourism market because of better accessibility
and business opportunity. They are representatives of the other cities, and their successes or failures can be
leading cases for the others to learn. Meanwhile, the time span of 2005 to 2012 is chosen, indicating that
eight years of annual statistics will be used into the analysis.
Note that tourists from the Special Administration Regions – Hong Kong and Macau, and Taiwan Province
current ruled by Kuomintang, are not included in this research, in order to avoid confusion and acquire more
accurate estimated results for inbound tourism to Chinese cities. This group has to be distinguished from the
other ‘pure’ international visitors. Travelling to China is easier for them than the other overseas tourists,
because they have much stronger attachment (nationality, language, culture commitment, and etc.) with the
mainland regions. Although statistical departments record them as international tourists, they are unique
and they actually belong to neither domestic nor international category. To investigate the difference
between this group and other overseas international tourists will be interesting as well, but unfortunately
precise statistical numbers for SAR tourists in city level are missing, they are recorded only in Beijing,
Shanghai and Guangzhou.
4.2.2 The dependent variable
Tourist Arrival
Tourist arrivals will be used as the dependent variable to measure inbound tourism, in fact, in the researches
53
recent years, it has been the most popular factor adopted of measuring the tourism demand (Song & Li,
2008). In the review study of Li, Song and Witt (2005), they discovered that in the majority of the previous
studies of tourism demand, researchers frequently used tourism expenditure or tourist arrivals as the
dependent variable, while a few other dependent variables were used in the minority, such as budget share
of tourism expenditures, tourism imports and exports, numbers of nights.
Considering the database can be accessed, the tourist arrivals will be the most suitable dependent variable
for this dissertation. While following the common sense that larger city are usually better known, the size of
city may influence the tourist arrivals. Thus a correction should be applied. In each year, the city’s population
will divide tourist arrivals, in order to eliminate the effect of city size. Population size is counted on per
thousand basis.
4.2.3 The independent variables
Inclusion
The following variables listed will be included into the estimation equations going to be made, and the
details of these variables will be illustrated one by one.
Sulfur dioxide
Media publicity
Foreign direct investment
Hotels
Mega events
Property Price
Sulfur Dioxide
This is the most important independent variable because it measures air quality, confronting the essence of
the main research question.
In Chapter 2, it indicated that the concentration of PM 2.5 can measure air quality as well, and many
countries (U.S. and western European countries) have adopted it as a measurement, called AQI (air quality
index), measuring how microgram of PM 2.5 concentrated in the air per cubic meter on average. However,
54
AQI had not accepted by the official department of China Environment Protection as an indicator of air
quality before 2014. Meanwhile, PM 2.5 is a relatively new concept to Chinese public, most of websites
provide real time statistics for each city, and historical data is rare. A Chinese website 2 provides historical
data from October 2013, but no data available from 2005 to 2012, indicating that there is not sufficient data
to sustain a reliable result of estimation. Moreover, the official measurement of air quality in China was API,
abbreviated for air pollution index; the difference between it and AQI is that API is measuring the
concentration of PM 10. Some historical data of API can be pursuit in the website of Ministry of
Environmental Protection of PRC3, but lack of completeness for the entire needed time span (2005 to 2012)
will deteriorate the reliability of estimation, thus it is not adopted as well, unfortunately.
Finally, the annual emission of sulfur dioxide on thousand tons basis for each prefecture city was found in
CEIC database, thus it is adopted in this empirical model as the indicator of air quality. Sulfate, mostly sulfur
dioxide, which has been discussed in Section 2.5.1, is an important composition of air pollutant PM 2.5. It can
be a proxy of measurement because they are in the same direction, more sulfur dioxide probably causes
more PM 2.5 generation. Comparing with PM 2.5, this proxy might have less visible to the public, since it does
not directly reported by media. It might lead the influence of media publicity is overestimated.
Still, the emission of sulfur dioxide is an appropriate proxy, since sulfur dioxide influences the air quality. It
thus will also influence tourists’ behavior and decisions, since it is a threat to human’s health, e.g. increase
the probability of CVD. Similarly to PM 2.5, WHO also set up guidelines for sulfur dioxide, which duration of
exposing to sulfur dioxide more than 500 micro gram per cubic meter on average should not exceed 10
minutes (World Health Organization, 2006).
There is no need for city-size correction because it is not definite that bigger city will generate more sulfur
dioxide based on the evidence found about PM 2.5, the cities in north are more polluted than the south, but
both north and south have big cities.
2 http://www.tianqihoubao.com/aqi/beijing.html3 http://datacenter.mep.gov.cn/report/air_daily/air_dairy_aqi.jsp
55
In addition, since time lagged effect of air pollution is also going to be investigated, the one-year lagged value
of sulfur dioxide will be also included as a independent variable.
Media publicity
Confronting with the second hypothesis, media publicity of air pollution has to be included. There is no
definite way to measure this variable, or quantify it. In this research, it is assumed that more media publicity
will generate more impact on tourists, so the number of information available would be an appropriate
method for measurement. Therefore, certain terms were searched in the Internet searching engine Bing.com,
and the total numbers of results found were recorded. The terms used to search were following the structure
as “City name +’air pollution’ + year”, for example:
Beijing “air pollution” 2005
The result of this search includes all the webpages mentioned Beijing, air pollution and 2005, but also
perfectly matched the order of the words “air pollution”. Likewise, Beijing can be replaced by Tianjin,
Shanghai, or any other cities needed, and 2005 should be altered by 2006, 2007 till 2012.
Note that using different searching engine like “Google” and “Bing” will get different results. The results from
google.com were not adopted because they sometimes were not comparable, and bing.com gave more stable
results. Besides, searching in a different time and different PC will also lead to a distort result. To clarify, the
data used in this dissertation was collected on 14th August 2014, in a laptop with a Chinese PC system.
This proxy of measuring media publicity is rough, and has several limitations need to be pointed out. First, it
included every webpage mentioning these terms, but not the actual number of people’s hits. There might be
some webpages never get a hit, which indeed have no impact on tourists, but are still counted into the result.
Google Trends gives the comparable trend over years based on the number of hits, but for some cities which
are cleaner in common sense, are rarely generate hits, and therefore cannot produce enough data to be used
as a variable. Another database named LexisNexis was also used to attempt to get news published in certain
years, but it was found the same problem as Google Trends.
Second, using the year “2005…2012” directly does not show the webpage reported air pollution in 2005.
56
Instead, every webpage mentioned 2005 will be included, but people cannot reach a webpage published in
2008 but mentioned 2005 before 2008. Furthermore, there will be increasing number of webpages
mentioning terms like 2005, because people are keeping publishing, but they are not available for previous
searching as well. Since the actual number of webpages published in a certain time period is not shown in
both Google and Bing, as a result, the data collected is more than actual webpages existed at that year.
Third, the Internet is just one of the mass media channels, other information sources, such as the local
newspapers, are excluded from the results. However, newspapers is much more reachable for aged people
than the Internet.
Fourth, this result might have excluded the information sources for non-English speakers. Even though major
components of international tourists understand English, niche markets cannot be neglected, for example,
Urumqi receives a lot of visitors from middle Asia countries, and they communicate by Uyghur language.
Together with the third limitation, the data collected is less than the actual information available.
Additionally, most of webpages mentioned air pollution discussed about PM 2.5, but annual emission of
sulfur dioxide will be used as the independent variable in the following analysis. It created a slight
inconsistency, because most tourists probably search terms like “air pollution” “PM 2.5” or “air pollutant”, but
not “sulfur dioxide emission” when they want to prepare for their travels. But as stated above, it is a pity that
the data of PM 2.5 is not sufficient to be adopted in the estimation.
Combining all the limitations above, it is difficult to judge the number in the database is larger or smaller
than what can be reached in reality, but it is an indication of the intensity of media publicity, since more
information published the more probable people will be influenced.
Foreign Direct Investment
It is an indicator referring to market ties. In the previous context we measured it by GDP. FDI and GDP are all
economic indicators, which can be used as proxy of market ties. Figure 6 depicts GDP (in billion Chinese
Yuan) and FDI (in million US dollar) for all Chinese prefecture level cities from 2005 to 2012. It is clear that
they are positively correlated, which suggests that only one of these two indicators can be included in the
57
estimation model.
Figure 6: GDP and FDI for all prefecture level cities in China from 2005 to 2012
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66525
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66526
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66527
0tan28aa5
66528
0tan29aa5
66529
0tan30aa5
66530
0tan1aa5
66510tan28aa566028
0tan8aa593480tan16aa6207160tan25aa648125
GDPFDI
Source: CEIC China Premium Database (2014)
FDI is chosen instead of GDP in the sense that more FDI in the city, more business visits would generate. GDP
can be seen as a potential of more business opportunities, but FDI is the business has already done, which
have a stronger connection with the actual business trips of international visitors took place in that time
period.
The data of FDI for each city from 2005 to 2012 comes from CEIC China Premium Database. The total capital
utilized for each year is recorded and counted in million US dollar. Additionally, it is also corrected by the city
size (population).
Hotels
Accommodation is always important of travelling arrangement. It has been discussed that international
visitors prefer qualified hotels, which indicates that 4-star or 5-stars hotel should be taken into account by
measuring international tourism demand. However when looking for the data of 4 and 5-star hotels, there
are still some values missing. In CEIC China Premium Database, the number of all registered hotel in the city
is found, thus it is adopted as a proxy of qualified accommodation. It is also corrected by the city size
(population).
Mega Event
As stated in Section 2.2.3, China started to participate holding mega events. To control the impact of mega
58
event on tourism demand, this is set up as a dummy variable. If the city held a mega event in the year, then its
value equal to 1, otherwise equal to 0. There are two mega eents took place in Chinese cities in time span
from 2005 to 2012, which are Beijing Olympics 2008 and Shanghai Expo 2010. Value for Beijing in the year of
2008 and Shanghai in the year of 2010 equal to 1, all the rest cities in the rest years equal to 0.
Property Price
Price level of the destination also has impact on tourism demand, because the basis of demand and supply
relationship suggests a higher price leads to a lower quantity. An expensive destination, to some extent,
probably will keep tourists away, and vice versa.
The best variable for the price level of tourism is Consumer Price Index (CPI), because tourism activities
covers a wide range of consumer products, and CPI is the one that comprehensively evaluated the price level
in a certain place. It is a surprise that a comprehensive CPI is difficult to find. CEIC provides retail prices for
agricultural, food, industrial, consumer goods, and service charges in very details (e.g. daily price of eggs per
kilo), but they are average values in the country level. The only prefectural level data is only available for
property price, so it is taken as a proxy of consumer price level. It is counted based on Chinese Yuan per
square meter.
Property price can be relevant to tourists’ expenditure, because hotel price is largely influenced since they
are both related to real estate sector. Accommodation expenditure also has great proportion in total travel
expenses, so property price can partly represent how expensive a destination is. The limitation of this proxy
is obvious, it ignores the prices of other tourist products. However it is still difficult to include every product
the tourists might consume, so another two representatives are picked up. The retail price of egg is an
indicator of normal food expense, and the price of taxi per kilometer is an indicator of service/transportation
expense. Figure 7 illustrates the changes of price level for property, egg and taxi in Chinese cities from 2005
to 2012(price in Jan 2008 equals to 1). It shows that property price has a greater increase than the other two
products, and taxi price almost remains stable over years. Thus taking property price as a proxy of price level
may lead to overestimation of the impact of price level on tourism demand.
Figure 7: Price changes of property, egg and taxi over years
59
Jan, y
yyy
Sep, yy
yy
May, yy
yy
Jan, y
yyy
Sep, yy
yy
May, yy
yy
Jan, y
yyy
Sep, yy
yy
May, yy
yy
Jan, y
yyy
Sep, yy
yy
PropertyEggTaxi
Source: CEIC China Premium Database (2014)
The above-mentioned variables are all needed for building up the estimation model for this dissertation.
Table 8 presents all the variables and their units, when model is being specified in Section 4.3, for simplicity,
they will be mentioned by their abbreviated name.
Table 8: List of selected variables
Function Name Abbreviation Unit Remarks
Dependent
variable
Tourist arrivals TA Thousand people Corrected by city
size
Independe
nt variable
Sulfur dioxide SD Thousand tons
1-year lagged sulfur dioxide SD_1 Thousand tons
Media publicity MP Thousand results
1-year lagged media publicity MP_1 Thousand results
Foreign direct investment FDI Million US Dollar Corrected by city
size
Hotels Hotel Unit Corrected by city
size
Mega Event ME N/A Dummy variable
Property price PP Thousand Chinese
Yuan / square meter
Source: own elaboration
Exclusion
Likewise, the following variables are excluded from this research; the reasons will also be explained for each
60
of them.
Trade-weighted effective exchange rate index (BIS)
Number of heritages
Transportation cost
Airport ranking
Income of tourists from origins
Trade-weighted effective exchange rate index (BIS)
BIS is an overall exchange rate index that complies weighted average of exchange rate of home currency
against foreign currencies with the weight of each foreign country equal to its share in trade. Exchange rate
between Chinese Yuan and other currencies is another determinants of how expensive for travelling in
Chinese cities, which can be seen as a composition of price level, indicating an impact on tourism demand.
In fact, the exchange rate has changes quite an extent in these years, due to the financial crisis in western
countries and strong performance of Chinese economy. Table 8 illustrated the changes of BIS over years,
which can be found a trend of appreciation of Chinese Yuan.
Table 9: BIS of Chinese Yuan from 2005 to 2012 (2010=100)
Year 2005 2006 2007 2008 2009 2010 2011 2012
BIS 87.2467 89.24 90.38197 96.37333 102.0108 100 100.0833 105.6792
Source: CEIC China Premium Database (2014)
The reason of dropping it is that BIS is an universal number for every city. Although it changes over years, but
in this sense it can only explain the changes in the number of visitors in different years, which occurs in all
cities. Meanwhile, as time-demeaned variables are added, which already have caught characteristic differs
over the years but remains the same for every city; BIS as such a variable is not necessarily to be added into
the model.
Number of heritages
Cultural and natural heritages are important attractions for international tourists. It is not picked into the
model because the number of heritages keeps constant over time, because they cannot be created. In panel
61
data estimation, this type of effect is automatically omitted because the impact does not change.
Transportation cost
Lower transport cost leads to an expansion of tourism – it might be true for short distance international
travelling, like a city break travel in European countries. For example, the introduction of low-cost airline in
Alghero significantly boosted the number of tourists, because of reduction of transportation cost resulted
from greater accessibility (Pulina & Cortes-Jimenez, 2010). However, it is inferred not applicable for
travelling to Chinese cities, because expense on air transport is always generating the most transportation
cost, and the ticket prices from Europe or US to China do not differ too much. Another reason to exclude it is
the difficulties of data collection, because ticket prices vary in a large range, and depend on many factors, e.g.
booking time prior to travel date, the airline company selected, the travel agency contacted.
Airport ranking
Since accessibility is one of the major determinants of destination attractiveness, the existence of
international airport and how many passengers it receives seem that need to be taken into account.
However, it is not selected because of two reasons. First, it might become a dominant variable, which causes
other variables to become insignificant because it has too strong impact. A large number of incoming
passengers mean either that the airport has many connections, or that it is located in a city that attracts
many tourists; it is difficult to distinguish what resulted a high rank of airport.
Subsequently, even if you could construct a variable that exactly measures the number of destinations
reachable from an airport, it would probably suffer from endogeneity. For example, if a city attracts many
tourists in the previous year, its airport is likely to have a bigger number of destinations in the next year to
cope with the high number of incoming tourists. Endogeneity is a problem of causation, it keeps unknown
that whether an accessible airport causes more visitors to come to the city, or whether more visitors coming
to the city causes the airport to become more accessible by having an increased number of
connections/destinations. Therefore, it is very difficult to add a variable for transport into the estimation.
Income of the tourists from origins
62
In the previous discussions, the property prices of Chinese cities are included as a sign of travel expenses in
China, which will matter for destination selection. If the expenses of travelling to Chinese cities are far more
than the price they can afford, then tourists will visit other places instead. The affordability is certainly linked
to income, in this case, is income of the tourists from the origins. However, since the origins cover all over the
world, the types of inbound tourists are diversified, and the proportion of different types varies across
different years, and again very detailed data is needed because 31 Chinese cities received different numbers
of tourists. Therefore, it is very difficult to find an appropriate proxy to measure their incomes, or to find a
suitable weighted factor to leverage all those incomes in one variable. In order to avoid disturbance of model
reliability, this variable have to be left out.
It does not mean that there is no indication of how expensive the Chinese cities are for international visitors;
a rough comparison could be a note for this variable. The Big Mac Index is useful to compare the price level
across the countries. Besides the meanings of the other economic meanings behind, another reason for
presenting the price of Big Mac is that it directly reflected the price of a Big Mac hamburger in McDonald, the
most popular food in the world, which could possibly be a purchase of international tourists in China when
they get tired of Chinese cuisine, which tastes very different from food from other parts of the world, and
want a bite of ‘taste of home’ (for Americans) or something they are familiar with.
Regarding the origins, albeit there are many, six origins can still be the representatives to make comparisons
of price levels with China. South Korea, Japan, Russia, the United States, Malaysia and Singapore have always
been the origins contributing the most numbers of inbound tourists to China from 2007 to 2012 (Travel
China Guide, 2012). The prices of a Big Mac from 2005 to 2012 were listed in Table 10, and China is also
included for comparison purpose. The value in one month of each year is selected because McDonald usually
does not adjust price of Big Mac too much during the year, and naturally, all the prices have been already
adjusted to the same currency – US Dollar. From Table 10 it can be discovered that the price of Big Mac in
China is the lowest among all these countries with just a few exceptions. Therefore, it gives an indication that
China is not an expensive destination for the major groups of international tourists.
Table 10: The prices of a Big Mac hamburger (in USD)
Year 2005 2006 2007 2008 2009 2010 2011 2012
63
Country
China 1.27 1.30 1.41 1.83 1.83 1.95 2.27 2.44
South Korea 2.49 2.56 3.08 3.14 2.39 2.82 3.50 3.19
Japan 2.34 2.19 2.31 2.62 3.23 3.67 4.08 4.08
Russia 1.48 1.60 1.85 2.54 1.73 2.33 2.70 2.55
United States 3.06 3.15 3.22 3.57 3.54 3.73 4.07 4.20
Malaysia 1.38 1.47 1.57 1.70 1.52 2.19 2.42 2.34
Singapore 2.17 2.20 2.34 2.92 2.61 3.08 3.65 3.75
Source: (The Economist, 2005; The Economist, 2006; The Economist, 2007; The Economist, 2008; The
Economist, 2009; The Economist, 2010; The Economist, 2011; The Economist, 2012)
The limitation of taking income out of the estimation is that it might lead to an overestimation of the impact
from other variables, since the income and tourist arrivals are positively related. However, because of the
difficulties mentioned previously, the inclusion of this variable can be a topic of further research.
4.3 Model Specification
There is a general demand model proposed by Lim (1997) typically for econometric analysis of international
tourism, stated that tourism demand is a function of:
Income of tourists from origins
Transportation cost between destinations and origins
Relative prices
Currency exchange rate
Qualitative factors in destinations
Considering what have been demonstrated in the last two sections, the estimation model for our analysis is
quite different from this general model. Income of tourists is neglected from the discussion. It is difficult to
be included, because the origins of tourists are too many and income level might differ too much, there is no
proxy can represent this variable. Transportation cost is excluded because of the worry of dominant effect
and problem of endogeneity. Currency exchange rate, which is BIS in this case, not get involved because the
64
time demeaned variables already controlled its effect. Besides, relative prices and qualitative factors in
destinations are taken into account for the following analysis.
As a result, the baseline mode of this dissertation is formulated as follows, which only includes price level
and some qualitative factors of destination:
Baseline Model (Model 1):
TA ij=β0+β1∗FDI ij+β2∗Hotel ij+β3∗MEij+β4∗PP ij+ε
The two hypotheses are testing the effect of sulfur dioxide and media publicity, thus two other models should
be formulated respectively, and there is a fully specified model with these two variables at the same time. The
fully specified model is going to be used for verifying assumptions of OLS regression and other tests of
hypothesis.
Sulfur dioxide effect model (Model 2):
TA ij=β0+β1∗FDI ij+β2∗Hotel ij+β3∗MEij+β4∗PP ij+β5∗SD ij+ε
Media publicity effect model (Model 3):
TA ij=β0+β1∗FDI ij+β2∗Hotel ij+β3∗MEij+β4∗PP ij+β5∗MPij+ε
Fully specified model (Model 4):
TA ij=β0+β1∗FDI ij+β2∗Hotel ij+β3∗MEij+β4∗PP ij+β5∗SD ij+β6∗MPij+ε
Since there is a wonder of exaggerate effect of media publicity, an interactive variable of sulfur dioxide and
media publicity is created to test it.
Model of moderated media publicity (Model 5):
TA ij=β0+β1∗FDI ij+β2∗Hotel ij+β3∗MEij+β4∗PP ij+β5∗SD ij+β6∗MPij+β7∗MPij∗SD ij+ε
65
Since there showed an approximate one year recovery time of negative incidents in Section 3.5, leaves a
necessity of testing time lagged effect of air pollution, accordingly, media publicity need also to take values of
the previous year. Model 6 tests that sulfur dioxide in the previous year might influence the tourist arrivals in
the current year, and Model 7 tests that sulfur dioxide in the previous and current years both influence the
tourist arrivals in the current year.
Time lagged effect model with previous year only (Model 6):
TA ij=β0+β1∗FDI ij+β2∗Hotel ij+β3∗MEij+β4∗PP ij+β5∗SD1ij+ β6∗MP1 ij+ε
Time lagged effect model with previous and current years (Model 7):
TA ij=β0+β1∗FDI ij+β2∗Hotel ij+β3∗MEij+β4∗PP ij+β5∗SD ij+β6∗SD1ij+β7∗MPij+β8∗MP1ij+ε
The results of tests of significance and assumptions are going to be presented in the next chapter.
66
Chapter 5 Results
This chapter will present the results of the models formulated in the last chapter, in order to facilitate find
the conclusion of hypothesis. Meanwhile, some relevant tests of assumptions of OLS regression will also be
conducted. Therefore, Section 5.1 will present the result of models, and Section 5.2 will present results of
goodness-of-fit of the fully specified model.
5.1 Models
5.1.1 Results in general
First, in order to guarantee the fixed effect model is more appropriate than the random effect model, a
Hausman test is carried out using the baseline model. It shows a P-value equal to 0.000, which suggests there
is systematic difference between fixed and random effect model. Therefore it is reasonable to use fixed effect
model for the following analysis.
Second, the independent variables – sulfur dioxide and media publicity – are added in step by step into the
model. Table 11 shows the results from Model 1 to Model 4. It does not include year dummy variables
because those are all insignificant.
Table 11 Results from Model 1 to Model 4
ModelVariable Model 1 Model 2 Model 3 Model 4FDI .077116***(.000) .068491***(.000) .076263***(.000) .067037***(.000)Hotel .042369(.891) .071308(.815) -.029316(.923) -.001498(0.996)ME .023142**(.059) .019781(.104) .029928**(.014) .026640**(.027)PP .006309***(.000) .005599***(.000) .008675***(.000) .008021***(.000)
67
SD -.000118**(.011) -.000125***(.005)MP -.001044***(.001) -.001089***(.001)
Source: own elaboration
Note: * Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level
It can be found that two variables in the baseline model keeps significant in 1% level, which are FDI and
property price. Mega Event is significant except for in Model 2, indicating that adding sulfur dioxide takes
away its impact. After controlling media publicity it turns to significant again, suggests that the focus of
media of air pollution was more intensive when mega events taken place. Property price has a positive
impact on tourist arrivals, which is slightly beyond the expectation, since it is a proxy of price level, indicating
that higher travelling expenses would keep tourists away. This might be explained by low price for China in
the aggregated level, as discussed in the prices of a Big Mac hamburger, China is less costly comparing with
the major source countries of international tourism. Therefore, Chinese cities are still affordable, though the
price level keeps increasing. Moreover, those expensive cities (three hotspots – Beijing, Shanghai and
Guangzhou in particular) are more famous and in rich of tourist products for international tourists, enabling
them still willing to visit there.
Hotel number is insignificant in all the models, thus can conclude that it has no impact on tourist arrivals.
One reason might be its relatively small changes over the years. A city usually has hundreds of hotels, and
there were about 10 hotels newly opened or closed in each year, after dividing by population size (on
thousand basis), the magnitude of change becomes very small.
All the coefficients need to be elaborated carefully. As Model 4 is the fully specified one, it is taken for the
example. Keep in mind that some variables are corrected by population size, which will have impacts on the
interpretation.
FDI:
For each year and each city, if FDI increases by one million US Dollar, tourist arrivals will increase 670 on
average, ceteris paribus.
68
ME:
If there is a city holding a mega event in the certain year, there are 266 tourist arrivals than the year that no
mega event is held, ceteris paribus.
PP:
For each year and each city, if property price increase by one thousand Chinese Yuan per square meter,
tourist arrivals will increase by 8 on average, ceteris paribus.
SD:
For each year and each city, if emission of sulfur dioxide increases by one thousand tons, tourist arrivals will
decrease by 0.125*population size, on average, ceteris paribus.
MP:
For each year and each city, if there is one thousand more webpages mentioned air pollution, tourist arrivals
will decrease 1.089* population size, on average, ceteris paribus.
Model 5 to Model 7 has checked the interaction between sulfur dioxide and media publicity, time-lagged
effects of air pollution and time–lagged effects of media publicity. Table 12 presents the results. The result
from model 4 is presented again to make comparison easier.
Table 12:
Results
from
Model 5
to Model
7
69
ModelVariable Model 4 Model 5 Model 6 Model 7FDI .067037***(.000) .066451***(.000) .057123***(.002) .056715***(.002)Hotel -.001498(0.996) -.010599(.972) -.020015(0.950) .023231(.942)ME .026640**(.027) .025811**(.034) .020127*(.094) .026715**(.039)PP .008021***(.000) -.007769***(.005) .005926***(.000) .006422***(.000)SD -.000125***(.005) -.000106*(.092) -.000036(.668)SD_1 -.000156***(.004) -.000128(.133)MP -.001089***(.001) -.001016***(.005) -.000786(.146)MP_1 -.000609*(.082) -.000049(.925)SD*MP -1.49e-06(.658)
§Source: own elaboration
Note: * Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level
Model 5 confirmed that there is no statistically significant impact of interaction between sulfur dioxide and
media publicity, indicating media is not a moderate variable. The other variables stayed almost the same as
Model 4, which is another support of no impact of interaction. Model 6 showed that both sulfur dioxide and
media publicity in the previous year have significant influence on tourist arrivals in the current year, but
media publicity in the previous year receives less statistical support comparing with the others.
After adding both the sulfur dioxide and media publicity in the present year back to the model, the
coefficients of SD, SD_1, MP and MP_1 have become all insignificant. This is resulted from multicollinearity
problem. Table 13 shows that the correlation between SD and SD_1 is 0.9896, between MP and MP_1 is
0.9795, indicating Model 7 is a high collinear model. In this case, either the data from the current year or the
previous year should be dropped. Furthermore, this correlation table warns the reliability of the result of the
other models, because in this highly correlated model case, it cannot be distinguished that which one
(previous/current year) leads to the impact, and it decreases the confidence of the estimations. However,
there is barely method to solve this problem, which implies further research is needed.
70
Table 13 Correlation of variables
Year Code TA SD FDI Hotel ME MP PP SD_1 MP_1
Year 1.0000
Code 0.0386 1.0000
TA 0.0948 -0.1077 1.0000
SD -0.0887 -0.0257 0.0404 1.0000
FDI 0.1991 -0.4333 0.4223 0.0423 1.0000
Hotel -0.1088 -0.1386 0.2933 -0.2158 0.0646 1.0000
ME -0.0010 -0.1214 0.1592 0.0252 0.0896 0.1247 1.0000
MP 0.0421 -0.3302 0.5441 0.2457 0.4220 0.1689 0.3375 1.0000
PP 0.4412 -0.3243 0.7212 -0.0180 0.5016 0.2372 0.2652 0.7268 1.0000
SD_1 -0.0970 -0.0251 0.0588 0.9896 0.0464 -0.2086 0.0396 0.2646 -0.0034 1.0000
MP_1 0.0765 -0.3216 0.5578 0.2581 0.4448 0.1362 0.2590 0.9795 0.7340 0.2747 1.0000
Source: own elaboration
5.1.2 Comparisons between different regions
In Section 2.2.3, the uneven geographical distribution of international tourism in China has been identified.
In short, three hotspot cities attracted more tourists than the others, and the east regions attracted more
tourists than the west. Therefore, it is also meaningful to check if air pollution has produced different effects
in different geographical locations. The fully specified model has been applied to the investigation for this s
section. Note that the correlation problem of variables is not mitigated yet, which might still defect the
confidence of these results.
Three hotspots vs. other regions
It is clear that three hotspots (Beijing, Shanghai and Guangzhou) received more tourists than others.
However it is difficult to formulate a regression with only three cities because of lack of observations (only
24). Instead, a regression excluded these three cities is built with 210 observations. The result of regression
is showed in following Table 13, the original result of Model 4 is also showed for comparison.
Table 13: Results of in/excluding three hotspot cities
Source: own elaboration
Note: * Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level
ModelVariable Model 4With all observations Model 4 Without hotspotsFDI .067037***(.000) .069611***(.000)Hotel -.001498(0.996) -.037387(.888)ME .026640**(.027) OmittedPP .008021***(.000) -.006740***(.000)SD -.000125***(.005) -.000060(.171)MP -.001089***(.001) -.000560(.441)
It shows that a disappearance of significant impacts from SD and MP in other Chinese cities, and variable ME
is omitted because of multicolliearity – mega events were only held in Beijing and Shanghai. It might be
explained by less public attention and less tourism prosperity of those cities, thus international tourists
would not be disturbed by air quality. While FDI and PP are still significant, they play dominant effect on
international tourism demand in the non-hotspot cities. Another reason caused this insignificance of SD and
MP might be that other factors among those cities are not caught into this analysis, but since this research is
not in depth of statistics, further researches are needed to detect those variables.
East vs. West
Unequal distribution of wealth was an important reason resulting difference of international tourism
between eastern and western China. The border of east and west is drawn according to Figure 2, the cities in
white area are included in the regression of eastern China, and the cities in grey area are included in the
regression of western China. As a result, in eastern regions, there are 156 observations used for regression,
while western are 76. Comparable results of regression models are presented in Table 14.
Table 14: Results of eastern and western China
Source: own elaboration
Note: * Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level
73
ModelVariableModel 4With all observations
Model 4With eastern ChinaModel 4With western China
FDI .067037***(.000) .051921***(.010) .095114***(.000)Hotel -.001498(0.996) -.6535992*(.093) -1.092886**(0.004)ME .026640**(.027) .0198166(.134) OmittedPP .008021***(.000) -.0048531**(.012) .006419(.117)SD -.000125***(.005) -.0002766***(.000) -.000052(.231)MP -.001089***(.001) -.0009122**(.012) -.001571(.175)
There are several differences have to be pointed out from this table of result. Regarding significance
perspective, eastern China has almost the same result gained from the estimation with the complete dataset.
ME lost its significance, and hotel gained a little significance. The reasons behind it might be that in this
smaller size of the dataset, the differences between cities in the east are also smaller. The estimation with
only data from western cities has very different result from the other two, and most importantly, SD and MP
are not significant any more. But this result is short of reliability, and the critical reason behind is lack of
observations. This reason also can be applied to Hotel, which has a quite surprising significance in the
estimation of west. Additionally, ME is omitted again since there is no mega events held in western China.
From the perspective of magnitude, first, FDI has less magnitude in the east than west. It might because of
rich tourist products from the east, attracting tourist visiting for various reasons other than FDI. The
magnitude of Hotel changed a lot, which might result from discredited reliability.
5.2 Goodness-of-fit
Hausman test
This test is conducted to detect systematic difference of the coefficients between fixed effect and random
effect in order to select an appropriate method to analyze this panel database.
The result of Hausman test (See Appendix 1) shows a p-value 0.000, suggests that differences between fixed
effect and random effect model are systematic. Thus, the fixed effect model is adopted – all the regressions
showed in the above section are estimated based on this method.
Heteroskedasticity of residuals
Another assumption of OLS regression is that the residuals are homoskedastic. Breush-Pagan test is a typical
one to detect it. However BP test is not applicable for panel dataset, as this dissertation is not statistics
dominant, the heteroskedasticity of residuals has to be left for further research.
Normality of residuals
74
Assumption of normality of residuals of Model 4 (fully specified model) has been also investigated. In Figure
8, Kernel density estimate graph depicts that the middle of residuals are almost following normal
distribution, but some residuals in the tails are not, which might slightly violent the assumption of normality.
It is in line with P- and Q-plot, which are Figure 9 and 10, showing that there are some outliers in the tails.
Figure 11 scattered the residuals, and the scatters in the upper right corner show clearly some extreme
values existing in the current dataset.
Figure 8: Kernel density estimate of residuals of Model 4
Source: own elaboration
Figure 9: P-Plot of residuals in Model 4
Source: own elaboration
Figure 10: Q-Plot of residuals in Model 4
75
Source: own elaboration
Figure 11: Scatter plot of residuals in Model 4
Source: own elaboration
There are indeed some extreme values caused this violence of normality in the tails. For example, tourist
arrivals in Lhasa 2008 plunged considerably (from 223.7 to 46). It resulted from the terrorism attack
happened in March, and then the visiting of tourist was almost banned in the next months. Similar case
happened in Urumqi 2010. Figure 12 and 13 depict the abnormal drop of tourist arrivals in these two places.
Figure 12: tourist arrivals (TA) of Lhasa
76
.1.1
5.2
.25
.3.3
5ar
rival
2007 2008 2009 2010 2011Year
Source: own elaboration
Figure 13: tourist arrivals (TA) of Urumqi
.06
.08
.1.1
2.1
4ar
rival
2004 2006 2008 2010 2012Year
Source: own elaboration
Outliers sometimes can largely change the estimation results, in this case, Lhasa and Urumqi are clearly the
outliers suggested to be excluded in order to acquire a more reasonable regression. After dropping Lhasa, the
dot in down left position has disappeared from the new Q-plot, showing in Figure 14. It implies eliminating
extreme values will gradually correct abnormality of the residuals.
Figure 14: Q-plot without Lhasa
77
-.06
-.04
-.02
0.0
2.0
4e[
Cod
e,t]
-.04 -.02 0 .02 .04Inverse Normal
Source: own elaboration
Furthermore, Model 4 is estimated again, the results do not differ much from the results we gained above
(See Appendix 2). It suggests that the outliers are not problematic for the results of estimations already
gained. Therefore, from the above analysis, it can be fairly concluded that the estimation results are reliable.
These results presented above eventually are going to facilitate the tests of hypotheses proposed in Section
3.6. In the next chapter, the conclusions of the tests will be discussed in order to find the way to final
conclusion of quantitative analysis of this dissertation.
78
Chapter 6 Discussion
This chapter is going to find conclusion of hypotheses made in Section 3.5 and attempt to further explain
them. First it would be convenient to quickly look back to the four hypotheses, which are:
H1: Lower air quality will lead to a decline on inbound tourism of Chinese cities.
H1.1: Lower air quality will result in a decline of inbound tourism of Chinese cities one year later.
H2: More media publicity of air pollution will lead to a decline on inbound tourism of Chinese
cities.
H2.1: More media publicity of air pollution in the previous year would lead to a decline of tourism
in the current year.
As a consequence, the following sections will answer these hypotheses separately.
6.1 Effects of air pollution
From the significant tests throughout the models contain variable of sulfur dioxide, it can be conclude that
H1 is not rejected. In other words, it is confirmed that lower air quality had lead to a decline on inbound
tourism of Chinese cities from 2005 to 2012. Regarding to H1.1, it is not rejected either, since the lagged
effect model (Model 6) evidenced its significant effect on tourist arrivals.
To look into the details, Model 2, 4 and 5 included the annual emission of sulfur dioxide in the present year.
The magnitude of its impact keeps stable, which are -.000118, -.000125, and -.000106. Although they seem
to be small numbers, the real decrease of the tourist arrivals depends on the population size, so the impact
can be enlarged, especially in those cities with dense population. Using the coefficient in the fully specified
79
model (Model 4), for instance, the population in Beijing in 2005 was 15380 thousand, so one thousand tons
more sulfur dioxide emissions resulted 1923 less inbound tourists in Beijing.
The coefficients of SD are both significant in 1% level in Model 2 and 4, demonstrated a strong support of its
effect. In Model 5 it is only in 10% significant level. It was added an interactive variable with media publicity,
which might takes the significance from SD.
Regarding the time-lagged effect of the emission of sulfur dioxide, Model 6 has present a strong statistically
support of the variable of SD in the previous year, which is significant in 1% level. It suggests H1.1 is not
rejected, which the air pollution in the previous year will lead to a decline of inbound tourism in Chinese
cities in the current year.
However, a limitation has already identified that SD and SD_1 are highly collinear. Therefore, neither of the
current effect nor time-lagged effect should be over interpreted, because it is unknown for which data caused
this estimation result. But still, taking Beijing 2006 for example, the population was 16010, indicated that
one thousand more emission of sulfur dioxide in 2005 lead to a decrease of tourist arrivals in 2006 by 2498
people. The magnitude of time lag impact is slightly larger, though the result is not confident enough, it still
can be reasonable. Travelling to China usually takes a long time to prepare, due to visa application,
unfamiliarity of Chinese language, and probably also financial difficulties, so it is quite possible that tourist
need to decide whether going to China or not one year earlier than the departure date, and a high level of air
pollution might let them quit this idea during this time period.
6.2 Effects of media publicity
Likewise, another important variable, media publicity, has also been proved to be significant by the models
containing it, indicating that H2 is not rejected – More media publicity of air pollution has lead a decline of
inbound tourism in Chinese cities from 2005 to 2012. They showed a strong support of this coefficient by
being significant in 1% level, even in Model 5, while the interactive variable has not taken significance from
it.
80
Regarding to H2.1, the result of Model 6 shows that there is a significant impact of media publicity in the
previous year, but this support is relatively weak (only significant when in 10% level).
Indeed, Model 3,4 and 5 contains media publicity. The magnitude is also stable as sulfur dioxide, which
fluctuate around -.0010. The actual number of tourist decline depends on the population size as well. Taking
Beijing 2005 for example, the coefficient in Model 4 indicated that one thousand more webpages mentioning
Beijing “air pollution” 2005 leads to 16759 less inbound tourists coming in the year of 2005. Regarding to
the time-lagged estimation, the magnitude has turned smaller, which is -.000609. Again for instance, on
thousand more webpages mentioning Beijing “air pollution” 2005 decreases inbound tourism of Beijing in
2006 by 9750 people. It suggests that media publicity in the previous year has less impact on tourists.
However this result might be suspect, because it contradicts with sulfur dioxide in the previous year, which
has larger impact than that in the current year. Media publicity is a access of knowing the situation of air
pollution (sulfur dioxide), and since mentioned above, travelling to China usually need about one year to
prepare, so the information in the previous year were supposed to be more important. This smaller
magnitude of coefficient can be explained by that tourists might change their minds just before the departure
when they heard air pollution in China is serious now, but since this dissertation does not study travelling
behavior of preparation for tourists going to China, it leaves space for further researches for digging deeper
from this point. Another reason leading to this result can be the limitations of variable MP itself which were
discussed in Section 4.2.3, claiming that it is difficult to judge how those limitations will distort the results.
However based on the result of Model 6, it might be able to resolve this doubt by stating that they have made
the influence of MP underestimated. Finally the high correlation between the variables is also a reason that
created this inconsistent change of magnitude, which is also calling for further researches of detecting and
eliminating correlation.
In summary, the main conclusion from all the quantitative analysis is that both sulfur dioxide emissions and
media publicity have significant impact on international tourist arrivals of Chinese cities. This will lead the
way to the final conclusion of answering the main research question in the next chapter.
81
82
Chapter 7 Conclusion and recommendations
7.1 Conclusion
This research is set out to answer the following question: How has air quality influenced
international tourists for the Chinese cities? It aims to investigate the impact of poor air quality,
which has been frequently exposed and progressively criticized by international media in recent years, on
international tourism demand in Chinese cities, by using literature reviews and applying quantitative
approach in the prefecture city level.
International tourism has been growing throughout the years after in 1978. Accordingly, tourism as an
important component of modern industry has also experienced a fast expansion over these 30 years. In fact,
all the industries in China have been developed comprehensively thanks to the implementation of “Open
Door” policy, which contributed to promote an attractive image of destination for tourists together with
other tourist products, e.g. heritage reservoirs. Currently, China receives more than 200 million international
tourists every year, which has created great economic benefits of foreign exchange receipts.
However, the impressively fast industrialization has made Chinese cities become nearly “invisible” because of
the heavy smog of air pollutants, and it also caught a lot of media attention. Since there are few scholars
conducted about air pollution of urban tourism, similar cases which epidemic disease and street crime are
investigated because of common characters share with air pollution as negative incidences. Based on the
experiences learnt from the case studies, both air pollution and media publicity of air pollution are suspected
to lead a decline of tourism demand, because favorable natural environment is always a crucial desire of
tourists, and destination image can be easily tarnished by such a negative factor by media exposure.
The first two hypotheses are dedicated to test the impact of air pollution. As stated in Chapter 5, they are not
rejected because the coefficients are significant in reasonable levels. Likewise, the other two hypotheses of
media publicity are also not rejected. Thus, this research has clearly evidenced their negative impacts on
83
international tourism demand of Chinese cities, which is in line with the expectations based on the
experiences gained from the other cases in the past.
As a consequence, the answer to the main research question is easily provided. It can be concluded that poor
air quality has negatively influenced demand of international tourists in Chinese cities. In other words,
international tourism in Chinese cities has been jeopardized under the impact of air pollution.
7.2 Recommendations
With regard to policy recommendations, the negative impacts manifested by statistical evidence are calling
for the attention from policy makers of mitigation air pollution, which is contradictory to the goals of
sustainable tourism development set up by the State Council. The attempts of decreasing emission of air
pollutant by restricting transport volume and moving heavy pollutant-generating industry to another place
before mega events were successful, but it lacks sustainable effectiveness and long-term implications. Some
solutions had been identified in Section 2.5.2, that cool surfaces, urban trees and green roofs are effective for
eliminating air pollution, which could be taken into account in urban planning in future.
Furthermore, this research focuses on urban areas in China only, which may limit its application to the other
areas e.g. rural areas. Importantly, however, this does not imply that generalization and application of this
research is problematic. Cities are appropriate representatives because they serve the majority of
international tourists, thus the conclusions obtained in urban tourism will give an indication of other niche
tourism markets. Other countries could also use the conclusion for reference because it is widely
acknowledged that negative factors, such as air pollution, will jeopardize tourism demand of a destination.
Although the impact of air quality on international tourism has been concluded to be significant, there still
leaves room for further researches, which could mitigate the high correlation between data from the
previous and current year of sulfur dioxide and media publicity, and improve the confidence of the results, by
applying more exquisite econometrical methods.
84
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Appendix
1. Result of Hausman test
(V_b-V_B is not positive definite) Prob>chi2 = 0.0000 = 47.03 chi2(10) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xtreg b = consistent under Ho and Ha; obtained from xtreg year7 -.0004034 -.0029364 .002533 .0042971 year6 -.003516 -.0063017 .0027857 .0034006 year5 -.0055142 -.0057003 .0001861 . year4 -.0030478 -.0014625 -.0015853 . year3 .0074578 .0099977 -.00254 . year2 .0030142 .0096984 -.0066842 .PropertyPr~e .0063091 .0094414 -.0031322 . MegaEvent .0231422 .0177777 .0053646 . hhotel .0423686 1.023733 -.981364 .1350881 fdi .0771161 .0810847 -.0039686 . fe re Difference S.E. (b) (B) (b-B) sqrt(diag(V_b-V_B)) Coefficients
96
2. Regression result excluding Lhasa
end of do-file.
F test that all u_i=0: F(29, 188) = 194.15 Prob > F = 0.0000 rho .98315831 (fraction of variance due to u_i) sigma_e .01494925 sigma_u .11421899 _cons .0740862 .0137371 5.39 0.000 .0469876 .1011848 year8 0 (omitted) year7 -.0020963 .0039766 -0.53 0.599 -.0099408 .0057482 year6 -.0056848 .0042064 -1.35 0.178 -.0139827 .0026131 year5 -.0071748 .0048355 -1.48 0.140 -.0167136 .0023639 year4 .0002848 .005823 0.05 0.961 -.011202 .0117717 year3 .0084212 .0058489 1.44 0.152 -.0031168 .0199592 year2 .0031924 .0062692 0.51 0.611 -.0091747 .0155594 year1 .0003183 .0067976 0.05 0.963 -.0130912 .0137277PropertyPrice .0080208 .0014238 5.63 0.000 .0052122 .0108294 MegaEvent .0266395 .0119354 2.23 0.027 .0030949 .0501841 hhotel -.0014983 .2977474 -0.01 0.996 -.5888534 .5858569 fdi .0670367 .015182 4.42 0.000 .0370877 .0969856 Publicity -.0010889 .0003169 -3.44 0.001 -.001714 -.0004638 Sul_Dio -.0001254 .0000445 -2.82 0.005 -.0002131 -.0000377 arrival Coef. Std. Err. t P>|t| [95% Conf. Interval]
corr(u_i, Xb) = 0.1877 Prob > F = 0.0000 F(13,188) = 21.23
overall = 0.1712 max = 8 between = 0.1772 avg = 7.7R-sq: within = 0.5948 Obs per group: min = 5
Group variable: Code Number of groups = 30Fixed-effects (within) regression Number of obs = 231
97