Climate change vulnerability assessment for ninh thuan’s tourism sector

MINISTRY OF EDUCATION AND TRAINING NHA TRANG UNIVERSITY TRAN THI MY PHUONG CLIMATE CHANGE VULNERABILITY ASSESSMENT FOR NINH THUAN’S TOURISM SECTOR MASTER THESIS KHANH HOA - 2019...

MINISTRY OF EDUCATION AND TRAINING

NHA TRANG UNIVERSITY

TRAN THI MY PHUONG

CLIMATE CHANGE VULNERABILITY ASSESSMENT FOR NINH

THUAN’S TOURISM SECTOR

MASTER THESIS

KHANH HOA - 2019

MINISTRY OF EDUCATION AND TRAINING

NHA TRANG UNIVERSITY

TRAN THI MY PHUONG

Climate Change Vulnerability Assessment for Ninh Thuan’s Tourism Sector

MASTER THESIS

Climate Change

Decision on establishing the

Committee:

Supervisors:

Prof MARGRETHE AANESEN

Dr QUACH THI KHANH NGOC

Chairman:

Faculty of Graduate Studies:

KHANH HOA - 2019

UNDERTAKING

I undertake that the thesis entitled: “CLIMATE CHANGE VULNERABILITY ASSESSMENT FOR NINH THUAN’S TOURISM SECTOR” is my work The work has not been presented elsewhere for assessment until the time this thesis is submitted

Nha Trang, Date 26 month 05 year 2019

Author

Tran Thi My Phuong

ACKNOWLEDGMENTS

During the time of writing, I received support and help from many people

I would first like to thank my supervisors, Prof Margrethe Aanesen and Dr Quach Thi Khanh Ngoc for their constant support which was very generous with their time and knowledge and assisted me in each step to complete the thesis They steered me in the right direction whenever they thought I needed it It would never have been possible for

me to take this work to completion without their support and encouragement

I am grateful to the sponsored and scholarship by NORAD and Nha Trang University (NTU) I am also thankful to Prof Dr Nguyen Thi Kim Anh, who was always

so helpful and provided me with their assistance throughout my years of study

I want to thank the experts of South Central Regional Hydro-meteorology center who were involved in the focus group interview and help me to the choice of criteria for this thesis Without their enthusiastic participation and input, the focus group interview and the choice of criteria could not have been successfully conducted

I would also thank my colleagues in the South Central Regional Hydro-meteorology center They were supporting me wholeheartedly I’m grateful for all of their help

Finally, I must express my very profound gratitude to my parents, my brother, and

my sisters for providing me with unfailing support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis This accomplishment would not have been possible without them

TABLE OF CONTENTS

UNDERTAKING i

ACKNOWLEDGMENTS ii

TABLE OF CONTENTS iii

LIST OF SYMBOLS v

LIST OF ABBREVIATIONS vii

LIST OF TABLES viii

LIST OF FIGURES ix

LIST OF GRAPHS x

ABSTRACT 1

CHAPTER 1: INTRODUCTION 2

1.1 Background 2

1.2 Research objectives 3

1.3 Research significance 4

1.4 Scope of research 4

1.5 Research outline 5

CHAPTER 2: LITERATURE REVIEW 6

2.1 Key concepts 6

2.1.1 Climate change 6

2.1.2 Tourism 6

2.1.3 Vulnerability 7

2.2 The theoretical basis for assessing vulnerability to climate change on tourism 7

2.2.1 Approaches to vulnerability assessment to climate change 7

2.2.2 Analysis framework 8

2.3 Review of related literature and studies 9

2.4 Ninh Thuan Climatic and Socio-economic conditions 10

2.4.1 Ninh Thuan Socio-economic conditions 10

2.4.2 Ninh Thuan climatic 11

CHAPTER 3: METHODOLOGY 15

3.1 The index method to assess climate change vulnerability 15

3.2 The analytic Hierarchy process (AHP) and calculating the index of the components by AHP 18

3.2.1 The AHP structure 19

3.2.2 Flow chart of the AHP 21

3.2.3 Judgment Scales and Consistency Measure in AHP 22

CHAPTER 4: RESULTS AND DISCUSSION 24

4.1 Results 24

4.1.1 Weight value of the Exposure Index 24

4.1.2 Weight value of the Sensitivity Index 31

4.1.3 Weight value of the Adaptive Capacity Index 36

4.1.4 Calculating the Vulnerability Index 42

4.2 Discussion 43

4.2.1 The level of the Exposure Index 43

4.2.2 The level of the Sensitivity Index 44

4.2.3 The level of the Adaptive Capacity Index 45

4.2.4 The level of the Vulnerability Index 46

CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS 48

LIST OF REFERENCES 51

APPENDIX 54

Min Xij Smallest real value of ij indicator in all localities Max Xij Largest real value of ij indicator in all localities; λmax Largest eigen-value

n Number of criteria

RI Random Index (depends on the number of criteria) CE1 Criteria 1 of the Exposure

CE2 Criteria 2 of the Exposure

CE3 Criteria 3 of the Exposure

CS1 Criteria 1 of the Sensitivity

CS2 Criteria 2 of the Sensitivity

CS3 Criteria 3 of the Sensitivity

CA1 Criteria 1 of the Adaptive Capacity CA2 Criteria 2 of the Adaptive Capacity CA3 Criteria 3 of the Adaptive Capacity

LIST OF ABBREVIATIONS

AHP Analytical Hierarchy Process

CC Climate Change

IPCC Intergovernmental Panel on Climate Change

OECD Organization for Economic Co-operation and Development

UNWTO World Tourism Organization

SWOT Strength, Weakness, Opportunity, and Threat Analysis

FCEM The Fuzzy Comprehensive Evaluation Method

TOPSIS The Technique for Order of Preference by Similarity to Ideal solution GIS Geographic Information Systems

RCP4.5 Representative Concentration Pathway 4.5

LIST OF TABLES

Table 3-1 The suite of indicator (Source: Lan Huong, 2015) 15

Table 3-2 The scale for judgments in the AHP method (Source: Saaty, 1994) 23

Table 4-1 The Weight value of The Exposure Indicators 28

Table 4-2 The Exposure Data 29

Table 4-3 The Exposure data was normalized 30

Table 4-4 The Exposure Index 30

Table 4-5 The Weight value of The Sensitivity Indices 34

Table 4-6 The data of Sensitivity 35

Table 4-7 The Sensitivity Data was normalized 36

Table 4-8 The Sensitivity Index 36

Table 4-9 The Weight value Of the Adaptive Capacity 39

Table 4-10 The data of Adaptive Capacity 41

Table 4-11 The Adaptive Capacity Data was normalized 42

Table 4-12 The Adaptive Capacity Index 42

Table 4-13 The Climate change vulnerability index and components index 42

LIST OF FIGURES

Figure 2-1 Top-down and bottom-up approaches 8

Figure 2-2 Basic Frame Work for Integrated Topdown Bottom up Approach 9

Figure 2-3 Rainfall Variability in Ninh Thuan (1978-2017) 12

Figure 2-4 The number of rain days with daily rainfall from 50mm or more 12

Figure 2-5 Distribution of mean annual rainfall (2000 compared with 2017) 13

Figure 2-6 Distribution of annual temperature from 1978-2017 13

Figure 3-1 The simple AHP structure 19

Figure 3-2 The AHP hierarchy structure we have used for calculating the Weight value of the Exposure Index 20

Figure 3-3 The AHP hierarchy structure we have used for calculating the Weight value of the Sensitivity Index 20

Figure 3-4 The AHP hierarchy structure we have used for calculating the Weight value of the Adaptive Capacity Index 21

Figure 3-5 Flow chart of the Analytic Hierarchy Process 21

Figure 3-6 The scale for judgments in the AHP method 22

LIST OF GRAPHS

Graph 4-1 The Value of Exposure Index 43Graph 4-2 The degree of Exposure to climate change on Ninh Thuan's tourism (Drawn by MapInfo Tool) 44Graph 4-3 The value of Sensitivity Index 44Graph 4-4 The degree of Sensitivity to climate change on Ninh Thuan's tourism (Drawn

by MapInfo Tool) 45Graph 4-5 The value of Adaptive Capacity Index 45Graph 4-6 The degree of Adaptive Capacity to climate change on Ninh Thuan's tourism (Drawn by MapInfo Tool) 46Graph 4-7 The value of the Vulnerability Index 46Graph 4-8 The degree of Climate change vulnerability on Ninh Thuan's tourism (Drawn

by MapInfo Tool) 47

ABSTRACT

Climate change vulnerability on tourism is emerging research for sustainable development of the tourism sector This thesis uses the AHP model and vulnerability index method for assessing vulnerability to climate change on tourism of Ninh Thuan province The process of vulnerability assessment includes: first - identify the indicators, the indicators, and the criterion; second - calculate the Weight value of indicators by AHP model, third - calculate the vulnerability index, finally - assessing the climate change vulnerability on tourism through the Climate Change Vulnerability Index (CCVI) Research results show that climate change causes vulnerability to the tourism sector of Ninh Thuan province, the most vulnerable area is the coastal region and lower in the mountainous region Therefore, the needs for an appropriate specific climate change policy when making strategic plans for developing Ninh Thuan's

tourism

Key words: Climate change, Vulnerability, Tourism, The analytic hierarchy

process

CHAPTER 1: INTRODUCTION

1.1 Background

Ninh Thuan is a province in the coastal Southern Central region of Vietnam It is

an important area of the economic, and a crucial transportation node with the South Railway, the 1A, and 27 Highway

North-In recent years, Ninh Thuan tourism sector has vigorously developed and become

an important economic branch of the area Ninh Thuan province has abundant natural and culturally diverse It is also a famous place in the region with many attractive tourism destinations with untouched beauty and unique culture Ninh Thuan is famous for beautiful landscapes, majestic Cham towers, and unique Cham culture According

to the Ninh Thuan Department of Culture, Sports and Tourism, the province has 149 cultural heritage, including two special national relic sites, and 12 national relic sites Besides the unique architectural works, traditional villages are also attractions in Ninh Thuan such as Bau Truc pottery village, My Nghiep village with the traditional brocade weaving Situated in Viet Nam Central coast, Ninh Thuan province has much attraction the beauty of nature, forests, sea or rivers, and streams following the sea with the white sandy beaches, blue water Therefore, Ninh Thuan is an ideal attraction

of tourists who love to explore the sea and vast nature

Since 2013, Ninh Thuan province has been identified as one of the especially target areas in the Strategy of Viet Nam's tourism development up to 2020 In 2017, over 1.9 million people arrived in Ninh Thuan, increasing 12 percent over 2016; total tourism sector revenue in 2017 reached VND 883 billion VNĐ, up by 17.7 percent compared to 2016 In the first eight months of 2018, Ninh Thuan welcomed over 2.13 million visitors total revenue reached 1000 billion VNĐ (Ninh Thuan Department of culture, sports, and tourism 2018) Moreover, it is also expected to welcome 2.35 million visitors in 2019 and earn 1.150 billion VND (49.95 million USD) from tourism

However, the main tourism resource identified is climatic conditions It is a key driver, and an important destination attribute makes tourism activities possible and enjoyable (Hu & Ritchie, 1992) Climatic conditions influence destination choice an

important component in tourists’ satisfaction and activity participation, as well as safety

According to WB (World Bank) director in Vietnam 2016, Ninh Thuan province, which is one of the poorest provinces in Viet Nam's and is most affected by climate change The extreme weather phenomena such as heat waves, storm, flood, drought, heavy rain increasing in the frequency and intensity and will occur more in the future According to the climate change scenario in Ninh Thuan, the annual average temperature can increase to 3.4o C, the average yearly rainfall may increase by 10%, sea level may rise between 0.74 - 1.05 m, implying that about 500 ha of the coastal area of Ninh Thuan flooding at the end of the 21st century

During 2015-2016, Ninh Thuan experienced the worst and prolonged drought, when the local government in the first time declared a state of emergency of drought and declared a natural disaster due to drought By the end of 2017, many sections of the dike in Ninh Thuan were seriously damaged by Typhoon Tembin In 2018, Toraji storm lead over 20 people were injured and around 150 homes have been damaged Also 2018, Ninh Thuan must cancel grape festival due to drought and shortage of water; the festival was first organized in 2016 to promote the province’s landscapes and culture, as well as its local specialties, at bringing around 120000 tourists per year (Ninh Thuan Department of Culture, Sports and Tourism 2018) Total Economic damage annual by the disaster in Ninh Thuan is Hundreds of billions VND (Ninh Thuan Steering Committee on Disaster Prevention) All this cause major losses to the local tourism sector and negatively related to economic growth

Consequently, Ninh Thuan' tourism sector was selected as the object of study because it faces high exposure-sensitivity to climate change And the vulnerability assessment the impact of climate change is important for sustainable tourism development in Ninh Thuan province

1.2 Research objectives

What are the physical Input to exposure Identifying character, magnitude,

impacts of climate change

on tourism sector?

indicators and rate of extreme climate events

What are the components

of tourism sector losses

associated with climate

change?

Input to sensitivity indicators

Identifying important components

of tourism sector was impacted by climate change

What actions can be the

adaptation and mitigation

options the impacts

climate change in the

tourism sector?

Input to the adaptive capacity indicators

Determining the potential of social-economics can be cope with climate change

What is the value of

Research?

The output of research

Finding solutions to minimize vulnerability and enhance the ability to respond to climate change to ensure the sustainable development of the tourism industry in Ninh Thuan province 1.3 Research significance

The potential benefit that can be gained to Ninh Thuan's tourism after the completion of this study is to identify areas as hotspots vulnerability due to climate change Research results will serve the state managers to have a basis in action and development appropriate policies for the tourism sector to adapt to climate change 1.4 Scope of research

Data were collected from January to April 2019 These were collected from Official publications and focused group interviews The Official publications such as Ninh Thuan statistics yearbook; The reports of Steering Committee on Natural Disaster Prevention and Control and Search and Rescue Ninh Thuan, The report of Ninh Thuan Department of culture, sports, and tourism; Ninh Thuan Department of Natural resources and Environment, the Souther Central Regional Hydro-meteorology Center, and by Ninh Thuan Hydro-meteorology Center Articles in the newspaper, from journals publications about Tourism and climatic of Ninh Thuan in 2017., etc

The structured of the questionnaire was prepared in Vietnamese, and data collection was done through a meeting of climate change experts (focused group interviews) 1.5 Research outline

Abstract

Chapter 1: Introduction

Chapter 2: Literature review

Chapter 3: Methodology

Chapter 4: Results and Discussions

Chapter 5: Conclusions and Recommendations

References

Appendix

CHAPTER 2: LITERATURE REVIEW

2.1 Key concepts

2.1.1 Climate change

Any change of climate over decades or longer compared to the present averages

is called climate change Climate change can be due to natural processes (internal or external components) or because of human activities (IPCC, 2007)

According to IPCC, ‘warming of the climate system is unequivocal.’ The mean temperature has increased by 0.76 °C between 1850 to nowadays and will continue to change globally At the end of the 21st century, IPCC predicts that average surface temperatures are estimated to rise by 1.8 °C to 4.0 ° C Extreme climate events such as heat waves, heavy precipitation, flood, and drought will be increasing, the typhoon will likely become more intense, with more massive peak wind speeds and more rain heavy rainfall associated with the increase of sea surface temperatures Ice melting is already observed in two poles are projected to continue Most of the coastal tourist cities are affected by these climate changes (UNWTO 2007b) Because visitor decision-making for the destination depends on the weather and climate conditions at the destination so that CC may influence tourism directly through the decision-making process and this is leading to resulting in the loss tourism revenue of attractions (Agnew and Viner, 2001)

2.1.2 Tourism

'Tourism is a social, cultural and economic phenomenon related to the movement

of people to places outside their usual place of residence, pleasure being the usual motivation.' (UNWTO, 2008) The tourism sector is a service industry in which consumers are tourists

A tourist (overnight visitor) is a person who visits a person or place and spends at least a night in the country visited

Accommodation is a paid service which is a specific place for a tourist to stay overnight The price of accommodation services is usually a market price, but can also

be non-commercial

Tourism infrastructure is structures or public utility to serve people and tourists

Tourism can be of the three forms: (a) Domestic tourism; (b) Inbound tourism; (c) Outbound tourism (UNWTO, 2008) The three forms above can be combined in various ways to derive other forms of tourism Using purpose of travel, so tourism in Viet Nam could be classified as 13 types follows Adventure tourism; Agri-tourism; Archeological tourism; Business tourism; Cultural heritage tourism; Craft village tourism; Eco-tourism; Educational tourism; Festival tourism; Leisure Travel; Medical tourism; Travel tourism; Sports tourism

2.1.3 Vulnerability

'Vulnerability to climate change is the degree to which systems are susceptible to and unable to cope with, adverse impacts.' The vulnerability is a function including changes in exposure, sensitivity, and adaptive capacity of the system (IPCC, 2007) Exposure is the rate and magnitude of climate change; Sensitivity is the degree to which a system is affected (directly or indirectly) by the climate change-related stimuli; Adaptive Capacity is the ability of a system adapting to climate change, in order to minimize the negative impacts of climate change (IPCC, 2007)

2.2 The theoretical basis for assessing vulnerability to climate change on tourism

2.2.1 Approaches to vulnerability assessment to climate change

Vulnerability Assessment is a process of testing the degree of threats and risks to the climate, natural, and human systems These can be socio-economic (e.g., a group

of people, human health, livelihoods) or biophysical (e.g., ecosystems, species, habitats, water) exposure units or combinations of the two (e.g., localities, sectors), or regional geography (global, regional, national, district, state, community, household,

or individual level)

Two approaches are used to assess vulnerability, including a top-down approach and a bottom-up approach (Dessai and Hulme 2004) The top-down approach focused

on global climate models and their downscaling of projections that are used as inputs

to project regional climate impacts and to evaluate the physical vulnerability The bottom-up approach focuses on understanding the past and present vulnerability that is

used to estimate future vulnerability and adaptation options to reduce future vulnerability and to evaluate the social vulnerability (Dessai & Hulme, 2004)

Figure 2-1 Top-down and bottom-up approaches

(Source: Dessai and Hulme 2004)

A top-down approach assessment needing to find out the probable biophysical and socioeconomic impacts for a given scenario and a bottom-up approach assessment should help in the participation of all the stakeholders in the assessment process and tries to get their opinion

2.2.2 Analysis framework

Today, many different types of frameworks can use for vulnerability assessment The choice the types of frameworks depends upon for what purposes the assessment is being conducted If elements of both top-down and bottom-up approach are combined

to complement each other to vulnerability analysis and measurement, yield results that are relevant for decisionmakers and information that is clear, understandable and useful for all actors And it calls an integrated approach And analysis framework in this thesis will use this integrated approach

The framework of integrated approaches (top-down and bottom-up approach) shown in Figure 2-2:

Figure 2-2 Analysis Framework (Source: United Nations Framework Convention on Climate Change)

2.3 Review of related literature and studies

The studies discussed the connection between climate change and tourism in earlier the 1960s (Scott et al 2012) The climate revolution from 1960 -1970 was a result of the long-term government of nationals investment into metrology and climate research The improved technology of metrological and climatology enabled man to understand the relationship between climate and social-economic conditions and enabled humans to use climate information in planning (Scott et al 2012)

In recent years, the analytic hierarchy process has been widely used to estimate the weight value of the climate change vulnerability index in many economic sectors

To compare with other multi-criteria methods, the advantages of the AHP method is flexible, intuitive appeal to the decision-makers, and it can check possible inconsistencies in judgments of the decision-makers (Saaty, 2000)

In the field of tourism, R Fabac, I Zver (2011) combined the SWOT and AHP method to contribute to the formulation of future tourist orientation of The Gornje Međimurje area, Croatia J Lee and H.Lee (2015) used the AHP method to determine the policy priorities for the creative tourism industry in Korea By measuring tourist preferences of smart tourism attractions via the Fuzzy Comprehensive Evaluation Method (FCEM) - Analytic hierarchy process (AHP) and integrated prioritization

approach (IPA); X Wang, X Li, et al (2016) offered insights into the theoretical and practical development of smart tourist attraction in China

In the other field, physical coastal vulnerability assessment in the context of climate change was studied by Yin et al (2012), with the weights of the variables assigned using AHP Murali et al (2013) adopted AHP and derived various criterion weights for calculating a coastal vulnerability index for Puducherry coast in India Cozannet et al (2013) used an AHP derived method for mapping the physical vulnerability of coastal areas at regional scales To estimate a tsunami hazard vulnerability components, Mohammad R Poursaber and Y Ariki (2016) by using Integrating Remote Sensing, GIS and AHP, estimated and classified vulnerability and inundation areas under the Tohoku tsunami event 2011 in the Ishinomaki, Miyagi prefecture, Japan Their research results show that the roles of topographical components in a tsunami disaster are vital In 2018, Md Shaharier Alam and Shamim Mahabubul Haque combined an AHP model and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model to assess the physical seismic vulnerability, and in addition used the Geographic Information Systems (GIS) tool to produce physical a seismic vulnerability map of residential neighborhoods of Mymensingh city in Bangladesh L Nahayo et al (2019) used the AHP model to calculate the weight value and rank the vulnerability conditioning components in assessing of landslides vulnerability for the western province of Rwanda

2.4 Ninh Thuan Climatic and Socio-economic conditions

2.4.1 Ninh Thuan Socio-economic conditions

Ninh Thuan is a province in the Central coastal region of Viet Nam, it is adjacent

to Khanh Hoa, Binh Thuan, and Lam Dong provinces Ninh Thuan has six rural districts (Ninh Son, Ninh Phuoc, Ninh Hai, Bac Ai, Thuan Bac, and Thuan Nam) and one urban district (Phan Rang-Thap Cham) It’s area is 3358.3 square km; in which, Phan Rang –Thap Cham, 78.9; Ninh Son, 770.6; Ninh Phuoc, 341.0; Ninh Hai, 215.3; Bac Ai,1030.9; Thuan Bac, 319.9; and Thuan Nam, 564.5 Ninh Thuan topography is down sloping from North-West to South-East It consits mostly of mountains (63%), the rests are coastal zones (23%), and Midland (14%) (Ninh Thuan Statistical yearbooks 2017)

The average population was 607,000 people, in which the urban population 220,000 people and the rural population of 387,000 people; the natural population growth rate is 11.6% The labor force from the age 15 and over in the province reached 352,000 people; the labor force in urban areas accounted for 35.1%, and rural labor force accounted for 64.9% In the academic year 2017-2018, Ninh Thuan province has

335 educational and training establishments; the number of teachers was 13073 people; the percentage of secondary school graduates aged 15-18 years was 85.3% The number of medical facilities in 2017 was 249; including has eight hospitals, seven regional polyclinics, and 65 commune health stations The rate of communes/wards meeting national health standard is 75.4% (Ninh Thuan Statistical yearbooks 2017) The total value of goods produced and services provided in Ninh Thuan in 2017 growth was 9.5% from 2016 Per Capita Income is 35 million VND per year; economic structure is as follows: Agriculture – Forestry - Aquaculture (38.35%), Industry – Construction (21.26%), and Sevices (40.39%) (Ninh Thuan Statistical yearbooks 2017)

According to Ninh Thuan Department of culture, sports, and tourism over 1.9 million people arrived in Ninh Thuan in 2017, increasing 12% from 2016 The number

of classified markets, supermarket and commercial center is 106; 233 relics (including temples, pagoda, the historical relics, and other relics); 132 accommodation establishments; 2 national parks (Nui Chua and Phuoc Binh); and many other tourism facilities

2.4.2 Ninh Thuan climatic

Ninh Thuan has a typical tropical monsoon climate with low rainfall, high temperatures, strong wind, and it is dry The weather has two distinct seasons with the rainy season from September to November and the dry season from December to September

Figure 2-3 Rainfall Variability in Ninh Thuan (1978-2017) (Source: Souther Central Regional Hydro-meteorological Center)

Figure 2-4 The number of rain days with daily rainfall from 50mm or more (Source: South Central Regional Hydro-meteorological Center)

Figure 2-5 Distribution of mean annual rainfall (2000 compared with 2017)

(Source: South Central Region Hydro-meteorological Center)

Average annual temperature of Ninh Thuan is around 26-27oC Climatological information about changes in temperature over the years 1978-2017 are shown below:

Figure 2-6 Distribution of annual temperature from 1978-2017

According to Ninh Thuan Department of Natural Resources and Environment,

average nine times annually Ninh Thuan is a region with low annual average storm frequency (0.5 per year) The storm season concentrates in November and December, sometimes in January The average number of days that have temperature higher than

35 ° C is 20 In 2017, natural disasters caused the death of three people, 148 houses collapsed, 1449.92 ha of cultivated area was flooded, 872 domestic animals died The total value of losses caused by natural disasters in 2017 was estimated to 181 billion VND (Ninh Thuan Statistical yearbooks 2017)

CHAPTER 3: METHODOLOGY

3.1 The index method to assess climate change vulnerability

The Climate Change Vulnerability Index (CCVI) is used to assess the degree of human vulnerability to the variation in climate and some extreme climate-related events The CCVI includes exposure to climate change, human sensitivity, and capacity for prevention, mitigation, and adaptation to climate change for the community CCVI is made up of three component indices: Exposure Index (E), Sensitivity Index (S), and Adaptive Capacity Index (AC)

The first goal of the index method is assessing climate change vulnerability through to construct a suite of indicators, where each indicator is a measurable variable used to represent an associated (but non-measured or non-measurable) component or quantity In 2015, the Viet Nam Institute of Meteorology, Hydrology, and Climate was constructing a suite of indicators to assess the vulnerability to climate change in these various fields in Viet Nam The suite of indicators was used for the tourism sector that are displayed in table 3-1:

Table 3-1 The suite of indicator (Source: Viet Nam Institute of Meteorology,

Hydrology, and Climate, 2015)

Exposure

Temperature Variations

The variations in the annual

(> = 35 ° C)

Precipitation

The number of heavy rain days

in the year (daily rainfall >=

Number of classified markets, super markets, and commercial centers

Establishment

Number of accommodation

Number of national parks National park

Number of Historical and

Adaptive

capacity

Infrastructure - Social

Employed population labor force

%

The rate of households with %

solid houses The rate of communes with

The budget for responding to

Number of plans, strategies, and actions to respond to climate change

Plan

The second goal is calculating each indicator index and then its component index The steps in calculating the index of the component are as follows: Normalizing indicator values of each component, to take the values between “0” and “1” The purpose of the normalization is to bring all indicators into the same dimensionless unit The real value can normalize with the following formula:

Xij: Normalized value of indicator j in the locality i;

Xij(r): The real value of the indicator ij;

Min Xij: The smallest real value of ij indicator in all localities;

Max Xij: The largest real value of ij indicator in all localities;

The component index (E, S, or AC) can be calculated by the following formula:

ij Xij 1

Xij 1

W

k

j k

W : The Weight value of the j-th indicator in locality i;

Finally, the CCVI can be calculated by the following formula:

( 1 ) 3

AC: Adaptive Capacity Index

CCVI: Climate Vulnerability Index

The CCVI was ranging between 0 and 1 inclusive That is, 0 represents low vulnerable and 1 represents high vulnerable Based on IPCC (2001) and local context, the tourism sector in Ninh Thuan province were categorized into low (0 ≤ CCVI ≤ 0.45), medium (0.45 < CCVI ≤ 0.70), and high (0.70 < CCVI ≤ 1.00) vulnerable by vulnerability index

3.2 The analytic Hierarchy process (AHP) and calculating the index of the components by AHP

The analytic hierarchy process (AHP) was firstly developed by Thomas L Saaty

in the 1980s and has been extensively studied and refined in decision making AHP is

an analysis method based on mathematics and psychology

The approach of AHP is a suitable multi-criteria approach to quantify and rank the possible set of initiatives and activities that a human could employ to mitigate and adapt to climate change risks on available opportunities

The main advantage of AHP method is the relative ease, with which it handles multiple criteria In addition to this, the use of AHP does not involve cumbersome mathematics The AHP involves the principles of decomposition, pair-wise comparisons, and priority vector generation and synthesis (Duran, Aguilo 2008) Saaty uses the eigenvector method to determine the relative weights among the various criteria based on the pair-wise comparison matrix, positive reciprocal matrix A = [aij]

3.2.1 The AHP structure

Figure 3-1 is a simple AHP structure, it consists of an overall goal, a group of criteria for reaching the goal, the criteria that relate the group of alternatives to the goal

Figure 7 The simple AHP structure

(Source: Saaty, 1994)

Figures 3-2 to 3-4 represents the AHP hierarchy structures will be use in this thesis, they will be used to calculating the weight value for three components including exposure, sensitivity and adaptive capacity

Figure 8 The AHP hierarchy structure we have used for calculating the

Weight value of the Exposure Index

(Source: Spires, 1991)

Figure 9 The AHP hierarchy structure we have used for calculating the

Weight value of the Sensitivity Index

(Source: Spires, 1991)

Figure 10 The AHP hierarchy structure we have used for calculating the

Weight value of the Adaptive Capacity Index

(Source: Spires, 1991)

3.2.2 Flow chart of the AHP

Figure 11 Flow chart of the Analytic Hierarchy Process

Figure 3-5 meaning, in the first step of the process, the goal is defined In the second step, criteria, and indicators were set up (the elements of one level must belong

to the same category) That means they are comparable to each other Furthermore, it

is necessary that the evaluations are independent of evaluations in the same or in other levels of the hierarchy Finally, it has to be assured that all relevant criteria and indicators are considered (Götze 2008)

3.2.3 Judgment Scales and Consistency Measure in AHP

Figure 3-6 gives the scales of intensity importance used to compare indicators

Experience and judgment slightly favor one

element over another

importance

Experience and judgment strongly favor

one element over another

Figure 12 The scale for judgments in the AHP method

(Source: Saaty, 1994)

In the AHP procedure, the consistency of the pair-wise comparisons made in has

to be checked for all criteria and indicators Saaty’s approach has provided with the capability to assess the consistency of the assigned relative importance in the pair-wise

comparison matrix (Saaty 1990) This can be done through computing the consistency ratio (CR) from the consistency index (CI), as follows:

In which:

CI: Consistency Index

λmax: the largest eigenvector (Appendix 2.b)

n: The number of components

RI: The values of RI given by Table 3-1

Table 3-2 The scale for judgments in the AHP method (Source: Saaty, 1994)

RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.51

The Consistency Ratio in the pairwise comparison of CR < 0.10 If CR ≥ 0.10,

we need rethinking the evaluation of the pairwise comparisons (Saaty 1990)

CHAPTER 4: RESULTS AND DISCUSSION

4.1 Results

Step to step calculating the weight value of the components:

The Pairwise comparison matrix has been divided from the questionnaire used in the focus group interviews (see ‘Appendix 1’) The principle when taking value from the questionnaire for use in pairwise comparison is ‘The value is above '1'; if 'Priority indicator' is 'A' in the actual judgment in the questionnaire from the focus group interview, if 'Priority indicator' is 'B' the reciprocal value of the actual judgment value

is used The values below '1' are the reciprocal value divided by the formula: (Meixner and Haas 2002)

The pairwise comparisons with normalized as follows: First, the column sums of the evaluation matrix is formed Then the ratios are divided by the column sums The result is the normalized matrix scaled to 1 (Meixner and Haas 2002)

The priority vector of the corresponding elements is achieved when the row sum

of the normalized matrix is divided by the number of elements With this procedure corresponding to the hierarchy level the individual priority vectors can be achieved (Meixner and Haas 2002)

The consistency ratio (CR) is calculated by equation (5) and (6) in chapter 3 If the consistency ratio in the pairwise comparison is lower than 10% (CR<10%), the pairwise comparison matrix is accepted, if CR is greater than 10%, we need to revise the subjective judgment and make a focus group interview again (Alonso and Lamata, 2006)

4.1.1 Weight value of the Exposure Index

E5 0.20 0.33 0.25 0.50 1.00 1.00 0.33 0.33 1.00 E6 0.20 0.33 0.25 0.50 1.00 1.00 0.33 0.33 1.00 E7 0.25 0.50 0.33 1.00 3.00 3.00 1.00 1.00 3.00 E8 0.25 0.50 0.33 1.00 3.00 3.00 1.00 1.00 4.00 E9 0.20 0.33 0.25 0.50 1.00 1.00 0.33 0.25 1.00 Consistency ratio: CR= 3% is acceptable because the

requirement condition is CR<10%

The matrix

was

normalized

aij E1 E2 E3 E4 E5 E6 E7 E8 E9 Average E1 0.34 0.36 0.33 0.39 0.38 0.27 0.24 0.38 0.19 0.32 E2 0.17 0.18 0.33 0.16 0.13 0.18 0.2 0.16 0.12 0.18 E3 0.11 0.06 0.11 0.16 0.19 0.23 0.2 0.11 0.15 0.15 E4 0.07 0.09 0.06 0.08 0.06 0.09 0.1 0.11 0.15 0.09 E5 0.06 0.09 0.04 0.08 0.06 0.09 0.05 0.05 0.12 0.07 E6 0.06 0.05 0.02 0.04 0.03 0.05 0.1 0.05 0.08 0.05 E7 0.07 0.05 0.03 0.04 0.06 0.02 0.05 0.05 0.08 0.05 E8 0.05 0.06 0.06 0.04 0.06 0.05 0.05 0.05 0.08 0.05 E9 0.07 0.06 0.03 0.02 0.02 0.02 0.02 0.03 0.04 0.03

Criteria 3: Tourism Resources (CE3)

E7 0.05 0.03 0.03 0.03 0.02 0.01 0.04 0.08 0.05 0.04 E8 0.05 0.03 0.03 0.02 0.02 0.05 0.02 0.04 0.09 0.04 E9 0.06 0.04 0.03 0.03 0.1 0.05 0.04 0.02 0.05 0.05

The priority vector for indicators

E1 0.29 0.32 0.38 E2 0.13 0.18 0.16 E3 0.20 0.15 0.16 E4 0.07 0.09 0.08 E5 0.04 0.07 0.05 E6 0.04 0.05 0.06 E7 0.09 0.05 0.04 E8 0.09 0.05 0.04 E9 0.04 0.03 0.05

Calculating the Priority vector for criteria follows as:

Pairwise comparison

matrix The matrix was normalized

CE3 0.15 Multiplying the priority vector of the indicator and the priority vector of the criteria, we get the Weight value of the exposure indices as follows:

CE1 CE2 CE3

E2 0.13 0.18 0.16 Criteria Priority level E2=0.13x0.69+0.18x0.16+0.16x0.15=0.14 E3 0.2 0.15 0.16 CE1 0.690 E3=0.2x0.69+0.15x0.16+0.16x0.15=0.19 E4 0.07 0.09 0.08 CE2 0.161 E4=0.07x0.69+0.09x0.16+0.08x0.15=0.07 E5 0.04 0.07 0.05 CE3 0.149 E5=0.04x0.69+0.07x0.16+0.05x0.15=0.05

The variations in the annual mean temperatures

Number of hot days in the year (> = 35 ° C) (E7) 0.08

Number of heavy rain days in the year (> =

The variations in the annual mean rainfall (E9) 0.04