Mapping Climate Change Vulnerability Using CBMS Data:

A Pilot in Vietnam

Vu Tuan Anh *

1. Vulnerability of Climate Change in Vietnam

Climate change, illustrated mainly by global warming and sea level rise, is one of the most serious challenges facing human being in the 21st century. Natural disasters and other extreme climate phenomena are more often observed all around the world, whereas average temperature and global sea level are increasing at unprecedented rate, becoming a major concern of all nations.

Climate change impacts to Vietnam are considered to be serious. Vietnam is among the five countries most affected by climate change and is the second in the world (after the Bahamas) to be most affected by sea level rise.

Climate change heavily influences many aspects of production and business, life and people's livelihoods, natural resources, social structure, infrastructure and the economy. The major features of climate change in Vietnam are typhoons and tropical cyclones, floods, droughts, landslides, sea level rise and extreme temperature changes (too cold or too hot weather).

The Government of Vietnam, with a clear vision on climate change impacts, has promulgated the National Target Programme to Respond to Climate Change (NTP) in December 2008. The strategic objective of the NTP is to assess climate change’s impacts on sectors/areas and regions in specific periods and develop feasible action plan to effective respond to climate change in each short-term and long-term periods to ensure sustainable development of Vietnam, take over opportunities to develop towards a low-carbon economy.

NTP requires industries and localities to integrate climate change issues into their development strategies and plan, and to prepare action plans for adaptation and mitigation of climate change impacts. There are two main objectives of the action plan: (i) To identify climate change features and vulnerability, to assess climate change impacts (including climate variability, sea level rise and climatic extremes) on every sector and locality; (ii) To identify measures to respond to climate change impacts.

Basing on historical data collected by climate observatories and world’s estimated scenarios of green house gas emission, Vietnam Ministry of Natural Resources and Environment (MONRE) has studied scenarios of sea level rise for * Socio-Economic Development Centre, Hanoi, Vietnam. Email: vtanh@yahoo.com

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the whole country and some most affected big regions. Three scenanios have selected and recommended to be used for planning (August 2009). Detail scenarios for small-scalled regions are still not available yet. In May 2011 MONRE with UNDP technical support has published “Guidance to Assessment of Climate Change Impacts and Defining Responses”, which includes steps and methods of CC assessment applied for local governments. Several projects sponsored by the World Bank, UNDP, ADB and other international donors have focus on response to climate change in different provinces in Vietnam. Some international non-governmental organizations operating in Vietnam (CARE International, Oxfam, etc.) are also intensively working together with selected communities for meanstreaming climate change factors into socio-economic development plans. Almost all mentioned projects do not use mapping methods yet due to lack of data of spacial distribution of climate impacts.

Statistical data of climate change (such as number of storms, typhoons, extreme temperature changes, etc.) and human and material losts are collected by some central and local agencies in Vietnam, such as Department of Meteorology, Hydrology and Climate Change, Agency for Emergency and Disaster Management. Maps of rainfall, flood, storms, landslides, etc. have been built and used at national and provincial levels. The Department of Meteorology, Hydrology and Climate Change regularly issues maps and charts of climate monitoring. However, at the local levels (district and commune) mapping of climate change exposure and response capacity is still rarely implemented yet.

2. Methodology

2.1. Methodology of an EEPSEA study:

According to the framework of the United Nations’ Inter-governmental Panel on Climate Change (IPCC), the climate change vulnerability including exposure, sensitivity and adaptive capacity can be mapped using the statistical and survey data.

Vulnerability is defined by IPCC as: “The degree to which a system is susceptible to, or unable to cope with the adverse effects of climate change, including climate variability and extremes. Vulnerability is a function of the character, magnitude, and rate of climate variation to which a system is exposed, its sensitivity, and its adaptive capacity” (IPCC 2001, p.995). Vulnerability can thus be defined as a function of exposure, sensitivity, and adaptive capacity.

Vulnerability = f(exposure, sensitivity, adaptive capacity)

According to IPCC, exposure is defined as “the nature and degree to which a system is exposed to significant climatic variations”; sensitivity is defined as “the degree to which a system is affected, either adversely or beneficially, by climate-related stimuli1”; and adaptive capacity is defined as “the ability of a system to adjust to

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climate change including climate variability and extremes), to moderate the potential damage from it, to take advantage of its opportunities, or to cope with its consequences”.

In their study on “Climate Change Vulnerability Mapping for Southeast Asia”, Yusuf and Fransisco (Arief Anshory Yusuf and Herminia A. Fransisco, 2009) derived the climate change vulnerability index through the following steps:

• Assessing exposure using information from historical records of climate-related hazards. Data of five climate-related risks: frequency of tropical cyclones, floods, landslides, droughts for about 20 years (1980-2000), physical exposure to landslides, and inundation zones of a five-meter sea level rise, were collected for drawing climate hazard maps and calculating exposure index.

• The population density was used as a proxy for human sensitivity to climate-hazard exposure. The assumption here is that regions that are relatively less inhabited will be less vulnerable compared to regions with high population densities, given the same degree of exposure to climate hazards.

• In addition to the human aspect of vulnerability, the ecological sensitivity of the region was calculated using biodiversity information as a proxy variable. A biodiversity-rich region, measured by the percentage of protected areas, is considered here as more vulnerable than other areas to climate hazards, other things being equal.

• An index of adaptive capacity was constructed as a function of socio-economic factors, technology, and infrastructure:

Adaptive capacity = f(socio-economic factors, technology, infrastructure)

The socio-economic factors include three indicators: human development index, poverty incidence and income inequality (Gini coefficient). The technology is reflected by two indicators: electricity coverage (the percentage of household with access to electricity) and irrigation extent (percentage of irrigated agricultural land).

The infrastucture includes road density (the length of road per square kilometer) and communication (the number of fixed phone lines per person).

• Based on the above, an index of the overall climate change vulnerability of the region was constructed. Weights of indicators in the indeces were defined by simple distribution or expert assessment of importance of selected indicators.

• Maps of hazard exposure, sensibility, adaptive capacity and overal climate change vulnerability were constructed using the calculated indeces and collected statistical data from different sources.

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Table 1: Indicators used for climate change vulnerability mapping by the EEPSEA study

Index Index element Indicator Weight

1. Cyclones (0.2)

2. Floods (0.2)

3. Droughts (0.2)

4. Landslides (0.2)

Hazard Exposure (1/3)

5. Sea level rise (0.2)

Human sensibility

6. Population density (0.7)

Sensibility (1/3) Ecological

sensibility 7. Protected area (0.3)

8. Human development index

(0.5)

9. Poverty incidence (0.28)

Socio-economic capacity

10. Income inquality (0.22)

11. Electricity coverage (0.53) Technology

12. Extent of irrigation (0.47)

13. Road density (0.5)

Vulnerability

Adaptive capacity (1/3)

Infrastructure

14. Communication (0.5)

Source: Arief Anshory Yusuf and Herminia A. Fransisco, 2009

2.2. Methodology of our study using CBMS data:

CBMS as a tool for monitoring socio-economic development in general and poverty in particular, has been piloted in Vietnam during some last years in a series of communes of 5 provinces. CBMS methodology includes a household census procedure, a set of socio-economic indicators and respective data at household and village level, involvement of local staff and people in data collection, processing and validation. Using GIS, this data could be mapped and present spacial distribution of socio-economic features of households and villages.

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Adopting the EEPSEA methodology of mapping climate change vulnarability, we have improved CBMS tools with aims to reflect the climate change impacts to households and communities. There are some changes of CBMS questionaire and indicators:

• Adding indicators reflecting impacts of climate hazard (drought, flood, cyclones and typhoons, soil salinization, pest, etc.) to household questionnaires.

• Adding indicators reflecting adaptive capacity of local communities (infrastructure, technology, human and financial capacity) to commune questionnaire.

We have consulted with local experts and defined the indicators to be added to the CBMS indicator set. Three criteria were used for selection of indicators: (i) Availability of data at community and household levels; (ii) Reflecting the climate change impacts and responses of local communities; (iii) Understandable for local people.

In the preliminary phase of the study, we use simple distribution of weights of indicators in each index of climate change exposure, sensibility and adaptive capacity.

The following indicators were selected by the local experts for measuring and mapping climate change vulnerability.

Table 2: Indicators used for climate change vulnerability mapping by the CBMS team

Index Index element

Indicator Weight

Cyclones, sea level rise

1. Percentage of HHs affected by floods during 3 years (2008-2010)

(1/6)

Extreme temperature change

2. Percentage of HHs affected by droughts during 3 years (2008-2010)

(1/6)

Sea level rise 3. Percentage of agricultural land affected by salinization during 3 years (2008-2010)

(1/6)

Climate change exposure (1/3)

Landslide 4. Percentage of HHs affected by landslides during 3 years (2008-2010)

(1/6)

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Cyclones 5. Percentage of HHs affected by typhoons, cyclones during 3 years (2008-2010)

(1/6)

Extreme temperature change

6. Percentage of HHs affected by animal pest (bird flu, pig flu, etc.) during 3 years (2008-2010)

(1/6)

7. Population density compared to the highest density

(1/5)

8. Percentage of oldaged and children in total population

(1/5)

Human sensibility

9. Percentage of the poor in total population

(1/5)

Ecological sensibility

10. Percentage of agricultural land possibly affected by salinization and landslides

(1/5)

Sensibility (1/3)

Economic sensibility

11. Agricultural and fishery production value possibly affected by natural disasters (flood, drought, salinization, landslide) compared to total production value

(1/5)

Management capacity

12. Percentage of villages having action plan for response to disasters

(1/7)

Human capacity

13. Percentage of trainned persons in total adult population

(1/7)

Finance capacity

14. Fund of response to disasters compared to total production value

(1/7)

15. Percentage of HHs having permanent dwelling

(1/7)

Infrastucture 16. Number of vehicles (car, boats) per HH

(1/7)

Adaptive capacity (1/3)

Communica- 17. Percentage of HHs having tivi (1/7)

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tion 18. Percentage of HHs having telephone

(1/7)

3. Measuring climate change vulnerability

The dominant hazards of climate change in Vietnam are impact of extreme climate patterns and natural disasters (cyclones, typhoons, floods, droughts, heavy rainfall, heat waves, windstorms) and sea level rise. This research project focuses on the coastal and mountainous areas where these dominant climate hazards affected heavily. Three districts1 in the North mountainous area, North coastal area and Centre regions of Vietnam are selected for the pilot study. These districts represent different geographical conditions, human livings and climate change features.

(a) Kim Son district, Ninh Binh province in the North of Vietnam.

Ninh Binh is one province in the Red River Delta in the North Vietnam. Ninh Binh has experience in CBMS. During the last phase of CBMS-Vietnam project (2006-2008), 25 communes in Nho Quan - a mountainous and poorest district of the province - have implemented CBMS and used for development planning, poverty reduction and women advancement.

In this project, Kim Son – a coastal district is selected for project activities. This district has a population of 172 thousands persons. The district covers an area of 165 km2. There are 25 communes and two townships in the district.

Agriculture, fishing on the sea and aquaculture of shrimp, crabs, shell, fish are main occupations of population.

The district is often affected by typhoons, extreme sea waves, soil salinization, floods and droughts. The population faces with fresh water shortage.

(b) Nghia Lo municipal, Yen Bai province in the North Mountainous Region of Vietnam

Yen Bai is one province in the Northern Mountainous Region in Vietnam. Yen Bai has experience in CBMS. During the last phase of CBMS-Vietnam project (2006-2008), CBMS has been implemented in 6 communes and used for development planning and poverty reduction. The major partner of CBMS project was the Department of Labour, Invalids and Social Affairs, beside local (commune and district) administration.

Nghia Lo municipal is the centre of the south part of the province. In the previous CBMS phase, some of wards and communes in this municipal were surveyed. 1 The administrative hierachy in Vietnam consists of 4 levels: central (national) government, province, district (or municipal) and commune (ward). There are 698 districts/municipals and more than 11,000 communes/wards. Commune (xa) is the basic government unit in rural areas. A commune consists of several villages.

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Agriculture and sevices are main occupation of the population.

The typical impacts of climate change here are affects of typhoons, droughts, landslide and extreme temperature.

(c) Tam Ky town, Quang Nam province in the Central Region of Vietnam.

Quang Nam province is in the middle of Vietnam and approximately 800 km far from Hanoi, 80 km far from Da Nang, the third big city of Vietnam.

Tam Ky town is the administrative centre of Quang Nam province. It includes some urban wards and rural communes. Agriculture and fishing are main occupation of the rural population, while services are main occupation of the urban population.

Tam Ky is located in sea coast and river bank. It’s affected by typhoons, flood, drought, landslide.

In each of the above sites, one commune was selected for CBMS survey.

In this paper, we will present preliminary results of survey in the Kim Son district and the Nghia Lo municipal. Basing on collected data from the wards and communes in these two site, we have computed the indexes of climate change sensibility, exposure and adaptive capacity. Then, the overall vulnerability index was calculated as average sum of the three above indexes. The indexes are presented in the below tables.

Table 3: Climate change exposure index of the Kim Son district and Nghia Lo municipal

Commune 1. Flood 2. Drough 3. Salinization

4. Landslide

5. Disasters

6. Pest Exposure index

Kim Son district

Phatdiem 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Kimdong 0.0000 0.0000 0.0000 0.0000 0.0953 0.0000 0.0159

Thuongkiem 0.1424 0.1041 0.0000 0.0000 0.0000 0.0000 0.0411

Binhminh 0.0000 0.0000 0.0000 0.0000 0.0074 0.0000 0.0012

Quangthien 0.0486 0.0584 0.0000 0.0000 0.0000 0.0000 0.0178

Anhoa 0.0362 0.0414 0.0000 0.0000 0.0000 0.0040 0.0136

Kimchinh 0.0683 0.0677 0.0233 0.0000 0.0000 0.0018 0.0269

Luuphuong 0.1087 0.1341 0.0000 0.0000 0.0000 0.0000 0.0405

Donghuong 0.0819 0.0664 0.0024 0.0000 0.0000 0.0000 0.0251

Yenloc 0.0893 0.1166 0.0000 0.0000 0.0070 0.0102 0.0372

Tanthanh 0.2751 0.5668 0.0000 0.0000 0.0000 0.0000 0.1403

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Nhuhoa 0.1659 0.0674 0.0000 0.0000 0.0000 0.0073 0.0401

Dinhhoa 0.0487 0.1745 0.0000 0.0000 0.0000 0.0000 0.0372

Laithanh 0.0599 0.0565 0.0004 0.0000 0.0012 0.0000 0.0197

Hungtien 0.0722 0.0557 0.0001 0.0000 0.0000 0.0057 0.0223

Kimdinh 0.0607 0.0486 0.0000 0.0000 0.0000 0.0030 0.0187

Yenmat 0.1958 0.0783 0.0000 0.0000 0.0000 0.0000 0.0457

Chatbinh 0.1260 0.0516 0.0000 0.0000 0.0000 0.0086 0.0310

Vanhai 0.0667 0.0714 0.1449 0.0000 0.0029 0.0000 0.0477

Kimmy 0.0925 0.1098 0.0000 0.0000 0.0023 0.0013 0.0343

Hoininh 0.0980 0.0365 0.0000 0.0000 0.0000 0.0055 0.0233

Chinhtam 0.0794 0.0276 0.0225 0.0000 0.0000 0.0034 0.0221

Kimtan 0.0871 0.1065 0.0000 0.0000 0.0063 0.0034 0.0339

Kimhai 0.0000 0.0000 0.0000 0.0000 0.0407 0.0000 0.0068

Conthoi 0.0735 0.0242 0.0000 0.0000 0.0059 0.0053 0.0182

Kimchung 0.0000 0.0000 0.0000 0.0000 0.0218 0.0000 0.0036

Xuanthien 0.0368 0.0250 0.0000 0.0000 0.0000 0.0000 0.0103

Nghia Lo municipal

Putrang 0.2941 0.3898 0.0000 0.0096 0.0128 0.0048 0.1185

Nghiaphu 0.2001 0.2514 0.0000 0.0225 0.0341 0.0189 0.0878

Trungtam 0.1560 0.2371 0.0000 0.0000 0.0067 0.0053 0.0675

Nghialoi 0.2270 0.2709 0.0000 0.0120 0.0267 0.0225 0.0932

Nghiaan 0.1686 0.1882 0.0000 0.0010 0.0124 0.0093 0.0632

Cauthia 0.1637 0.1569 0.0000 0.0146 0.0170 0.0165 0.0614

Tanan 0.1602 0.1657 0.0000 0.0060 0.0129 0.0047 0.0582

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Table 4: Climate change sensibility index of the Kim Son district and Nghia Lo municipal

Commune 7. Density 8. Oldaged & child

9. Poor 10. Effected land

11. Effected economy

Sensibility index

Kim Son district

Phatdiem 0.9784 0.2249 0.0547 0.0000 0.0000 0.2516

Kimdong 0.0520 0.3610 0.1449 0.3255 0.0007 0.1768

Thuongkiem 0.1005 0.2052 0.0680 0.0000 0.0000 0.0747

Binhminh 0.0437 0.4497 0.0938 0.0778 0.0001 0.1330

Quangthien 0.1061 0.3624 0.0670 0.0000 0.0000 0.1071

Anhoa 0.1004 0.3572 0.1336 0.0000 0.0000 0.1182

Kimchinh 0.0998 0.3148 0.0967 0.0000 0.0000 0.1022

Luuphuong 0.1194 0.4314 0.0611 0.0000 0.0000 0.1224

Donghuong 0.1255 0.3584 0.1025 0.0000 0.0000 0.1173

Yenloc 0.1273 0.2891 0.0759 0.0000 0.0000 0.0985

Tanthanh 0.1123 0.2077 0.1196 0.0000 0.0000 0.0879

Nhuhoa 0.1109 0.3732 0.1136 0.0000 0.0000 0.1196

Dinhhoa 0.1042 0.3070 0.1205 0.0000 0.0000 0.1063

Laithanh 0.1085 0.3545 0.1213 0.0000 0.0000 0.1168

Hungtien 0.1226 0.3389 0.0952 0.0000 0.0000 0.1113

Kimdinh 0.1253 0.2899 0.1689 0.0000 0.0000 0.1168

Yenmat 0.1019 0.2183 0.1159 0.0000 0.0000 0.0872

Chatbinh 0.1033 0.1837 0.1685 0.0000 0.0000 0.0911

Vanhai 0.1216 0.3669 0.1156 0.0000 0.0000 0.1208

Kimmy 0.1341 0.2522 0.1342 0.0546 0.0001 0.1151

Hoininh 0.1138 0.2142 0.1389 0.0000 0.0000 0.0934

Chinhtam 0.1161 0.2015 0.2130 0.0000 0.0000 0.1061

Kimtan 0.0908 0.2205 0.2145 0.0472 0.0001 0.1146

Kimhai 0.0557 0.3026 0.2571 0.2025 0.0007 0.1637

Conthoi 0.1211 0.4192 0.1983 0.0935 0.0002 0.1665

Kimchung 0.0813 0.2831 0.2487 0.2656 0.0009 0.1759

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Xuanthien 0.0833 0.3493 0.2228 0.0000 0.0000 0.1311

Nghia Lo municipal

Putrang 0.0919 0.3101 0.1036 0.1295 0.0021 0.1274

Nghiaphu 0.0515 0.3100 0.1840 0.1121 0.0028 0.1321

Trungtam 0.4634 0.3050 0.0741 0.0481 0.0004 0.1782

Nghialoi 0.1005 0.3126 0.4374 0.0388 0.0002 0.1779

Nghiaan 0.0245 0.3103 0.2860 0.0626 0.0013 0.1369

Cauthia 0.2169 0.3151 0.0740 0.0663 0.0007 0.1346

Tanan 0.1908 0.3100 0.0679 0.0413 0.0001 0.1220

Table 5: Adaptive capacity index of the Kim Son district and Nghia Lo municipal

Commune 12. Action_plan

13. Funds

14. Human_resources

15. Transport

16. Housing

17. Tivi 18. Telephon

e

Adaptive capacity

index

Kim Son district

Phatdiem 1.0000 0.0006 0.0046 0.0065 0.6700 0.9000 0.9300 0.5853

Kimdong 1.0000 0.0011 0.0115 0.0107 0.6400 0.8500 0.8800 0.5656

Thuongkiem 1.0000 0.0007 0.0072 0.0109 0.6600 0.8600 0.8300 0.5615

Binhminh 1.0000 0.0004 0.0232 0.0138 0.6100 0.8700 0.8100 0.5546

Quangthien 1.0000 0.0007 0.0095 0.0048 0.5100 0.9000 0.8200 0.5408

Anhoa 1.0000 0.0011 0.0101 0.0090 0.5300 0.8600 0.8200 0.5384

Kimchinh 1.0000 0.0010 0.0066 0.0055 0.5000 0.8500 0.8600 0.5372

Luuphuong 1.0000 0.0006 0.0107 0.0075 0.6000 0.8300 0.7600 0.5348

Donghuong 1.0000 0.0009 0.0088 0.0049 0.5200 0.8700 0.8000 0.5341

Yenloc 1.0000 0.0008 0.0075 0.0029 0.5200 0.8200 0.8200 0.5285

Tanthanh 1.0000 0.0011 0.0075 0.0041 0.5250 0.7700 0.8100 0.5196

Nhuhoa 1.0000 0.0009 0.0096 0.0057 0.4500 0.8500 0.8000 0.5194

Dinhhoa 1.0000 0.0015 0.0090 0.0056 0.4500 0.8460 0.8000 0.5187

Laithanh 1.0000 0.0011 0.0082 0.0019 0.4300 0.8000 0.8000 0.5069

Hungtien 1.0000 0.0011 0.0112 0.0048 0.4100 0.8350 0.7700 0.5053

Kimdinh 1.0000 0.0010 0.0102 0.0036 0.4650 0.8200 0.7300 0.5050

Yenmat 1.0000 0.0003 0.0121 0.0035 0.3800 0.7600 0.8200 0.4960

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Chatbinh 1.0000 0.0008 0.0082 0.0039 0.3550 0.7900 0.8000 0.4930

Vanhai 1.0000 0.0011 0.0099 0.0051 0.4000 0.8100 0.7100 0.4894

Kimmy 1.0000 0.0011 0.0045 0.0029 0.3850 0.7350 0.7900 0.4864

Hoininh 1.0000 0.0007 0.0081 0.0083 0.3200 0.7600 0.7800 0.4795

Chinhtam 1.0000 0.0005 0.0097 0.0058 0.3200 0.7600 0.7700 0.4777

Kimtan 1.0000 0.0012 0.0078 0.0071 0.3600 0.7600 0.7000 0.4727

Kimhai 1.0000 0.0005 0.0111 0.0098 0.3300 0.7100 0.7600 0.4702

Conthoi 1.0000 0.0008 0.0069 0.0044 0.3200 0.7500 0.7100 0.4654

Kimchung 1.0000 0.0005 0.0094 0.0099 0.3250 0.7500 0.6000 0.4491

Xuanthien 1.0000 0.0007 0.0139 0.0084 0.3000 0.7000 0.6700 0.4488

Nghia Lo municipal

Putrang 1.0000 0.0109 0.0783 0.0206 0.6000 0.9800 0.9700 0.6100

Nghiaphu 1.0000 0.0000 0.2045 0.0087 0.0980 0.9400 0.9500 0.5335

Trungtam 0.2174 0.0000 0.0762 0.0562 0.8500 0.9900 0.9800 0.5283

Nghialoi 1.0000 0.0094 0.1057 0.0012 0.1000 0.9300 0.9500 0.5160

Nghiaan 1.0000 0.0000 0.1227 0.0155 0.0600 0.9600 0.9300 0.5147

Cauthia 0.4444 0.0000 0.1477 0.0297 0.3370 0.9800 0.9700 0.4848

Tanan 0.0000 0.0000 0.0681 0.0386 0.5000 0.9700 0.9700 0.4245

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Table 6: Climate change vulnerability index of the Kim Son district and Nghia Lo municipal

Commune Vulnerability index

Kim Son district

Phatdiem 0.2790

Kimdong 0.2528

Thuongkiem 0.2258

Binhminh 0.2296

Quangthien 0.2219

Anhoa 0.2234

Kimchinh 0.2221

Luuphuong 0.2325

Donghuong 0.2255

Yenloc 0.2214

Tanthanh 0.2493

Nhuhoa 0.2263

Dinhhoa 0.2207

Laithanh 0.2145

Hungtien 0.2130

Kimdinh 0.2135

Yenmat 0.2096

Chatbinh 0.2050

Vanhai 0.2193

Kimmy 0.2119

Hoininh 0.1987

Chinhtam 0.2020

Kimtan 0.2071

Kimhai 0.2136

Conthoi 0.2167

Kimchung 0.2096

Xuanthien 0.1967

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Nghia Lo municipal

Putrang 0.2853

Nghiaphu 0.2512

Trungtam 0.2580

Nghialoi 0.2624

Nghiaan 0.2383

Cauthia 0.2270

Tanan 0.2016

4. Mapping climate change vulnerability:

We used the software MapInfor for mapping the vulnerability index and it’s components. The local agencies can use these maps for quick assessment of climate change situation and responding capacity of communes.

Some examples of Kim Son district maps are presented below.

Map 1: Administrative map of Kim Son district

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Map 2-7: Climate change exposure of the communes in Kim Son district

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Map 8-13: Climate change sensibility of the communes in Kim Son district

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Map 14-17: Climate change adaptive capacity of the communes in Kim Son district

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Map 18: Climate change vulnerability of the communes in Kim Son district

REFERENCES

• Arief Anshory Yusuf and Herminia A. Fransisco, (2009). Climate Change Vulnerability Mapping for Southeast Asia. IDRC.

• Inter-governmental Panel on Climate Change (IPCC), (2001). J. McCarthy; O. Canziani; N. Leary; D. Dokken; and K. White (eds) In Climate Change 2001: Impacts, Adaptation, and Vulnerability. Cambridge University Press. Cambridge.

• Vietnam General Statistics Office (2011). Statistical Yearbook 2010. Hanoi.

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