IJERD – International Journal of Environmental and Rural Development (2013) 4-1

Research article
erd

Climate Change Vulnerability: Household Assessment
Levels in Kampong Speu Province, Cambodia
NYDA CHHINH*Royal University of Phnom Penh, Phnom Penh, Cambodia
Email: chhinh.nyda@rupp.edu.kh

HOEURN CHEBRoyal University of Phnom Penh, Phnom Penh, Cambodia
Received 9 December 2012

Accepted 30 January 2013(*Corresponding Author)

Abstract Cambodia’s   Ministry   of   Environment   (MoE)   conducted   vulnerability   and  adaptation assessments using two different methodologies: the Global Circulation Model(MoE, 2001) and Household Surveys (MoE, 2005). They found that Cambodia isvulnerable to climate change and has a low adaptive capacity compared to other countries
in Southeast Asia. Flood and drought were identified as the climatic hazards that imposed
the greatest threats to rural farmers. This study, which assessed vulnerability at thehousehold level in the drought-prone Kampong Speu (KPS) province, calculates climatechange vulnerability based on the framework developed by the Inter-governmental Panelon Climate Change (IPCC). The results indicate that drought is the most severe climate
hazard experienced in KPS. Farmers reported that they regularly experienced irregular
rainfall distribution during cropping season that results in crop damage and/or loss.
Adaptations to drought adopted by farmers include water storage, introducing droughttolerant crop varieties, and improving knowledge about farming techniques. Other
mechanisms that are also feasible are providing or enhancing secondary income capacity.
Keywords climate change, vulnerability, adaptation, assessment, rural development

INTRODUCTION
Climate change is the term most frequently used in global studies to refer to significant and lastingchanges  in  the  Earth’s  weather  pattern  that  are  evident  in  effects  such  as worldwide changes in in
precipitation, and temperature (IPCC, 2007).  However,  ‘climate  change’  is  not  commonly used in
Cambodia,  where  this  phenomenon  is  instead  called  ‘climate  variability’.  The  impacts  of  climate  change can be seen spatially, temporally, socio-economically and through many other factors. An
assessment of vulnerability to climate change is required before any prescriptions are given to
mitigate and/or to adapt to climate change.
As defined in the IPCC report Climate Change 2001, vulnerability is a function of thesensitivity of a system to changes in climate (the degree to which a system will respond to a givenchange in climate, including beneficial and harmful effects), adaptive capacity (the degree to which
adjustments in practices, processes, or structures can moderate or offset the potential for damage ortake advantage of opportunities created by a given change in climate), and the degree of exposureof the system to climatic hazards (IPCC, 2001 , p. 89). The definitions of exposure, sensitivity, andadaptive capacity can be found in climate change literature (Adger, 2006; Fankhauser et al., 1999;
Eriksen and O'Brien, 2007). Explanations of how to quantify vulnerability can be found in Fussel(2007) and Hinkel (2011).
When studying vulnerability on a fine scale, for example at a household level, the study ofclimate change is associated with the outcomes of changes in climate in a particular geographicalarea such as a commune. A study may focus on a single element, such as changes in productivity,
economic loss, or local knowledge of adaptation.
This paper aims to estimate and quantify vulnerability to climate change in Cambodia usingⒸ ISERD

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household data and to identify the household characteristics that are most impacted by climatechange effects such as flood and drought.
Background
One study on vulnerability related to climate change at the household level in Cambodia wascompleted by the Ministry of Environment (MoE) in 2005. Climatic hazards including flood,
drought, and windstorms were studied. That study (MoE, 2005) demonstrates that Cambodians
have observed changes in weather patterns and experienced losses in farming production due toclimatic hazards because of their low adaptive capacity. A number of possible coping andadaptation mechanisms were identified by the study, which noted there were a number of
constraints to effectively adapting to climate change such as lacks in the financial, knowledge andskill  aspects  required  to  mitigate  impact.  Following  the  MoE’s  study,  there  have  been  a  number  of  studies related to climate change assessment that use different frameworks, such as Try Toun(2009) and Yusuf and Francisco’s  (2010) EEPSEA’s  study.  From  these  studies,  it  can  be  concluded  that Cambodia is exposed to climate variability and that even with low degree of exposure; ruralcommunities are highly vulnerable to changes in climate due to low adaptive capacity.
Fig. 1 shows the production lost due to flood and drought between 1984 and 2011. During the
1990s, droughts were more common than floods and occurred with very high severity, except for in
1996 when both disasters hit Cambodia at the same time during growing seasons. During the 2000s,
the both floods and drought occurred every year, with the highest concentration of disasters
occurring in the middle of the decade.
There was flooding in Cambodia every year between 1998 and 2011, with the most severe
floods occurring in the 2000-01 growing season, based on the average level of flooding in
Cambodia as recorded over the last 70 years (the 2011 flood is not included for the comparison due
to lack of literature). The 2000-01 floods caused extensive damage to many social infrastructure
systems, properties and agricultural plantations and during the wet season affected both people andanimals in 22 provinces.
The Kampong Speu (KPS) province is the second priority project implementation in the
National Adaptation Program of Action to Climate Change (NAPA) of Cambodia. The NAPA
priority for adaptation is consistent with the finding of EEPSEA vulnerability mapping study,
which identified that Kampong Speu is the third most vulnerable province of the 17 provinces in
Cambodia. As mentioned earlier, KPS has a high incidence of poverty, and experienced drought
more often and intensively than other provinces in Cambodia.

Fig. 1 Distribution of paddy rice destroyed by flood and drought in Cambodia, 1984-2011
Kampong Speu is about 40 km to the west of Phnom Penh. The province is comprised of 8
districts. The total population of the province is 716,944, of which 368,432 are female. Similar toother provinces in Cambodia, the economy of this province is agriculture-dominant and primarilyⒸ ISERD

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involved in rice production. The total paddy rice area of this province in 2007 was 78,000 ha,
which accounted for 3.81% of the total paddy rice in Cambodia. KPS produces 245,000 tons of riceannually, approximately 4% of  the  county’s  total  rice  production.
Data gathering
The household survey samples were selected from 6 communes in 6 districts within two
geographical areas: highland areas (2 districts/communes: Phnom Sruch/Morhasang, Oral/Tasal)
and lowland areas (4 districts/communes: Chbar Mon/Chbarmon, Oudong/Peng Lavea, Bor
Sedth/Kork, Somrong Tong/Rolang Chork). A total of 600 questionnaires were collected of which
200 from highland and 400 from lowland. The occurrence of natural hazards, including flash
flooding, drought and windstorm, were recorded based on yes/no questions. Other variables that
contributed to the index calculation were collected based on Table 1, 2 and 4.
METHODOLOGIESComposite indicators involve two steps. First, it involves in normalizing of each indicator based on
Eq. (1) and then, aggregating the indicators into one index as in Eq. (2).
The normalizing procedure is based on Eq. (1).

xi  x min / x max x min

Ii

(1)

Where Ii is the normalized value of indicator i, x is the original value for indicator forindividual household, and xmax and xmin are the highest and lowest values of the indicator.
In order to aggregate the indicators (as in Eq. 2), weighting among the indicators is employed.
The weights are obtained from focus group discussion, expert judgment and key informant
interview from the study site. This is to reduce subjectivity * . It is very important to quantify
vulnerability according to the different weights for each indicator and dimensions. The consensus
method was used to gather the weight of different determinants and indicators. Eight Focus Group
Discussions (FGDs) were conducted with participants from a range of backgrounds at theprovincial, district and commune levels in order to come up with different weights. Weights werethen averaged among all FGDs and levels.
Djn¦ IiWii 1(2)

Where Dj is the aggregate value from the product of normalized value of indicator, j is thename of index, n is the number of indicators within a particular index and Ii and Wi are thenormalized value of indicator and weight of each indicator respectively.
Exposure Index (EI)
Table 1 Example of Exposure Index calculation
HH No.
12
3
4
5
6

Number of hazard events
Drought
Flood
10
3
5
0
2
2
3
1
0
4
1
0

Windstorm
0
1
0
2
0
3

Normalized Value (index)
Drought
Flood
Windstorm
1.00
0.75
0.00
0.50
0.00
0.33
0.20
0.50
0.00
0.30
0.25
0.67
0.00
1.00
0.00
0.10
0.00
1.00

Hazard
Index
0.77
0.31
0.27
0.34
0.33
0.22

Exposure index in this study is composed of three hazards indicators: flash flooding, drought andwindstorms. Based on social perception of hazard, we recorded number of events that household*

In our study site, the weights are 0.53, 0.32, and 0.15 for drought, flash flood, and windstorm respectively.

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experienced. For example, within 1990-2010 one household may experience 10 drought events, 3
flash floods and 1 windstorm. Different localities of household experienced differently (Table 1).
Each hazard event, such drought, is normalized to get the value between 0 and 1.
Sensitivity index (SI)
Sensitivity of a household composed of four dimensions (with different weight) namely: humancapital (0.23), livelihood (0.26), infrastructure (0.21), and financial capital (0.30). Meaning thatsensitivity index is the aggregated value of the four dimensions after applying weight. Eachdimension is measured by different indicator as in Table 2.
After normalizing the indicator based on (Eq.1), we aggregate the normalized value ofindicators with weighting of each indicator by their own dimension (Table 3). We assume that thehigher the weight, the more sensitive to natural hazard. For example, in human dimension, ahousehold may experience more hardship if there are more children compared to other households.
Table 2 Sensitivity indicators and weight
Dimension
Haman
Livelihood
Infrastructure
Financial

Indicator
Dependent ratio (S1)
Number of family laborers working in agriculture (S2)Percentage of annual income generated from agriculture (S3)Distance from household to a body of water (S6)Percentage of debt in (S9)

Indicator Weight
0.55
0.45
1.00
1.00
1.00

Table 3 Example of Sensitivity Index calculation
HH No.
1
2
3
4
5
6

Indicator
S1
25
20
14
33
16
50

S2
2
2
5
2
1
2

Normalized Value (index)
S1
S2
0.31
0.25
0.17
0.25
0.00
1.00
0.53
0.25
0.06
0.00
1.00
0.25

Weight Value
0.28
0.20
0.45
0.40
0.03
0.66

Adaptive capacity index (ACI)This index composed of five dimensions (with different weight) namely: infrastructure (0.19),
economics (0.21), technology (0.20), social capital (0.18) and human capital (0.22). The adaptive
capacity index is also based on the same principle as in sensitivity index. Each dimension has
different indicator with different weight as in Table 4.
Vulnerability index
Vulnerability assessment is based on Eq. 3. For a comprehensive discussion of the Indexes
Approach,   see   ‘Handbook   on   Constructing   Composite   Indicators:   Methodology   and   User   Guide’  jointly developed by OECD and JRC European Commission (OECD, 2008). The example related
to this approach can be found in Yusuf and Francisco (2010) and International Crop ResearchInstitute for the Semi-Arid Tropics (2009).
The Vulnerability Index (VI) is based on three-dimensional indexes (see the definition above)
including exposure index, sensitivity index, and adaptive capacity index. The VI’s formula isshown in Eq. (3).
VI

EI  SI  (1  ACI ) / 3(3)

Before aggregating vulnerability index (VI), there is a manipulation of adaptive capacity index(ACI) by ‘1   – ACI’ so that the direction of ACI is the same as exposure and sensitivity index,
meaning that for EI and SI the higher the value, the higher vulnerability while the higher the ACI,Ⓒ ISERD

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the low vulnerability. So, the manipulation of ACI will keep ACI value the same direction as EI andSI.
Table 4 Adaptive capacity and their weight
Dimension
Infrastructure
Economic

Indicator
Percentage of irrigated agricultural land of household
Income per headAmount of remittance per year
Percentage of income generated from non-agriculture
Number of TVs and radios
Number of line phones and cell phones
Number of motorcycles
Amount of money that can be borrowed from relatives and/or friends
in case of disaster
Number of laborers
Level of education

Technology
Social capital
Human

Indicator Weight
1.00
0.31
0.31
0.38
0.30
0.30
0.40
0.10
0.53
0.47

RESULTS AND DISCUSSION
Hazard exposure
Almost every household in the sample (574 out of 600 cases) reported that they were impacted bydrought at least once between 1999 and 2010. During this 12-year period, 20% of households
experienced drought once every two years. Flash floods and windstorms were very rare and, out of
600 cases, 540 were not impacted by flash floods and 558 by windstorms at all during the study
period. Compared to flash flooding and windstorms, drought is the major thread to rural households.
Table 5 shows the number of occurrence of natural hazards (windstorms, flash floods anddrought) reported by households from 1999 to 2011. It can be inferred from the data thatwindstorms occurred with a low frequency and had a greater effect on lowland communities morethan highland communities. Based on the FGD data, highland residents reported that windstorms
are not a major concern and only 6% of both highland and lowland respondents reported
experiencing windstorms over the last 12 years.
Table 5 Hazard events experienced by respondents in KPS, 1999-2010
Class
0-1
2-3
4-5
6-7
8-9
10-12

Drought
58
79
23
10
0
30

HighlandFlash flood
192
8
0
0
0
0

Windstorm
198
2
0
0
0
0

Drought
107
160
54
7
6
66

Lowland
Flash flood
378
20
1
1
0
0

Windstorm
393
7
0
0
0
0

Sensitivity
Figure 2 shows the comparison of the means of the four determinants of the sensitivity index andmeaning that the higher the value, the more sensitive to natural hazards. Horizontal axis is thesensitivity dimensions that will be composited into sensitivity index and vertical axis is the meanvalue of weighted of each dimension from highland and lowland. The livelihood determinant had
the highest mean value at 0.684, followed by human at 0.396. The other determinants were
negligible. This implies that the livelihood of households is highly sensitive to the changes causedby climatic events and that the ratio of dependency is also high.
The mean value of human sensitivity in the lowland area (N=400) was 0.26 compared to thehighland area (N=200) at 0.29 with the standard deviations of each commune at 0.10 and 0.01
respectively. The independent samples test found that the group means of sensitivity index ofⒸ ISERD

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highland community are significantly different as the value in the p-value at 0.001 (less than 0.05).
Therefore it can be concluded that the highland area is more sensitive to climate change impacts
than the lowland area. This is due to either highland households are highly depending on
agriculture.

Fig. 2 Comparison of sensitive indicators to climate change in the KPS province
Adaptive Capacity
Fig. 3 compares adaptive capacity dimensions which finally be composed into adaptive capacity
index. The values on top of the bar are the mean of the dimension, meaning that the higher thevalue, the higher adaptive capacity. The graph illustrates that the human component of adaptive
capacity is twice as significant as the other components (infrastructure, economics and technology).
It can be interpreted that labor is the best way to handle shock. Other factors, such as the level ofsocial capital, play an insignificant role compared to other determinants.
By disaggregating adaptive capacity index (ACI) into highland (N=200) and lowland (N=400),
the means of ACI of lowland and highland commune are at 0.77 and 0.83 with the standard
deviations of 0.11 and 0.09 respectively. The independent samples test shows that the group meansof the lowland and highland are significantly different as the p-value is 0.00 (less than 0.05). Basedon this statistics, we can conclude that highland community is statically lower than lowland
communes.

Fig. 3 Comparison of adaptive capacity of household in the KPS province
Vulnerability assessment
Figure 4 also presents the index of hazard, sensitivity, adaptive capacity by disaggregating theindex value into topographical areas (highland and lowland) based on household data. Both areas
experienced hazards to a similar degree. Given the index value of hazards is low, the main hazard
is drought, which is considered the most serious hazard among the three hazards studied in thispaper (drought, windstorms and flash floods).
There is a slightly variation between highland and lowland areas in term of adaptive capacity
index value. However, it is concluded that the highland area is more vulnerable than the lowland
area because of a lower adaptive capacity and higher sensitivity to climate stimuli. It is worth
noticing here that livelihood of households who are heavily depending on paddy rice is more
vulnerable than those with raising livestock and having engaging in non-agricultural activities.
Fig. 5 shows a comparison of households with the vulnerability index categorized into low andhigh vulnerability. The graph shows that 43% of households are in the medium vulnerability
category with 19% and 7% in the high and very high vulnerable categories respectively. By lumpⒸ ISERD36

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summing these three categories it was found that there are 67% of household in the KPS provincethat are potentially impacted by hazards, while the rest are in the low vulnerability category.
From a qualitative point of view, natural disasters, particularly drought, have resulted in
insecurity amongst communities. Due to losses in rice production, some people turn to theft inorder to support their families. The FGDs disclosed this had happened in the Prekdey village(Rolang Chork commune, Somrong Tong district). FGDs in the Chrok Trach village (Morha Sang
commune, Phnom Srouch district) confirmed that some families were in hardship because of thedrought and did not have enough money for their children go to school. Some families, often theones who had suffered the most difficulties and who experienced serious damage to rice production
from drought, had decided to sell their properties and migrate other places, especially to Phnom
Penh. Moreover, the FGD in the Krang Troak village (Kok commune, Borsedth district) reported
that some families experienced increased domestic violence between husbands and wives which
was attributed to stresses caused by drought that damaged their rice production.

Fig. 4 Vulnerability comparison of high and lowland households of the KPS province(1999-2010)
Very High
High

MediumLow

Very Low
.0

10.0

20.0

30.0

40.0

50.0

Fig. 5 Vulnerability Index by Households
Adaptation identification
In this study, the practices of adapting to drought were determined through a household survey,
FGDs and expert recommendations. Some farmers stated they did not employ any adaptive
practices, while others protected their paddy rice from drought impacts by pumping water fromnearby water bodies such as ponds or wells and/or changing to drought-resistant practices (forexample by planting a short-duration rice variety). Some farmers also increased the amount of
fertilizer they used in order to maximize their yield, especially during years when drought occurred.
The key informant interviews also found that participants believed that modern crop varieties
should be and in some cases have been introduced to areas which experience frequent and/or severe
drought. This is one of the primary interventions of the Department of Agriculture at the provincial
level through their line authorities. While establishing a supplemental irrigation scheme is a longterm drought mitigation strategy, providing ad hoc water pumping to local community is a must
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during drought spells. The practice of water pumping has been used throughout Cambodia,
especially in Kampong Speu province, with the assistance of the Department of Water Resources
and Meteorology. Although it would involve many different government agencies, the next urgent
drought  mitigation  intervention  should  be  increasing  farmers’  knowledge  about  farming  techniques  including Systematic of Rice Intensification (SRI) and improving general education.
CONCLUSION
Cambodia is highly dependent on agriculture, therefore it is vulnerable to climate stimuli such as
flood and drought. Every province in Cambodia is vulnerable to climate change to some degree,
however some are more affected by flood and others by drought. This research found the Kampong
Speu province is prone to drought, with many residents reporting they experienced drought impacts
almost once every two years.
This empirical study reconfirmed that the present vulnerability of households in Kampong
Speu is high due to high sensitivity, low adaptive capacity and low exposure factors, and the study
identified that 67% of this province is in the medium to very high vulnerable range. People haveadapted to drought, but their practices are not productive. Therefore, enhancing adaptive capacity isvery critical, including promoting more drought-resistant crops, (perhaps, as suggested in theliterature, on a small scale) implementing an irrigation scheme, and improved knowledge about
how to use farming techniques to mitigate drought.
ACKNOWLEDGEMENTS
This work was carried out with assistance from a grant from the International Development
Research Centre (IDRC) in Ottawa, Canada and the Economy and Environmental Program forSoutheast Asia (EEPSEA). Thanks must be made to respondents participating in the survey,
government agencies in KPS, and students of the Royal University of Phnom Penh.
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