Households’ vulnerability to climate change in Thua Thien Hue province

JOURNAL OF SCIENCE, Hue University, Vol. 70, No 1 (2012) pp. 227-236 227 HOUSEHOLDS’ VULNERABILITY TO CLIMATE CHANGE IN THUA THIEN HUE PROVINCE Bui Dung The, Bui Duc Tinh College of Economics, Hue University Abstract. This paper measures the relative vulnerability of households living in Thua Thien – Hue province using the indicator approach. Information was collected via key informant interview, focus group discussion and a questionnaire survey of 597 households in the coastal, delta and upland areas of Thua Thien Hue province. It is established in the present study that households in the province are highly exposed to climatic hazards, particularly aquaculture and fishing households in the coastal and lowland areas. There is significant difference in adaptive capacity across different household groups. Household with aquaculture, cropping and capture fishery as the main livelihoods are highly sensitive to climactic hazards. Given the situation, agriculture and aquaculture should be given priority in interventions to enhance local adaptive capacity. High levels of exposure and low level of adaptive capacity are the main contributors to the vulnerability of households in the province. Keywords: climate change, vulnerability, households. 1. Introduction The IPCC Assessment Report (2010) defines vulnerability as: “The degree to which a system is susceptible to, or unable to cope with, 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” (McCarthy et al., 2001). Vulnerability includes an external dimension that is represented by the exposure of a system to climate variations, as well as a more complex internal dimension comprising its sensitivity and adaptive capacity to these stressors. A highly vulnerable system would be one that is very sensitive to modest changes in climate, where the sensitivity includes the potential for substantial harmful effects, and for which the ability to adapt is severely constrained. Thus, vulnerability is understood as a function of three components: exposure, sensitivity and adaptive capacity, which are influenced by a range of biophysical and socio-economic factors. Exposure can be interpreted as the direct danger (the stressor) together with the nature and extent of changes in a region’s climate variables (temperature, precipitation, and extreme weather events). Sensitivity describes the 228 Households’ vulnerability to climate change in human–environmental conditions that exacerbate or ameliorate the hazard or trigger an impact. Exposure and sensitivity are intrinsically linked and mutually influence potential impacts. Adaptive capacity represents the potential to implement adaptation measures in efforts to avert potential impacts (Füssel and Klein 2006, Yusuf and Francisco 2010). Thua Thien Hue is located in the Central Viet Nam, bordered by the South China Sea to the east and by Laos to the west. The province has an area of 5,053 square kilometres 49,107 hectars of which are used as agricultural land. Another 180,412 hectars are occupied by forests. Except Nam Dong and Aluoi districts that are located in the mountainous area, other districts are in the plain and strongly affected by inundation. Thua Thien Hue comprises of basins of four main rivers: O Lau, Bo, Huong, and Truoi rivers. The topography slopes downwards from the western Truong Son mountain range to the coast and is divided into three areas, i.e., higher mountain area, low-lying area, and coastal plain. The province of Thua Thien Hue is considered amongst the most disaster prone areas of Vietnam. In the past few decades, the frequency and severity of disasters increased significantly in Thua Thien Hue. Climate changes, especially extreme disasters killed many people and destroyed livelihoods of, and push many local communities dropped back poverty (PCFSC 2008 and 2009). Responses to reduce impacts by climate induced events such as floods and storm are not only the responsibility of the community itself but also a mandate of government agencies. The government must have adequate capacity to carry out tasks for climate change adaptation because successful implementations of adaptation strategy will be dependent on government’s performance. In fact, capacity for planning and action on climate change adaptation by local governments is lacking. An insight into how different household are vulnerable to climate change is of great importance to LGUs. Therefore, this study was to measure and explain households’ relative vulnerability to climatic hazards in Thua Thien – Hue province in order to suggest policy implications to local governments and for adaptation interventions at household level. 2. Research methods 2.1. Method to calculate vulnerability index This study used the indicator approach to measure the vulnerability of households in Thua Thien Hue province. The indicator approach indentifies indicators that reflect vulnerability and measures vulnerability by computing indices, averages or weighted averages for those selected variables or indicators. This approach can be applied at different levels (household, county/district, province and national). The indicator approach is valuable for monitoring trends and exploring conceptual frameworks. According to Leichenko and O’Brien (2002), composite indices capture the BUI DUNG THE, BUI DUC TINH 229 multi-dimensionality of vulnerability in a comprehensible form. The indicator approach is the most common method adopted for quantifying vulnerability in the global change community. It is used to develop a better understanding of the socio-economic and biophysical factors contributing to vulnerability (Hebb and Mortsch 2007). Vulnerability indices of households were constructed based on the interrogation of a wide range of data sources following the notion that vulnerability is a function of exposure to climate change and variability, sensitivity to the impacts of that exposure, and the ability to adapt to ongoing and future changes (Hahn, Riederer, and Foster, 2009). The measurement of relative vulnerability using the indicator approach includes a number of important steps, such as indicator identification, assigning weight and calculating vulnerability index. Identification of indicators: The selection of indicators was done through an extensive review of previous reports; in particular, we draw from Gbetibouo and Ringler (2008), Smith et al (2006), Jusuf and Francisco (2010). These indicators were then pragmatically assessed through a workshop with the participation of LGUs and social scientists. This is to ensure that each indicator is practical, specific, measurable and time-bond. The study identified exposure indictors for five dominant hazards, namely storm, flood, drought, flashflood and extreme cold. Sensitivity indicators describe the natural, human, infrastructure and livelihood conditions that can either worsen the hazard or trigger an impact. Adaptive capacity indicators covered the types of assets that the local households have. Assigning weights: The issue of weightings is highly controversial largely due to the subjectivity inherent in assigning weightings. While the application of weights facilitates an indication of importance of the different variables, it also leaves the results open to manipulation. To take the local context and situation into account, weighting for each indicator, parameter and dimension should be used. Our review of literature indicates that there are several prevailing methods to assign weights to indicators. They are: (1) arbitrary choice of equal weight, (2) expert judgment, (3) statistical methods such as principal component analysis, and (4) consensus among policy makers and stakeholders. Each method has its own pros and cons. In the present study we do not assign equal weights because this strategy is too subjective, and the literature shows that indicators do not equally affect the vulnerability (Hebb and Mortsch 2007). The development of weights via expert judgment is often constrained by the availability of expert knowledge in smaller communities and difficulties in reaching a consensus on the weights among expert panel members. The use of statistical methods appears complicated and it is hard to involve the stakeholders in the exercise. Therefore we herein use the method to assign weights to indicators/dimensions through consensus among policy makers and stakeholders. Policy makers, local government units (LGUs) and stakeholders discussed and agreed on weights for each indicators/dimensions. 230 Households’ vulnerability to climate change in Calculating vulnerability indices: As discussed earlier, the vulnerability of a given system largely depends on its exposure, sensitivity, and adaptive capacity. The climate change vulnerability index was derived through the following steps: - We assessed the exposure using information from historical data of climate- related hazards. We considered the past exposure to climate risks as the best available proxy for future climate risks. - We calculate hazard index for the climate hazard that households face, such as storms, floods, droughts, and extreme cold. - We analyzed socio-economic data of surveyed households and calculated the sensitivity indices. - We calculated the adaptive capacity indices for all surveyed households. - To obtain the overall index of climate change vulnerability, we get the weighted average of exposure (multiple hazard risk exposure), sensitivity, and the reverse of adaptive capacity indices. It should be noted that to make the indicator values are comparable across households we normalize indicator values using the following formula: Zij = (Xij – Xi min )/ (Xi max – Xi min) Where Zij is the normalized value of indicator i of commune j; Xij is the original value of indicator i of commune j; Xi max is the highest value of all communes; and Xi min is the lowest value of all communes. 2.2. Data collection Data and information necessary for the study are collected using several methods including focus group discussions, key informant interview, secondary data collection, and household survey. The sample for the household survey is 600 households. They were chosen using stratification and random sampling methods. At first stage, the study stratified all communes into three groups based on their topographical feature: upland, delta and coastal. In consultation with LGU staff two communes were selected from each group. They are the upland communes of Huong Giang, Thuong Quang in Nam Dong upland district; the delta communes of Quang Thanh commune in Quang Dien and Phong Binh commune in Huong Tra district; and the coastal communes of Vinh Hai Phu Loc district and Hai Duong in Huong Tra district. Using the lists of households available at the communes, 100 households were selected from each commune. In-person interviews were undertaken for the sampled households. The number of interviews completed and used in the present study is 597. BUI DUNG THE, BUI DUC TINH 231 3. Results and discussion 3.1. Exposure to climatic hazards Hazard exposure is the main play in disaster risks to local communities. Local communities in Thua Thien – Hue are affected by various types of climate hazards. Table1 shows that household groups with different livelihoods are exposed to different hazards at different levels. The households who live largely on aquaculture and fishing activities and forestry and cropping are of more exposure to climatic hazards. Non- farming household group is considered as least exposed to hazard. Over 46% of total households have exposure index of over 0.41 to 0.6 scales and about one fourth of households have exposure index of 0.61 – 1.0. There is a significant difference in hazard exposure of households who live in different topographical areas of the province. Households in coastal and delta area have higher level of exposure, as compared with households in the uplands. Table 1. Households hazard exposure by types of hazards and livelihoods Types of households Storm Floods Drought Landslide Flash flood Extreme colds Weighted Means Cropping 0.69 0.19 0.11 0.04 0.06 0.24 0.46 Livestock husbandry 0.63 0.13 0.09 0.09 0.10 0.21 0.42 Aquaculture & fishing 0.78 0.15 0.06 0.05 0.02 0.28 0.49 Forestry 0.81 0.03 0.09 0.05 0.25 0.24 0.48 Non-farming 0.74 0.13 0.09 0.06 0.06 0.20 0.38 (Source: Calculation by authors using the household survey data). Table 2. Households hazard exposure by types of hazards and livelihoods Type of region Mean T-Test F Sig. Coastal communes 0.46 13.18389 2.5E-06 Delta communes 0.52 Upland communes 0.42 Total 0.47 232 Households’ vulnerability to climate change in 3.2. Sensitivity Sensitivity is defined as the degree to which as system is affected either adversely or beneficially by climate – related disasters (Yusuf and Francisco, 2010). In this study sensitivity to climate change-induced disaster is measured by function of human sensitivity, livelihood sensitivity, infrastructure sensitivity and financial sensitivity. As presented in Table 3 and 4, there is a statically significant difference in the household sensitivity across type of livelihood but not across topographical area. Households with livelihood relied on aquaculture and fishing households was rated as the most sensitive livelihood practices in Thua Thien Hue. Households with livestock raising and forestry as main livelihoods are also sensitive to climate hazards. Non- farming practices are the least sensitive livelihood practices. Table 3. Sensitivity index of households by types of livelihoods Types of household livelihood Mean T-Test F Sig. Cropping households 0.40 131.3463 3.06E-80 Livestock husbandry households 0.51 Aquaculture and fishing households 0.55 Forestry households 0.56 Non-farming households 0.18 All 0.38 (Source: Calculation by authors using the household survey data). Table 4. Sensitivity index of household by topographical areas Type of region Mean T-Test F Sig. Coastal area 0.38 2.415999 0.28967 Delta area 0.37 Upland area 0.39 All 0.38 (Source: Calculation by authors using the household survey data). 3.3. Adaptive Capacity Adaptive capacity of households is defined as ability to adjust to climate change, including climate variability and extreme events in order to moderate the potential damage from it or to take advantage of its opportunities to deal with consequences BUI DUNG THE, BUI DUC TINH 233 (Yusuf and Francisco, 2010). It was established in the present study that non-farming households have the highest adaptive capacity to climatic hazards than any other groups (Table 5). The cropping household group also has high adaptive capacity. Households with aquaculture and fishing as main livelihoods have lowest adaptive capacity to climate hazards. Result of assessment also highlighted that forestry households also have low adaptive capacity to climatic disaster. This explained why hazards cause severe impacts on local communities, particularly to households who relied on aquaculture and fishing and forestry. Table 5. Adaptive Capacity of Households by type of livelihood Type of households Mean T-Test F Sig. Cropping households 0.46 10.66957 2.35E-08 Livestock husbandary households 0.43 Aquaculture and fishing households 0.34 Forestry households 0.41 Non-farming households 0.47 All 0.45 (Source: Calculation by authors using the household survey data). The comparison of adaptive capacity between households living in different topographical areas shows that households in delta area of the province have better adaptive capacity to climatic hazards than that of households living in upland and coastal areas (Table 6). About 15% of households living in delta communes have adaptive capacity index of 0.61 – 1.0 in comparison with about 14% of households living in upland communes and about 10% of households living in coastal regions. About 50% of households living in coastal communes and about 35% of households living in upland region have adaptive capacity lower than mean level of the whole sampled households. Table 6. Adaptive Capacity of Households by Topographical Area Type of region Mean T_Test F Sig. Coastal area 0.39 18.37644 1.8E-08 Delta area 0.47 Upland area 0.42 All 0.45 (Source: Calculation by authors using the household survey data). 234 Households’ vulnerability to climate change in 3.4. Vulnerability Households living in Thua Thien – Hue are highly vulnerable to climate variability and extreme events. There is significant difference in vulnerability index to climatic hazards between different groups of households with different livelihoods. As shown in Table 7 households with aquaculture and fishing practices and forestry as main livelihoods are more vulnerable to climate hazards. Non-farm households are least vulnerable to climatic hazards. Households that live on cropping practices and livestock are also less vulnerable (Table 7). Our analysis also indicate that about 20% of households with livelihood practices related to aquaculture and fishing practices and forestry have vulnerability index greater than 0.6. It is important to note that over 88% of non-farming household groups have vulnerability index smaller than 0.2. Table 7. Vulnerability index of households by type of livelihood Types of household livelihood Mean T- Test F Sig. Cropping .3197 50.003 0.000 Livestock .3875 Aquaculture and fishing .5939 Forestry .5449 Non-farming .0932 All .4215 (Source: Calculation by authors using the household survey data). Table 8 shows that households living in upland regions and coastal regions are the most vulnerable groups to climate hazards with average vulnerability index of 0.60 and 0.54, while that of households in the delta area is 0.33. Table 8. Household vulnerability index by type of livelihoods Topographical area Mean T-Test F Sig. Coastal area 0.54 3.195708 0.041642 Delta area 0.33 Upland area 0.60 All 0.42 (Source: Calculation by authors using the household survey data). BUI DUNG THE, BUI DUC TINH 235 4. Conclusion It is to conclude that there is a significant difference in hazard exposure of households living in different topography and different livelihood practices. Storm is assessed as the most severe hazards to households. Households living in coastal communes of this province are the most exposure to storms than households living in upland regions. Households with aquaculture and fishing as main livelihood are most exposed to storms. In terms of sensitivity, aquaculture and fishing household are more sensitive to climatic hazards. Forestry households and livestock raising household groups are also highly sensitive to climatic hazards. This is reason why these household groups suffer more damages than other groups. Adaptive capacity of households in the study site is relatively low. Households living in coastal and upland communes have lower adaptive capacity in terms of technology indicators, as compared with households living in delta region. However, it should be recognized the fact that social capital indicator is an important play in local adaptive capacity to extreme climate events in the context of low economically adaptive capacity. 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