2016 International Conference on Environment, Climate Change and Sustainable Development (ECCSD 2016) ISBN: 978-1-60595-358-8

A Synthesized Vulnerability Assessment: Case Study of Biophysical and Social Vulnerability in Taitung ,

* Yung-Jaan LEE #75 Chang-Hsing Street, , Taiwan 106 *Corresponding author

Keywords: Climate change, Biophysical vulnerability, Social vulnerability, Synthesized vulnerability.

Abstract. Strategies and action plans for reducing climate-related disaster risk can be pursued through mitigation and adaptation. In the last ten years, extreme climate events have severely caused flash floods, debris flows, landslides, etc. and have damaged many sectors, including agriculture, infrastructure and health. Since climate change is expected to have continued effects on social functioning and human development, this study proposes a vulnerability assessment framework combining both biophysical and social vulnerability and further carries out synthesized vulnerability analyses to identify vulnerable areas at the county level. Because of its geographical, geological and climatic features, Taiwan is susceptible to earthquakes, typhoons and numerous induced disasters. In many areas of Taiwan, potential hazards include debris flows and flash floods, but these problems are particularly severe in . Moreover, the impact of climate change will further cause severe damages and disasters. Therefore, an urgent task for Taitung County is to establish a vulnerability assessment framework as a policy tool to identify not only the regions needing attention but also the hotspots in which efforts could be made to reduce vulnerability. To analyze the biophysical vulnerability of Taitung, this study examined hazards in seven maps from Taiwan’s National Science and Technology Center for Disaster Reduction (NCDR) and made some adjustments according to local characteristics. Statistical data from the NCDR regarding social vulnerability were also used. Finally, GIS overlaying technique was used to perform a synthesized analysis of biophysical vulnerability and social vulnerability for each .

Introduction According to the Fifth Assessment Report by United Nations Intergovernmental Panel on Climate Change (IPCC), the global average land and sea surface temperature has risen linearly since 1950. Climate change is already causing impacts on natural and human systems on all continents and the oceans. If the rate of GHG emissions remains the same or even increases, global climate change may accelerate in the near future [1]. Consequently, the severe impact of the risk and uncertainties stemming from the accelerating climate change are likely to increase [2,3] and may exceed adaptation limits [1]. Climate change is expected to increase the frequency of extreme weather events. The expected effects of climate change on the global hydrological cycle include increased water vapor, increased rainfall intensity, and decreased snowfall. In terms of temperature, global warming will increase possibilities of heat waves, and some regions will become more arid [4]. Many institutions and researchers have paid attention to climate change vulnerability, mitigation and adaptation practices relating to this context [5,6,7,8]. In the late 20th century, temperatures in cities have increased significantly faster compared to rural areas [9]. The climate has also been changing over the same time period [10,11]. Furthermore, over the past decades, economic losses have been increasing due to the rising concentration of people and assets in hazardous areas, turning many of today’s cities a potential risk due to natural hazards [2,12]. The trend is expected to persist unless disaster and climate risk management becomes part of urban planning theories and practices [3,13].

To improve the adaptation capacity of Taiwan, to reduce vulnerabilities, and to establish integrated operational mechanisms, Taiwan’s National Development Council approved the "Adaptation Strategy to Climate Change in Taiwan" in 2012. Ministries and subordinate agencies are expected to continue performing adaptation planning, actions, implementation and management under this policy framework, in order to meet the objectives of climate change adaptation. Additionally, the National Development Council completed the "Local Climate Change Adaptation Plan and Practice Guidelines" in December, 2012 to guide local governments in exploring and proposing local climate change adaptation strategies and action plans. The objectives of the vulnerability assessment are mostly related to the risk identification, the understanding of the factors of vulnerability [14]. Vulnerability assessment frameworks are based on multiple conceptual models, and how the factors in the models interact to affect vulnerability [5]. At current stage, risk assessment information for Taiwan is inadequate, and a holistic and complete assessment requires long-term observations and research. However, the impacts of climate change on Taiwan’s environment in the short-term only focus on such disasters as flash floods, debris flows and landslides. The impacts of these disasters on society can be analyzed through the perspectives of prevention and relief management. Therefore, this study examines the historical background and development conditions of Taitung County. Quantitative data for biophysical and social vulnerability, obtained from Taiwan’s National Science and Technology Center for Disaster Reduction (NCDR), are adopted to explore synthesized vulnerability to climate change as a foundation for future climate-related adaptation planning.

Literature Review

Vulnerability Expansions of cities cause over concentrated areas in terms of population and economy and turn many of today’s cities into disaster hotspots [13]. This risk is varying rapidly with time, location, exposure, vulnerability and resilience [12]. Vulnerability and resilience are not only about the present status, but are also about what society has done to itself over the long period of time; why and how society has taken certain set of actions to reach the current state; and how society might change the current state to improve in the future [15]. This information is important for increasing sustainability [16]. The key requirement for contemporary urban planners and managers is to understand the issues of vulnerability and resilience [3,13,16,17,18,19]. Authorities must integrate resilience into their decision makings about urban development and governance [13]. However, throughout the world, many cities facing the effects of drastic climate change are expected to work towards a "resilient city", in order to reduce the impact of climate change [3,13,15,18,20]. Vulnerability is a persistent, ‘‘normal’’ condition related to poor development and unsustainability practices [15,21]. Vulnerability can be described as a characteristic to environmental change directing toward possible impacts [5]. Adger’s definition of vulnerability can fully describe this characteristic: “Vulnerability is the state of susceptibility to harm from exposure to stresses associated with environmental and social change and from the absence of capacity to adapt” [22]. The vulnerability of specific domains can be evaluated using a synthesized and comprehensive analysis which consists of exposure assessment on various local domains (a group experiences of stressors or a specific area) of climate change, sensitivity assessment of climate change impacts on a specific area, potential impact analysis on each domain in climate change, and analysis of the adaptability of each domain to respond to climate change [5,6]. Restated, synthesized and comprehensive vulnerability analyses require the explorations of “how?”, “why?”, and “to what?” people and the environment are vulnerable [5,6]. The concept of vulnerability is extremely complex and particularly difficult to operationalize and/or to measure with both biophysical (e.g. climatic conditions, natural hazards, topography, land cover) and socio-economic (e.g. demography, poverty, employment, gender) factors that influence the potential for harm [5, 8, 23]. Climate change and other risks have different effects on vulnerability and have different social effects [3, 17, 24]. Vulnerability assessment frameworks as a policy tool identified not only the specific areas needing immediate attention but also the hotspots in which efforts could be made to reduce vulnerability to anticipated climate change impacts [3, 23, 25, 26]. Undertaking this propensity will allow us to identify particular measurements for constructing resilience when coping with the disturbance and impacts [3, 26, 27]. Expanding the Approach to Vulnerability and Resilience Academics and practitioners have worked hard to explore vulnerability and resilience to the impacts of potential change, at all time and space scales and based on numerous types of change [15,21]. No longer must ways of living and livelihoods be categorized as vulnerability and/or resilience, but instead they are acknowledged as interconnected multiple sustainability goals, dealing with multiple exposures. Restated, academics and practitioners recognize the potential linkages between vulnerability and resilience frameworks [5, 13]. Vulnerability and resilience are assumed to be connected through response capacity, which is proposed to be a core component of vulnerability [28]. If this depiction is accepted, it is uncontroversial to suggest that the interconnectedness between vulnerability and resilience is that they are both about responding to a disturbance and its implications for humans and nature. This is not the same as setting up vulnerability and resilience as absolute opposites. Instead, it does capture the idea that a more vulnerable area can often, though not necessarily always, mean being less resilient to unexpected disturbances [5,27,28]. Resilience does not refer to “bouncing back” to a pre-existing state [15], rather the ability of ecosystems, institutions, infrastructure and knowledge networks to evolve and adapt so that urban residents can survive and thrive even when dealt with numerous unpredictable disturbances [29]. Rather than “bouncing back,” resilience and sustainability could instead be established through a society that does not get “back to normal,” but instead does better, even through “bouncing forward” [15] “building back better” [2] or “building back better and smarter” [3]. Recently, resilience has been considered as a more basic component than vulnerability in socio-institutional dynamics [28]. “A ‘Resilient City’ is prepared to absorb and recover from any shock or stress while maintaining its essential functions, structures, and identity, as well as adapting and thriving in the face of continual change. This requires evidence-based, long-term, and inclusive strategies that take an integrated, systems approach to reduce vulnerability and disaster risk while increasing adaptive capacity in line with sustainable development goals” [20]. Vulnerability, resilience and adaptation are vital concepts of climate change and have been important constructs in relation to climate change and disaster-related issues [3,4,17]. Assessing vulnerability is essential for effective disaster mitigation and for adapting to environmental and climate changes [26]. Moreover, a clear understanding and effective assessment of vulnerability to environmental change and extreme climate events are needed to make sure that policymakers can develop appropriate mitigation and adaptation policies [8,13,18]. Local governments therefore have a crucial role and responsibility to prepare for and adapt to climate change. Resilience as a planning and governance main concern for cities is rising with governments, social scientists, planners, architects and engineers taking up the resilience agenda. Moreover, resilience must be connected to sustainability so that the resilience it is trying to plan and design for actually helps the society move toward preferred future sustainability states, and not undesired ones [12].

Method

Study Site Often situated along coastlines, in flood plains, or on slopeland, cities and townships with their concentrations of people and assets are vulnerable to numerous disasters [18]. They must adapt to past and future impacts of climate change, despite uncertainties and unknown risks and its local effects [13,

16, 30]. Furthermore, coastal and communities are particularly vulnerable to the impacts of climate change due to their direct dependence on the goods and services provided by marine ecosystems, the closeness of houses and infrastructure to rising seas and extreme weather events, and the increasing unpredictability of weather patterns. For this reason, an increasing focus has been on climate change vulnerability, adaptation and resilience policies and practices relating to this context [6, 7]. Climate change scientists report that the current average global temperature is 4°C higher than that of the preindustrial level and may continue to rise until 2060 [31]. Since Taiwan is an island, it is susceptible to natural hazards, including severe climate change impacts. According to a recent IPCC report, the global temperature and sea level have risen significantly, and the speed of the change is accelerating [1]. Taitung County, one of the 22 municipalities and counties of Taiwan, is located in Southeast coast of Taiwan and includes two small offshore , Green Island and Orchid Island. Taitung County is 3,515 km 2, or 10% of the area of Taiwan, and is the third largest county in Taiwan. The population of Taitung County is approximately 240,000, or 1% of the population of Taiwan. The north to the south border of Taitung County is about 176 km. Taitung County is the longest and narrowest county in Taiwan. Mountains and hillside areas comprise 85% of its area. The mountainous geographical characteristics of Taitung County limit its arable land. In terms of the effects of topographical and geographical conditions on economic development, the main economic activities of Taitung County are farming, fishing and tourist industries. That is, the county is mostly rural. Moreover, more than one-third of the population in Taitung County is aboriginal peoples. Due to its geographically narrow characteristics, its 16 cities and townships are very different. Furthermore, due to the insufficient financial resources, decision makers must thoroughly examine vulnerability in each city and township in order to reduce the potential climate change impacts and achieve the goal of sustainability and resilience. Research Methods Vulnerability assessment frameworks are based on different conceptual models of how these factors interact to influence vulnerability [3]. This study adopts the biophysical and social vulnerability focuses specifically on characterizing the geography of socio-political factors of vulnerability that influence how human and natural systems cope with or respond to hazards and stress [5,8,14]. More comprehensive and integrated approaches to vulnerability assessment require a more systemic understanding of human-environment interaction [3,14]. A visual tool for communicating the results of vulnerability assessment to policymakers, practitioners and the general public is the spatial vulnerability assessment providing maps of the vulnerability distribution and using geographic information system (GIS) and remote sensing data [3,5,26,32]. Biophysical Vulnerability. In this study, the data used to evaluate biophysical vulnerability included areas affected by mudslides, geographical distribution of rock falls, potential flooding caused by daily rainfall, potential debris slides, potential rock slides, potential tsunami, and dip slope distribution maps provided by the National Science and Technology Center for Disaster Reduction (NCDR). The ratio of biophysical vulnerability area to township area was also calculated to analyze the biophysical vulnerability of each township in Taitung County. The formula for spatial vulnerability is as follows:

. (1) where , 1, 2, …, 16, represents each city and township in Taitung County: Haiduan, Changbin, Daren, Yanping, Dawu, Beinan, Donghe, Taimali, Jinfeng, Chenggong, Guanshan, Chishang, Luye, , Ludao, Lanyu. area of effect from dip slope, debris slide, olistolith, tsunami, inundation potential (600mm), inundation potential (350mm), rock fall, mudslide. Social Vulnerability. Four dimensions of social vulnerability included in the NCDR statistics for 2011 were considered: exposure, mitigation & preparedness, response capacity, and recovery capacity. Figure 1 shows that exposure factors include (1) industry, (2) household assets, (3) population; mitigation & preparedness factors include (1) prevention engineering, (2) regulations and enforcement, (3) disaster prevention education; response capacity factors include (1) refuge shelters, (2) disaster disadvantaged group, (3) rescue, (4) medication; recovery capacity factors include (1) economy and (2) social network.

Figure 1. Four main dimensions of social vulnerability. Social Vulnerability Method by NCDR. Each dimension includes several statistical items. Positive and negative correlations between statistical items and vulnerability are expressed by (+) and (-), respectively. Values are multiplied by + and - when calculating vulnerability. There are mainly two types of composite index, that is composite vulnerability index without weight and composite vulnerability index with weight. A number of studies have either used equal weights or no weights to represent a composite vulnerability index [8, 33, 34]. Arguments for not using weights include its simplicity, the subjectivity associated with weighting procedures, and unavailability of all supporting information for weighting [8, 26].

(2) where is the degree of social vulnerability and represents , , , , which are indicators of exposure, mitigation & preparedness, response capacity, and recovery capacity, respectively. , 1, 2, 3 (industry, household assets and population, respectively). ， 1, 2, 3 (prevention engineering, regulations and enforcement, and disaster prevention, respectively). ， 1, 2, 3, 4 (refuge shelter, disaster disadvantaged group, rescue, and medication, respectively). ， 1, 2 (economy and social network, respectively). The values for each township under each item are z scores standardized from the original data using the following equation:

(3) : an original score before standardization : average for the population standard deviation for the population

the distance between the original score and the average for the population. The standard deviation is calculated. When the original score is lower than the average, is negative. When the original score is higher than the average is positive. After normalizing the social vulnerability data provided by NCDR, vulnerability can be compared between each township in Taitung County. The following definitions are applied. 1, 2,…, 27 represents the following factors that affect social vulnerability: gross output value of agriculture, forestry, fishing, and animal husbandry; gross output value of industry and business; value of household property; households in potential debris-flow areas; population; ratio of low seismic design level buildings in Taiwan Earthquake Loss Estimation System; the number of drainage projects; ratio of overused slopeland; the frequency of disaster prevention drills; number of access roads; number of shelters; number of elderly persons living alone; number of persons with disabilities; number of nursing homes; number of firefighters; number of fire engines; number of ambulances; number of hospital service areas; number of medical personnel; number of hospital beds; number of low income households; ratio of recovering time; ratio of typhoon and floods insurance; ratio of earthquake insurance; revenue from subvention; ratio of social welfare. : The lowest value obtained after standardizing vulnerability score : The highest value obtained after standardizing vulnerability score

Normalization formula:

Research Results and Discussion

Biophysical Vulnerability Notably, Taitung City had the highest proportion of total biophysical vulnerability area to township area because its daily inundation potential (600mm) reaches 10.35%, potential area of tsunami also reaches 5.66%, and the total area of biophysical vulnerability minus overlapping areas result in a total of 14.42%. The second most vulnerable area is Chenggong, mainly due to its dip slope area which stands 2.13%, potential area of tsunami which stands 2.16%, area of debris slide which stands 2.13%, and the total of each biophysical vulnerability area is 9.85%. The third most vulnerable area is Donghe, mainly due to its dip slope area which stands 3.90%, area of debris slide which stands 2.59%, and the total of each biophysical vulnerability area is 7.52%. In contrast, Haiduan had the lowest proportion of land with biophysical vulnerability (0.19%). The most widely distributed area of biophysical vulnerability in the County is dip slope (0.92% of total area) followed by areas with daily inundation potential (600mm) (0.79% of total area). The biophysical vulnerability with the smallest distribution in the county is olistolith (0.01% of total area) followed by mudslide potential with a 0.12% area of affect. Figure 2 shows the biophysical vulnerability distribution in Taitung County. Social Vulnerability

Normalization of Social Vulnerability Data. The social vulnerability data provided by NCDR was normalized to enable comparisons of townships of Taitung County. The following definitions were applied. : Value obtained by normalizing vulnerability. : Value obtained by standardizing vulnerability. : Lowest value obtained by standardizing vulnerability score : Highest value obtained by standardizing vulnerability score The normalization formula is where] 1, 2,…, 27 represents the following social vulnerability factors: gross output value of agriculture, forestry, fishing, and animal husbandry; gross output value of industry and business; value of household property; households in potential debris-flow areas; population; ratio of low seismic design level buildings in TELES; the number of drainage projects; ratio of overused slopeland; the frequency of disaster prevention drills; number of access roads; number of shelters; number of elderly persons living alone; number of persons with disabilities; number of nursing homes; number of firefighters; number of fire engines; number of ambulances; number of hospital service areas; number of medical personnel; number of hospital beds; number of low income households; ratio of recovering time; ratio of typhoon and floods insurance; ratio of earthquake insurance; revenue from subvention; ratio of social welfare (Table 1).

Figure 2. Biophysical vulnerability distribution for Taitung County.

Table 1. Normalized social vulnerability exposure scores for Taitung County. Mitigation & Exposure Response Capacity Recovery Capacity Preparedness Haiduan Township 0.05 0.26 0.66 0.93 Changbin Township 0.08 0.90 1.00 0.84 Daren Township 0.06 0.77 0.45 0.96 Yanping Township 0.03 0.79 0.55 0.98 Dawu Township 0.10 0.53 0.59 0.92 Beinan Township 0.32 0.00 0.41 0.76 Donghe Township 0.15 0.51 0.66 0.85 Taimali Township 0.15 0.69 0.79 0.83 Jinfeng Township 0.03 0.49 0.40 0.93 Chenggong Township 0.17 0.41 0.63 0.70 Guanshan Township 0.14 0.90 0.53 0.73 Chishang Township 0.16 0.92 0.40 0.48 Luye Township 0.14 0.75 0.00 0.84 Taitung City 1.00 1.00 0.05 0.00 The total standardized social vulnerability score for each township can be classified as high, medium, or low. The areas with the highest total standardized scores for social vulnerability are Changbin, Haiduan, Daren, Yanping, Taimali and Donghe. Areas with medium total standardized scores for social vulnerability are Dawu, Chenggong, Guanshan and Chishang. Areas with the lowest total standardized scores for social vulnerability are Beinan, Jinfeng, Luye and Taitung City.

Overall Results for Biophysical and Social Vulnerability Assessment. Considering the potential impacts of climate change and the domain of climate change impacts, a high vulnerability domain is defined as an area with a high potential impact of climate change and low adaptability. Conversely, a low vulnerability domain is defined as an area with a low potential impact of climate change and high adaptability. That is, vulnerability is defined by the potential impact and adaptability matrix. Moreover, climate change vulnerability assessment results can be plotted on a risk map, which can be used to interpret the vulnerability assessment results in each township. It also provides a basis for developing adaptation and action plans. According to the area ratio of the regional distribution of biophysical vulnerability for each township in Taitung County, the biophysical vulnerability for each township can be classified as high, medium, and low. Based on these area ratios, the townships with high biophysical vulnerability were Taitung City, Chenggong, Donghe, Dawu and Changbin. Townships with medium biophysical vulnerability were Taimali, Chishang, Luye, Guanshan and Daren. Townships with low biophysical vulnerability were Beinan, Yanping, Jinfeng and Haiduan. A similar method was used to classify the social vulnerability of townships as high, medium, or low according to their normalized scores for social vulnerability. Townships with high normalized scores for social vulnerability included Changbin, Taimali, Yanping and Guanshan. Townships with medium levels of normalized scores of social vulnerability are: Daren, Donghe, Dawu and Taitung City. Townships with low levels of normalized scores of social vulnerability are: Chishang, Haiduan, Chenggong, Jinfeng, Luye and Beinan. Nine categories are defined through cross-comparison of biophysical and social vulnerability: Low-Low, Low-Medium, Low-High, Medium-Low, Medium-Medium, Medium-High, High-Low, High-Medium and High-High. On a 3x3 grid, five colors (V1~V5) are used to distinguish the comprehensive vulnerability score ratings from low to high. The V1 is the low-low layer; V2 is the low-mid and mid-low layer; V3 is the mid-mid, low-high, high-low layer; V4 is the mid-high, high-mid layer; and V5 is the high-high layer (Figure 3).

Social Vulnerability

Low Medium High Vulnerability Vulnerability V1 V3 Low V2 Biophysical Biophysical Beinan, Jinfeng, Haiduan Yenping V2 V3 V4 Medium Chishang, Luye Daren Taimali, Guanshan V3 V4 High V5 Chenggong, Changbin Taitung, Donghe, Dawu Note: Vulnerability: V5 ＞V4 ＞V3 ＞V2 ＞V1 Figure 3. Comprehensive assessment of biophysical and social vulnerability. Figure 4 shows the five categories (V1 to V5) used for comprehensive assessment of biophysical and social vulnerability. Areas in category V4 (second most vulnerable) include Taimali, Guanshan, Taitung City, Donghe and Dawu. Areas in V3 include Yanping, Daren, Chenggong and Changbin. Areas in V2 include Chishang and Luye. Areas in V1 (least vulnerable) include Beinan, Jinfeng and Haiduan. The results indicate that Taitung County does not have townships which are both high in biophysical and social vulnerability because townships in the coastal areas have high levels of biophysical vulnerability and medium levels of social vulnerability. The likely explanation is that, because of the relatively sparse population density in Taitung County, fewer household assets along coastal regions, resulting in fewer losses when disaster strikes. Figure 4 comprehensively depicts the spatial distribution of vulnerability for Taitung County.

Figure 4. Relative vulnerability for each township. Consolidating previous analysis and assessment results improves understanding of the impact of vulnerability caused by climate change in Taitung County and facilitates follow up of adjustment strategies, identification of priority adjustment areas, and identification of areas that are most vulnerable. The causes of climate change today are bound to have impacts in the future. After assessing vulnerability, the natural environment and society of Taitung must enhance its disaster mitigation, response, and recover capacities to address these impacts. Appropriate land use plans, restrictions, and other supporting measures are needed to supplement its capacity for climate change adaptability. The vulnerability of Taitung is lower than the national average, probably due to its lower total agricultural production, total industrial production, household and building value, and household population. Restated, social vulnerability involves population factors, and Taitung County is scarcely populated. Therefore, from a scientific perspective, social vulnerability is lower than the national average. However, exposure and vulnerability are dynamic; they change with time and space and with factors associated with the economy, society, geography, population, culture, system, governance, and environment. Based on the degree of wealth, education level, disabilities and health, and gender, age, social class and other inequalities in social and cultural characteristics, individuals and communities also differ in their exposure and vulnerability. Therefore, Taitung County should carefully compare the causes of vulnerability in each township to ensure an effective response to the impacts of climate change.

Conclusions In the near future, a critical challenge for cities will be measuring climate-related vulnerability [3, 4, 13, 17, 18]. Identifying highly vulnerable areas and proposing adaptation plans and strategies are urgent tasks for effective and efficient city governance. Vulnerability studies are applicable in this field and can help identify important sites for carrying out adaptation strategies. The results of this study indicated that the most vulnerable cities and townships in Taitung County are Taimali, Guanshan, Taitung, Donghe and Dawu. The second most vulnerable cities and townships include Yanping, Daren, Chenggong and Changbin. Based on the findings of this study, four additional vulnerability studies at the county level are proposed:

Future Vulnerability Studies Can Select Different Vulnerability Variables According to Local Characteristics The current study integrated vulnerability by combining biophysical vulnerability and social vulnerability. However, different counties can estimate their own vulnerability level according to scenarios and their history of local disasters. The background data analyzed in this study included the area of effect from dip slope, debris slide, olistolith, tsunami, inundation potential (600mm), rock fall, and mudslide. These data were then used to calculate the coverage of each biophysical vulnerability in different cities and townships and to evaluate the biophysical vulnerability of cities and townships in Taitung County. However, decision makers in different counties can use other variables to measure local biophysical vulnerability. Social vulnerable was measured in terms of gross output value of agriculture, forestry, fishing, and animal husbandry; gross output value of industry and business; value of household property; households in potential debris-flow areas; population; ratio of low seismic design level buildings in TELES; the number of drainage projects; ratio of overused slopeland; the frequency of disaster prevention drills; number of access roads; number of shelters; number of elderly persons living alone; number of persons with disabilities; number of nursing homes; number of firefighters; number of fire engines; number of ambulances; number of hospital service areas; number of medical personnel; number of hospital beds; number of low income households; ratio of recovering time; ratio of typhoon and floods insurance; ratio of earthquake insurance; revenue from subvention; ratio of social welfare. However, different areas can select different variables according to their local characteristics and can perform further sensitivity analyses to prioritize the input of future resources. Decision Makers Require Sufficient Climate Change Adaptation Resources Local governments that are implementing climate change adaptation plans to reduce climate change vulnerability should focus on those cities and townships that are most vulnerable [3,23,25,26]. Moreover, vulnerability is only one of the measurements required for making climate change adaptation plans. Due to the extensive fields and disciplines involved, vulnerability studies are one evaluation tool to integrate different perspectives and can be easy for people to understand local vulnerability and further to promote people’s climate change crisis consensus. Applications in Regional Governance When discussing the issue of city-region spatial integration from the governance perspective, the objective is not to compare operational management of the different governments within a city-region, but to avoid rigid administrative limitations and to emphasize territorial governance. Studies of climate systems in Taiwan normally use the Dajia River as the boundary between the North and the South. In contrast, the Taiwan Central Government adopts the political boundary for each county and city to draft climate change adaptation plans and does not consider cross-boundary cooperation. By applying the proposed evaluation method of combining cross-boundary governance, Taiwan can perform regional vulnerability evaluations that improve local climate change plan only focusing on each county’s political boundary. The Central Government can also perform cross-boundary climate change adaptation plans to increase the comprehensiveness of proposed climate change policies. Vulnerability Can Further Integrate with Future Climate Change Scenario Simulation Currently, no prediction values have been set for simulations of future scenarios in Taiwan. Therefore, no vulnerability evaluation for different future scenario can be performed; only secondary statistical data can be used to estimate possible conditions for different counties and cities. Future studies should include variables for different situations and should integrate simulations of various climate change scenarios to compare vulnerability in different situations. Climate change should not be undertaken at the expense of other challenges and opportunities in everyday life [15]. Therefore, non-climate-related phenomena (such as land subsidence intensive fish farming, salinization of freshwater supplies due to excessive drawdown, and slopeland exploitation because of illegal plantation and earthquakes) should also be taken into consideration for disaster risk reduction [2]. Furthermore, other contributors including social injustices, discrimination, poor wealth distribution, and a value system that allows unsustainable use of environmental resources should also be considered [3]. Many authors have suggested that it is important to understand the interactions between global environmental changes (including climate change) and other socio-economic and political changes happening at multiple spatial and temporal scales in order to create effective and efficient adaptation policy [3,6]. Moreover, the domain within development and governance work that is best appropriate for emphasizing climate change adaptation is disaster risk reduction [13,15,35] which deserves further investigation in addition to vulnerability explored in this study. Climate has a cultural history, which is interwoven with its physical history [36]. This Taitung study is a case study in the Southeast coast of Taiwan, an island state lying in the west side of the Pacific Ocean. Although this study has its unique cultural and natural characteristics, this study can serve as an example for other island states to explore the climate change impacts and the related biophysical and social vulnerabilities.

Acknowledgments The author would like to thank the Taitung County Government and the Ministry of Science and Technology of the Republic of , Taiwan, for financially supporting this research under Contract No. NSC102-2410-H-170-005-SS2. Ms. Te-Hsiu Huang and Ms. Chiao-Chi Chen are appreciated for their data collection.

References [1] IPCC, Report of Working Group II on Impacts, Adaptation and Vulnerability to the Fifth Assessment Report of the IPCC, IPCC, Geneva, 2014. [2] I. Davis, K. Yanagisawa and K. Georgieva, Disaster Risk Growth and Livelihood: Investing in Resilience and Development, first ed., Routledge, London and New York, 2015. [3] Y. Jabareen, The Risk City: Cities Countering Climate Change: Emerging Planning Theories and Practices around the World, Springer, New York and London, 2015. [4] C.B. Field, V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea and L.L. White, Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, ed., Cambridge University Press, Cambridge, 2014. [5] R. Aretano, T. Semeraro, I. Petrosillo, A. De Marco, M.R. Pasimeni and G. Zurlini, Mapping ecological vulnerability to fire for effective conservation management of natural protected areas, Eco. Model. 295 (2015) 163-175. [6] N.J. Bennett, P. Dearden and A.M. Peredo, Vulnerability to multiple stressors in coastal communities: a study of the Andaman coast of Thailand, Clim. Dev. (2014) DOI:10.1080/17565529.2014.886993. [7] R.J.T. Klein, A.G. Patt, Assessing Vulnerability to Global Environmental Change: Making Research Useful for Adaptation Decision Making and Policy, Earthscan, London, 2012. [8] Y.J. Lee, Social vulnerability indicators as a sustainable planning tool, Environ. Impact Assess. Rev. 44 (2014) 31-42.

[9] S.L. Harlan, D.M. Ruddell, Climate change and health in cities: impacts of heat and air pollution and potential co-benefits from mitigation and adaptation, Curr. Opin. Environ. Sustainability 3 (2011) 126-134. [10] R. Gelca, K. Hayhoe and I. Scott-Fleming, Observed trends in air temperature, precipitation, and water quality for Texas reservoirs: 1960-2010, Texas Water J. 5 (2014) 36-54. [11] N. Michelutti, A.P. Wolfe, C.A. Cooke, W.O. Hobbs, M. Vuille and J.P. Smol, Climate change forces new ecological states in Tropical Andean Lakes, PLoS ONE 10(2) (2015) e0115338. doi:10.1371/journal.pone.0115338. [12] C. Tarhan, C. Aydin and V. Tecim, How can be disaster resilience built with using sustainable development? Procedia Soc. Behav. Sci. 216 (2016) 452- 459. [13] A. Jha, H. Brecht and Z. Stanton-Geddes, Building resilience to disasters and climate change in the age of urbanization, in: I. Davis, K. Yanagisawa and K. Georgieva (Eds.), Disaster Risk Growth and Livelihood: Investing in Resilience and Development. Routledge, New York, 2015, pp.7-27. [14] B.L. Preston, E.J. Yuen and R.M. Warburton, Putting vulnerability to climate change on the map: a review of approaches, benefits and risks, Sustain. Sci. 6 (2011) 177-202. [15] I. Kelman, J.C. Gaillard and J. Mercer, Climate change’s role in disaster risk reduction’s future: beyond vulnerability and resilience, Int. J. Disaster Risk Sci. 6 (2015) 21-27. [16] Y.-J. Lee, Building resilient cities through community empowerment: principles and strategies for Taiwan Island. The 2015 International Conference on Spatial Planning and Sustainable Development (SPSD 2015). August 7-8, 2015, Taipei, Taiwan. [17] Y. Jabareen, Planning the resilient city: concepts and strategies for coping with climate change and environmental risk, Cities 31 (2013) 220-229. [18] A.K. Jha, T.W. Miner and Z. Stanton-Geddes, Building Urban Resilience: Principles, Tools, and Practice. Directions in Development, first ed.,World Bank, Washington, 2013. [19] D.K. Khailania, R. Pererab, Mainstreaming disaster resilience attributes in local development plans for the adaptation to climate change induced flooding: a study based on the local plan of Shah Alam City, Malaysia, Land Use Policy 30 (2013) 615-627. [20] ICLEI, Resilient Cities Report 2015, ICLEI, Bonn, 2015. [21] B. Wisner, J.C. Gaillard and I. Kelman, Handbook of Hazards and Disaster Risk Reduction, first ed., Routledge, Abingdon, 2012. [22] W.N. Adger, Vulnerability, Glob. Environ. Chang. 16(3) (2006) 268-281. [23] H. Sajjad, P. Jain, Assessment of socio-economic vulnerabilities among urban migrants in South-East Delhi, India, J. Stud. Soc. Sci. 7(1) (2014) 65-81. [24] IPCC, Climate Change 2007: Synthesis Report. Contribution of working groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, 2007. [25] S.L. Cutter, L. Barnes, M. Berry, C. Burton, E. Evans, E. Tate and J. Webb, A place-based model for understanding community resilience to natural disasters, Glob. Environ. Chang. 18 (2008) 598-606. [26] S. Khan, Vulnerability assessments and their planning implications: a case study of the Hutt Valley, New Zealand, Nat. Hazards 64 (2012) 1587-1607. [27] C. Béné, R.G. Wood, A. Newsham and M. Davies, Resilience: New Utopia or New Tyranny? Reflection about the Potentials and Limits of the Concept of Resilience in Relation to Vulnerability Reduction Programs, IDS Working Paper 405, Institute of Development Studies, Brighton, 2012. [28] M.N. Ahsan, K. Takeuchi, The dynamics among poverty, vulnerability, and resilience: evidence from coastal Bangladesh, Nat. Hazards (2015) DOI 10.1007/s11069-015-1950-0. [29] S. Kernaghan, J. da Silva, Initiating and sustaining action: experiences building resilience to climate change in Asian cities, Urban Clim. 7 (2014) 47-63. [30] D. Archer, F. Almansi, M. DiGregorio, D. Roberts, D. Sharma and D. Syam, Moving towards inclusive urban adaptation: approaches to integrating community-based adaptation to climate change at city and national scale, Clim. Dev. 6 (2014) 345-356. [31] R. Betts, M. Collins, D. Hemming, C. Jones, J. Lowe and M. Sanderson, When could global warming reach 4°C? Philos. Trans. R. Soc. 369 (2011) 67-84. [32] B.Y. Liu, Y.L. Siu, G. Mitchell and W. Xu, The danger of mapping risk from multiple natural hazards, Nat. Hazards (2016) (DOI 10.1007/s11069-016-2184-5). [33] S.L. Cutter, B.J. Boruff and W.L. Shirley, Social vulnerability to environmental hazards, Soc. Sci. Q. 84 (2003) 242-261. [34] L. Schipper, Meeting at the crossroads? exploring the linkages between climate change adaptation and disaster risk reduction, Clim. Dev. 1(1) (2009) 16-30 [35] A. Planitz, Disaster risk governance and the principles of good governance, in: I. Davis, K. Yanagisawa and K. Georgieva (Eds.), Disaster Risk Growth and Livelihood: Investing in Resilience and Development. Routledge, London and New York, 2015, pp. 225-237. [36] M. Hulme,Climate and its changes: a cultural appraisal, Geo. 2(1) (2015) 1-11.