Mapping Cross-Boundary Climate Change Vulnerability—Case Study of the Hualien and Taitung Area, Taiwan

Abstract

This study uses biophysical vulnerability and social vulnerability data from Taiwan's National Science and Technology Center for Disaster Reduction (NCDR) to assess the vulnerable areas in Hualien County and Taitung County (Hualien-Taitung Area). The most vulnerable townships are Yanping and Chenggong townships in Taitung County, and the least vulnerable townships are Fuli, Jian, Yuli, Shoufeng and Fenglin townships in Hualien County. The severity of potential impacts to townships in Taitung County is greater than that to townships in Hualien County. In the context of climate change, the Hualien-Taitung Area must develop policies that prioritize policies and strategies for hotspot townships. Furthermore, the Hualien-Taitung Area should integrate long-term cross-boundary policies and strategies to lobby for increased funding from the central government. Moreover, cross-boundary resource integration and regional adaptation strategies should be merged to help the Hualien-Taitung Area move toward sustainability.

Relationships among Environmental Attitudes, Risk Perceptions, and Coping Behavior: A Case Study of Four Environmentally Sensitive Townships in Yunlin County, Taiwan

“…The above results imply that for a given degree of high synthesized vulnerability, residents of Mailiao (high EF) have a low coefficient for the path from "attitudes to and knowledge of climate change" to "adaptation behavioral intention", and have a positive coefficient for the path from "disaster risk perceptions" to "adaptation behavioral intention". Residents of areas with higher EF typically consume more land and water resources [67,68]. In the future, whether residents of areas with high synthesized vulnerability/high EF have an inadequate awareness of disaster risk awareness should be examined.…”

A Systematic Review of Coastal Vulnerability Mapping

“…For example, some studies introduced innovative aspects to a standard methodology by including novel variables, methodological improvements, and validation or sensitivity analysis. A few papers included primary data collection via focus groups, interviews, questionnaires, and surveys to identify variables for index construction, validate their selection, or weight them for analysis [62][63][64][65].…”

“…These variables are often included as proxies for the socioeconomic factors that make people more or less vulnerable to stressors. Only four studies included variables related to governance and institutions [64,67,96,104], even though these aspects play a vital role in coastal decision-making that shapes their vulnerability. This is not surprising as spatially delineated proxies for such conceptual variables are difficult to quantify and generally lack spatially explicit delineations.…”

A New Framework for Understanding Urban Social Vulnerability from a Network Perspective

“…Cutter et al provided generally accepted factors that influence social vulnerability: (1) frail and physically limited individuals (e.g., the elderly, children, special needs people, and even females); (2) the type and density of infrastructure and lifelines; (3) building stock; (4) limited access to public resources (e.g., knowledge, information and technology); (5) insufficient access to service resources (e.g., education and medicine); (6) lack of access to political power and social capital; and, (7) beliefs and customs [22,25]. Owing to its "mediating role", social vulnerability is not only registered by exposure to hazards alone, but also resides in the local sensitivity to external stress and the capacity of the system to prepare, cope, and recover from damage [5,7,26,27]. Therefore, social vulnerability typically includes three basic components (or dimensions): exposure, sensitivity, and adaptability [28,29].…”