인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
The management of climate adaptation information requires a classification framework capable of accommodating the climatic, environmental, and socio-economic characteristics of diverse countries, while ensuring international interoperability. The Korea national climate ADaptation information Taxonomy (KADT), optimized for Korea’s climatic–environmental context based on the National Adaptation Plans (NAP) and climate risks, requires flexible extension to capture a broader range of regional characteristics and achieve wider global applicability. This study aims to develop a generalizable classification methodology to enhance the KADT’s practical applicability and international utility. We propose a three-phase methodology combining a conceptual framework with Shannon information entropy theory. Five normalized environmental assessment indicators—Climate Exposure Index (CEI), Topographic Complexity Index (TCI), Socioeconomic Exposure Index (SEI), Social Vulnerability Index (SVI), and Adaptive Capacity Index (ACI)—are integrated into a Total Environmental Assessment Index (TEAI) using weights. For operational deployment, a formal expert-elicitation procedure (Delphi/AHP) is proposed to derive specific weights. The TEAI operationalizes the IPCC AR6 adaptation formulation and guides efficient adaptation budget allocation. The three-phase methodology uses normalized entropy (H) to select target-based, impact-based, or hybrid grouping strategies. Pilot applications in South Korea, the United Kingdom, and Mongolia demonstrated cross-context applicability: H results favored target-based grouping for South Korea and impact-based grouping for the UK and Mongolia. The methodology produced 29/8, 33/7, and 35/3 detailed subclasses/subclass items, respectively, derived from entropy-based grouping rules. This research advances the KADT from a static taxonomy to a dynamic, verifiable tool for evidence-based adaptation planning, supporting international compatibility and offering a pathway toward global standardization.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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UCI(KEPA) : I410-151-26-02-094499639
