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
As extreme meteorological events intensify, effective climate adaptation strategies has become urgent. Climate adaptation requires significant resources across multiple sectors, necessitating reliable, science-based information. South Korea lacks a standard management system for adaptation information, with various scattered sectors. To address this, the Korean Climate Adaptation Information Taxonomy (KADT, NIER, 2023) was developed. This study enhances completeness and practicality of KADT by refining its structure and introducing an efficiency evaluation method. First, the Korea National Climate Risk completeness diagnostic system was improved to a four-step process, expanding 72 National Climate Risks into 118 sub-risks. The bottom-up CID-based classification system was subdivided into five Types, 14 Mid-Categories, and 29 Sub-Categories, using KMA and IPCC AR6 WG-1 standards. The top-down structure was improved, focusing on Sub-Categories: 1) Science-Based Information (SCI) reclassifies risk management targets into 40 Sub-categories; 2) Adaptation Solutions & Options (SOL) reorganized 226 tasks into four Sub-categories and 36 details; and 3) Policy Evaluation and Governance (PEV) classified 8,374 local measures into 12 Sub-categories and 108 details. To evaluate the efficiency of KADT, ISO/IEC 20926:2009(IFPUG FPA) was employed. Results showed that the KADT-based approach significantly reduces functional points compared to the manual approach, improving work efficiency by 50% to 120%. This study provides a refined framework for managing climate adaptation information in South Korea, enhancing risk assessment and adaptation planning at national and regional levels, fostering more effective strategies.
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.
최근 본 자료 전체보기
UCI(KEPA) : I410-151-25-02-091125324
