RISS 학술연구정보서비스

검색
다국어 입력

http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

변환된 중국어를 복사하여 사용하시면 됩니다.

예시)
  • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
  • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
닫기
    인기검색어 순위 펼치기

    RISS 인기검색어

      KCI등재후보

      설명가능한 인공지능을 활용한 COVID-19 이후 한국기업 ESG 등급 하락 예측 연구 = A Study on Predicting ESG Rating Downgrades of Korean Companies Post-COVID-19 using Explainable Artificial Intelligence

      한글로보기

      https://www.riss.kr/link?id=A109896911

      • 0

        상세조회
      • 0

        다운로드
      서지정보 열기
      • 내보내기
      • 내책장담기
      • 공유하기
      • 오류접수

      부가정보

      다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

      The importance of environmental, social, and governance (ESG) performance and related risk management has grown significantly in the aftermath of the COVID-19 pandemic. This study aims to predict ESG rating downgrades among publicly listed firms in Korea during the post-pandemic period (2020–2024) by comparing the performance of various machine learning models. Furthermore, explainable artificial intelligence (XAI) techniques are employed to interpret the prediction outcomes of top-performing models in greater depth. By integrating XAI methods, this study addresses the “black box” nature of AI-driven ESG prediction models, enhancing both interpretability and credibility of the results. The findings are expected to offer empirical insights for corporate managers, investors, and policymakers to proactively identify and manage ESG-related risks and make more informed, data-driven decisions.
      번역하기

      The importance of environmental, social, and governance (ESG) performance and related risk management has grown significantly in the aftermath of the COVID-19 pandemic. This study aims to predict ESG rating downgrades among publicly listed firms in Ko...

      The importance of environmental, social, and governance (ESG) performance and related risk management has grown significantly in the aftermath of the COVID-19 pandemic. This study aims to predict ESG rating downgrades among publicly listed firms in Korea during the post-pandemic period (2020–2024) by comparing the performance of various machine learning models. Furthermore, explainable artificial intelligence (XAI) techniques are employed to interpret the prediction outcomes of top-performing models in greater depth. By integrating XAI methods, this study addresses the “black box” nature of AI-driven ESG prediction models, enhancing both interpretability and credibility of the results. The findings are expected to offer empirical insights for corporate managers, investors, and policymakers to proactively identify and manage ESG-related risks and make more informed, data-driven decisions.

      더보기

      동일학술지(권/호) 다른 논문

      동일학술지 더보기

      더보기

      분석정보

      View

      상세정보조회

      0

      Usage

      원문다운로드

      0

      대출신청

      0

      복사신청

      0

      EDDS신청

      0

      동일 주제 내 활용도 TOP

      더보기

      주제

      연도별 연구동향

      연도별 활용동향

      연관논문

      연구자 네트워크맵

      공동연구자 (7)

      유사연구자 (20) 활용도상위20명

      이 자료와 함께 이용한 RISS 자료

      나만을 위한 추천자료

      해외이동버튼