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
Abstract The aviation industry’s growing carbon footprint necessitates data-driven evaluation tools.This study assesses the CO 2 efficiency of airlines operating at Istanbul Airport by integrating operational flight data with the Atmosfair Airline Index through a machine learning framework. A multiple linear regression model was developed to predict CO 2 Efficiency Points (EP) using two primary predictors: total payload and daily landing frequency. Flight observations were collected from FlightRadar24 for passenger aircraft operating on March 28, 2025, while EP values were obtained from the 2024 Atmosfair Index. The model demonstrated a strong explanatory capacity (Adjusted R 2 ≈ 0.73) and acceptable predictive accuracy (MAE = 3.82; RMSE = 4.45), indicating that flight frequency and payload are statistically significant determinants of CO 2 efficiency.The findings underscore that larger payloads and higher operational intensity are associated with improved efficiency scores, reflecting the critical role of data-informed scheduling and capacity management in sustainable aviation. Despite the limited sample size, the model exhibits robust internal validity and offers a transparent, reproducible approach for airport-level carbon performance assessment. By linking empirical aviation data with environmental performance metrics, this research contributes a lightweight yet scalable analytical framework that aligns with the International Civil Aviation Organization’s (ICAO) net-zero carbon target for 2050. The proposed model provides practical implications for airport operators and policymakers aiming to integrate predictive analytics into emissions monitoring and green airport management systems.
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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