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GCAM을 이용한 국내 수송부문 모델링

Modeling Domestic Transportation Sector Using Global Change Assessment Model

  • 전승호 (아주대학교 에너지시스템학과) ;
  • 김수덕 (아주대학교 에너지시스템학과)
  • 투고 : 2016.10.20
  • 심사 : 2017.04.26
  • 발행 : 2017.04.30

초록

본 연구에서는 통합모형인 GCAM을 활용한 국내수송부문을 모델링에 대해 논의한다. GCAM은 IPCC 5차 보고서 평가에도 활용된, 국제적으로 널리 쓰이는 모형이다. 그럼에도 불구하고 이를 국내수송부문에 그대로 적용하는 데 상당한 문제가 있다. 첫째, GCAM의 기준년도(2010년) 수송 서비스수요가 국가통계와 일치하지 않다는 점. 둘째, 수송부문 시뮬레이션 결과가 관련 부문별 서비스수요의 과거추이를 제대로 반영하고 못하고 있다는 점이다. GCAM을 활용한 국내 수송부문 모델링에서 가장 중요하게 영향을 미치는 수송서비스수요 항등식을 상세히 점검함으로써, 기준년도의 서비스수요를 국가통계와 일치시키도록 노력하였다. 또 GCAM의 시뮬레이션 결과가 과거 통계추이를 제대로 반영할 수 있도록 기존모형을 점검, 수정하였다. 점검 및 수정결과, 기존 GCAM의 시뮬레이션 결과와 어떤 부분에서 문제가 있는지, 또 수송부문별 과거 서비스 수요의 추이가 어떻게 제대로 반영되고 있는지를 상세히 보고하였다. 본 연구는 향후 수송부문의 정책, 기술평가 및 온실가스저감 대책 마련 등을 위한 시나리오 분석의 기본분석도구로 유용하게 쓰일 수 있을 것으로 보인다.

In this study, we discuss the modeling of domestic transport sector using GCAM(Global Change Assessment Model). The GCAM is one of integrated assessment models widely used in internationally modeling community, and applied for the evaluation of IPCC 5th Report. Nevertheless, it is noted that there are a considerable number of problems in its application to domestic transport sector. First, the base year information of GCAM for detailed transportation service demand is found not consistent with national statistics. Second, the transportation sector simulation results do not properly reflect the past trends of service demand. Thus, the base year service demand is carefully matched with the detailed national statistics. In addition, the existing models were checked and modified so that the simulation results of service demand can accurately reflect past trends of national statistics. As a result, it is reported in detail how the current GCAM simulation results are corrected and how the trend of past transportation sector service demands is properly reflected. This study is expected to be useful as a basic tool for future scenario analysis for transportation policy, technology evaluation and greenhouse gas reduction measures.

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