差异(会计)
过程(计算)
计算机科学
质量(理念)
过程管理
风险分析(工程)
分解
管理科学
运筹学
系统工程
业务
工程类
会计
生态学
哲学
认识论
生物
操作系统
作者
Sara Giarola,Shivika Mittal,Marc Vielle,Sigit Perdana,Lorenza Campagnolo,Elisa Delpiazzo,Ha Bui,Annela Anger Kraavi,A. Yu. Kolpakov,Ida Sognnæs,Glen P. Peters,Adam Hawkes,Alexandre C. Köberle,Neil Grant,Ajay Gambhir,Αλέξανδρος Νίκας,Haris Doukas,Jorge Moreno,Dirk-Jan Van de Ven
标识
DOI:10.1016/j.scitotenv.2021.146861
摘要
Harmonisation sets the ground to a solid inter-comparison of integrated assessment models. A clear and transparent harmonisation process promotes a consistent interpretation of the modelling outcomes divergences and, reducing the model variance, is instrumental to the use of integrated assessment models to support policy decision-making. Despite its crucial role for climate economic policies, the definition of a comprehensive harmonisation methodology for integrated assessment modelling remains an open challenge for the scientific community. This paper proposes a framework for a harmonisation methodology with the definition of indispensable steps and recommendations to overcome stumbling blocks in order to reduce the variance of the outcomes which depends on controllable modelling assumptions. The harmonisation approach of the PARIS REINFORCE project is presented here to layout such a framework. A decomposition analysis of the harmonisation process is shown through 6 integrated assessment models (GCAM, ICES-XPS, MUSE, E3ME, GEMINI-E3, and TIAM). Results prove the potentials of the proposed framework to reduce the model variance and present a powerful diagnostic tool to feedback on the quality of the harmonisation itself.
科研通智能强力驱动
Strongly Powered by AbleSci AI