首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Applying engineering and fleet detail to represent passenger vehicle transport in a computable general equilibrium model
Institution:1. National Center for Risk and Economic Analysis of Terrorism Events (CREATE), University of Southern California, Los Angeles, CA 90089, USA;2. Econometrica, Inc., Bethesda, MD 20814, USA;3. Texas Tech University, Department of Economics, Lubbock, TX 79409, USA;1. Institute of Energy, Environment and Economy (3E), Tsinghua University, Beijing 100084, PR China;2. China Automotive Energy Research Center (CAERC), Tsinghua University, Beijing 100084, PR China;1. Technical University of Denmark, Department of Management Engineering, Produktionstorvet, Building 426, 2800 Kgs. Lyngby, Denmark;2. E4SMA, Via Livorno 60, 10144 Turin, Italy;3. VTT Technical Research Centre of Finland, P.O. Box 1000, 02044 VTT, Finland
Abstract:A well-known challenge in computable general equilibrium (CGE) models is to maintain correspondence between the forecasted economic and physical quantities over time. Maintaining such a correspondence is necessary to understand how economic forecasts reflect, and are constrained by, relationships within the underlying physical system. This work develops a method for projecting global demand for passenger vehicle transport, retaining supplemental physical accounting for vehicle stock, fuel use, and greenhouse gas (GHG) emissions. This method is implemented in the MIT Emissions Prediction and Policy Analysis Version 5 (EPPA5) model and includes several advances over previous approaches. First, the relationship between per-capita income and demand for passenger vehicle transport services (in vehicle-miles traveled, or VMT) is based on econometric estimates and modeled using quasi-homothetic preferences. Second, the passenger vehicle transport sector is structured to capture opportunities to reduce fleet-level gasoline use through the application of vehicle efficiency or alternative fuel vehicle technologies, introduction of alternative fuels, or reduction in demand for VMT. Third, alternative fuel vehicles (AFVs) are represented in the EPPA model. Fixed costs as well as learning effects that could influence the rate of AFV introduction are captured explicitly. This model development lays the foundation for assessing policies that differentiate based on vehicle age and efficiency, alter the relative prices of fuels, or focus on promoting specific advanced vehicle or fuel technologies.
Keywords:
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号