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Detailed assessment of global transport-energy models’ structures and projections
Institution:1. Institute of Transportation Studies, University of California, Davis, USA;2. Department of Energy and Environment, Chalmers University of Technology, Sweden;3. Precourt Energy Efficiency Center, Stanford University, USA;4. Joint Global Change Research Institute, Pacific Northwest National Laboratory, USA;5. Energy Program, International Institute for Applied Systems Analysis, Austria;6. Howard H. Baker Jr. Center for Public Policy, University of Tennessee, USA;7. International Council on Clean Transportation, USA;8. International Energy Agency, France;1. Fondazione Eni Enrico Mattei (FEEM) and Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Milan, Italy;2. Centre for Health Economics and Research Evaluation (CHERE), University of Technology Sydney, Australia;1. Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, USA;2. National Renewable Energy Laboratory, Golden, CO, USA;3. Pacific Northwest National Laboratory, Seattle, WA, USA;4. University of Washington, Seattle, WA, USA;1. Institute of Energy Economics, University of Cologne, Vogelsanger Strasse 321a, 50827 Cologne, Germany;2. Department of Economics, University of Cologne, Universitaetsstrasse 22a, 50937 Cologne, Germany
Abstract:This paper focuses on comparing the frameworks and projections from four global transportation models with considerable technology details. We analyze and compare the modeling frameworks, underlying data, assumptions, intermediate parameters, and projections to identify the sources of divergence or consistency, as well as key knowledge gaps. We find that there are significant differences in the base-year data and key parameters for future projections, especially for developing countries. These include passenger and freight activity, mode shares, vehicle ownership rates, and energy consumption by mode, particularly for shipping, aviation and trucking. This may be due in part to a lack of previous efforts to do such consistency-checking and “bench-marking.” We find that the four models differ in terms of the relative roles of various mitigation strategies to achieve a 2 °C/450 ppm target: the economics-based integrated assessment models favor the use of low carbon fuels as the primary mitigation option followed by efficiency improvements, whereas transport-only and expert-based models favor efficiency improvements of vehicles followed by mode shifts. We offer recommendations for future modeling improvements focusing on (1) reducing data gaps; (2) translating the findings from this study into relevant policy implications such as gaps of current policy goals, additional policy targets needed, regional vs. global reductions; (3) modeling strata of demographic groups to improve understanding of vehicle ownership levels, travel behavior, and urban vs. rural considerations; and (4) conducting coordinated efforts in aligning historical data, and comparing input assumptions and results of policy analysis and modeling insights.
Keywords:Transportation scenarios  Transportation behaviors  Energy use  Climate mitigation  GHG emissions  Transportation demand  Model comparison
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