Representing a Deployment of Light-Duty Internal Combustion and Electric Vehicles in Economy-Wide Models

Technical Note 17 February 2019 Representing a Deployment of Light-Duty Internal Combustion and Electric Vehicles in Economy-Wide Models Abbas Ghandi and Sergey Paltsev MIT Joint Program on the Science and Policy of Global Change Woods Hole and short- and long-term visitors—provide the united combines cutting-edge scientific research with independent policy vision needed to solve global challenges. analysis to provide a solid foundation for the public and private At the heart of much of the program’s work lies MIT’s Integrated decisions needed to mitigate and adapt to unavoidable global Global System Model. Through this integrated model, the program environmental changes. Being data-driven, the Joint Program uses seeks to discover new interactions among natural and human climate extensive Earth system and economic data and models to produce system components; objectively assess uncertainty in economic and quantitative analysis and predictions of the risks of climate change climate projections; critically and quantitatively analyze environmental and the challenges of limiting human influence on the environment— management and policy proposals; understand complex connections essential knowledge for the international dialogue toward a global among the many forces that will shape our future; and improve response to climate change. methods to model, monitor and verify greenhouse gas emissions and To this end, the Joint Program brings together an interdisciplinary climatic impacts. group from two established MIT research centers: the Center for This reprint is intended to communicate research results and improve Global Change Science (CGCS) and the Center for Energy and public understanding of global environment and energy challenges, Environmental Policy Research (CEEPR). These two centers—along thereby contributing to informed debate about climate change and the with collaborators from the Marine Biology Laboratory (MBL) at economic and social implications of policy alternatives. —Ronald G. Prinn and John M. Reilly, Joint Program Co-Directors MIT Joint Program on the Science and Policy Massachusetts Institute of Technology T (617) 253-7492 F (617) 253-9845 of Global Change 77 Massachusetts Ave., E19-411 [email protected] Cambridge MA 02139-4307 (USA) http://globalchange.mit.edu/ FEBRUARY 2019 Representing a Deployment of Light-Duty Internal Combustion and Electric Vehicles in Economy-Wide Models Abbas Ghandi1 and Sergey Paltsev1 Abstract: Representing the fleet of light-duty vehicles (LDV) in economy-wide models is important for projections of transportation demand, energy use, and the resulting emissions. We describe a methodology for incorporating the private transportation details into economy-wide models and, using an example of the MIT Economic Projection and Policy Analysis (EPPA) model, provide a description of calibrating the model to the data. We provide the results both for light-duty internal combustion engine (ICE) vehicles and electric vehicles (EV). For the EV fleet, both plug-in hybrid vehicles (PHEV) and battery electric vehicles (BEV) are considered. First, for initial calibration we provide a consistent representation of the historic data at the level of regional disaggregation of the EPPA model. We find that the global LDV stock increased by about 45% in ten years, from 735 million in 2005 to 1.1 billion in 2015. China has been the fastest growing market, where LDV stock increased from 20 million in 2005 to 140 million in 2015, a 7-fold increase. Second, we assess relative costs of ICE, PHEV, and BEV vehicles. Based on consumer prices (top-down approach) and battery pack/vehicle components cost estimates (bottom-up approach) in USA, PHEVs are about 30-60% more expensive than ICEs and BEVs are about 40-90% more expensive than ICEs. Finally, we apply our methodology for a long term projection of LDV stock. We find that global LDV stock is projected to grow from 1.1 billion vehicles in 2015 to 1.8 billion in 2050, while global EV stock is growing from about a million in 2015 to about 500 million in 2050. Our methodology can be applied in other energy-economic models to test a sensitivity of the results to different input assumptions and specifications. 1. INTRODUCTION ........................................................................................................................................................2 2. PRIVATE TRANSPORTATION DETAILS IN THE EPPA MODEL .......................................................................3 3. NUMBER OF PRIVATE LIGHT-DUTY VEHICLES .................................................................................................4 3.1 DATA SOURCES ..................................................................................................................................................................4 3.2 NUMBER OF LDVS .............................................................................................................................................................5 3.3 REFINED OIL CONSUMPTION ........................................................................................................................................6 4. BEV/PHEV MARKUP ESTIMATION ......................................................................................................................6 4.1 EV GLOBAL MARKET STATUS .......................................................................................................................................6 4.2 BEV/PHEV TOP-DOWN MARKUP ESTIMATION ......................................................................................................8 4.3 BEV/PHEV BOTTOM-UP MARKUP ESTIMATION ................................................................................................. 10 4.4 TOP-DOWN/BOTTOM-UP MARKUP SUMMARY ....................................................................................................13 5. LIGHT-DUTY VEHICLE PROJECTION ................................................................................................................ 14 6. CONCLUSION ......................................................................................................................................................... 15 7. REFERENCES ........................................................................................................................................................... 16 APPENDIX ..................................................................................................................................................................... 18 1 MIT Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA TEchnicAL NotE 17 MIT JOINT PROGRAM ON THE SCIENCE AND POLICY OF GLOBAL CHANGE 1. Introduction creating top-down (i.e., based on manufacturer suggested Understanding the future trends in providing private mobil- retail prices, MSRP) and bottom-up (i.e., based on individual ity services is crucial for projecting fuel use and emissions. components of a vehicle) calculations of the relative costs of Light-duty (i.e., cars and light trucks) vehicles (LDV) provide a ICEs, plug-in hybrid electric vehicles (PHEV) and battery substantial source of fuel demand and the resulting greenhouse electric vehicles (BEV), and discuss the future trends in the gas (GHG) emissions—in USA, they currently account for relative costs of ICE, PHEVs and BEVs. almost half of petroleum demand (Heywood et al., 2015). In Despite the importance of an adequate representation of private 2015, GHG emissions from LDVs in USA were about 1,000 transportation in energy and emission scenarios, the corre- million tonnes of CO2-equivalent (MtCO2e), which accounted sponding data with a global coverage for a stock of LDVs, their for about 16% of the total GHG emissions in USA (EPA, miles-driven and fuel use are sparse. In addition, data from 2017a). Improving fuel efficiency of internal combustion different sources are often inconsistent due to their different engine-based cars (ICE) and switching from gasoline and approaches for reporting and different definitions of what is diesel ICEs to electric vehicles (EV) and other alternative “a private light-duty vehicle”. We provide a discussion of the fuel vehicles are critical options for GHG emission reduction. ways to achieve consistent representation of LDVs, assess the Computable general equilibrium (CGE) models are im- historic data from different sources and provide a methodology portant tools for projecting future energy use and GHG of calibrating the data to the regions of the MIT Economic emissions, but usually these models provide projections at Projection and Policy Analysis (EPPA) model (Chen, et al., an aggregated level of sectoral representation of economy, 2016). Our approach can be used by other modeling teams to with private transportation usually combined with other represent the characteristics of the private LDVs in different sectors (IPCC, 2014; EPA, 2017b). Traditional datasets like modeling platforms. the Global Trade Analysis Project (GTAP) dataset (Agu- iar et al., 2016) do not provide any disaggregated data for The paper structure is as follows. Section 2 introduces private transportation. As a result, modeling groups that the main transport-related features of the EPPA model. are interested in transportation modeling rely on additional In Section 3, we discuss our methodology for estimating data routines that provide the necessary details for LDV the

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