Modeling the GHG emissions intensity of plug-in electric vehicles using short-term and long-term perspectives

https://doi.org/10.1016/j.trd.2019.01.027Get rights and content

Highlights

  • We model short and long-term well-to-wheel GHG impacts of plug-in electric vehicles (PEVs).

  • PEVs offer substantial GHG emissions reductions in all contexts explored.

  • In short-term (2015), PEVs cut GHGs by 34% to 98%, varying by regional grid mix.

  • In long-term (2050), PEV GHG intensity is 36% to 74% lower than 2015 levels.

  • Even the higher-carbon electricity region sees strong PEV GHG benefit in long-term.

Abstract

Plug-in electric vehicles (PEVs) can contribute to deep greenhouse gas (GHG) reduction targets but their efficacy depends on the sources of electricity. PEV GHG intensity can vary over time (and regionally), making it unclear how policymakers should regulate PEVs in the short and long-term. To explore this uncertainty, we model the short-term (Study 1) and long-term (Study 2) well-to-wheels GHG intensity of PEVs in three regions with very different electricity grid profiles: the Canadian provinces of British Columbia, Alberta, and Ontario. Study 1 uses empirical data on vehicle preferences, driving patterns, and recharge access from a representative survey of new vehicle buyers in Canada (n = 1754) to construct a temporally-explicit model of PEV usage in 2015. Fleet-wide emissions intensity of PEVs varies substantially between regions, with the greatest reduction potential relative to conventional gasoline vehicles seen in British Columbia (78–98%), followed by Ontario (58–92%) and Alberta (34–41%). Study 2 simulates the potential long-term dynamics of technology, behavior, and emissions with the CIMS energy-economy model. With the emissions intensity of electricity decreasing by at least one-third by 2050 and vehicle energy efficiency improving over time, simulation results find that, compared to 2015, 2050 fleet average PEV emissions are 40–52% lower in British Columbia, 57–74% lower in Alberta, and 36–46% lower in Ontario. Overall, we find that PEVs offer substantial GHG emissions benefits compared to conventional vehicles in all scenarios explored. Policy makers seeking deep GHG cuts may want to support PEV adoption, even in jurisdictions that presently use relatively carbon-intensive electricity.

Introduction

Transportation electrification can play a key role in achieving long-term climate change targets (International Energy Agency, 2015, McCollum et al., 2013, Williams et al., 2012). Numerous studies have estimated the greenhouse gas (GHG) emission impacts of plug-in electric vehicles (PEVs) compared to conventional vehicles, using a full lifecycle or well-to wheels approach (Axsen et al., 2011, Elgowainy et al., 2009, Garcia et al., 2015, Ma et al., 2012, Nguyen et al., 2013, Rangaraju et al., 2015, Raykin et al., 2012, Samaras and Meisterling, 2008, Shen et al., 2012, Silva et al., 2009). Estimates of GHG reductions range widely – from 4% to 97% – depending on various assumptions, notably the source(s) of electricity, which can vary regionally and over time. Such dynamics lead to difficult questions for policy makers around how to design and evaluate policies related to PEVs over the long-term, such as vehicle emission standards, low carbon fuel standards, and zero-emission vehicle mandates. We presently focus on the importance of the presumed timeline of such analyses—that is, how different the emission impacts of PEVs can look if calculated based on the energy sources used in the present electrical grid, or a future, potentially decarbonized version of the electrical grid.

In light of such dynamics, our paper includes two studies. Study 1 follows a short-term (static) perspective, using detailed empirical data on PEV preferences and usage and present-day regional electrical grid information. We consider electricity demand and generation on an hourly basis, and also model GHG impacts on a “marginal” or “consequential” basis (based on the incremental electricity supplied to recharge PEVs at the time of demand). However, this short-term perspective does not represent how the electrical system and fleets may change over time. In Study 2, we use insights from Study 1 to inform a long-term (dynamic) energy-economy model (CIMS) that considers changes to the electricity grid mix and vehicle fleet over several decades as new, cleaner technologies replace emissions-intensive power plants and conventional vehicles as they are retired. This dynamic perspective is particularly important when considering the impact of emerging technologies such as PEVs within the context of deep, long-term GHG reduction targets (e.g. 80% cuts by 2050) and electricity market pressures from natural gas and renewable generation.

We demonstrate these methods using the case of Canada, where PEVs are a particularly promising solution to reduce emissions from light-duty vehicles, given that the country has one of the cleanest electricity systems in the world (151 gCO2/kWh vs. world average of 525 gCO2/kWh) (IEA, 2016). We model three Canadian provinces covering a range of electricity grid types and emission intensities: British Columbia (mainly hydro-based), Alberta (coal and natural gas-based), and Ontario (mixed nuclear, natural gas, and renewables). Despite the large range of emissions intensities between regions and over time, we find that PEVs offer substantial GHG emissions benefits compared to conventional gasoline vehicles in all scenarios modeled, with even larger reductions in the long-term and even in business-as-usual electricity scenarios.

The next section summarizes the relevant literature. Section 3 describes the data collection methods, modeling approaches, and results of Study 1 (short-term model). Study 2 (long-term model) is described in Section 4, which includes an overview of the CIMS energy-economy model, a summary of the data inputs from Study 1, and the long-term results. Section 5 discusses key results from both studies and their policy implications, and identifies some of the limitations of this work to be addressed in future studies.

Section snippets

Literature review

Quantifying the GHG emissions impacts of PEVs is difficult because calculations depend on numerous technical and behavioral factors that can differ between regions and change over time, such as the number of vehicles and their characteristics (including PEV type), how vehicles are driven and recharged by consumers, and the electricity grid mix. Previous studies employ a range of assumptions and methodologies; Table 1 compares the approaches and results of selected studies.

As one starting point

Study 1: Short-term modeling

This section describes the methods and results of the short-term model (Study 1). We first describe the survey instrument used to collect consumer data to improve the behavioral realism of the analysis. Next, we summarize the methods and scenarios used to model potential PEV use and emissions, and finally summarize the GHG intensity results.

Study 2: long-term modeling

This section describes the methods and results of the long-term modeling (Study 2). We first introduce the CIMS energy-economy model and the three policy scenarios used in the analysis. Next, we discuss how the model was calibrated using assumptions and results from Study 1. Finally, we summarize the GHG intensity results of the long-term model.

Discussion and conclusions

Our analysis considers two perspectives on GHG reductions from PEVs: a detailed, static analysis over the short-term and a broader, dynamic analysis over the longer term. The short-term model (Study 1) reveals important micro-level considerations when estimating the GHG impacts of PEVs such as charge timing and recharge availability. In contrast, the long-term model (Study 2) captures technology dynamics over several decades, such as the declining capital and intangible costs of PEVs and the

Acknowledgements

This work was supported by BC Hydro, the Pacific Institute for Climate Solutions, Natural Resources Canada, and the Social Sciences and Humanities Research Council of Canada.

References (71)

  • H. Ma et al.

    A new comparison between the life cycle greenhouse gas emissions of battery electric vehicles and internal combustion vehicles

    Energy Policy

    (2012)
  • P. Mau et al.

    The “neighbor effect”: Simulating dynamics in consumer preferences for new vehicle technologies

    Ecol. Econ.

    (2008)
  • N.C. Onat et al.

    Conventional, hybrid, plug-in hybrid or electric vehicles? State-based comparative carbon and energy footprint analysis in the United States

    Appl. Energy

    (2015)
  • S. Rangaraju et al.

    Impacts of electricity mix, charging profile, and driving behavior on the emissions performance of battery electric vehicles: A Belgian case study

    Appl. Energy

    (2015)
  • L. Raykin et al.

    Impacts of driving patterns on tank-to-wheel energy use of plug-in hybrid electric vehicles

    Transp. Res. Part D Transp. Environ.

    (2012)
  • C.C. Rolim et al.

    Impacts of Electric Vehicle Adoption on Driver Behavior and Environmental Performance

    Procedia - Soc. Behav. Sci.

    (2012)
  • C. Silva et al.

    Evaluation of energy consumption, emissions and cost of plug-in hybrid vehicles

    Energy Convers. Manage.

    (2009)
  • M. Sykes et al.

    No free ride to zero-emissions: Simulating a region’s need to implement its own zero-emissions vehicle (ZEV) mandate to achieve 2050 GHG targets

    Energy Policy

    (2017)
  • C. Weiller

    Plug-in hybrid electric vehicle impacts on hourly electricity demand in the United States

    Energy Policy

    (2011)
  • C. Yang

    A framework for allocating greenhouse gas emissions from electricity generation to plug-in electric vehicle charging

    Energy Policy

    (2013)
  • S.A.H. Zahabi et al.

    Fuel economy of hybrid-electric versus conventional gasoline vehicles in real-world conditions: A case study of cold cities in Quebec, Canada

    Transp. Res. Part D Transp. Environ.

    (2014)
  • Axhausen, K.W., 2006. Definition of movement and activity for transport modelling. In: Handbooks in Transport:...
  • Axhausen, K.W., 1995. Travel Diaries: An Annotated...
  • Axsen, J., Bailey, H.J., Kamiya, G., 2013. The Canadian Plug-in Electric Vehicle Survey (CPEVS 2013): Anticipating...
  • Axsen, J., Goldberg, S., Bailey, J., Kamiya, G., Langman, B., Cairns, J., Wolinetz, M., Miele, A., 2015. Electrifying...
  • J. Axsen et al.

    Early U.S. market for plug-in hybrid electric vehicles

    Transp. Res. Rec. J. Transp. Res. Board

    (2009)
  • J. Axsen et al.

    The early US market for PHEVs: Anticipating consumer awareness, recharge potential, design priorities and energy impacts

    (2008)
  • M.R. Busse et al.

    Are consumers myopic? Evidence from new and used car purchases

    Am. Econ. Rev.

    (2013)
  • R. Butcher et al.

    The use of diaries in data collection

    J. R. Stat. Soc. Ser. D (The Stat.

    (1990)
  • Cai, H., Brandt, A.R., Yeh, S., Englander, J.G., Han, J., Elgowainy, A., Wang, M.Q., 2015. Well-to-Wheels Greenhouse...
  • Canadian Association of Petroleum Producers, 2015. Crude Oil: Forecast, Markets & Transportation....
  • N.D. Caperello et al.

    Households’ stories of their encounters with a plug-in hybrid electric vehicle

    Behav. Environ.

    (2011)
  • A. Elgowainy et al.

    Well-to-Wheels Energy Use and Greenhouse Gas Emissions Analysis of Plug-in Hybrid Electric Vehicles

    (2009)
  • A. Elgowainy et al.

    Cradle-to-grave lifecycle analysis of U.S. light-duty vehicle-fuel pathways: a greenhouse gas emissions and economic assessment of current (2015) and future (2025-2030) technologies

    Argonne Natl. Lab.

    (2016)
  • Environment and Climate Change Canada, 2018. National Inventory Report 1990-2016: Greenhouse Gas Sources and Sinks in...
  • Cited by (0)

    View full text