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