Zero Emission Vehicles: Forecasting Fleet Scenarios and their Emissions Implications (2019) / Chapter Skim
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30 CHAPTER 4 MODELING ASSUMPTIONS AND RESULTS With the addition of one MA3T simulation for the Base Case analysis scenario, 16 sets of simulations were conducted for analysis of emissions changes due to advanced adoption of ZEVs. Each of the simulation sets consist of three separate simulations (with "Low", "Medium", and "High" parameter adjustment)
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31 Table 8. MA3T parameter adjustments for ZEV adoption scenario simulations.
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32 Simulation Set ID MA 3T Parameters Adjusted Parameter Adjustment Values Defaults: 1.1 kW for Year #1 (2005)
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33 Simulation Set ID MA 3T Parameters Adjusted Parameter Adjustment Values Medium: Unsubsidized vehicle manufacturer cost parity between ZEVs with ICEVs counterparts by category in 2035 High: Unsubsidized vehicle manufacturer cost parity between ZEVs with ICEVs counterparts by category in 2030 C2 Gasoline Price Defaults: variable by region and analysis year Low: A minimum rate of increase ($0.05/year) slightly lower than the increase in annual average price from U.S EIA data between 2002 and 2019, applied to all regions beginning in 2019 Medium: Rate of increase ($0.07/year)
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34 4.1 ASSUMPTIONS FOR INFRASTRUCTURE SCENARIOS A range of infrastructure scenarios that reflect likely changes in charging availability in public, workplace, and home locations were considered. First, it was validated that baseline inputs in MA3T were similar to "current" (i.e., approximately 2018)
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35 the three California utilities. An additional 12,500 electric vehicle charging stations are planned in this IOUs initiative, of which 6,250 would be located either at homes or at workplaces.
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36 proprietary data that was not obtained for this study. The Medium case assumes the midpoint between the high and low adjustments for 2050.
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37 on vehicle sales. Test simulations where the credit amount was changed, not presented in this report, indicated that the effect of the credit amount on future ZEV population was small.
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38 4.2.3 HOV lane access duration assumptions (P6 simulations) The default start year for HOV lane access policy in MA3T is 2014, and the default duration is five years.
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39 Appendix A From 2002 through 2018, theses prices increased by a small constant rate (on an annual average, $0.07 for gasoline and $0.08 for diesel)
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40 Although ZEV purchase cost was identified as the greatest barrier to ZEV adoption in the literature review, and financial incentives such as rebates effectively reduce the cost of ZEVs, changes to the ARRA tax credit parameters and state rebate amounts had only a modest impact on the ZEV populations estimated by MA3T. Increasing the number of OEMs producing eligible ZEVs had a slightly greater impact than increasing the cap on the number of vehicles eligible for the ARRA credit.
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41 Table 9. Model results of total ZEV population (millions of vehicles)
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42 simulations is 0.5%. For comparison, the market share of BEVs, FCEVs, and PHEVs in California in 2018 was 8%.
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43 increased adoption of ZEVs would generally reduce emissions; an example of this would be a reduction of evaporative HC from the light-duty fleet. MOVES2014b provides emission factors in grams per vehicle mile traveled (VMT)
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44 The emissions reductions for each simulation and pollutant modeled are summarized in Table 12 (also in Figure A-3 through Figure A-5) , Table 13 (also in Figure A-6 through Figure A-8)
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45 • The High case simulation for rebates applied to other states (states that have not had rebates since as early as 2011) produced a 9% reduction in emissions.
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46 • Emissions reductions for the state rebate amount simulation set are 0%, but up to 2% for the rebate duration set. ‒ MA3T values rebates, tax credits, and vehicle manufacturer cost differently; although rebates and tax credits effectively reduce the purchase cost of ZEVs, MA3T did not produce substantial changes in estimated ZEV populations when rebate amounts were increased.
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47 The DOE Alternative Fuels Data Center provides summaries of state-level electricity sources and annual emissions per vehicle for EVs, PHEVs, HEVs, and gas-powered vehicles (see https://afdc.energy.gov/vehicles/electric_emissions.html)
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48 Table 12. Reduction in modeled light-duty passenger vehicle emissions (in tons)
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49 Simulation Set ID Simulation Description NOx (%)
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50 Simulation Set ID Simulation Description NOx (%)
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51 Simulation Set ID Simulation Description NOx (%)
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52 Simulation Set ID Simulation Description NOx (%)
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53 Table 13. Reduction in modeled light-duty passenger vehicle emissions (in tons)
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54 Simulation Set ID Simulation Description 1,3-Butadiene (%) Acetaldehyde (%)
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55 Simulation Set ID Simulation Description 1,3-Butadiene (%) Acetaldehyde (%)
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56 Simulation Set ID Simulation Description 1,3-Butadiene (%) Acetaldehyde (%)
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57 Simulation Set ID Simulation Description 1,3-Butadiene (%) Acetaldehyde (%)
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58 Table 14. Reduction in modeled light-duty passenger vehicle emissions (in tons)
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59 Simulation Set ID Simulation Description Ethylbenzene (%) Formaldehyde (%)
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60 Simulation Set ID Simulation Description Ethylbenzene (%) Formaldehyde (%)
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61 Simulation Set ID Simulation Description Ethylbenzene (%) Formaldehyde (%)
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62 Simulation Set ID Simulation Description Ethylbenzene (%) Formaldehyde (%)
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63 Table 15. Reduction in modeled light-duty passenger vehicle emissions of GHGs (million metric tons CO2; metric tons CH4 and N2O)
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64 Simulation Set ID Simulation Description CO2 (%)
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65 Simulation Set ID Simulation Description CO2 (%)
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66 Simulation Set ID Simulation Description CO2 (%)
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67 important determinant of purchase cost is battery cost; MA3T modeled consumer preference consistently with these findings. The largest ZEV population increases modeled with MA3T (and corresponding reductions in emissions)
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68 Another example limitation is the estimation of the distribution of daily vehicle use for which longitudinal travel data are not available. The current version of MA3T (V20190404)
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69 Combinations of parameter adjustments in MA3T that lead to increased ZEV adoption could lead to greater ZEV adoption outcomes than could be achieved by adjusting a single parameter, depending on the magnitude of increased ZEV adoption from the individual parameters. The results of combined factors are not necessarily additive, since MA3T includes important feedback mechanisms that result in a synergistic effect on ZEV adoption.
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