Credit: ACS, Keshavarzmohammadian et al. Click to enlarge.

Total NO_x, VOCs, and SO₂ emissions are similar in OPT and BAU due to inter-sectoral shifts. They found that moderate, economy-wide GHG fees have little effect on GHG emissions from the LDV sector—but are more effective in the electricity sector.

In the OPT scenario, estimated well-to-wheels GHG emissions from full-size BEVs with 100-mile range are 62 gCO₂-e mi^–1 in 2050, while those from full-size ICEVs are 121 gCO₂-e mi^–1.

Compared to prior studies, the model includes a relatively comprehensive suite of available and viable forthcoming technologies, focusing on improved ICEVs and EVs, for meeting demand for energy services in all economic sectors, including the LDV portion of the transportation sector. Rather than pre-specifying mixes of energy sources and shares of technologies in any of the sectors of the economy, we use an optimization model to determine the least costly choices for meeting demand.

—Keshavarzmohammadian et al.

The researchers explored their different scenarios using the ANSWER-MARKAL model with a modified version of the Environmental Protection Agency’s 9-region database (EPA US9R). They adapted cost and performance projections for LDV technologies from the National Research Council (2013) optimistic case.

MARKAL was developed in a cooperative multinational project over a period of almost two decades by the Energy Technology Systems Analysis Programme (ETSAP) of the International Energy Agency. It uses linear programming to estimate energy supply shifts over a multi-decadal timeframe, finding the least-cost means to supply specified demands for energy services subject to user-defined constraints, assuming a fully competitive market. The model computes energy balances at all levels of an energy system from primary sources to energy services, supplies energy services at minimum total system cost, and balances commodities in each time period.

The EPA US9R database specifies technical and cost features of current and future technologies at five-year intervals, with a structure that connects energy carriers (e.g., output of mining or importing technologies) to conversion or process technologies (e.g., power plants and refineries) and in turn to the transportation, residential, industrial, and commercial end-use sectors. The database includes joint Corporate Average Fuel Economy (CAFE) and GHG emission standards for LDVs.

Future LDV transportation demand in the US9R database is specified based on US EIA Annual Energy Outlook (AEO) 2014 projections, allocated to the model’s nine regions and to seven vehicle size classes ranging from mini-compacts to light trucks.

The Boulder team’s BAU reference scenario was unmodified from the 2014 EPA US9R database, including EPA’s efficiency and cost estimates for future gasoline ICEV, HEV, PHEV, BEV, and ethanol vehicles. The OPT scenario substitutes optimistic efficiency and cost improvement for gasoline ICEV, HEV, PHEV, BEV, and ethanol vehicles from the NRC report, and adds and refines upstream emissions and refines cost and performance estimates for other sectors.

The team explored other scenarios including different levels of CO₂ and CH₄ fees applied to the BAU and OPT scenarios; different levels of LDV demand; and different oil prices.

The researches used the results from MARKAL to estimate well-to-wheel (WTW) emissions rates for GHG, NO_x, and SO₂ for ICEV and BEV technologies.

Among their findings:

• Gasoline vehicles dominate in the BAU scenario for the entire time horizon. In the OPT scenario BEVs gain a LDV market share of about 15%—all from 100-mile range EVs (BEV100) by 2030. By 2050, the share climbs to 47% (with 20% share from BEV100 and 27% from BEV200).

• Total electricity generation is equal in the BAU and OPT scenarios in 2030, but is 4% higher in 2050 in the OPT scenario, mainly due to the increased use of BEVs. Electricity generation from natural gas increases over time in both scenarios, whereas generation from existing coal plants declines. Electricity generation from other technologies including wind and solar are similar between the BAU and OPT scenarios, with these two renewable technologies contributing about 9% of generation in 2050.

• In the BAU scenario, GHG emissions from the LDV sector decline by 53% from 2010 to 2030 and by an additional 21% from 2030 to 2050, due largely to existing CAFE regulations. Correspondingly, in the BAU scenario the LDV share of total emissions is reduced from 21% in 2010 to 10% in 2030 and 7% in 2050. Direct emissions from the LDV sector contribute 9% and 5% of GHG emissions in the OPT scenario in 2030 and 2050, respectively. The OPT scenario results in GHG emissions from LDVs that are 36% lower than in the BAU scenario in 2050.

• Total GHG emissions in the OPT scenario are 5% lower than those in the BAU scenario in 2030 and 9% lower in 2050.

• BAU LDV NO_x emissions decline from about 2 million tonnes in 2010 to 0.4 million tonnes in 2030 and to 0.3 million tonnes in 2050, due to tailpipe emissions limits that have already been promulgated. In the OPT scenario, direct NO_x emissions from LDV are about 0.3 million tonnes in 2030 and 0.2 million tonnes in 2050—35% lower than in the BAU scenario. However, total energy system NO_x emissions are about the same in 2050 in the OPT and BAU scenarios, because industrial sector emissions are 27% higher in the former.

• In the BAU scenario, SO₂ emissions from electricity generation fall from 5 million tonnes in 2010 to 1.4 million tonnes in 2050, due to existing control requirements and the shift away from coal-fired generation. This reduction is modestly countered in the BAU scenario by an increase in SO₂ emissions from the industrial sector. In the OPT scenario, SO₂ emissions from the industrial sector in 2050 are 20% higher than in the BAU scenario in 2050, due to increased direct fuel use in place of electricity. This change offsets the reductions from the electric sector that result from less use of coal-fired power generation in the OPT scenario.

Although not directly examined in this study, vehicle cycle emissions are also important in comparing across technologies. ANL (2016) estimated emissions from vehicle manufacturing of about 41 gCO₂-eq mi^-1 for current midsize ICEV, and about 64 gCO₂-eq mi^-1 for BEV90 vehicles. In the future, these emissions are expected to decline as electricity sector emissions are reduced and manufacturing processes become more efficient. However, as light duty vehicles generally become more efficient, with lower WTW emissions, the vehicle production cycle will likely comprise an increasing fraction of total life cycle emissions. Thus, in future assessments greater attention needs to be focused on vehicle manufacturing and recycling impacts, as well as on upstream emissions and on the potential for inter-sectoral shifts.

—Keshavarzmohammadian et al.

Resources

• Azadeh Keshavarzmohammadian, Daven K. Henze, and Jana B. Milford (2017) “Emission Impacts of Electric Vehicles in the US Transportation Sector Following Optimistic Cost and Efficiency Projections” Environmental Science & Technology doi: 10.1021/acs.est.6b04801