Electric vehicles can contribute to climate change mitigation if coupled with decarbonized electricity, but only if vehicle range matches travelers' needs. Evaluating electric vehicle range against a population's needs is challenging because detailed driving behavior must be taken into account. Here we develop a model to combine information from coarse-grained but expansive travel surveys with high-resolution GPS data to estimate the energy requirements of personal vehicle trips across the U.S. We find that the energy requirements of 87% of vehicle-days could be met by an existing, affordable electric vehicle. This percentage is markedly similar across diverse cities, even when per-capita gasoline consumption differs significantly. We also find that for the highest-energy days, other vehicle technologies are likely needed even as batteries improve and charging infrastructure expands. Car-sharing or other means to serve this small number of high-energy days could play an important role in the electrification and decarbonization of transportation.

Resistance to adopting a cap on greenhouse gas emissions internationally, and across various national contexts, has encouraged alternative climate change mitigation proposals. These proposals include separately targeting clean energy uptake and demand-side efficiency in individual end-use sectors, an approach to climate change mitigation which we characterize as segmental and technology-centered. A debate has ensued on the detailed implementation of these policies in particular national contexts, but less attention has been paid to the general factors determining the effectiveness of a segmental approach to emissions reduction. We address this topic by probing the interdependencies of segmental policies and their collective ability to control emissions. First, we show for the case of U.S. electricity how the set of suitable energy technologies depends on demand-side efficiency, and changes with the stringency of climate targets. Under a high-efficiency scenario, carbon-free technologies must supply 60-80% of U.S. electricity demand to meet an emissions reduction target of 80% below 1990 levels by midcentury. Second, we quantify the enhanced propensity to exceed any intended emissions target with this approach, even if goals are set on both the supply and demand side, due to the multiplicative accumulation of emissions error. For example, a 10% error in complying with separate policies on the demand and supply side would combine to result in a 20% error in emissions. Third, we discuss why despite these risks, the enhanced planning capability of a segmental approach may help counteract growing infrastructural inertia. The emissions reduction impediment due to infrastructural inertia is significant in the electricity sectors of each of the greatest emitters: China, the U.S., and Europe. Commonly cited climate targets are still within reach but, as we show, would require more than a 50% reduction in the carbon intensity of new power plants built in these regions over the next decade.

Estimating personal vehicle energy consumption is important for nationwide climate policy, local and statewide environmental policy, and technology planning. Transportation energy use is complex, depending on vehicle performance and the driving behavior of individuals as well as on travel patterns of cities and regions. Previous studies typically combine large samples of travel behavior with fixed estimates of per-mile fuel economy or use detailed models of vehicles with limited samples of travel behavior. Here we introduce a model for estimating privately operated vehicle energy consumption, TripEnergy, that accurately reconstructs detailed driving behavior across the U.S. and simulates vehicle performance for different driving conditions. TripEnergy consists of a demand model, linking GPS drive cycles to travel survey trips, and a vehicle model, efficiently simulating energy consumption across different types of driving. Because of its ability to link small-scale variation in vehicle technology and driver behavior with large-scale variation in travel patterns, we expect it to be useful for a variety of applications, including technology assessment, cost and energy savings from eco-driving, and the integration of electric vehicle technologies into the grid.

<p>Suppressive effects of FABP7 depletion on gene expression in Accx11 cells.</p>