An NBER conference on Energy Use in Transportation took place on Zoom.us June 11-12. Research Associates Meghan R. Busse of Northwestern University and Christopher R. Knittel of MIT; and Kate S. Whitefoot of Carnegie Mellon University organized the meeting, sponsored by the Alfred P. Sloan Foundation. These researchers' papers were presented and discussed:
Arik Levinson, Georgetown University and NBER, and Lutz Sager, Georgetown University
Do Car Buyers Undervalue Future Fuel Savings? Post-Purchase Evidence
Regulators attest that tightened automotive fuel efficiency standards save drivers money. The more efficient cars cost more upfront but reduce drivers' annual fuel expenses by more than enough to pay for those upfront costs. That claim implies a market failure or irrationality: absent the regulation, drivers would underinvest in fuel economy. Levinson and Sager use survey data on 180,000 American cars and their drivers to examine whether each individual driver would in fact have been better off in a more expensive but more fuel efficient car, given their actual annual miles of driving and local gasoline prices. They find the regulators' claim to be true only on average. Many drivers could have been better off financially by paying less upfront for less fuel efficient cars. Their use of post-purchase survey data allows us to classify those choices by driver demographics. The differences across groups vary depending on how the researchers compare efficient and inefficient cars. Drivers that are older, male, and college-educated are more likely to be overinvesting in fuel economy, driving hybrid gas-electric models of vehicles that also come with standard gasoline engines, even though they do not save enough annually to pay for the extra upfront cost. But when Levinson and Sager look at all cars, not just the pairs with gas and hybrid gas-electric versions, those same older, male, educated drivers appear more likely to be underinvesting in fuel economy.
Fiona Burlig, University of Chicago and NBER; James B. Bushnell, University of California, Davis and NBER; David S. Rapson, University of California, Davis; and Catherine Wolfram, University of California, Berkeley and NBER
Cars of the Future, Today? Estimating the Contribution of Electric Vehicles to California's Residential Electricity Demand (slides)
California is now home to over 650,000 electric vehicles (EVs), less than 5% of which are charged at home using a meter dedicated to EV use. State policy has thus been forced to rely upon either survey data or approximations based on selected samples to estimate the extent and timing of residential electricity use devoted to EVs. Burlig, Bushnell, Rapson, and Wolfram match a novel dataset comprised of 1.7 billion household electricity meter readings to electric vehicle adoption events at the address-level from 2014-2017 in California. They use these rich data in conjunction with a panel fixed effects approach to estimate the effects of EV adoption on electricity load. In the sample, EVs increase household load by 0.10 to 0.15 kWh per hour, or 17-25 kWh per week, the majority of which is concentrated during evening and nighttime hours. While these estimates are roughly half of the estimates used as an input into state EV-related forecasts and policies, the load impacts are concentrated in the late night and early morning, corresponding to higher marginal emissions factors than if charging had taken place mid-day.
James B. Bushnell and Erich Muehlegger, University of California, Davis and NBER, and David S. Rapson, University of California, Davis
Energy Prices and Electric Vehicle Adoption (slides)
Connor R. Forsythe, Akshaya Jha, Jeremy J. Michalek, Carnegie Mellon University, and Kate S. Whitefoot
Externalities of Policy-Induced Scrappage: The Case of Automotive Regulations
Matthew B. Bruchon and Jeremy J. Michalek, Carnegie Mellon University, and Ines Azevedo, Stanford University
Effects of Internalizing Air Emissions Externalities on Optimal Ride-Hailing Fleet Technology Composition and Operations (slides)
Ride-hailing services from transportation network companies, such as Uber and Lyft, serve the fastest growing share of U.S. passenger travel demand. The high use-intensity of ride-hailing vehicles is economically attractive for electric vehicles, which typically have lower operating costs and higher capital costs than conventional vehicles. Bruchon, Michalek, and Azevedo optimize fleet technology composition (mix of conventional vehicles (CVs), hybrid electric vehicles (HEVs), and battery electric vehicles (BEVs)) and operations to satisfy exogeneous trip demand at minimum cost, and they compare results across scenarios. In nearly all cases, the optimal fleet includes a mix of technologies. In present and future scenarios for Austin, TX, Los Angeles, CA and New York, NY, HEVs and BEVs make up the largest portion of vehicle distance traveled in optimized fleets, and CVs are used primarily for periods of peak demand (if at all). Across a wide range of scenarios for the three cities, internalizing life cycle air pollution and greenhouse gas emission externality costs (via a Pigovian tax) leads to increased fleet electrification, a shift in charging toward periods when the grid is cleaner, and a reduction in emissions externalities of 12-31% in the base cases and 2-80% across the sensitivity scenarios. In all cases, the optimal fleet mix, its dispatch strategy, and resulting air emissions change substantively when air emissions externalities are internalized, suggesting a role for policy.
Rhiannon Leigh Jerch, Temple University; Panle Jia Barwick and Shanjun Li, Cornell University and NBER; and Jing Wu, Tsinghua University
Road Rationing Policies and Housing Markets (slides)
Canonical urban models postulate transportation cost as a key element in determining urban spatial structure. This paper examines how road rationing policies impact the spatial distribution of households using rich micro data on housing transactions and resident demographics in Beijing. Jerch, Barwick, Li, and Wu find that Beijing’s road rationing policy significantly increased the demand for housing near subway stations as well as central business districts. The premium for proximity is stable in the periods prior to the driving restriction, but shifts significantly in the aftermath of the policy. The composition of households living close to subway stations and Beijing’s central business districts shifts toward wealthier households, consistent with theoretical predictions of the monocentric city model with income-stratified transit modes. Their findings suggest that city-wide road rationing policies can have the unintended consequence of limiting access to public transit for lower income individuals.