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Margaret Taylor, Lawrence Berkeley National Lab 2A: Tranportation Moderator: Margaret Taylor, Lawrence Berkeley National Lab John Anderson, Center for Sustainable Energy Identifying areas with high proclivity to adopt electric vehicles Researchers focused on clean transportation have produced a large body of knowledge about factors that predict the likelihood of electric vehicle (EV) adoption. The literature routinely informs policy aimed at reducing barriers to EV adoption, as well as for targeting and tailoring outreach and education aimed at “adding fuel to the fire,” or increasing adoption among consumers already predisposed to adopting electric vehicles. Consumer attributes thought to be related to higher likelihood of adoption include single-family home ownership, multiple vehicles in a household, higher income levels, high levels of EV adoption among neighbors, and prior adoption of clean technologies like solar. The authors build on these identified attributes, and use data from a variety of sources — including California’s Clean Vehicle Rebate Project (CVRP) Consumer Survey — to identify census tracts with high proclivity for electric vehicle adoption. These results will inform an interactive tool for use by EV stakeholders. Results will be useful for identifying areas with high potential for EV adoption, especially in areas where adoption levels are presently low. These results will also provide CVRP outreach implementation teams with a tool for identifying areas where their outreach efforts might have high impacts on increasing adoption. Alec Beall, The University of British Columbia A carbon price by another name may seem sweeter: Consumers prefer upstream offsets to equivalent downstream taxes Consumers are influenced not only by prices, but also by how those prices are labelled. Prices for carbon emissions can be framed in a variety of ways, such as carbon "taxes", "permits", or "offsets." Furthermore, the emissions can be regulated at many different points in the production and usage system: "upstream" regulations are applied to the extraction and importation of fossil fuels, while "downstream" regulations are applied to the sale of products and services. From a conventional economic standpoint, these points of regulation should have roughly equivalent impacts on carbon emissions. However, the impact of "upstream" vs "downstream" frames on consumer perceptions and preferences is largely unknown. This talk presents data from three studies examining U.S. consumer preferences in the airline industry (N = 1097). In all three studies, participants were presented with several scenarios in which they were asked to choose between two ostensibly identical flights for purchase (e.g., two flights to little-known Caribbean islands): One was a flight that carried a $14.00 carbon fee and the other was a flight that did not carry a $14.00 carbon fee. Across all three studies, consumers reported being more likely to purchase a flight that carried a $14.00 carbon fee when that fee was labelled as an “upstream offset” (a “carbon offset on aviation fuel production and importation”) than when it was framed in other ways (i.e., as a “carbon tax” or “carbon permit”) or if the point of regulation of the fee was downstream (i.e., “…on airplane travel”). Strikingly, individuals in the “upstream offset” labelling condition were actually no less likely to prefer flights carrying a carbon fee when compared to a control condition in which no description was given and the $14.00 carbon fee was not even applied. These framing differences were moderated by political ideology, such that Republicans show a particular distaste for downstream taxes. Countries or states that wish to enact a carbon fee may want to use the "upstream offset" frame, especially if competing with other countries without a carbon fee. Our results suggest that consumers may in fact prefer airline flights with an upstream carbon offset; this preference may be strong enough to counteract any additional cost to the country or state that implements it; and the implementing country could potentially realize further benefits if the offset investment helps finance sustainable low- carbon development in that country. Furthermore, aviation consumers might be more accepting of "upstream offset" regulation than "downstream tax" regulation. In fact, our findings suggest that customers may be more willing to purchase tickets that include appropriately described carbon offsets, even if the cost is higher. Other implications and future directions for this work are discussed. Mersiha McClaren, Research Into Action, Inc. Race to the Bottom: Using Advanced Analytics, Operator Training, and Feedback to Improve Electric Bus Fuel Economy Fleet operators can incorporate a few electric buses with little impact to daily operations; there are capital costs, but for the most part it’s “business as usual.” Transitioning an entire fleet to electric buses, however, is disruptive – for both the transit agency and the utility. A transit authority in California has committed to electrifying its 80-bus fleet by the end of 2018. For their electric buses to be cost-effective compared to internal combustion, the average fuel economy needs to be 2.0 kWh/mile. The fuel economy of their two currently deployed buses for trips along the same route range from 1.2 kWh/mile to 5 kWh/mile, representing significant risk to the transit authority due to highly variable operating costs. The transit agency has partnered with a team to design and implement an E-Bus Operator Training and Feedback Program (E-Bus Program) that maximizes E-Bus fuel economy by coupling education and training for E-bus operators with advanced analytics and proven behavior modification strategies. The data model for the E-Bus Program includes processing data from: 1) The E-Bus onboard telemetry system; 2) The transit agency’s administrative system that tracks driver assignments to buses and routes, tracks the location of the bus during its route (using GPS), and forecasts estimated departure times; 3) Traffic data to identify slow zones (e.g., hospitals, schools, etc.) and development density factors that may impact E-Bus operational efficiency; and 4) Existing driving habits (good and bad) and training/guidance provided to operators on driving best practice. These data will help the transit authority to better understand key drivers of fuel economy (including both good and bad habits) and inform the development of the E-Bus operator training model. The agency will also use feedback mechanisms and other behavioral modification strategies to encourage driving techniques and practices that improve fuel economy. The authors of this paper will discuss the design and evaluation of this effort, including: how the program will develop strategies that are scalable and fleet-wide; ways to assess and optimize operational parameters and operator performance to maximize fuel economy and minimize operating costs; and how to establish a transit agency outreach, education, and technical assistance platform to scale-up E-Fleet tools, programs, lessons learned and best practices. The authors will also discuss the benefits of and need for developing an evaluation plan at the initial stage of the design so that the data model is comprehensive. Lastly, this paper will highlight the truly disruptive nature of fleet electrification, and how the E-Bus Program uniquely addresses four key dimensions of organizational systems change: technology, people, process, and policy Shiqi Ou, Oak Ridge National Laboratory Quantitative estimation for residential vehicle parking rate in China and its potential influence on PEV purchasing China has become the largest vehicle market in the world since 2009, and is ambitious to expand the population of the plug-in electric vehicles (PEVs) to 5 million units by 2020. Accompanied with the rapid urbanization and motorization, the residential vehicle parking and home PEV charging issues have been causing concerns. This study probes the residential parking rates by provinces in China and projects the residential vehicle parking rates in years, and adopted the discrete choice model to simulate the potential influence of the residential parking rates on the plug-in electric vehicles (PEVs) purchasing. The residential parking rates in China are firstly quantitatively revealed with limited data resources. By data mining in several major real estate trading network platforms in China, this study obtained the raw information on the residential communities in 31 provinces (areas) in mainland China, including household numbers, residential parking numbers, price, building ages etc. By quantitatively estimating the housing lifetime and urbanization rate in China, this study calculated the average residential vehicle parking rates in metropolitan, suburb, and rural areas in every province (area) in China for years in 2005, 2015, 2025, and 2050. These 372 residential parking rates comprehensively present a full picture of the residential parking level in China, which shows an inequality in areas in China: varies by provinces, by regions with different economic levels, and by urban types. Shown by the distributions of the results for the residential parking rates, the development of the residential parking rates is positive related to the economic development, urbanization rate, and urban planning. The results also reveal that the residential parking rates vary in the same metropolitan cities with different urban planning. These values directly reflect the contemporary urbanization changes and
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