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This study explores the potential to promote lower-emissions air travel by providing consumers with information about the carbon emissions of alternative flight choices in the context of online flight search and booking. We surveyed over 450 employees of the University of California, Davis, asking them to choose among hypothetical flight options for university-related business trips. Emissions estimates for flight alternatives were prominently displayed alongside cost, layovers, and airport, and the lowest-emissions flight was labeled “Greenest Flight.” We found an impressive rate of willingness to pay for lower-emissions flights: around $200/ton of CO 2 e saved, a magnitude higher than that seen in carbon offsets programs, and consistent with findings from a prior study with a non-university-based sample. In a second step of analysis, we estimated the carbon and cost impacts if the university were to adopt a flight-search interface that prioritizes carbon emissions information and displays alternatives from multiple regional airports in their employee travel-booking portal. We estimated potential annual savings of 79 tons of CO 2 e, while reducing airfare costs by $56,000, mainly through an increased willingness of travelers to take advantage of cheaper nonstop (lower-emissions) flights from a more distant airport in the region over indirect flights from their preferred airport for medium-distance flights. Institutionalizing this strategy within organizations with large travel budgets could reduce personal and organizational carbon footprints. If implemented across major flight-search engines, it could potentially reduce the demand for higher-emissions flights, leading to an industry-wide impact on aviation emissions. 

We develop a dynamic dictionary data structure for the GPU, supporting fast insertions and deletions, based on the Log Structured Merge tree (LSM). Our implementation on an NVIDIA K40c GPU has an average update (insertion or deletion) rate of 225 M elements/s, 13.5x faster than merging items into a sorted array. The GPU LSM supports the retrieval operations of lookup, count, and range query operations with an average rate of 75 M, 32 M and 23 M queries/s respectively. The trade-off for the dynamic updates is that the sorted array is almost twice as fast on retrievals. We believe that our GPU LSM is the first dynamic general-purpose dictionary data structure for the GPU.