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Impacts of E-bike Ownership on Travel Behavior: Evidence from three Northern California rebate programs


E-bike incentive programs are being utilized across the United States to encourage the adoption of active transportation. This study assesses the impacts of three e-bike rebate programs in Northern California using survey results by the three agencies that administered the programs. Through this research, I answer three research questions: “How has e-bike ownership impacted the mode choices, trip purpose, and travel frequency of new adopters”, “How much do e-bike rebate recipients reduce their transportation-related GHG emission?”, and “How did the design of each program impact who was able to participate and the program outcomes?”. To answer these questions, we explored survey responses through descriptive statistics and undertook an estimation of GHG emissions reductions. I decided against more complex data analysis given data quality issues that arose during the cleaning process. Despite that, our analysis revealed changes in travel behavior, car travel replacement, the impact of program designs, and various equity impacts. E-bike recipients reported more regular bike use after getting their e-bike, although their frequency of bicycle use began to decline in the long-term while remaining above previous rates. Respondents also reported high rates of occasional car trip replacement (1-3 times per week and 1-3 times per month), indicating that e-bikes substituted occasional car trips. While there was evidence of regular car trip replacement, the vast majority of e-bike use in our sample was for recreational travel. Given that this data was collected during the COVID-19 pandemic when many restrictions were still in place, these high rates of reported recreational travel were unsurprising. Our GHG reduction analysis estimated a monthly diversion of 12-44 kilograms of CO2 per rebate participant, similar to the GHG emissions reductions observed in other research. We conclude with an equity analysis that explores how program design influenced who participated in these rebate programs. This found that low-income requirements successfully target those with the most need for financial assistance. However, these requirements do not help meet other equity metrics (a just age, gender, racial, and ethnic distribution) by association.

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