research report

Barriers to Reducing the Carbon Footprint of Transportation Part 2: Investigating Evolving Travel Behaviors in the Post-Pandemic Period in California

Publication Date

May 1, 2024

Author(s)

Basar Ozbilen, Siddhartha Gulhare, Keita Makino, Aurojeet Jena, Xiatian Iogansen, Patrick Loa, Yongsung Lee, Giovanni Circella

Abstract

During the early months of the pandemic, stay-at-home orders and concerns about infection catalyzed a shift toward online activities, such as remote work and e-shopping, resulting in a significant decrease in conventional travel. However, as the effects of the pandemic diminished, the pandemic-induced online activities began to subside, and conventional travel started to rebound. To understand evolving travel-related activities spurred by the COVID-19 pandemic, researchers at ITS-Davis conducted four waves of mobility surveys in California between Spring 2020 and Fall 2023. Key findings from the analysis of these data reveal that remote work and a combination of remote work and physical commuting (i.e., hybrid work) emerge as an enduring outcome of the pandemic. The pandemic accelerated the rise of e-shopping, both for grocery and non-grocery purchases, with findings demonstrating the critical influence of socio-demographic factors, including age, gender, and income, on e-shopping adoption and frequency. The findings show that socio-demographic factors such as work status, income level, and work arrangements are associated with household vehicle ownership changes and individual vehicle miles traveled (VMT). In particular, an increase in commute frequency reduces the likelihood of vehicle shedding (i.e., getting rid of a vehicle), while amplifying the likelihood of vehicle acquisition. In the meantime, remote workers exhibit lower commuting VMT but higher non-commuting VMT compared to hybrid workers. The findings demonstrate a similarity between the percentage of respondents who used public transit, bikes, e-bikes, and e-scooters for commuting and non-commuting trips to some degree between 2019 and 2023.

published journal article

Driving A-loan: Automobile Debt, Neighborhood Race, and the COVID-19 Pandemic

Abstract

COVID-19 altered travel patterns in the U.S. Studies have analyzed the effect of the pandemic on travel mode, including working from home, but few have focused on automobile ownership—a relationship with potentially long-term consequences for accessibility, household budgets, and debt, and policy efforts to meet climate goals.

To understand the association between the pandemic and automobile ownership, we rely on a unique credit panel dataset from Experian and examine three different automobile loan-related outcome measures: annualized growth rate of new automobile loan balances, average new loan size, and the number of new loans. We focus specifically on changes across loans in neighborhoods by race/ethnicity, hypothesizing larger increases in automobile debt in Black and Latino/neighborhoods, where workers are less likely to be able to telework. The annualized growth rate of new automobile loans increased during the pandemic across all neighborhoods by race/ethnicity, increasing most rapidly in Latino/a neighborhoods. Controlling for other factors, loan size increased similarly across neighborhoods by race/ethnicity. The increase in automobile lending in Latino/a neighborhoods, therefore, likely was explained by a significant uptick in the number of new loans.

The growth in automobile lending during the pandemic was potentially prompted by pandemic-induced changes in the need for automobiles and facilitated by an expanded social safety net. As the pandemic and its various forms of public financial assistance recede, the findings underscore the importance of ongoing assistance in enabling automobile ownership or shared access among households with limited means whose livelihoods depend on the access that vehicles provide.

presentation

Understanding the “New Normal:” Activity and Mobility Patterns of Low-Income and Disadvantaged Communities in the Era of Hybrid Work and High Gas Prices

research report

Updated Fuel Portfolio Scenario Modeling to Inform 2024 Low Carbon Fuel Standard Rulemaking

Publication Date

November 1, 2023

Author(s)

Colin Murphy, Jin Wook Ro, Qian Wang

Abstract

The Low Carbon Fuel Standard (LCFS) plays a critical role in California’s efforts to reduce greenhouse gas (GHG) and air pollutant emissions from transportation. The LCFS incentivizes the use of fuels with lower life cycle GHG emissions by using a credit market mechanism to provide incentives for low-carbon fuels, using revenue generated by charges applied to high-carbon ones. Maintaining an approximate balance between LCFS credit and deficit supplies helps support a stable LCFS credit price and the broader transition to low-carbon transportation. The Fuel Portfolio Scenario Model, presented here, evaluates bottom-up fuel supply and LCFS compliance to inform LCFS policy decisions. We considered two key fuel demand scenarios: (1) the Low Carbon Transportation scenario, reflecting the expected transition to low-carbon transportation in California over the next 15 years, and (2) the Driving to Zero scenario, featuring a significantly higher consumption of petroleum gasoline. In both scenarios, 2030 LCFS targets around 30% resulting in a near-balance between credits and deficits, with some banked credits remaining. Several additional scenarios were modeled to explore the impact of target trajectory timing, alternate post-2030 targets, greater biofuel use, and other parameters. This fuel portfolio scenario modeling work can meaningfully inform policy development.

presentation

Fuel Portfolio Scenario Modeling of 2030 LCFS Targets

Publication Date

July 6, 2023

Author(s)

Colin Murphy, Jin Wook Ro

blog

Making Policy in the Absence of Certainty: Biofuels and Land Use Change

Publication Date

October 26, 2023

Author(s)

Colin Murphy

presentation

Fuel Portfolio Scenario Modeling of 2030 Low Carbon Fuel Standard Targets in California

Publication Date

January 9, 2024

Author(s)

Jin Wook Ro, Colin Murphy

op-ed

Opinion: How a California Climate Win Could End up Destroying Rainforests — and What to do About it

Publication Date

March 14, 2024

Author(s)

Colin Murphy, Dan Sperling

presentation

New Insights into how Micromobility Services Affect Vehicle Miles Traveled: Evidence from the American Micromobility Panel

published journal article

Examining Spatial Disparities in Electric Vehicle Charging Station Placements Using Machine Learning

Abstract

Electric vehicles (EVs) are an emerging mode of transportation that has the potential to reshape the transportation sector by significantly reducing carbon emissions thereby promoting a cleaner environment and pushing the boundaries of climate progress. Nevertheless, there remain significant hurdles to the widespread adoption of electric vehicles in the United States ranging from the high cost of EVs to the inequitable placement of EV charging stations (EVCS). A deeper understanding of the underlying complex interactions of social, economic, and demographic factors that may lead to such emerging disparities in EVCS placements is, therefore, necessary to mitigate accessibility issues and improve EV usage among people of all ages and abilities. In this study, we develop a machine learning framework to examine spatial disparities in EVCS placements by using a predictive approach. We first identify the essential socioeconomic factors that may contribute to spatial disparities in EVCS access. Second, using these factors along with ground truth data from existing EVCS placements we predict future ECVS density at multiple spatial scales using machine learning algorithms and compare their predictive accuracy to identify the most optimal spatial resolution for our predictions. Finally, we compare the most accurately predicted EVCS placement density with a spatial inequity indicator to quantify how equitably these placements would be for Orange County, California. Our method achieved the highest predictive accuracy (94.9%) of EVCS placement density at a spatial resolution of 3 km using Random Forests. Our results indicate that a total of 11.04% of predicted EVCS placements in Orange County will lie within a high spatial inequity zone – indicating populations with the lowest accessibility may require greater investments in EVCS placements. 69.52% of the study area experience moderate accessibility issues and the remaining 19.11% face the least accessibility issues w.r.t EV charging stations. Within the least accessible areas, 7.8% of the area will require a low density of predicted EVCS placements, 3.4% will require a medium density of predicted EVCS placements and 0.55% will require a high density of EVCS placements. The moderately accessible areas would require the highest placements of EVCS but mostly with low-density placements covering 54.42% of the area. The findings from this study highlight a generalizable framework to quantify inequities in EVCS placements that will enable policymakers to identify underserved communities and facilitate targeted infrastructure investments for widespread EV usage and adoption for all. The findings from this study highlight a generalizable framework to quantify inequities in EVCS placements that will enable policymakers to identify underserved communities and facilitate targeted infrastructure investments for widespread EV usage and adoption for all.