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Forecasting Revenues of Motor Fuel Taxes in California: an Application of Time-Series Regression Model to Estimate Changes in Vehicle Miles-travelled (VMT) among Light-duty Vehicles (LDVs) - Transportation infrastructure funding in the United States has been supported by motor fuel taxes for the past century. The revenue-generating capacity of motor fuel taxes is contingent on vehicle miles-travelled (VMT), vehicle fuel efficiency, and the tax rates. To forecast revenues of motor fuel taxes, this paper focuses on constructing a time-series model to estimate state-level VMT in California from 2023 to 2030. The model was fitted on county-level sociodemographic and public roadway data, and the selected model specification is an autoregressive integrated moving average (ARIMA) model with two lags of VMT, one-period error term and two lags of explanatory variables. We leveraged the estimated VMT to compute gasoline tax revenues, including both excise and sales taxes, under three scenarios: 1) no impact of light-duty vehicles (LDV) electrification, 2) modest growth of zero-emissions vehicles (ZEVs), and 3) aggressive growth of ZEVs. Based on our scenario analysis, we found that under both scenarios of modest and aggressive ZEV market growth, the gap in gasoline taxes revenue in combination with the Road Improvement Fee, a flat fee levied on ZEVs, would continue to widen between the present and 2030. By 2030, the revenue loss is expected to be around $0.5 billion, approximately 6% of gasoline excise tax revenues raised in 2023. In the face of addressing revenue shortfalls in our transportation infrastructure funding, re-creating the linkage between road usage and payment is a crucial policy solution. Based on our study, we computed the per-mile road-usage charge (RUC) rate to be 2.4 cents.
