Passenger Car Ownership Estimation toward 2030 in Japan: BAU Scenario with Socio-economic Factors

Hirota, Keiko (2006): Passenger Car Ownership Estimation toward 2030 in Japan: BAU Scenario with Socio-economic Factors. Published in: Studies in Regional Science , Vol. 37, No. 1 (2007): pp. 25-39.

Abstract

The estimation of passenger car ownership is a crucial estimation for auto-related production and for the analysis of many transportation-related policies such as Green House Gas (GHG), emissions, and energy consumption policies. Previous studies of car ownership estimation have generally focused on accurate adherence to the track record, statistical signification, or model structure; however, there are problems in focusing on all these factors together. A variation in the assumptions can produce different forecast results. Further, the uncertainties in the forecasting processes were enormous, and this made the final results unreliable. It is important for these previous studies with economic variables to have accurate results of passenger car ownership with regard to the various estimation factors such as emission levels, CO2, and car parts production. For the production estimation and for the policy analysis, it is necessary to draw a car ownership pattern as a baseline scenario “the Business as Usual.” The purpose of our passenger car ownership estimation model with the Business as Usual scenario　―JARI BAU Model―. JARI BAU Model is to estimate passenger car ownership by resolving these difficulties. Our passenger car ownership estimation model with the JARI BAU Model for the demand function is intended to provide information on the total passenger car ownership in Japan from the present time until the year 2030. This paper is an attempt at methodological amelioration by conducting a fairly comprehensive literature survey on the estimation models of passenger car ownership. The estimated results will be strictly examined by t-value, and regression coefficients will be estimated at the 1% significance level. The accuracy of the estimated result will be compared to the statistical record. This paper is unique in that it attempts to estimate car ownership solely on the basis of socioeconomic trends, without including the physical characteristics of automobiles such as fuel economy, vehicle age, or infrastructure development. Considering an aging society with a declining birth rate and an increasing governmental debt, the population may be polarized into high- and low-income groups. The polarization of income distribution affects the polarization of car ownership. We assume that the driver’s license holders in the high-income group can own their vehicles. The BAU model estimates 60.09 million passenger vehicles in 2010 and 61.59 million in 2030. The estimation model improves both the accuracy and statistical estimation. From the viewpoint of accuracy, the deviation is between –4% and +8% as compared with the actual record. The estimated t-values are significant for the entire data set and the limited data set (the 1970s, 1980s, and 1990–2002).

• Item Type: MPRA Paper
• Language: English
• Keywords: Time series models, Model construction and estimation, passenger car ownership, Japan, Demographic trends
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; N - Economic History > N3 - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy > N35 - Asia including Middle East; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R4 - Transportation Economics > R41 - Transportation: Demand, Supply, and Congestion ; Travel Time ; Safety and Accidents ; Transportation Noise; J - Labor and Demographic Economics > J1 - Demographic Economics > J11 - Demographic Trends, Macroeconomic Effects, and Forecasts

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