Professor Shanjun Li, Associate Professor, Dyson School of Applied Economics & Management, Cornell University.

The talk will first broadly discuss the important environmental and energy challenges in China. Then the presentation will illustrate how important economic principles combined with big data can be powerful tools to address some of these challenges in the context of traffic congestion in Beijing. Major urban areas in China are experiencing world’s worst traffic congestion due to the dramatic increase in vehicle ownership and travel demand in the past decade. Central and local governments have been employing various policies to address this challenge yet with little or no visible impacts because these policies fundamentally failed to get the price right for road usage. This paper provides the first empirical estimate of the marginal cost of traffic congestion and the optimal congestion pricing in China by estimating the relationship between average vehicle speed and traffic density using rich vehicle traffic density and speed data from over 1500 monitoring stations throughout Beijing. To identify the causal effect of traffic density on speed, we use the driving restriction policy that restricts vehicle driving based on the last digit of the license plate to generate exogenous variation. Our analysis shows that the average marginal cost of congestion is about 0.5 Yuan (or $0.08) per km, nearly three times as much as what OLS regressions would imply and larger than estimates from transportation engineering models. Based on the marginal cost estimates for different road segments and time of the day, we estimate the optimal congestion pricing, the resulting congestion level, and social welfare under various designs of the pricing strategy.