Paul Natsuo Kishimoto

Modeling Regional Transportation Demand in China and the Impacts of a National Carbon Policy

Chinas climate and energy policy commitments are stated at the national level, but they may have uneven impacts on the countrys regionally heterogeneous transport system. This work quantifies the expected provincial-level response of freight transport to an economywide policy targeting reductions in carbon emissions intensity. The analysis applies the China Regional Energy Model, a multisector, static, global, computable general equilibrium (CGE) model representing 30 individual provinces with physical accounts of energy and greenhouse gas emissions. The structure of road and nonroad freight (and passenger) sectors, the preparation of transport activity data, and a policy similar to announced goals that specify a 17% reduction in the carbon dioxide emissions intensity of gross domestic product are described. In the national aggregate and in most provinces, the road freight sector is most affected by the emissions intensity cap. The road freight sector contributes 24%–-versus 18% from nonroad freight and 51% from nontransport sectors–-of a 5.1% reduction in national refined oil demand. Significant regional differences are found in the impacts of a national-level, economywide policy. Steep reductions in freight activity occur in some of the poorest provinces, partly because they offer low-cost abatement opportunities, and the resulting adjustments across the economy affect transport demand. This research contributes a new tool capable of capturing the transport impact of sector- and province-specific policies in detail and providing a rigorous foundation for future dynamic CGE analyses. Potential impacts of energy and climate policy on regional transport systems are important inputs to policy and infrastructure investment decisions at the central and local levels.

Incorporating Multiple Uncertainties into Projections of Chinese Private Car Sales and Stock

China is in a fast-growing stage of mobility development, and its increasing demand for private cars comes with growing energy consumption and pollutant emissions. Uncertainty in Chinese parameterization of car ownership models makes forecasting these trends a challenge. We develop an application of the Monte Carlo method, conditioned on historical data, to sample parameters for a model projecting aspects of private car diffusion, such as the mix of new and replacement sales. Our model includes changes in per-capita disposable income—both the mean and level of inequality—and a measure of car affordability. By incorporating multiple uncertainties, we show a distribution of possible future outcomes: a low stock of 280 million (1st decile); median of 430 million; and high of 620 million vehicles (9th decile) in 2050. This illustrates the limitations of attempts to model vehicle markets at the national level, by showing how uncertainties in fundamental descriptors of growth lead to a broad range of possible outcomes. While uncertainty in projected per-capita ownership grows continually, the share of first-time purchases in sales is most uncertain in the near term and then narrows as the market saturates. Replacement purchases increasingly capture the sales market from 2025. Our results suggest that stakeholders have a narrow window of opportunity to regulate the fuel economy, pollution and other attributes of vehicles sold to first-time buyers. These may, in turn, shape consumers’ experience and expectations of car ownership, affecting their additional and replacement purchases.