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Summary

Research Question

What might we expect for the future of mobility in China in 2030? Responses to this question will help transportation decisionmakers in China—national, provincial, and local officials—and the private sector better prepare for the future. Long-range transportation planning involves many difficult choices, especially in an era of constrained resources. Which modes of transportation should be prioritized? Which investments should be funded? How will the pace of economic growth affect auto manufacturing and purchasing? How will economic and demographic changes affect long-distance travel? These questions are hard to answer, particularly because transportation decisionmakers must make decisions with a time horizon that extends 30 to 50 years into the future.

Although the country's mobility (how people travel from point to point) will be considerably different in 2030 from what it is today, figuring out how it will be different is a significant challenge. Some changes happen slowly, while others can take place relatively quickly. Demographic change happens relatively slowly, although the trend in China points toward rapidly slowing population growth rates and an aging population structure. Investments that change travel patterns can happen quickly; China went from no high-speed rail less than a decade ago to the world's most extensive network today. Both types of changes can dramatically affect travel demand.

Answers to our research question cannot be reliably addressed through straight-line trend analysis or improved travel demand forecast models. These approaches are lacking because the data and information needed to support long-term thinking about the future of mobility are uncertain, incomplete, evolving, or conflicting. Instead, we have applied scenario techniques, which are increasingly used to deal with the opportunities and risks associated with complex, long-term issues. As we look to 2030, multiple mobility futures are possible. The relationship between today's situation and a long-term future outcome is not linear. It is not even relevant to study the two points in time—now and then. It takes a systematic process of identifying possible, plausible futures and then understanding the paths leading to those alternative futures.

Our study, which was the result of collaboration between RAND and the Institute for Mobility Research (ifmo), focused on long-term scenarios for passenger travel, including travel by car, transit,1 domestic air, and intercity rail. Long-term scenarios in this area are multilayered and complex, influenced by demographics, economics, energy, and transportation supply and constraints. How these forces play out over the next 15 years will depend on whether and how decisionmakers sort out and address current and upcoming challenges. Although we cannot know these outcomes in advance, we can apply scenario planning to develop plausible mobility futures that can be used to anticipate and prepare for change.

Methodology

To develop alternative scenarios of the future of mobility, we applied a process that combined expert opinion gathered in workshops, cross-impact analysis, consistency analysis, and cluster analysis. The study began with identifying four influencing areas and descriptors (variables of interest) within each area. Then RAND and ifmo staff convened four workshops, one for each influencing area: demographics, economics, energy, and transportation supply and constraints.

Six to eight subject-matter experts from government, academia, nonprofit organizations, and consulting firms were involved in each workshop, for a total of 28 people who brought considerable substantive experience in a variety of fields and disciplines. At each workshop, experts were asked to make projections for each descriptor for 2030, along with their assumptions regarding the projections and their qualitative estimates of their impact on mobility. Where there was little uncertainty and high consensus, the group identified only one projection per descriptor. Otherwise, two or three alternative projections surfaced.

We subjected the descriptors and projections to a cross-impact analysis and consistency analysis to identify relationships between the descriptors. We then put these into a computer support system, which used cluster analysis to group them into distinct scenario frameworks. This produced two scenarios: the Great Reset and Slowing but Growing. We developed the resulting scenario narratives based on the assumptions and projections that surfaced during the expert workshops. Given the importance of economic growth in each scenario, economists—both workshop experts and other RAND experts on China—vetted the economic framework for each.

 
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