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Key Drivers

Although similarities between the two scenarios exist, the key drivers that cause one path to emerge over another are the important elements for anticipating and preparing for change. We identify key drivers partly through the information in Figure A.2 in Appendix A, which shows how active or passive each descriptor is. An active descriptor influences many other descriptors, while many descriptors influence a passive descriptor. Active descriptors are therefore more important in influencing the entire system. Additionally, in the process of writing the scenario narratives based on linking the descriptors and the reasons for the projections, we identified an additional driver with high uncertainty—environmental conditions—that played a dominant role in distinguishing the scenarios from each other. The future development of these three selected key drivers will strongly affect other descriptors in the scenarios. Table 3.2 summarizes the three key drivers.

Table 3.2. Key Drivers with High Uncertainty in Future Development

SCENARIO

Key Driver

The Great Reset

Slowing but Growing

Economic growth

High

Moderate

Constraints on driving and vehicle ownership

Widespread in cities

Only in tier 1 cities

Environmental conditions

Improve

Worsen

Economic Growth

Economic growth, the second most highly active descriptor in the system, received very different projections. Because of its influence on motorization and both intra- and intercity travel demand, we consider this a key driver of future mobility in China.

The experts agreed that China's historically high GDP growth rates would decrease by 2030; most observers agree that no country can sustain double-digit growth rates indefinitely. However, that rate might slow slightly or significantly. Indeed, in the past few years, China's GDP growth has declined to between 7 and 9 percent. The question for these scenarios is whether growth stabilizes at a higher rate or a lower one (still high by international standards, but well below past rates).

Economic growth depends on many factors, only some of which are under direct government control. Higher growth could occur as a result of a successful shift to consumption-driven growth, a reduction in corruption, reform of the hukou system, or policies to allow more competition among companies (as opposed to propping up state-owned enterprises). On the other hand, lower growth might result from turmoil in the financial markets as a result of extremely high levels of debt, continuing trends of low labor productivity, or a lack of a safety net for both retirement and health care that discourages consumption. Political unrest (especially if unemployment increases) or demands for higher environmental quality (which was, to some extent, sacrificed in the interest of increasing GDP) could hinder the government's ability to encourage and steer growth.

Constraints on Driving and on Vehicle Ownership

A second key driver is constraints on driving and on vehicle ownership. Although these were separate descriptors in the analysis, we combine them here because they both represent attempts to use regulations or channel market forces to dampen the amount of personal car ownership and the use of cars. Both were rated fairly active. Chinese cities have been far more assertive in developing such restrictions than cities in other developed countries have (with the exception of Singapore, whose vehicle ownership constraints, adopted several decades ago, were used as a model by Chinese cities), and these might well have a substantial effect on motorization and driving that would lead to very different mobility outcomes.

These constraints already exist to a limited extent in several of the largest cities, and the question is whether the enormous problems with congestion, air pollution, and lack of parking that led to the adoption of such policies in first-tier cities might spread to second- and third-tier cities. These cities might look to their larger neighbors and develop similar policies if they are shown to be effective.

There are two sources of uncertainty with regard to this driver. The first is whether the policies succeed where they are already implemented. They might be less effective if people find ways around them, if they are not strictly enforced, or if they are watered down over time. (For example, Beijing has a black market in license plates, which can be legally obtained only through a lottery that began in 2011 [Song, 2014]). The second is whether cities will have the political will to implement them. In an atmosphere of general political discontent, such a policy might become a flashpoint for protests. Although the need for such policies might become more pronounced with higher economic growth, such growth might also mean that residents feel as though their desires for increased mobility and status are being thwarted.

Environmental Conditions

The third driver is environmental conditions, which we did not include in our initial set of descriptors but rather identified in the process of writing the scenario narratives. Participants at several of the workshops discussed the impact of environmental conditions intensively as a reason for certain projections, especially in the demographic and economic areas. We think that environmental conditions in China might play a key role in influencing some of the descriptors. For example, prolonged drought might influence internal migration, which could affect the percentage of population living in the eastern region. Extremely low air quality could discourage business owners from locating factories and offices in certain cities, which could affect the proportion of the economy generated in the eastern region.

Second, although the extent of environmental degradation has become more apparent over time, the trajectory of these conditions is uncertain. For example, rainfall patterns could affect whether droughts become more severe; climate change, whose influence is highly uncertain, might, in turn, affect rainfall. Third, both political and economic considerations might affect environmental conditions through regulations, public pressure, and spending on measures to clean up polluted land, air, and water.

 
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