Constraints on Car Ownership and Driving
In contrast, constraints on car ownership and driving are largely determined by policy. Some of these are market-based constraints; for example, the high cost of passenger cars might keep them financially out of reach for many. The base cost of a car depends on a range of variables, but policymakers can increase the cost by levying various types of taxes. They can and have used a mechanism seldom used in developed countries: restrictions on the number of license plates available. Constraints on driving are similarly a combination of market- and policy-driven factors. The price of gas can be an important determinant of driving, although, as China moves toward more hybrid and electric vehicles, gas prices might have a lesser impact than in a country with a predominantly gasoline-powered fleet. Road pricing (which already exists in the form of tolls) is another policy lever to influence demand.
Environmental Conditions
Environmental conditions are more like economic growth in that a variety of factors only partially under government control influence them. The key factor that the government can control is the adoption of—and even more importantly, the enforcement of—environmental regulations. In the past, regulations have been subservient to the demands of economic growth, and incentives to protect the environment have not been well-aligned with other incentives. The financial resources of city, provincial, and the central government to build the appropriate infrastructure (such as water treatment plants) can also influence environmental conditions. Although both scenarios assume that comprehensive laws to address climate change will be promulgated, there is more than one way to enact such laws. In addition, we assume that environmental problems themselves—that is, their severity and their role in shaping public opinion—might differ between the two scenarios.
Implications for Transportation Decisionmaking
Our two scenarios present transportation policymakers, planners, transportation suppliers, and private-sector users of the system with the different challenges and opportunities that they might face under one scenario versus another. This information provides a context for understanding how today's decisions, among any of these players, might play out in the future. In this section, we suggest two ways in which to apply and use the scenarios in transportation decisionmaking. Depending on who is using the scenarios, different implications for planning can be drawn.
Identifying Leading Indicators
One of the fundamental uses of scenarios is that, if considered plausible, they can help decisionmakers anticipate and prepare for change. The systematic, long-term view of different paths of mobility development supports creative but focused "what-if" thinking. As an initial step toward further action and planning, monitoring key trends in relation to each scenario is useful. Leading indicators of directions in which critical uncertainties might go can and should be discerned now and monitored over time. We can categorize them by the relative strength of their connection to demographic, economic, energy, or transportation supply and constraint issues. Considering all of the influencing areas when identifying leading indicators forces the acknowledgment of shifts in trends outside the transportation-specific domain. The purpose of this exercise is to ask, "Toward which scenario are we moving, and with what implications?"
Specific leading indicators can be developed on the basis of the key trends set out in the scenarios, supported by appropriate data sources that are monitored on a regular basis. For example, under the Great Reset scenario, potential leading indicators include adoption and effective enforcement of economic reform measures, growth in consumer spending, and gradual adoption of constraint measures on ownership and driving. Under Slowing but Growing, leading indicators include a lack of economic reforms and continuation of unsustainable debt growth, slowing of growth in car manufacturing, and lack of investment by non-first-tier cities in transit and nonmotorized modes.
Determining Opportunities, Risks, and Contingencies
Because multiple scenarios force planners to consider a wider range of futures than in typical short- to medium-term planning, scenarios serve to uncover new opportunities on the horizon and to highlight key risks. In this way, the scenarios presented in this report can be used to influence government bodies at all levels, as well as private firms, to consider a wider set of options within their planning process. Such planning typically begins with the desired end state and works backward to the current status. At every stage, the planner asks, "What must be done at the previous stage to reach this stage?" Making sense of past events and monitoring potential future developments when working in a high-pressure environment (as transportation often is) is a challenge. Scenarios enable planners to look at a wider set of opportunities and risks and therefore to identify a more robust set of strategic options.
In the Chinese context, such scenarios might be especially helpful in understanding why previous plans have not come to fruition. For example, actual NEV registrations have fallen far short of ambitious past targets for adoption. The two scenarios presented both have optimistic targets as well, but they help explain why other factors might influence the number of NEVs sold. Such factors as economic growth, special treatment of NEVs in the system of licensing restraints, and the adoption of non-ownership-based motorization are all thought to have some effect on NEV purchases. These could constitute other policy levers by which to influence NEV demand.
From the private-sector side, scenarios also point out opportunities for new markets, such as the possibility of more constraints on ownership and driving leading to markets for providing nonownership use of cars or the possibility of growth in other technologies (such as driver assistance and telematics), which could suggest a need to build more infrastructure to take advantage of these opportunities.
Finally, scenarios point to some risks based on continuing current trends. For example, there is risk in continuing current patterns of building new apartments on the outskirts based on local governments' need for revenues. In the Slowing but Growing scenario, this leads to declines in property values and a financial crisis resulting from overreliance on debt financing. Another risk is overbuilding intercity transportation infrastructure if demand does not grow strongly; this has economic consequences (wasting money that could have been spent more productively), as well as more-direct transportation consequences (lower-than-anticipated demand could result in a lack of revenues to maintain the infrastructure in a state of good repair).
Utility of the Wild-Card Scenario
Too narrow a focus, on what we can imagine today, can constrain future planning, even long-term future planning. Our scenario process, like others, followed a systematic approach of drawing out and analyzing possible future projections on a specific set of descriptors based on past and current trends. The systematic approach provides greater credibility to the scenarios, but it does constrain the scenarios to what might be plausible given current conditions.
The value of the wild-card scenario is that it escapes the condition that it must be believable today, because it represents a break in trends. Although our study identified only one such wild card, Debt Comes Due, other wild cards should be considered. These could be either positive or negative. Positive wild cards might be based on health technology breakthroughs that produce extreme longevity and declines in disability, rapid developments in autonomous-vehicle technology, or successful use of geo-engineering to address the threat of climate change. Negative wild cards could include military conflicts in Asia, a global pandemic, or destructive effects from climate change, such as a widespread refugee crisis or a sharp decline in food production.
Planners can use wild-card scenarios in the same ways they use the more-plausible scenarios. Planners can assume that those scenarios are possible, if unlikely, and test their policies against the possibility of such events coming to pass. Ideally, policies would be robust; that is, they would have positive impacts regardless of the circumstances, instead of being tailored to the likelihood of one future.
