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Step 3: Integrate into Scenario Frameworks
We performed two types of analysis to develop the input to the scenarios. First, we conducted a cross-impact analysis between the descriptors across all influencing areas. This supported the identification of the key drivers in the system (see Gausemeier, Fink, and Schlake, 1998, for a further description of this type of analysis). We recorded the impacts that the different descriptors have on each other in a cross-impact matrix (or influence matrix) using a scale from 0 (no impact) to 3 (strong impact). For example, we determined that the total population has a strong influence on urbanization, so that relationship would be rated 3, while total population has no effect on the convenience of public transit, so that was rated 0. This exercise establishes the degree of interconnectedness among the descriptors.
The outcome of this analysis is the system diagram illustrated in Figure A.2. The higher the activity index of a descriptor, the more it influences other descriptors. For example, urbanization and economic growth affected a large number of other descriptors, so they are highly active. The higher the passivity index, the more other descriptors drive that descriptor. Many other descriptors affect oil consumption, so it is considered highly passive. A descriptor with both a high activity index and a high passivity index is strongly interconnected in the system, being driver and driven at the same time. This analysis was the basis for identifying some descriptors as key drivers.
Figure A.2. System Dynamics as an Outcome of the Cross-Impact Analysis
The second type of analysis is based on consistency logic, which establishes consistency (or lack thereof) among projections across all descriptors. Consistency here means how well the projection of a particular row and column would "fit" and how realistic it would be for both of them to appear simultaneously. The matrix entry is a numerical value that represents the level of consistency, with 5 being totally consistent and 1 being totally inconsistent. We created a consistency matrix created using all projections for each descriptor (see extract in Figure A.3). We judged how consistent or compatible a projection in a row is with the projections in each column. For example, strong growth in demand for air travel was deemed consistent with the lower price of oil because demand is affected by price and higher oil prices mean more-expensive airfares. On the other hand, the experts deemed increasing concentration of economic output in the eastern region inconsistent with a decrease in income inequality because this region is already wealthier than the rest of the country, and greater concentration of economic activity in one region is likely to lead to a concentration of wealth as well.
Figure A.3. Extract from the Consistency Matrix, Including Projection Pairs
1 = Totally inconsistent
2 = Partially inconsistent
3 = Neutral or independent
4 = Consistent
5 = Strongly consistent
The consistency matrix was then fed into an online tool, the RAHS platform.1 RAHS is a prototype of a web-based foresight platform that the Future Analysis Branch of the German Federal Ministry of Defence developed and funded to enhance external cooperation with industrial and scientific partners and thus to strengthen the methodological fundamentals of its own foresight work. Instead of providing a single software solution only for scenario development, it supports foresight projects with a variety of alternative foresight methods within a web-based online environment (Brockmann, 2012; Durst, Kolonko, and Durst, 2012). RAHS was designed based on a comprehensive scanning of internationally applied foresight methods and tools, including the Z_punkt Foresight-Toolbox, the Joint Research Centre (JRC) FOR-LEARN Online Foresight Guide, the Foresight Horizon Scanning Centre (HSC) toolkit, the European Union (EU) research project iKnow, and compilations of future research methodologies in the Millennium Project by Glenn and Gordon (2009) and
For this project, ifmo researchers led the use of RAHS to analyze millions of mathematically possible pairs of projections for the descriptors across all influencing areas and to eliminate the pairs deemed inconsistent in the consistency analysis that preceded this step. The exploratory scenario-construction toolbox in RAHS isolated clusters made up of homogeneous groups of descriptors and projections based on the consistency-analysis results. (More details on the application of consistency logic and cluster analysis implemented in the RAHS platform can be found in Gausemeier, Fink, and Schlake, 1998).
The RAHS output (see Table A.2) enabled the experts to identify scenarios that differ as widely as possible from each other. Of the four clusters produced, numbered 1 through 4 in Table A.2, the research team selected two to develop further. Clusters 1, 2, and 3 were similar across many descriptors, so first the team decided that only one of the three should be carried forward. Cluster 4 was included specifically because it varied so greatly from the other three. For example, cluster 4 had a lower average GDP growth rate. We thought it was important to include different growth rates because, at this stage of development, economic growth is generally an important, though hardly the only, determinant of mobility. The research team finally chose cluster 2 because its projections for total population, share of the economy in the eastern region, and domestic vehicle production differed from those in cluster 4.
Table A.2. Share of Projections Within Each Cluster
Table A.2. Share of Projections Within Each Cluster — Continued
Step 4: Produce Scenario Narratives
Using the two selected scenario frameworks, the research team wrote a narrative for each scenario. We developed the storylines by interlinking active and passive descriptors. We highlighted and interpreted key developments and interrelations. Thus, the scenarios describe not only the situation in 2030 but also how a situation developed step by step during that time frame. The scenarios represent a dynamic path, starting today and continuing to 2030. Using standard convention, we wrote all narratives from the vantage point of 2030.
Step 5: Draw Consequences for Future Mobility
This step generally consists of developing future estimates of mobility based empirically on past trends and ratings of directional influence (that is, whether a projection would encourage higher or lower use of a mode), as well as the strength of the influence in each scenario on travel. However, the lack of reliable Chinese data on personal travel at the national level made conducting this type of exercise difficult because it cannot be based on past trends. As such, we instead discussed changes in travel demand in a more qualitative manner (e.g., strong increase versus moderate increase). We also wove in more broadly some thoughts about the prevalence of new technologies (such as advanced driver-assistance systems) and new access models (such as car-sharing) based on other drivers in each scenario.
Step 6: Create Wild-Card Scenario
In scenario development, wild cards are highly unlikely but possible events that have a major impact on the future. They are disruptive and surprising, and they undermine the trends or developments presented in a scenario. During the workshops, we asked the experts to think about which wild cards would have a strong and sustained impact on future mobility in China. We determined that, given the importance of economic growth, we would include a wild card in which China falls into a period of sustained weak growth, which would represent a very different future.
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