Strategic Issues Facing Transportation, Volume 4: Sustainability as an Organizing Principle for Transportation Agencies (2014)

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66 3.1 Background Transportation agencies face growing uncertainty. Changing state and federal budget priori- ties, potential major regulatory changes related to greenhouse gas (GHG) emissions, and major technology changes (e.g., connected vehicles, high-speed rail) mean that state transportation executives are increasingly uncertain about the future (AASHTO, 2010). In particular, these issues are likely to affect the organizational principles around which state transportation agencies are established. These challenges are even greater when long-term perspectives are required from transportation decisionmakers, such as that necessitated by a commitment to sustainability (i.e., sustainability requires that decisionmakers consider the impact of their transportation decisions on future generations’ ability to support and sustain their society). Specifically, shifting demographics, disruptive technologies, major statutory and regulatory changes, increasing environmental stress, and uncertainty about the future distribution and rate of economic growth all mean that tra- ditional organizations will be challenged to find new operating principles (Friedman, G., 2010, 2011; Friedman, T. L., 2007, 2009). Traditional input-output planning models have been of little value in helping agencies navigate environments characterized by a high degree of uncertainty (Shoemaker, 1995, 1997; Shoemaker and Gunther, 2002; van der Heijden, 2005; Schwartz, 1991). Instead, scenario planning has emerged as a means to help analysts and decisionmakers envision the different requirements that organizations will have to address in the radically different future conditions (often referred to as future worlds) (AASHTO, 2010). Scenario planning helps organizations look into the future and anticipate events and trends, understand risk, provide ideas for preemptive organizational response, and help managers break out of their established mental models as they become aware of alternative future possibilities. A scenario is a set of related possibilities that describe one possible future that the strategist cannot control. Although there is no consensus on the definition or approach to scenario planning, typically, a scenario is a rich narrative or story describing a possible outcome (Schwartz, 1991). Despite the difficulties in defining scenarios or developing a single approach to scenario devel- opment, there is a clear consensus that scenarios are not predictions (van der Heijden, 2005). Instead, “Scenarios are consistent and coherent descriptions of alternative hypothetical futures that reflect different perspectives on past, present, and future developments which can serve as a basis for action” (van Notten, 2005). Scenario planning has a long history, dating back to its roots in 19th century military operational planning. In the 20th century, interest in scenario planning reemerged in the work of the RAND Corporation and the writings of Herman Kahn (Kaplan, 1991). Essentially, Kahn’s approach was to develop three basic scenarios: (1) the most likely (or baseline) case scenario, (2) a worst-case C H A P T E R 3 Scenario Development Methodology

Scenario Development Methodology 67 scenario, and (3) a best-case scenario. By the 1960s, the scenario-planning approach was mov- ing beyond the worst-case/most likely–case/best-case paradigm to consider more open-ended alternative futures in large part because, while the simple, linear, single-dimension approach to scenario development was well suited to military planning (where, originally, outcomes were anticipated to occur as a single event, with a single, logical, short-term and long-term chain emerg- ing from that event), it was hopelessly limited when facing the ambiguous and open-ended environment of government and business (Michel and Roubelat, 1996, 2000). General Electric and Royal Dutch Shell pioneered an alternative approach to scenario planning that was based on the development of a number of different future worlds that were neither good nor bad, but possible. The goal of this approach to scenario planning was to sensitize management and planning staff to alternative planning assumptions (Diffenbach, 1983). In particular, Royal Dutch Shell’s Group Planning Department, led by Pierre Wack, explored the environment for events that might affect the price of oil. The team identified several issues, including the steady exhaustion of U.S. oil reserves and the expanding role of the Organization of Petroleum Exporting Countries (OPEC; which might demand higher prices for oil), and developed full scenarios for two cases: (1) steady oil prices and (2) massive oil crisis triggered by OPEC. In October 1974, the second scenario was realized, and Shell was the only major oil company able to respond. Shell’s adept response enabled the firm to move from seventh to first in profitability in the industry (Cornelius et al., 2005). The perceived success of the Shell experience led to a sudden increase in the use of scenario planning and an enormous proliferation in scenario-planning approaches and goals. Policy theorist Philip Van Notten conducted a detailed analysis of the use of scenario planning in gov- ernment, industry, and the private sector and developed a typology that described the range of goals and methodologies. This typology used three broad macro characteristics and nine micro characteristics. Macro characteristics addressed the why, how, and what of a scenario exercise (i.e., its goals, the process design, and the scenario contents); micro characteristics described the specific goals, participants, methodologies, and analytical techniques used to build these scenarios, such as the following (van Notten, 2000): • Function of the scenario exercise • Role of values in the scenario process • Subject area and issues covered • Nature of change addressed (discontinuity versus evolutionary) • Nature of the inputs into the scenario process • Methods used in the scenario process • Degree of group participation versus model-based analysis • Role of time in the scenario • Level of integration of different scenario elements The result of this analysis is that there is no firm consensus concerning the correct approach to scenario analysis and that the methods are highly context dependent. Thus, the use of specific techniques (e.g., the use of “wild card”—high-impact, low-probability—events) in scenario- planning exercises is best in (1) tabletop or wargaming/strategic simulation exercises, where decisionmakers explore how their plans and assumptions might be disrupted by unexpected, high-impact events, or (2) crisis-prone systems, where sudden radical discontinuities can change long-held assumptions and methods of doing business (Rockfellow, 1994; Petersen, 1999). In longer-term, society-wide scenario analysis, individual events are less likely to have major long-term effects due to the relative strength of macro-level trends. For example, despite numer- ous policy changes and major historical and social events, U.S. gross domestic product (GDP) has

68 Sustainability as an Organizing Principle for Transportation Agencies shown a remarkable stable upward pattern that can be modeled easily as an exponential trend (see Figure 10). Furthermore, when the entire period is considered, a remarkably stable growth pattern emerges. In fact, when the entire period from 1870 to 2009 is considered, despite numerous wild cards (e.g., wars, economic fluctuations, policy changes), U.S. real GDP grew at an annual rate of approximately 3 percent. From a scenario-planning perspective, the power of long-term trends suggests that many events are over-determined (i.e., there are many drivers that are shaping long-term changes) and that the power of any one event or wild card to affect long-term change is vastly overrated [for more discussion of this point, see Thompson (1978)]. This does not mean that individuals or events cannot change the direction of the future; it simply means that their power to disrupt decades-old forces and trends that are the combination of millions of individuals’ single acts is limited and that these events or individuals rarely have truly long-term consequences. While such paradigm shifts do occur (e.g., the shift from rail to car), they are often the result of numerous long-term forces acting on each other (e.g., technology, economic development, national policy shifts, and millions of individual decisions led to the adoption of the automobile). Paradigm shifts that occur separate from these long-term trends are difficult to identify and, for purposes of this phase of the project (i.e., identification of requirements in the shape of challenges and opportunities for Phase II), are of limited value. Thus, for longer-term, macro scenario building, an approach that focuses on long-term, powerful social, economic, tech- nological, and geographical factors is more appropriate than considering shorter-term events. Specifically, this approach is more likely to capture the main forces to which individual organi- zations (such as state DOTs) will have to respond over several decades rather than the day-to-day, year-to-year events that may distract organizational planners from important long-term changes. This approach is consistent with the FHWA scenario-based planning methodology. The FHWA approach involves six general steps and is a dynamic methodology, allowing transportation planners to generate new scenarios as events occur (see Section 3.2). The first step in the FHWA process is to identify driving forces, or macro-level trends. Driving forces are “the major sources of change that impact the future” (FHWA, 2010). Commonly used driving forces include local 0 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000 12,000,000 14,000,000 16,000,000 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 Re al GD P (m ill io ns of 20 05 do lla rs ) Time US GDP Expon. (US GDP) Source: Johnston and Williamson (2011). Figure 10. Real U.S. GDP growth, 1870–2009.

Scenario Development Methodology 69 land use, levels of congestion, and local demographics. The second step is to determine patterns of interactions. Patterns of interactions between driving forces can be determined in a variety of ways. The FHWA recommends that transportation planners use a matrix and develop a metric related to positive or negative outcomes. The third step involves creating scenarios from these matrices by fitting realistic situations to predicted patterns between the driving forces. The FHWA describes the goal of creating scenarios as bringing life to possible alternatives in a way that community stakeholders can easily recognize and connect the various components. The fourth step is to analyze the implications of the scenario. In this step, transportation planners and stakeholders develop potential transportation policies that mesh with the scenarios. Evaluating scenarios is the fifth step in the FHWA’s methodology. The FHWA describes a variety of methods for evaluating scenarios, such as using various criteria and presenting the scenarios to community stakeholders (e.g., through a decision-analysis session or individual interviews). The sixth and last step is monitoring relevant indicators of the scenario. 3.2 Scenario Development Methodology Figure 11 provides an overview of the research team’s approach. The project began with a meeting with the NCHRP 20-83(7) panel to agree on key assumptions, strawman drivers that could affect organizing principles, and the scope of the project. Based on this meeting, the research team conducted an in-depth scan of the current futurist literature, conducted interviews with subject matter experts (SMEs), held internal SME discussions and panels, and identified a series of drivers that will affect the long-term development of U.S. society and thus affect state transportation agencies’ organizing principles. Table 24 presents the drivers identified using the research team’s approach; Appendix A describes each driver in detail. For each driver, the research team established a series of alternative outcomes or stories that describe how that driver might evolve or change between 2010 and 2050. Detailed descriptions of each driver, the data and methodology used to develop driver outcomes, and additional supporting information can be found in Appendix A. The research team then used a scenario-building technique known as “morphological analysis” (Zwicky, 1969; Zwicky and Wilson, 1967). General morphological analysis was developed as a method for structuring and investigating the total set of relationships contained in multi- dimensional, non-quantifiable problems (Ritchey, 2006). Traditional scenario planning emerged as an alternative to formal (mathematical) methods and causal modeling as a form of non- quantified modeling that relied on judgmental processes and internal consistency rather than on causality. However, scenario planning did not provide any guidelines as to how to place the non-quantifiable dimensions of scenario development on a sound methodological basis. Morphological analysis offers a solution to this problem by extending the traditional scenario-planning techniques through a cross-consistency assessment (CCA) approach. CCA is a method for rigorously structuring and investigating the internal properties of inherently non-quantifiable problem complexes, which contain any number of disparate parameters. It encourages the investigation of boundary conditions and virtually compels practitioners to examine numbers of contrasting configurations and policy solutions. Essentially, general morphological analysis is a method for identifying and investigating the total set of possible relationships or configurations contained in a given problem complex. In this sense, it is closely related to typology construction, although it is more generalized in form and Role of Projections For many drivers that lent themselves to quantitative treatment (e.g., population), the research team used a variety of projections to express different trends that are identified by experts. The research team included more qualitative judgments, where appropriate, in the development of the overall scenarios.

70 Sustainability as an Organizing Principle for Transportation Agencies conceptual range. The approach begins by identifying and defining the parameters (or dimensions) of the problem complex to be investigated and assigning each parameter a range of relevant val- ues or conditions. A morphological box (also known as a Zwicky box) is constructed by setting the parameters against each other in an n-dimensional matrix. Each cell of the n-dimensional box contains one specific value or condition from each of the parameters and thus marks out a specific state or configuration of the problem complex. For example, imagine a simple problem complex, defined as consisting of three parameters or dimensions (e.g., color, texture, and size). Further, assume that the first two dimensions Figure 11. Overview of the scenario development approach. World 1 World 5 World 4 World 3 World 2 ? ? ? ? ? Synthesize and integrate drivers to create scenarios Review data, literature, and conduct SME interviews and panels to identify potential range of outcomes Driver 1 Driver 2 Driver 3 Driver 4 Driver 5 Analyze trends and alterative visions of the future Identify and analyze drivers Establish scope, key assumptions, and review strawman drivers and scenarios with the NCHRP panel Conduct in-depth scan of literature and interviews with SMEs Review, refine, and revise through discussions, interviews, and inputs from SMEs Finalize scenarios and determine challenges and opportunities for state transportation agencies “Requirements” for Phase II “Describe Transportation Agency Models” Reference and review as needed Challenges Opportunities

Scenario Development Methodology 71 consist of five discrete values or conditions each (e.g., color = red, green, blue, yellow, brown) and the third consists of three values (size = large, medium, small). Then there are 75 (= 5 × 5 × 3) cells in the Zwicky box, each containing three conditions—one from each dimension (e.g., red, rough, large). The entire three-dimensional matrix is a morphological field that contains all of the (formally) possible relationships involved. For this study, the research team identified sev- eral different drivers and identified the range of potential values that each driver could take on. The team then considered each potential combination of drivers. Those that did not make logi- cal sense were excluded from further analysis. Those that were included were expanded to form full-scenario descriptions for further analysis. Using this approach, the research team developed a series of scenarios that expressed a number of alternative worlds for 2050. The NCHRP panel then reviewed and slightly modified the general Scenario Driver Definition Impact on State Transportation Agencies and Organizational Principles Demographic Factors The size, distribution, and characteristics (e.g., age, sex, ethnicity) of the U.S. population This driver will affect organizing principles by helping to determine travel demand and, indirectly, the resources that are available to transportation agencies. Economic Growth and Public-Sector Spending on Transportation Future patterns of economic growth (e.g., GDP, inflation, investment, employment, income growth) and public- sector spending (e.g., federal, state, and local) on transportation This driver will affect organizing principles by determining the resources that are available for transportation agencies and the level of transportation demand (as generated by economic activity). Energy (Includes Transportation Energy Uses and Fuel Prices) Future changes in energy use and the proportion of energy derived from different sources; Includes the price and fuel sources used, by modes of transportation This driver will affect organizing principles by helping to determine travel demand (e.g., via fuel prices and energy availability). Climate Change, Environment, and Resource Use Future changes in the environment (in particular, climate change), resource availability, and resource use This driver will affect organizing principles through impacts on environmental resource shocks, travel demand, and state-specific environmental challenges that transportation agencies will face. Transportation Technology The development of future transportation technologies and the degree to which these technologies are adopted by individuals and networks This driver will affect organizing principles by determining the transportation options available, travel demand, requirements for investment and capital decisions in the future, and the choices that need to be made. Land Use Population distribution, demographics, land use patterns, and development factors This driver will affect organizing principles through travel demands and the requirements for state transportation agencies. Future Transportation System Funding, Operation, and Control Funding, degree of shared ownership with the private sector, and centralization/ decentralization (i.e., the roles of federal, state, regional, and local governments) This driver will affect organizing principles via the resources they have; challenges and opportunities caused by shared ownership; and the role of federal, state, regional, and local governments. *Detailed descriptions of each driver, the data and methodology used to develop driver outcomes, and additional supporting information can be found in Appendix A. Table 24. Scenario drivers affecting organizing principles*.

72 Sustainability as an Organizing Principle for Transportation Agencies descriptions of the scenarios. Based on the panel’s comments, the research team conducted detailed research to fill out the scenarios, held interviews with SMEs and futurists, and developed detailed descriptions of the scenarios. Booz Allen SMEs then reviewed these scenarios in an all-day session to validate and review assumptions. These SMEs included transportation planners and experts, transportation technology experts, environmental experts, economists, and individuals who formerly had been state and local transportation officials before joining Booz Allen. These SMEs were supplemented with several academics external to Booz Allen to rule out the potential for organizational biases. Based on their comments, the research team again revised the scenarios and submitted a draft report describing them to the NCHRP panel. The panel made additional comments, and the research team revised the scenarios to reflect the consensus of the future. These finalized scenarios formed the basis for identifying the challenges and opportunities that different types of state transportation agencies could face in the next 40 years.