The Importance of Why

Image courtesy of David Castillo Dominici at

It’s an exciting time to be doing scenario planning.  A variety of new tools and data sources are available, many of them open source. Federal agencies are strongly encouraging local governments to engage in scenario planning, and putting grant funding where their mouth is.  With all the excitement, it can be tempting to dive headlong into a scenario planning process without giving adequate thought to a very basic question:

Why do you want to do scenario planning anyway?

There are many different purposes that scenario planning can serve, and being muddled about your intentions can lead to wasted time and resources.  Only after you have answered “why” can you answer “what” tools and data to use, “how” to design and evaluate the scenarios, and “who” should be involved at different stages in the process.  Knowing “why” also helps answer other questions, such as:

  • Which data points will you manipulate, and which will you measure?

  • What do you assume will remain constant across scenarios?

  • What is the appropriate “baseline” scenario?  Do you even need a baseline?

  • How plausible should the scenarios be?

  • Should the scenarios be mutually exclusive (i.e., either/or options that can’t be combined)?

  • Is it OK if one of the scenarios seems like an obvious “winner” or “loser”?

Below I discuss a variety of reasons why communities may want to engage in scenario planning, and how those reasons inform the types of questions outlined above.

Reason #1: To explore how decisions today might affect future outcomes.

The primary focus here is on factors that are under the community’s control – infrastructure investments, zoning policies, environmental regulations, etc.  Ideally, the only data points that are manipulated as inputs to the scenarios are those that correspond with actual decisions, and other data points are held constant or allowed to vary as outputs.  In the case of land use, for example, communities can control zoning but the private sector is primarily responsible for any development that actually occurs.  Therefore a robust scenario analysis would manipulate allowed densities as an input, and the location and density of development would be the output of a model that considers market conditions and other factors beyond the community’s control.  Any assumptions about these external factors – such as consumer preferences – should be made explicit.

In this case, an appropriate baseline might be a “do-nothing” scenario where current policies are continued unchanged into the future.  Designating one particular scenario as a baseline may be unnecessary, however, if the scenarios are all plausible and distinctly different.  Scenarios that seem implausible because the underlying decision is highly unlikely (e.g., a historically anti-tax community deciding to significantly increases taxes) may undermine the credibility of the effort.  Including one scenario that dramatically outperforms the others may also affect credibility, by creating the impression that the scenarios were deliberately designed favor one decision over others.  For these reasons, involving stakeholders in designing the scenarios is particularly important.

Reason #2: To explore how external factors (beyond the community’s control) may impact the future.

Here the factors that a community has control over are held constant across scenarios, to isolate the effects of external factors.  What if energy prices skyrocket?  What if sea levels rise? Analyzing how current plans and policies respond to these and other future uncertainties can point the way toward strategies for increasing resiliency. A scenario where external factors remain unchanged from today is a useful baseline, as it illustrates the magnitude of impact that changes may have on a community, and the importance of preparing for change. The fact that this baseline scenario may be an obvious “winner” in terms of desirable outcomes does not negate the need to prepare for alternative scenarios.   Improbable scenarios may also be worth exploring if the magnitude of impact is quite large, much as it behooves a community to plan for rare but catastrophic natural disasters.

Reason #3: To increase understanding of the connections between issues.

In this case, exactly how changes come about (through specific decisions or as the result of external factors) is less important than illustrating how change in one domain affects other domains.  This gives the practitioner much more freedom to manipulate data points as inputs to the scenarios.  Using land use as an example again, scenarios may “force” development to occur at particular densities and in particular locations, to illustrate the impact of these different land use patterns on travel behavior, public health, government spending, or other outcomes.

If the intention is to highlight inherent trade-offs, it helps if the scenarios are mutually exclusive.  For example, one scenario may include primarily low-density development and new roads, while another scenario may include primarily high-density development and new transit – two scenarios that would not be logical to combine.  A baseline scenario is not necessary if the scenarios are all reasonably plausible and distinct.  Scenarios that seem implausible from a political perspective may be OK, if they illustrate relationships or trade-offs that would otherwise not be apparent.

Reason #4: To ask “what would it take” to achieve certain outcomes.

Perhaps your community is considering or has already adopted specific goals such as a reduction in greenhouse gas emissions, or an increase in transit mode share.  A scenario analysis can serve as a reality check by illustrating what it might take to actually meet those goals. The analysis could also explore different paths for achieving the same desired outcomes, embodied in distinctly different scenarios. The currently adopted plan may be an appropriate baseline, illustrating how close the plan comes (or how far it falls short) in meeting the goals.

The plausibility of the alternative scenarios in itself becomes a metric – if the scenarios are highly implausible, perhaps the goals are unrealistic.  On the other hand, if the scenarios are highly plausible, perhaps the community could consider more aggressive “stretch” goals.  Assumptions about external factors that may affect the desired outcomes should be carefully thought through and made explicit.

Reason #5: To illustrate what is possible given different funding levels.

This type of analysis is helpful if future funding levels are uncertain, or if a community is considering developing new funding sources (e.g., a tax dedicated to transit).  If higher funding levels are highly implausible, however, this can undermine credibility.  The scenario with the highest funding level is likely to be the obvious winner, in terms of beneficial outcomes, but this is OK if the intention is to discuss how much the community is willing to pay for the benefits, or to help the community understand the impact of potential funding cuts.  A scenario that assumes continuation of current funding levels would be an obvious baseline.

Reason #6: To identify a preferred scenario.

The preference could be stated as a package of policies and strategies drawn from either one scenario or a combination of scenarios, or as an end-state represented by one of the scenarios.  In the latter case, subsequent discussions would focus on policies and strategies that could achieve that end state. The plausibility of the scenarios is key, as is the engagement of stakeholders early in the process of defining the scenarios.   If one of the scenarios clearly outperforms the others, stakeholders may feel they are not being presented with real choices, but rather being led down a predetermined path.

Reason #7: To spark people’s imaginations about possible futures.

In contrast to the previous examples, here the practitioner has the most freedom to play God and design scenarios that may be improbable or even implausible, but illustrate radically different visions of what may be possible.  These kinds of scenarios can serve as a platform for out-of-the-box discussions, and may lead to new ideas that wouldn’t otherwise be considered.   In this case, a useful baseline may be a scenario that seems highly probable, which can help highlight the commonly-held assumptions that the alternative scenarios call into question.

Several of the reasons for conducting scenario planning outlined above can be combined in productive ways.  The matrix below provides a simple example of how scenarios that explore the impacts of today’s decisions on future outcomes can be combined with scenarios that explore external factors:

 Example illustration of how to consider external factors against planning decisions

Other purposes are more difficult to combine.  For example, it may be challenging to design a scenario analysis that sparks people’s imaginations and encourages out-of-the-box thinking, but also results in the selection of a realistic preferred scenario.  This just underscores the need for clear thinking about why you want to do scenario planning in the first place.  If you enter the process with clear intentions, you will save yourself and your stakeholders a lot of frustration in the long run.


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