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Scenario analysis: a review of methods and applications for engineering and environmental systems

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Abstract

Changing environment, uncertain economic conditions, and socio-political unrest have renewed interest in scenario analysis, both from theoretical and applied points of view. Nevertheless, neither the processes for scenario analysis (SA) nor evaluation criteria and metrics have been regularized. In this paper, SA-reported applications and implementation methodology are discussed in the context of an extensive literature review covering papers published between 2000 and 2010. Over 340 papers were identified through a series of queries in the web of science database. The papers were classified based on the North American Industrial Classification System and SA application goals (environmental, business, and social). SA methodology used in each paper was assessed based on four main criteria: coverage, consistency, uncertainty assessment, and efficiency. We find a significant increase in SA applications, especially in the environmental field. Theoretical developments in the field represent a small fraction of published studies and do not increase in time. The methods used to develop different scenarios vary widely across the academic literature and applications reviewed. Similarly, the methods and data used to characterize the scenarios and develop response strategies are extremely diverse and are limited by factors such as computational tractability and available time and resources. Based on this review, we recommend a regular process for scenario analysis that includes the steps of analysis, scenario definition, and evaluation.

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Acknowledgments

The authors would like to thank Kelsie Baker, Zachary Collier, Elisa Tatham, and Daniel Eisenberg for their thoughtful and constructive suggestions, which led to substantial improvement of this article. They also want to thank Matthew Wood, Fausto Morales, and John Coles for their help on database construction and on classification criteria definition. Permission was granted by the US Army Chief of Engineers to publish this information. The views and opinions expressed in this paper are those of the individual authors and not those of the US Army or other sponsor agencies.

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Authors and Affiliations

  1. US Army Engineer Research and Development Center, Vicksburg, MS, USA

    Yousra Tourki & Igor Linkov

  2. University of Massachusetts, Boston, MA, USA

    Jeffrey Keisler

  3. US Army Corps of Engineers, Concord, MA, 01742, USA

    Igor Linkov

Authors
  1. Yousra Tourki
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  2. Jeffrey Keisler
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  3. Igor Linkov
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Correspondence to Igor Linkov.

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Appendix: Theoretical coding for the classification

Appendix: Theoretical coding for the classification

Project goals and process designs were coded by evaluating the three sub-components of each factor. Each of these sub-components will be coded as “0” if they relate more to exploratory project goals or intuitive process designs, and “1” if they relate more to decision-support project goals or formal process designs.

To assess each sub-criterion, we answer to these questions

1.1 Project goals (Table 2)

  1. 1.

    Function: Is the goal of the SA to produce a product?

    1. a.

      “0” = No, the goal is to understand a process instead.

    2. b.

      “1” = Yes, the goal is to produce a decision aid, piece of policy, etc.

  2. 2.

    Inclusion of norms: Is the goal of the SA normative in nature?

    1. a.

      “0” = No, the goal is to describe possible futures.

    2. b.

      “1” = Yes, the goal is to describe probable or preferable (aka normative) futures.

  3. 3.

    Subject: Is the subject of the SA a specific decision maker like an institution?

    1. a.

      “0” = No, the subject is a survey of a geographic area or particular issue.

    2. b.

      “1” = Yes, an organization or sector is the subject of the SA.

1.2 Process design (Table 3)

  1. 1.

    Input: Is the input for the SA process quantitative in nature?

    1. a.

      “0” = No, inputs are primarily qualitative.

    2. b.

      “1” = Yes, inputs are mostly quantitative.

  2. 2.

    Method: Is the process model based?

    1. a.

      “0” = No, the process relies mostly on participation from individuals.

    2. b.

      “1” = Yes, the process depends on a formal model.

  3. 3.

    Group composition: Does the process include an exclusive and homogeneous group?

    1. a.

      “0” = No, the process is inclusive and participants represent heterogeneous groups.

    2. b.

      “1” = Yes, the process is exclusive and participants are relatively homogeneous.

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Tourki, Y., Keisler, J. & Linkov, I. Scenario analysis: a review of methods and applications for engineering and environmental systems. Environ Syst Decis 33, 3–20 (2013). https://doi.org/10.1007/s10669-013-9437-6

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