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An Evolutionary Model of Industry Transformation and the Political Sustainability of Emission Control Policies

An Evolutionary Model of Industry Transformation and the Political Sustainability of Emission Control Policies

Steven C. Isley
Robert J. Lempert
Steven W. Popper
Raffaele Vardavas
Copyright Date: 2013
Published by: RAND Corporation
Pages: 104
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  • Book Info
    An Evolutionary Model of Industry Transformation and the Political Sustainability of Emission Control Policies
    Book Description:

    Limiting climate change will require transformation of energy and other systems. This report presents an agent-based, game theoretic model designed to compare the long-term sustainability of alternative carbon emission reduction policies. The model tracks the co-evolution of an industry sector, its technology base, and political coalitions that influence government policy. It uses robust decision making methods to compare alternative policies.

    eISBN: 978-0-8330-8308-1
    Subjects: Physics, Mathematics, Technology

Table of Contents

  1. Front Matter
    (pp. i-ii)
  2. Preface
    (pp. iii-iv)
  3. Table of Contents
    (pp. v-vi)
  4. Figures
    (pp. vii-viii)
  5. Tables
    (pp. ix-x)
  6. Summary
    (pp. xi-xiv)
  7. Acknowledgments
    (pp. xv-xvi)
  8. Abbreviations
    (pp. xvii-xviii)
  9. 1. Introduction
    (pp. 1-4)

    Standard economic theory provides an excellent understanding of the efficiency-enhancing potential of markets. However, the introduction of markets often also leads to significant changes in society’s values, technology, and institutions, and these types of market-induced transformations are generally not well understood. This presents a significant limitation because potential policy solutions to many problems require transformations in various sectors of society, such as energy, transportation, and housing. Our current analytic tools are often inadequate for comparing and evaluating policies that might promote such transformations. Therefore, a better understanding of the interacting socioeconomic mechanisms and processes involved in market-induced transformations would be...

  10. 2. Design of Robust Decision Making Analysis
    (pp. 5-14)

    Our simulation model displays characteristics that make it useful for our purposes, while making it difficult to employ the traditional tools of policy analysis. Addressing an important issue of interest in this project, the model can display properties that the complex systems literature callsemergenceand a dynamics shaped by self-referential expectations in the presence of imperfect information. Such features can generate regions of extreme sensitivity to particular assumptions yet can, at the same time, exhibit important regularities of macroscopic behavior. Traditional policy analytic tools, which employ a probabilistic representation of uncertainty and rank alternative strategies according to expectations contingent...

  11. 3. Model Design
    (pp. 15-38)

    This section describes in more detail the components of the model shown in Figure 2.2. The appendixes provide further details regarding some components. As will rapidly become apparent, the model contains many uncertain parameters that can potentially prove important to comparisons among alternative policies. The RDM process described above and the calibration procedure described in Section 4 are designed to use this model to make useful policy arguments by identifying the combinations of conditions for which one type of policy leads to more favorable results than others.

    The model proceeds by iterating through the steps schematically illustrated in Figure 2.2....

  12. 4. Calibration
    (pp. 39-42)

    We developed this model to conduct an RDM analysis that compares the ability of alternative near-term carbon reduction policies to result in a long-term transformation to a low-carbon economy. We thus required a suitable experimental design over the model input parameters that enables testing policy performance over a wide range of plausible futures, as represented by the model. Two types of information can help guide the choice of cases. First, estimates exist for the upper and lower bounds for individual model input parameters. Second, the model should reproduce the historical record reasonably well. We sought a set of cases consistent...

  13. 5. Representative Analysis
    (pp. 43-48)

    To help verify the model and explore some interesting aspects of the political feedback mechanism, we conducted an exploratory analysis of approximately 20,000 cases. The range of inputs differed from the calibration analysis in important ways. First, the representative analysis included many new inputs that were not applicable in the calibration analysis, such as the parameters relating to the government’s response to lobbying and the shape parameters for the carbon R&D beta distribution. Second, a new input parameter, called thestarting case, was added. This parameter takes values 1 through 10 and corresponds to the ten starting cases generated during...

  14. 6. Next Steps
    (pp. 49-50)

    The evolutionary simulation model we have set forth in this report, and the RDM framework for exercising it, are intended to serve as a laboratory for examining how the choice of the initial design of GHG emission reduction policies may affect how such policies evolve over time and the extent to which they achieve their intended goals. In particular, we were interested in the situation in which policymakers have an opportunity to put in place a GHG regulatory system, which will then evolve over time outside their control. This evolutionary agent-based formalism aims to examine how policymakers might use their...

  15. Appendix A. Computation of the Social Cost of Carbon
    (pp. 51-54)
  16. Appendix B. The Lobbying Game
    (pp. 55-60)
  17. Appendix C. Adaptive Learning Model for R&D Decisions
    (pp. 61-64)
  18. Appendix D. Starting Cases
    (pp. 65-68)
  19. Appendix E. Representative Analysis Details
    (pp. 69-72)
  20. Appendix F. Parameter List
    (pp. 73-80)
  21. References
    (pp. 81-86)