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Using Systems Thinking and System Dynamics Modeling to Understand Rebound Effects

  • Mohammad Ahmadi AchachloueiEmail author
  • Lorenz M. Hilty
Part of the Progress in IS book series (PROIS)

Abstract

Processes leading to an increase of demand for a resource as a consequence of increasing the efficiency of using this resource in production or consumption are known as (direct) rebound effects. Rebound effects at micro and macro levels tend to offset the reduction in resource consumption enabled by progress in efficiency. Systems thinking and modeling instruments such as causal loop diagrams and System Dynamics can be used to conceptualize the structure of this complex phenomenon and also to communicate model-based insights. In passenger transport, the rebound effect can be invoked by increased cost efficiency (direct economic rebound) and/or increase in speed (time rebound). In this paper we review and compare two existing models on passenger transport—including a model on the role of information and communication technology—with regard to the feedback loops used to conceptualize rebound effects.

Keywords

Rebound effect Energy efficiency Systems thinking Systems modeling System dynamics Causal loop diagrams Passenger transport ICT Time rebound Direct rebound 

Notes

Acknowledgements

The authors would like to thank Empa (Technology and Society Lab), KTH (Centre for Sustainable Communications), and Vinnova, which made this work possible as a part of the first author’s Ph.D. project.

References

  1. 1.
    O. Edenhofer, R. Pichs-Madruga, Y. Sokona, S. Agrawala, I. A. Bashmakov, G. Blanco, J. Broome, and others, Climate Change 2014: Mitigation of Climate Change: Working Group III Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 2014.Google Scholar
  2. 2.
    P. H. G. Berkhout, J. C. Muskens, and J. W. Velthuijsen, “Defining the rebound effect,” Energy Policy, vol. 28, no. 6–7, pp. 425–432, Jun. 2000.Google Scholar
  3. 3.
    A. Arvesen, R. M. Bright, and E. G. Hertwich, “Considering only first-order effects? How simplifications lead to unrealistic technology optimism in climate change mitigation,” Energy Policy, vol. 39, no. 11, pp. 7448–7454, Nov. 2011.Google Scholar
  4. 4.
    S. Sorrell and J. Dimitropoulos, “The rebound effect: Microeconomic definitions, limitations and extensions,” Ecol. Econ., vol. 65, no. 3, pp. 636–649, Apr. 2008.Google Scholar
  5. 5.
    L. A. Greening, D. L. Greene, and C. Difiglio, “Energy efficiency and consumption — the rebound effect — a survey,” Energy Policy, vol. 28, no. 6–7, pp. 389–401, Jun. 2000.Google Scholar
  6. 6.
    S. Sorrell, J. Dimitropoulos, and M. Sommerville, “Empirical estimates of the direct rebound effect: A review,” Energy Policy, vol. 37, no. 4, pp. 1356–1371, Apr. 2009.Google Scholar
  7. 7.
    I. Røpke and T. H. Christensen, “Energy impacts of ICT – Insights from an everyday life perspective,” Telemat. Inform., vol. 29, no. 4, pp. 348–361, Nov. 2012.Google Scholar
  8. 8.
    W. Abrahamse, L. Steg, C. Vlek, and T. Rothengatter, “A review of intervention studies aimed at household energy conservation,” J. Environ. Psychol., vol. 25, no. 3, pp. 273–291, Sep. 2005.Google Scholar
  9. 9.
    J. W. Forrester, Industrial Dynamics. Cambridge Massachusetts: The MIT Press, 1961.Google Scholar
  10. 10.
    J. D. Sterman, Business dynamics: systems thinking and modeling for a complex world, vol. 19. Irwin/McGraw-Hill Boston, 2000.Google Scholar
  11. 11.
    G. P. Richardson, “System dynamics, the basic elements of,” in Complex Systems in Finance and Econometrics, Springer, 2011, pp. 856–862.Google Scholar
  12. 12.
    L. M. Hilty, P. Arnfalk, L. Erdmann, J. Goodman, M. Lehmann, and P. A. Wäger, “The relevance of information and communication technologies for environmental sustainability – A prospective simulation study,” Environ. Model. Softw., vol. 21, no. 11, pp. 1618–1629, Nov. 2006.Google Scholar
  13. 13.
    M. D. Stepp, J. J. Winebrake, J. S. Hawker, and S. J. Skerlos, “Greenhouse gas mitigation policies and the transportation sector: The role of feedback effects on policy effectiveness,” Energy Policy, vol. 37, no. 7, pp. 2774–2787, Jul. 2009.Google Scholar
  14. 14.
    P. Peeters, “Chapter 4 Tourism transport, technology, and carbon dioxide emissions,” in Tourism and the Implications of Climate Change: Issues and Actions, vol. 3, 0 vols., Emerald Group Publishing Limited, 2010, pp. 67–90.Google Scholar
  15. 15.
    E. Dace, G. Bazbauers, A. Berzina, and P. I. Davidsen, “System dynamics model for analyzing effects of eco-design policy on packaging waste management system,” Resour. Conserv. Recycl., vol. 87, pp. 175–190, Jun. 2014.Google Scholar
  16. 16.
    S. Sorrell, “Jevons’ Paradox revisited: The evidence for backfire from improved energy efficiency,” Energy Policy, vol. 37, no. 4, pp. 1456–1469, Apr. 2009.Google Scholar
  17. 17.
    C. Gossart, “Rebound Effects and ICT: A Review of the Literature,” in ICT Innovations for Sustainability, L. M. Hilty and B. Aebischer, Eds. Springer International Publishing, 2015, pp. 435–448.Google Scholar
  18. 18.
    M. Börjesson Rivera, C. Håkansson, Å. Svenfelt, and G. Finnveden, “Including second order effects in environmental assessments of ICT,” Environ. Model. Softw., vol. 56, pp. 105–115, Jun. 2014.Google Scholar
  19. 19.
    S. Borenstein, “A microeconomic framework for evaluating energy efficiency rebound and some implications,” National Bureau of Economic Research, 2013.Google Scholar
  20. 20.
    L. Erdmann and L. M. Hilty, “Scenario Analysis: Exploring the Macroeconomic Impacts of Information and Communication Technologies on Greenhouse Gas Emissions,” J. Ind. Ecol., vol. 14, no. 5, pp. 826–843, 2010.CrossRefGoogle Scholar
  21. 21.
    J. D. Khazzoom, “Economic Implications of Mandated Efficiency in Standards for Household Appliances,” Energy J., vol. 1, no. 4, pp. 21–40, Oct. 1980.Google Scholar
  22. 22.
    T. H. Oum, W. G. Waters, and J.-S. Yong, “Concepts of price elasticities of transport demand and recent empirical estimates: an interpretative survey,” J. Transp. Econ. Policy, pp. 139–154, 1992.Google Scholar
  23. 23.
    P. Hjorth and A. Bagheri, “Navigating towards sustainable development: A system dynamics approach,” Futures, vol. 38, no. 1, pp. 74–92, Feb. 2006.Google Scholar
  24. 24.
    N. C. Nguyen, D. Graham, H. Ross, K. Maani, and O. Bosch, “Educating systems thinking for sustainability: experience with a developing country,” Syst. Res. Behav. Sci., vol. 29, no. 1, pp. 14–29, 2012.CrossRefGoogle Scholar
  25. 25.
    J. Mingers and L. White, “A review of the recent contribution of systems thinking to operational research and management science,” Eur. J. Oper. Res., vol. 207, no. 3, pp. 1147–1161, Dec. 2010.Google Scholar
  26. 26.
    E. F. Wolstenholme, “Qualitative vs quantitative modelling: the evolving balance,” J. Oper. Res. Soc., pp. 422–428, 1999.Google Scholar
  27. 27.
    N. Videira, F. Schneider, F. Sekulova, and G. Kallis, “Improving understanding on degrowth pathways: An exploratory study using collaborative causal models,” Futures, vol. 55, pp. 58–77, 2014.CrossRefGoogle Scholar
  28. 28.
    L. Erdmann and F. Wurtenberger, “The future impact ICT on environmental sustainability. First Interim Report. Identification and global description of economic sectors,” Institute for Prospective Technology Studies (IPTS), Sevilla, 2003.Google Scholar
  29. 29.
    L. Erdmann and S. Behrendt, “The future impact ICT on environmental sustainability. Second Interim Report,” Institute for Prospective Technology Studies (IPTS), Sevilla, 2003.Google Scholar
  30. 30.
    J. Goodman and V. Alakeson, “The future impact ICT on environmental sustainability. Third Interim Report. Scenarios,” Institute for Prospective Technology Studies (IPTS), Sevilla, 2003.Google Scholar
  31. 31.
    L. M. Hilty, P. Wäger, M. Lehmann, R. Hischier, T. F. Ruddy, and M. Binswanger, “The future impact of ICT on environmental sustainability. Fourth Interim Report. Refinement and quantification,” Institute for Prospective Technological Studies (IPTS), Sevilla, 2004.Google Scholar
  32. 32.
    P. Arnfalk, “The future impact ICT on environmental sustainability. Fifth Interim Report. Evaluation and Recommendations,” Institute for Prospective Technology Studies (IPTS), Sevilla, 2004.Google Scholar
  33. 33.
    L. Erdmann, L. M. Hilty, J. Goodman, and P. Arnfalk, “The future impact ICT on environmental sustainability. Synthesis Report,” Institute for Prospective Technology Studies (IPTS), Sevilla, 2004.Google Scholar
  34. 34.
    P. Wäger, L. M. Hilty, P. Arnfalk, L. Erdmann, and J. Goodman, “Experience with a System Dynamics model in a prospective study on the future impact of ICT on environmental sustainability,” in IEMSs 3rd Biennial Meeting Summit on Environmental Modeling and Software, Burlington, USA, 2006.Google Scholar
  35. 35.
    M. A. Achachlouei and L. M. Hilty, “Modeling the Effects of ICT on Environmental Sustainability: Revisiting a System Dynamics Model Developed for the European Commission,” in ICT Innovations for Sustainability, L. M. Hilty and B. Aebischer, Eds. Springer International Publishing, 2015, pp. 449–474.Google Scholar
  36. 36.
    L. M. Hilty, Ökologische Bewertung von Verkehrs-und Logistiksystemen: Ökobilanzen und Computersimulation. IWÖ, 1994.Google Scholar
  37. 37.
    L. M. Hilty, “Umweltbezogene Informationsverarbeitung–Beiträge der Informatik zu einer nachhaltigen Entwicklung,” Habil Hambg., 1997.Google Scholar
  38. 38.
    G. Hupkes, “The law of constant travel time and trip-rates,” Futures, vol. 14, no. 1, pp. 38–46, Feb. 1982.Google Scholar
  39. 39.
    D. Metz, “The Myth of Travel Time Saving,” Transp. Rev., vol. 28, no. 3, pp. 321–336, May 2008.Google Scholar
  40. 40.
    M. Höjer and L.-G. Mattsson, “Determinism and backcasting in future studies,” Futures, vol. 32, no. 7, pp. 613–634, Sep. 2000.Google Scholar
  41. 41.
    L. M. Hilty, R. Meyer, and T. F. Ruddy, “A general modelling and simulation system for sustainability impact assessment in the field of traffic and logistics,” Environ. Inf. Syst. Ind. Public Adm. Idea Group Publ., pp. 167–185, 2001.Google Scholar
  42. 42.
    E. Wolstenholme, “Using generic system archetypes to support thinking and modelling,” Syst. Dyn. Rev., vol. 20, no. 4, pp. 341–356, 2004.CrossRefGoogle Scholar
  43. 43.
    J. Schleich, B. Mills, and E. Dütschke, “A brighter future? Quantifying the rebound effect in energy efficient lighting,” Energy Policy, vol. 72, pp. 35–42, Sep. 2014.Google Scholar
  44. 44.
    R. Kok, R. M. J. Benders, and H. C. Moll, “Measuring the environmental load of household consumption using some methods based on input–output energy analysis: A comparison of methods and a discussion of results,” Energy Policy, vol. 34, no. 17, pp. 2744–2761, Nov. 2006.Google Scholar
  45. 45.
    S. Grepperud and I. Rasmussen, “A general equilibrium assessment of rebound effects,” Energy Econ., vol. 26, no. 2, pp. 261–282, Mar. 2004.Google Scholar
  46. 46.
    I. Røpke, “Theories of practice — New inspiration for ecological economic studies on consumption,” Ecol. Econ., vol. 68, no. 10, pp. 2490–2497, Aug. 2009.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mohammad Ahmadi Achachlouei
    • 1
    • 2
    • 3
    Email author
  • Lorenz M. Hilty
    • 2
    • 3
    • 4
  1. 1.Division of Environmental Strategies Research (fms)KTH Royal Institute of TechnologyStockholmSweden
  2. 2.Centre for Sustainable Communications (CESC)KTH Royal Institute of TechnologyStockholmSweden
  3. 3.Empa – Swiss Federal Laboratories for Materials Science and Technology, Technology and Society LabSt. GallenSwitzerland
  4. 4.Department of InformaticsUniversity of ZurichZurichSwitzerland

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