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Agent-based modelling of consumer energy choices

Nature Climate Change volume 6, pages 556–562 (2016)Cite this article

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Abstract

Strategies to mitigate global climate change should be grounded in a rigorous understanding of energy systems, particularly the factors that drive energy demand. Agent-based modelling (ABM) is a powerful tool for representing the complexities of energy demand, such as social interactions and spatial constraints. Unlike other approaches for modelling energy demand, ABM is not limited to studying perfectly rational agents or to abstracting micro details into system-level equations. Instead, ABM provides the ability to represent behaviours of energy consumers — such as individual households — using a range of theories, and to examine how the interaction of heterogeneous agents at the micro-level produces macro outcomes of importance to the global climate, such as the adoption of low-carbon behaviours and technologies over space and time. We provide an overview of ABM work in the area of consumer energy choices, with a focus on identifying specific ways in which ABM can improve understanding of both fundamental scientific and applied aspects of the demand side of energy to aid the design of better policies and programmes. Future research needs for improving the practice of ABM to better understand energy demand are also discussed.

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Figure 1: Common elements of an ABM.

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References

  1. Weber, C. & Perrels, A. Modelling lifestyle effects on energy demand and related emissions. Energ. Policy 28, 549–566 (2000).

    Article  Google Scholar 

  2. Dietz, T., Gardner, G. T., Gilligan, J., Stern, P. C. & Vandenbergh, M. P. Household actions can provide a behavioral wedge to rapidly reduce US carbon emissions. Proc. Natl Acad. Sci. USA 106, 18452–18456 (2009).

    Article  CAS  Google Scholar 

  3. Wilkerson, J. T., Cullenward, D., Davidian, D. & Weyant, J. P. End use technology choice in the national energy modeling system (NEMS): an analysis of the residential and commercial building sectors. Energ. Econ. 40, 773–784 (2013).

    Article  Google Scholar 

  4. Shavel, I. et al. Exploring Natural Gas and Renewables in ERCOT, Part III: The Role of Demand Response, Energy Efficiency, and Combined Heat & Power (The Brattle Group, 2014).

    Google Scholar 

  5. Carrico, A. R., Vandenbergh, M. P., Stern, P. C. & Dietz, T. US climate policy needs behavioural science. Nature Clim. Change 5, 177–179 (2015).

    Article  Google Scholar 

  6. Ipakchi, A. & Albuyeh, F. Grid of the Future. IEEE Power Energ. Mag. 7, 52–62 (2009).

    Article  Google Scholar 

  7. Rai, V. & Robinson, S. A. Agent-based modeling of energy technology adoption: empirical integration of social, behavioral, economic, and environmental factors. Environ. Modell. Softw. 70, 163–177 (2015).

    Article  Google Scholar 

  8. Stokes, D. E. Pasteur's Quadrant: Basic Science and Technological Innovation (Brookings Institution, 1997).

    Google Scholar 

  9. Clark, W. C. & Dickson, N. M. Sustainability science: the emerging research program. Proc. Natl Acad. Sci. USA 100, 8059–8061 (2003).

    Article  CAS  Google Scholar 

  10. Kates, R. W. What kind of a science is sustainability science? Proc. Natl Acad. Sci. USA 108, 19449–19450 (2011).

    Article  CAS  Google Scholar 

  11. Worrell, E., Ramesohl, S. & Boyd, G. Advances in energy forecasting models based on engineering economics. Annu. Rev. Env. Resour. 29, 345–381 (2004).

    Article  Google Scholar 

  12. Loulou, R. & Labriet, M. ETSAP-TIAM: The TIMES Integrated Assessment Model Part I: model structure. Comput. Manag. Sci. 5, 7–40 (2008).

    Article  Google Scholar 

  13. Popp, D., Newell, R. G. & Jaffe, A. B. in Handbook of the Economics of Innovation Vol. 2 (eds Hall, B. H. & Rosenberg, N.) 873–937 (Elsevier, 2010).

    Book  Google Scholar 

  14. Kemp, R. & Volpi, M. The diffusion of clean technologies: a review with suggestions for future diffusion analysis. J. Clean. Prod. 16, S14–S21 (2008).

    Article  Google Scholar 

  15. Wilson, C. & Dowlatabadi, H. Models of decision making and residential energy use. Annu. Rev. Environ. Resour. 32, 169–203 (2007).

    Article  Google Scholar 

  16. Aguirregabiria, V. & Mira, P. Dynamic discrete choice structural models: a survey. J. Econometrics 156, 38–67 (2010).

    Article  Google Scholar 

  17. Sargent, T. J. Evolution and intelligent design. Am. Econ. Rev. 98, 3–37 (2008).

    Article  Google Scholar 

  18. Tesfatsion, L. in Handbook of Computational Economics Vol. 2 (eds Tesfatsion, L. & Judd, K. L.) 831–880 (Elsevier, 2006).

    Google Scholar 

  19. Dubé, J.-P., Hitsch, G. J. & Jindal, P. The joint identification of utility and discount functions from stated choice data: an application to durable goods adoption. QME-Quant. Mark. Econ. 12, 331–377 (2014).

    Article  Google Scholar 

  20. Henry, A. D. & Vollan, B. Networks and the challenge of sustainable development. Annu. Rev. Environ. Resour. 39, 583–610 (2014).

    Article  Google Scholar 

  21. Kiesling, E., Günther, M., Stummer, C. & Wakolbinger, L. M. Agent-based simulation of innovation diffusion: a review. Cent. Eur. J. Oper. Re. 20, 183–230 (2012).

    Article  Google Scholar 

  22. Barabasi, A.-L. Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life (Basic Books, 2014).

    Google Scholar 

  23. Watts, D. J. Six Degrees: The Science of a Connected Age (Norton, 2003).

    Google Scholar 

  24. Dietz, T., Fitzgerald, A. & Shwom, R. Environmental values. Annu. Rev. Environ. Resour. 30, 335–372 (2005).

    Article  Google Scholar 

  25. Henry, A. D. The challenge of learning for sustainability: a prolegomenon to theory. Hum. Ecol. Rev. 16, 131–140 (2009).

    Google Scholar 

  26. Axsen, J. & Kurani, K. S. Interpersonal influence within car buyers' social networks: applying five perspectives to plug-in hybrid vehicle drivers. Environ. Plann. A 44, 1047–1065 (2012).

    Article  Google Scholar 

  27. Claudy, M. C., Michelsen, C. & O'Driscoll, A. The diffusion of microgeneration technologies–assessing the influence of perceived product characteristics on home owners' willingness to pay. Energ. Policy 39, 1459–1469 (2011).

    Article  Google Scholar 

  28. Bollinger, B. & Gillingham, K. Peer effects in the diffusion of solar photovoltaic panels. Market. Sci. 31, 900–912 (2012).

    Article  Google Scholar 

  29. Noll, D., Dawes, C. & Rai, V. Solar community organizations and active peer effects in the adoption of residential PV. Energ. Policy 67, 330–343 (2014).

    Article  Google Scholar 

  30. Robinson, S. A. & Rai, V. Determinants of spatio-temporal patterns of energy technology adoption: an agent-based modeling approach. Appl. Energ. 151, 273–284 (2015).

    Article  Google Scholar 

  31. Durlauf, S. N. Complexity, economics, and public policy. Pol. Philos. Econ. 11, 45–75 (2012).

    Article  Google Scholar 

  32. Henry, A. D. & Dietz, T. Understanding environmental cognition. Organ. Environ. 25, 238–258 (2012).

    Article  Google Scholar 

  33. Easley, D. & Kleinberg, J. Networks, Crowds, and Markets: Reasoning About a Highly Connected World (Cambridge Univ. Press, 2010).

    Book  Google Scholar 

  34. An, L. Modeling human decisions in coupled human and natural systems: review of agent-based models. Ecol. Model. 229, 25–36 (2012).

    Article  Google Scholar 

  35. Filatova, T., Verburg, P. H., Parker, D. C. & Stannard, C. A. Spatial agent-based models for socio-ecological systems: challenges and prospects. Environ. Modell. Softw. 45, 1–7 (2013).

    Article  Google Scholar 

  36. Macal, C. M. & North, M. J. Tutorial on agent-based modelling and simulation. J. Simulat. 4, 151–162 (2010).

    Article  Google Scholar 

  37. Zhang, H., Vorobeychik, Y., Letchford, J. & Lakkaraju, K. in Proc. 2015 International Conference on Autonomous Agents and Multiagent Systems 513–521 (International Foundation for Autonomous Agents and Multiagent Systems, 2015).

    Google Scholar 

  38. Valente, T. W. Network Models of the Diffusion of Innovations (Hampton, 1995).

    Google Scholar 

  39. Helbing, D. & Balietti, S. in Social Self-Organization: Agent-Based Simulations and Experiments to Study Emergent Social Behavior 25–70 (Springer, 2013).

    Google Scholar 

  40. Schelling, T. C. Micromotives and Macrobehavior (Norton, 2006).

    Google Scholar 

  41. Epstein, J. M. Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Univ. Press, 2006).

    Google Scholar 

  42. Gilbert, G. N. Agent-Based Models (Sage, 2008).

    Book  Google Scholar 

  43. Stern, P. C., Dietz, T., Abel, T., Guagnano, G. A. & Kalof, L. A value-belief-norm theory of support for social movements: the case of environmentalism. Hum. Ecol. Rev. 6, 81–87 (1999).

    Google Scholar 

  44. Wolf, I., Schröder, T., Neumann, J. & de Haan, G. Changing minds about electric cars: an empirically grounded agent-based modeling approach. Technol. Forecast. Soc. 94, 269–285 (2015).

    Article  Google Scholar 

  45. Grimm, V. Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310, 987–991 (2005).

    Article  Google Scholar 

  46. Bonabeau, E. Agent-based modeling: methods and techniques for simulating human systems. Proc. Natl Acad. Sci. USA 99, 7280–7287 (2002).

    Article  CAS  Google Scholar 

  47. Rand, W. & Rust, R. T. Agent-based modeling in marketing: guidelines for rigor. Int. J. Res. Mark. 28, 181–193 (2011).

    Article  Google Scholar 

  48. Eppstein, M. J., Grover, D. K., Marshall, J. S. & Rizzo, D. M. An agent-based model to study market penetration of plug-in hybrid electric vehicles. Energ. Policy 39, 3789–3802 (2011).

    Article  Google Scholar 

  49. Mueller, M. G. & de Haan, P. How much do incentives affect car purchase? Agent-based microsimulation of consumer choice of new cars — Part I: model structure, simulation of bounded rationality, and model validation. Energ. Policy 37, 1072–1082 (2009).

    Article  Google Scholar 

  50. Tran, M. Agent-behaviour and network influence on energy innovation diffusion. Commun. Nonlinear Sci. 17, 3682–3695 (2012).

    Article  Google Scholar 

  51. Zhang, T., Gensler, S. & Garcia, R. A study of the diffusion of alternative fuel vehicles: an agent-based modeling approach. J. Prod. Innovat. Manag. 28, 152–168 (2011).

    Article  CAS  Google Scholar 

  52. Günther, M., Stummer, C., Wakolbinger, L. M. & Wildpaner, M. An agent-based simulation approach for the new product diffusion of a novel biomass fuel. J. Oper. Res. Soc. 62, 12–20 (2011).

    Article  Google Scholar 

  53. Stummer, C., Kiesling, E., Günther, M. & Vetschera, R. Innovation diffusion of repeat purchase products in a competitive market: an agent-based simulation approach. Cent. Eur. J. Oper. Re. 245, 157–167 (2015).

    Article  Google Scholar 

  54. van Vliet, O., de Vries, B., Faaij, A., Turkenburg, W. & Jager, W. Multi-agent simulation of adoption of alternative fuels. Transport Res. D-Tr. E. 15, 326–342 (2010).

    Article  Google Scholar 

  55. Zhao, J., Mazhari, E., Celik, N. & Son, Y.-J. Hybrid agent-based simulation for policy evaluation of solar power generation systems. Simul. Model. Pract. Th. 19, 2189–2205 (2011).

    Article  Google Scholar 

  56. Kowalska-Pyzalska, A., Maciejowska, K., Suszczyński, K., Sznajd-Weron, K. & Weron, R. Turning green: agent-based modeling of the adoption of dynamic electricity tariffs. Energ. Policy 72, 164–174 (2014).

    Article  Google Scholar 

  57. Ma, T. & Nakamori, Y. Modeling technological change in energy systems – from optimization to agent-based modeling. Energy 34, 873–879 (2009).

    Article  Google Scholar 

  58. Kim, S., Lee, K., Cho, J. K. & Kim, C. O. Agent-based diffusion model for an automobile market with fuzzy TOPSIS-based product adoption process. Expert Syst. Appl. 38, 7270–7276 (2011).

    Article  Google Scholar 

  59. Sopha, B. M., Klöckner, C. A. & Hertwich, E. G. Adoption and diffusion of heating systems in Norway: coupling agent-based modeling with empirical research. Environ. Innovat. Soc. Trans. 8, 42–61 (2013).

    Article  Google Scholar 

  60. Rahmandad, H. & Sterman, J. Heterogeneity and network structure in the dynamics of diffusion: comparing agent-based and differential equation models. Manag. Sci. 54, 998–1014 (2008).

    Article  Google Scholar 

  61. Huang, Q., Parker, D. C., Filatova, T. & Sun, S. A review of urban residential choice models using agent-based modeling. Environ. Plann. B 41, 661–689 (2014).

    Article  Google Scholar 

  62. Narayanan, S. & Nair, H. S. Estimating causal installed-base effects: a bias-correction approach. J. Mark. Res. 50, 70–94 (2013).

    Article  Google Scholar 

  63. Rai, V. & Robinson, S. A. Effective information channels for reducing costs of environmentally-friendly technologies: evidence from residential PV markets. Environ. Res. Lett. 8, 014044 (2013).

    Article  Google Scholar 

  64. Kleinberg, J., Ludwig, J., Mullainathan, S. & Obermeyer, Z. Prediction policy problems. Am. Econ. Rev. 105, 491–495 (2015).

    Article  Google Scholar 

  65. Bidwell, D., Dietz, T. & Scavia, D. Fostering knowledge networks for climate adaptation. Nature Clim. Change 3, 610–611 (2013).

    Article  Google Scholar 

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Acknowledgements

V.R. acknowledges support from the US Department of Energy under its Solar Energy Evolution and Diffusion Studies (SEEDS) programme within the SunShot Initiative (award no. DE-EE0006129) and from The University of Texas at Austin's Summer Research Assignment. A.D.H. also acknowledges support from the US Department of Energy SEEDS programme (award no. DE-AC36-08GO28308), as well as from the University of Arizona's Institute of the Environment.

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Authors and Affiliations

  1. The University of Texas at Austin, LBJ School of Public Affairs, 2315 Red River Street, Austin, 78712, Texas, USA

    Varun Rai

  2. University of Arizona, School of Government and Public Policy, 319 Social Sciences Building, Tucson, 85721, Arizona, USA

    Adam Douglas Henry

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V.R. and A.D.H. conceived, designed and co-wrote the paper.

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Correspondence to Varun Rai.

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Rai, V., Henry, A. Agent-based modelling of consumer energy choices. Nature Clim Change 6, 556–562 (2016). https://doi.org/10.1038/nclimate2967

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