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Generating linked technology-socioeconomic scenarios for emerging energy transitions

Author

Listed:
  • Small, Mitchell J.
  • Wong-Parodi, Gabrielle
  • Kefford, Benjamin M.
  • Stringer, Martin
  • Schmeda-Lopez, Diego R.
  • Greig, Chris
  • Ballinger, Benjamin
  • Wilson, Stephen
  • Smart, Simon

Abstract

The formulation and use of scenarios is now a fundamental part of national and global efforts to assess and plan for climate change. While scenario development initially focused on the technical dimensions of energy, emissions and climate response, in recent years parallel sets of shared socio-economic pathways have been developed to portray the values, motivations, and sociopolitical and institutional dimensions of these systems. However, integrating the technical and social aspects of evolving energy systems is difficult, with transitions dependent on highly uncertain technological advances, social preferences, political governance, climate urgency, and the interaction of these elements to maintain or overcome systemic inertia. A broad range of interdisciplinary knowledge is needed to structure and evaluate these processes, many of which involve a mix of qualitative and quantitative factors. To structure and facilitate the necessary linkages this paper presents an approach for generating a plausible range of scenarios for an emerging energy technology. The method considers influences among technical and social factors that can encourage or impede necessary improvements in the performance and cost of the technology, as well the processes affecting public acceptance and the establishment of governance structures necessary to support effective planning and implementation. A Bayesian network is used to capture relationships among the technological and socioeconomic factors likely to affect the probability that the technology will achieve significant penetration and adoption. The method is demonstrated for carbon capture and storage (CCS): a potential technology on the pathway to deep decarbonization. A preliminary set of expert elicitations is conducted to illustrate how relationships between these factors can be estimated. This establishes a prior or baseline network that can be subsequently analyzed by choosing either optimistic or pessimistic assumptions for respective groups of technical and social variables, identifying sets of key factors that limit or encourage successful deployment.

Suggested Citation

  • Small, Mitchell J. & Wong-Parodi, Gabrielle & Kefford, Benjamin M. & Stringer, Martin & Schmeda-Lopez, Diego R. & Greig, Chris & Ballinger, Benjamin & Wilson, Stephen & Smart, Simon, 2019. "Generating linked technology-socioeconomic scenarios for emerging energy transitions," Applied Energy, Elsevier, vol. 239(C), pages 1402-1423.
  • Handle: RePEc:eee:appene:v:239:y:2019:i:c:p:1402-1423
    DOI: 10.1016/j.apenergy.2019.01.215
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    References listed on IDEAS

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    1. Ya‐Mei Yang & Mitchell J. Small & Egemen O. Ogretim & Donald D. Gray & Arthur W. Wells & Grant S. Bromhal & Brian R. Strazisar, 2012. "A Bayesian belief network (BBN) for combining evidence from multiple CO 2 leak detection technologies," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 2(3), pages 185-199, June.
    2. Hall, Lisa M.H. & Buckley, Alastair R., 2016. "A review of energy systems models in the UK: Prevalent usage and categorisation," Applied Energy, Elsevier, vol. 169(C), pages 607-628.
    3. Fortes, Patrícia & Alvarenga, António & Seixas, Júlia & Rodrigues, Sofia, 2015. "Long-term energy scenarios: Bridging the gap between socio-economic storylines and energy modeling," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 161-178.
    4. Evelina Trutnevyte & Céline Guivarch & Robert Lempert & Neil Strachan, 2016. "Reinvigorating the scenario technique to expand uncertainty consideration," Climatic Change, Springer, vol. 135(3), pages 373-379, April.
    5. Vanessa Schweizer & Brian O’Neill, 2014. "Systematic construction of global socioeconomic pathways using internally consistent element combinations," Climatic Change, Springer, vol. 122(3), pages 431-445, February.
    6. Price, James & Keppo, Ilkka, 2017. "Modelling to generate alternatives: A technique to explore uncertainty in energy-environment-economy models," Applied Energy, Elsevier, vol. 195(C), pages 356-369.
    7. Céline Guivarch & Julie Rozenberg & Vanessa Schweizer, 2016. "The diversity of socio-economic pathways and CO2 emissions scenarios: Insights from the investigation of a scenarios database," Post-Print halshs-01292901, HAL.
    8. Gregory F. Nemet & Laura Diaz Anadon & Elena Verdolini, 2017. "Quantifying the Effects of Expert Selection and Elicitation Design on Experts’ Confidence in Their Judgments About Future Energy Technologies," Risk Analysis, John Wiley & Sons, vol. 37(2), pages 315-330, February.
    9. Andrea Herbst & Felipe Andrés Toro & Felix Reitze & Eberhard Jochem, 2012. "Introduction to Energy Systems Modelling," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 148(II), pages 111-135, June.
    10. Elena Verdolini & Laura Díaz Anadón & Erin Baker & Valentina Bosetti & Lara Aleluia Reis, 2018. "Future Prospects for Energy Technologies: Insights from Expert Elicitations," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 12(1), pages 133-153.
    11. repec:bla:wireae:v:6:y:2017:i:4:p:n/a-n/a is not listed on IDEAS
    12. Elmar Kriegler & Jae Edmonds & Stéphane Hallegatte & Kristie Ebi & Tom Kram & Keywan Riahi & Harald Winkler & Detlef Vuuren, 2014. "A new scenario framework for climate change research: the concept of shared climate policy assumptions," Climatic Change, Springer, vol. 122(3), pages 401-414, February.
    13. Borunda, Mónica & Jaramillo, O.A. & Reyes, Alberto & Ibargüengoytia, Pablo H., 2016. "Bayesian networks in renewable energy systems: A bibliographical survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 32-45.
    14. Jägemann, Cosima & Fürsch, Michaela & Hagspiel, Simeon & Nagl, Stephan, 2013. "Decarbonizing Europe's power sector by 2050 — Analyzing the economic implications of alternative decarbonization pathways," Energy Economics, Elsevier, vol. 40(C), pages 622-636.
    15. Detlef Vuuren & Elmar Kriegler & Brian O’Neill & Kristie Ebi & Keywan Riahi & Timothy Carter & Jae Edmonds & Stephane Hallegatte & Tom Kram & Ritu Mathur & Harald Winkler, 2014. "A new scenario framework for Climate Change Research: scenario matrix architecture," Climatic Change, Springer, vol. 122(3), pages 373-386, February.
    16. Gambelli, Danilo & Alberti, Francesca & Solfanelli, Francesco & Vairo, Daniela & Zanoli, Raffaele, 2017. "Third generation algae biofuels in Italy by 2030: A scenario analysis using Bayesian networks," Energy Policy, Elsevier, vol. 103(C), pages 165-178.
    17. Brian O’Neill & Elmar Kriegler & Keywan Riahi & Kristie Ebi & Stephane Hallegatte & Timothy Carter & Ritu Mathur & Detlef Vuuren, 2014. "A new scenario framework for climate change research: the concept of shared socioeconomic pathways," Climatic Change, Springer, vol. 122(3), pages 387-400, February.
    18. Markusson, Nils & Kern, Florian & Watson, Jim & Arapostathis, Stathis & Chalmers, Hannah & Ghaleigh, Navraj & Heptonstall, Philip & Pearson, Peter & Rossati, David & Russell, Stewart, 2012. "A socio-technical framework for assessing the viability of carbon capture and storage technology," Technological Forecasting and Social Change, Elsevier, vol. 79(5), pages 903-918.
    19. Krause, Jette & Small, Mitchell J. & Haas, Armin & Jaeger, Carlo C., 2016. "An expert-based bayesian assessment of 2030 German new vehicle CO2 emissions and related costs," Transport Policy, Elsevier, vol. 52(C), pages 197-208.
    20. Kristie Ebi & Stephane Hallegatte & Tom Kram & Nigel Arnell & Timothy Carter & Jae Edmonds & Elmar Kriegler & Ritu Mathur & Brian O’Neill & Keywan Riahi & Harald Winkler & Detlef Vuuren & Timm Zwickel, 2014. "A new scenario framework for climate change research: background, process, and future directions," Climatic Change, Springer, vol. 122(3), pages 363-372, February.
    21. Sergey Paltsev, 2017. "Energy scenarios: the value and limits of scenario analysis," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 6(4), July.
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