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Scenario techniques for energy and environmental research: An overview of recent developments to broaden the capacity to deal with complexity and uncertainty

Author

Listed:
  • Céline Guivarch

    (CIRED - centre international de recherche sur l'environnement et le développement - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - EHESS - École des hautes études en sciences sociales - AgroParisTech - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique)

  • Robert Lempert

    (RAND Corporation)

  • Evelina Trutnevyte

    (D-USYS - Department of Environmental Systems Science [ETH Zürich] - ETH Zürich - Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich])

Abstract

Scenario techniques are a teeming field in energy and environmental research and decision making. This Thematic Issue (TI) highlights quantitative (computational) methods that improve the development and use of scenarios for dealing with the dual challenge of complexity and (deep) uncertainty. The TI gathers 13 articles that describe methodological innovations or extensions and refinements of existing methods, as well as applications that demonstrate the potential of these methodological developments. The TI proposes two methodological foci for dealing with the challenges of (deep) uncertainty and complexity: diversity and vulnerability approaches help tackle uncertainty; multiple-objective and multiple-scale approaches help address complexity; whereas some combinations of those foci can also be applied. This overview article to the TI presents the contributions gathered in the TI, and shows how they individually and collectively bring new capacity to scenarios techniques to deal with complexity and (deep) uncertainty.

Suggested Citation

  • Céline Guivarch & Robert Lempert & Evelina Trutnevyte, 2017. "Scenario techniques for energy and environmental research: An overview of recent developments to broaden the capacity to deal with complexity and uncertainty," Post-Print halshs-01579281, HAL.
    • Handle: RePEc:hal:journl:halshs-01579281
    DOI: 10.1016/j.envsoft.2017.07.017
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01579281v1
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    Citations

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    Cited by:

    1. Pei-Hao Li & Steve Pye & Ilkka Keppo & Marc Jaxa-Rozen & Evelina Trutnevyte, 2023. "Revealing effective regional decarbonisation measures to limit global temperature increase in uncertain transition scenarios with machine learning techniques," Climatic Change, Springer, vol. 176(7), pages 1-23, July.
    2. Wolfgang Weimer-Jehle & Stefan Vögele & Wolfgang Hauser & Hannah Kosow & Witold-Roger Poganietz & Sigrid Prehofer, 2020. "Socio-technical energy scenarios: state-of-the-art and CIB-based approaches," Climatic Change, Springer, vol. 162(4), pages 1723-1741, October.
    3. Bouw, Kathelijne & Noorman, Klaas Jan & Wiekens, Carina J. & Faaij, André, 2021. "Local energy planning in the built environment: An analysis of model characteristics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    4. Thomas Pregger & Tobias Naegler & Wolfgang Weimer-Jehle & Sigrid Prehofer & Wolfgang Hauser, 2020. "Moving towards socio-technical scenarios of the German energy transition—lessons learned from integrated energy scenario building," Climatic Change, Springer, vol. 162(4), pages 1743-1762, October.
    5. Vanessa J. Schweizer, 2020. "Reflections on cross-impact balances, a systematic method constructing global socio-technical scenarios for climate change research," Climatic Change, Springer, vol. 162(4), pages 1705-1722, October.
    6. Witold-Roger Poganietz & Wolfgang Weimer-Jehle, 2020. "Introduction to the special issue ‘Integrated scenario building in energy transition research’," Climatic Change, Springer, vol. 162(4), pages 1699-1704, October.
    7. Sharma, Tarun & Glynn, James & Panos, Evangelos & Deane, Paul & Gargiulo, Maurizio & Rogan, Fionn & Gallachóir, Brian Ó, 2019. "High performance computing for energy system optimization models: Enhancing the energy policy tool kit," Energy Policy, Elsevier, vol. 128(C), pages 66-74.
    8. van Beuzekom, Iris & Hodge, Bri-Mathias & Slootweg, Han, 2021. "Framework for optimization of long-term, multi-period investment planning of integrated urban energy systems," Applied Energy, Elsevier, vol. 292(C).
    9. van Sluisveld, Mariësse A.E. & Hof, Andries F. & Carrara, Samuel & Geels, Frank W. & Nilsson, Måns & Rogge, Karoline & Turnheim, Bruno & van Vuuren, Detlef P., 2020. "Aligning integrated assessment modelling with socio-technical transition insights: An application to low-carbon energy scenario analysis in Europe," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    10. Xexakis, Georgios & Hansmann, Ralph & Volken, Sandra P. & Trutnevyte, Evelina, 2020. "Models on the wrong track: Model-based electricity supply scenarios in Switzerland are not aligned with the perspectives of energy experts and the public," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).

    More about this item

    Keywords

    scenarios; uncertainty; complexity; diversity; vulnerability; multiple-objective; multiple- scale;
    All these keywords.

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