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Actors, Decision-making, and Institutions in Quantitative System Modelling

Author

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  • De Cian, Enrica
  • Dasgupta, Shouro
  • Hof, Andries F.
  • van Sluisveld, Mariësse A.E.
  • Köhler, Jonathan
  • Pfluger, Benjamin
  • van Vuuren, Detlef P.

Abstract

Increasing the realism with respect to the representation of actors, decision-making, and institutions is critical to better understand the transition towards a low-carbon sustainable society since actors, decision-making, and institutions are the defining elements of transition pathways. In this paper, we explore how this can be done by conducting a model-based scenario analysis. The increasing focus on implementation and transition dynamics towards long-term objectives requires a better comprehension of what drives change and how those changes can be accelerated. We explore opportunities that arise from a deeper engagement of quantitative systems modeling with socio-technical transitions studies, initiative-based learning, and applied economics. We argue that a number of opportunities for enriching the realism in model-based scenario analysis can arise through model refinements oriented towards a more detailed approach in terms of actor heterogeneity, as well as through integration across different analytical and disciplinary approaches.

Suggested Citation

  • De Cian, Enrica & Dasgupta, Shouro & Hof, Andries F. & van Sluisveld, Mariësse A.E. & Köhler, Jonathan & Pfluger, Benjamin & van Vuuren, Detlef P., "undated". "Actors, Decision-making, and Institutions in Quantitative System Modelling," MITP: Mitigation, Innovation and Transformation Pathways 263485, Fondazione Eni Enrico Mattei (FEEM).
    • Handle: RePEc:ags:feemmi:263485
    DOI: 10.22004/ag.econ.263485
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    Cited by:

    1. Odenweller, Adrian, 2022. "Climate mitigation under S-shaped energy technology diffusion: Leveraging synergies of optimisation and simulation models," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    2. Galster, H.S. & Van der Wal, A.J. & Batenburg, A.E. & Koning, V. & Faaij, A.P.C., 2025. "A comprehensive review of integrating behavioral drivers of technology adoption and energy service use in energy system models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 214(C).
    3. Hof, Andries F. & van Vuuren, Detlef P. & Berkhout, Frans & Geels, Frank W., 2020. "Understanding transition pathways by bridging modelling, transition and practice-based studies: Editorial introduction to the special issue," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    4. Claire Copeland & Britta Turner & Gareth Powells & Kevin Wilson, 2022. "In Search of Complementarity: Insights from an Exercise in Quantifying Qualitative Energy Futures," Energies, MDPI, vol. 15(15), pages 1-21, July.
    5. Cheng Wang & Tao Lv & Rongjiang Cai & Jianfeng Xu & Liya Wang, 2022. "Bibliometric Analysis of Multi-Level Perspective on Sustainability Transition Research," Sustainability, MDPI, vol. 14(7), pages 1-31, March.
    6. Ansari, Dawud & Holz, Franziska, 2020. "Between stranded assets and green transformation: Fossil-fuel-producing developing countries towards 2055," World Development, Elsevier, vol. 130(C).
    7. Jahel, Camille & Bourgeois, Robin & Bourgoin, Jérémy & Daré, William's & De Lattre-Gasquet, Marie & Delay, Etienne & Dumas, Patrice & Le Page, Christophe & Piraux, Marc & Prudhomme, Rémi, 2023. "The future of social-ecological systems at the crossroads of quantitative and qualitative methods," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    8. Ansari, Dawud & Holz, Franziska, 2019. "Anticipating global energy, climate and policy in 2055: Constructing qualitative and quantitative narratives," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 58, pages 1-23.
    9. Fields, Neve & Leonard, Alycia & Mutembei, Martin & Nganga, Anne & Martindale, Leigh & Bergman, Marissa & Kaoma, Mwansa & Howells, Mark & Brown, Ed, 2025. "Endogenous integration of qualitative factors into quantitative energy transition modelling for development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 220(C).
    10. Motlaghzadeh, Kasra & Schweizer, Vanessa & Craik, Neil & Moreno-Cruz, Juan, 2023. "Key uncertainties behind global projections of direct air capture deployment," Applied Energy, Elsevier, vol. 348(C).
    11. Hof, Andries F. & Carrara, Samuel & De Cian, Enrica & Pfluger, Benjamin & van Sluisveld, Mariësse A.E. & de Boer, Harmen Sytze & van Vuuren, Detlef P., 2020. "From global to national scenarios: Bridging different models to explore power generation decarbonisation based on insights from socio-technical transition case studies," Technological Forecasting and Social Change, Elsevier, vol. 151(C).

    More about this item

    Keywords

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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General
    • P16 - Political Economy and Comparative Economic Systems - - Capitalist Economies - - - Capitalist Institutions; Welfare State
    • Q00 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics

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