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Towards agent-based integrated assessment models: examples, challenges, and future developments

  • Francesco Lamperti
  • Antoine Mandel
  • Mauro Napoletano
  • Alessandro Sapio
  • Andrea Roventini
  • Tomas Balint
  • Igor Khorenzhenko
Original Article

Abstract

Understanding the complex, dynamic, and non-linear relationships between human activities, the environment and the evolution of the climate is pivotal for policy design and requires appropriate tools. Despite the existence of different attempts to link the economy (or parts of it) to the evolution of the climate, results have often been disappointing and criticized. In this paper, we discuss the use of agent-based modeling for climate policy integrated assessment. First, we identify the main limitations of current mainstream models and stress how framing the problem from a complex system perspective might help, in particular when extreme climate conditions are at stake and general equilibrium effects are questionable. Second, we present two agent-based models that serve as prototypes for the analysis of coupled climate, energy, and macroeconomic dynamics. We argue that such models constitute examples of a promising approach for the integrated assessment of climate change and economic dynamics. They allow a bottom-up representation of climate damages and their cross-sectoral percolation, naturally embed distributional issues, and traditionally account for the role of finance in sustaining economic development and shaping the dynamics of energy transitions. All these issues are at the fore-front of the research in integrated assessment. Finally, we provide a careful discussion of testable policy exercises, modeling limitations, and open challenges for this stream of research. Notwithstanding great potential, there is a long way-to-go for agent-based models to catch-up with the richness of many existing integrated assessment models and overcome their major problems. This should encourage research in the area.

Keywords

Climate change Climate policy Integrated assessment Transitions Agent-based models 

Notes

Acknowledgements

The authors express their gratitude to two anonymous referees whose comments improved the quality of the paper. The authors would like to acknowledge financial support from different European Projects. Francesco Lamperti, Antoine Mandel, Mauro Napoletano, Andrea Roventini, and Alessandro Sapio acknowledge financial support from European Union 7th FP grant agreement no. 603416—IMPRESSIONS. Tomas Balint and Antoine Mandel acknowledge financial support from European Union 7th FP grant agreement no. 610704—SIMPOL. Antoine Mandel, Mauro Napoletano, and Andrea Roventini acknowledge financial support from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 640772—DOLFINS and no. 649186—ISIGrowth. Alessandro Sapio acknowledges financial support by Parthenope University, Bando di sostegno alla ricerca individuale per il triennio 2015–2017, annualitá 2015 & 2016. All the usual disclaimers apply.

Supplementary material

10113_2018_1287_MOESM1_ESM.pdf (240 kb)
ESM 1 (PDF 240 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Francesco Lamperti
    • 1
    • 2
  • Antoine Mandel
    • 3
    • 4
  • Mauro Napoletano
    • 5
    • 6
  • Alessandro Sapio
    • 6
    • 7
  • Andrea Roventini
    • 6
    • 8
  • Tomas Balint
    • 3
  • Igor Khorenzhenko
    • 3
    • 9
  1. 1.Scuola Superiore Sant’AnnaInstitute of EconomicsPisaItaly
  2. 2.Fondazione Eni Enrico MatteiMilanItaly
  3. 3.Université Paris 1 Panthéon-SorbonneParisFrance
  4. 4.CNRSParisFrance
  5. 5.OFCE Sciences Po, Université Côte d’Azur, GREDEG, SKEMA, CNRSSophia AntipolisFrance
  6. 6.Scuola Superiore Sant’AnnaPisaItaly
  7. 7.Parthenope University of NaplesNaplesItaly
  8. 8.OFCE Sciences PoSophia AntipolisItaly
  9. 9.Bielefeld UniversityBielefeldGermany

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