An innovative approach to multi-method integrated assessment modelling of global climate change

Siebers, Peer Olaf; Lim, Zhi En; Figueredo, Grazziela P.; Hey, James

Abstract

© 2020, University of Surrey. All rights reserved. Modelling and simulation play an increasingly significant role in exploratory studies for informing policy makers on climate change mitigation strategies. There is considerable research being done in creating Integrated Assessment Models (IAMs), which focus on examining the human impacts on climate change. Many popular IAMs are created as steady state optimisation models. They typically employ a nested structure of neoclassical production functions to represent the energy-economy system, holding aggregate views on variables, and hence are unable to capture a finer level of details of the underlying system components. An alternative approach that allows modelling populations as a collection of individual and unevenly distributed entities is Agent-Based Modelling, often used in the field of Social Simulation. But simulating huge numbers of individual entities can quickly become an issue, as it requires large amounts of computational resources. The goal of this paper is to introduce a conceptual framework for developing hybrid IAMs. This novel modelling approach allows us to reuse existing rigid, but well-established IAMs, and adds more flexibility by replacing aggregate stocks with a community of vibrant interacting entities. We provide a proof-of-concept of the application of this conceptual framework in form of an illustrative example. Our test case takes the settings of the US. It is solely created for the purpose of demonstrating our hybrid modelling approach; we do not claim that it has predictive powers.

Citation

Siebers, P. O., Lim, Z. E., Figueredo, G. P., & Hey, J. (2020). An innovative approach to multi-method integrated assessment modelling of global climate change. Journal of Artificial Societies and Social Simulation, 23(1), Article 10. https://doi.org/10.18564/jasss.4209

• Journal Article Type: Article
• Acceptance Date: Dec 10, 2019
• Online Publication Date: Jan 31, 2020
• Publication Date: Jan 31, 2020
• Deposit Date: Feb 25, 2020
• Publicly Available Date: Mar 9, 2020
• Journal: Journal of Artificial Societies and Social Simulation
• Print ISSN: 1460-7425
• Publisher: SimSoc Consortium
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 1
• Article Number: 10
• DOI: https://doi.org/10.18564/jasss.4209
• Keywords: Computer Science (miscellaneous); General Social Sciences
• Public URL: https://nottingham-repository.worktribe.com/output/4034618
• Publisher URL: http://jasss.soc.surrey.ac.uk/23/1/10.html
• Additional Information: The Journal of Artificial Societies and Social Simulation (JASSS) is an Open Access journal published by the SIMSOC Consortium. All work published in JASSS is licensed under a Creative Commons Attribution 4.0 International License. [http://jasss.soc.surrey.ac.uk/admin/copyright.html]