Exploring the impact of modelling assumptions on distributive justice using JUSTICE

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

21 Downloads (Pure)

Abstract

Integrated Assessment Models (IAMs) vary widely in complexity and underlying assumptions. There have been considerable efforts to increase the complexity of IAMs for improved representation of socioeconomic and environmental outcomes. However, less attention has been given to the foundational assumptions of these models and their distributional consequences. These assumptions are fraught with deep and normative uncertainty and can significantly impact IAM projections. If these assumptions are not explicit, IAMs can perpetuate existing mistakes and exacerbate inequalities due to their black-box nature. This paper introduces a novel IAM called JUSTICE (Justice Universality Spatial Temporal Integrated Climate Economy) to explore the influence on distributive justice outcomes due to underlying modelling assumptions across model components and functions: the economy and climate components, and the damage and social welfare functions. JUSTICE is a simple IAM inspired by the long-established RICE and is designed to be a surrogate for more complex IAMs for eliciting normative insights. As illustrated in Figure 1, JUSTICE contains two distinct economic and climate sub-models, three damage functions, and four social welfare functions (SWFs), each based on fundamentally different assumptions. This modular structure enables JUSTICE to uncover assumptions with nontrivial normative and distributional consequences. Also, the simplicity of JUSTICE makes it suitable for assessing the consequences of these modelling assumptions under deep and normative uncertainty using MS-MORDM and EMODPS frameworks, promoting a more equitable approach to decision-making. Using JUSTICE, we investigate the effects of three SWFs—Utilitarianism, Egalitarianism, and Prioritarianism—on global temperature rise, with two levels of aggregation. We also explore the sensitivity of distributional outcomes for two different climate models. Our findings reveal that different assumptions lead to significantly distinct optimal abatement pathways, underscoring the importance of explicating assumptions and exploring their uncertainties to facilitate deliberation and identify common ground among policymakers with diverse perspectives.

Original languageEnglish
Title of host publicationProceedings of the 25th International Congress on Modelling and Simulation, MODSIM 2023
EditorsJai Vaze, Chris Chilcott, Lindsay Hutley, Susan M. Cuddy
PublisherModelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ)
Pages90-96
Number of pages7
ISBN (Electronic)9780987214300
Publication statusPublished - 2023
Event25th International Congress on Modelling and Simulation, MODSIM 2023 - Darwin, Australia
Duration: 9 Jul 202314 Jul 2023

Publication series

NameProceedings of the International Congress on Modelling and Simulation, MODSIM
ISSN (Electronic)2981-8001

Conference

Conference25th International Congress on Modelling and Simulation, MODSIM 2023
Country/TerritoryAustralia
CityDarwin
Period9/07/2314/07/23

Bibliographical note

Publisher Copyright:
© 2023 Proceedings of the International Congress on Modelling and Simulation, MODSIM. All rights reserved.

Keywords

  • deep uncertainty
  • distributive justice
  • Integrated assessment model
  • normative uncertainty

Fingerprint

Dive into the research topics of 'Exploring the impact of modelling assumptions on distributive justice using JUSTICE'. Together they form a unique fingerprint.

Cite this