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Developing model-based narratives of society’s response to climate change is challenged by two factors. First, society’s response to possible future climate change is subject to many uncertainties. Second, we argue that society’s mitigation action emerge out of the actions and interactions of the many actors in society. Together, these two factors imply that the overarching dynamics of society’s response to climate change are unpredictable. In contrast to conventional processes of developing scenarios, in this study the emergence of climate change mitigation action by society has been represented in an agent-based model with which we developed two narratives of the emergence of climate change mitigation action by applying exploratory modelling and analysis. The agent-based model represents a two-level game involving governments and citizens changing their emission behaviour in the face of climate change through mitigation action. Insights gained from the exploration on uncertainties pertaining to the system have been used to construct two internally consistent and plausible narratives on the pathways of the emergence of mitigation action, which, as we argue, are a reasonable summary of the uncertainty space. The first narrative highlights how and when strong mitigation action emerges while the second narrative highlights how and when weak mitigation action emerges. In contrast to a conventional scenario development process, these two scenarios have been discovered bottom up rather than being defined top down. They succinctly capture the possible outcomes of the emergence of climate change mitigation by society across a large range of uncertain factors. The narratives therefore help in conveying the consequences of the various uncertainties influencing the emergence of climate change mitigation action by society.

Original languageEnglish
Article number9
JournalJournal of Artificial Societies and Social Simulation
Volume19
Issue number3
DOIs
Publication statusPublished - 1 Jun 2016

    Research areas

  • Agent-based modeling, Climate change mitigation, Exploratory modeling, Scenario discovery, Uncertainty

ID: 28677376