Abstract
Low-carbon transitions are long-term complex processes that are driven by multiple factors. To provide a theoretical and practical framework of this process, we argue that the combination of the multi-level perspective (MLP) and agent-based modeling (ABM) enables us to reach a deeper and detailed analysis of low-carbon transitions. As an extensively applied theoretical form, MLP conceptualizes low-carbon transitions as a nonlinear process and allows a system to be analyzed and organized into multiple dimensions (landscape, regime, and niche). However, MLP cannot explain the many details of complex transitions, whereas ABM can estimate the influence of interacting behaviors in a complex system. Therefore, the main advantages of the combined approach for the analysis of low-carbon transition are verified: the MLP can contribute to the overall design of ABM, and ABM can provide a dynamic, continuous, and quantitative description of the MLP. To construct this combination framework, this paper offers a guiding principle that combines the two perspectives under a low-carbon transitional background to create an integrated strategy using three procedures: defining the common concepts, their interaction, and their combination. Through the proposed framework, the goal of this work was to reach a better understanding of social system evolution from the present high-carbon state to a low-carbon state under the pressure of ambitious climate goals, providing specific policy recommendations.
Highlights
The concept of low-carbon transition refers to a significant evolving process of industrialization and urbanization
In addition to the above comparison of the pros and cons of agent-based modeling (ABM) and multi-level perspective (MLP) in terms of analysis, methods, and policies, there are other comparisons, such as temporality and treatment of complexity [12]. Both methods can provide a long-term perspective; for complexity treatment, modeling of a system is achieved through internally consistent parameters and decision-rules for ABM, and in-depth cases generate a rich understanding of sociotechnical dynamics and uncertainties for MLP
Low-carbon transitions have received a significant amount of attention by researchers and organizations, and various theoretical frameworks have emerged thatofprovided different insights into
Summary
The concept of low-carbon transition refers to a significant evolving process of industrialization and urbanization. Previous studies relied too much on qualitative data from the MLP side, and only simple computational modeling approaches, such as the integrated assessment method (IAM) [11,12], were adopted as the counterpart in the integration These integrated approaches cannot fully reflect the multi-level scale and the interaction between different levels. To reflect a multi-level scale with the interaction between levels in different transition stages in integrated approaches, we used the MLP combined with ABM to better interpret multiscale factors and provide reliable information for policymakers when forming policy strategies [9,10]. Active two-way feedback and strong correspondence compensate for some of the shortcomings of each approach, and the combination of ABM and MLP can provide a new and robust analytic framework for the low-carbon transition.
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