Muse-rasa Captures Human Dimension In Climate-energy-economic Models Via Global Geoai-ml Agent Datasets

This article introduces a new Geospatial Agent-Based Modelling Framework called MUSE-RASA, which is used to create a large dataset of geospatial agents to assess the impact of the climate-energy-economy system on the residential sector globally. The model uses geospatial big data analytics to capture the human dimension in the modelling approach, which is limited to traditional models. The MUSE-RASA model uses five components-heterogeneity, diversity, evolution, decision-making, and exogenous constraints-to represent the complexities of agents’ structures, diversity, and evolving attributes. The model produces global metrics that can be used to analyse transition and design policy recommendations.