Abstract
AbstractThis article provides a combined geospatial artificial intelligence-machine learning, geoAI-ML, agent-based, data-driven, technology-rich, bottom-up approach and datasets for capturing the human dimension in climate-energy-economy models. Seven stages were required to conduct this study and build thirteen datasets to characterise and parametrise geospatial agents in 28 regions, globally. Fundamentally, the methodology starts collecting and handling data, ending with the application of the ModUlar energy system Simulation Environment (MUSE), ResidentiAl Spatially-resolved and temporal-explicit Agents (RASA) model. MUSE-RASA uses AI-ML-based geospatial big data analytics to define eight scenarios to explore long-term transition pathways towards net-zero emission targets by mid-century. The framework and datasets are key for climate-energy-economy models considering consumer behaviour and bounded rationality in more realistic decision-making processes beyond traditional approaches. This approach defines energy economic agents as heterogeneous and diverse entities that evolve in space and time, making decisions under exogenous constraints. This framework is based on the Theory of Bounded Rationality, the Theory of Real Competition, the theoretical foundations of agent-based modelling and the progress on the combination of GIS-ABM.
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
Reference41 articles.
1. Nikas, A., Doukas, H. & Papandreou, A. A detailed overview and consistent classification of climate-economy models. Understanding risks and uncertainties in energy and climate policy, 1–54 (2019).
2. Shaikh, A. Capitalism: Competition, conflict, crises. (Oxford University Press, 2016).
3. Simon, H. A Behavioral Model of Rational Choice. The Quarterly Journal of Economics 69, 99–118, https://doi.org/10.2307/1884852 (1955).
4. Simon, H. A. in Utility and Probability (eds Eatwell, J., Milgate, M. & Newman, P.) 15–18 (Palgrave Macmillan UK, 1990).
5. Petracca, E. Simulating Marx: Herbert A. Simon’s cognitivist approach to dialectical materialism. History of the Human Sciences, 09526951211031143 (2021).