ENERGY Pro: Spatially explicit agent-based model on achieving positive energy districts

Area of Science:

• Environmental social science
• Computational social science
• Urban planning

Background:

• Understanding homeowner decisions is crucial for energy transition.
• Agent-based modeling (ABM) offers a powerful tool for simulating complex social dynamics.
• Integrating spatial microsimulation enhances the empirical grounding of ABMs.

Purpose of the Study:

• To describe the ENERGY Pro agent-based model using the ODD+D protocol.
• To investigate homeowner adoption of energy-efficient retrofitting (EER) measures in Amsterdam.
• To promote the replicability and accessibility of the model for energy transition research.

Main Methods:

• Agent-based modeling (ABM) combined with spatial microsimulation.
• Utilizing the Overview, Design Concept, and Details + Human Decision-making (ODD+D) protocol for model description.
• Empirical explicitness in model construction and data expansion.

Main Results:

• The article details the conceptual framework, data expansion, and implementation of the ENERGY Pro model.
• Sensitivity analysis and validation results are presented.
• The model is designed for adaptability to different urban contexts.

Conclusions:

• The ENERGY Pro model provides a robust framework for studying energy transition dynamics.
• The combination of social and spatial microsimulation is effective for empirical ABM.
• The model serves as a valuable resource for researchers and policymakers in urban energy planning.