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Abstract Combating environmental degradation requires global cooperation. We here argue that institutional designs for such efforts need to account for human behavior. The voyage of the Titanic serves as an analogous case to learn from, and we use behavioral insights to identify critical aspects of human behavior that serve as barriers or opportunities for addressing the challenges we face. We identify a set of public goods that may help us mitigate identified negative aspects of human behavior, while leveraging the positive aspects: standards and best practices, mechanisms for large-scale coordination, and curation of information to raise awareness and promote action. We use existing international organizations, providing at least one of these capacities, as cases to learn from before applying our insights to existing institutional solutions for global environmental protection. We identify institutional design features that, if adapted to better account for human behavior, could lead to more effective institutional solutions.

This article describes the ENERGY Pro agent-based model using the Overview, Design Concept, and Details + Human Decision-making (ODD+D protocol). The model is empirically explicit and aims to investigate the adoption decisions of homeowners in Amsterdam on different energy-efficient retrofitting (EER) measures. Following the ODD+D protocol, this study uncovers the conceptual framework used for model construction, the spatial microsimulation process of expanding the data, and the model implementation details. The article also describes sensitivity analysis, validation results, and how to use and adapt the model. With this article, the authors aim to make the model replicable and accessible to other researchers and inspire them using the combination of social simulation and spatial microsimulation in studying the energy transition. • The agent-based model is described using the ODD+D protocol. • The combination of simulation methods is used for constructing an empirical model. • The model on energy transition can be adapted for other cities.

To create quality decision-making tools that would contribute to transport sustainability, we need to build models relying on accurate, timely, and sufficiently disaggregated data. In spite of today’s ubiquity of big data, practical applications are still limited and have not reached technology readiness. Among them, passively generated telecom data are promising for studying travel-pattern generation. The objective of this study is twofold. First, to demonstrate how telecom data can be fused with other data sources and used to feed up a traffic model. Second, to simulate traffic using an agent-based approach and assess the emission produced by the model’s scenario. Taking Novi Sad as a case study, we simulated the traffic composition at 1-s resolution using the GAMA platform and calculated its emission at 1-h resolution. We used telecom data together with population and GIS data to calculate spatial-temporal movement and imported it to the ABM. Traffic flow was calibrated and validated with data from automatic vehicle counters, while air quality data was used to validate emissions. The results demonstrate the value of using diverse data sets for the creation of decision-making tools. We believe that this study is a positive endeavor toward combining big data and ABM in urban studies.