One way researchers analyze real-world, sustainability questions is by utilizing computational models.  These models use data to derive the relationships between economic, social, and natural systems and quantify the potential outcomes of public policies and behavioral changes.  Sustainability models have provided useful insights on a wide range of societal challenges,  such as energy transition, climate mitigation, and environmental protection.  

These models have played an important role in supporting environmental decision making and problem solving, but it can be difficult to codify the complexities of human systems and institutional rules  that influence sustainability outcomes.  Collaborating with researchers at the University of California San Diego and Delft University, C-PREE faculty member Wei Peng co-authored a study that uses modeling experiments to demonstrate the importance of institutional factors in computational sustainability models.

“Most energy models have been built around the physical rules governing the technologies,” explains Peng. “Modeling institutions is much harder. Some institutional factors can be represented in models through technology cost and deployment, such as electricity market rules. Others are deeply uncertain and complex, such as social norms, which makes quantitative modeling quite challenging.  In essence, the right model choice depends on what institutional factor one hopes to capture, and that's exactly the motivation of this study.”

Given the results of the study, the incorporation of institutional changes might be the next frontier for researchers looking to improve the accuracy of sustainability models.