Equity and modeling in sustainability science: Examples and opportunities throughout the process
Equity is core to sustainability. Models are important tools for informing action, and their development and use present opportunities to center equity in process and outcomes. Members of the Engineering Systems Lab (ESL) highlight progress in integrating equity into systems modeling in sustainability science, as well as key challenges, tensions, and future directions.
Authors: Amanda Giang, Morgan R Edwards, Sarah M Fletcher, Rivkah Gardner-Frolick, Rowenna Gryba, Jean-Denis Mathias, Camille Venier-Cambron, John M Anderies, Emily Berglund, Sanya Carley, Jacob Shimkus Erickson, Emily Grubert, Antonia Hadjimichael, Jason Hill, Erin Mayfield, Destenie Nock, Kimberly Kivvaq Pikok, Rebecca K Saari, Mateo Samudio Lezcano, Afreen Siddiqi, Jennifer B Skerker, Christopher W Tessum
Citation: Proceedings of the National Academy of Sciences. March 18, 2024. 121 (13) e2215688121
Abstract:
Equity is core to sustainability, but current interventions to enhance sustainability often fall short in adequately addressing this linkage. Models are important tools for informing action, and their development and use present opportunities to center equity in process and outcomes. This Perspective highlights progress in integrating equity into systems modeling in sustainability science, as well as key challenges, tensions, and future directions. We present a conceptual framework for equity in systems modeling, focused on its distributional, procedural, and recognitional dimensions. We discuss examples of how modelers engage with these different dimensions throughout the modeling process and from across a range of modeling approaches and topics, including water resources, energy systems, air quality, and conservation.
Synthesizing across these examples, we identify significant advances in enhancing procedural and recognitional equity by reframing models as tools to explore pluralism in worldviews and knowledge systems; enabling models to better represent distributional inequity through new computational techniques and data sources; investigating the dynamics that can drive inequities by linking different modeling approaches; and developing more nuanced metrics for assessing equity outcomes. We also identify important future directions, such as an increased focus on using models to identify pathways to transform underlying conditions that lead to inequities and move toward desired futures. By looking at examples across the diverse fields within sustainability science, we argue that there are valuable opportunities for mutual learning on how to use models more effectively as tools to support sustainable and equitable futures.
