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Sustainability challenges related to food production arise from multiple nature-society interactions occurring over long time periods. Traditional methods of quantitative analysis do not represent long-term changes in the networks of system components, including institutions and knowledge that affect system behavior. Here, we develop an approach to study system structure and evolution by combining a qualitative framework that represents sustainability-relevant human, technological, and environmental components, and their interactions, mediated by knowledge and institutions, with network modeling that enables quantitative metrics. We use this approach to examine the water and food system in the Punjab province of the Indus River Basin in Pakistan, exploring how food production has been sustained, despite high population growth, periodic floods, and frequent political and economic disruptions. Using network models of five periods spanning 75 y (1947 to 2022), we examine how quantitative metrics of network structure relate to observed sustainability-relevant outcomes and how potential interventions in the system affect these quantitative metrics. We find that the persistent centrality of some and evolving centrality of other key nodes, coupled with the increasing number and length of pathways connecting them, are associated with sustaining food production in the system over time. Our assessment of potential interventions shows that regulating groundwater pumping and phasing out fossil fuels alters network pathways, and helps identify potential vulnerabilities for future food production. 

Abstract Built infrastructure for water and energy supply, transportation, and other such services underpins human well‐being and socioeconomic development. A fundamental understanding of how infrastructure design and user strategies interact can guide important design decisions as well as policy formulation for ensuring long‐term infrastructure viability in conjunction with improved individual user benefits. In this work, an agent based model (ABM) is developed to study this issue for the specific case of irrigation canals. Cooperatively maintained irrigation canals serve essential roles in sustaining agriculture‐based economies in many regions. Canal system design can strongly affect benefits derived by distributed users, regional agricultural output, and the long‐term viability of the shared infrastructure itself. Here, an ABM is used to investigate how an option to use an independent water source interacts with canal design to affect canal maintenance cooperation and farmer income. The independent water source is stylized as a well that provides access to groundwater and represents astrategically robustdesign option; a design option that reduces the implementer's utility vulnerability to unfavorable actions by other actors. Research in other systems has demonstrated that strategically robust designs can improve both implementer utility and the probability of collaboration. The results of this research, in contrast, demonstrate that the option of individual resource access, the strategically robust design option, as represented by a well, reduces cooperative maintenance in most cases. However, wells also improve farmer income, especially for downstream farmers that are most affected by water theft. 

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.