Social and Material Sustainability

This project developed and empirically grounded the concept of urban symbiosis — the mutually beneficial, citizen-led sharing of materials and resources within cities that leverages urban density and social networks to keep materials in circulation. Working with a team from the University of Michigan, we first established a typology of resource circularity in cities that distinguishes urban symbiosis from top-down forms like industrial symbiosis and conventional waste management. A follow-on empirical study used London as a case study to examine why grassroots circularity projects emerge, how they develop, and what benefits they produce — finding that social and wellbeing outcomes frequently outweigh environmental motivations, and that urban symbiosis represents a mechanism for citizens to assert their right to a sustainable city.

This project examines how machine learning is being used in industrial ecology and what methodological standards should govern its application. A comprehensive review of 150 peer-reviewed ML–IE studies — spanning life cycle assessment, carbon footprinting, material flow analysis, and industrial symbiosis — classifies studies into three levels of ML integration: surrogate models (54%), partial integration (34%), and full workflow integration (12%). Troublingly, only 5% of studies achieve strong reproducibility, just 20% employ external validation, and fewer than 7% engage with sociopolitical dimensions — raising significant concerns about the credibility and equity implications of ML-generated evidence in environmental research. A companion preprint makes a direct call for improved standards around reproducibility, representativity, and transparency in supervised ML applications to IE. Together, these contributions provide both an empirical map of current ML practice in the field and a framework for raising methodological quality.

This project contributes to a major international synthesis effort that distills the scientific literature on the circular economy into 64 "stylized facts" — empirically grounded, topically relevant statements designed to make CE knowledge accessible to policymakers, practitioners, and the broader public. Drawing on decades of industrial ecology research on material stocks and flows, R-strategies, business models, and sustainability assessment, the stylized facts span the full scope of CE theory and practice — from the fundamental limits of material recycling to the socioeconomic and regulatory conditions that enable circular strategies to succeed. This effort, coordinated from within the global IE community, provides the first broad scientific synthesis of CE knowledge in this format.

GLO-FORCE — Blockchain and AI for Optimizing Food Supply Chain Resilience and Security — is an interdisciplinary project that integrates blockchain, artificial intelligence, and geospatial intelligence to strengthen food supply chain resilience. The platform combines a county-resolved food security data model, blockchain-enabled farm-to-fork traceability, and AI-assisted optimization tools for routing supplies during disruptions — treating food supply chains as critical infrastructure and prioritizing communities most vulnerable to supply shocks. Piloted in the St. Louis region using the 2025 EF3 tornado as a case study, GLO-FORCE aims to compress the time from disaster detection to routed food shipments. The HES lab is contributing a critical infrastructure modeling framework and geospatial computing capacity to the project. Funding is currently being sought.

FEW-Meter (Food-Energy-Water Meter) was a large-scale international research project that measured the resource performance of urban agriculture across five countries in Europe and North America. Using a standardized measurement protocol developed by the project team, FEW-Meter quantified the food, energy, and water flows of urban farms and gardens — establishing a rigorous empirical foundation for evaluating where urban agriculture outperforms, and where it falls short of, conventional food production. HES lab contributions span the full arc of the project: from field data collection and cross-national analysis to high-impact publications comparing the carbon footprints of urban and conventional agriculture, assessing urban agriculture's potential to scale in the Global North, and synthesizing what the field knows about urban agriculture's impacts on people, places, and the planet. The project also developed the FEWP (Food-Energy-Water-People) framework to foreground social dimensions alongside resource flows, and produced a public roadmap for resource-efficient urban agriculture.