Distributed 7 days in advance with approval from Associate Chair of Graduate Programs, the CEE Graduate Committee and the Institute.

School of Civil and Environmental Engineering

Ph.D. Thesis Defense Announcement

DATA-DRIVEN STRATEGIES FOR MUNICIPAL SOLID WASTE MANAGEMENT: IMPLICATIONS FOR POLICY, INFRASTRUCTURE AND REGIONAL CIRCULARITY

By Catharina Hollauer

                                                                   Advisor:  Dr. Joe Frank Bozeman III                   

Committee Members:  Dr. Omar Asensio (IDEaS, Public Policy), Dr. Oliver Heidrich (University of New Castle), Dr. Yongsheng Chen (CEE), Dr. Diego Pecin (Amazon Research), Dr. Sofía Perez-Guzmán (CEE)

Date and Time: April 29th, 2025.  9:00 AM - 11:00 AM EST

Location: virtual link, Coda C1103.

ABSTRACT
In the circular economy, a central point of discussion is the potential economic and environmental gains that emerge from more efficient waste management strategies and practices. This dissertation explores policy, infrastructural, and regional circularity aspects of the transition to circular waste management, focusing on plastic waste streams in the United States. It applies decision-support tools—including optimization, machine learning (ML), and artificial intelligence (AI)—to enable data-driven design of circular strategies and enhance policy evaluation related to the circular economy and climate regulations. The research centers on three applications: (1) circular policy evaluation, (2) circular infrastructure and strategic planning, and (3) analysis of regional drivers and barriers to circularity adoption. To begin, a generative AI approach for real-time evaluation of public perception toward circular waste policies is introduced. By analyzing social media discourse using Large Language Models (LLMs), this research assesses preferences regarding single-use plastics regulation and highlights LLMs as scalable, low-cost alternatives to traditional surveys.
Building on this understanding of public engagement, the next focus turns to the practical implications for small and medium-sized enterprises operating within localized circular infrastructure. An optimization-based framework models a Polyethylene Terephthalate bottle manufacturer’s procurement strategy under extended producer responsibility and policy constraints, identifying trade-offs and decision-making strategies to mitigate resource scarcity, enhance supply chain resilience, and support circular business models.
Finally, regional drivers and barriers to circularity adoption are investigated using interpretable ML to highlight key sociodemographic, infrastructural, environmental, and technological factors. A composite circularity index reveals critical influences on regional adoption of circular waste strategies in the U.S.
This dissertation contributes to the circular economy literature by integrating industrial ecology, optimization, and data analytics to support policy evaluation, infrastructure planning, and regional strategy in circular waste management.