PhD Dissertation Defense: Gloria Schmitz

Date/Time: Thu, March 28th, 2024 at 3:00 pm

Location: 426 Renaissance Park and Zoom

Title: “The Circular Economy Before and During the Covid-19 Pandemic: A Global Analysis of the Role of Waste Proliferation and Climate Change Resilience”

Abstract:

This three-article Dissertation examines the impacts of waste generated from personal protective equipment (PPE), and how changing consumer needs, preferences and behaviors before and during the COVID-19 pandemic have influenced the transition to the circular economy (CE; i.e., reusing and recycling materials instead of linear product life cycles ending in disposal) at three levels of analysis: (1) the micro (neighborhoods in a city), (2) meso (municipalities in a state), and (3) macro (global corporations). I apply an integrated population health, resilience and CE theoretical approach to empirically assess the increased global waste burden and to examine implications for cities, the environment and human health. Article 1 is based on data I collected during the Fall 2022 period in Boston, MA, and uses geospatial (GIS) analyses to identify patterns of PPE waste and how these patterns collocate with sociodemographic variables. Article 2 integrates GIS and supervised machine learning analyses to explore waste generation for 2018 – 2022 in the 351 Massachusetts cities and towns. Article 3 uses unsupervised machine learning to analyze progress toward CE indicators across 2,485 global corporations from 2010 – 2019.

Using geospatial mapping methods in ArcGIS Pro and statistical modeling approaches in R, including supervised and unsupervised machine learning, I test whether waste significantly increased during the COVID-19 pandemic — compared to prior to the pandemic — by addressing four overarching research questions: (1) How has the COVID-19 pandemic affected the transition to CE worldwide?, (2) Have waste generation trends changed solely because of medically-necessary items (e.g., PPE), or has there also been a shift in consumer sustainability behaviors and waste generating practices?, (3) Are waste generation trends consistent at multiple 8 levels (i.e., the local, national and global level)?, and (4) How can the findings inform policy recommendations for both the public and private sector to create a synergized approach to CE?

I found that waste increased overall during the COVID-19 pandemic, driven by both increased PPE waste and changes in consumer preferences (e.g., ordering more takeout food, using disposable products, disruptions to the supply chain, packaging of online ordering), with spatial and temporal differences across Massachusetts cities and towns, and heterogeneity across neighborhoods within the City of Boston. The analyses of corporate sustainability practices identified four distinctive clusters with varying levels of CE indicators. The results suggest that macro-level waste management policies diffuse down to influence individualistic sustainability behaviors at the micro-level, which provides insights into how this increased waste burden impacts population health outcomes (e.g., increased disease incidence and poorer air quality).

These findings can inform evidence-based policies at the municipal, state, country, and intergovernmental levels to advise decisionmakers about effective waste reduction strategies, including incorporating CE practices. Specifically, Article 1 suggests that installing more waste disposal bins (e.g., WiFi-enabled smart bins that notify staff when full) and engaging in public awareness campaigns to promote CE behaviors (e.g., consumption reduction, recycling) can help reduce PPE and other types of litter. Article 2 can inform efforts to ensure that elevated consumption and waste disposal habits developed during the COVID-19 pandemic do not persist into the future, such as by reducing bin sizes and instituting legislation aimed at reducing waste (e.g., plastic bag bans, mandating reusable take-out containers over disposable containers). In Article 3, my results found that unsupervised machine learning methods are a feasible method for segmenting companies across the world into clusters based on CE indicators. These findings can help to highlight firms that do well on these metrics and can also identify laggards, who might benefit from sustainability life cycle analyses to maximize their use of CE practices.

Committee Members:

Daniel Aldrich, Professor of Political Science and Public Policy at Northeastern (Chair)

Dietmar Offenhuber, Associate Professor of Public Policy and Urban Affairs and Art + Design at Northeastern

Panagoula Diamanti-Karanou, Associate Teaching Professor in International Affairs at Northeastern 

Risa Kitagawa-Amano, Assistant Professor of Political Science and International Affairs at Northeastern