Abstract
While there is a growing literature on the circular economy (CE) and waste outcomes, climate change, circularity, and the global waste crisis are not always theoretically integrated. To advance towards an integrated theoretical model of waste proliferation and climate change, the present study explores two key research questions: (1). How did global waste and recycling volumes, GHG emissions, sea level rise, average global temperature, and the frequency of extreme weather events change over the past 25 years (2000 – 2024? (2). Can unsupervised machine learning cluster countries with similar waste and recycling outputs, and climate change outcomes? The analytic strategy in the present study includes aggregating country-level data on global waste and recycling amounts, GHG emissions, sea level rise, average global temperature, and the frequency of extreme weather events from 2000 – 2024. Time series analyses are used to show the correlation between waste and recycling increases, and the indicators of global climate change. Unsupervised machine learning (k-means) is applied to determine whether there are distinct clusters of countries with similar waste outputs and climate change outcomes. To fully understand how to mitigate the climate crisis, we must reconceptualize waste as a climate, social, and policy issue. Without the integration of these two literatures, we will be unable to find solutions that match the urgency of the climate crisis. By integrating a CE framework with quantitative econometric analyses and unsupervised machine learning modeling, we can begin to untangle the complex relationship between waste and climate change.
Presenters
Gloria SchmitzPostdoctoral Research Associate, Defense Industrial Base Institute, Northeastern University, Massachusetts, United States
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
2026 Special Focus—Unseen Unsustainability: Addressing Hidden Risks to Long-Term Wellbeing for All
KEYWORDS
Circular Economy, Global Waste Proliferation, Machine Learning, Theoretical Integration, Recycling
