Date of Award
5-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
College/School
College of Science and Mathematics
Department/Program
Earth and Environmental Studies
Thesis Sponsor/Dissertation Chair/Project Chair
Danlin Yu
Committee Member
Josh Galster
Committee Member
Pankaj Lal
Committee Member
Nicholas Procopio III
Abstract
Environmental justice, as both a movement and a theoretical construct, continues to evolve in response to shifting societal, environmental, and technological conditions. This dissertation investigates the integration of big data, such as social media, remote sensing imagery, and internet search frequencies, into the identification, analysis, and remediation of environmental injustices. Framing environmental justice through the lenses of distributive and data justice, the project explores both the promises and pitfalls of using emergent data sources to enhance the spatial and temporal precision of environmental equity investigations. Through a combination of systematic literature review, spatial analysis, system dynamics simulation, and policy evaluation, these studies examine environmental injustice in New Jersey, with a particular focus on lead exposure in Newark. The findings underscore the potential of big data to supplement traditional datasets and enable more responsive and participatory environmental governance, while also highlighting the risks of technological determinism and the need for ethically grounded data practices. By bridging environmental and data justice frameworks, this dissertation contributes to ongoing discussions about the role of technology in advancing equity in environmental decision-making.
File Format
Recommended Citation
Knoble, Charles C. II, "Don't Let Lead Lead on Environmental Justice: A Simulative Approach to Lead Remediation in the Big Data Era" (2025). Theses, Dissertations and Culminating Projects. 1571.
https://digitalcommons.montclair.edu/etd/1571