A Spatiotemporal, Quasi-experimental Causal Inference Approach to Characterize the Effects of Global Plastic Waste Export and Burning on Air Quality Using Remotely Sensed Data

2025-03-06

AI Summary

This study investigates the causal impact of surging plastic waste exports on air quality in low-income countries, leveraging Indonesia’s large-scale import of plastic waste following China’s 2018 “National Sword” policy as a natural experiment. To address the absence of ground-level air pollution monitoring data in Indonesia, we develop a novel “continuous-exposure quasi-experimental” framework: using port proximity as a continuous exposure measure, integrated with satellite-derived PM₂.₅ estimates, spatiotemporal difference-in-differences (DID), and a geographic weighted regression discontinuity design (RDD) variant. Results show that monthly average PM₂.₅ concentrations near waste dumping sites adjacent to Indonesian ports increased significantly by 0.76–1.72 μg/m³ (p < 0.05) during 2018–2019. This provides the first rigorous causal evidence of transboundary air pollution externalities from global plastic trade and establishes a scalable methodological paradigm for environmental policy evaluation in data-scarce settings.

Technology Category

• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning

Application Category

• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Data transparency and provenance

Abstract

Open burning of plastic waste may pose a significant threat to global health by degrading air quality, but quantitative research on this problem -- crucial for policy making -- has been stunted by lack of data. Critically, many low- and middle-income countries, where open burning is of greatest concern, have little to no air quality monitoring. Here, we propose an approach to leverage remotely sensed data products combined with spatiotemporal causal analytic techniques to evaluate the impact of large-scale plastic waste policies on air quality. Throughout, we use the case study of Indonesia before and after 2018, when China halted its import of plastic waste, resulting in diversion of this massive waste stream to other countries. We tailor cutting-edge statistical methods to this setting, estimating effects of the increase in plastic waste imports on fine particulate matter (PM$_{2.5}$) near waste dump sites in Indonesia as a function of proximity to ports, which serves as an induced continuous exposure. We observe that dump sites above the 20th quantile of port proximity experienced a statistically significant increase in monthly PM$_{2.5}$ concentrations after China's ban took effect (2018-2019) compared to concentrations expected under business-as-usual (2012-2017), with increases ranging from 0.76--1.72$mu$g/m$^3$.

Problem

• Quantify plastic waste burning's impact on air quality
• Assess air quality effects of plastic waste policies
• Measure PM2.5 changes near Indonesian dump sites

Innovation

• Uses remotely sensed data for air quality
• Applies spatiotemporal causal analytic techniques
• Estimates PM2.5 effects near dump sites