Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM 2.5 Pollution

James Ming Chen, Mira Zovko, Nika Šimurina and Vatroslav Zovko
Additional contact information
James Ming Chen: College of Law, Michigan State University, East Lansing, MI 48824, USA
Mira Zovko: Ministry of Economy and Sustainable Development, 10000 Zagreb, Croatia
Nika Šimurina: Faculty of Economics & Business, University of Zagreb, 10000 Zagreb, Croatia
Vatroslav Zovko: Faculty of Teacher Education, University of Zagreb, 10000 Zagreb, Croatia

IJERPH, 2021, vol. 18, issue 16, 1-59

Abstract: This study evaluates numerous epidemiological, environmental, and economic factors affecting morbidity and mortality from PM 2.5 exposure in the 27 member states of the European Union. This form of air pollution inflicts considerable social and economic damage in addition to loss of life and well-being. This study creates and deploys a comprehensive data pipeline. The first step consists of conventional linear models and supervised machine learning alternatives. Those regression methods do more than predict health outcomes in the EU-27 and relate those predictions to independent variables. Linear regression and its machine learning equivalents also inform unsupervised machine learning methods such as clustering and manifold learning. Lower-dimension manifolds of this dataset’s feature space reveal the relationship among EU-27 countries and their success (or failure) in managing PM 2.5 morbidity and mortality. Principal component analysis informs further interpretation of variables along economic and health-based lines. A nonlinear environmental Kuznets curve may describe the fuller relationship between economic activity and premature death from PM 2.5 exposure. The European Union should bridge the historical, cultural, and economic gaps that impair these countries’ collective response to PM 2.5 pollution.

Keywords: air pollution; particulate matter; PM 2.5; public health; environmental Kuznets curve; machine learning; supervised learning; unsupervised learning; clustering; manifold learning; dimensionality reduction; principal component analysis; European Union (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/16/8688/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/16/8688/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:16:p:8688-:d:616028

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().