Environmental Complexity and Respiratory Health: A Data-Driven Exploration Across European Regions

Onofrio Resta, Emanuela Resta, Alberto Costantiello, Piergiuseppe Liuzzi () and Angelo Leogrande ()
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Onofrio Resta: UNIBA - Università degli studi di Bari Aldo Moro = University of Bari Aldo Moro
Emanuela Resta: Unifg - Università degli Studi di Foggia = University of Foggia
Alberto Costantiello: LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro
Angelo Leogrande: LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro

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Abstract: This paper examines the environmental and infrastructure determinants of respiratory disease mortality (TRD) across European nation-states through an original combination of econometric, machine learning, clustering, and network-based approaches. The primary scientific inquiry is how structural environmental variables, such as land use, energy mix, sanitation, and climatic stress, co-interact to affect respiratory mortality across regions. Although prior literature has addressed individual environmental predictors in singleton settings, this paper fills an integral gap by using a multi-method, systems-level analysis that accounts for interdependencies as well as contextual variability. The statistical analysis draws on panel data covering several years and nation-states using fixed effects regressions with robust standard errors for evaluating the effects of variables such as agricultural land use (AGRL), access to electricity (ELEC), renewable energy (RENE), freshwater withdrawals (WTRW), cooling degree days (CDD), and sanitation (SANS). We employ cluster analysis and density-based methodology to identify spatial and environmental groupings, while machine learning regressions-specifically, K-Nearest Neighbors (KNN)-are utilized for predictive modeling and evaluating feature importance. Lastly, network analysis identifies the structural connections between variables, including influence metrics and directional weights. We obtain the following results: Consistently, across all regressions, AGRL, WTRW, and SANS feature importantly when determining the effect for TRD. Consistently across all networks, influencer metrics identify AGRL, WTRW, and SANS as key influencers. Consistently across all models, the best-performing predictive regression identifies the nonlinear (polynomial or non-monotone), context-sensitive nature of the effects. Consistent with the network results, the influencer metrics suggest strong connections between variables, with a particular emphasis on the importance of holistic environmental health approaches. Combining the disparate yet complementary methodological tools, the paper provides robust, understandable, yet policy-relevant insights into the environmental complexity driving respiratory health outcomes across Europe.

Keywords: Q53; I10; C45; C38; Panel Data Models JEL CODES: C23; Network Analysis; Machine Learning Regression; Environmental Determinants; Respiratory Disease Mortality; Respiratory Disease Mortality Environmental Determinants Machine Learning Regression Network Analysis Panel Data Models JEL CODES: C23 C38 C45 I10 Q53 (search for similar items in EconPapers)
Date: 2025-09-06
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