The disadvantaged populations often bear a disproportionate burden of environmental pollution, especially when air quality exceeds regulatory standards. This link is particularly pronounced in areas with housing deprivation. As deprivation levels rise, the number of people exposed to excessive pollution increases. The industrial sector is one of the major contributors to air pollution and greenhouse gas emissions. Given the close relationship between industrial sites and socioeconomic, environmental, and health factors in cities, analyzing the real estate market can provide valuable insights for guiding sustainable development strategies. The present research aims to determine if a correlation exists between residential property prices and polluting sites, by considering factors that could represent housing deprivation. By examining the type of relationship and changes in property prices, the study intends to inform decision-making on housing deprivation policies. Through a genetic algorithm (Multi-Case Strategy of Evolutionary Polynomial Regression) applied to a sample of a limited available sample of polluting sites in Italy, first results have been obtained, that align with empirical observations and local user expectations. The results highlight the importance of implementing effective housing deprivation policies that address the environmental impacts of polluting industrial sites on real estate market dynamics. The innovative contribution of this work lies in identifying critical ‘pollution-poverty’ areas that require urgent remedial action.

Environmental Impacts and Housing Deprivation: A Study of the Effects of Industrial Polluting Sites in the Italian Context / Anelli, D.; Morano, P.; Tajani, F.; Di Liddo, F.; Locurcio, M.. - 15889:(2026), pp. 381-392. ( Workshops of the International Conference on Computational Science and Its Applications, ICCSA 20252025) [10.1007/978-3-031-97603-2_24].

Environmental Impacts and Housing Deprivation: A Study of the Effects of Industrial Polluting Sites in the Italian Context

Morano P.;Di Liddo F.;Locurcio M.
2026

Abstract

The disadvantaged populations often bear a disproportionate burden of environmental pollution, especially when air quality exceeds regulatory standards. This link is particularly pronounced in areas with housing deprivation. As deprivation levels rise, the number of people exposed to excessive pollution increases. The industrial sector is one of the major contributors to air pollution and greenhouse gas emissions. Given the close relationship between industrial sites and socioeconomic, environmental, and health factors in cities, analyzing the real estate market can provide valuable insights for guiding sustainable development strategies. The present research aims to determine if a correlation exists between residential property prices and polluting sites, by considering factors that could represent housing deprivation. By examining the type of relationship and changes in property prices, the study intends to inform decision-making on housing deprivation policies. Through a genetic algorithm (Multi-Case Strategy of Evolutionary Polynomial Regression) applied to a sample of a limited available sample of polluting sites in Italy, first results have been obtained, that align with empirical observations and local user expectations. The results highlight the importance of implementing effective housing deprivation policies that address the environmental impacts of polluting industrial sites on real estate market dynamics. The innovative contribution of this work lies in identifying critical ‘pollution-poverty’ areas that require urgent remedial action.
2026
Workshops of the International Conference on Computational Science and Its Applications, ICCSA 2025
9783031976025
9783031976032
Environmental Impacts and Housing Deprivation: A Study of the Effects of Industrial Polluting Sites in the Italian Context / Anelli, D.; Morano, P.; Tajani, F.; Di Liddo, F.; Locurcio, M.. - 15889:(2026), pp. 381-392. ( Workshops of the International Conference on Computational Science and Its Applications, ICCSA 20252025) [10.1007/978-3-031-97603-2_24].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/293142
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