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Enhancing Sustainability: Quantifying and Mapping Vulnerability to Extreme Heat Using Socioeconomic Factors at the National, Regional and Local Levels

Author

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  • Konstantinos Ziliaskopoulos

    (Department of Environmental Sciences, University of Thessaly, 41500 Larissa, Greece
    Sustainable Development Unit, Athena Research and Innovation Centre, 15125 Marousi, Greece
    Department of Industrial and Systems Engineering, Auburn University, Auburn, AL 36849, USA)

  • Christos Petropoulos

    (EETAA Hellenic Agency for Local Development and Local Government, 10436 Athens, Greece)

  • Chrysi Laspidou

    (Sustainable Development Unit, Athena Research and Innovation Centre, 15125 Marousi, Greece
    Department of Civil Engineering, University of Thessaly, 38334 Volos, Greece)

Abstract

Population-dense urban areas often concentrate high commercial and industrial activity and intricate transportation systems. In crowded cities, extreme events can be even more damaging due to the high population they affect and the social inequalities that are likely to emerge. Extreme heat is a climate hazard that has been linked to high morbidity and mortality, especially in cities with high population densities. The way extreme heat events are felt in the population varies depending on a variety of factors, such as age, employment status, living conditions, air-conditioning, housing conditions, habits, behaviors and other socio-demographic parameters. In this article, we quantify and locate vulnerabilities of populations to extreme heat in order to formulate policy and practice recommendations that will make communities resilient and will shape the transition to a more sustainable future. This work contributes towards the achievement of Sustainable Development Goal 11—Sustainable Cities and Communities—by developing the tools to make cities and settlements resilient and sustainable. To this end, we analyze socioeconomic data at the NUTS3 level for the national case study of Greece and at the census tract level for the local case study of the city of Athens. The target variable for this study is defined as the average daily mortality during heatwaves per 100,000 individuals, and a methodology is developed for constructing this variable based on socioeconomic data available in public databases. The independent variables were selected based on their contribution to socioeconomic vulnerability; they include the percentage of elderly individuals, retirees, unemployed persons, renters, those living alone, those residing in smaller houses, those living in older houses and immigrants from developing countries. An ensemble gradient boosted tree model was employed for this study to obtain feature importance metrics that was used to construct a composite index of socioeconomic heat vulnerability. The socioeconomic heat vulnerability index (SHVI) was calculated for each prefecture in Greece and for each census tract in the city of Athens, Greece. The unique feature of this SHVI is that it can be applied to any geographical resolution using the same methodology and produces a result that is not only quantifiable, but also facilitates a comparison between vulnerability scores across different regions. This application aimed to map the SHVI of both prefecture and city, to examine the significance of scale, to identify vulnerability hotspots, and rank the most vulnerable areas, which are prioritized by authorities for interventions that protect public health.

Suggested Citation

  • Konstantinos Ziliaskopoulos & Christos Petropoulos & Chrysi Laspidou, 2024. "Enhancing Sustainability: Quantifying and Mapping Vulnerability to Extreme Heat Using Socioeconomic Factors at the National, Regional and Local Levels," Sustainability, MDPI, vol. 16(17), pages 1-16, September.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7603-:d:1469831
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    References listed on IDEAS

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    1. Kaiser, R. & Le Tertre, A. & Schwartz, J. & Gotway, C.A. & Daley, W.R. & Rubin, C.H., 2007. "The effect of the 1995 heat wave in Chicago on all-cause and cause-specific mortality," American Journal of Public Health, American Public Health Association, vol. 97(S1), pages 158-162.
    2. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
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