High‐Resolution Downscaling of Disposable Income in Europe Using Open‐Source Data

Mehdi Mikou, Améline Vallet (), Céline Guivarch () and David Makowski ()
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Mehdi Mikou: ESE - Ecologie Systématique et Evolution - AgroParisTech - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique, CIRED - Centre International de Recherche sur l'Environnement et le Développement - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - EHESS - École des hautes études en sciences sociales - AgroParisTech - ENPC - École nationale des ponts et chaussées - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique
Améline Vallet: ESE - Ecologie Systématique et Evolution - AgroParisTech - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique, CIRED - Centre International de Recherche sur l'Environnement et le Développement - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - EHESS - École des hautes études en sciences sociales - AgroParisTech - ENPC - École nationale des ponts et chaussées - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique
Céline Guivarch: CIRED - Centre International de Recherche sur l'Environnement et le Développement - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - EHESS - École des hautes études en sciences sociales - AgroParisTech - ENPC - École nationale des ponts et chaussées - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique
David Makowski: MIA Paris-Saclay - Mathématiques et Informatique Appliquées - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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Abstract: Income maps have been extensively used for identifying populations vulnerable to global changes. The frequency and intensity of extreme events are likely to increase in coming years as a result of climate change. In this context, several studies have hypothesized that the economic and social impact of extreme events depend on income. However, to rigorously test this hypothesis, fine‐scale spatial income data is needed, compatible with the analysis of extreme climatic events. To produce reliable high‐resolution income data, we have developed an innovative machine learning framework, that we applied to produce a European 1 km‐gridded data set of per capita disposable income for 2015. This data set was generated by downscaling income data available for more than 120,000 administrative units. Our learning framework showed high accuracy levels, and performed better or equally than other existing approaches used in the literature for downscaling income. It also yielded better results for the estimation of spatial inequality within administrative units. Using SHAP values, we explored the contribution of the model predictors to income predictions and found that, in addition to geographic predictors, distance to public transport or nighttime light intensity were key drivers of income predictions. More broadly, this data set offers an opportunity to explore the relationships between economic inequality and environmental degradation in health, adaptation or urban planning sectors. It can also facilitate the development of future income maps that align with the Shared Socioeconomic Pathways, and ultimately enable the assessment of future climate risks.

Keywords: machine learning; random forest; income; Europe; spatial modeling; economic vulnerability (search for similar items in EconPapers)
Date: 2025
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Published in Earth's Future, 2025, 13 (1), pp.e2024EF004576. ⟨10.1029/2024EF004576⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04906700

DOI: 10.1029/2024EF004576

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