Unveiling Tipping Points in European Sustainability: A Nonlinear MARS Approach to People, Planet, and Prosperity

This study investigates sustainable development in Europe, moving beyond simple SDG scores and linear models. Using data from 2000 to 2024 across European countries, Principal Component Analysis (PCA) was employed to create indices for the People, Planet, and Prosperity domains of sustainable development. Multivariate Adaptive Regression Splines (MARSs) captured nonlinearities and interactions in the influence of these domains on sustainable development on the European continent. The results show that progress depends on crossing key thresholds rather than gradual change. In the People domain, exceeding minimum levels leads to significant gains. The Planet domain acts as both stabiliser and constraint: moderate ecological performance fosters progress, while extremes hinder it. Prosperity has modest direct effects but becomes influential when combined with social and environmental factors. Interactions reveal that sustainable development arises from domain synergies, with social progress offering the most significant leverage. The findings indicate the need to target lagging regions to surpass social thresholds, protect ecological stability, and ground prosperity in inclusive, eco-friendly frameworks. Overall, the study highlights how data-driven methods can identify tipping points in sustainability and provides a transferable framework for achieving the 2030 Agenda.

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Publication Details

Journal
Sustainability
Published
2025-09-26
DOI
https://doi.org/10.3390/su17198692
Primary Topic
Sustainable Development and Environmental Policy
Type
article
Field-Weighted Citation Impact
0.00

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article

Unveiling Tipping Points in European Sustainability: A Nonlinear MARS Approach to People, Planet, and Prosperity

Tomasz Lechowicz, Robert Szarota, Oksanа Liashenko, Павлова Олена Миколаївна et al.
Sustainability
Sustainable Development and Environmental Policy
article

Unveiling Tipping Points in European Sustainability: A Nonlinear MARS Approach to People, Planet, and Prosperity

Tomasz Lechowicz, Robert Szarota, Oksanа Liashenko, Павлова Олена Миколаївна, Павлов Костянтин Володимирович, Natalia Hrytsiuk, Maryna Nagara
article en

Abstract

This study investigates sustainable development in Europe, moving beyond simple SDG scores and linear models. Using data from 2000 to 2024 across European countries, Principal Component Analysis (PCA) was employed to create indices for the People, Planet, and Prosperity domains of sustainable development. Multivariate Adaptive Regression Splines (MARSs) captured nonlinearities and interactions in the influence of these domains on sustainable development on the European continent. The results show that progress depends on crossing key thresholds rather than gradual change. In the People domain, exceeding minimum levels leads to significant gains. The Planet domain acts as both stabiliser and constraint: moderate ecological performance fosters progress, while extremes hinder it. Prosperity has modest direct effects but becomes influential when combined with social and environmental factors. Interactions reveal that sustainable development arises from domain synergies, with social progress offering the most significant leverage. The findings indicate the need to target lagging regions to surpass social thresholds, protect ecological stability, and ground prosperity in inclusive, eco-friendly frameworks. Overall, the study highlights how data-driven methods can identify tipping points in sustainability and provides a transferable framework for achieving the 2030 Agenda.

SustainabilityVol. 17(19)
Loughborough University (GB), West Ukrainian National University (UA), University of Agriculture in Krakow (PL), Lesya Ukrainka Volyn National University (UA), National-Louis University (PL), Państwowa Wyższa Szkoła Zawodowa w Nowym Sączu (PL), University College London (GB), AGH University of Krakow (PL)
European Commission
Openalex Percentile: Top 64%
Sustainable Development and Environmental Policy
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