A dynamic decomposition for the Environmental Kuznets Curve: A state-space framework

S. J. Koopman, C. Amoroso, C. García-Martos

This paper proposes a structured yet flexible way to model the Environmental Kuznets Curve (EKC) in a dynamic nonlinear panel setting. Instead of the usual quadratic form, we represent the EKC as the difference between two sigmoid functions: one reflecting the emissions pressure linked to economic growth, the other the effect of the green transition. Unobserved stochastic trends are included to capture time-varying features such as changes in energy mixes and technological progress. The model can be written in state-space form and estimated with standard Kalman filtering and smoothing methods. Using CO2 emissions and GDP data for 15 major economies (the G7, Australia, and the E7), we find evidence of a common global EKC. The results also suggest that European countries began the green transition earlier and reached lower CO2 peaks, consistent with the view that climate policy can accelerate the transition.

Keywords: nonlinear panel data model, stochastic trends, state-space methods

SI Statistics and the Sustainable Development Goals (SDGs): Methodological Approaches and Applications in Health, Climate Action, and Energy
September 3, 2026  9:00 AM
Aula 30

M. Díaz-Gay

C. García-Martos, A. M. Alonso, E. Martínez, S. Monge, R. Rodríguez Fernández, M. J. Sierra, J. Sánchez de la Mata

M. Oviedo de la Fuente, R. Fernández-Casal, M. A. Florez

M. Wiper, C. Ausín, A. Sarhadi, M. Pérez García