Politická ekonomie X:X | DOI: 10.18267/j.polek.1552

Impact of minerals rent on green growth: Evidence from the wavelet and quantile-based approaches

Yongliang Zhao, Shabir Mohsin Hashmi, Qasim Raza Syed, Tomiwa Sunday Adebayo
Yongliang Zhao, School of Economics and Management, Yancheng Institute of Technology, Yancheng, China
Shabir Mohsin Hashmi (corresponding author), School of Digital Economics and Management, Suzhou City University, Suzhou, China
Qasim Raza Syed, ARUCAD Research Centre, Arkin University of Creative Arts and Design, Northern Cyprus, Turkey
Tomiwa Sunday Adebayo, Department of Business Administration, Faculty of Economics and Administrative Science, Cyprus International University, Lefkoşa, Northern Cyprus, Turkey; University of Economics and Human Sciences, Warsaw, Poland; Research Center of Development Economics, State University of Economics (UNEC), Baku, Azerbaijan

The aim of this study is to investigate whether minerals rent (MR) impacts green growth (GG). To this end, annual time series data for BRICS countries is adopted that covers the period 1990-2021. To provide robust empirical outcomes, we employ two novel methods: the novel wavelet quantile regression (WQR) approach; and the novel quantile-on-quantile kernel-based regularized least squares (QQKRLS) model. The findings from the novel WQR delineate that minerals rent promotes green growth across the quantiles, in all BRICS members, during the short and medium run. However, minerals rent hinders green growth in all BRICS members (except for South Africa) in the long run. Next, the general findings from the novel QQKRLS model report that minerals rent leads to low levels of green growth across the quantiles of minerals rent and green growth, with the exception of South Africa. The results are also consistent during the sensitivity analysis. Based on the core findings that validate the natural resource curse hypothesis, we propose pertinent policy implications.

Keywords: Green growth, minerals rent, natural resources curse hypothesis, wavelet quantile regression, quantile-on-quantile kernel-based regularized least squares, machine learning

Received: April 19, 2025; Revised: December 10, 2025; Accepted: February 16, 2026; Prepublished online: May 31, 2026 

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