Title: Role of income in pollution and growth across Belt and Road Initiative countries: new insights from dynamic functional regression model

Authors: Mohanned Abduljabbar Hael; Haiqiang Ma; Fahmi Al-Selwi; Mushref Mohammed; Khin Sandi Myint; Hamas A. Al-Kuhali

Addresses: School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang 330013, China; Department of Data Science and Information Technology, Taiz University, Yemen ' School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang 330013, China ' Department of Data Science and Information Technology, Taiz University, Yemen ' Business School, Hohai University, Nanjing, China; College of Administrative Sciences, Taiz University, Yemen ' School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang 330013, China ' School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China

Abstract: This study employed function-on-scalar regression (FOSR) to model the complex nexus and spatial-temporal dynamic impact of income distribution on carbon dioxide emissions (CO₂) and economic growth (EG) for 80 Belt and Road Initiative (BRI) countries (1990-2021). Results discovered a robust positive nexus between income, CO₂, and EG across the spatial-temporal BRI domain. Increased income levels are associated with heightened EG, subsequently driving higher CO₂. Decreased income levels were linked to reduced EG and a corresponding decline in CO₂ intensity. Accordingly, we suggested policy implications to guide BRI towards a balanced and sustainable development pattern.

Keywords: function-on-scalar regression; FOSR; penalised least square estimator; dynamic modelling; economic growth; carbon emissions; income level; Belt and Road Initiative; BRI.

DOI: 10.1504/IJGW.2024.139903

International Journal of Global Warming, 2024 Vol.33 No.4, pp.315 - 329

Received: 12 Dec 2023
Accepted: 13 Mar 2024
Published online: 09 Jul 2024 *

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