Affiliation:
1. School of Management Guangdong University of Science and Technology Dongguan China
2. School of Economics Jiangxi University of Finance and Economics Nanchang China
3. E‐Commerce Research Center in Guangdong‐Hong Kong‐Macao Greater Bay Area, Guangzhou College of Commerce Guangzhou China
4. Center for Socio‐Economic Development Research Lahore Pakistan
5. Department of Economics College of Business Administration, Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia
Abstract
AbstractOver the past few years, the Association of Southeast Asian Nations (ASEAN) has experienced great economic expansion, which has resulted in varied degrees of diversified commerce, an elevated level of government revenue, and an increase in the demand for energy. The purpose of this study is to provide a solution to this conundrum by analyzing the effects of trade diversification (TDF), government revenues (GRN), gross domestic product (GDP), and natural resource rent (NTR) on the sustainable development of the ASEAN countries between the years 1981 and 2022. In order to accurately portray the concept of environmental sustainability, the ecological footprint (EFP) is utilized to represent sustainable development. The quantile‐based econometrics technique known as the Method of the Moments Quantile Regression (MMQR) has been utilized in order to investigate the direction and amplitude of the asymmetric correlation that exists between the interaction of GRN, TDF, NTR, and EFP. According to the estimations of the MMQR, it is proposed that government revenues, which include significant financial incentives that promote the stringent execution of environmental rules, hence avoiding deleterious impacts on the environment, have negative coefficients at all quantiles (Q0.25 − Q0.90). Conversely, TDF and GDP have a positive and statistically strong significant correlation across all quantiles (Q0.25 − Q0.90), revealing that TDF reduces environmental sustainability and expands the size of the world's EFP by making energy‐intensive products more accessible. In addition, the Augmented Mean Group (AMG) and the Common Correlated Effect Mean Group (CCEMG) both provide evidence that supports the correlation study by demonstrating that there is a similar pattern of causality across variables.
Funder
Princess Nourah Bint Abdulrahman University