Leveraging AI for Promoting Sustainable Environments in G-7: The Impact of Financial Development and Digital Economy via MMQR Approach

Mohammad Ridwan; Shewly Bala; Sarder Abdulla Al Shiam; Afsana Akhter; Md Mahdi Hasan; Md Asrafuzzaman; Shake Ibna Abir; Shaharina Shoha; Robeena Bibi

doi:10.56556/gssr.v3i3.971

Authors

Mohammad Ridwan Department of Economics, Noakhali Science and Technology University, Sonapur, Noakhali-3814, Bangladesh https://orcid.org/0000-0003-2239-2706
Shewly Bala Department of Finance, University of Dhaka, Bangladesh https://orcid.org/0009-0002-6547-8366
Sarder Abdulla Al Shiam Department of Management -Business Analytics, St Francis College, USA https://orcid.org/0009-0007-7032-6339
Afsana Akhter Department of Economics, Noakhali Science and Technology University, Sonapur, Noakhali-3814, Bangladesh https://orcid.org/0009-0001-1748-2654
Md Mahdi Hasan Department of Management -Business Analytics, St Francis College, USA https://orcid.org/0009-0000-4434-249X
Md Asrafuzzaman Department of Management -Business Analytics, St Francis College, USA
Shake Ibna Abir Department of Mathematics, Western Kentucky University, Bowling Green, KY, USA https://orcid.org/0009-0004-0724-8700
Shaharina Shoha Department of Mathematics, Western Kentucky University, Bowling Green, KY, USA https://orcid.org/0009-0008-8141-3566
Robeena Bibi School of Public Administration, Hohai University, Nanjing China https://orcid.org/0009-0003-6672-4212

DOI:

https://doi.org/10.56556/gssr.v3i3.971

Keywords:

Artificial Intelligence, Digital Economy, Financial development, LCC Hypothesis, MMQR approach

Abstract

This study investigates the role of Artificial Intelligence (AI) in promoting a sustainable environment within the G-7 countries by testing the Load Capacity Curve (LCC) hypothesis. Additionally, it examines the effects of financial development, the digital economy, and urbanization on the load capacity factor using data from 2010 to 2022. The research employs cross-sectional dependence and slope homogeneity tests, revealing issues of cross-sectional dependence and heterogeneity. Panel unit root tests, both first and second generation, confirm that the variables are free from unit root problems. Furthermore, panel cointegration tests indicate that the variables are cointegrated in the long run. To assess the impact of the explanatory variables on the load capacity factor, the study utilizes the Method of Moments Quantile Regression (MMQR). The findings reveal a U-shaped relationship between income and the load capacity factor, supporting the LCC hypothesis in the G-7 region. The results also indicate that AI innovation and financial development have a significant positive correlation with the load capacity factor. In contrast, the digital economy and urbanization are found to significantly reduce the load capacity factor. Robustness checks, including the Driscoll-Kraay standard error, Augmented Mean Group, and Common Correlated Effect Mean Group estimation approaches, validate the findings obtained from the MMQR method. Moreover, the Dumitrescu-Hurlin (D-H) causality assessment is utilized to explore the causal connections between variables. The results reveal a unidirectional causal relationship between income and the load capacity factor. Additionally, bidirectional causal relationships are the remaining explanatory variables and load capacity factors.