AI-DRIVEN GROWTH IN AN ENVIRONMENTAL CONTEXT: ANALYZING THE EFFECTS OF ENVIRONMENTAL POLICY STRINGENCY AND TAXES IN G19 COUNTRIES

Abstract

The impacts of technological progress and its adoption, especially in the shape of AI, have been unprecedented in changing the socioeconomic landscapes of the economies. However, the socioeconomic impacts of AI may differ from country to country and region to region depending on the socioeconomic structure and levels of socioeconomic development of the economies or regions. One of the major contributions of the AI adoption is its driving potential for economic growth (EG). The contribution of AI to EG warrants further data-driven evidence-based insights for the formulation of macroeconomic policies ensuring sustainable economic growth. The primary focus of the current study is to examine the impact of AI on EG in G-19 economies from 2011 to 2023. In addition, the study also includes Environmental Degradation (ED), Environmental Policy Stringency (EPS), and Environmental Tax (ET) as control variables. The estimates of the Panel Corrected Standard Errors (PCSE) and Kernel-Regularized Least Squares (KRLS) Machine Learning Method (MLM) show that AI and EPS have a positive and significant impact on EG, while ED and ET have a negative impact on EG. Policymakers should promote economic growth, reduce environmental degradation, and leverage AI adoption to achieve a more sustainable and prosperous future.