Exploring the effects of Air Pollution, Renewable Energy usage, and AI-driven Technologies on Australia's Economic Development Using the ARDL Model

Abstract

This study uses the Autoregressive Distributed Lag (ARDL) model to investigate the relationship between air pollution, renewable energy usage, AI-driven technologies, and Australia’s economic development over the period 2010–2020. The analysis provides important insights into the dynamic interactions between these variables in both the short and long term. Renewable energy adoption shows a negative impact on economic growth in the short term, likely due to initial investment costs and infrastructure adjustments, it demonstrates a significant positive effect on GDP in the long term. This finding underscores the critical role of renewable energy in fostering sustainable growth over extended periods. The incorporation of AI-driven technologies is found to enhance economic performance, particularly in the short term. The immediate benefits likely stem from improved productivity, cost efficiency, and the expansion of innovative industries supported by AI advancements. The study identifies a detrimental impact of air pollution on economic growth, highlighting the need for stringent environmental regulations and policies aimed at reducing air pollutants. This negative relationship emphasizes the importance of addressing environmental challenges as a core component of economic development strategies. The bounds test for co-integration confirms a stable and consistent long-term relationship among the variables. This suggests that policies addressing these factors can have enduring implications for Australia’s economic trajectory. This study contributes to the discourse on sustainable economic growth by highlighting the interplay between technology, environment, and economic performance. It underscores the necessity of aligning AI and renewable energy policies to foster a balanced, sustainable development pathway.