Artificial Intelligence and Environmental Sustainability: Insights from PLS-SEM on Resource Efficiency and Carbon Emission Reduction

Abstract

This study investigates the relationship between Artificial Intelligence (AI) and environmental sustainability, focusing on how AI-driven resource efficiency, energy consumption, environmental monitoring, and carbon emission reduction contribute to sustainability outcomes. The purpose of this research is to examine the dual nature of AI's impact on sustainability by testing both positive and negative effects using Partial Least Squares Structural Equation Modeling (PLS-SEM). Drawing from a sample of 233 firms in the energy, transportation, and manufacturing sectors, this study collects data through an online survey measured on a five-point Likert scale. Four hypotheses are tested, revealing that AI-driven resource efficiency and environmental monitoring positively affect sustainability, while AI energy consumption has a negative impact. Furthermore, AI integration in industrial processes helps reduce resource depletion. The findings suggest that governments should incentivize AI adoption aimed at resource efficiency and environmental monitoring through policies like tax breaks or subsidies, particularly for firms reducing their carbon footprint. To mitigate the negative effects of AI energy consumption, policymakers are urged to promote energy-efficient AI models and invest in renewable energy infrastructure. A balanced policy approach is crucial to optimize the environmental benefits of AI while minimizing its energy-related drawbacks.