Beyond Traditional Drivers: Artificial Intelligence and the Evolving Structure of Carbon Emissions in the United States

Kuan  Mo; Tahia  Tasnuva; Md Omar  Farukh; Shamina Israt  Tithi

doi:10.31181/sa42202673

Authors

Kuan Mo Department of Earth and Environmental Sciences, Brooklyn College, State University of New York, New York, USA. Author https://orcid.org/0009-0002-3127-5683
Tahia Tasnuva Department of Business Administration, Manarat International University, Ashulia, Dhaka-1314, Bangladesh. Author https://orcid.org/0009-0000-6191-8219
Md Omar Farukh Department of Applied Science in Organizational Management and Leadership, Eastern Florida State College, USA. Author https://orcid.org/0009-0000-4471-4070
Shamina Israt Tithi Department of Earth and Environmental Sciences, Brooklyn College, State University of New York, New York, USA. Author https://orcid.org/0009-0008-4482-2424

DOI:

https://doi.org/10.31181/sa42202673

Keywords:

Artificial intelligence, Carbon emissions, Energy consumption, Foreign direct investment, Urbanization

Abstract

This study examines the determinants of carbon emissions in the United States over the period 1990–2022, with particular attention to the role of Artificial Intelligence (AI) within a broader macroeconomic setting. While traditional literature has primarily emphasized economic growth and energy consumption as the main drivers of environmental degradation, this paper extends the analysis by incorporating technological change alongside Foreign Direct Investment (FDI) and urbanization in a unified time-series framework. The empirical results indicate that economic growth and energy use continue to exert upward pressure on carbon emissions, reflecting the continued reliance on energy-intensive production and consumption patterns in the U.S. economy. FDI and urban expansion also contribute to higher emissions, although their effects vary across time and may depend on structural conditions within the economy. These findings confirm that conventional development pathways remain closely associated with environmental stress. In contrast, AI is found to have a mitigating association with carbon emissions. This relationship likely reflects improvements in production efficiency, optimization of energy use, and better allocation of economic resources driven by technological progress. However, the results also suggest that the environmental effect of AI is not independent, as it interacts with existing economic and energy structures in shaping overall emission outcomes.

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