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

従来の要因を超えて：人工知能と米国における炭素排出の進化する構造 (AI 翻訳)

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

Systemic Analytics📚 査読済 / ジャーナル2026-05-24#エネルギー転換Origin: US
DOI: 10.31181/sa42202673
原典: https://doi.org/10.31181/sa42202673
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🤖 gxceed AI 要約

日本語

1990～2022年の米国データを用い、経済成長・エネルギー消費・FDI・都市化に加えAIがCO2排出に与える影響を分析。AIは排出抑制に寄与するが、その効果は経済・エネルギー構造と相互作用する。伝統的な排出要因は依然として有意。

English

Using US data from 1990-2022, this study analyzes the impact of AI on carbon emissions alongside economic growth, energy use, FDI, and urbanization. AI shows a mitigating association with emissions, but its effect interacts with economic and energy structures. Traditional drivers remain significant.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

米国の事例だが、日本でもAIを活用した排出削減が注目される。本論文はAIの環境効果をマクロ経済学的に示し、日本のGX政策でのAI位置づけに示唆を与える。ただし米国特有の構造に留意。

In the global GX context

This paper contributes to the global discourse on AI-enabled decarbonization, offering empirical evidence that AI can reduce emissions in a large economy. Relevant for ISSB and transition finance frameworks exploring technology-driven decarbonization.

👥 読者別の含意

🔬研究者:Provides time-series evidence on AI's mitigating role in US carbon emissions, controlling for traditional drivers like GDP and energy.

🏢実務担当者:May inform corporate AI adoption for energy efficiency, but lacks sector-specific guidance.

🏛政策担当者:Suggests AI investment as a potential lever for national climate policy, though causality is not firmly established.

📄 Abstract（原文）

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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