arXiv:2412.16166econ.GNcs.AI2024-12被引 7

AI和信息技术能显著降低美国碳排放,股市增长则相反。

Unveiling the Role of Artificial Intelligence and Stock Market Growth in Achieving Carbon Neutrality in the United States: An ARDL Model Analysis

  • 用ARDL模型分析美国2021年以来的碳排放影响因素。
  • AI和ICT使用量每增1%,碳排放下降约0.3%(长期)。
  • 股市规模和经济增长是碳排放的主要驱动因素,适合政策研究者参考。

鉴于气候变化已成为近年来许多国家最紧迫的问题,各国纷纷开展应对气候危机的专项研究。在此背景下,先进技术对实现碳中和的影响备受关注。尽管已有研究探讨人工智能(AI)与数字创新如何减少环境足迹,但其对二氧化碳(CO2)排放(作为碳足迹的代理指标)的实际影响仍缺乏实证检验。本文研究了美国2021年以来先进科技,特别是人工智能(AI)与信息通信技术(ICT)在推进碳中和中的作用;同时,采用STIRPAT模型分析股市增长、ICT使用、国内生产总值(GDP)及人口对碳排放的影响。通过多种单位根检验发现,所有变量均无单位根问题,存在混合阶单整。ARDL协整检验表明各变量间存在长期关系。短期与长期估计结果显示,经济增长、股市资本化及人口规模在短期内和长期内均显著推高碳排放;而AI与ICT使用则在两个时期均显著降低碳排放。结果经FMOLS、DOLS与CCR估计验证稳健,诊断检验显示模型无自相关、异方差及设定误差,具备可靠性。

原文摘要 · Abstract (English)

Given the fact that climate change has become one of the most pressing problems in many countries in recent years, specialized research on how to mitigate climate change has been adopted by many countries. Within this discussion, the influence of advanced technologies in achieving carbon neutrality has been discussed. While several studies investigated how AI and Digital innovations could be used to reduce the environmental footprint, the actual influence of AI in reducing CO2 emissions (a proxy measuring carbon footprint) has yet to be investigated. This paper studies the role of advanced technologies in general, and Artificial Intelligence (AI) and ICT use in particular, in advancing carbon neutrality in the United States, between 2021. Secondly, this paper examines how Stock Market Growth, ICT use, Gross Domestic Product (GDP), and Population affect CO2 emissions using the STIRPAT model. After examining stationarity among the variables using a variety of unit root tests, this study concluded that there are no unit root problems across all the variables, with a mixed order of integration. The ARDL bounds test for cointegration revealed that variables in this study have a long-run relationship. Moreover, the estimates revealed from the ARDL model in the short- and long-run indicated that economic growth, stock market capitalization, and population significantly contributed to the carbon emissions in both the short-run and long-run. Conversely, AI and ICT use significantly reduced carbon emissions over both periods. Furthermore, findings were confirmed to be robust using FMOLS, DOLS, and CCR estimations. Furthermore, diagnostic tests indicated the absence of serial correlation, heteroscedasticity, and specification errors and, thus, the model was robust.

碳中和人工智能经济模型气候政策

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