揭示人工智能与股票市场增长在美国实现碳中和中的作用：基于ARDL模型的分析
Unveiling the Role of Artificial Intelligence and Stock Market Growth in Achieving Carbon Neutrality in the United States: An ARDL Model Analysis
AI总结：
本文利用STIRPAT模型和ARDL方法分析2021年美国数据，发现AI与ICT使用显著减少碳排放，而经济增长、股市资本化和人口增加则加剧碳排放。
摘要：
鉴于气候变化已成为近年来许多国家最紧迫的问题之一，许多国家已经开展了关于如何缓解气候变化的专门研究。在这一讨论中，先进技术在实现碳中和方面的影响得到了探讨。尽管已有几项研究调查了如何利用AI和数字创新来减少环境足迹，但AI在减少CO2排放（衡量碳足迹的代理指标）方面的实际影响仍有待研究。本文研究了先进技术的总体作用，特别是人工智能（AI）和信息通信技术（ICT）的使用，在2021年推进美国碳中和方面的作用。其次，本文利用STIRPAT模型考察了股票市场增长、ICT使用、国内生产总值（GDP）和人口如何影响CO2排放。在使用多种单位根检验考察变量的平稳性后，本研究得出结论，所有变量均不存在单位根问题，且具有混合的积分阶数。用于协整的ARDL边界检验表明，本研究中的变量存在长期关系。此外，从ARDL模型中得出的短期和长期估计结果表明，经济增长、股票市场资本化和人口在短期和长期均对碳排放有显著贡献。相反，AI和ICT的使用在两个时期内都显著减少了碳排放。此外，使用FMOLS、DOLS和CCR估计证实了研究结果的稳健性。诊断检验表明不存在序列相关、异方差和设定误差，因此模型是稳健的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
