AI、清洁能源与数字经济能有效降低美国碳排放
Role of AI Innovation, Clean Energy and Digital Economy towards Net Zero Emission in the United States: An ARDL Approach
- 用自回归分布滞后模型分析1990-2022年美国碳排放影响因素
- AI创新等三项因素显著减排，经济增长与工业化加剧污染
- 结果对政策制定者推动绿色技术与低碳产业有重要参考价值
本文基于1990至2022年数据，采用ARDL方法研究人工智能创新、GDP增长、可再生能源利用、数字经济和工业化对美国二氧化碳排放的影响。结果显示，人工智能创新、可再生能源使用和数字经济有助于降低碳排放，而经济扩张和工业化则加剧生态破坏。单位根检验（ADF、PP、DF-GLS）表明各变量存在异质性平稳性，保障了ARDL分析的稳健性。互补方法（FMOLS、DOLS、CCR）进一步验证了结果可靠性。成对格兰杰因果检验发现碳排放与人工智能创新、数字经济之间存在强单向因果关系，凸显其在生态可持续中的关键作用。研究强调需通过推进人工智能技术、绿色能源应用和环境友好型工业发展，实现平衡增长与环境质量提升。
原文摘要 · Abstract (English)
The current paper investigates the influences of AI innovation, GDP growth, renewable energy utilization, the digital economy, and industrialization on CO2 emissions in the USA from 1990 to 2022, incorporating the ARDL methodology. The outcomes observe that AI innovation, renewable energy usage, and the digital economy reduce CO2 emissions, while GDP expansion and industrialization intensify ecosystem damage. Unit root tests (ADF, PP, and DF-GLS) reveal heterogeneous integration levels amongst components, ensuring robustness in the ARDL analysis. Complementary methods (FMOLS, DOLS, and CCR) validate the results, enhancing their reliability. Pairwise Granger causality assessments identify strong unidirectional connections within CO2 emissions and AI innovation, as well as the digital economy, underscoring their significant roles in ecological sustainability. This research highlights the requirement for strategic actions to nurture equitable growth, including advancements in AI technology, green energy adoption, and environmentally conscious industrial development, to improve environmental quality in the United States.
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