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<p>This study examines how artificial intelligence (AI), financial development (FD), renewable energy consumption (REEC), and fertilizer consumption (FER) affect environmental quality in the United States, using the load capacity factor (LF) as a sustainability-based proxy for ecological quality. Using data from 1990Q1–2021Q2. To reduce nonlinearity and distributional concerns, we apply a wavelet–quantile framework to capture heterogeneous effects across both time horizons (short-, medium-, and long-term) and distributional states (low to high quantiles). The results show that AI and REEC generally enhance ecological quality, with effects that are more pronounced in the short- to medium-term and around higher sustainability states, while fertilizer use exhibits positive associations with LF mainly in the short and medium horizons but weakens over longer horizons, consistent with potential ecological saturation and degradation risks from prolonged intensification. In contrast, financial development is predominantly associated with lower LF across most horizons and quantiles, whereas economic growth tends to improve LF, with stronger effects in the medium term and sustained positive influence across the distribution. The results suggest that achieving regional sustainable development in the U.S. requires combining AI-enabled efficiency and renewable expansion with sustainable fertilizer governance and a financial system that reorients capital toward green technologies and ecosystem-preserving investment.</p>

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Impacts of artificial intelligence, renewables and fertilizer use on environmental quality: insights from wavelet quantile approaches in the United States

  • 期刊论文

  • 发表日期：

    2026年7月22日

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  • Seyed Alireza Athari,
  • Berna Uzun,
  • Mohamed Djafar Henni,
  • Dilber Uzun Ozsahin,
  • Victoria Olushola Olanrewaju

摘要

This study examines how artificial intelligence (AI), financial development (FD), renewable energy consumption (REEC), and fertilizer consumption (FER) affect environmental quality in the United States, using the load capacity factor (LF) as a sustainability-based proxy for ecological quality. Using data from 1990Q1–2021Q2. To reduce nonlinearity and distributional concerns, we apply a wavelet–quantile framework to capture heterogeneous effects across both time horizons (short-, medium-, and long-term) and distributional states (low to high quantiles). The results show that AI and REEC generally enhance ecological quality, with effects that are more pronounced in the short- to medium-term and around higher sustainability states, while fertilizer use exhibits positive associations with LF mainly in the short and medium horizons but weakens over longer horizons, consistent with potential ecological saturation and degradation risks from prolonged intensification. In contrast, financial development is predominantly associated with lower LF across most horizons and quantiles, whereas economic growth tends to improve LF, with stronger effects in the medium term and sustained positive influence across the distribution. The results suggest that achieving regional sustainable development in the U.S. requires combining AI-enabled efficiency and renewable expansion with sustainable fertilizer governance and a financial system that reorients capital toward green technologies and ecosystem-preserving investment.