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Green economy and artificial intelligence: pathways to better air quality and health in China
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DOI：10.1007/s11869-026-02041-1.png)
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Abstract
En 中文
China is under growing pressure from rising healthcare costs, persistent air-pollution exposure, rapid urbanization, and fast digital transformation. While previous studies have examined how pollution, energy production, and technological change affect health expenditure, most rely on average-based methods that overlook how these relationships differ when healthcare spending is low, moderate, or high. This study addresses that gap by treating health expenditure as a nonlinear, distribution-sensitive outcome shaped by artificial intelligence, green growth, energy production, and air quality. The study applies quantile ADF and quantile KPSS stationarity tests, and multivariate quantile-on-quantile regression, with conventional quantile regression used for robustness. The findings show clear nonlinear patterns, stable conditional distributions, and strong quantile-specific differences. Artificial intelligence has a mixed effect: it may increase spending through digital infrastructure and diagnostic investment, but may also reduce cost pressure through efficiency, early detection, and better resource allocation. For long-term policy, China’s health expenditure reflects a complex technology–energy–environment nexus, requiring coordinated policies on AI, green growth, clean energy, urban planning, and air quality improvement.
Keywords:
Artificial intelligence
Green economy
Health
Air quality
China
MQQR
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