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How Does New Quality Productive Forces Affect Green Total Factor Energy Efficiency in China? Consider the Threshold Effect of Artificial Intelligence

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  • Boyu Yuan

    (Institute of Geographical Sciences, Hebei Academy of Sciences (Hebei Engineering Research Center for Geographic Information Application), Shijiazhuang 050011, China
    College of Business & Economics, Australian National University, Canberra, ACT 2601, Australia)

  • Runde Gu

    (Institute of Geographical Sciences, Hebei Academy of Sciences (Hebei Engineering Research Center for Geographic Information Application), Shijiazhuang 050011, China
    School of Management Science and Information Engineering, Hebei University of Economics and Business, Shijiazhuang 050061, China)

  • Peng Wang

    (Institute of Geographical Sciences, Hebei Academy of Sciences (Hebei Engineering Research Center for Geographic Information Application), Shijiazhuang 050011, China)

  • Yuwei Hu

    (School of Management Science and Information Engineering, Hebei University of Economics and Business, Shijiazhuang 050061, China)

Abstract

China’s economy is shifting from an era of rapid expansion to one focused on high-quality development, making it imperative to tackle environmental degradation linked to energy use. Understanding how New Quality Productive Forces (NQPF) interact with energy efficiency, along with the mechanisms driving this relationship, is essential for economic transformation and long-term sustainability. This study establishes an evaluation framework for NQPF, integrating technological, green, and digital dimensions. We apply fixed-effects models, the spatial Durbin model (SDM), a moderation model, and a threshold model to analyze the influence of NQPF on Green Total Factor Energy Efficiency (GTFEE) and its spatial implications. This underscores the necessity of distinguishing it from traditional productivity frameworks and adopting a new analytical perspective. Furthermore, by considering dimensions such as input, application, innovation capability, and market efficiency, we reveal the moderating role and heterogeneous effects of artificial intelligence (AI). The findings are as follows: The development of NQPF significantly enhances GTFEE, and the conclusion remains robust after tail reduction and endogeneity tests. NQPF has a positive spatial spillover effect on GTFEE; that is, while improving the local GTFEE, it also improves neighboring regions GTFEE. The advancement of AI significantly strengthens the positive impact of NQPF on GTFEE. AI exhibits a significant U-shaped threshold effect: as AI levels increase, its moderating effect transitions from suppression to facilitation, with marginal benefits gradually increasing over time.

Suggested Citation

  • Boyu Yuan & Runde Gu & Peng Wang & Yuwei Hu, 2025. "How Does New Quality Productive Forces Affect Green Total Factor Energy Efficiency in China? Consider the Threshold Effect of Artificial Intelligence," Sustainability, MDPI, vol. 17(15), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:15:p:7012-:d:1715953
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