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<p>Industrialised river basins worldwide face a shared sustainability challenge: sustaining economic growth while easing pressures on water resources, energy systems and the ecological environment. Taking the Yellow River Basin in China as a representative water-constrained and energy-intensive industrial region, this study examines whether artificial intelligence (AI) technology innovation enhances coupling coordination in the water–energy–environment–economy (WEEE) system. Using panel data for 55 cities from 2013 to 2024, this study applies fixed-effects models, mediation analysis, spatial econometric models and threshold models. The results show that AI technology innovation significantly enhances local WEEE coupling coordination, with resource-allocation efficiency acting as an important transmission channel. Spatial-effect decomposition reveals a positive local effect but a negative spillover effect, suggesting that AI-related talent, investment, data and digital infrastructure may concentrate in cities with stronger innovation capacity and generate competitive pressure on neighbouring areas. Regional analysis shows that the local benefits and spillover risks of AI innovation vary across different parts of the basin. Threshold tests indicate that R&amp;D human capital is a key condition for translating AI innovation into sustainability gains: the positive effect of AI innovation becomes significant after the first threshold and strengthens further after the second. This study reveals a previously overlooked interdisciplinary link among AI innovation, resource allocation, spatial competition and WEEE coupling coordination. By quantifying the local effect, spatial spillover and threshold boundary of AI technology innovation, this study shows that AI innovation can support environmental governance and industrial sustainability transitions by enhancing WEEE coordination through resource-allocation efficiency as a transmission channel. These findings provide empirical evidence for industrialised regions seeking to use AI innovation to support sustainable transitions, while highlighting future challenges related to data sharing, skilled labour supply, regional inequality and cross-regional governance.</p>

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Artificial intelligence technology innovation enhances water–energy–environment–economy coupling coordination: evidence from baseline regression, spatial spillovers and threshold effects

  • 期刊论文

  • 发表日期：

    2026年7月16日

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  • xiaobao Zheng,
  • guibin Suo

摘要

Industrialised river basins worldwide face a shared sustainability challenge: sustaining economic growth while easing pressures on water resources, energy systems and the ecological environment. Taking the Yellow River Basin in China as a representative water-constrained and energy-intensive industrial region, this study examines whether artificial intelligence (AI) technology innovation enhances coupling coordination in the water–energy–environment–economy (WEEE) system. Using panel data for 55 cities from 2013 to 2024, this study applies fixed-effects models, mediation analysis, spatial econometric models and threshold models. The results show that AI technology innovation significantly enhances local WEEE coupling coordination, with resource-allocation efficiency acting as an important transmission channel. Spatial-effect decomposition reveals a positive local effect but a negative spillover effect, suggesting that AI-related talent, investment, data and digital infrastructure may concentrate in cities with stronger innovation capacity and generate competitive pressure on neighbouring areas. Regional analysis shows that the local benefits and spillover risks of AI innovation vary across different parts of the basin. Threshold tests indicate that R&D human capital is a key condition for translating AI innovation into sustainability gains: the positive effect of AI innovation becomes significant after the first threshold and strengthens further after the second. This study reveals a previously overlooked interdisciplinary link among AI innovation, resource allocation, spatial competition and WEEE coupling coordination. By quantifying the local effect, spatial spillover and threshold boundary of AI technology innovation, this study shows that AI innovation can support environmental governance and industrial sustainability transitions by enhancing WEEE coordination through resource-allocation efficiency as a transmission channel. These findings provide empirical evidence for industrialised regions seeking to use AI innovation to support sustainable transitions, while highlighting future challenges related to data sharing, skilled labour supply, regional inequality and cross-regional governance.