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Investigating the status of water pollution in the planting industry and its influencing factors can help promote the intensive utilization of agricultural water resources and the control of water pollution. The grey water footprint of the planting industry (PGWF) in Zhejiang and its influencing factors from 2010 to 2021 were analyzed using the GWF model and the Logarithmic Mean Divisia Index (LMDI) model. The results indicate that: (1) The PGWF in Zhejiang exhibited a downward trend from 2010 to 2021, decreasing from 24.50 × 108 m3 in 2010 to 11.08 × 108 m3 in 2021. (2) From 2010 to 2021, the spatial pattern of PGWF shifted from being ‘higher in the northeastern regions and lower in the southwestern parts’ to ‘higher in the central area and lower around the periphery’. (3) Economic development and technological progress drive up the PGWF in Zhejiang, whereas adjustments in industrial structure, reduced water intensity, and population act as constraints on its expansion. Notably, water intensity exerts the most significant negative influence on the variation of the PGWF, whereas economic development has the strongest positive effect.

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Spatiotemporal Variations and Influencing Factors of Grey Water Footprint of Planting Industry in Zhejiang Province, China

  • 会议论文

  • 发表日期：

    2025年

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  • Yiling Weng,
  • Jiayan Song,
  • Hua Zhu

摘要

Investigating the status of water pollution in the planting industry and its influencing factors can help promote the intensive utilization of agricultural water resources and the control of water pollution. The grey water footprint of the planting industry (PGWF) in Zhejiang and its influencing factors from 2010 to 2021 were analyzed using the GWF model and the Logarithmic Mean Divisia Index (LMDI) model. The results indicate that: (1) The PGWF in Zhejiang exhibited a downward trend from 2010 to 2021, decreasing from 24.50 × 108 m3 in 2010 to 11.08 × 108 m3 in 2021. (2) From 2010 to 2021, the spatial pattern of PGWF shifted from being ‘higher in the northeastern regions and lower in the southwestern parts’ to ‘higher in the central area and lower around the periphery’. (3) Economic development and technological progress drive up the PGWF in Zhejiang, whereas adjustments in industrial structure, reduced water intensity, and population act as constraints on its expansion. Notably, water intensity exerts the most significant negative influence on the variation of the PGWF, whereas economic development has the strongest positive effect.