山东沿海城市灰水足迹变化特征及其影响因素

Spatio-temporal evolution of grey water footprint and its influencing factors in Shandong’s coastal cities

  • 摘要: 为探究山东沿海地区2002—2021年灰水足迹时空变化特征及其影响机制,本文以青岛、潍坊等7个沿海城市为研究区域,核算并分析其灰水足迹时序变化与空间分异格局;采用随机森林模型,通过相对重要性与偏依赖分析识别关键影响因素;运用结构方程模型,定量解析各因素对灰水足迹的直接效应与间接传导路径.结果表明,区域灰水足迹以年均3.4%速度减小,呈“先升后稳再降”三阶段特征,农业灰水足迹平均贡献率最大,为首要来源;在空间上,灰水足迹与农业灰水足迹呈“中部高、四周低”格局,生活与工业灰水足迹分布分别与人口规模、产业结构密切相关.单位耕地面积化肥施用量、单位面积水产养殖产量、单位畜禽当量污染物排放量、工业废水排放强度为区域灰水足迹的核心影响因素,偏依赖关系揭示了各因素对灰水足迹的边际作用规律,结构方程模型进一步印证了农业面源污染是灰水足迹增加的主要影响因素,农业污染管控和工业污染治理是实现山东沿海地区灰水足迹减排的主要驱动力.

     

    Abstract: To investigate the spatiotemporal variation characteristics and influencing mechanism of grey water footprint ( Q ) in coastal areas of Shandong province from 2002 to 2021, this paper takes seven coastal cities as the study area, calculates and analyzes the temporal change and spatial differentiation pattern of Q . The random forest model is adopted to identify the key influencing factors through relative importance analysis and partial dependence analysis. Furthermore, the structural equation model is used to quantitatively analyze the direct and indirect transmission paths among different factors. The results indicate that the regional Q decreases at an average annual rate of 3.4%, showing a three-stage characteristic of "initial increase, subsequent stability, and final decrease". Q_\textagr contributes the largest proportion on average, which is the primary source of the total Q. In terms of spatial distribution, both the total Q and Q_\textagr present a pattern of "high in the central region and low in the surrounding areas". Meanwhile, the distribution of Q_\textdom and Q_\textind is highly correlated with population size and industrial structure, respectively. Fertilizer application rate per unit cultivated land (Un-irr), aquaculture yield per unit area (Yaqp), pollutant emission per standard livestock equivalent (Upol−liv), and industrial wastewater discharge intensity (Uind) were the core influencing factors of the regional Q. The partial dependence relationship revealed the marginal effect of each factor on the Q. The structural equation model further confirmed that agricultural non-point source pollution was the main driving factor for the increase of Q, while agricultural pollution control and industrial pollution control were the main driving forces for the reduction of Q.

     

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