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Determinants of single family residential water use across scales in four western US cities

机译:美国西部四个城市不同规模的单户住宅用水的决定因素

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A growing body of literature examines urban water sustainability with increasing evidence that locally-based physical and social spatial interactions contribute to water use. These studies however are based on single-city analysis and often fail to consider whether these interactions occur more generally. We examine a multi-city comparison using a common set of spatially-explicit water, socioeconomic, and biophysical data. We investigate the relative importance of variables for explaining the variations of single family residential (SFR) water uses at Census Block Group (CBG) and Census Tract (CT) scales in four representative western US cities - Austin, Phoenix, Portland, and Salt lake City, - which cover a wide range of climate and development density. We used both ordinary least squares regression and spatial error regression models to identify the influence of spatial dependence on water use patterns. Our results show that older downtown areas show lower water use than newer suburban areas in all four cities. Tax assessed value and building age are the main determinants of SFR water use across the four cities regardless of the scale. Impervious surface area becomes an important variable for summer water use in all cities, and it is important in all seasons for arid environments such as Phoenix. CT level analysis shows better model predictability than CBG analysis. In all cities, seasons, and spatial scales, spatial error regression models better explain the variations of SFR water use. Such a spatially-varying relationship of urban water consumption provides additional evidence for the need to integrate urban land use planning and municipal water planning.
机译:越来越多的文献研究了城市水的可持续性,越来越多的证据表明,基于本地的物理和社会空间相互作用会促进用水。但是,这些研究是基于单一城市的分析,因此常常无法考虑这些相互作用是否更为普遍。我们使用一组常见的空间明确的水,社会经济和生物物理数据来检验多城市比较。我们调查变量的相对重要性,以解释美国西部四个有代表性的城市(奥斯汀,凤凰城,波特兰和盐湖)的人口普查区块组(CBG)和人口普查区域(CT)规模的单户住宅用水量(SFR)变化城市-涵盖广泛的气候和发展密度。我们使用普通最小二乘回归和空间误差回归模型来确定空间依赖性对用水模式的影响。我们的结果表明,在所有四个城市中,较旧的市中心地区的用水量均低于较新的郊区地区。无论规模如何,税收评估值和建筑年龄是四个城市SFR用水的主要决定因素。不可渗透的表面积成为所有城市夏季用水的重要变量,对于诸如凤凰城这样的干旱环境在所有季节都非常重要。 CT水平分析显示出比CBG分析更好的模型可预测性。在所有城市,季节和空间规模中,空间误差回归模型都能更好地解释SFR用水的变化。城市用水量的这种空间变化关系为将城市土地利用规划和市政用水规划进行整合提供了额外的证据。

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