4.6 Article

Informing Sustainable Consumption in Urban Districts: A Method for Transforming Household Expenditures into Physical Quantities

期刊

SUSTAINABILITY
卷 12, 期 3, 页码 -

出版社

MDPI
DOI: 10.3390/su12030802

关键词

urban metabolism; district material flow analysis; sustainable consumption; household consumption; consumption-based impact; environmental impact; sharing economy

资金

  1. Swedish Research Council Formas [2015-11271-29632-31]
  2. Swedish Energy Department [P37684-1]
  3. Mistra Urban Futures Platform

向作者/读者索取更多资源

Interventions targeted at district-level are a potentially effective way to reduce consumption-based urban impacts; however, a systematic method for accounting these impacts at district scale has not yet been developed. This article outlines a method for transforming household expenditure data into consumption quantified on a physical basis. Data sources are combined to calculate monetary value per unit mass for different products consumed by households. Socio-economic household archetypes are selected, and typical consumption for these archetypes is calculated by combining expenditure data from a household budget survey with the calculated monetary values per unit mass. The resulting physical quantities of different products consumed are envisaged as an essential part of performing district scale material flow analysis and urban metabolism studies, also as an input for assessing consumption-based environmental impacts and for designing sustainable consumption policies. The method was applied to characterise consumption in urban districts. The obtained results were used to assess of districts' consumption-based impacts with life cycle assessment (LCA) and to inform design of sharing economy. The method was found to be an effective way to evaluate the demand for products in different districts; this in turn could inform objective measures to aid more sustainable urban consumption.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据