水负担能力分析:关键文献综述

IF 2.1 4区 环境科学与生态学 Q2 ENGINEERING, CIVIL AQUA-Water Infrastructure Ecosystems and Society Pub Date : 2023-07-31 DOI:10.2166/aqua.2023.035
T. Fagundes, R. Marques, T. Malheiros
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引用次数: 0

摘要

过去几年,由于最近的经济危机和关税在资助服务方面的作用越来越大,水和卫生设施的可负担性引起了研究人员的注意。本文对以前关于水和/或卫生设施可负担性的研究进行了系统审查,并对所采用的方法进行了关键的定性讨论。研究框架依赖于一种混合方法,结合了系统的定量分析和叙述分析。应用的文献综述方案返回了期刊和公认的国际组织发表的79项相关研究,并确定了四个关键主题,即负担能力指标、阈值、数据来源和分析的实际应用。本文通过提出和讨论最重要的水负担能力问题,并启发分析在公共财政政策中的潜在作用,从而为文献做出贡献。
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Water affordability analysis: a critical literature review
Water and sanitation affordability has drawn researchers’ attention over the past years due to the recent economic crisis and the growing role of tariffs on funding services. This paper conducted a systematic review of previous research on water and/or sanitation affordability and a critical qualitative discussion on the methodologies adopted. The research framework relied on a hybrid approach that combines systematic quantitative and narrative analyses. The literature review protocol applied returned 79 relevant studies published by journals and recognized international organizations and four key topics, namely affordability indicators, threshold, data source, and practical use of the analysis were identified and critically discussed. This paper contributes to the literature by presenting and discussing the most important water affordability issues and enlightening the potential role of the analysis in public financial policies.
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来源期刊
CiteScore
4.10
自引率
21.10%
发文量
0
审稿时长
20 weeks
期刊最新文献
Biogas production from water lilies, food waste, and sludge: substrate characterization and process performance How suitable is the gold-labelling method for the quantification of nanoplastics in natural water? Corrigendum: AQUA – Water Infrastructure, Ecosystems and Society 72 (7), 1115–1129: Application of system dynamics model for reservoir performance under future climatic scenarios in Gelevard Dam, Iran, Ali Babolhakami, Mohammad Ali Gholami Sefidkouhi and Alireza Emadi, https://dx.doi.org/10.2166/aqua.2023.193 Exploring the rise of AI-based smart water management systems Unraveling air–water two-phase flow patterns in water pipelines based on multiple signals and convolutional neural networks
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