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Urban water infrastructure (UWI) in cities faces enormous pressure to cope with increased water demands, handle extreme events and improve the service with minimum resource consumption and environmental impacts. The current study presents an approach for addressing the challenges in UWI, specifically in water supply and sewerage. The article argues a need for a paradigm shift that simultaneously includes the sustainability and resilience aspects throughout the life cycle of UWI. The article further highlights the issues in the prevailing approach of centralized infrastructure and demonstrates the necessity of moving away from such an approach and shifting towards decentralized infrastructure. Understanding the factors accelerating decentralization to attain a paradigm shift to decentralization is necessary. Hence, the study first identifies the drivers of decentralization. Secondly, the need for an appropriate scale to be considered while implementing decentralized UWI is highlighted in this study. Furthermore, the effect of the scale of infrastructure is discussed through the trade-offs between life-cycle costs, ease of governance, resilience and recycling benefits. The approach of scaled decentralization outlined in the study will be useful for developing countries to plan new infrastructure and also for developed countries to replace the ageing UWI to create future sustainable and resilient urban systems [ABSTRACT FROM AUTHOR]

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method determines the best solution from a set of alternatives with certain attributes. The best alternative is chosen based on its Euclidean distance from the ideal solution. TOPSIS is widely used in various multi-attribute decision making problems such as supply chain logistics, marketing management, environmental management or chemical engineering. Despite the extensive use of this method, there is no free and open-source software (FOSS) for TOPSIS with comprehensive post-analysis extensions. Therefore, this paper describes a Python-3 based tool PyTOPS for TOPSIS with the Berkeley Software Distribution (BSD)-3-Clause license.

In the construction sector, LCAs typically apply an approach based on fixed or partially fixed building lifespans/service lives/reference study period. The temporal scopes applied in building LCAs are hence typically not reflecting that the timeframes buildings can provide the service they are intended to provide, are (highly) dependent on numerous factors e.g.: building location, materials used to construct the building, energy supply and the use of the building. Inaccurate estimation of the temporal scope of a building LCA will lead to incorrect quantification of the environmental impacts of buildings. Incorrect quantification of the environmental performance of buildings may, in the worst case, derange/decelerate the development within the building sector towards more sustainable buildings. In this paper, a data set consisting of 20999 Danish buildings, demolished between 2009 and 2015, is analyzed. A multiple linear regression model is derived and used to quantify the temporal scope (often referred to as the reference study period) of building LCAs in an attempt to improve the accuracy of sustainability assessment of buildings, taking several influencing factors into account. The results obtained from the derived model are subsequently compared with several fixed/partially fixed building lifespan/service life/reference study period quantification approaches The regression model proved to estimate the lifespan with lower errors (compared to observed values) than the prevailing approach relying on a single fixed value for all building locations, uses and building materials. The application of model based site, use, and/or material specific etc. temporal scope quantification in LCA is new and provides a mean to reduce the uncertainty of LCA results; however, the approach needs to be formalized.