Nested Phoenix: a bottom-up Python model for the life cycle environmental performance of urban built stocks

Abstract

Buildings and infrastructure assets in cities represent the dominant majority of the anthropogenic material stock and with the expected population growth this is set to double by 2100. It is therefore critical to quantify the life cycle environmental performance of built stocks, existing and forthcoming, to better manage them, modify their designs and mitigate climate change and resource depletion. Yet existing models fail to provide the required spatial and temporal resolution, are not comprehensive enough and often do not capture shifts in environmental effects. This paper presents Nested Phoenix, a bottom-up Python model that addresses these gaps and provides one of the most sophisticated models for built stocks to date. We present the scope of the model, its functionalities and development solutions before describing the different Python packages used, the overall approach and the database and model architecture. Nested Phoenix enables quantifying material stocks and flows and life cycle embodied, operational and transport environmental flows, alongside carbon sequestration in green infrastructure and biogenic carbon. This is coupled with a dynamic modelling approach that enables the investigation of myriad scenarios over time. This capacity, coupled with spatialization using geographic information systems, represents the breadth of Nested Phoenix.