Assessing water circularity in cities: Methodological framework with a case study

With significant efforts made to consider water reuse in cities, a robust and replicable framework is needed to quantify the degree of urban water circularity and its impacts from a systems perspective. A quantitative urban water circularity framework can benchmark the progress and compare the impacts of water circularity policies across cities. In that pursuit, we bring together concepts of resource circularity and material flow analysis (MFA) to develop a demand- and discharge-driven water circularity assessment framework for cities. The framework integrates anthropogenic water flow data based on the water demand in an urban system and treated wastewater discharge for primary water demand substitution. Leveraging the water mass balance, we apply the framework in evaluating the state of water circularity in Singapore from 2015 to 2019. Overall, water circularity has been steadily increasing, with 24.9% of total water demand fulfilled by secondary flows in 2019, potentially reaching 39.6% at maximum water recycling capacity. Finally, we discuss the wider implications of water circularity assessments for energy, the environment, and urban water infrastructure and policy. Overall, this study provides a quantitative tool to assess the scale of water circularity within engineered urban water infrastructure and its application to develop macro-level water systems planning and policy insights

Book Review: Triumph of the City, Edward Glaeser

JTLU vol. 6, no.3, pp. 87-89 (2013)The authors review the book Triumph of the City by Edward Glaeser (Penguin Press, 2011)

Causation versus Prediction: Comparing Causal Discovery and Inference with Artificial Neural Networks in Travel Mode Choice Modeling

This study compares the performance of a causal and a predictive model in modeling travel mode choice in three neighborhoods in Chicago. A causal discovery algorithm and a causal inference technique were used to extract the causal relationships in the mode choice decision making process and to estimate the quantitative causal effects between the variables both directly from observational data. The model results reveal that trip distance and vehicle ownership are the direct causes of mode choice in the three neighborhoods. Artificial neural network models were estimated to predict mode choice. Their accuracy was over 70%, and the SHAP values obtained measure the importance of each variable. We find that both the causal and predictive modeling approaches are useful for the purpose they serve. We also note that the study of mode choice behavior through causal modeling is mostly unexplored, yet it could transform our understanding of the mode choice behavior. Further research is needed to realize the full potential of these techniques in modeling mode choice