AUM2020 Global Workshop: Session 10: The economics of cities
Duration: 2 hours
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1. Development of an Integrated Land Use and Transportation Model for Planning Deep Decarbonization in the Greater Toronto Metropolitan Region, Canada
Mr Saeed Shakib, Mr Jason Hawkins, Dr Mark Purdon, and Prof. Khandker Nurul Habib (University of Toronto) 2. Simulating urban negotiations using agent-based modelling: the case of Section 106 negotiations in UK Ms Aya Badawy, Dr Nuno Pinto, and Prof. Richard Kingston (University of Manchester) 3. Parameter estimation for an integrated choice model of daily activity scheduling Dr Janody Pougala, Dr Tim Hillel, Dr Rico Krueger, and Prof. Michel Bierlaire (EPFL) 4. Spatial Implications of Telecommuting Dr Andrii Parkhomenko (University of Southern California) and Dr Matthew Deventhal (Claremont McKenna College) Discussant: Dr Jamil Nur (University of Cambridge) Host: Dr Li Wan (University of Cambridge) |
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Created: | 2021-02-08 21:32 |
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Collection: | Martin Centre AUM2020: Modelling the New Urban World |
Publisher: | University of Cambridge |
Copyright: | The Martin Centre |
Language: | eng (English) |
Keywords: | AUM2020; Modelling the New Urban World; Online Global Workshop; Architecture; Martin Centre; |
Abstract: | 1. This paper presents on the development of a land use and transportation integrated (LUTI) model for the Greater
Toronto Metropolitan Region (also known as the Greater Golden Horseshoe: GGH) and its application to evaluating policy scenarios for low-carbon transportation. Road transportation and residential land use account for 29.3% and 13.2% of the GHG area emissions produced in Ontario . The paper focuses on objectives of model development, demonstration of the implications for the GGH region of adopting regional climate plan targets, and the challenges in providing the data requirements for developing a large-scale LUTI model based on the TRANUS modeling platform. 2. Section 106 negotiations have been a means to secure affordable housing in UK. In August 2020, the Government proposed the abolition of this mechanism due to its uncertainty, inconsistency and delays, and there has been a heated debate on the issue since then. The research presents two agent-based models that simulate S106 negotiations with different levels of complexity. The aim is to understand the process of negotiation, hence contribute to the debate. 3. This talk presents the estimation of a choice model of daily activity schedules, which integrates the discrete dimensions of the activity scheduling process (activity participation, activity scheduling, location choice, mode choice) in a single random-utility framework. We use data from the 2015 Swiss Mobility and Transport Microcensus (MTMC) to show how the approach can be used to estimate model parameters and draw likely schedules for different individuals from a continuous distribution. 4. If the 2020 surge in working from home became permanent, how would the distribution of jobs and residents within and across U.S. cities change? To study this question, we build a quantitative spatial equilibrium model of job and residence choice with commuting frictions between 4,502 sub-metropolitan locations in the contiguous U.S. A novel feature of our model is the heterogeneity of workers in the fraction of time they work on-site: some workers commute daily, some always work at home, while others alternate between working on-site and remotely. In a counterfactual where remote work becomes more common, residents move from central to peripheral areas within cities, and from large coastal to small interior cities, on average. The reallocation of jobs is less monotonic, with increases both in peripheral locations and in the highest-productivity metropolises. Agglomeration externalities from in-person interactions are crucial for welfare effects. If telecommuters keep contributing to productivity as if they worked on-site, better job market access drives considerable welfare gains, even for those who continue to commute. But if productivity declines in response to the reduction in face-to-face interactions, wages fall and most workers are worse off. |
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