Off-campus UMass Amherst users: To download campus access dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.

Non-UMass Amherst users: Please talk to your librarian about requesting this dissertation through interlibrary loan.

Dissertations that have an embargo placed on them will not be available to anyone until the embargo expires.

Author ORCID Identifier

N/A

AccessType

Open Access Dissertation

Document Type

dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Environmental Conservation

Year Degree Awarded

2016

Month Degree Awarded

September

First Advisor

Simi T. Hoque

Second Advisor

Benjamin S. Weil

Third Advisor

Robert L. Ryan

Subject Categories

Construction Engineering and Management | Environmental Engineering | Urban, Community and Regional Planning

Abstract

A number of tools are available today for simulating different aspects of urban activity, but these efforts are fragmented and do not effectively reflect the interrelationships between very diverse groups of urban sectors and resource flows. There is a critical need for robust and reliable urban metabolism analysis tools that integrate socio-economic elements of urbanization and physicality of the built environment into evaluating sustainability in cities. This dissertation outlines the development of an Integrated Urban Metabolism Analysis Tool (IUMAT) that dynamically measures the environmental impacts of land cover, transportation, and consumption of energy, water and materials employing a holistic framework. It includes examination of the existing scholarship on urban metabolism as well as description of the calculative framework for IUMAT. The scope of work is establishment of the Residential Energy Model that would serve as a template for the larger Energy, Water and Materials (EWM) Model. The EWM model takes a bottom-up approach to generate spatial resource demand profiles based on building and neighborhood characteristics. Finally, Residential Energy Consumption Survey (RECS) 2009 data is used to explain how the proposed framework makes use of actual data to find determinants of resources’ demand and unravel correlations between environmental consequences and myriad of urban variables. Quantile regression is explored as a robust method for large-scale energy modeling that is a prototype for resource use projection within other urban sectors.

DOI

https://doi.org/10.7275/9058786.0

Download

Share

COinS