Ph.D. Thesis Defense by Zhongming Lu

Event Details
  • Date/Time:
    • Wednesday January 7, 2015
      1:00 pm - 3:00 pm
  • Location: BBISS Conference Room C, 828 W. Peachtree St. NW
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Summaries

Summary Sentence: MANAGING THE COMPLEXITY OF SUSTAINABE CITIES: THE INTERDEPENDENCE BETWEEN INFRASTRUCTURE SYSTEM AND SOCIOECONOMIC ENVIRONMENT

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School of Civil and Environmental Engineering

 Ph.D. Thesis Defense Announcement

MANAGING THE COMPLEXITY OF SUSTAINABE CITIES: THE INTERDEPENDENCE BETWEEN INFRASTRUCTURE SYSTEM AND SOCIOECONOMIC ENVIRONMENT

 

by:

Zhongming Lu

 

Advisor: 

Dr. John Crittenden(CEE)

 

Committee Members: 

Dr. Yongsheng Chen (CEE), Dr. Ellen Dunham-Jones (COA), Dr. Richard Fujimoto (CSE), & Dr. Frank Southworth (CEE)

 Date & Time:

Wednesday, January 7, 2:00 PM

 Location: 

BBISS Conference Room C, 828 W. Peachtree St. NW

ABSTRACT

As a critical component of the city, urban infrastructures emerge through the interactions with socioeconomic environment. Managing the complexity behind
the interactions can make the city more sustainable. Complexity involves the two aspects: 1) understanding social preference and adoption of green
infrastructure designs (e.g., low-impact development (LID) to control storm water, transit-oriented development (TOD) to reduce car dependence and
incentivize denser land use); 2) developing an urban model that accounts for the complexity of the urban system to predict the emergent property of the city
(e.g., land use, water consumption, tax revenues and carbon emissions). These two aspects constitute the research content of this dissertation.
This dissertation consists of four sections. In the first section, I developed an agent-based model (ABM) to predict the land use pattern. The ABM is an
approach suited to simulating and understanding the dynamics of the complex system. To reduce the complexity and uncertainty of the ABM, the model
simulated the decisions and interaction of agents (i.e., home buyer, the developer and the local government) at the neighborhood scale. The output of the
ABM serves as the baseline scenario of land use pattern for evaluating the effect of tax investment and fees on the adoption of green infrastructure designs
and more compact land use pattern. Second, with the help of the ABM, I evaluated and compared the policies (i.e., impact fees, subsidy) on the adoption of
green infrastructure designs and more compact land use pattern. I developed a more sustainable development (MSD) scenario that introduces an impact fee
that developers must pay if they choose not to use LID (i.e., rainwater harvesting, porous pavement) to build houses or apartment homes. Model simulations
show homeowners selecting apartment homes 60% of the time after 30 years of development in MSD. In contrast, only 35% homeowners selected apartment
homes after 30 years of development in business as usual (BAU) scenario where there is no impact fee for LID. The increased adoption of apartment homes
results from the lower cost of using LID (i.e., rain garden, native vegetations and porous pavement) in public spaces and improved quality of life for
apartment homes relative to single-family homes. The MSD scenario generates more tax revenues and water savings than does BAU. Third, as an initial
effort to calibrate the home buyer's preference for community design in the ABM, I developed an analytic modeling based on the existing community
preference survey. The data available for this effort is from National Association of Realtors' 2011 community preference survey. I applied a latent class
choice model and discovered four classes of individuals that reveal distinctive behaviors when choosing smart growth neighborhoods, based on the interplay
between aspects of community design, socioeconomic characteristics, and personal attitudes. Linking the results of the latent class choice to an agent-based
market diffusion model enables planners to evaluate the effectiveness of a proposed smart growth neighborhood design in inducing less sprawling
development. In the fourth section, I developed a survey that focuses on preferences of metro Atlanta residents for LID and TOD. With the responses
collected on Mechanical Turk, I developed a latent-class residential community choice model of four distinctive classes that reveal heterogeneous
preferences for community designs. Spatial distribution of the four classes was mapped out to visualize the locations of the demand for different community
designs in the metro Atlanta. The analysis of the impact of increase in housing price on the adoption of LID and TOD shows a low risk of investing LID and
TOD in the metro Atlanta. Residents are willing to adopt the community with LID and TOD as compared to the corresponding one without LID and TOD.
Further, I demonstrated an integrated framework of managing the complexity of urban sustainability which feeds the bottom-level calibrated decision making
simulation into an agent-based model to predict the emergent land use pattern with the intervention of policies. Results show that more compact development
can be achieved with a proper design of LID requirement on low-density communities. Lastly, a simple environmental impact assessment on land use
patterns provides a rough estimation of 28% carbon emission reduction from more compact development.

 

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Keywords
graduate students, Phd Defense
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  • Created By: Danielle Ramirez
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  • Created On: Dec 31, 2014 - 8:31am
  • Last Updated: Oct 7, 2016 - 10:10pm