Advancing Green Stormwater Infrastructure Through Understanding the Influences of Social Factors

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Qi, J. (2023). Advancing Green Stormwater Infrastructure Through Understanding the Influences of Social Factors. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Green stormwater infrastructure (GSI) is a nature-inspired engineering solution to stormwater management that has gained increasing attention over the last two decades. While the technical evidence supporting the efficacy and efficiency of GSI is crucial, it alone does not necessarily translate to a significant increase in GSI adoption. Even with the recent research focus gradually turning toward the social benefits of GSI implementation, the social factors that influence its implementation remain underexplored. Furthermore, successful GSI adoption and implementation requires a collaborative effort in governance transitioning, public engagement, and adequate consideration of demographic constraints. Therefore, it is essential to understand the social barriers that hinder the adoption of GSI. This dissertation draws interdisciplinary linkages between social barriers and the cognitive biases that may affect rational decision-making for GSI adoption.Mecklenburg County, the most population-dense county in North Carolina, is an ideal case study location to represent future scenarios for other urbanized areas across the United States. The case study, including an online survey and interviews with local officials, reveals patterns that resonate with the literature's findings that negative public opinions hinder long-term support for GSI. This study created a simulation model to streamline decision-making processes based on individual behaviors to explore long-term local GSI adoption patterns. The simulation model developed in this study shows that cognitive biases, such as loss aversion and status quo, could impede broader GSI adoption. The contribution of this work is drawing attention from both academia and practitioners in terms of long-term planning for sustainable infrastructure development in residential areas where government incentives are limited. Furthermore, it facilitates improved data collection on residents' opinions of GSI over time, allowing the refinement and validation of the proposed simulation model to improve accuracy. Simultaneously, the survey can serve as a consistent means of public education and engagement, working to bridge the knowledge gap. This dissertation lays the groundwork for identifying potential conflicting decision-making patterns related to eco-friendly behaviors, specifically focusing on small-scale GSI in residential properties. Such insights are crucial for securing resident financial support for stormwater management, thereby alleviating pressure on already stretched federal resources.

Details
Author

Qi, Jingyi

Title

Advancing Green Stormwater Infrastructure Through Understanding the Influences of Social Factors

Physical Description

1 online resource (145 pages) : PDF

Date

2023

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Green stormwater infrastructure (GSI) is a nature-inspired engineering solution to stormwater management that has gained increasing attention over the last two decades. While the technical evidence supporting the efficacy and efficiency of GSI is crucial, it alone does not necessarily translate to a significant increase in GSI adoption. Even with the recent research focus gradually turning toward the social benefits of GSI implementation, the social factors that influence its implementation remain underexplored. Furthermore, successful GSI adoption and implementation requires a collaborative effort in governance transitioning, public engagement, and adequate consideration of demographic constraints. Therefore, it is essential to understand the social barriers that hinder the adoption of GSI. This dissertation draws interdisciplinary linkages between social barriers and the cognitive biases that may affect rational decision-making for GSI adoption.Mecklenburg County, the most population-dense county in North Carolina, is an ideal case study location to represent future scenarios for other urbanized areas across the United States. The case study, including an online survey and interviews with local officials, reveals patterns that resonate with the literature's findings that negative public opinions hinder long-term support for GSI. This study created a simulation model to streamline decision-making processes based on individual behaviors to explore long-term local GSI adoption patterns. The simulation model developed in this study shows that cognitive biases, such as loss aversion and status quo, could impede broader GSI adoption. The contribution of this work is drawing attention from both academia and practitioners in terms of long-term planning for sustainable infrastructure development in residential areas where government incentives are limited. Furthermore, it facilitates improved data collection on residents' opinions of GSI over time, allowing the refinement and validation of the proposed simulation model to improve accuracy. Simultaneously, the survey can serve as a consistent means of public education and engagement, working to bridge the knowledge gap. This dissertation lays the groundwork for identifying potential conflicting decision-making patterns related to eco-friendly behaviors, specifically focusing on small-scale GSI in residential properties. Such insights are crucial for securing resident financial support for stormwater management, thereby alleviating pressure on already stretched federal resources.

Genre

doctoral dissertations

Subjects--Topics

Environmental engineering

Degree

Ph.D.

Keywords

Cognitive Biases
Stormwater Infrastructure
Stormwater Management

Subject Area

Engineering

Advisor(s)

Barclay, Nicole

Committee Members

Diemer, John
Hadzikadic, Mirsad
Malinowski, Patricia
Wu, Jy

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2023.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Qi_uncc_0694D_13406

Permalink

http://hdl.handle.net/20.500.13093/etd:3211