Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/63554
Title: An agent-based platform for decision making on the green retrofit of public buildings
Authors: Liang, Xin
Advisors: Shen, Qiping Geoffrey (BRE)
Keywords: Public buildings -- Environmental aspects.
Sustainable architecture.
Issue Date: 2016
Publisher: The Hong Kong Polytechnic University
Abstract: In the last two decades, energy consumption in China has increased rapidly due to fast urbanization and industrialization. In 2010, China replaced the United States as the largest energy consumer, accounting for more than 20% of the total global energy consumption. The building sector was responsible of 40% of total energy consumption and 15% of greenhouse gas (GHG) emission. Within this sector, the energy consumption per square meter in public buildings is 5-15 times of that in residential buildings. In addition, most energy consumption occurs during the actual occupancy operation stage rather than during the construction stage. Therefore, green retrofit, which can improve the energy efficiency of public buildings, plays an important role in energy saving and GHG reduction. The decision making on green retrofit is more complex compared to new green buildings, since green retrofit involves more restrictions (e.g., the structures and locations of buildings) and stakeholders (e.g., tenants and facility managers). Most previous studies investigated the decision making on green retrofit from the technical, economic, and environmental perspectives. Few studies, if not none, have investigated the decision-making behaviors of the key stakeholders and their different interaction relationships under different circumstances. Therefore, the behaviors and strategies of stakeholders in decision making on green retrofit remain unexplored areas. The primary aim of this research is to examine whether an agent-based platform that models decision-making behaviors of stakeholders can support decision making on green retrofit of public buildings. The specific objectives of this research are as follows: (1) To analyze the relationship of stakeholders and their priorities in green retrofit through a two-mode social network analysis. (2) To build a model of decision-making behaviors based on game theory for optimizing decisions of key stakeholders on green retrofit. (3) To develop and validate an agent-based platform on the basis of the proposed model to support decision making on green retrofit. This study reviewed previous studies related to green retrofit and summarized the state-of-the-art research in this area. The stakeholders and their characteristics were identified by literature review and interviews. A two-mode social network was developed to analyze the stakeholder relationship and a game model was built to optimize the decision making of stakeholders on green retrofit. Based on the model, an agent-based platform was developed, which can facilitate decision making on green retrofit. To validate the platform, the policies, launched by the Shanghai and Shenzhen local governments for energy efficiency retrofit of public buildings, were simulated on the developed platform.
The key findings obtained from this study are as follows. First, a two-mode network of stakeholders was developed to analyze the relationship and priority of stakeholders in green retrofit projects. Second, a game theory-based model was built, which can explain the reasons for stakeholder decision-making behaviors in green retrofit. The split incentives between owners and tenants were identified as main barriers of green retrofit projects. The proposed model can optimize the decision making on green retrofit by balancing benefits among key stakeholders. Third, an agent-based platform for supporting decision making on the green retrofit of public buildings was developed on the basis of the proposed model. A sensitivity analysis was conducted and the results showed the platform is robust. The factors influencing decision making on green retrofit were investigated and the results showed that the two factors (i.e., the cost of green retrofit and the energy price) influence the decision making on green retrofit most significantly. This study made original contributions to the decision making on green retrofit from both theoretical and practical perspectives. From the theoretical perspective, first, this study proposed an innovative method to improve the understanding of stakeholder relationship through a two-mode social network analysis. Second, this study first analyzed the behaviors and strategies of the stakeholders in green retrofit through a game theory-based model. From the practical perspective, this study developed an agent-based platform to support decision making on green retrofit, which can facilitate information sharing, simplify the process of decision making and improve the collaboration among stakeholders in green retrofit. This is the first integrated platform designed for both the government and other stakeholders (e.g., owners, tenants and facility managers). For the government, the platform can provide customized policy suggestions for different types of buildings. For other stakeholders, it can provide decision-making support for an individual green retrofit project. In summary, the proposed agent-based platform is an effective tool to assess costs and benefits of green retrofit projects, support decision making and provide policy suggestions, and consequently, it can further promote green retrofit of public buildings and reduce energy consumption and GHG emission.
Description: PolyU Library Call No.: [THS] LG51 .H577P BRE 2016 Liang
xvi, 246 pages :color illustrations
URI: http://hdl.handle.net/10397/63554
Rights: All rights reserved.
Appears in Collections:Thesis

Files in This Item:
File Description SizeFormat 
b29291343_link.htmFor PolyU Users208 BHTMLView/Open
b29291343_ira.pdfFor All Users (Non-printable)3.6 MBAdobe PDFView/Open
Show full item record

Page view(s)

65
Last Week
2
Last month
Checked on Jun 25, 2017

Download(s)

44
Checked on Jun 25, 2017

Google ScholarTM

Check



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.