Browsing by Author "Lai X"

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    Big Data Analytic Framework for Organizational Leverage
    (MDPI (Basel, Switzerland), 6/03/2021) Mathrani S; Lai X
    Web data have grown exponentially to reach zettabyte scales. Mountains of data come from several online applications, such as e-commerce, social media, web and sensor-based devices, business web sites, and other information types posted by users. Big data analytics (BDA) can help to derive new insights from this huge and fast-growing data source. The core advantage of BDA technology is in its ability to mine these data and provide information on underlying trends. BDA, however, faces innate difficulty in optimizing the process and capabilities that require merging of diverse data assets to generate viable information. This paper explores the BDA process and capabilities in leveraging data via three case studies who are prime users of BDA tools. Findings emphasize four key components of the BDA process framework: system coordination, data sourcing, big data application service, and end users. Further building blocks are data security, privacy, and management that represent services for providing functionality to the four components of the BDA process across information and technology value chains.
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    Carbon footprint accounting of prefabricated buildings: A circular economy perspective
    (Elsevier Ltd, 2024-06-15) Li CZ; Tam VWY; Lai X; Zhou Y; Guo S
    Prefabricated buildings have gained significant attention as a solution for reducing carbon footprint in the construction industry. However, there is a lack of a comprehensive accounting approach tailored to the construction features of prefabricated buildings. To address this issue, this study develops a framework to account the life-cycle carbon footprint of prefabricated buildings within the circular economy principles. The developed framework is applied to a prefabricated building in Chengdu, China, revealing that a significant carbon footprint is attributed to the material production and transportation stages. Moreover, an uncertainty analysis is conducted based on Data Quality Indicator evaluation method and Monte Carlo Simulation to validate the accounting framework, and a minor deviation of 0.085 % between the uncertain and calculated values validates the proposed accounting framework. Furthermore, key processes to mitigate the carbon footprint are identified. The results show that cast-in-place concrete is a key process at the construction materials level. Building upon these findings, this study offers recommendations on accurately and efficiently accounting the carbon footprint of prefabricated buildings. The research findings contribute to fostering the accounting of the life-cycle carbon footprint for prefabricated buildings, establishing a theoretical and empirical basis for circular economy implementation in prefabricated buildings, and offering insights for making informed carbon emission reduction decisions in prefabricated buildings.