Latent Dirichlet Allocation in Public Procurement Documents Analysis for Determining Energy Efficiency Issues in Construction Works at Polish Universities

Anna Pamula (), Zbigniew Gontar, Beata Gontar and Tetiana Fesenko
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Anna Pamula: Department of Computer Science, Faculty of Management, University of Lodz, 90-136 Łódź, Poland
Beata Gontar: Department of Computer Science, Faculty of Management, University of Lodz, 90-136 Łódź, Poland
Tetiana Fesenko: Department of Electronic Computers, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine

Energies, 2023, vol. 16, issue 12, 1-23

Abstract: This paper presents a comprehensive analysis of public procurement documents in the domain of university buildings taken from the e-procurement platform, particularly focusing on their transformation towards more efficient energy consumption. Using a corpus of the titles of the public procurement documents from 2020 to 2022, we used Latent Dirichlet Allocation (LDA) for topic modeling to understand the key thematic areas of focus. The methodology presented in this study incorporated a bifurcated approach. This two-stage procedure began with preprocessing and dictionary creation from the corpus of titles of procurement documents. Following this, the Latent Dirichlet Allocation (LDA) model was employed for topic extraction and trend analysis, thereby providing a comprehensive understanding of the thematic progression in procurement practices over time. Our analysis revealed a shift in emphasis from modernization towards the adoption of energy-saving technologies as well as a growing focus on broader sustainability initiatives. However, a less prevalent topic was adherence to cooling & heating systems, suggesting potential areas for improvement in procurement practices. These findings contribute to the growing body of knowledge on sustainable procurement in university buildings and offer valuable insight for universities to enhance their energy efficiency strategies.

Keywords: public procurement; energy efficiency; topic modeling; Latent Dirichlet Allocation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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