## 7 Trend Analysis  
To understand the evolution of topics over time, we conducted a trend analysis for the past three years. This was achieved by segmenting our dataset into three subsets corresponding to each year and applying the same LDA model to each subset. Figure 6 presents the dynamics of the classified documents according to the cluster in the considered period. The clusters with the highest number of documents are highlighted in green.  
Figure 6: Classified documents according to clusters.  
2020: In the 2020 procurement documents, the dominant topics were \"Sanitary Facilities\" (Topic 11) and \"Cooling & Heating Systems\" (Topic 12). It seemed that during this year, the primary focus was on the modernization of sanitary facilities and transforming cooling and heating systems into more energy-efficient ones.  
2021: The subsequent year, 2021, saw a significant increase in the prominence of \"Reducing Energy Consumption\" (Topic 2) and \"Thermal Insulation Works\". This indicated a shift in focus towards procuring and implementing advanced energy-saving technologies.  
2022: The most recent year, 2022, showed a balanced emphasis on both \"Reducing Energy Consumption\" (Topic 2) and \"Sanitary Facilities\" (Topic 11). This suggested a strong focus on integrating energy-efficient technologies and considering the modernization of sanitary facilities of university buildings.  
Overall, the trend analysis revealed an evolving focus on energy efficiency in university building procurement practices. Starting from an emphasis on energy-efficient facilities installation, there was a noticeable shift towards adopting advanced energy-saving technologies and broader sustainability initiatives. This trend demonstrated the commitments of universities to continuous improvement in energy efficiency and sustainability.  
Figure 7 presents the dynamics of the classified documents according to the region in the considered period. The regions with the highest number of documents are highlighted in blue. The dynamic nature of the graph suggested that the distribution of documents across regions changed over time, potentially indicating variations in energy efficiency practices, policy focus, or regional priorities. The scope of the study included data from a three-year period. Although this is a short period of time, it allowed us to draw a general trend, which for most regions in the last year of the study showed an increasing interest in energy efficiency.  
Figure 8 presents a detailed frequency analysis of topics (clusters) according to region. The detailed frequency analysis in Figure 8 shed light on the regional variations in the prominence of energy efficiency topics within university buildings. It allowed for a comparative assessment of the adoption, emphasis, or challenges associated with these topics across different geographical contexts. Differentiation between regions was identified in the sense of clusters. The majority of the universities in the regions made investments related to clusters 2, 10, 11, and 12. The smallest numbers of orders were observed for clusters 6, 7, and 8. The study did not include causal analysis, but it can be assumed that this differentiation may be due to infrastructure and the avail  
Figure 7: Classified documents according to region.