This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.

This paper examines the relationship between renewable energy (RE) generation, economic factors, infrastructure, and governance quality in ASEAN countries. Based on the Fixed Effects regression model on panel data spanning the years 2002–2021, results demonstrate that domestic capital investment, foreign direct investment, governance effectiveness, and crude oil price exhibit an inverse yet significant relationship with RE generation. An increase in those factors will lead to a decline in RE generation. Meanwhile, economic growth and infrastructure have a positive relationship, which implies that these factors act as stimulants for RE generation in the region. Hence, it is advisable to prioritise policies that foster economic growth, including offering tax breaks specifically for RE projects. Additionally, it’s crucial to streamline governance processes to facilitate infrastructure conducive to RE generation, along with investing in RE infrastructure. This could be achieved by establishing one-stop centres for consolidating permitting processes, which would streamline the often-bureaucratic process. However, given the extensive time period covered, future research should examine the short-term relationship between the variables to address any potential temporal trends between the factors and RE generation.