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.

Assessment of water resources carrying capacity (WRCC) is of great significance for understanding the status of regional water resources, promoting the coordinated development of water resources with environmental, social and economic development, and promoting sustainable development. This study focuses on the Longdong Loess Plateau region and utilized panel data spanning from 2010 to 2020, established a three-dimensional evaluation index system encompassing water resources, economic, and ecological dimensions, uses the entropy-weighted TOPSIS model coupled with global spatial autocorrelation analysis (Global Moran’s I) and the hot spot analysis (Getis-Ord Gi* index) method to comprehensively evaluate the spatial distribution of the WRCC in the study region. It can provide scientific basis and theoretical support for decision-making on sustainable development strategies in the Longdong Loess Plateau region and other regions of the world.From 2010 to 2020, the overall WRCC of the Longdong Loess Plateau area show some fluctuations but maintained overall growth. The WRCC in each county and district predominantly fell within level III (normal) and level IV (good). The spatial distribution of the WRCC in each county and district is featured by clustering pattern, with neighboring counties displaying similar values, resulting in a spatial distribution pattern characterized by high carrying capacity in the south and low carrying capacity in the north. Based on these findings, our study puts forth several recommendations for enhancing the WRCC in the Longdong Loess Plateau area.

The territorial planning approach to allocating productive forces is based on the fact that territories have competitive advantages in producing specific products. However, in agriculture, the advantages principle cannot be used to shape the allocation patterns, due to a variety of intervening factors, such as the climatic and environmental conditions for agricultural production and the quality of land and availability of water. In the case of Russia, one of the most diverse countries in terms of the territorial disparities in agricultural production, this study examines the location and development patterns of the agricultural sector. The study identifies the competitive advantages of territories by comparing localization of agricultural production, production costs, performance, and profitability of agricultural producers, as well as prices of agricultural products in 78 different administrative regions in Russia. The study reveals which regions have more advantageous conditions for over-concentrating energy capacities, labor resources, fixed capital, and investments. However, at a certain point, over-concentrated production forces can lead to a deterioration in the performance of farmers due to an increase in capital intensity. Therefore, countries with significant regional differences in agricultural production should adjust their spatial development patterns according to the parameters of territories’ comparative advantages.

This article emphasizes the critical role of the subsidiarity principle in facilitating adaptation to climate change. Employing a comparative legal analysis approach, the paper examines how this principle, traditionally pivotal in distributing powers within the European Union, could be adapted globally to manage climate change displacement. Specifically, it explores whether subsidiarity can surmount the challenges posed by national sovereignty and states’ reluctance to cede control over domestic matters. Findings indicate that while domestic efforts and local adaptations should be prioritized, international intervention becomes imperative when national capacities are overwhelmed. This article proposes that ‘causing countries’ and the global community bear a collective responsibility to act. The Asia-Pacific region, characterized by diverse and vulnerable ecosystems like small islands, coastal areas, and mountainous regions, serves as the focal point for this study. The research underscores the necessity of developing policies and further research to robustly implement the subsidiarity principle in protecting climate-displaced populations.