This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

In most studies on hydroclimatic variability and trend, the notion of change point detection analysis of time series data has not been considered. Understanding the system is crucial for managing water resources sustainably in the future since it denotes a change in the status quo. If this happened, it is difficult to distinguish the time series data’s rising or falling tendencies in various areas when we look at the trend analysis alone. This study’s primary goal was to describe, quantify, and confirm the homogeneity and change point detection of hydroclimatic variables, including mean annual, seasonal, and monthly rainfall, air temperature, and streamflow. The method was employed using the four-homogeneity test, i.e., Pettitt’s test, Buishand’s test, standard normal homogeneity test, and von Neumann ratio test at 5% significance level. In order to choose the homogenous stations, the test outputs were divided into three categories: “useful”, “doubtful”, and “suspect”. The results showed that most of the stations for annual rainfall and air temperature were homogenous. It is found that 68.8% and 56.2% of the air temperature and rainfall stations respectively, were classified as useful. Whereas, the streamflow stations were classified 100% as useful. Overall, the change point detection analyses timings were found at monthly, seasonal, and annual time scales. In the rainfall time series, no annual change points were detected. In the air temperature time series except at Edagahamus station, all stations experienced an increasing change point while the streamflow time series experienced a decreasing change point except at Agulai and Genfel hydro stations. While alterations in streamflow time series without a noticeable change in rainfall time series recommend the change is caused by variables besides rainfall. Most probably the observed abrupt alterations in streamflow could result from alterations in catchment characteristics like the subbasin’s land use and cover. These research findings offered important details on the homogeneity and change point detection of the research area’s air temperature, rainfall, and streamflow necessary for the planers, decision-makers, hydrologists, and engineers for a better water allocation strategy, impact assessment and trend analyses.

While infrastructure provides necessary public services and is vital for the socio-economic development of a nation, public funds alone cannot finance all infrastructure needs in society, especially after the COVID-19 pandemic, where many countries are facing budget deficits. Although private financing schemes, such as public-private partnerships (PPPs) and land value capture, have been considered intensively, they have yet to produce adequate private capital flows to infrastructure projects due to a lack of incentives for private investors. Against the background, this paper proposes a new financing mechanism in which governments might divert some of the increased tax revenue from the spillover effects of newly constructed infrastructures to fund the private sector through grants or subsidies. The empirical work in Vietnam shows a significant increase in tax revenues after completing two expressways, supporting our idea about spillover effects, which includes small- and medium-sized enterprise (SME) development. This study’s results suggest that spillover effects can bring new opportunities for governments and multilateral development banks (MDBs) to implement infrastructure projects with greater private sector involvement in the region. It also proposes some financial schemes, such as land capture and financing for business startups, including SMEs, to enhance the spillover effects of infrastructure.

The dairy industry is considered one of the most needed industries in almost every country; this is due to the continuous daily demand of its different products. Nevertheless, this industry consumes large amount of water, energy and material resources, and generates large quantities of liquid and solid wastes. In the sequel, under the pressure of fulfilling the 17 sustainable development goals (17 SDGs), it is important to address the sustainability of this sector in the world and particularly in developing countries. This study aims at assessing the impact of environmental, economic and social sustainability practices on the organizational performance of dairy industry in Palestine. To this end, a quantitative-research approach, based on a questionnaire for data collection, was adopted. Data has been collected from a convenient sample of 15 dairy factories working in West Bank in Palestine during a three-month period from March to May, 2023. Inferential statistical analyses were conducted as well. The results revealed that there is a difference between the median values of environmental and economic practices. In addition, the results showed that there is a medium relationship between sustainability practices and organizational performance. However, the economic practices proved to have the strongest impact then social practices; while, there is no impact of environmental practices on organizational performance. Furthermore, the results showed that this industry consumes larger amount of water as well as it generates large amounts of wastewater that mainly discharged to the drainage system without treatment for recycling or reuse. Several sound recommendations are given at the end of this paper. It worth mentioning that there are no previous studies conducted on the dairy industry sector in Palestine about sustainability assessment.

This study analyzes the impact of a high-speed rail line on tax revenues and on the economy of affected regions within the country. The economic impact of infrastructure investment can be induced by changes in tax revenues when the infrastructure is in operation. Accurate regional GDP data are not necessarily available in many Asian countries. However, tax data can be collected. Therefore, this study uses tax revenue dates in order to estimate spillover effects of infrastructure investment. The Kyushu high-speed rail line was constructed in 1991 and was completed in 2003. In 2004, the rail line started operating from Kagoshima to Kumamoto. The entire line was opened in 2011. We estimated its impact in the Kyushu region of Japan by using the differencein- difference method, and compared the tax revenues of regions along the high-speed railway line with other regions that were not affected by the railway line. Our findings show a positive impact on the region’s tax revenue following the connection of the Kyushu rapid train with large cities, such as Osaka and Tokyo. Tax revenue in the region significantly increased during construction in 1991–2003, and dropped after the start of operations in 2004–2010. The rapid train’s impact on the neighboring prefectures of Kyushu is positive. However, in 2004–2013, its impact on tax revenue in places farther from the rapid train was observed to be lower. When the Kyushu railway line was connected to the existing high-speed railway line of Sanyo, the situation changed. The study finds statistically significant and economically growing impact on tax revenue after it was completed and connected to other large cities, such as Osaka and Tokyo. Tax revenues in the regions close to the high-speed train is higher than in adjacent regions. The difference-in-difference coefficient methods reveal that corporate tax revenue was lower than personal income tax revenue during construction. However, the difference in corporate tax revenues rose after connectivity with large cities was completed. Public–private partnership (PPP) has been promoted in many Asian countries. However, PPP-infrastructure in India failed in many cases due to the low rate of return from infrastructure investment. This study shows that an increase of tax revenues is significant in the case of the Kyushu rapid train in Japan. If half of the incremental tax revenues were returned to private investors in infrastructure, the rate of return from infrastructure investment would significantly rise for long period of time. It would attract stable and long-term private investors, such as pension funds and insurance funds into infrastructure investment. The last section of the paper will address how incremental tax revenues created by the spillover effects of infrastructure will improve the performance of private investors in infrastructure investment.

One significant importance of street vending in South Africa is its role in providing livelihoods and economic opportunities, especially for marginalized and vulnerable populations. However, Street vendors, particularly those selling agricultural commodities, face numerous challenges. Street vending in Moletjie Mmotong is a vital source of income and employment, offering affordable goods and services, including food, clothing, and household items. One potential solution is online selling, but there is limited knowledge about it in the informal sector. This study aims to analyze the factors affecting street vendors’ willingness to sell fruits and vegetables online in Moletjie Mmotong under Polokwane Municipality. Data was collected from 60 street vendors using a questionnaire and simple random sampling. Descriptive statistics identified and described the socio-economic characteristics of the vendors, while a binary logistic regression model analyzed the factors influencing their willingness to sell online. The study found that age, education level, gender, household size, and access to online selling information significantly influenced their willingness to sell online. The findings highlight the potential benefits of online selling for street vendors, such as increased sales and a broader customer base. The study recommends that governments provide training and workshops on online selling, develop educational programs, distribute educational materials, and create marketing strategies to support street vendors in transitioning to online platforms.