This study critically examines the implications of international transport corridor projects for Central Asian countries, focusing on the Western-backed Transport Corridor Europe-Caucasus-Asia (TRACECA), the Chinese initiative “One Belt—One Road”, and the International North-South Transport Corridor (INSTC) supported by the Russian Federation, India, and Iran. The analysis underscores the risks associated with Western projects, highlighting a need for a more explicit commitment to substantial infrastructure investments and persistent contradictions among key investors and beneficiaries. While the Chinese initiative presents significant benefits such as transit participation, infrastructure development, and economic investments, it also carries risks, notably an increased debt burden and potential monopolization by Chinese corporations. The study emphasizes that Central Asian countries, though indirect beneficiaries of INSTC, may not be directly involved due to geographical constraints. Study findings advocate for Central Asian nations to balance foreign investments, promote economic integration, and safeguard political and economic sovereignty. The study underscores the region’s wealth of natural and human resources, emphasizing the potential for increased demand for goods and services with improved living standards, strategically positioning these countries in the evolving global economic landscape.

While there has been much discussion about the large infrastructure needs in Asia and the Pacific, less attention has been paid to public expenditure efficiency in infrastructure services delivery. New constructions are not the only solution, especially when governments have limited capital to invest. Globally, new infrastructure projects face delays and cost overruns, leading to an inefficient use of public resources. The root causes include the lack of transparency in project selection, the lack of project preparation, the silo approach by public entities in assessing feasibility studies, and the lack of public sector capacity to fully develop a bankable pipeline of projects. To tackle these issues, governments need a smarter investment approach and to do so, enhancing public service efficiency is very crucial. The paper suggests a “whole life cycle” (WLC) approach as the main strategic solution for the discussed issues and challenges. We expand the definition of WLC to include the entire life cycle of the infrastructure asset from need identification to its disposal. The stages comprise planning, preparation, procurement, design, construction, operation and maintenance, and disposal. This is because we believe any efficient or inefficient decision throughout such a wide life cycle influences the quality of public services. Hence, in this holistic approach, infrastructure life cycle consists of four phases: planning, preparation, procurement, and implementation. Governments could enhance public efficiency and thus improve access to finance throughout the WLC by several solutions. These are (i) preparing infrastructure master plan and pipelines and long-term budgeting during the planning phase; (ii) establishing framework and guidelines and improving governance during preparation phase; (iii) promoting standardization, transparency, open government, and contractual consistency during the procurement phase; and finally (iv) continued role of government and total asset management during the implementation phase. In addition to these phase-specific means, key WLC solutions include proper use of technology, capacity building, and private participation in general and public-private partnership (PPP) in particular.

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.

Developing Asia’s infrastructure gap results from both inadequate public resources and a lack of effective channels to mobilize private resources toward desired outcomes. The public-private partnership (PPP) mechanism has evolved to fill the infrastructure gap. However, PPP projects are often at risk of becoming distressed, or worst, being terminated because of the long-term nature of contracts and the many different stakeholders involved. This paper applies survival-time hazard analysis to estimate how project-related, macroeconomic, and institutional factors affect the hazard rate of the projects. Empirical results show that government’s provision of guarantees, involvement of multilateral development banks, and existence of a dedicated PPP unit are important for a project’s success. Privately initiated proposals should be regulated and undergo competitive bidding to reduce the hazard rate of the project and the corresponding burden to the government. Economic growth leads to successful project outcomes. Improved legal and institutional environment can ensure PPP success.

The US Infrastructure Investment and Job Act (IIJA), also commonly referred to as the Bipartisan Infrastructure Bill, passed in 2021, has drawn international attention. It aims to help to rebuild US infrastructure, including transportation networks, broadband, water, power and energy, environmental protection and public works projects. An estimated $1.2 trillion in total funding over ten years will be allocated. The Bipartisan Infrastructure Bill is the largest funding bill for US infrastructure in the recent history of the United States. This review article will specifically discuss funding allocations for roads and bridges, power and grids, broadband, water infrastructure, airports, environmental protection, ports, Western water infrastructure, electric vehicle charging stations and electric school buses in the new spending of the Infrastructure Investment and Job Act and why these investments are urgently necessary. This article will also briefly discuss the views of think tank experts, the public policy perspectives, the impact on domestic and global arenas of the new spending in the IIJA, and the public policy implications.

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.