This paper assesses South Africa’s massive infrastructure drive to revive growth and increase employment. After years of stagnant growth, this is now facing a deep economic crisis, exacerbated by the COVID-19 pandemic. This drive also comes after years of weak infrastructure investment, widening the infrastructure deficit. The plan outlines a R1 trillion investment drive, primarily from the private sector through the Infrastructure Fund over the next 10 years (Government of South Africa, 2020). This paper argues that while infrastructure development in South Africa is much-needed, the emphasis on de-risking for private sector buy-in overshadows the key role the state must play in leading on structurally transforming the economy.

A method for studying the resilience of energy and socio-ecological systems is considered; it integrates approaches developed at the International Institute of Applied Systems Analysis and the Melentyev Institute of Energy Systems (MESI) of the Siberian Branch of the Russian Academy of Sciences. The article discusses in detail the methods of using intelligent information technologies, in particular semantic technologies and knowledge engineering (cognitive probabilistic modeling), which the authors propose to use in assessing the risks of natural and man-made threats to the resilience of the energy sector and social and ecological systems. More attention is paid to the study and adaptation of the integral indicator of quality of life, which makes it possible to combine these interdisciplinary studies.

In this study, we explore the impact of contemporary bank run incidents on stock market performance, taking into consideration insured deposit concentration. Specifically, we use data from the recent downfall of the Silicon Valley Bank (SVB). By employing event study methods with the mean-adjusted return model and market models, we evaluate the cumulative abnormal returns (CARs). Our findings reveal a substantial negative CAR for all the listed companies in our sample, suggesting that the SVB crisis adversely affected stock returns. Further analysis shows an even more pronounced effect on the banking sector and that banks with a high concentration of insured deposits experienced economically and statistically less negative CARs. We also find that the response by the Treasury Department, the Federal Reserve, the Federal Deposit Insurance Corporation, and other agencies—aimed at fully safeguard all depositors—led a rebound in CARs. Our results highlight the importance of deposit insurance policy and regulatory responses in protecting the financial system during panic events.

This research analyzes disaster risk financing within the framework of the disaster management policy in Indonesia as the implementation of the Disaster Management Law, Number 24 of 2007, by examining recent issues, challenges, and opportunities in disaster financing. Utilizing a qualitative approach, the research systematically reviews various studies, reports, and existing regulations and policies to understand the current landscape comprehensively. Recent developments in disaster risk financing in Indonesia highlight the need for a nuanced exploration of the existing policy framework. Fiscal constraints, evolving risk landscapes, and the increasing frequency of disasters underscore the urgency of effective disaster risk financing strategies. Through a qualitative examination, this study identifies challenges while illuminating opportunities for innovation and improvement within the current policy framework. The contribution of this research extends to both theoretical and practical levels. Theoretically, it enriches the academic discourse on disaster risk financing by offering a nuanced understanding of the complexities involved. On a practical level, the findings derived from the examination provide actionable recommendations for policymakers and practitioners engaged in disaster management in Indonesia. The insights aim to inform the refinement of disaster management policies and practices, fostering resilience and adaptability in the face of evolving disaster scenarios.

This study aims to identify the risk factors causing the delay in the completion schedule and to determine an optimization strategy for more accurate completion schedule prediction. A validated questionnaire has been used to calculate a risk rating using the analytical hierarchy process (AHP) method, and a Monte Carlo simulation on @RISK 8.2 software was employed to obtain a more accurate prediction of project completion schedules. The study revealed that the dominant risk factors causing project delays are coordination with stakeholders and changes in the scope of work/design review. In addition, the project completion date was determined with a confidence level of 95%. All data used in this study were obtained directly from the case study of the Double-Double Track Development Project (Package A). The key result of this study is the optimization of a risk-based schedule forecast with a 95% confidence level, applicable directly to the scheduling of the Double-Double Track Development Project (Package A). This paper demonstrates the application of Monte Carlo Simulation using @RISK 8.2 software as a project management tool for predicting risk-based-project completion schedules.