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Container shipping is a crucial component of the global supply chain that is affected by a large range of operational risks with high uncertainty, threatening the stability of service, manufacture, distribution and profitability of involved parties. However, quantitative risk analysis (QRA) of container shipping operational risk (CSOR) is being obstructed by the lack of a well-established theoretical structure to guide deeper research efforts. This paper proposes a methodological framework to strengthen the quality and reliability of CSOR analysis (CSORA).

Focusing on addressing uncertainties, the framework establishes a solid, overarching and updated basis for quantitative CSORA. The framework consists of clearly defined elements and processes, including knowledge establishing, information gathering, aggregating multiple sources of data (social/deliberative and mathematical/statistical), calculating risk and uncertainty level and presenting and interpreting quantified results. The framework is applied in a case study of three container shipping companies in Vietnam.

Various methodological contributions were rendered regarding CSOR characteristics, settings of analysis models, handling of uncertainties and result interpretation. The empirical study also generated valuable managerial implications regarding CSOR management policies.

This paper fills the gap of an updated framework for CSORA considering the recent advancements of container shipping operations and risk management. The framework can be used by both practitioners as a tool for CSORA and scholars as a test bench to facilitate the comparison and development of QRA models.

The literature on economic forecasting, is showing an increase in criticism, of the inaccuracy of forecasts, with major implications for economic, and fiscal policymaking. Forecasts are subject to the systemic uncertainty of human systems, considerable event-driven uncertainty, and show biases towards optimistic growth paths. The purpose of this study is to consider approaches to improve economic foresight.

This study describes the practice of economic foresight as evolving in two separate, non-overlapping branches, short-term economic forecasting, and long-term scenario analysis of development, the latter found in studies of climate change and sustainability. The unique case of Ireland is considered, a country that has experienced both steep growth and deep troughs, with uncertainty that has confounded forecasting. The challenges facing forecasts are discussed, with brief review of the drivers of growth, and of long-term economic scenarios in the global literature.

Economic forecasting seeks to manage uncertainty by improving the accuracy of quantitative point forecasts, and related models. Yet, systematic forecast failures remain, and the economy defies prediction, even in the near-term. In contrast, long-term scenario analysis eschews forecasts in favour of a set of plausible or possible alternative scenarios. Using alternative scenarios is a response to the irreducible uncertainty of complex systems, with sophisticated approaches employed to integrate qualitative and quantitative insights.

To support economic and fiscal policymaking, it is necessary support advancement in approaches to economic foresight, to improve handling of uncertainty and related risk.

While European Union Regulation (EC) 1466/97 mandates pursuit of improved accuracy, in short-term economic forecasts, there is now a case for implementing advanced foresight approaches, for improved analysis, and more robust decision-making.

Building economic resilience and adaptability, as part of a sustainable future, requires both long-term strategic planning, and short-term policy. A 21st century policymaking process can be better supported by analysis of alternative scenarios.

To the best of the authors’ knowledge, the article is original in considering the application of scenario foresight approaches, in economic forecasting. The study has value in improving the baseline forecast methods, that are fundamental to contemporary economics, and in bringing the field of economics into the heart of foresight.

The purpose of the paper is 1) to address the importance of contingency at the right level when defining project control baseline, including cost reserves / “room to manoeuvre” and 2) present proactive uncertainty management as a regime to ensure cost effective management of project reserves and contribute to project success.

The paper is a combination of literature study and quantitative research on how contingency develops during the lifetime of a case project. The investigation into the case project includes document study into quantitative material from the case project. The combination of empirical material and theory makes the discussion robust.

Unrealistic low cost uncertainty will lead to unrealistic low contingency. The case study from a Norwegian mega project shows a contingency of 15 per cent in addition to expected costs. The case study shows that by continuous opportunity management and risk reduction, the needs for management reserves are systematically reduced and the contingency is controlled.

This research is limited to one case study. A higher number of cases are necessary to generalise the findings. However, the authors would claim that the systematic mapping of need for management reserve towards the project contingency, and a continuous uncertainty management system will help to obtain cost effective management. The findings from the case study could be applied on similar cases.

The case study shows a way of setting contingencies and managing contingencies through systematic uncertainty management.

Improved management of project provisions will increase the value of future projects.

This study aims to analyze the contagious effects of economic policy uncertainties in the USA on the economies of its important trading partners, such as Japan, Canada, Mexico and the Eurozone.

In the study using the uncertainty index created by Baker et al. (2016), the interaction between variables was analyzed with structural VAR (SVAR) models.

According to the results obtained from the analysis, economic policy uncertainties in the USA had significant effects on the economies of its high-volume trading partners. The internal debt crisis experienced in the Eurozone after the 2008 crisis caused the European Central Bank to respond to the economic policy uncertainties in the USA with contractionary monetary policies, unlike other countries. In addition to these results, Mexico, which has a more fragile economic structure than other countries in the analysis, was more impacted by increasing uncertainties, as expected.

The present study aimed to bring a new perspective to the literature by evaluating the contagiousness of local uncertainty in the globalizing world and the monetary policies implemented as a precaution against this situation on an empirical plane.

Port throughput analysis is a challenging task, as it consists of intertwined interactions between a variety of cargos and numerous influencing factors. This study aims to propose a quantitative method to facilitate port throughput analysis by identification of important cargos and key macroeconomic variables.

Mutual information is applied to measure the linear and nonlinear correlation among variables. The method gives a unique measure of dependence between two variables by quantifying the amount of information held in one variable through another variable.

This study uses the mutual information to the Port of Isafjordur in Iceland to underpin the port throughput analysis. The results show that marine products are the main export cargo, whereas most imports are fuel oil, industrial materials and marine product. The aggregation of these cargos, handled in the port, meaningfully determines the non-containerized port throughput. The relation between non-containerized export and the national gross domestic product (GDP) is relatively high. However, non-containerized import is mostly related to the world GDP. The non-containerized throughput shows a strong relation to the national GDP. Furthermore, the results reveal that the volume of national export trade is the key influencing macroeconomic variable to the containerized throughput.

Application of the mutual information in port throughput analysis effectively reduces epistemic uncertainty in the identification of important cargos and key influencing macroeconomic variables. Thus, it increases the reliability of the port throughput forecast.

This paper analyses the determinants of transport demand for maritime container transport. Such an analysis is relevant, among others for port planning, since port expansion plans are based on forecasts. Inevitably, different opinions about the future development of (container) transport flows exist, and decisionmakers are confronted with uncertainty. This paper analyses the variables of container transport demand. Seven variables are identified, four related to the overall volume of trade and international transport flows (the GDP, export quote of economies, the direction of trade and the value density of trade) and three related to the containerised proportion of transport flows (the containerisable share of transport flows, the containerisation rate and the share of shipping in international trade). The rise of containerised transport flows from 1980 to 1995 is based on different 'underlying factors'. The future development of the variables is highly uncertain, and a 'extrapolation' of the high growth rates of the past, is not likely to lead to a good forecast for the future. Thus, decision-makers confronted with the uncertainty about future trade flows, should try to maximise flexibility in port planning.

This paper aims to study the determinants of private investment in Egypt while accounting for uncertainty associated with financing decisions of the firm using time series analysis over the period 1982-2015. The analysis is based on Tobin’s (1969) Q-theory of investment. The variables used in the empirical model are investment rate, average q index, prices of capital goods, internal finance and external finance.

This research is concerned with the model specification of a dynamic Average Q model. In that respect, the current research describes the data, presents the empirical methodology and estimates the Average Q model of investment and obtains the results. The empirical procedures and results of studying the average Q model. It includes testing for the unit root in the time series, vector error correction model (VECM) and cointegration long run analysis, and finally estimations of the model under uncertainty and empirical results.

Stochastic shocks to the determinants of private investment in Egypt have their impact on investment rate. The representation of impulse response in VECM shows that a one standard deviation shock to the value of the firm has a positive impact on investment rate. Stochastic shocks to both internal finance and external finance have slightly positive response from investment rate. Also, a stochastic shock to investment rate has a positive yet declining response from itself. However, a stochastic shock to prices of capital goods has a negative impact on investment rate. The representation of variance decomposition in VECM shows that investment rate is positively affected yet at a declining rate by a one standard deviation shock in both internal and external finance during the period 1982-2015. Also, a stochastic shock in the value of the firm or in the prices of capital goods has a slightly positive impact on investment rate.

Investment and capital accumulation are the main vehicles for economic growth and development. There have been fluctuations in Egypt’s investment rates since mid-1970s due to variations in saving rates. Thus, it is important to present some policy implications that could potentially assist the enhancement of the Egyptian economy. In that respect, the estimated results of the empirical model show that changes in the prices of capital goods in Egypt are significant factors that have negative impact on investment rate. Prices of imported capital goods in Egypt are affected by foreign exchange market conditions in the form of significant changes in the pound exchange rate. Thus, foreign exchange market reforms, as adopted recently in the Egyptian economy and improvements in trade balance, are important steps to alleviate obstacles that hinder investment. Regarding the source of finance, the estimated results showed that changes in both internal and external finance have a positive impact on investment rate. In this case, it is the firm’s decision to choose the method of financing its investment depending on factors such as its market value, institutional size and capacity and the opportunity cost of the funds used in financing the required investment.

This study aims to create an efficient approach to validate building energy simulation models amidst challenges from time-intensive data collection. Emphasizing precision in model calibration through strategic short-term data acquisition, the systematic framework targets critical adjustments using a strategically captured dataset. Leveraging metrics like Mean Bias Error (MBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)), this methodology aims to heighten energy efficiency assessment accuracy without lengthy data collection periods.

A standalone school and a campus facility were selected as case studies. Field investigations enabled precise energy modeling, emphasizing user-dependent parameters and compliance with standards. Simulation outputs were compared to short-term actual measurements, utilizing MBE and CV(RMSE) metrics, focusing on internal temperature and CO₂ levels. Energy bills and consumption data were scrutinized to verify natural gas and electricity usage against uncertain parameters.

Discrepancies between initial simulations and measurements were observed. Following adjustments, the standalone school 1’s average internal temperature increased from 19.5 °C to 21.3 °C, with MBE and CV(RMSE) aiding validation. Campus facilities exhibited complex variations, addressed by accounting for CO₂ levels and occupancy patterns, with similar metrics aiding validation. Revisions in lighting and electrical equipment schedules improved electricity consumption predictions. Verification of natural gas usage and monthly error rate calculations refined the simulation model.

This paper tackles Building Energy Simulation validation challenges due to data scarcity and time constraints. It proposes a strategic, short-term data collection method. It uses MBE and CV(RMSE) metrics for a comprehensive evaluation to ensure reliable energy efficiency predictions without extensive data collection.