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The global economy has already experienced recession in 1975, 1982, 1991, 2007 (the Great Recession or the Global Financial Meltdown), and 2020 (The Pandemic Recession). When hope of recovery has just started to emerge with real gross domestic product (GDP) growth reaching 5.7% in 2021, dark clouds are gathering ominously on the horizon again. In January 2022, the World Bank's forecasts (for global growth) were 4.1% for 2022 and 3.3% for 2023. Not only the indicators like industrial activity are shrinking, consumer and investor confidence is at a low point, workers are losing jobs, profit margins and global trade are showing a downward trend, after a period of post-pandemic expansion. But most worrying is the slowing down of the Big Trio of USA-China-Euro Zone that currently accounts for over 50% of real global GDP in Purchasing Power Parity (PPP) terms and contributed 62% to global growth over 2015–2019. Moreover, energy and food prices are showing rising trend, and Ukraine war is showing no sign of ending in the near future. In this context, the present chapter attempts to analyze whether with prices rising and productive activities (including trade) shrinking (described as “stagflation”), is the world headed for another serious slump, and if that happens, what will be its severity? It also tries to point out some limitations of the policy suggestions of the structural projection models used by the World Bank. It also tries to point out the policy suggestions of the models used by the World Bank.

This paper aims to develop a compound measure, which is fiscal vulnerability index, provides early warning signals of fiscal sustainability problems for Türkiye's economy.

The index is constructed using twelve distinct fiscal indicators and applying the portfolio method, which considers the time-varying cross-correlation structure between the subindices.

Dynamics of the fiscal vulnerability index indicate that it accurately predicts to the well-known fiscal crisis occurring in Türkiye's recent history. As a result, such a compound measure should be used in the early identification of fiscal vulnerability in Türkiye.

The main contribution of this paper, relative to existing papers, is that a fiscal vulnerability index was constructed by employing the most contemporaneous method and evaluating its performance in terms of capturing historical stress periods.

Public debt forecasts represent a key policy issue. Many methodologies have been employed to predict debt sustainability, including dynamic stochastic general equilibrium models, the stock flow consistent method, the structural vector autoregressive model and, more recently, the neuro-fuzzy method. Despite their widespread application in the empirical literature, all of these approaches exhibit shortcomings that limit their utility. The present research adopts a different approach to public debt forecasts, that is, the random forest, an ensemble of machine learning.

Using quarterly observations over the period 2000–2021, the present research tests the reliability of the random forest technique for forecasting the Italian public debt.

The results show the large predictive power of this method to forecast debt-to-GDP fluctuations, with no need to model the underlying structure of the economy.

Compared to other methodologies, the random forest method has a predictive capacity that is granted by the algorithm itself. The use of repeated learning, training and validation stages provides well-defined parameters that are not conditional to strong theoretical restrictions This allows to overcome the shortcomings arising from the traditional techniques which are generally adopted in the empirical literature to forecast public debt.

This paper explores whether the Euro-area sovereign credit default swap market is prone to contagion effects. It investigates whether the sharp increase in sovereign CDS spread of a given country is due to a deterioration of the macroeconomic variables or some form of contagion.

For this purpose, the authors use an innovative approach, i.e. spatial econometrics. Although modeling spatial dependence is an attractive challenge, its application in the field of finance remains limited.

The empirical findings show strong evidence of spatial dependence highlighting the presence of pure contagion. Furthermore, evidence of wake-up call contagion-increased sensitivity of investors to fundamentals of neighboring countries and shift contagion-increased sensitivity to common factors are well recorded.

This study aims to study a crucial financial issue that gained increased research interest, i.e. financial contagion. A methodological contribution is made by extending the standard spatial Durbin model (SDM) to analyze and differentiate between several forms of contagion. The results can be used to understand how shocks are spreading through countries.

This study investigates the interactions between the US daily public debt and currency power under impacts of the Covid-19 crisis.

The authors employ the multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) modeling to explore the interactions between daily changes in the US Debt to the Penny and the US Dollar Index. The data sets are from April 01, 1993, to May 27, 2022, in which noticeable points include the Covid-19 outbreak (January 01, 2020) and the US vaccination campaign commencement (December 14, 2020).

The authors find that the daily change in public debt positively affects the USD index return, and the past performance of currency power significantly mitigates the Debt to the Penny. Due to the Covid-19 outbreak, the impact of public debt on currency power becomes negative. This effect remains unchanged after the pandemic. These findings indicate that policy-makers could feasibly obtain both the budget stability and currency power objectives in pursuit of either public debt sustainability or power of currency. However, such policies should be considered that public debt could be a negative influencer during crisis periods.

The authors propose a pioneering approach to explore the relationship between leading and lagging indicators of an economy as characterized by their daily data sets. In accordance, empirical findings of this study inspire future research in relation to public debt and its connections with several economic indicators.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2022-0581

This study aims to enhance the assessment of green governance in energy projects along the Belt and Road, reduce the influence of fuzzy judgment, and construct a grey network analysis model from the perspective of Environmental, Social, and Governance (ESG).

The ESG concept is used to establish an evaluation indicator system. The Analytic Network Process (ANP) and the Grey System Theory are applied sequentially to determine the green governance grade of energy projects, exemplified by an evaluation of five projects.

The Karot hydropower project has the best green governance status among the five projects and is of excellent grade. This is followed by the Hongfeng photovoltaic project, the De Aar wind power project, and the Yamal liquefied natural gas project, which are of good grade. The Lamu coal power station project has the worst green governance and is at a medium level.

This study can assist Belt and Road energy projects in identifying their deficiencies and promoting sustainable development by providing a robust framework for green governance evaluation.

The indicator system developed in this study includes social and project governance aspects in addition to environmental performance, reflecting the comprehensive green governance status of projects. The combined use of ANP and grey system theory fully considers the mutual influence relationship between indicators and improves the objectivity of green governance grade judgment.

This paper aims to study the effects of plum powder and apple pomace powder additions on the quality properties of buffalo meat emulsion.

Buffalo meat emulsions were prepared using different levels (2%, 4% and 6%) of plum powder and apple pomace powder, respectively. The meat emulsions were analysed for the physico-chemical, sensory and textural properties of the meat emulsion.

The pH of meat emulsions decreased significantly (p < 0.05) with an increased level of plum powder and apple pomace powder. Water-holding capacity (43.1%–48.1%), emulsion stability (80.2%–92.2%) and cooking yield (85.4%–91.0%) were significantly (p < 0.05) higher in plum powder and apple pomace powder added than the water-holding capacity (42.1%), emulsion stability (79.7%) and cooking yield (85.0%) of control emulsion. The moisture content was decreased significantly (p < 0.05), and crude fibre content was increased significantly (p < 0.05) with the increase in plum powder and apple pomace powder additions in meat emulsions. The total phenolic content and colour values (a* and b*) were significantly higher in plum powder and apple pomace powder added to meat emulsions. The sensory scores of meat emulsions were affected by the addition of plum powder and apple pomace powder. The meat emulsion added with 6% plum powder and 6% apple pomace powder showed significantly lower values of sensory overall acceptability. The hardness of meat emulsions increased with the addition of plum powder and apple pomace powder.

The results indicated that meat emulsions with a good cooking yield, fibre content, sensory acceptability and textural properties can be prepared by using plum powder and apple pomace powder.