The impact of OFDI and institutional quality on domestic capital formation at the disaggregated level: Evidence for developed and emerging countries

In this study, we investigate whether outbound foreign direct investment (OFDI) either augments or impedes domestic public and private investment, incorporating the role of institutional quality into the context of developed and emerging countries. To this end, we apply a cross-sectional-autoregressive-distributed lag (CS-ARDL) approach to analyze panel data from the period 1996-2017. Our empirical findings suggest that OFDI augments private capital formation for developed countries. Institutional quality (IQ) is found to be a driving factor that promotes private capital formation in the established economies of developed countries. However, OFDI has a negative association with the public capital formation in the established economies of developed countries, while IQ has a positive association with it. In the context of emerging economies, OFDI is found to be too insignificant to have an effect on private and public capital formation. Interestingly, IQ has a detrimental effect on both private and public capital formation in emerging economies. Our findings are robust. The empirical findings of this study imply that institutional quality should continue to be improved in developed countries, while it should surpass a certain threshold for emerging economies to promote domestic capital formation. © 2020 by the authors.16ZDA038Funding: This research was funded by the National Social Science Foundation Project of China, grant number 16ZDA038

What does the clean energy transition look like for Russian oil exports?

The commitment of governments to achieving the goals of the Paris Agreement and the expected decrease in fossil fuel consumption indicate the importance of studying the risks facing the oil exporters and the range of options and responses. This study adopts the Trade Gravity model to examine the effect of clean energy policies on Russian oil exports during the period 1996–2019. Our results based on Cross-Sectional Autoregressive Distributed Lags modeling suggest that Russian oil exports are negatively affected by the energy transitions. This indicates that investing in cleaner energy technologies as a channel toward reducing fossil fuel demand can reduce the oil trade globally. Policies and strategies to retain and support the financial stability of the Russian government under the current scenario are suggested. © 2022 The Authors. Energy Science & Engineering published by Society of Chemical Industry and John Wiley & Sons Ltd.Russian Foundation for Basic Research, РФФИIran National Science Foundation, INSF, (20‐510‐56021)This research was funded by Russian Foundation for Basic Research (RFBR) and Iran National Science Foundation (INSF) (grant number 20‐510‐56021)

The interaction of finance and innovation for low carbon economy: Evidence from Saudi Arabia

Saudi Arabia sets an ambitious plan to install 58.7 GW renewable capacity along with several mitigation measures. For the smooth accomplishment of undertaken projects, the role of the financial market is crucial. This paper investigates the role of financial development in explaining the low carbon economy (LCE), incorporating the role of innovation in the context of Saudi Arabia, where fossil fuel contributes almost 100% share of the total energy mix. We apply the standard, asymmetric, and quantile Autoregressive Distributed Lag (ARDL) Approaches to analyse our time-series data from 1981 to 2016 (both yearly and monthly forms). Our empirical analysis demonstrates that finance asymmetrically fosters the carbon economy, but it helps to achieve a low carbon economy through the channel of innovation under different economic circumstances. Our empirical evidence reinforces the role of the financial market in overcoming the financial constraints for launching green projects. © 2022 The AuthorsKCR-KFL-06-20; King Abdulaziz University, KAUThis project was funded by King Abdulaziz University , Jeddah, Saudi Arabia, and King Abdullah City for Atomic and Renewable Energy , Riyadh, Saudi Arabia under grant no. ( KCR-KFL-06-20 ). Therefore, the authors gratefully acknowledge their technical and financial support

A hybrid modular approach for dynamic fault tree analysis

YesOver the years, several approaches have been developed for the quantitative analysis of dynamic fault trees (DFTs). These approaches have strong theoretical and mathematical foundations; however, they appear to suffer from the state-space explosion and high computational requirements, compromising their efficacy. Modularisation techniques have been developed to address these issues by identifying and quantifying static and dynamic modules of the fault tree separately by using binary decision diagrams and Markov models. Although these approaches appear effective in reducing computational effort and avoiding state-space explosion, the reliance of the Markov chain on exponentially distributed data of system components can limit their widespread industrial applications. In this paper, we propose a hybrid modularisation scheme where independent sub-trees of a DFT are identified and quantified in a hierarchical order. A hybrid framework with the combination of algebraic solution, Petri Nets, and Monte Carlo simulation is used to increase the efficiency of the solution. The proposed approach uses the advantages of each existing approach in the right place (independent module). We have experimented the proposed approach on five independent hypothetical and industrial examples in which the experiments show the capabilities of the proposed approach facing repeated basic events and non-exponential failure distributions. The proposed approach could provide an approximate solution to DFTs without unacceptable loss of accuracy. Moreover, the use of modularised or hierarchical Petri nets makes this approach more generally applicable by allowing quantitative evaluation of DFTs with a wide range of failure rate distributions for basic events of the tree.This work was supported in part by the Dependability Engineering Innovation for Cyber Physical Systems (CPS) (DEIS) H2020 Project under Grant 732242, and in part by the LIVEBIO: Light-weight Verification for Synthetic Biology Project under Grant EPSRC EP/R043787/1

The response of exchange rates to economic policy uncertainty: Evidence from Russia

The Russian economy has encountered substantial exchange rate volatility due to many endogenous and exogenous shocks, including the adoption of different exchange rate systems, the global financial crisis, sanctions, and the COVID pandemic. The economy has long experience with a managed floating exchange rate system, which motivates us to investigate the exchange rate response to domestic economic policy uncertainty, incorporating oil prices and the trade volume under different economic circumstances. We apply quantile-based time-series approaches to deal with extreme values. Our empirical investigation demonstrates that the local currency appreciates in response to increased Russian economic policy uncertainty under different quantiles of the managed floating exchange rate, but it depreciates under most quantiles in a floating exchange rate period. Our findings confirm that the Russian currency appreciates with the rise in international oil prices and trade as Russia is an oil-exporting country. Moreover, the findings are robust under the quantile-on-quantile approach. © 2021 The AuthorsRussian Science Foundation, RSF: 19-18-00262This study was supported by the grant of the Russian Science Foundation , Code: 19-18-00262 . “Empirical modelling of balanced technological and socioeconomic development in the Russian regions”

The Spillover of Inflation among the G7 Countries

Many global shocks, including the renegotiation of NAFTA, the United States–China trade war, the Brexit, and the COVID-19 pandemic, may have recently influenced the inflation spillover in the G7 countries. The current literature overlooks the influence of these important events on the inflation spillover of the G7 countries. This study fulfills this gap and investigates the nature of inflation spillover in the short, medium, and long term. Using the monthly data from 1956:6 to 2020:12, the study finds that Japan and the United States are the main transmitters of inflation. International trade, purchasing power parity, low-cost technology, and the Abenomics policy were found to be responsible for the inflation spillover. We suggest that the central banks of these countries collaborate to achieve the targeted inflation rate. © 2021 by the authors

Mineral import demand and clean energy transitions in the top mineral-importing countries

The clean energy transitions require a large volume of minerals to handle its diverse technologies, such as solar photovoltaics (PV), wind turbines etc. Therefore, mineral importing countries concentrated on cleaner energy production confront an uprising trend in critical mineral prices due to thriving demands. We quest for the response of the top mineral importing countries' import demand for minerals to the clean energy transitions from 1996 to 2019 within the import-demand function analysis. Using the cross-sectional autoregressive distributed lag (CS-ARDL) method, our findings divulge a significantly positive response of mineral import demand to solar and wind energy productions in the long run. We also find that mineral price elasticity holds the Marshallian demand hypothesis in the mineral-laden solar energy generation while contradicting it in wind energy production. In addition, the oil price substitution effect does not sustain, whereas exchange rate depreciates mineral import demands in the long run. Therefore, our policy implications encompass optimizing the mineral resources for clean energy transitions to materialize the 21st century's global agenda of a decarbonized or net-zero emissions trajectory. © 2022 Elsevier LtdMinistry of Education and Science of the Russian Federation, Minobrnauka; Ural Federal University, UrFUAcknowledgement: The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University project within the Priority-2030 Program) is gratefully acknowledged

Влияние эффектов перетекания волатильности на политическую неопределенность, цены на нефть, биржу и рынки драгоценных металлов в российской экономике

The Russian economy is emerging, meaning that natural resources play a dominant role in economic development. Given the considerable volatility in resource prices, we investigate the volatility spillovers among policy uncertainty, international oil prices, exchange rate, stock index and metal prices covering the period of 2 July 2008 to 15 May 2020 for the Russian economy applying Dynamic Connectedness based on Time-Varying Parameter Vector Autoregression (TVP-VAR). Our empirical investigation demonstrates that gold price, Russian policy uncertainty, oil price and stock index are net volatility contributors, whereas palladium, platinum, silver and exchange rate are net volatilities receivers. Market capitalisation and silver market are found to be the highest net contributor and net receiver, respectively. The palladium appears as a net volatility receiver initially, just after the global financial crisis. The Russian economic policy uncertainty appears to be the dominant volatility contributor from 2008 to 2014, but onward it turned to be a net volatility receiver. Over the year 2014, gold price was the prominent volatility contributor to another market when the oil price dropped significantly. The total connectivity of the markets are highly anchored with several exogenous shocks, including economic sanction, adoption of floating exchange rate, oil price plunge. Our empirical findings provide several policy implications to portfolio managers and Russian regional stakeholders. © Sohag K., Husain S., Chukavina K., Al Mamun Md Text. 2022.Russian Foundation for Basic Research, РФФИ; Iran National Science Foundation, INSFThe article has been prepared with the support of the grant of RFBR and INSF, code: 20-510-56021 “Modeling the future of Oil Demand and Fiscal Sustainability: Evidence from Iran and Russia”

Dynamic Fault Tree Analysis: State-of-the-Art in Modeling, Analysis, and Tools

YesSafety and reliability are two important aspects of dependability that are needed to be rigorously evaluated throughout the development life-cycle of a system. Over the years, several methodologies have been developed for the analysis of failure behavior of systems. Fault tree analysis (FTA) is one of the well-established and widely used methods for safety and reliability engineering of systems. Fault tree, in its classical static form, is inadequate for modeling dynamic interactions between components and is unable to include temporal and statistical dependencies in the model. Several attempts have been made to alleviate the aforementioned limitations of static fault trees (SFT). Dynamic fault trees (DFT) were introduced to enhance the modeling power of its static counterpart. In DFT, the expressiveness of fault tree was improved by introducing new dynamic gates. While the introduction of the dynamic gates helps to overcome many limitations of SFT and allows to analyze a wide range of complex systems, it brings some overhead with it. One such overhead is that the existing combinatorial approaches used for qualitative and quantitative analysis of SFTs are no longer applicable to DFTs. This leads to several successful attempts for developing new approaches for DFT analysis. The methodologies used so far for DFT analysis include, but not limited to, algebraic solution, Markov models, Petri Nets, Bayesian Networks, and Monte Carlo simulation. To illustrate the usefulness of modeling capability of DFTs, many benchmark studies have been performed in different industries. Moreover, software tools are developed to aid in the DFT analysis process. Firstly, in this chapter, we provided a brief description of the DFT methodology. Secondly, this chapter reviews a number of prominent DFT analysis techniques such as Markov chains, Petri Nets, Bayesian networks, algebraic approach; and provides insight into their working mechanism, applicability, strengths, and challenges. These reviewed techniques covered both qualitative and quantitative analysis of DFTs. Thirdly, we discussed the emerging trends in machine learning based approaches to DFT analysis. Fourthly, the research performed for sensitivity analysis in DFTs has been reviewed. Finally, we provided some potential future research directions for DFT-based safety and reliability analysis

Do Geopolitical Tensions and Economic Policy Uncertainties Reorient Mineral Imports in the USA? A Fat-Tailed Data Analysis Using Novel Quantile Approaches

Mineral resources are essential raw materials to generate electricity, fuel vehicles, and heat homes and workplaces. Besides, the global agenda of clean energy deployment, including solar photovoltaics (PV), wind turbines, electric vehicles (EV), and storage facilities, calls for a considerable volume of critical minerals, which elevates their respective import demands. This highly concentrated source of those minerals poses a significant concern triggered by the augmented geopolitical tensions and economic policy uncertainties. In light of this context, our objective is to estimate the response of mineral import demand to global geopolitical risk events and economic policy uncertainty covering monthly data from January 1996 to December 2020. In doing so, we apply the cross-quantilogram (CQ) and the quantile-on-quantile (QQ) regression approaches due to the fat-tailed nature of the data property. Besides, these quantile-based data analysis procedures are appropriate for non-normal data sets and show the co-movement of the variables of interest under a bi-variate modelling approach. More importantly, these two techniques also exhibit the quantile connectedness among the variables in the bearish and bullish conditions. Moreover, our findings show that mineral import demand responds negatively to the USA’s (own) and global geopolitical risk events at the high quantiles under long memory. In addition, this demand reacts positively to the USA’s (own) and global economic policy uncertainty in entire quantiles under long memory. Therefore, our policy suggestions are concerned with tackling geopolitical tensions and economic policy uncertainty by adopting pre-emptive measures within a viable institutional mechanism to continue impressive mineral trade flows. © 2022 by the authors.Ministry of Education and Science of the Russian Federation, Minobrnauka; Ural Federal University, UrFUThe research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University project within the Priority-2030 Program) is gratefully acknowledged