Global Uncertainty Research Articles

Do the Energy-Related Uncertainties Stimulate Renewable Energy Demand in Developed Economies? Fresh Evidence from the Role of Environmental Policy Stringency and Global Economic Policy Uncertainty

Despite earlier research on green energy, there is still a significant gap in understanding how energy-related uncertainties affect renewable energy consumption (REN), especially in developed nations. Thus, this study explicitly looks into how the energy-related uncertainty index (EUI) can promote (or diminish) REN in sixteen wealthy nations between 2000 and 2020. Furthermore, we attempt to specify the factors of REN and explore whether environmental policy stringency (EPS) and global economic policy uncertainty (GEPU) could help moderate (or intensify) the EUI-REN nexus. To achieve this, we employ different panel data methods. The results underscore that the EUI significantly impacts REN, denoting that higher uncertainties related to energy markets lead to promoting REN. Additionally, the (EUI × EPS) underlines that EPS has a favorable role in increasing the positive effect of the EUI on REN in sample developed countries while (EUI × GEPU) has a detrimental effect. Remarkably, the findings underline that the effect of the EUI on REN is more positive in high EPS countries and that the positive effect of the EUI is more moderate when GEPU is high. The findings also underscore that the development of the financial market, FDI, personal remittances, and EPS positively stimulate REN whereas CO2, total natural resources rents, economic activity, and GEPU have a detrimental impact. The results are robust, and authorities and policymakers are advised to implement a wide range of policy proposals to accomplish sustainable development goals (SDGs) 7 and 13.

Quantile-frequency connectedness among climate change stocks: The roles of climate change attention and global uncertainties

Concerns regarding climate change and the environment have heightened interest in climate-conscious investing, necessitating a deeper understanding of climate-friendly financial products. This study investigates climate change stock market connectedness and spillover effects, focusing on quantile-frequency dependency. The study utilizes a novel quantile-frequency connectedness with seven MSCI climate change market indices and daily data from January 2014 to December 2022. The empirical analysis reveals that climate change stocks are highly interconnected across various market conditions, including bearish and bullish periods. Spillover effects have amplified during increased stock market turbulence, regardless of market directions. Short-term spillover effects have a stronger impact than long-term effects, regardless of market conditions. Climate change stock markets in Japan and the Asia Pacific consistently experience net shocks, while the United States, Europe, Emerging Markets (EM), North America, and the European Economic and Monetary Union (EMU) exhibit variations based on market conditions and timeframes. The study identifies events including the 2015 Paris Agreement, the 2016 US withdrawal from the Paris Agreement, the COVID-19 pandemic, the 2021 US rejoining of the Paris Agreement, and the 2022 Russian-Ukraine War that have influenced the dynamic connectedness and spillover of climate change markets. The analysis also reveals that increased investor attention to climate change leads to higher interconnectedness among climate change stock markets across various market scenarios. Moreover, global financial stress, economic policy uncertainty, geopolitical risk, global carbon emission futures, oil market uncertainty, and stock market uncertainty significantly impact the interdependence of climate change markets. The findings have implications for portfolio managers, emphasizing the need for hedging techniques to mitigate risks in climate change markets.

Global Economic Policy Uncertainty and Global Economic Leaders’ Influence on Regional Economic Growth

This study aims to evaluate the regional economic growth of select American, European, and Asian countries amidst global economic uncertainty. The proposed methodology incorporates mixed frequencies in the data (VAR-MIDAS), facilitating the assessment of their reliance on lagged values, the impact of economic growth in other countries within the same region, and the influence of Global Economic Policy Uncertainty (GEPU). This sheds light on the effects of economic news from relevant newspapers on GDP growth. The primary hypothesis suggests a significant influence of other countries within the same region and the two major global economic powers on the Gross Domestic Product (GDP) growth of each studied region, namely the United States and China. This research also examines this hypothesis across 19 GEPU-included economies, categorizing them regionally by continent. The econometric results confirm the influence of the two leading economies on economic growth and identify causal relationships, with GEPU also exhibiting effects on GDP. Additionally, in-sample estimations reveal disparities in the influence of leaders on the economic growth of the examined economies.

Global banking systems, financial stability, and uncertainty: How have countries coped with geopolitical risks?

The study provides global evidence on the effects of news-based geopolitical risk (GPR), events, and associated risks on the financial stability of banking systems. With extensive large panel data from 179 economies over the past decades, we document strong adverse effects of GPR on financial stability with the predicted decrease (increase) in the bank (non-performing loans) Z-score. Commercial banking systems are heterogeneously exposed to GPR. Possible mechanisms to mitigate the effects of GPR include multidimensional financial development, enhanced world governance, and public finance. Our findings are robust with a battery of econometric approaches controlling for time-varying macroeconomic factors and banking characteristics (e.g., capitalization, concentration, performance) across countries and regions. The impacts of GPR on global financial stability are persistent when we control for systematic banking crises, economic policy, world uncertainty, and global uncertainty spillovers. While higher capitalized banks could secure ample financial stability with substantial military expenditure, the Asia Pacific and emerging economies are sensitive to widespread GPR. The study highlights the roles of institutional quality and world governance factors through times of uncertainty. In The context of global risks, greater global and comprehensive collaborations with mitigated GPR threats are inevitable for our inclusive growth toward Sustainable Development Goals. Our findings are critical to policymakers, advisers, and economists for our global financial stability and sustainable development.

Examining cryptocurrency Trends in the Wake of COVID-19: Addressing volatility challenges

This study examines the effect of the COVID-19 on the liquidity and volatility of nine major cryptocurrencies Trends in the Wake of the pandemic, including Cardano, BNB, Bitcoin, BUSD, Dogecoin, Ethereum, Coin, Tether, and XRP. Using daily data from January 22, 2020, to December 14, 2022, the research reveals a significant increase in overall cryptocurrency market volatility during the pandemic. Contrary to the initial vision of cryptocurrencies as efficient transaction mediums, the study suggests that heightened volatility challenges their suitability for rapid settlement. Additionally, the findings highlight cryptocurrencies’ speculative nature, lacking intrinsic ties to tangible assets and proving ineffective as secure havens or hedges against inflation. The dynamic and unpredictable nature of cryptocurrencies poses challenges to their envisioned roles in the financial landscape, prompting a reevaluation of their utility and stability amid global economic uncertainties.

Trading in Turbulent Times: Unravelling the Interplay between Trade Policy Uncertainty and Geopolitics in Russian Mineral Resource Supply

Trade policy plays a pivotal role in shaping import and export volumes within the global economy. However, the escalation of geopolitical tensions has posed significant challenges to the smooth flow of mineral resource trade. This study aims to analyse the consequences of global trade policy uncertainty (TPU) and various measures of geopolitical risk (GPR), including terror threats, war threats and nuclear threats (NCT), on Russian mineral exports (MEX). The analysis covers the period from January 2013 to December 2021. By employing sophisticated quantile regression methodologies, such as the cross-quantilogram (CQ) and cross-spectral quantile coherency (CQC) techniques, this study uncovers a compelling interaction between TPU and Russian MEX across different market conditions. Interestingly, TPU exerts a significantly positive impact on the export volumes of Russian mineral resources. In contrast, the presence of GPRs, particularly warfare and terrorism, proves to be detrimental to MEX during bullish market phases across various memory horizons. Surprisingly, NCT stimulates mineral trade during bearish market conditions, both in monthly and annual timeframes. These findings remain robust and consistent even when the CQC technique is employed. In light of these pressing geopolitical challenges, this study underscores the importance of developing sustainable mineral trade policies that foster enhanced global cohesion. It is crucial to address these challenges effectively to ensure the smooth and efficient flow of mineral resources in the international trade landscape. JEL Codes: C22, F13, F14, P28, Q21, Q27

The firms’ perception of global uncertainty and R&D investment: Evidence from China

This study explores how the firms’ perception of global uncertainty (FGU) affects R&D investment of listed firms in China. Our analysis is rooted in the understanding and measurement of FGU. Consistent with the theory of strategic growth, we find a significantly positive correlation between FGU and R&D investment. We further elucidate the mechanism underlining this correlation. When FGU increases, financing and labour resources are shifted towards R&D activities, promoting R&D investment. We also examine and explain how this correlation is magnified for firms in less competitive industries, those with higher tangible asset ratios, those related to states, and those with lower deleveraging levels. Finally, we divide FGU into five categories and investigate which subcomponent of FGU has the relatively greatest impact on R&D investment. FGU related to criminal and political events has a greater influence on R&D investment than other types of FGU.

Connection between urbanization and CO2 emissions in South Africa: does global uncertainty matter?

Purpose Given the interest in sustainable development, this study aims to assess the relationship between CO2 and urbanization as well as the role of world uncertainty in this association in a South African context. Design/methodology/approach This study focuses on yearly data from 1968 to 2020. To do this, the authors use the autoregressive distributed lag (ARDL) approach. Findings The authors find that urbanization’s effect on CO2 emissions is only significant when it is augmented with world uncertainty. Moreover, this effect is negative (referring to a reduction in CO2 emissions). Meanwhile, the authors find that GDP has a positive (that is, increasing) and significant effect on CO2 emissions. Overall, policymakers should focus on decoupling economic growth from traditional fossil fuels that produce greenhouse gas emissions. Originality/value The existing body of research contains numerous studies examining the relationship between urbanization and CO2 emissions. However, the dearth of research on the impact of global uncertainty on this connection is weak. Hence, this study aims to fill this gap and make a significant contribution to the field.

Enhancing global sensitivity and uncertainty quantification in medical image reconstruction with Monte Carlo arbitrary-masked mamba

Deep learning has been extensively applied in medical image reconstruction, where Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represent the predominant paradigms, each possessing distinct advantages and inherent limitations: CNNs exhibit linear complexity with local sensitivity, whereas ViTs demonstrate quadratic complexity with global sensitivity. The emerging Mamba has shown superiority in learning visual representation, which combines the advantages of linear scalability and global sensitivity. In this study, we introduce MambaMIR, an Arbitrary-Masked Mamba-based model with wavelet decomposition for joint medical image reconstruction and uncertainty estimation. A novel Arbitrary Scan Masking (ASM) mechanism “masks out” redundant information to introduce randomness for further uncertainty estimation. Compared to the commonly used Monte Carlo (MC) dropout, our proposed MC-ASM provides an uncertainty map without the need for hyperparameter tuning and mitigates the performance drop typically observed when applying dropout to low-level tasks. For further texture preservation and better perceptual quality, we employ the wavelet transformation into MambaMIR and explore its variant based on the Generative Adversarial Network, namely MambaMIR-GAN. Comprehensive experiments have been conducted for multiple representative medical image reconstruction tasks, demonstrating that the proposed MambaMIR and MambaMIR-GAN outperform other baseline and state-of-the-art methods in different reconstruction tasks, where MambaMIR achieves the best reconstruction fidelity and MambaMIR-GAN has the best perceptual quality. In addition, our MC-ASM provides uncertainty maps as an additional tool for clinicians, while mitigating the typical performance drop caused by the commonly used dropout.

Global uncertainties and stock returns under heterogeneous market conditions: Evidence from wavelet coherence analysis

Over the years, the world has faced numerous global uncertainties, each leaving a profound impact on the global economy and the financial market. This has increased the research on how these uncertainties affect various stocks. However, limited attention has been directed toward comprehending the combined influence of geopolitical risk, global policy uncertainty, and infectious disease equity market volatility on heterogeneous stock returns. This study sets out to investigate the impact of geopolitical risk (GPRH), global policy uncertainty (GPU), infectious disease equity market volatility (ID-EMV), and their correlations on stock returns in a heterogeneous market context, employing wavelet coherence analysis. The study's outcomes emphasize the temporal nature of these uncertainties’ effects on financial markets. Furthermore, our research shows that there is a stronger influence on stock returns from various markets when the combined effects of GPU, ID-EMV, and GPRH are taken into account than when the effects are taken into account separately. This implies that times when all of these factors are operating concurrently will see greater fluctuations in markets than times when only one of them acts in the market. The findings of the study provide significant insight into the complex relationship between economic indicators and stock market performance, allowing investors and policymakers to gain a better understanding of the dynamics of global financial markets.

A Monte Carlo Propagation of the Full Variance‐Covariance of GRACE‐Like Level‐2 Data With Applications in Hydrological Data Assimilation and Sea‐Level Budget Studies

AbstractUnderstanding mass (re‐)distribution within the Earth system, and addressing global challenges such as the impact of climate change on water resources requires global time‐variable terrestrial water storage (TWS) estimates along with reasonable uncertainty fields. The Gravity Recovery and Climate Experiment (GRACE) and GRACE‐FO satellite missions provide time‐variable gravity fields with full variance‐covariance information. A rigorous uncertainty propagation of these errors to TWS uncertainties is mathematically challenging and computationally inefficient. We propose a Monte Carlo Full Variance‐Covariance (MCFVC) error propagation approach to precisely compute TWS uncertainties. We also establish theoretical criteria to predict the actual convergence and accuracy of MCFVC, showing a convergence after 10,000 realizations with the relative error of 2.8% for variance and 4.7% for covariance at the confidence level of 95%. This can be achieved in few seconds using a single CPU to compute the uncertainties of each 1° resolution globally gridded TWS field. A validation against the rigorous error propagation method indicates relative differences of less than 0.8%. A global uncertainty assessment shows that neglecting the covariance of gravity coefficients can considerably bias the TWS uncertainties, that is, up to 60%, in some basins like Eyre. Flexibility of MCFVC allows the quantification of filtering impacts on the uncertainty of TWS fields, for example, up to 35% in the Tocantins River Basin. An empirical model is provided to reproduce GRACE‐like TWS uncertainty fields for hydrological studies. Finally, experiments of GRACE(‐FO) data assimilation for hydrological applications and sea‐level budget estimation are presented that indicate the importance of accounting for the full covariance information in these studies.

Fetus descent simulation with the active uterine contraction during the vaginal delivery: MRI-based evaluation and uncertainty quantification

Finite element models ranging from single to multiscale models have been widely used to gain valuable insights into the physiological delivery process and associated complication scenarios. However, the fetus descent simulation with the active uterine contraction is still challenging for validation and uncertainty quantification issues. The present study performed a fetus descent simulation using the active uterine contraction. Then, simulation outcomes were evaluated using theoretical and in vivo MRI childbirth data. Moreover, parameter uncertainty and propagation were also performed. A maternal pelvis model was developed. The active uterine contraction was modeled using a transversely isotropic Mooney–Rivlin material. Displacement trajectories were compared between simulation, theoretical and in vivo MRI childbirth data. Monte Carlo (M.C) and Polynomial Chaos Expansion (PCE) methods were applied to quantify uncertain parameters and their propagations. Obtained results showed that fetal descent behavior is consistent with the MRI-based observation as well as the theoretical trajectory (curve of Carus). The head downward vertical displacement ranges from 0 to approximately 47 mm. A reduction of 50% in uterine size was observed during the simulation. Three high-sensitive parameters ( C 1 , C 2 , Ca 0 ) were also identified. Our study suggested that the use of the active uterine contraction is essential for simulating vaginal delivery but the global parameter sensitivity, parameter uncertainty, and outcome evaluation should be carefully performed. As a perspective, the developed approach could be extrapolated for patient-specific modeling and associated delivery complication simulations to identify risks and potential therapeutic solutions.

The Influence of Macroeconomic Variables on Non-Performing Finance in Sharia Commercial Banks

One of the advantages of the sharia banking business is financing productive sectors that do not contain haram elements. Financing in the micro, small and medium sectors is a sharia banking financing business line, but there is a big risk of bad credit. In recent years, increasing global and domestic economic uncertainty has led to depreciation of the exchange rate, unstable prices of basic commodities, thereby reducing people's purchasing power. This research aims to examine macroeconomic variables such as the exchange rate, inflation and production index as a proxy for GDP towards NPF of sharia banking. The research uses monthly time-series data and the OLS method. The research results show that macroeconomic variables have an influence on NPF. The research implication is that the stability of macroeconomic conditions can maintain people's purchasing power which can influence the NPF level of sharia banking.

Analysis of effect of global uncertainty on domestic uncertainty using connectedness index

Analysis of effect of global uncertainty on domestic uncertainty using connectedness index

New-type infrastructure and urban economic resilience: Evidence from China

Rising global uncertainties pose challenges to the resilience of economic development, and new-type infrastructure, referring to a system that is supported by next-generation information technology, can be a crucial stabilizing factor in the digital era to overcome uncertainties. This study uses 5244 observations from 276 cities in China from 2003 to 2021 to investigate the impact of new-type infrastructure on urban economic resilience. The empirical findings indicate that new-type infrastructure can effectively enhance regional economic resilience, and urban economic resilience can be improved in the digital age. New-type infrastructure can effectively mitigate the adverse impact of Corona Virus disease 2019 (COVID-19) on economic resilience. The effect of new-type infrastructure on economic resilience is larger in central regions, cities with medium and large economic scales, and cities with well-developed traditional infrastructure. New-type infrastructure exhibits threshold effects with changes in mechanism variables, decreasing as the threshold of resource dependence is crossed and increasing as the threshold of capital deepening and human capital is crossed. New-type infrastructure can enhance economic resilience by promoting innovation, including digital and green innovation. In addition, new-type infrastructure can cultivate emerging industries, advance industrial structure optimization, and promote collaborative agglomeration, which also contributes to enhancing economic resilience.

Economical hybrid novelty detection leveraging global aleatoric semantic uncertainty for enhanced MRI-based ACL tear diagnosis

This study presents an innovative hybrid deep learning (DL) framework that reformulates the sagittal MRI-based anterior cruciate ligament (ACL) tear classification task as a novelty detection problem to tackle class imbalance. We introduce a highly discriminative novelty score, which leverages the aleatoric semantic uncertainty as this is modeled in the class scores outputted by the YOLOv5-nano object detection (OD) model. To account for tissue continuity, we propose using the global scores (probability vector) when the model is applied to the entire sagittal sequence. The second module of the proposed pipeline constitutes the MINIROCKET timeseries classification model for determining whether a knee has an ACL tear. To better evaluate the generalization capabilities of our approach, we also carry out cross-database testing involving two public databases (KneeMRI and MRNet) and a validation-only database from University General Hospital of Larissa, Greece. Our method consistently outperformed (p-value<0.05) the state-of-the-art (SOTA) approaches on the KneeMRI dataset and achieved better accuracy and sensitivity on the MRNet dataset. It also generalized remarkably good, especially when the model had been trained on KneeMRI. The presented framework generated at least 2.1 times less carbon emissions and consumed at least 2.6 times less energy, when compared with SOTA. The integration of aleatoric semantic uncertainty-based scores into a novelty detection framework, when combined with the use of lightweight OD and timeseries classification models, have the potential to revolutionize the MRI-based injury detection by setting a new precedent in diagnostic precision, speed and environmental sustainability. Our resource-efficient framework offers potential for widespread application.

The examination of the impact of global uncertainties on developed and developing markets with structural VAR model

This study aims to examine the impact of global uncertainties on developed and developing markets with the help of the Structural VAR model. Due to the integration of financial markets, it is of great importance for securities market investors to be able to predict the direction of the markets. One of the variables to be used to predict the development of financial markets is the MSCI indices. In the study, the Developed Markets Index (MSCIWO) was used to represent developed markets, and the MSCIEF Emerging Markets Index was used to represent emerging markets. In the study, two separate models were established to measure the impact of global uncertainties on developed and developing markets. Using monthly data from 2014-2023, this study identifies differences between developed and emerging markets and shows how these markets react to different types of uncertainty. The results obtained from the study show that the effect of the Global Economic Policy Uncertainty Index (GEPU) variable on developed and developing capital markets is positive in the long term. Additionally, it was determined that the Geopolitical Risk Index (GPR) variable has a long-term negative impact on both markets. Analysis results indicate that the effect of the VIX variable on advanced markets is not significant, whereas it shows a negative impact on developing markets.

Efficiency of Healthcare Financing: Case of European Countries

Global turbulence and uncertainty force civil servants and executors to optimise public finance distribution. The COVID-19 pandemic aligned with the necessity of assessing the efficiency of healthcare financing due to its capability in overcoming the negative consequences. The paper analyses the peculiarities of healthcare financing in 34 European countries and points out trends and changes in its structure and dynamics. It also realises cluster analysis to reveal models of healthcare financing and their specific features. Panel data regression analysis was used to assess the efficiency of healthcare financing within each cluster by clarifying the relationship between healthcare expenditures and public health outcome—life expectancy. The distributed lag model was also used to test for time lags between financial inflows in healthcare and its outcome. Empirical results highlight key tips for optimising healthcare financing and creating the benchmark model.

Global evidence on the energy–environment dilemma: the role of energy-related uncertainty across diverse environmental indicators

ABSTRACT Several existing studies show that macroeconomic uncertainties intensify global environmental and climate challenges, putting the globe at risk of not being able to achieve the United Nations’ Sustainable Development Goals by 2030. In this study, we provide global evidence on the role of energy-related uncertainty in the energy – environment dilemma between 1996 and 2021. We employ three distinct environmental indicators – load capacity factor (LCF), carbon dioxide emissions (CO2), and ecological footprint (EFP) – alongside a comprehensive global energy-related uncertainty index and time-frequency-quantile methods based on the Wavelet Quantile Correlation, Cross-Quantilogram, and Wavelet Local Multiple Correlation with Dominance. The empirical results suggest negative and strong nonlinear dependencies between energy-related uncertainty and the LCF across periods and quantiles. The results further suggest that the energy-related uncertainty has positive and strong nonlinear dependences not only with CO2 emissions but also EFP across various periods and quantiles. The results further suggest that the dependences between energy-related uncertainty and environmental indicators vary across periods and quantiles, with evidence of stronger dependency structures in the long run. These findings underscore the substantial influence of energy-related uncertainties on contemporary environmental challenges. We suggest that governments and policymakers need to reshape policy directives toward mitigating the environmental effects of energy-related uncertainties.

Volatility dynamics in energy and agriculture markets: An analysis of domestic and global uncertainty factors

PurposeThis study aims to explore the intricate relationship between uncertainty indicators and volatility of commodity futures, with a specific focus on agriculture and energy sectors.Design/methodology/approachThe authors analyse the volatility of Indian agriculture and energy futures using the GARCH-MIDAS model, taking into account different types of uncertainty factors. The evaluation of out-sample predictive capability involves the application of out-sample R-squared test and computation of various loss functions.FindingsThe research outcomes underscore the significant impact of diverse uncertainty factors such as domestic economic policy uncertainty (EPU), global EPU (GEPU), US EPU and geopolitical risk (GPR) on long-run volatility of Indian energy and agriculture (agri) futures. Additionally, the study demonstrates that GPR exhibits superior predictive capability for crude oil futures volatility, while domestic EPU stands out as an effective predictor for agri futures, particularly castor seed and guar gum.Practical implicationsThe study offers practical implications for market participants and policymakers to adopt a comprehensive perspective, incorporating diverse uncertainty factors, for informed decision-making and effective risk management in commodity markets.Originality/valueThe research makes an inaugural attempt to examine the impact of domestic and global uncertainty indicators on modelling and predicting volatility in energy and agri futures. The distinctive feature of considering an emerging market also adds a novel dimension to the research landscape.