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Abstract Quantum technologies (QTs) hold the promise to transform a wide range of industries, such as computing, communications, finance, healthcare, defense, space, and beyond. Of the various QTs, the most relevant is presently quantum computing (QC), of significant projected market potential, with some estimates forecasting it to reach many billion dollars over the next few years. There are, however, risks to factor, as highlighted in this perspective, the most relevant technical, economic, and societal risks. Those financial and societal are projected to become increasingly relevant phased with the progressing solution of the technical issues. The synergy with artificial intelligence comes with further opportunities as development can be faster and more effective, but also increases risks. AI presents numerous opportunities, but it also comes with several risks and challenges. Some of the major risks associated with AI include bias and fairness, transparency and explainability, job displacement, security concerns, ethical concerns, privacy issues, lack of regulation and standards, and exponential growth and unintended consequences. Balancing the advantages of AI-driven quantum technologies with associated risks presents a significant challenge. It necessitates careful consideration of ethical, economic, and technical aspects to ensure that these technologies are developed and deployed in a manner that is beneficial and equitable for everyone. Graphical Abstract

The pursuit of a sustainable environment is a common goal for many nations; however, achieving this has become increasingly difficult due to country‐specific risks, often referred to as country risk. Despite its importance, the relationship between country risk and environmental outcomes is still in its early stages of exploration. This study aims to bridge this gap by examining the influence of country risk on India's ecological footprint within an asymmetric empirical framework, while accounting for economic growth and coal energy consumption. Utilizing the nonlinear autoregressive distributed lag (NARDL) estimator, the analysis covers data from 1984 to 2018. The findings reveal that positive changes in economic and financial risk contribute to environmental improvement by significantly reducing the ecological footprint, whereas negative changes in these risks lead to an increase in ecological footprint. Additionally, positive changes in political risk, economic growth, and coal energy usage exacerbate the ecological footprint, while reductions in coal energy use and political risk lead to environmental benefits. Based on these outcomes, policymakers are encouraged to establish a clear risk benchmark and reduce the ecological footprint by fostering economic growth in a sustainable manner. Governments should also prioritize strong support for green technology investments and ensure stability across political, economic, and financial sectors to promote long‐term environmental sustainability. [ABSTRACT FROM AUTHOR]

Quantum technologies (QTs) hold the promise to transform a wide range of industries, such as computing, communications, finance, healthcare, defense, space, and beyond. Of the various QTs, the most relevant is presently quantum computing (QC), of significant projected market potential, with some estimates forecasting it to reach many billion dollars over the next few years. There are, however, risks to factor, as highlighted in this perspective, the most relevant technical, economic, and societal risks. Those financial and societal are projected to become increasingly relevant phased with the progressing solution of the technical issues. The synergy with artificial intelligence comes with further opportunities as development can be faster and more effective, but also increases risks. AI presents numerous opportunities, but it also comes with several risks and challenges. Some of the major risks associated with AI include bias and fairness, transparency and explainability, job displacement, security concerns, ethical concerns, privacy issues, lack of regulation and standards, and exponential growth and unintended consequences. Balancing the advantages of AI-driven quantum technologies with associated risks presents a significant challenge. It necessitates careful consideration of ethical, economic, and technical aspects to ensure that these technologies are developed and deployed in a manner that is beneficial and equitable for everyone. [ABSTRACT FROM AUTHOR]

The current agriculture system has become complex and fragile in recent years. With an increase in population, the demand for food is increasing, but the resources such as arable land and water are limited, and clearing forest land for cultivation and over-extraction of groundwater are changing land-use patterns and depleting groundwater resources, which again are responsible for multiple risks in agriculture and food system. The limited land and water resources with increased global population and its demand for food have mainly stressed small farmers. The rising environment, social and economic risks such as crop disease outbreaks, climate risk causing natural hazards such as floods, famine, drought, exposure to chemicals, technology risks such as genetically modified crops, and biofuels, food demand disparities, demographic and dietary changes, financial risk, conflict and political unrest, biological diversity loss, psychological factors in long-term decision making, and emerging complexity within agriculture system network are the some of the examples of multiple risks faced by small farmers in developing nations. Understanding the link among multiple domains such as environment, soil and hydrology, science, technology, finance, psychology, nutrition, and relation and conflicts is vital to study the multiple risks associated with the agriculture system as these domains overlap. Thus, sustainable long-term solutions cannot be confined to a single discipline approach. Therefore, there is a need to understand the intertwined nature of multiple risks affecting farmers. First, the author emphasizes on understanding the interconnected nature of rising multiple risks in modern industrial agriculture and food system in terms of social, environmental, and economic dimensions, this understanding is crucial for sustainable agriculture policy framing. Second, providing policy implications that will help policy makers to develop legalize mechanism to reduce rising risk of hazards. [ABSTRACT FROM AUTHOR]

The establishment of acceptable risk standards serves as a crucial connection between earthquake disaster risk assessment and risk management. Currently, there lacks a standardized criterion for determining the acceptable level of risk pertaining to earthquake disasters. To establish a standardized system of acceptable risk of earthquake disaster, the data of disaster-affected toll, death toll, and direct economic losses resulting from earthquakes disaster with MS ≥ 5.0 in Chinese Mainland between 1991 and 2020 are utilized. These data are employed to construct the acceptable life risk F–N curve (the accumulated probability F of annual death toll ≥ N—death toll N), the acceptable economic risk F–D curve (the accumulated probability F of annual direct economic losses ≥ D—direct economic losses), and the acceptable comprehensive risk F–L curve (the accumulated probability F with earthquake occurrence of annual disaster degree ≥ L—disaster degree). Then, the acceptable risk level of earthquake disasters with MS5.0–5.9, MS6.0–6.9, MS ≥ 7.0, and MS ≥ 5.0 are determined. Moreover, the classification and consistency of acceptable types of earthquake disaster comprehensive risk, life risk and economic risk in the past 30 years are compared. Through analysis, the acceptable life risk, acceptable economic risk, and acceptable comprehensive risk criteria of earthquake disasters with MS5.0–5.9, MS6.0–6.9, MS ≥ 7.0, and MS ≥ 5.0 in Chinese Mainland are obtained. It is found that the comprehensive risk of earthquake disasters with different magnitudes is mainly at an unacceptable level, while the main types that are consistent with life risk and economic risk are tolerable level and acceptable level, respectively. The research results can provide more comprehensive theoretical data and practical basis for the effective implementation of earthquake disaster risk management. [ABSTRACT FROM AUTHOR]

This study examines the risks associated with investing in Indonesia, as well as the impact of foreign direct investment (FDI) on the country's macroeconomy. It uses the autoregressive distributed lag (ARDL) method to analyze the short-term and long-term relationship between macroeconomic variables and FDI inflows. The study utilizes annual time series data spanning from 1984 to 2015. The exchange rate is critical in the short term because it has an inverse effect on foreign direct investment (FDI). On the other hand, factors like inflation, GDP growth risk, and economic and political concerns have a major impact on foreign direct investment (FDI) over a long period of time. Financial factors, however, do not exhibit long-term cointegration with FDI. Prudent international investors prioritize the instability of macroeconomic variables to optimize profits on invested funds, highlighting the necessity of government regulation over these variables to enhance foreign direct investment inflows. This study contributes to the current body of work regarding the influence of country risk on foreign direct investment (FDI) inflows into Indonesia.

Abstract A reliable economic risk map is critical for effective debris-flow mitigation. However, the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its application. To estimate the economic risks caused by future debris flows, a machine learning-based method was proposed to generate an economic risk map by multiplying a debris-flow hazard map and an economic vulnerability map. We selected the Gyirong Zangbo Basin as the study area because frequent severe debris flows impact the area every year. The debris-flow hazard map was developed through the multiplication of the annual probability of spatial impact, temporal probability, and annual susceptibility. We employed a hybrid machine learning model—certainty factor-genetic algorithm-support vector classification—to calculate susceptibilities. Simultaneously, a Poisson model was applied for temporal probabilities, while the determination of annual probability of spatial impact relied on statistical results. Additionally, four major elements at risk were selected for the generation of an economic loss map: roads, vegetation-covered land, residential buildings, and farmland. The economic loss of elements at risk was calculated based on physical vulnerabilities and their economic values. Therefore, we proposed a physical vulnerability matrix for residential buildings, factoring in impact pressure on buildings and their horizontal distance and vertical distance to debris-flow channels. In this context, an ensemble model (XGBoost) was used to predict debris-flow volumes to calculate impact pressures on buildings. The results show that residential buildings occupy 76.7% of the total economic risk, while road-covered areas contribute approximately 6.85%. Vegetation-covered land and farmland collectively represent 16.45% of the entire risk. These findings can provide a scientific support for the effective mitigation of future debris flows.

The main objective of this article is to investigate the impact of various financial, economic, and political risks and economic complexity on the development of the Sukuk market in the Iranian economy. The data required to conduct this research based on the variables of the proposed model were used from the Capital Market Central Asset Management Company, the International Country Risk Guide (ICRG) database, and the MIT University website. The data relating to 2010-2022 is seasonal, and REVIEWS 13 software was used. The model estimation results using the Nonlinear Autoregressive Distributed Lag Model Approach (NARDL) show that the negative shock of political risk reduces the development of the Sukuk market in the short and long term. The negative shock of financial risk in the long term has a negative impact on the development of the Sukuk market. The negative shock of economic complexity reduces the development of the Sukuk market in the short term. The positive shocks of political risk, financial risk, economic risk, and economic complexity in the short and long term led to the development of the Sukuk market. Among the three types of risk, political risk and financial risk have the most impact on sukuk market development. The error correction coefficient in this estimate is negative and statistically significant, which shows that 0.42% of the short-term imbalance is adjusted to reach the long-term balance every year. [ABSTRACT FROM AUTHOR]

China's social safety net is still underdeveloped, hence family support in the form of intergenerational transfers often serves as a substitute for the public transfer system. Using data from the China Health and Retirement Longitudinal Study, this paper finds that both upstream inter‐vivos transfers (from children to parents) and downstream inter‐vivos transfers (from parents to children) are prevalent in urban China. Moreover, the relative income status of the parent and children has an impact on inter‐vivos transfers. To investigate what economic factors generate the observed patterns of inter‐vivos transfers, this paper adopts a general equilibrium life‐cycle model in which overlapping generations are altruistically linked and calibrates the model to match data from urban China. Counterfactual experiments of removing one source of economic risk or modifying the social security replacement rate from the baseline model at a time reveal that intergenerational transfers mainly serve as informal insurance against the income risk of the children. [ABSTRACT FROM AUTHOR]

Given the increased frequency of extreme rainfall events, pre-disaster countermeasures against landslides triggered by heavy rainfall are important to enhance disaster resilience. This study presents a methodology for economic risk assessment of structures affected by rainfall-induced landslides using machine learning (ML). Random Forest and LightGBM algorithms were applied to develop ML-based landslide prediction models considering the spatial distributions of landslide conditioning and triggering factors. The rainfall index was calculated considering the temporal variation in rainfall and was used as a feature associated with rainfall intensity. The rainfall hazard curve, representing the relationship between the rainfall index and its annual exceedance probability, was statistically estimated using a generalised extreme value distribution. Rainfall-induced landslide susceptibility was assessed using an ML-based landslide prediction model and rainfall hazard curve. Finally, the risk curve associated with the economic loss from structures damaged by rainfall-induced landslides was estimated based on landslide susceptibility and structure distribution maps. In this study, LightGBM showed better prediction performance for evaluating rainfall-induced landslide susceptibility than Random Forest. An illustrative example is presented to demonstrate that the proposed methodology can be used to develop an appropriate risk-based disaster mitigation strategy. [ABSTRACT FROM AUTHOR]

A reliable economic risk map is critical for effective debris-flow mitigation. However, the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its application. To estimate the economic risks caused by future debris flows, a machine learning-based method was proposed to generate an economic risk map by multiplying a debris-flow hazard map and an economic vulnerability map. We selected the Gyirong Zangbo Basin as the study area because frequent severe debris flows impact the area every year. The debris-flow hazard map was developed through the multiplication of the annual probability of spatial impact, temporal probability, and annual susceptibility. We employed a hybrid machine learning model—certainty factor-genetic algorithm-support vector classification—to calculate susceptibilities. Simultaneously, a Poisson model was applied for temporal probabilities, while the determination of annual probability of spatial impact relied on statistical results. Additionally, four major elements at risk were selected for the generation of an economic loss map: roads, vegetation-covered land, residential buildings, and farmland. The economic loss of elements at risk was calculated based on physical vulnerabilities and their economic values. Therefore, we proposed a physical vulnerability matrix for residential buildings, factoring in impact pressure on buildings and their horizontal distance and vertical distance to debris-flow channels. In this context, an ensemble model (XGBoost) was used to predict debris-flow volumes to calculate impact pressures on buildings. The results show that residential buildings occupy 76.7% of the total economic risk, while road-covered areas contribute approximately 6.85%. Vegetation-covered land and farmland collectively represent 16.45% of the entire risk. These findings can provide a scientific support for the effective mitigation of future debris flows. [ABSTRACT FROM AUTHOR]

The relationship between economic, financial, political risks and per capita carbon emission (CO2) is considered as one of the major global challenges. The effect of these three factors on carbon emissions is very important. Therefore, the current research seeks to investigate the role of economic, financial, and political risk in reducing per capita carbon emissions (CO2) by using the very new and fresh approach of quantile-on-quantile regression (QQR) modeling in the annual period from 1990 to 2018. The statistical relationship between the variables mentioned in Eviews12 and Matlab2022 software platform has been investigated for Iran. The results show that the economic risk variable in all quantiles (0.5 to 0.95) had a positive effect on carbon emissions per capita in all quantiles (0.5 to 0.95), and this positive relationship was relatively stronger in the quantiles (0.3 to 0.95), of the economic risk variable. the financial risk variable in all quantiles (0.5 to 0.95) had a positive effect on carbon emissions per capita in all quantiles (0.5 to 0.95), and this positive relationship was relatively weak in all quantiles (0.5 to 0.95) of the financial risk variable, as well as Politics risk has a positive effect on carbon emissions per capita in all quantiles (0.5 to 0.95) and this positive relationship is relatively weak in all quantiles (0.5 to 0.95) of the Politics risk variable. Thus, the need to pay attention to Iran's economic, financial, and political stability to improve the environment's quality and reduce carbon emission (CO2) is very important.1.IntroductionThe most significant global threat in the 21st century is climate change and global warming, primarily driven by carbon dioxide (CO2) emissions (Akadiri et al., 2021; Oladipopo et al., 2021). The rapid development of modern industrial societies worldwide in recent years has led to a gradual rise in the consumption of fossil fuels, including coal, oil, and natural gas. The increased consumption has resulted in the substantial emission of CO2 (Danish et al., 2019; Dong et al., 2018; Zhao et al., 2021). In recent years, there has been an enhanced awareness among governments and international organizations worldwide regarding the impact of climate change on the economy, society, and the environment (Gambier et al., 2022). The heightened awareness has prompted the adoption of environmental protection policies (Roncroni et al., 2021). However, the implementation of these policies requires significant expenditures. Consequently, the role of financial stability in addressing the risks associated with climate change and reducing greenhouse gas (GHG) emissions has gained increasing importance (Sun et al., 2022). Research indicates that a stable financial environment is conducive to stimulating production and investment, albeit with a potential increase in energy consumption and CO2 emissions (Solimana et al., 2017).Global warming and CO2 emissions are closely intertwined with economic and political risks (Adoms et al., 2018). Global uncertainties have increased the volatility of economic and political policies on a global scale. Any form of uncertainty, be it social, political, economic, or war-related, invariably impacts economic activities (Blatman & Miguel, 2010; Guidolin & La Ferrara, 2010). Economic (in)stability plays a crucial role in shaping the environment in which companies operate, influencing the decision-making processes of economic entities. Similarly, political instability can significantly impact investors’ decision-making. Moreover, political risk is on the rise in nearly all countries, exerting pressure on military budgets at the expense of construction budgets. This situation leads to a reduction in overall production within the country, and the decreased production results in a further decline in energy consumption, and ultimately leading to a decrease in carbon emissions (Ahmad et al., 2022). Employing a novel methodology known as quantile-on-quantile regression (QQR), the present research aimed to explore the impact of economic, financial, and political risks on per capita carbon emissions in Iran during 1990–2018. Regarding the methodology and the specific focus, no similar research has been conducted in Iran. Therefore, the current study stands out for its innovation in terms of subject matter, methodology, and the targeted context, potentially yielding significant findings.2. Materials and MethodsThe QQR approach is a novel method for analyzing bivariate equations. Introduced by Sim and Zhu (2015), it combines ordinary regression and nonparametric estimation, providing more comprehensive insights compared to traditional estimation methods. QQR examines the intricate relationship between the lower and upper quantiles of the data series, which yields a more realistic analytical perspective than conventional methods (Yu et al., 2022). This study used the QQR approach to investigate the relationship between economic, financial, and political risks and per capita carbon emissions. In this line, the econometric model was formulated as in Equation (1): (1) In Equation (1), CO2t denotes per capita carbon emissions in year t. ERt represents economic risk in year t. FRt is financial risk in year t. PRt indicates political risk in year t, and 𝜀𝑡 is a component of the model error.Several methods were used to analyze the data, including the descriptive analysis, assessment of variable reliability, the diagnostic test (esp., the disruption components autocorrelation test), the correlation test, Johansen’s co-accumulation, and finally the quantile-by-quantile model estimation.3. Results and DiscussionUtilizing the innovative econometric approach of quantile-on-quantile regression (QQR), the research explored the statistical relationship between economic, financial, and political risk variables and per capita carbon emissions in Iran during 1990–2018. The findings revealed that the economic risk variable had a positive effect on carbon emissions per capita across all quantiles (0.5 to 0.95), with this positive relationship being relatively stronger in the 0.3–0.95 quantiles of the economic risk variable. Similarly, the financial risk variable had a positive effect on carbon emissions per capita in all quantiles (0.5 to 0.95), although this positive relationship is relatively weak across all quantiles of the financial risk variable. Likewise, political risk positively influenced carbon emissions per capita in all quantiles (0.5 to 0.95), with this positive relationship being relatively weak across all quantiles of the political risk variable. The research results align with the findings of Zhang and Chiu (2020), Abbasi and Riaz (2016), Mehmet et al. (2018), and Zaidi et al. (2019).4.ConclusionThe present study aimed to examine the correlation between economic, financial, political risks, and per capita carbon emissions in Iran during 1990–2018. The findings emphasize the significance of maintaining economic, financial, and political stability in Iran as it is crucial for enhancing the quality of the environment and mitigating carbon emissions.

Analysts' perspectives on economic risk are a crucial element in the investor decision-making process. Several researchers have immersed themselves in analyst forecasts and stock prices. However, it is imperative to examine the consistency of findings from previous studies in order to draw general conclusions. This analysis is necessary to establish whether analysts' forecasts actually influence stock market returns. Consistency between studies was assessed using a meta-analysis approach, facilitated by Comprehensive Meta-Analysis (CMA) software. Metaanalysis provides an effect size estimate to assess the relationship between the independent variable (analysts' forecasts) and the dependent variable (stock market returns). The results of the heterogeneity test indicate an Isquared value in excess of 50%, signifying substantial variation. Similarly, the correlation analysis reveals a p-value of 0.004, suggesting heterogeneity in the studies of analyst forecasts and stock market returns. Heterogeneity refers to disparities in data within or between studies, attributable to varying research locations and economic conditions. Consequently, the results of this study are heterogeneous due to significant sampling error arising from various research locations and economic circumstances. Thus, it is demonstrated that analysts' forecasts have an impact on stock market returns. [ABSTRACT FROM AUTHOR]

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The purpose of this research is to investigate different levels of country risk and its role in the relationship between renewable energy consumption and economic growth in Iran, during the period (1997-2021). In terms of purpose, this research is of applied type and in terms of causal-analytical method, and the method of collecting information is of documentary-library type. After calculating the threshold value of each variable, we analyzed the effect of renewable energy consumption on economic growth using the threshold distance of different country risks. The results indicate the non-linear effect of renewable energy consumption on economic growth under different risks in the country. This research is one of the first studies in Iran that analyzed the relationship between renewable energy consumption and economic growth based on a risk-based approach. According to the regression model described in the current research, this research provides suggestions for developing a suitable strategic plan with the aim of specifying short-term and long-term goals, and future vision, as a road map for those involved. .

The U.S. catfish industry has adopted several production practices which embody varying degrees of economic risk. Employing commercial farm data, this study quantified the economic risks associated with six catfish food fish production strategies. Stochastic Monte Carlo simulations employing established enterprise budgets found fish yield, feed price, and feed conversion ratio contributing highly to variations in the cost of production. While multiple-batch (MB) farming of channel catfish was the least risky strategy, both MB and intensively aerated production were stochastically dominant (second-order) to low-intensity single-batch production. Split-pond and intensively aerated hybrid catfish production demonstrated consistently lower production costs and were stochastically dominant (second-order) to medium-intensity single-batch production. Multiple-batch and intensively aerated production of channel catfish were more susceptible to price (market) risk while hybrid catfish production was more susceptible to yield (production) risks. First order stochastic dominance of split-pond technology on larger farms as compared to low-intensity culture on smaller farms suggested that yield increasing intensive production practices supersede low-intensity technologies and help achieve economies of scale. Study results provide critical information on the relative risk associated with different catfish production strategies under varying economic and market conditions. [ABSTRACT FROM AUTHOR]

Flood risk quantification is a complex process involving a chain of models. The approach contains many uncertainties which accumulate in the calculated flood risk. Risk-based flood safety standards are therefore prone to large uncertainty. This study aims to quantify the uncertainty of risk-based flood safety standards for a Dutch riverine case study. Through a systematic approach using expert elicitation, the key uncertainty sources in the Dutch safety standard derivation are identified. Uncertainty magnitudes are quantified for five main sources of uncertainty, after which these uncertainties are propagated through the model chain in a scenario analysis to derive flood safety standards. The results show that the Dutch risk-based flood safety standards are highly uncertain given the relative uncertainty bandwidth of 70%. The flood safety standards are found to be most sensitive to damage function and evacuation uncertainty. Also, we show that the impact of uncertainty strongly depends on local characteristics. [ABSTRACT FROM AUTHOR]

The rising demand for reducing the shadow economy (SE) through financial development (FD) has stimulated researchers worldwide. However, various issues that could affect the FD and SE nexus must be highlighted. Therefore, it is necessary to understand the topic of interest related to this area and to expand the engaging network for advanced research by integrated efforts. The main goal of this bibliometric analysis is to assess the global research trends in the FD and SE literature based on publication outputs, co-occurrences of author keywords, and co-authorships between authors and affiliated nations. The study retrieved 386 research articles from the Scopus database and employed VOSviewer software for results visualization. The study results have shown that until 1998, the FD-SE literature has increased very slowly, while from 2008, the research publications have increased largely. The United States (US) and United Kingdom (UK) are the most contributing countries, while the University of Houston-Victoria of US and the University of Manchester of UK are the leading universities in most productive institutions. Moreover, fintech, institutional change, market-based financial system, credit cycle, bankable adults, and non-bank financial institutions are the recent variables that are used in SE literature. [ABSTRACT FROM AUTHOR]

In the last two decades, environmental degradation has been a topic of concern. The rising level of CO2 emissions (CO2E) has adversely affected life in the E7 countries, which comprise of Brazil, China, India, Indonesia, Mexico, Russia, and Turkey. The increased in CO2E is the cause of rising sea levels in the E7 countries. Visibly, E7 nations which are considered as the largest emitters of CO2 are facing the most severe environmental challenges. This study investigates the impact of eco-innovation, economic growth (EG), renewable energy consumption (REC), economic risk (ERI), and globalization on the CO2E, using the Feasible Generalized Lease Squares (FGLS) and Panel Corrected Standard Errors (PCSE) techniques for the period 1995 to 2018. The results indicate an inverted N-shaped relationship between eco-innovation and CO2E. Also, eco-innovation, REC, and economic risk are observed to be significant factors in abating CO2 emissions. On the contrary, globalization and GDP are responsible for rising CO2E in E7 countries. According to empirical estimates, eco-innovation improves the efficiency of carbon emissions, which lowers CO2E. In addition, because they are immune to changes in the price of oil and gas and disruptions brought about by geopolitical events, renewable energy sources can offer countries a more secure energy source than fossil fuels. Alternative energy sources can reasonably cut CO2E while offering a more reliable and secure energy source. Therefore, it is crucial that policies be put in place to cut CO2E by giving priority to environmental innovative policies. [ABSTRACT FROM AUTHOR]

In recent time, Turkey could be said to have experienced different levels of Economic Risk, Financial Risk, and Political Risk from low- to high-level. This study investigates the linkage between country risks, namely Financial Risk, Economic Risk, and Political Risk (FEP risk) in Turkey for the period 1984Q1 to 2019Q1 by using threshold cointegration, Markow-switching regression (given the nonlinearity and structural breaks observed in the time series variables), and frequency domain causality approaches. The empirical findings of this study reveal that (i) nonlinear cointegration between Economic Risk, Financial Risk, and Political Risk in Turkey is statistically significant given the evidence of threshold cointegration test, which determines the structural breaks endogenously; (ii) there is positive linkage among the component of country risk at different volatility periods; (iii) there is a significant Granger causal linkage between Economic Risk, Financial Risk and Political Risk at the different frequency levels. The study is likely to open debate about the literature since the study concludes with a discussion on short-run and long-run implications for economic, political, and financial stabilises, thus offering policy suggestions for the policymakers in Turkey. [ABSTRACT FROM AUTHOR]

Abstract This study specifically explores the effect of domestic political and economic risks on risk-taking in the banking sector for 105 countries operating in six various geographical regions between 2009 and 2017. To the best of our knowledge, this may be the first study that attempts to conduct this relationship from this perspective. Remarkably, the dynamic estimation results underscore that a rise in political and economic risks triggers risk-taking behavior in the banking sector globally, in particular in the OECD High-income region. Besides, the estimation results reveal that capital regulation, market power, and income diversification negatively impact risk-taking while credit risk, inefficiency, financial market development, and deposit insurance have a positive effect on risk-taking behavior. The results also stress that the extent of the effect of determinants and significance level vary by changing the region. The results are robust and have significant implications for policymakers and bank managers.

In this study, the focus is on examining the influence of renewable energy consumption, economic risk, and financial risk on the load capacity factor (LF) within the BRICS countries. The analysis covers the time span from 1990 to 2019. The empirical strategy uses the Method of Moments Quantile Regression (MMQR) and long-run estimators (Fixed Effects Ordinary Least Squares, FE-OLS; Dynamic Ordinary Least Squares, DOLS; and Fully Modified Ordinary Least Squares, FMOLS). The findings highlight the presence of a cointegrating relationship. Moreover, fossil fuels and economic growth cause LF to decrease, while economic risk and the use of renewable energy sources increase the deepening of the LF. Furthermore, the results of the MMQR method are confirmed by DOLS, FMOLS, and FE-OLS estimates. Causality results also demonstrate that these factors may forecast ecological quality, indicating that policies for renewable energy consumption, financial risk, renewable energy, and economic growth can all have an impact on the degree of LF. In light of this research, policymakers should strongly encourage expenditures on environmentally friendly technologies and economic and financial stability to increase energy efficiency as well as sustain the widespread adoption and use of energy-saving products. [ABSTRACT FROM AUTHOR]

The blend of economic stagnation and inflation is a challenge to the contemporary macroeconomic study. The causes, influence and policy implications of "stagflation" have been academically controversial. This paper starts with the connections between energy crises and stagflation to research the causes of "stagflation" in the 1970s and analyze the realistic impact of energy crises. It then comparatively studies the current economic landscape and the landscape in the 1970s for similarities and differences, digs into the origin of the current macroeconomic situation, and on such basis proposes policy suggestions. [ABSTRACT FROM AUTHOR]

Nonetheless, the risk factors such as economic risk, political risk, and financial risk have their respective pros and cons in various economic and financial investigations. Yet, the influence of these risk factors on sustainable venture capital is hardly studied in the existing literature. In this sense, the present research tends to investigate the influence of these risks on sustainable venture capital while considering the role of human capital in the US economy. This study uses novel time series approaches using quarterly data from 2006Q1 to 2020Q4. The estimated results validated each variable's stationarity and cointegration between the study variables. The asymmetric data distribution leads to the employment of a novel method of moment quantile regression, which illustrates the positive association between economic risk, political risk, human capital, and sustainable venture capital. On the contrary, financial risk is found adversely affecting the sustainable venture capital in the country. The robustness of the model is examined by employing the bootstrap quantile regression. This study suggests minimizing economic, political, and financial risks and increasing investment in human capital to encourage sustainable venture capital. [ABSTRACT FROM AUTHOR]

Extant literature suggests that the banking sector's sustainability is achievable by minimizing the risk factors, in particular, credit risk (CR). Despite prior studies, there are fewer attempts to considerably probe the role of country governance settings in managing CR and ultimately achieving sustainability. Therefore, this study aims to test this nexus for the banking sector operating in BRICS developing economies. Specifically, this research attempts to explore whether country governance has a moderator role between CR and the exposure of environments to risk factors. To achieve these objectives, we conduct panel data analysis using the quantile (QR) and fixed effects (FE) estimation methods. The results show that increasing liquidity, profitability, capital requirements, and income diversification lead to decreasing CR, whereas increasing inefficiency causes an increase in CR. In addition, the results reveal that a country's increasing vulnerability to a specific financial risk index (FRI), economic risk index (ERI), and political risk index (PRI); developing capital markets; increasing lending interest rates; and weakening country governance quality is significantly linked to increasing CR. Remarkably, the results underscore that country governance has a significant moderator role, and by enhancing the quality of country governance, the impact of country-specific FRI, ERI, and PRI on CR could be attenuated. [ABSTRACT FROM AUTHOR]

This study examines the linkage between economic risk and political risk in the United Kingdom. This linkage has attracted the attention of policymakers; however, there is no consequence of the linkage in the existing literature. The study aims to close this gap for the UK case by applying wavelet coherence (WTC) and quantile‐on‐quantile regression (QQR) approaches and using quarterly data between 1984/Q1 and 2020/Q4. The results of the WTC reveal that there is time–frequency dependency between economic risk and political risk majorly in the medium and low frequencies. Moreover, the direction of the causality changes over time. Furthermore, the outcomes of the WTC show that economic risk leads political risk between 1995 and 2005, whereas political risk leads economic risk from 2006 to 2019. The outcomes of the QQR approach disclose that in the higher tail (0.7–0.95) of political risk and lower and medium tail (0.05–0.60) of economic risk, the effect of political risk on economic risk is positive and strong. On the flip side, at all quantiles (0.05–0.95) of economic risk and lower quantiles (0.10–0.30) of political risk, the effect of political risk on economic risk is positive and strong. The results are also validated by the outcomes of partial wavelet coherence, multiple wavelet coherence, and quantile regression. Hence, the results highlight the importance of political risk (economic risk) for economic risk (political risk) in the UK case. [ABSTRACT FROM AUTHOR]

After the public health event of COVID-19, more academics are looking into how to predict combined economic hazards associated with public health incidents. There are currently just a few approaches for detecting aberrant behaviour in aggregated financial risk, and most only work after the economic risk has already been inappropriately aggregated. As a result, we provide a multi-scale convolutional neural network-based model for clustering financial risk anomaly detection (MCNN). First, we use MCNN to train a model for counting economic risks that are used to evaluate aberrant risk aggregating data. Second, we can use the test results to extract the financial risk statistics and economic risk precursor coordinate points. Then, we calculate the economic risk distribution entropy, distance, potential energy and density. To train the three elements of the development state and create the prediction model, we finally use the particle swarm optimization-based extreme learning machine (PSO-ELM). The results of the experiments demonstrate that, in comparison to existing algorithms, our model can efficiently realize early warning and detect abnormal behaviours of aggregated economic risks with high timeliness. Additionally, our method achieves a forecast accuracy of 97.68% and can give additional time to take emergency action.

As countries propose to develop their green economy strategies to achieve sustainable development goals, many researchers and practitioners have analyzed the various factors affecting this special economy and how export diversification impacts the environment. However, there is limited knowledge about the link between export diversification and the green economy. Thus, this research study explored the impact of such diversification on the green economy by considering the role of economic risk. A new dynamic panel threshold approach was applied to the global panel data of 112 countries from 1995 to 2014. The results support the U-shaped correlation between export diversification and the green economy with an increase in economic risk. Export diversification tends to weaken the green economy when economic risk is at lower levels, but it improves the economy after reaching a certain level of economic risk. We also found that the green economy has a persistent effect over time. Under all economic risk levels, the previous level of green economy development promotes current green economy development. These findings thus provide policymakers with crucial implications. [ABSTRACT FROM AUTHOR]

Today, banks, which act as financial intermediaries, ensure the growth of the economy. Banks, being a channel in monetary policies and monetary issues, are effective institutions in ensuring sustainable macroeconomic stability. In addition, banks are one of the institutions that are exposed to the most financial risk by nature. This article explores the long-term effects of economic, financial and political stabilities on the banking sector. This research covers the 21-year period between 1996–2017. The aim of this study is to examine the effects of economic, financial and political stabilities, which are the components of country risk, on the banking sector in selected Balkan countries and using the panel data analysis technique, which is one of the econometric analysis methods. Panel cross-sectional dependence, unit root tests, cointegration and causality tests were applied according to country risk indicators for the six selected Balkan countries. The selected Balkan countries have paid attention to their development in the country's economies and their involvement in global trade and to adopt the modern banking system. In addition, six Balkan countries have been selected as examples in the study due to their close relations with Turkey in the socio-cultural and economic fields. The results of the panel tests indicated the of unit root tests as well as the existence of cointegration. The p values obtained as a result of cointegration analysis are less than 0.005, indicating the existence of cointegration. It has been concluded that there is a cointegration relationship in all three hypotheses, which have the effects of economic, financial and political stabilities on the banking sector. According to the Granger causality test results, there is a bidirectional causality relationship between the banking variable and the economic stabilities variable. Having long-term and causal relationships between selected Balkan economies requires financial and economic stability to be supported simultaneously. In short, it has been determined that economic, financial and political stabilities have a relationship with sustainable banking in the long term. [ABSTRACT FROM AUTHOR]

The gravest challenge for economic sustainability is the undetermined growth in the financial and economic risks of the nation, which need to be overcome with adequate measures without compromising economic growth. The uncertainty of economic factors produces fluctuations in the financial sector and makes them more vulnerable. However, the existing literature has not significantly focused on the economic and financial risk challenge for sustainable economic growth. Therefore, to fill the gap, an in-depth study is imperative to explore the association between these risks. To do so, this study incorporates both economic and financial risk to determine how risks are interconnected across time (frequency) and how they are linked by utilizing quarterly data from 1984-Q1 to 2020-Q4 and by applying both the “wavelet power spectrum (WPS)” and “wavelet coherence (WTC)” approaches, to examine the time-frequency dependency of each variable on the other. The findings of WTC revealed that the economic and financial risks have a positive dependency on each other in India at high, medium, and low frequencies. Likewise, the wavelet power spectrum outcomes reflect the high economic and financial risks vulnerability during 1991, 1992, and 1996. In addition, for the robustness check, the study employed both the “quantile regression (QR)” and “quantile-on-quantile regression (QQR)”. Both the QQR and QR endorsed the positive association between FR and ER. Hence, our paper is the first research of its kind for the Indian economy, and it extends to the existing literature by examining the link between the two most significant indicators in terms of both time and frequency dependency. The findings in our paper offer excellent perspectives for investors and policymakers to assess prospects for investment and policy changes if necessary.

The article examines uncertainty as a category of economic science and as a characteristic of the business environment. Approaches to understanding uncertainty in the history of economic science are presented. Uncertainty as an economic category is considered a characteristic of an economic situation in which it is impossible to get an absolute idea about the internal and external conditions of the functioning of the socioeconomic system due to the ambiguity of its parameters, incompleteness, inaccuracy or contradiction of information and the influence of subjective factors, which causes the nondeterminism of economic processes, instability of economic development and economic risk. The components of the VUCA world and the BANI world are indicated, as well as the types of reactions to them in the business environment. Uncertainty is considered a norm of business functioning. [ABSTRACT FROM AUTHOR]

This study specifically explores the effect of domestic political and economic risks on risk-taking in the banking sector for 105 countries operating in six various geographical regions between 2009 and 2017. To the best of our knowledge, this may be the first study that attempts to conduct this relationship from this perspective. Remarkably, the dynamic estimation results underscore that a rise in political and economic risks triggers risk-taking behavior in the banking sector globally, in particular in the OECD High-income region. Besides, the estimation results reveal that capital regulation, market power, and income diversification negatively impact risk-taking while credit risk, inefficiency, financial market development, and deposit insurance have a positive effect on risk-taking behavior. The results also stress that the extent of the effect of determinants and significance level vary by changing the region. The results are robust and have significant implications for policymakers and bank managers. [ABSTRACT FROM AUTHOR]

This study uses a stochastic bioeconomic approach to estimate the COVID-19 pandemic economic impact on shrimp farming in Mexico. Seeding-harvesting schedules — March–June, May–August, and August–November — were analyzed using shrimp prices and production costs corresponding to 2017–2019 (pre-pandemic) and 2020 (pandemic). The analyses estimated net revenue varied within 597.97–2758.88 USD$ ha−1 and 1262.40–1701.32 USD$ ha−1 under the pre-pandemic and pandemic scenarios, respectively. Significant decreases (38%) were estimated in net revenue values in March–June and May–August under the pandemic scenario. However, probability distributions estimated that uncertainty on the expected net revenues was not affected by the pandemic conditions, and the probability of losing was null or negligible in all the cases. Unfavorable conditions under the pandemic also required significantly higher break-even production for March–June (25.7%) and May–August (28.5%) schedules. The cost of post-larvae was the most important economic factor influencing net revenue. To conclude, although the operating conditions during the pandemic were conducive to worsening the economic outcome, no evidence still exists that uncertainty and economic risk increased compared with pre-pandemic conditions. [ABSTRACT FROM AUTHOR]

Generation mix in the power system implies the combination of various sources of energy, renewable and conventional, where each of these sources behaves as a different asset class. The generation from renewable sources is highly variable due to the uncertainty in their primary source of energy. These uncertainties affect the expected returns from a portfolio corresponding to a generation mix. To apply any theories in finance to the energy spectrum for further clarity, the risk and reward have to be clearly defined. Since taking any single parameter will not justify the complete sense of reward and risk, expected returns, defined as a fuzzy index by combining the monthly percentage output of a generation asset, the Levelized Cost of Electricity and the expected profit the generation company produces by bidding that source in power market. The generation mix is then optimized by using Black–Litterman model. [ABSTRACT FROM AUTHOR]

This study provides fresh evidence regarding the dynamic association that is believed to exist in relation to green finance (GF), economic growth (GDP), political risk (PR), economic risk (ER), and carbon dioxide (CO2) emissions. It therefore uses the dataset pertaining to China from most recent time-series – covering the period spanning from the years of 1990 to 2020, by employing the Morlet Wavelet Analysis technique. The empirical findings of the wavelet power spectrum reveal that green finance GF and ER are vulnerable in the short- and long-run, and the short-run, respectively. At the same time, no vulnerability has been observed in the GDP, PR, and CO2 emissions. In addition to this, the wavelet coherence also reveals the bidirectional causal association that exists between GF-CO2 and ER-CO2, but only in the short run. It must also be taken into consideration that the causal influence of CO2 is deemed to be greater than the GF and ER, respectively. Besides this, a bidirectional causal nexus also exists between the GDP and CO2 emissions, only in the long run. Furthermore, the association between the economic growths follows both the phase and antiphase associations. Moreover, the study also reveals that there is no significant causal link between the PR and CO2 emissions. The results emphasize that the significance of green finance investment will tend to increase with strict policy implications, stabilization or minimization of economic risk and political risk. The same will also take place while promoting environmentally friendly production via economic growth, so as to reduce CO2 emission in the region taken into account. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of country-specific risk, namely, political and economic risks, global economic policy uncertainty, and also macroeconomic factors on the price of Turkish tourism firms' stocks during the 2000 to 2017 period. By considering the structural break, the results underscore that a rise of political and economic risks, global economic policy uncertainty, and real exchange rate correspond with decreasing stock prices in the long-run. Moreover, the short-run results show that the real exchange rate and global economic policy uncertainty impact stock prices negatively though stock prices react positively to a rise of political risk. To the best of our knowledge, this is the first study that attempted to conduct this nexus, and findings have important implications for managers, investors, and assets analysts. [ABSTRACT FROM AUTHOR]