Driving low-carbon mechanisms through smart investments in renewable resources and green finance initiatives among G20 nations.

In light of the escalating concerns regarding climate change and environmental decline, major nations are actively exploring strategies to mitigate environmental harm and achieve future sustainability. The surge in economic expansion in developed economies is linked to an increase in CO2 emissions. Consequently, in their pursuit of carbon-neutral policies, these countries are increasingly turning towards renewable energy as a means to enhance resource conservation and efficiency. This research investigates the varied impacts of renewable energy investment, green finance, geopolitical risk, GDP growth, foreign direct investment, and gross fixed capital formation on the carbon emissions of G20 countries. The study uses the CUP-FM (Continuously Updated Fully Modified) and CUP-BC (Continuously Updated Bias-Corrected) estimators, which are sophisticated econometric approaches designed to handle non-stationary panel data and cross-sectional dependency, to produce robust long-term parameter estimates. The CUP-FM estimator adjusts for potential endogeneity and serial correlation, improving the accuracy of long-run relationships in panel data. The CUP-BC estimator provides bias-corrected estimates to further enhance the precision of these long-run connections.The long-term parameter estimates reveal a negative correlation between renewable energy investment, green finance, and carbon emissions. In contrast, foreign direct investment, gross fixed capital formation, GDP growth, and geopolitical risk are positively associated with CO2 emissions. This suggests that financial stability often leads to investments in carbon-heavy economic ventures, thereby implicating economic growth as a contributing factor to environmental degradation in G20 countries.

The impact of fuel cell vehicles deployment on road transport greenhouse gas emissions through 2050: Evidence from 15 G20 countries.

As global concern over the negative impacts of global warming, primarily caused by using passenger vehicles (PVs), the transition to hydrogen fuel cell vehicles (HFCVs) is an essential alternative for reducing greenhouse (GHG) emissions. This research employs a bottom-up approach to analyze road vehicle fleet's GHG emissions. We calculated GHG emissions from PVs in 15 Group of Twenty (G20) countries based on four scenarios adopting the global HFCVs from 2024 to 2050. This paper introduces business-as-usual (BaU), moderate, aggressive, and non-HFCVs scenario. The results show that the aggressive scenario has the highest sales, estimated between 62,000 and 29.48 million vehicles by 2050, with global hydrogen market penetration rates 48.48%. Building on countries' respective national strategies, the findings highlight China and India as the leading markets for hydrogen demand, with Germany and Japan also showing significant interest. The aggressive scenario further demonstrates that transitioning from internal combustion engine vehicles (ICEVs) to battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), and HFCVs can significantly reduce annual GHG emissions. Ultimately, this study finds that the transition to HFCVs could reduce emissions by up to 67.09% by 2050.

Assessment of green development level performance in G20 countries: A dynamic evaluation framework.

To achieve sustainable development goals and foster future prosperity globally, the promotion of green development is essential. Nevertheless, persistent regional disparities in green development among the G20 nations stem from differing economic and environmental conditions in dynamic contexts. In light of these conditions, it is essential for governments to evaluate their country's current green development performance in comparison with higher-performing nations and to learn from their experiences. This study introduces a new dynamic evaluation framework that integrates the importance of evaluation indicators using the dynamic entropy weighting method, a Dynamic Technique for Order Preference by Similarity to Ideal Solution method, and principal component analysis. This framework serves as a tool for G20 countries to assess their green development levels between 2015 and 2021. A key contribution of this study is the facilitation of the G20's ability to implement relevant reform measures and monitor green development progress in a dynamic environment. This framework can serve as a model for similar evaluations in other regions globally. The study's main findings indicate that G20 countries with high levels of green development possess strong economic foundations and prioritise the balanced development of their economies, societies, environments, and innovation.

G20 roadmap for carbon neutrality: The role of Paris agreement, artificial intelligence, and energy transition in changing geopolitical landscape.

The rapid advancement of artificial intelligence (AI) in the 21st century is driving profound societal changes and playing a crucial role in optimizing energy systems to achieve carbon neutrality. Most G20 nations have developed national AI strategies and are advancing AI applications in energy, manufacturing, and agriculture sectors to meet this goal. However, disparities exist among these nations, creating an "AI divide" that needs to be addressed for regulatory consistency and fair distribution of AI benefits. Here, we look at the linear effects of AI and the Paris Agreement (AI), as well as their potential interaction on carbon neutrality. We also investigate whether geopolitical risk (GPR) can hinder or enhance efforts to attain carbon neutrality through energy transition (ET). To measure carbon neutrality of G20 countries, we employed a robust parametric Malmquist index combined with the fixed-effect panel stochastic frontier model to account for heterogeneity. Results indicate that from 1990 to 2022, carbon neutrality has improved primarily due to technological advancements. Developed G20 countries led in technological progress, while developing countries showed modest gains in carbon efficiency. Using the Driscoll-Kraay robust standard error method, we found that AI has a positive but insignificant linear effect on carbon neutrality. However, the interaction between PA and AI was positive and statistically significant, suggesting that PA augments AI's potential in accelerating carbon neutrality. Energy transition accelerates carbon neutrality in both developed and developing G20 countries. However, the role of energy transition in achieving carbon neutrality becomes negative when the interaction term between energy transition and geopolitical risk (ET × GRP) is incorporated. Regarding control variables, green innovation positively impacts carbon neutrality, whereas financial development has an insignificant effect. Industrial structure and foreign direct investment both negatively affect carbon neutrality, thereby supporting the pollution haven hypothesis. It is recommended that strategies to bridge the "AI divide" and uphold geopolitical stability are crucial to achieve carbon neutrality.

FinTech and financial instability. Is this time different?

We study the development of FinTech, defined as a set of innovations and an economic sector that apply recently developed digital technologies to financial services, with particular focus on payment and lending platforms, and digital asset management and online trading apps. We use mixed methods, including a theoretical exercise on the main balance sheet interactions involved in FinTech banking, and empirical insights from fieldwork in Latin America and the United States. Our analysis corroborates previous literature identifying several systemic risks in FinTech payment and lending platforms. These include the enhanced risk of a bank run, the increase in liquidity risk for incumbent banks, the fueling of precarious lending, and the potential compromise in the efficacy of monetary policy. Our discussion of online asset management and trading apps also highlights the risk of enhanced volatility in financial markets due to the increase in the participation of low-income and inexperienced investors. We observe that while the FinTech sector is still small in size, it already contains seeds of financial instability which should be all-too-familiar from recent history.

Resilient green infrastructure: Navigating environmental resistance for sustainable development, social mobility in climate change policy.

This study explores the complex dynamics of environmental resistance, policy stability, skill development, and green initiatives in light of the growing worldwide concerns about climate change. COP27 and the G20 Summit show that as microcosm of the global discourse, provides a unique opportunity to study it. This research sought to thoroughly investigate, the links among green infrastructure, green technological innovation, policy stability, skill development, and their combined effects on social mobility and climate change (Case-1), it also discusses the global talk on climate change in COP27 and G20 (Case-2). In Case-1 data were analyzed through the use of structural equation modeling (SEM) by adopting a quantitative approach, and in Case-2 data were analyzed using theme analysis by applying a qualitative approach. Using a mixed-method research approach, the study surveyed 375 locals living close to the CPEC corridor quantitatively and interviewed ten important stakeholders, including elected officials, environmental activists, and community leaders, qualitatively. The survey highlighted the complex perspectives and experiences of citizens with green programs and environmental legislation in the CPEC zone. The study revealed the perceptions and experiences of residents regarding green initiatives and environmental policies within the CPEC region. Key stakeholders provided valuable insights into policy formulation and ongoing environmental sustainability efforts. The analysis unveiled intricate relationships between green infrastructure, technological innovation, policy stability, skill development, and their collective impact on climate change and social mobility. Notably, the study identified a critical research gap in understanding these dynamics within regions undergoing substantial economic development. Policy formulation and continuing environmental sustainability efforts were aided by key stakeholders' ideas. Green infrastructure, technological innovation, policy stability, skill development, and their overall influence on climate change and social mobility were all examined. Notably, the study found a critical research vacuum in understanding these processes inside rapidly developing economies. Policymakers, environmental groups, and communities managing the fine line between economic success and environmental responsibility will find great value in the findings. This study is unique because it examines issues on climate change from a local perspective in a region that is rapidly developing economically, it also adds value to the climate change challenges on the global level. This study presents a substantial theoretical contribution by examining the intricate interactions among environmental opposition, policy stability, skill development, and green initiatives within the CPEC against the backdrop of global climate change concerns.

RANK-BASED INDICES FOR TESTING INDEPENDENCE BETWEEN TWO HIGH-DIMENSIONAL VECTORS.

To test independence between two high-dimensional random vectors, we propose three tests based on the rank-based indices derived from Hoeffding's D, Blum-Kiefer-Rosenblatt's R and Bergsma-Dassios-Yanagimoto's τ*. Under the null hypothesis of independence, we show that the distributions of the proposed test statistics converge to normal ones if the dimensions diverge arbitrarily with the sample size. We further derive an explicit rate of convergence. Thanks to the monotone transformation-invariant property, these distribution-free tests can be readily used to generally distributed random vectors including heavily tailed ones. We further study the local power of the proposed tests and compare their relative efficiencies with two classic distance covariance/correlation based tests in high dimensional settings. We establish explicit relationships between D,R,τ* and Pearson's correlation for bivariate normal random variables. The relationships serve as a basis for power comparison. Our theoretical results show that under a Gaussian equicorrelation alternative, (i) the proposed tests are superior to the two classic distance covariance/correlation based tests if the components of random vectors have very different scales; (ii) the asymptotic efficiency of the proposed tests based on D,τ* and R are sorted in a descending order.

Fintech and environmental efficiency: The dual role of foreign direct investment in G20 nations.

This study investigates the relationship between financial technology (fintech) and environmental efficiency across G20 countries, emphasizing the moderating effect of foreign direct investment (FDI) from 2010 to 2022. Employing Data Envelopment Analysis (DEA) through both Slack-Based Measure (SBM) and Epsilon-Based Measure (EBM), alongside Tobit regression and the Generalized Method of Moments (GMM) for analytical rigor, the research reveals that fintech exerts a positive influence on environmental efficiency within these countries. Furthermore, it demonstrates that FDI contributes to enhancing environmental efficiency. However, when FDI is combined with fintech investments, it yields a negative impact. This detrimental effect stems from FDI's emphasis on short-term gains, rapid expansion, and a globally oriented supply chain that favors cost efficiency at the expense of sustainability. The study highlights the necessity for investments in fintech that comply with environmental standards and offers policy recommendations to improve environmental efficiency. It urges policymakers to promote environmentally sustainable investment practices within the fintech sector to aid in achieving sustainable development goals.

Characterization of an endoplasmic reticulum stress-associated lncRNA prognostic signature and the tumor-suppressive role of RP11-295G20.2 knockdown in lung adenocarcinoma.

Endoplasmic reticulum stress (ERS) is commonly induced by accumulating misfolded or unfolded proteins in tumor microenvironment. Long non-coding RNAs (lncRNAs) play important roles in ERS response and lung adenocarcinoma (LUAD) progression. However, the role of ERS-related lncRNAs in LUAD remains unknown. In this study, we aimed to identify ERS-associated lncRNAs with prognostic value in LUAD and characterize their clinical implications. Cox and least absolute shrinkage and selection operator regression analyses identified nine ERS-related lncRNAs with independent prognostic abilities, including five protective factors (CROCCP2, KIAA0125, LINC0996, RPARP-AS1 and TBX5-AS1) and four risk factors (LINC0857, LINC116, RP11-21L23.2 and RP11-295G20.2). We developed an ERS-related lncRNA risk prediction model in predicting overall survival of LUAD patients, which classified TCGA cohorts into high-risk (HS) and low-risk (LS) groups. Comprehensive bioinformatic analyses revealed HS patients featured with late-stage tumors, greater mutation burdens, weaker anti-tumor immunity/responses, and lower sensitivity to targeted drugs compared to LS patients, contributing to tumor progression and a poor prognosis. Functional enrichment analysis implicated these ERS-related lncRNAs in cell migration, cell death, and immunity. Furthermore, expression of the most significantly upregulated risk lncRNA, RP11-295G20.2, was validated at the mRNA level using clinical LUAD samples. Knockdown of RP11-295G20.2 obviously reduced ERS and suppressed proliferation, invasion, and migration of LUAD cells. This novel ERS-related lncRNA signature provides a new biomarker for prognostic prediction, and ERS-associated RP11-295G20.2 serves as a potential therapeutic target in LUAD.

Genome-Wide Computational Prediction and Analysis of Noncoding RNAs in Oleidesulfovibrio alaskensis G20.

Noncoding RNAs (ncRNAs) play key roles in the regulation of important pathways, including cellular growth, stress management, signaling, and biofilm formation. Sulfate-reducing bacteria (SRB) contribute to huge economic losses causing microbial-induced corrosion through biofilms on metal surfaces. To effectively combat the challenges posed by SRB, it is essential to understand their molecular mechanisms of biofilm formation. This study aimed to identify ncRNAs in the genome of a model SRB, Oleidesulfovibrio alaskensis G20 (OA G20). Three in silico approaches revealed genome-wide distribution of 37 ncRNAs excluding tRNAs in the OA G20. These ncRNAs belonged to 18 different Rfam families. This study identified riboswitches, sRNAs, RNP, and SRP. The analysis revealed that these ncRNAs could play key roles in the regulation of several pathways of biosynthesis and transport involved in biofilm formation by OA G20. Three sRNAs, Pseudomonas P10, Hammerhead type II, and sX4, which were found in OA G20, are rare and their roles have not been determined in SRB. These results suggest that applying various computational methods could enrich the results and lead to the discovery of additional novel ncRNAs, which could lead to understanding the "rules of life of OA G20" during biofilm formation.

Assessing the environmental sustainability gap in G20 economies: The roles of economic growth, energy mix, foreign direct investment, and population.

There is serious debate among researchers regarding the sustainability implications of economic prosperity and energy dependence. Energy consumption has a critical linkage with economic growth, but it also degrades environmental quality. Therefore, it is important to investigate the relationship between economic growth, the energy mix, and environmental sustainability. However, empirical literature utilizes narrow variables to capture environmental sustainability. Because of this, this research introduces a new environmental sustainability variable using entropy weighting and combining deforestation, household carbon emissions, and life expectancy. This study examines the relationship between environmental sustainability, economic growth, and other selected variables using data from 2002 to 2019 for the G20 and its high-, upper-, and low-middle-income member countries. Since shocks in one G20 country can affect another, this study uses the Augmented Mean Group (AMG) technique for empirical analysis. The results of this study indicate that Gross Domestic Product (EG) and its square term did not support the Environmental Kuznets Curve (EKC) theory. The energy mix has a positive impact on the environmental sustainability gap across all the samples except for the upper-middle-income group. Foreign direct investment positively affects this gap, while population growth has no significant impact. These findings demonstrate that policymakers should support environmentally friendly and clean energy sources to foster long-term economic growth and sustainability.

Evaluation of Malnutrition Knowledge among Nursing Staff in the Czech Republic: A Cross-Sectional Psychometric Study.

Construct: The Knowledge of Malnutrition - Geriatric 2.0' (KoM-G 2.0) instrument was designed to quantify nursing staff malnutrition knowledge in inpatient medical and rehabilitation care facilities, as well as home health care. It has been used to assess grasp of current clinical practice guidelines and proficiency in addressing issues related to malnutrition. It provides insight into familiarity with and capacity to tackle issues pertaining to malnutrition in clinical practice. Furthermore, it has been used assess the effectiveness of educational interventions aimed at improving nursing professionals knowledge and awareness of malnutrition. Background: The quality of nursing education affects malnutrition risk assessment, monitoring of food intake, and effectiveness of nutrition care. Improvements in malnutrition education require determining the current level of knowledge and benchmarking with other countries. In the Czech Republic, no nationwide assessment of nursing staff malnutrition knowledge has ever been conducted. Approach: The purpose of the study was to translate the KoM-G 2.0 instrument, gather initial validity evidence, and evaluate nursing staff knowledge of malnutrition in inpatient medical, rehabilitation care facilities, and home care in the Czech Republic. All inpatient healthcare facilities and home healthcare facilities in the Czech Republic were invited to participate. The Czech version of the internationally standardized KoM-G 2.0 (KoM-G 2.0 CZ) was used to assess nursing staff malnutrition knowledge between 3 February 2021 and 31 May 2021. A total of 728 nurses began the questionnaire, and 465 (63.9%) of respondents completed it and were included in the study. Data analyses examined instrument difficulty, discriminability, and reliability, as well as sources of variation in knowledge scores. Findings: The psychometric characteristics of the KoM-G 2.0 CZ instrument included the difficulty index Q (0.61), the discriminant index (ULI 0.29, RIT 0.38, upper-lower 30% 0.67), and Cronbach alpha (0.619). The overall mean of correct answers was 6.24 (SD 2.8). There was a significant impact of educational attainment and nutrition training on KoM-G 2.0 CZ scores. Conclusions: Our findings provide initial validity evidence that KoM-G 2.0 CZ is useful and appropriate for assessing malnutrition knowledge among Czech nursing staff. Our research identified gaps in knowledge and examples of good practice in understanding malnutrition that can be applied internationally. The knowledge of academic nurses was greater; therefore, we suggest they play a key role in nutritional care. We recommend continuous education to improve understanding of malnutrition in this setting.

Exploring the impact of circular economy practices on ecological footprint, inflation rate, and renewable energy consumption: evidence from G20 economies.

The circular economy (CE) has acquired significant interest for its potential to contribute to sustainable development (SD). The present study utilizes empirical methodology, specifically panel data analysis, to examine the distinct effects and outcomes of the circular economy and its associated factors within a unified framework. The focus is on the G20 countries from 2008 to 2021. We evaluated the influence of various CE value sources (renewable energy consumption, composting rate, repair services availability, recycling rate) and a factor-analysis-derived measure of the CE on economic, environmental, and social aspects of SD. The objective was to assess the distinct effects and outcomes of CE and its components in a unified framework-the analysis utilized panel data from G20 countries from 2008 to 2021. Our findings show a substantial influence of CE in achieving SD, with positive implications for the economy, environment, and society. However, the impact of each CE value source on the SD dimensions shows variation. While renewable energy consumption (RENEC) and composting rate (CR) lessen environmental impact, recycling rate (RR) shows no significant effect, and repair services availability (RSA) increases the Ecological Footprint (EFP). Notably, RSA is the sole CE component, showing a positive economic impact at the national level. Additionally, RENEC, RSA, and RR contribute to reducing the inflation rate (INFR). Policymakers should undertake detailed impact assessments to develop effective, tailored strategies based on each country's unique goals. The findings of this study have important policy implications, particularly in terms of emphasizing targeted strategies for implementing CE practices to achieve sustainable development.

The Intersections of COVID-19 Global Health Governance and Population Health Priorities: Equity-Related Lessons Learned From Canada and Selected G20 Countries.

Background: COVID-19-related global health governance (GHG) processes and public health measures taken influenced population health priorities worldwide. We investigated the intersection between COVID-19-related GHG and how it redefined population health priorities in Canada and other G20 countries. We analysed a Canada-related multilevel qualitative study and a scoping review of selected G20 countries. Findings show the importance of linking equity considerations to funding and accountability when responding to COVID-19. Nationalism and limited coordination among governance actors contributed to fragmented COVID-19 public health responses. COVID-19-related consequences were not systematically negative, but when they were, they affected more population groups living and working in conditions of vulnerability and marginalisation. Policy options and recommendations: Six policy options are proposed addressing upstream determinants of health, such as providing sufficient funding for equitable and accountable global and public health outcomes and implementing gender-focused policies to reduce COVID-19 response-related inequities and negative consequences downstream. Specific programmatic (e.g., assessing the needs of the community early) and research recommendations are also suggested to redress identified gaps. Conclusion: Despite the consequences of the COVID-19 pandemic, programmatic and research opportunities along with concrete policy options must be mobilised and implemented without further delay. We collectively share the duty to act upon global health justice.

New nonparametric measures for instantaneous and granger-causality tail co-dependence.

We propose a new methodology to asses risk spillovers in a time-series framework. Firstly, we introduce an explicit nonparametric measure of cross-sectional conditional tail co-movement, which is intuitively comparable to the Conditional Value-at-Risk (CoVaR). We show that nonlinear CoVaR (NCoVaR) is able to capture even highly nonlinear dependence structures. Secondly, for the purpose of potential contagion analysis, we adapt the measure to be informative about the causality direction between the variables in the Granger causality sense. By showing that the natural estimators of the two metrics are U-statistics, we construct formal nonparametric tests for independence and Granger non-causality. Numerical simulations confirm that in common situations the nonparametric tests have better size and power properties than their parametric counterparts. The methodology is illustrated empirically by assessing risk transmissions between sovereigns and banking sectors in the euro area, which observed highly irregular co-movements between asset prices after the global financial crisis. The new measures seem to be less susceptible to these irregularities than their parametric analogues, providing a clearer overview of the underlying sovereign-bank risk feedback loops.

The structure and assembly mechanisms of T4-like cyanophages community in the South China Sea.

Marine ecosystems contain an immense diversity of phages, many of which infect cyanobacteria (cyanophage) that are largely responsible for primary productivity. To characterize the genetic diversity and biogeographic distribution of the marine T4-like cyanophage community in the northern South China Sea, the T4-like cyanophage portal protein gene (g20) was amplified. Phylogenetic analysis revealed that marine T4-like cyanophages were highly diverse, with g20 operational taxonomic units being affiliated with five defined clades (Clusters I-V). Cluster II had a wide geographic distribution, Cluster IV was the most abundant in the open sea, and Cluster I was dominant in coastal shelf environments. Our results showed T4-like cyanophages (based on g20) community was generally shaped via heterogeneous selection. Highly variable environmental factors (such as salinity and temperature) can heterogeneously select different cyanophage communities. Nevertheless, the dominant drivers of the T4-like cyanophage community based on the g20 and g23 (T4-like phage major capsid protein gene) were different, probably due to different coverages by the primer sets. Furthermore, the community assembly processes of T4-like cyanophages were affected by host traits (abundance and distribution), viral traits (latent period, burst size, and host range), and environmental properties (temperature and salinity).IMPORTANCECyanophages are abundant and ubiquitous in the oceans, altering population structures and evolution of cyanobacteria, which account for a large portion of global carbon fixation, through host mortality, horizontal gene transfer, and the modulation of host metabolism. However, little is known about the biogeography and ecological drivers that shape the cyanophage community. Here, we use g20 and g23 genes to examine the biogeographic patterns and the assembly mechanisms of T4-like cyanophage community in the northern part of the South China Sea. The different coverages of primer sets might lead to the different dominant drivers of T4-like cyanophage community based on g20 and g23 genes. Our results showed that characteristics of viral traits (latent period, burst size, and host range) and host traits (abundance and distribution) were found to either limit or enhance the biogeographic distribution of T4-like cyanophages. Overall, both virus and host properties are critical to consider when determining rules of community assembly for viruses.

The key to sustainability: In-depth investigation of environmental quality in G20 countries through the lens of renewable energy, economic complexity and geopolitical risk resilience.

The world is currently facing urgent climate and environmental issues, such as global warming, ecosystem collapse, and energy shortages. In this context, this study selected data from 2000 to 2021 and employed the Method of Moment Quantile Regression (MMQR) to thoroughly investigate the impact of renewable energy consumption, economic complexity, and geopolitical risks on the ecological footprint of the Group of Twenty (G20) countries. The results indicate that in countries with lower quantiles, renewable energy consumption significantly reduces the ecological footprint, whereas its effect is not prominent in countries with higher quantiles. Economic complexity has a negative impact on the ecological footprint, and this impact becomes stronger as the quantile of the ecological footprint rises. Additionally, economic complexity moderates the effect of renewable energy on the ecological footprint. Geopolitical risks facilitate the growth of the ecological footprint. Likewise, robustness tests such as DOLS, FMOLS, and quantile regression confirm these estimates in the same framework. This study has conducted a profound analysis of global environmental issues, offering innovative perspectives and recommendations for achieving goals related to sustainable energy utilization, mitigating climate change, and improving the ecological environment. The findings of this research will guide policymakers in G20 countries to adopt more effective environmental protection measures, thereby contributing to the construction of a sustainable future.

The dampening effect of geopolitical risk and economic policy uncertainty in the linkage between economic complexity and environmental degradation in the G-20.

The question remains whether high geopolitical risk and economic policy uncertainty will have a dampening or enhancing effect on pollution factors. In this regard, the study empirically investigates the effects of economic complexity, geopolitical risk, economic policy uncertainty, renewable energy consumption and economic growth on environmental pollution for G-20 countries from 1997 to 2018. The long-term coefficient estimates, derived from the FMOLS estimator, support the inverted U-shaped EKC linkages between economic complexity and ecological footprint, carbon footprint and carbon dioxide emissions. Furthermore, over the long term, geopolitical risks, renewable energy use, and the interaction between economic complexity and policy uncertainty have a positive impact on environmental quality in the G-20 economies. Conversely, economic growth and the interaction between economic complexity and geopolitical risk are negatively associated with environmental quality. Additionally, economic policy uncertainty has a positive effect on ecological footprint carbon footprint and carbon dioxide emissions. Finally, causality results revealed that explanatory variables are the cause of environmental pollution indicators. Hence, in order to advance environmental quality in these nations, precautions must be taken to mitigate the effects of economic policy uncertainty and boost the accessibility of renewable energy sources. Additionally, while not advised as a policy measure, the feasible economic fallout of geopolitical risk should also be considered.

Hölder regularity for parabolic fractional p-Laplacian.

Local Hölder regularity is established for certain weak solutions to a class of parabolic fractional p-Laplace equations with merely measurable kernels. The proof uses DeGiorgi's iteration and refines DiBenedetto's intrinsic scaling method. The control of a nonlocal integral of solutions in the reduction of oscillation plays a crucial role and entails delicate analysis in this intrinsic scaling scenario. Dispensing with any logarithmic estimate and any comparison principle, the proof is new even for the linear case.

The Impact of the COVID-19 Pandemic on Stock Market Performance in G20 Countries: Evidence from Long Short-Term Memory with a Recurrent Neural Network Approach.

In light of developing and industrialized nations, the G20 economies account for a whopping two-thirds of the world's population and are the largest economies globally. Public emergencies have occasionally arisen due to the rapid spread of COVID-19 globally, impacting many people's lives, especially in G20 countries. Thus, this study is written to investigate the impact of the COVID-19 pandemic on stock market performance in G20 countries. This study uses daily stock market data of G20 countries from January 1, 2019 to June 30, 2020. The stock market data were divided into G7 countries and non-G7 countries. The data were analyzed using Long Short-Term Memory with a Recurrent Neural Network (LSTM-RNN) approach. The result indicated a gap between the actual stock market index and a forecasted time series that would have happened without COVID-19. Owing to movement restrictions, this study found that stock markets in six countries, including Argentina, China, South Africa, Turkey, Saudi Arabia, and the United States, are affected negatively. Besides that, movement restrictions in the G7 countries, excluding the United States, and the non-G20 countries, excluding Argentina, China, South Africa, Turkey, and Saudi, significantly impact the stock market performance. Generally, LSTM prediction estimates relative terms, except for stock market performance in the United Kingdom, the Republic of Korea, South Africa, and Spain. The stock market performance in the United Kingdom and Spain countries has significantly reduced during and after the occurrence of COVID-19. It indicates that the COVID-19 pandemic considerably influenced the stock markets of 14 G20 countries, whereas less severely impacting 6 remaining countries. In conclusion, our empirical evidence showed that the pandemic had restricted effects on the stock market performance in G20 countries.