Recent Advances, Challenges, and Future Perspectives of ZnO Nanostructure Materials Towards Energy Applications.

In this approach, zinc oxide (ZnO) is a multipurpose substance with remarkable characteristics such as high sensitivity, a large specific area, non-toxicity, excellent compatibility, and a high isoelectric point, which make it attractive for discussion with some limitations. It is the most favorable possible option for the collection of nanostructures in terms of structure and their characteristics. The development of numerous ZnO nanostructure-based electrochemical sensors and biosensors used in health diagnosis, pharmaceutical evaluation, food hygiene, and contamination of the environment monitoring is described, as well as the production of ZnO nanostructures. Nanostructured ZnO has good chemical and temperature durability as an n-type semiconducting material, making it useful in a wide range of uses, from luminous materials to supercapacitors, batteries, solar cells, photocatalysis, biosensors, medicinal devices, and more. When compared to the bulk materials, the nanosized materials have both a higher rate of disintegration and a higher solubility. Furthermore, ZnO nanoparticles are regarded as top contenders for electrochemical sensors due to their strong electrochemical behaviors and electron transmission characteristics. The impact of many factors, including selectivity, sensitivity, detection limit, strength, and structures, arrangements, and their respective functioning processes, has been investigated. This study concentrated a substantial amount of its attention on the recent advancements that have been made in ZnO-based nanoparticles, composites, and modified materials for use in the application areas of energy storage and conversion devices as well as biological applications. Supercapacitors, Li-ion batteries, dye-sensitized solar cells, photocatalysis, biosensors, medicinal, and biological systems have been studied. ZnO-based materials are constantly analyzed for their advantages in energy and life science applications.

Progress and Perspectives on Promising Covalent-Organic Frameworks (COFs) Materials for Energy Storage Capacity.

In recent years, a new class of highly crystalline advanced permeable materials covalent-organic frameworks (COFs) have garnered a great deal of attention thanks to their remarkable properties, such as their large surface area, highly ordered pores and channels, and controllable crystalline structures. The lower physical stability and electrical conductivity, however, prevent them from being widely used in applications like photocatalytic activities and innovative energy storage and conversion devices. For this reason, many studies have focused on finding ways to improve upon these interesting materials while also minimizing their drawbacks. This review article begins with a brief introduction to the history and major milestones of COFs development before moving on to a comprehensive exploration of the various synthesis methods and recent successes and signposts of their potential applications in carbon dioxide (CO2 ) sequestration, supercapacitors (SCs), lithium-ion batteries (LIBs), and hydrogen production (H2 -energy). In conclusion, the difficulties and potential of future developing with highly efficient COFs ideas for photocatalytic as well as electrochemical energy storage applications are highlighted.

What does the EKC theory leave behind? A state-of-the-art review and assessment of export diversification-augmented models.

Over the past three decades, researchers have extensively examined the environmental Kuznets curve (EKC) hypothesis. Despite their early focus on the ecological impacts of anthropogenic development, associated conclusions differ and often conflict. In this study, we conducted a state-of-the-art review of this topic and shed light on the methodological challenges that the literature attempted to overcome so far. Since China is going through structural economic changes and environmental reforms, we relied on this illustrative case and developed an augmented-EKC framework to investigate whether this hypothesis holds between export product diversification and environmental pollution, stratifying by carbon energy content: renewable (Model 1) and fossil energy (Model 2). Quarterly data are collected over the most available and recent period (i.e., 1990Q1-2018Q4) and computed by applying the Quadratic Match-Sum Method (QMS) on annual series. Besides, per capita income and foreign direct investments are included as additional factors to the baseline models specifications. The empirical analysis comprises the Clemente-Montanes-Reyes unit root test with structural break and additive outlier, the autoregressive distributed lag (ARDL) bounds test for cointegration, the Granger causality test, and dynamic (DOLS) and fully modified OLS (FMOLS) estimators, followed by robustness checks confirming the stability of the coefficients exhibited in the two autoregressive settings. For both models, empirical results failed to support the existence of an inverted-U-shaped relationship among export product diversification and carbon release from fuel combustion in China. Also, as income grows, low-carbon resources seem improving export diversification and vice versa. Related findings are thought to bring robust inferences able to complement the existing literature and open a fruitful research direction.

Investigating the role of export product diversification for renewable, and non-renewable energy consumption in GCC (gulf cooperation council) countries: does the Kuznets hypothesis exist?

This study explores the effects of renewable and nonrenewable energy demand on export product diversification, economic growth, natural resources, human capital, and trade in GCC (Gulf Cooperation Council) countries using data of six countries from 1990 to 2019. The empirical analysis integrates the panel unit root tests (IPS and CIPS), panel quantile regression, and fully modified OLS models. The empirical results confirm that there exists a significant negative relationship between renewable energy and export diversification; signifying that diversification of products will reduce renewable energy. Similarly, when compared to the square of export product diversification, it shows a positive and significant correlation. The empirical findings highlighted the presence of Kuznets's hypothesis between export product diversification, renewable, and non-renewable energy consumption. Furthermore, the findings suggest that natural resources and economic growth may increase overall energy consumption in GCC countries. It implies an important policy suggestion that encouraging export diversification will reduce GCC countries' reliance on oil to meet energy demand.

Does economic complexity matter for environmental sustainability? Using ecological footprint as an indicator.

The current decade has witnessed the rise of empirical research in the domain of ecological footprint which has become a major scholarly area among environmental researchers. However, many key factors determining ecological footprint have been inadequately dealt within the existing body of knowledge. The current research aims to explore the association between economic complexity, human capital, renewable energy generation, urbanization, economic growth, export quality, trade and ecological footprint for the top ten economic complex countries. This study applied panel data estimators, for instance, fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS) and the system-GMM long-run estimators from 1980 to 2017. The long-run estimates reveal that economic complexity, economic growth, export quality, trade and urbanization increase ecological footprint. Human capital and renewable energy generation help to mitigate ecological footprint. We conclude that investment in more renewable energy generation and its consumption and efficient use of human capital will improve economic complexity, export quality, and environment in developed and developing countries.

Exploring the nexus between agriculture and greenhouse gas emissions in BIMSTEC region: The role of renewable energy and human capital as moderators.

Agriculture and the food system emit a considerable amount of Greenhouse Gas (GHG) emissions in the atmosphere. Hence, current researchers, policymakers, and other stakeholders are calling for improving the environmental performance of agriculture. This study utilizes the countries of The Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) to investigate the effect of agriculture value-added, pesticide use, renewable energy adoption, human capital, and economic growth on greenhouse gas emissions. The moderation effect of renewable energy use and human capital is also introduced to see whether they can offset agriculture's emissions in these BIMSTEC economies. Having reported a state-of-the-art literature review, the econometric procedure applies the second-generation unit root tests, panel cointegration and panel quantile regression for three preferred model specifications. The result from the Panel quantile regression method reveals a U-shaped relationship between agriculture value-added and greenhouse gas emissions, suggesting the significance of a small farming system. Human capital has a negative effect, whereas pesticide use has a positive effect on greenhouse gas emissions. Furthermore, the moderation effect of human capital and pesticide use suggests that human capital is not significant enough to offset the effect of pesticide use on the greenhouse gas emissions, whereas the interaction of renewable and pesticide use suggests that renewable energy adoption in the agriculture sector can mitigate the effect of pesticide use on GHG emissions. Finally, the conclusions of the study support the achievement of few sustainable development goals.

Exploring the role of green innovation and investment in energy for environmental quality: An empirical appraisal from provincial data of China.

To better analyze China's carbon neutrality target, this study investigates the effect of green innovation and investment in the energy industry on China's provincial and regional data from 1995 to 2017. Using Westerlund and Edgerton's panel cointegration test, the authors found a stable long-run relationship between CO2 emissions and its determinants. We found that under major structural breaks at the local, regional, and global levels, such as the East Asian crises of 1997, the financial crises of 2007-2008, China's RMB exchange rate reform announced on August 11, 2015, and mild recession in 2001, CO2 emissions, income, green innovation, renewable energy use, and energy industry investment are cointegrated. The environmental Kuznets curve hypothesis is valid. In the long run, income, environmental innovation, investment in the energy industry, and renewable energy consumption are key contributors in explaining CO2 emissions. The empirical evidence from augmented mean group (AMG) is consistent with the estimates of CS-ARDL. Concerning practical implications, the findings suggest that there is a need to switch the Chinese economy to more sustainable sources of energy, a viable solution to abate environmental degradation. China should introduce and shift investments to green innovation.

Investigating the Effects of Meteorological Parameters on COVID-19: Case Study of New Jersey, United States.

This research aims to explore the correlation between meteorological parameters and COVID-19 pandemic in New Jersey, United States. The authors employ extensive correlation analysis including Pearson correlation, Spearman correlation, Kendall's rank correlation and auto regressive distributed lag (ARDL) to check the effects of meteorological parameters on the COVID new cases of New Jersey. In doing so, PM 2.5, air quality index, temperature (°C), humidity (%), health security index, human development index, and population density are considered as crucial meteorological and non-meteorological factors. This research work used the maximum available data of all variables from 1st March to 7th July 2020. Among the weather indicators, temperature (°C) was found to have a negative correlation, while humidity and air quality highlighted a positive correlation with daily new cases of COVID-19 in New Jersey. The empirical findings illustrated that there is a strong positive association of lagged humidity, air quality, PM 2.5, and previous infections with daily new cases. Similarly, the ARDL findings suggest that air quality, humidity and infections have lagged effects with the COVID-19 spread across New Jersey. The empirical conclusions of this research might serve as a key input to mitigate the rapid spread of COVID-19 across the United States.

Recent Progress on Potentiometric Sensor Applications Based on Nanoscale Metal Oxides: A Comprehensive Review.

Electrochemical sensors have been the subject of much research and development as of late, with several publications detailing new designs boasting enhanced performance metrics. That is, without a doubt, because such sensors stand out from other analytical tools thanks to their excellent analytical characteristics, low cost, and ease of use. Their progress has shown a trend toward seeking out novel useful nano structure materials. A variety of nanostructure metal oxides have been utilized in the creation of potentiometric sensors, which are the subject of this article. For screen-printed pH sensors, metal oxides have been utilized as sensing layers due to their mixed ion-electron conductivity and as paste-ion-selective electrode components and in solid-contact electrodes. Further significant uses include solid-contact layers. All the metal oxide uses mentioned are within the purview of this article. Nanoscale metal oxides have several potential uses in the potentiometry method, and this paper summarizes such uses, including hybrid materials and single-component layers. Potentiometric sensors with outstanding analytical properties can be manufactured entirely from metal oxides. These novel sensors outperform the more traditional, conventional electrodes in terms of useful characteristics. In this review, we looked at the potentiometric analytical properties of different building solutions with various nanoscale metal oxides.

Recent Advancements on Sustainable Electrochemical Water Splitting Hydrogen Energy Applications Based on Nanoscale Transition Metal Oxide (TMO) Substrates.

The development of green hydrogen generation technologies is increasingly crucial to meeting the growing energy demand for sustainable and environmentally acceptable resources. Many obstacles in the advancement of electrodes prevented water electrolysis, long thought to be an eco-friendly method of producing hydrogen gas with no carbon emissions, from coming to fruition. Because of their great electrical conductivity, maximum supporting capacity, ease of modification in valence states, durability in hard environments, and high redox characteristics, transition metal oxides (TMOs) have recently captured a lot of interest as potential cathodes and anodes. Electrochemical water splitting is the subject of this investigation, namely the role of transition metal oxides as both active and supportive sites. It has suggested various approaches for the logical development of electrode materials based on TMOs. These include adjusting the electronic state, altering the surface structure to control its resistance to air and water, improving the flow of energy and matter, and ensuring the stability of the electrocatalyst in challenging conditions. In this comprehensive review, it has been covered the latest findings in electrocatalysis of the Oxygen Evolution Reaction (OER) and Hydrogen Evaluation Reaction (HER), as well as some of the specific difficulties, opportunities, and current research prospects in this field.

Potential Development of Porous Carbon Composites Generated from the Biomass for Energy Storage Applications.

Creating an innovative and environmentally friendly energy storage system is of vital importance due to the growing number of environmental problems and the fast exhaustion of fossil fuels. Energy storage using porous carbon composites generated from biomass has attracted a lot of attention in the research community. This is primarily due to the environmentally friendly nature, abundant availability in nature, accessibility, affordability, and long-term viability of macro/meso/microporous carbon sourced from a variety of biological materials. The energy density of symmetric supercapacitors is also considered, with values between 5.1 and 138.4 Wh/kg. In this review, we look at the basic structures of biomass and how they affect porous carbon synthesis. It also discusses the effects of different structured porous carbon materials on electrochemical performance and analyzes them. In recent developments, significant steps have been made across various fields including fuel cells, carbon capture, and the utilization of biomass-derived carbonaceous nanoparticles. Notably, our study delves into the innovative energy conversion and storage potentials inherent in these materials. This comprehensive investigation seeks to lay the foundation for forthcoming energy storage research endeavors by delineating the current advancements and anticipating potential challenges in fabricating porous carbon composites sourced from biomass.

Forecasting carbon emissions future prices using the machine learning methods.

Due to the uncertainty surrounding the coupling and decoupling of natural gas, oil, and energy commodity futures prices, the current study seeks to investigate the interactions between energy commodity futures, oil price futures, and carbon emission futures from a forecasting perspective with implications for environmental sustainability. We employed daily data on natural gas futures prices, crude oil futures prices, carbon futures prices, and Dow Jones energy commodity futures prices from January 2018 to October 2021. For empirical analysis, we applied machine learning tools including traditional multiple linear regression (MLR), artificial neural network (ANN), support vector regression (SVR), and long short-term memory (LSTM). The machine learning analysis provides two key findings. First, the nonlinear frameworks outperform linear models in developing the relationships between future oil prices (crude oil and heating oil) and carbon emission futures prices. Second, the machine learning findings establish that when oil prices and natural gas prices display extreme movement, carbon emission futures prices react nonlinearly. Understanding the nonlinear dynamics of extreme movements can help policymakers design climate and environmental policies, as well as adjust natural gas and oil futures prices. We discuss important implications to sustainable development goals mainly SDG 7 and SDG 12.

Resources curse hypothesis and COP26 target: Mineral and oil resources economies COVID-19 perspective.

In recent times, industrialized economies have focused more on achieving a sustainable environment while maintaining economic prosperity. However, it is clear from the current research that natural resource exploitation and decentralization substantially affect environmental quality. To experimentally validate such data, the current study examines decentralized economies during the previous three decades (1990-2020). This study discovered the existence of long-term cointegration between carbon emissions, economic growth, revenue decentralization, spending decentralization, natural resources, and human capital using panel data econometric techniques. The findings are based on non-parametric techniques, indicating that economic growth and revenue decentralization are the primary barriers to meeting the COP26 objective. Human capital drives down carbon emissions and contributes to meeting the COP26 objective. On the contrary, decentralization of spending and natural resources has a mixed influence on carbon emissions across quantiles. This report recommends investing in human capital, education, and research & development to speed up COP26's target accomplishment.

Utilizing Nanostructured Materials for Hydrogen Generation, Storage, and Diverse Applications.

The rapid advancement of refined nanostructures and nanotechnologies offers significant potential to boost research activities in hydrogen storage. Recent innovations in hydrogen storage have centered on nanostructured materials, highlighting their effectiveness in molecular hydrogen storage, chemical storage, and as nanoconfined hydride supports. Emphasizing the importance of exploring ultra-high-surface-area nanoporous materials and metals, we advocate for their mechanical stability, rigidity, and high hydride loading capacities to enhance hydrogen storage efficiency. Despite the evident benefits of nanostructured materials in hydrogen storage, we also address the existing challenges and future research directions in this domain. Recent progress in creating intricate nanostructures has had a notable positive impact on the field of hydrogen storage, particularly in the realm of storing molecular hydrogen, where these nanostructured materials are primarily utilized.

Studying the psychology of coping negative emotions during COVID-19: a quantitative analysis from India.

The outbreak of the COVID-19 virus adversely affected the material and mental well-being of the infected individuals and their families. The poor health system combined with lack of fear of infection has created significant negative health effects for people. The present research consider the notable models of coping with negative emotions, including '3Cs' and 'direct action and palliation approach'. With the observation method's help, a detailed perspective was found on people's coping processes, categorized as psychological, control, coherence, and connectedness coping. The present study considers the notable models of dealing with negative feelings, including '3Cs' and 'direct intervention and palliation strategy'. With the observation method's support, a detailed viewpoint was found on people's coping mechanisms, categorized as neurological, regulation, coherence, and connectedness coping. Using the ANOVA and t-tests, a significant augmentation in people's negative emotions was found since the beginning of the pandemic. Using GMM regression technique, 'avoidance', 'proactive preparedness', 'emotional resilience', 'entertainment', and 'spiritualism' were highly significant techniques in curbing the negative emotions during the COVID-19 pandemic. Meanwhile, the LOGIT regression found cumulative negative emotions and emotions about negative career outlooks to be the most significant to bring negative emotions to normalcy. The study suggests that policymakers design a national-level strategy to strengthen the mental health systems to boost mental well-being.

Analyzing the Nexus Between Geopolitical Risk, Policy Uncertainty, and Tourist Arrivals: Evidence From the United States.

This study attempts to explore the causal linkage of the COVID-19 pandemic, economic policy uncertainty, geopolitical risk, and tourism arrivals in the United States taking data from January to November 2020. In order to analyze the above relationship, this study uses a novel time-varying granger causality test developed by Shi et al. (2018), which incorporates its three causality algorithms such as forward recursive causality, rolling causality, and recursive evolving causality. The findings from forward recursive causality could not confirm any significant causal relationship between COVID-19 and tourism, geopolitical risk (GPR) and tourism, economic policy uncertainty and tourism, and geopolitical risk and COVID-19 but found causality between economic policy uncertainty and COVID-19. The rolling window causality reported bidirectional causality between COVID-19 and tourism and unidirectional causality running from tourism to geopolitical risk. However, the recursive evolving causality identified a significant bidirectional causal relationship between all the variables. Based on the findings, policy implications for the tourism sector are provided.

Impacts of environmental taxes and technologies on greenhouse gas emissions: contextual evidence from leading emitter European countries.

With growing concerns about climate change, global warming and greenhouse gas (GHG) emissions, environmental taxes, renewable energy consumption, and environmental technologies have caught attention from researchers, policymakers, and concerned organizations in developed and developing world. The environmental-related taxes and carbon and energy taxes are considered as effective tool and highly recommended by economists and environmental scientists in developed nations (e.g., European economies). To this end, the current study examines the role of environmental policies and regulations and cleaner energy consumption for GHG emissions across leading emitter countries in Europe. The study used annual data of variables from 1994 to 2018 for nine leading European economies. The empirical estimates of quantile regression, FMOLS, and DOLS revealed that environmental taxes and promotion of cleaner energy sources can be effective to reduce overall pollution efflux. The study reports new implications regarding sustainable development goals.

A study on the effects of meteorological and climatic factors on the COVID-19 spread in Canada during 2020.

The Coronavirus (COVID-19) pandemic has infected more than three million people, with thousands of deaths and millions of people into quarantine. In this research, the authors focus on meteorological and climatic factors on the COVID-19 spread, the main parameters including daily new cases of COVID-19, carbon dioxide (CO2) emission, nitrogen dioxide (NO2), Sulfur dioxide (SO2), PM2.5, Ozone (O3), average temperature, and humidity are examined to understand how different meteorological parameters affect the COVID-19 spread in Canada? The graphical quantitative analysis results indicate that CO2 emissions, air quality, temperature, and humidity have a direct negative relationship with COVID-19 infections. Quantile regression analysis revealed that air quality, Nitrogen, and Ozone significantly induce the COVID-19 spread across Canadian provinces. The findings of this study are contrary to the earlier studies, which argued that weather and climate change significantly increase COVID-19 infections. We suggested that meteorological and climatic factors might be critical to reducing the COVID-19 new cases in Canada based on the findings. This work's empirical conclusions can provide a guideline for future research and policymaking to stop the COVID-19 spread across Canadian provinces.

Environmental taxes, energy consumption, and environmental quality: Theoretical survey with policy implications.

Improving energy efficiency and mitigating environmental problems through environmental regulations and taxes are considered as fundamental driving forces of climate change policies. However, the current literature on the theoretical and empirical evidence focusing on the inter-linkages between environmental taxes, energy consumption, and environmental quality is rather meager. This article attempts to provide a detailed survey on the earlier literature for developed, developing, and emerging countries analysis by covering the literature up to 2020. The prime objective of this survey is the coverage of different level of economies, modeling, methodologies, time periods as well as empirical outcomes. The study mainly covers three types of causality direction: (i) environmental taxes, energy consumption, and energy efficiency; (ii) environmental taxes and environmental quality; (iii) energy consumption (renewables, non-renewable, and fossil fuels) and environment deterioration. Most of the empirical studies reported that the energy usage for economic activities significantly affects the pollutant emissions. However, the role of environmental taxes is still ambiguous and demands a more in-depth investigation. Comprehending the literature survey has provided the basis to address the policymaking, designing as well as the implementation of environmental regulations.

Effects of climatological parameters on the outbreak spread of COVID-19 in highly affected regions of Spain.

The coronavirus (COVID-19) pandemic is infecting the human population, killing people, and destroying livelihoods. This research sought to explore the associations of daily average temperature (AT) and air quality (PM2.5) with the daily new cases of COVID-19 in the top four regions of Spain (Castilla y Leon, Castilla-La Mancha, Catalonia, and Madrid). To this end, the authors employ Pearson correlation, Spearman correlation, and robust panel regressions to quantify the overall co-movement between temperature, air quality, and daily cases of COVID-19 from 29 February to 17 July 2020. Overall empirical results show that temperature may not be a determinant to induce COVID-19 spread in Spain, while the rising temperature may reduce the virus transmission. However, the correlation and regression findings illustrate that air quality may speed up the transmission rate of COVID-19. Our findings are contrary to the earlier studies, which show a significant impact of temperature in raising the COVID-19 spread. The conclusions of this work can serve as an input to mitigate the rapid spread of COVID-19 in Spain and reform policies accordingly.