Impact of COVID-19 pandemic on socio-economic, energy-environment and transport sector globally and sustainable development goal (SDG)

TikTok content on measles-rubella vaccine in Jordan: A cross-sectional study highlighting the spread of vaccine misinformation

Social media platforms, including TikTok, have become influential sources of health information. However, they also present as potential sources for the spread of vaccine misinformation. The aim of this study was to assess the quality of measles-rubella (MR) vaccine-related contents on TikTok in Jordan and to analyze factors associated with vaccine misinformation. A systematic search for MR vaccine-related TikTok contents in Jordan was conducted using pre-defined keywords and a specified time range. Content metrics (likes, comments, shares, and saves) were collected while the content quality of health information was evaluated using a modified version of the DISCERN, a validated instrument by two expert raters. The average modified DISCERN score ranged from 1, denoting poor content, to 5, indicating excellent content. A total of 50 videos from 34 unique content creators formed the final study sample. The majority of MR vaccine-related content was created by lay individuals (61.8%), followed by TV/news websites/journalists (23.5%), and healthcare professionals (HCPs) (14.7%). The Cohen κ per modified DISCERN item was in the range of 0.579-0.808, p<0.001), indicating good to excellent agreement. The overall average modified DISCERN score was 2±1.2, while it was only 1.3±0.52 for lay individuals' content, which indicated poor content quality. For the normalized per number of followers for each source, content by lay individuals had a significantly higher number of likes, saves, and shares with p=0.009, 0.012, and 0.004, respectively. Vaccine misinformation was detected in 58.8% of the videos as follows: lay individuals (85.7%), TV/news websites/journalists (25.0%), and HCPs content had none (p<0.001). Normalized per the number of followers for each source, videos flagged as having MR vaccine misinformation reached a higher number of likes, saves, and shares (p=0.012, 0.016, and 0.003, respectively). In conclusion, substantial dissemination of TikTok MR vaccine-related misinformation in Jordan was detected. Rigorous fact-checking is warranted by the platform to address misinformation on TikTok, which is vital to improve trust in MR vaccination and ultimately protect public health.

Epidemiologic and clinical updates on viral infections in Saudi Arabia

In the past two decades, the world has witnessed devastating pandemics affecting the global healthcare infrastructure and disrupting society and the economy worldwide. Among all pathogens, viruses play a critical role that is associated with outbreaks due to their wide range of species, involvement of animal hosts, easily transmitted to humans, and increased rates of infectivity. Viral disease outbreaks threaten public health globally due to the challenges associated with controlling and eradicating them. Implementing effective viral disease control programs starts with ongoing surveillance data collection and analyses to detect infectious disease trends and patterns, which is critical for maintaining public health. Viral disease control strategies include improved hygiene and sanitation facilities, eliminating arthropod vectors, vaccinations, and quarantine. The Saudi Ministry of Health (MOH) and the Public Health Authority (also known as Weqayah) in Saudi Arabia are responsible for public health surveillance to control and prevent infectious diseases. The notifiable viral diseases based on the Saudi MOH include hepatitis diseases, viral hemorrhagic fevers, respiratory viral diseases, exanthematous viral diseases, neurological viral diseases, and conjunctivitis. Monitoring trends and detecting changes in these viral diseases is essential to provide proper interventions, evaluate the established prevention programs, and develop better prevention strategies. Therefore, this review aims to highlight the epidemiological updates of the recently reported viral infections in Saudi Arabia and to provide insights into the recent clinical treatment and prevention strategies.

Estimating sectoral COVID-19 economic losses in the Philippines using nighttime light and electricity consumption data

Introduction

Economic loss estimation is critical for policymakers to craft policies that balance economic and health concerns during pandemic emergencies. However, this task is time-consuming and resource-intensive, posing challenges during emergencies.

Method

To address this, we proposed using electricity consumption (EC) and nighttime lights (NTL) datasets to estimate the total, commercial, and industrial economic losses from COVID-19 lockdowns in the Philippines. Regression models were employed to establish the relationship of GDP with EC and NTL. Then, models using basic statistics and weather data were developed to estimate the counterfactual EC and NTL, from which counterfactual GDP was derived. The difference between the actual and the counterfactual GDP from 2020 to 2021 yielded economic loss.

Results

This paper highlights three findings. First, the regression model results established that models based on EC (adj-R2 ≥ 0.978) were better at explaining GDP than models using NTL (adj-R2 ≥ 0.663); however, combining both EC and NTL improved the prediction (adj-R2 ≥ 0.979). Second, counterfactual EC and NTL could be estimated using models based on statistics and weather data explaining more than 81% of the pre-pandemic values. Last, the estimated total loss amounted to 2.9 trillion PhP in 2020 and 3.2 trillion PhP in 2021. More than two-thirds of the losses were in the commercial sector as it responded to both policies and the COVID-19 case surge. In contrast, the industrial sector was affected primarily by the lockdown implementation.

Discussion

This method allowed monitoring of economic losses resulting from long-term and large-scale hazards such as the COVID-19 pandemic. These findings can serve as empirical evidence for advocating targeted strategies that balance public health and the economy during pandemic scenarios.

Influence of Artificial intelligent in Industrial Economic sustainability development problems and Countermeasures

Economic Sustainability Development (ESD) helps improve the sustainable values needed to conserve resources via optimum use, recovery, and recycling. There should be a direct relationship between countermeasures and the cause of economic losses due to improper design of ESD. Therefore, combining big data and cutting-edge technology may facilitate real-time monitoring, encourage consumers to engage in more sustainable practices and foster the development of industry sustainability. However, countermeasures have unforeseen consequences and tradeoffs that are difficult to predict in ESD. In this research, ESD uses big data to enhance their operations and customer service, develop targeted marketing strategies, and boost sales and profitability. In ESD, Data analytics is being used by human resources to improve decision-making throughout the recruiting process and in evaluating employee performance. In the long run, Artificial Intelligence (AI) adoption may boost productivity and produce new goods, creating jobs and boosting the economy. AI may have a net beneficial impact on ESD. Therefore, ESD-AI helps to overcome the problems by minimizing costs and boosting the economy. AI-integrated ESD helps analyze vast amounts of data, which may increase the speed at which things are done and substantially enhance decision-making. Hence, a balanced approach is essential to guarantee that AI systems can tackle sustainability challenges without adversely compromising other aims to boost the economy.

Evaluating airports' Sustainable Development Goals by using multi-criteria decision making methodologies

BACKGROUND

The recent growth of the aviation industry, which poses significant environmental challenges, has heightened the pressure on the sustainability of airports. Airport sustainability requires a holistic approach that encompasses economic, social, environmental, and operational aspects. In this regard, the United Nations' 17 Sustainable Development Goals (SDGs) Agenda provides a roadmap for the aviation industry. However, despite recognizing the importance of SDGs, aviation authorities and airports often fail to effectively integrate them into their activities and annual reports.

OBJECTIVE

This study aims to evaluate the significance of SDGs for airports and select the airport that prioritizes SDGs the most using Multi-Criteria Decision Making (MCDM) methodologies.

METHODS

This study introduces a novel approach that integrates Step-wise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) methods, which are MCDM techniques, to enhance airport sustainability. The SWARA method is employed to evaluate and assign weights to the SDGs in the context of airports.

RESULTS

SDG 8 holds the highest level of significance among the goals concerning airports, while SDG 14 falls outside the scope of airport sustainability aspects. Then, five international airports that have been designated as green airports by aviation authorities and assessment organizations are selected, and the optimal alternative is determined using the WASPAS method, considering the weights obtained through SWARA.

CONCLUSION

Dallas/Fort Worth International Airport is the top choice due to its successful implementations and reports aligning with the SDGs.

Assessing the possibility of China reaching carbon emission peak by 2030 in the context of the COVID-19 pandemic

At this stage, it is an important goal for China to solve environmental problems to limit the carbon emission peak by 2030 and then to lower its quality gradually thereafter. The outbreak of the COVID-19 pandemic in 2020 seriously impacted all aspects of China's social economy and brought many uncertainties to the realization of the carbon emission peak. Based on the fact, it has important theoretical and practical significance to take the problem of China's carbon emissions during the COVID-19 as the research object. Thus, this paper analyzes the current situation of China's CO2 emissions and finds out that in the context of ongoing COVID-19 response, stimulated by China's economic stimulus policies, CO2 emissions decrease firstly and then gradually rebound. On this basis, the paper constructs a dynamic model of China's CO2 emission system to simulate the change in China's CO2 emissions under different economic stimulus policies. The relevant simulation results demonstrate the following: (1) China cannot realize the CO2 emission goal by 2030 only by adopting traditional economic stimulus policies. (2) The green economic stimulus policy oriented to the development of clean energy will rebound China's CO2 emissions in the short term, but it can effectively reduce CO2 emissions in the long run. The most important factors affecting CO2 emission reduction are industrial energy consumption and industrial structure, while the energy power generation structure and the transportation structure have a relatively weak impact on the emission reduction effect. (3) The green economic stimulus policy combined with economic stimulus measures and a variety of low-carbon measures can enable China to peak CO2 emissions before 2030, with a peak value of 11.059 billion tons. In general, green economic stimulus policies can achieve a win-win situation for China's economic recovery and carbon emission peak.

Data-driven models applying in household hazardous waste: Amount prediction and classification in Shanghai

Precisely predicting the amount of household hazardous waste (HHW) and classifying it intelligently is crucial for effective city management. Although data-driven models have the potential to address these problems, there have been few studies utilizing this approach for HHW prediction and classification due to the scarcity of available data. To address this, the current study employed the prophet model to forecast HHW quantities based on the Integration of Two Networks systems in Shanghai. HHW classification was performed using HVGGNet structures, which were based on VGG and transfer learning. To expedite the process of finding the optimal global learning rate, the method of cyclical learning rate was adopted, thus avoiding the need for repeated testing. Results showed that the average rate of HHW generation was 0.1 g/person/day, with the most significant waste categories being fluorescent lamps (30.6 %), paint barrels (26.1 %), medicine (26.2 %), battery (15.8 %), thermometer (0.03 %), and others (1.22 %). Recovering rare earth element (18.85 kg), Cd (3064.10 kg), Hg (15643.43 kg), Zn (14239.07 kg), Ag (11805.81 kg), Ni (4956.64 kg) and Li (1081.45 kg) from HHW can help avoid groundwater pollution, soil contamination and air pollution. HVGGNet-11 demonstrated 90.5 % precision and was deemed most suitable for HHW sorting. Furthermore, the prophet model predicted that HHW in Shanghai would increase from 794.43 t in 2020 to 2049.67 t in 2025.

Unleashing the link between the relaxation of the COVID-19 control policy and residents' mental health in China: the mediating role of family tourism consumption

Objective

COVID-19 has negatively influenced industrial development, family consumption, and residents' mental health. Unfortunately, it has not yet been studied whether this adverse situation can be alleviated after the relaxation of the COVID-19 control policy (RCC). Therefore, this study aimed to analyze the effect of the RCC on the resident's mental health and the mediating effect of family tourism consumption.

Methods

By using the PSM and mediating effetc model to research the panel data of two periods (April 2021 and April 2023) for Shaanxi province, China.

Results

The RCC negatively inhibited the mental health severity of residents, and the mental health severity decreased by 0.602. In particular, the RCC showed the most substantial negative effect on residents' stress, followed by anxiety and depression. Meanwhile, it is found that the impact of the RCC on the mental health of residents is highly heterogeneous. The RCC indicates a linear significant effect on the mental health of residents under 60 years of age, while the results were found insignificant for residents above 60 years of age. Meanwhile, the RCC's improvement effect on urban residents' mental health is greater than that of rural residents. In addition, mechanism analysis showed that tourism consumption plays a mediating role in the influence of the RCC on the mental health of residents, and the mediating effect accounted for 24.58% of the total effect.

Conclusion

Based on the findings, the study proposes that government and policymakers should strengthen mental health intervention, improve access to mental health counseling, stimulate economic development, expand the employment of residents, and track the mutation of the novel coronavirus.

Links between renewable and non-renewable energy consumption, economic growth, and climate change, evidence from five emerging Asian countries

One of the greatest challenges facing humanity in the current millennium is the need to mitigate climate change, and one of the most viable options to overcome this challenge is to invest in renewable energy. The study dynamically examines the links between renewable energy consumption, non-renewable energy consumption, climate change, and economic growth in five emerging Asian countries during the period 1975-2020. Variables selected in the model have long-term cointegration, as explored by the Pedroni cointegration test and the Westerlund cointegration test. The long-term estimated parameters of the augmented mean group (AMG) method show that renewable energy consumption significantly reduces climate change, while non-renewable energy consumption significantly promotes climate change. The results also show that GDP, investment in transport infrastructure, and urbanization can significantly contribute to climate change in selected emerging Asian countries. Moreover, the results validate the inverted U-shaped EKC hypothesis for emerging Asian economies. Country-specific analysis results using AMG estimates shows that renewable energy consumption reduces climate change in selected emerging Asian countries. Non-renewable energy consumption and investment in transport infrastructure have had significant progressive impacts on climate change in all countries. Urbanization contributes significantly to climate change, with the exception of Japan, which does not have any significant impact on climate change. GDP contributes significantly to climate change in all countries; however, GDP2 has significant adverse effects on climate change in India, China, Japan, and Korea, validating the inverted U-shaped EKC assumption for all countries except Bangladesh. Moreover, the Dumitrescu and Hurlin causality test confirmed a pairwise causal relationship between non-renewable energy consumption and GDP, supporting the feedback hypothesis. The results suggest that the best option for climate change mitigation in selected emerging Asian countries is to transition from non-renewable to renewable energy sources.

A prospective study of the impact of COVID-19-related restrictions on activities and mobility upon physical activity, travel behaviour and attitudes

Background and aims

Public health measures adopted to contain the spread of COVID-19 included restrictions on activities and mobility as people were asked to stay at home and schools moved to online learning. This may have increased risk of non-communicable disease by limiting recreational and transport-related physical activity. Building on an existing study, we assessed changes in self-reported and device-measured physical activity and travel behaviour before, during and after the peak of local COVID-19 outbreak and restrictions (March-July 2020). We examined beliefs in effectiveness of strategies to increase active and public transport after restrictions were reduced.

Methods

A longitudinal study of adult infrequent bus users (average ≤ 2 trips per week; n = 70; 67% women) in Hobart, Australia. One-week assessment periods at four separate timepoints (before, during, 0-3 months after, and 3-6 months after the peak restrictions period) involved wearing an accelerometer, daily transport diaries, online surveys and tracking bus smartcard boardings.

Results

Physical activity (especially among older participants), bus use and private motor vehicle use declined significantly during or 0-3 months after the peak restrictions period and returned to pre-restrictions levels by 3-6 months after the peak restrictions period, except bus use which remained significantly lower. Retrospective surveys overstated declines in bus use and active transport and self-reports understated declines in physical activity. Social distancing and improving service efficiency and frequency were seen as effective strategies for increasing bus use after restrictions but belief in effectiveness of distancing decreased over time.

Conclusions

When restrictions on mobility are increased, supportive health promotion measures are needed to prevent declines in physical activity, particularly for older adults. Public transport systems need capacity to implement temporary distancing measures to prevent communicable disease transmission. Providing convenient, flexible, and efficient options for public transport may help to replenish public transport use after restrictions are reduced.

Assessing the construction of a Healthy City in China: a conceptual framework and evaluation index system

OBJECTIVES

COVID-19 has brought challenges to the health of all mankind. It is particularly important to promote the construction of a 'Healthy China' and build a 'healthy community'. The aims of this study were to construct a reasonable conceptual framework for the Healthy City concept and to assess Healthy City construction in China.

STUDY DESIGN

This study combined qualitative and quantitative research.

METHODS

This study proposes the concept model of 'nature-human body-Healthy City' and accordingly constructs an evaluation index system for the construction of a Healthy City that integrates five dimensions, namely, the medical level, economic basis, cultural development, social services, and ecological environment to explore the spatial and temporal heterogeneity of Healthy City construction in China. Finally, the influencing factors of Healthy City construction patterns are explored using GeoDetector.

RESULTS

(1) The pace of Healthy City construction is generally on the rise; (2) the construction of Healthy Cities exhibits significant global spatial autocorrelation and gradually increasing agglomeration. The spatial distribution of cold hotspot areas was relatively stable; (3) medical and health progress is an important factor; the level of economic development is the leading support; the endowment of resources and environment is the basic condition; public service support provides important support; and scientific and technological innovation capabilities provide technical support for the construction of a Healthy City.

CONCLUSIONS

The spatial heterogeneity of Healthy City construction in China is evident, and the state of spatial distribution is relatively stable. The spatial pattern of Healthy City construction is shaped by a combination of factors. Our research will provide a scientific basis for promoting the construction of Healthy Cities and helping to implement the Health China Strategy.

The impact of COVID-19 on economy, air pollution and income: evidence from China

The global pandemic caused by the outbreak of COVID-19 has posed significant risks to our health. Preventive measures such as closed management have greatly affected the economies, environments and societies of various countries. Economy, air pollution and income are three important interconnected aspects of sustainable development. However, current research lacks systematic quantitative analysis of their relationships. To fill the gap, this study adopts monthly data from January 2016 to April 2022 and constructs both a Simultaneous Equation Model (SEM) and a Time Varying Parameter Stochastic Volatility Vector Autoregressive (TVP-SV-VAR) model to empirically analyze the impact of COVID-19 on China's economy, air pollution and income. This study finds that the COVID-19 has a negative impact on China's economy and income, and a positive impact on air pollution, and the impact of the COVID-19 is systematic. In addition, there is an inverted-U shaped relationship between air pollution and economics, and a positive correlation between economic and income. The impact of COVID-19 on the economy, air pollution and income show a process of sharp fluctuations to gradual stabilization that gradually stabilized over time. This process is time-varying in the short-term, medium-term and long-term. The impacts are persistent at three different time points (before, during and after the outbreak of COVID-19), but the negative impact on the economy and income is persistent, while the positive impact on air pollution is limited. This study provides a more systematic and dynamic understanding of the COVID-19 preventive and mitigation measures in China and even the world, which helps to provide insights into the formulation of more comprehensive planning strategies in the future.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00477-023-02450-z.

Do COVID-19 pandemic-related policy shocks flatten the bid-rent curve? Evidence from real estate markets in Shanghai

The COVID-19 pandemic has drastically affected the socioeconomic activities and peoples' daily life, resulting in a change in locational preferences in the real estate markets. Although enormous efforts have been devoted to examining the housing price impacts of the COVID-19 pandemic, little is known about the responses of the real estate markets to the evolving pandemic control measures. This study investigates the price gradient effects of various pandemic-related policy shocks using a hedonic price model on the district-level property transaction data in Shanghai, China over a 48-month period from 2018 to 2021. We found that these shocks have significantly altered the bid-rent curves. The price gradient for residential property units decreased in absolute value to - 0.433 after Wuhan's lockdown, demonstrating peoples' preferences to avoid the high infection risks in districts closer to the city center. However, in the post-reopening and post-vaccine periods, the price gradient increased to - 0.463 and - 0.486, respectively, implying rational expectations of a recovering real estate market for the low infection and mortality rates. In addition, we discovered that Wuhan's lockdown has steepened the price gradient for commercial property units, suggesting a decline in business volumes and an increase in operating costs in the low-density districts imposed by the strict pandemic control measures. This study contributes to the empirical literature on the price gradient effects of the COVID-19 pandemic by extending the study period to the post-vaccine era.

Co-occurrence of urban heat and the COVID-19: Impacts, drivers, methods, and implications for the post-pandemic era

Cities, the main place of human settlements, are under various mega challenges such as climate change, population increase, economic growth, urbanization, and pandemic diseases, and such challenges are mostly interlinked. Urban heat, due to heatwaves and heat islands, is the combined effect of climate change and urbanization. The COVID-19 is found to be a critical intervention of urban heat. However, the interrelationship between COVID-19 and urban heat has not been fully understood, constraining urban planning and design actions for improving the resilience to the dual impacts of heat and the pandemic. To close this research gap, this paper conducted a review on the co-occurrence of urban heat and the COVID-19 pandemic for a better understanding of their synergies, conflicts or trade-offs. The research involves a systematic review of urban temperature anomalies, variations in air pollutant concentrations, unbalanced energy development, and thermal health risks during the pandemic lockdown. In addition, this paper further explored data sources and analytical methods adopted to screen and identify the interventions of COVID-19 to urban heat. Overall, this paper is of significance for understanding the impact of COVID-19 on urban heat and provides a reference for coping with urban heat and the pandemic simultaneously. The world is witnessing the co-existence of heat and the pandemic, even in the post-pandemic era. This study can enlighten city managers, planners, the public, and researchers to collaborate for constructing a robust and resilient urban system for dealing with more than one challenges.

Transition to a sustainable food supply chain during disruptions: A study on the Brazilian food companies in the Covid-19 era

The COVID-19 pandemic has brought negative impacts to global supply chains, in particular to the agricultural sector. Although these companies have been developing programs to mitigate the impacts caused by COVID-19, researchers have been worried about a possible weakening of the adoption of sustainable initiatives due to a focus on dealing with the consequences of the pandemic. Grounded on the Resource Orchestration Theory, the goal of this study was to assess the effects of the COVID-19 outbreak on sustainable (environmental, social, and economic) performance in the context of agri-food supply chains. To do so, a questionnaire survey was used to collect data from 349 different medium and large agri-food companies in Brazil. Data were analyzed using Structural Equation Modeling-Partial Least Squares technique. This study has found out that, under the impacts arising from COVID-19 pandemic (1) supply management and transportation and logistics management had a positive impact on food supply chains' sustainable performance; (2) the effects of relationship management and supply chain wide impact management were found to be negative on sustainable performance; and (3) the effects of demand and production management on sustainable performance were not considered significant. We propose a framework that clearly represents the relationship between the disrupted supply chain areas and sustainable performance through the development of orchestration capabilities. By knowing which kinds of impacts produce the most significant effects on sustainable performance, policy makers and managers will be able to make decisions and take actions to avoid negative effects and to improve firms' sustainable performance.

A Statistical Synopsis of COVID-19 Components and Descriptive Analysis of Their Socio-Economic and Healthcare Aspects in Bangladesh Perspective

The aim of the work is to analyze the socio-economic and healthcare aspects that arise in the contemporary COVID-19 situation from Bangladesh perspective. We elaborately discuss the successive COVID-19 occurrences in Bangladesh with consequential information. The components associated with the COVID-19 commencement and treatment policy with corresponding features and their consequences are patently delineated. The effect of troublesome issues related to the treatment is detailed with supporting real-time data. We elucidate the applications of modern technologies advancement in epidemiological aspects and their existent compatibility in Bangladesh. We statistically analyze the real-time data through figurative and tabular approaches. Some relevant measures of central tendency and dispersion are utilized to explore the data structure and its observable specifications. For a clear manifestation, Z- scores of the COVID-19 components are analyzed through the Box-Whisker plot. We have discovered that the gathered data exhibit features that are unsatisfactory for the normal distribution, are highly positively skewed, and are predominated by the earliest occurrences. Infections and deaths were initially lower than the global average, but they drastically rose in the first quarter of 2021 and persisted for the remainder of the year. Substantial preventive results were produced by the region-wisetime-worthy moves. In the fourth quarter of 2021, the infections and deaths noticeably decreased, and the number of recoveries was highly significant. In the middle of 2022, a lethal rise in infections was observed in Bangladesh and that was quickly stabilized, and the pandemic ingredients were under control. According to our assessment, some concluding remarks are made at the end of this work.

Assessing the EKC hypothesis by considering the supply chain disruption and greener energy: findings in the lens of sustainable development goals

This paper investigates the effect of the supply chain disruption, greener energy consumption, and economic growth on carbon emissions in advanced economies and emerging markets from 1997 to 2021 using panel quantile autoregressive distributed lags (QARDL) and the panel quantile regression (QR). The results of the two models confirm, on the one hand, the validity of the environmental Kuznets curve (EKC) hypothesis and, on the other hand, the role of renewable energy consumption in mitigating carbon emissions in advanced and developing economies. Furthermore, the finding shows that the supply chain disruption for the long run is positive at all quantiles, indicating the evidence of association at the extreme low and high quantiles than at the intermediate quantile. In addition, the effect of the supply chain decreases at the lower quantile. It turns negative at the upper 90th quantile in the short run, indicating that the supply chain disruption reduces the environmental degradation under the bearish market conditions. In the future, the increasing supply chain disruptions due to the Russia-Ukraine conflict and further COVID-19 worldwide can consider sluggish economic growth and play an essential role in promoting renewable energy abundance and reducing CO2 emissions. Practical implications are reported in the lens of carbon neutrality and structural changes.

An empirical investigation of COVID-19 effects on herding behaviour in USA and UK stock markets using a quantile regression approach

This study investigates the effects of the COVID-19 pandemic on herding behaviour among investors in two well-developed markets. Utilizing daily prices of stock indexes from the period of December 5, 2017 to February 28, 2022 for USA and January 9, 2018 to February 28, 2022 for UK, we test for herding behaviour using the quantile regression approach in addition to the OLS model. We found no evidence of herding before the COVID-19 pandemic in both bullish and bearish markets for USA and UK. However, herding incidence was discovered in the USA and UK bullish market during the COVID-19 period. In the bearish market, herding behaviour was only found during the COVID-19 period in USA. The study provides policymakers and investors with information to draw significant measures in their investment portfolio management during crises and pandemics.

GIS-based AHP analysis to recognize the COVID-19 concern zone in India

This study was planned to identifying the Corona concerns zone during COVID-19 transmission in India. The death rate was very high due COVID-19 pandemic outbreaks which are one of the main reasons for impairment the countries, and it will takes several years for the re-establishment of the fundamental need to ensure the demand of public supply. Currently, like many countries around the world, India is also facing a drastic health crisis due to Corona virus disease. Analytical Hierarchy Process (AHP) and Geographical Information System (GIS) play important role in making the multi-criteria decisions and identifying the corona concern zone of a larger populated areas across the country in a single platform which can be further helpful for better control, planning, and management during several pandemic outbreaks. The present work is based on the AHP and GIS-assisted identification, analysis, and representation of the state-wise corona concern zone of India. Consequently, the current examination is essential to investigate the Corona concern zone in order to support the management and planning authority of India to improve their strategies in respect to reduce or check the health risk during the emergency of pandemic due to COVID-19. The present study indicated that the state-wise priority of corona concern zone recorded higher in state Maharashtra, Uttar Pradesh, and Kerala as compared to the other part of the India. Hence, GIS and AHP are the potential to identify, observe and analyze the COVID-19 Concern Zone.

Development and Characterization of Phage-Display-Derived Novel Human Monoclonal Antibodies against the Receptor Binding Domain of SARS-CoV-2

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has resulted in an ongoing global pandemic crisis, caused by the life-threatening illness coronavirus disease 2019 (COVID-19). Thus, the rapid development of monoclonal antibodies (mAbs) to cope with COVID-19 is urgently necessary. In this study, we used phage display to develop four human mAbs specific to the receptor-binding domain (RBD) of SARS-CoV-2. Our intensive in vitro functional analyses demonstrated that K102.1, an anti-SARS-CoV-2 RBD-specific mAb, exerted potent neutralizing activity against pseudoviral and live viral infection and the interaction between SARS-CoV-2 RBD and human angiotensin-converting enzyme 2. Monotherapy with K102.1 also revealed the therapeutic potential against SARS-CoV-2 infection in vivo. Further, this study developed a sandwich enzyme-linked immunosorbent assay with a non-competing mAb pair, K102.1 and K102.2, that accurately detected the RBDs of SARS-CoV-2 wild-type and variants with high sensitivity in the picomolar range. These findings suggest that the phage-display-based mAb selection from an established antibody library may be an effective strategy for the rapid development of mAbs against the constantly evolving SARS-CoV-2.

Microalgal remediation and valorisation of polluted wastewaters for zero-carbon circular bioeconomy

Overexploitation of natural resources to meet human needs has considerably impacted CO₂ emissions, contributing to global warming and severe climatic change. This review furnishes an understanding of the sources, brutality, and effects of CO₂ emissions and compelling requirements for metamorphosis from a linear to a circular bioeconomy. A detailed emphasis on microalgae, its types, properties, and cultivation are explained with significance in attaining a zero-carbon circular bioeconomy. Microalgal treatment of a variety of wastewaters with the conversion of generated biomass into value-added products such as bio-energy and pharmaceuticals, along with agricultural products is elaborated. Challenges encountered in large-scale implementation of microalgal technologies for low-carbon circular bioeconomy are discussed along with solutions and future perceptions. Emphasis on the suitability of microalgae in wastewater treatment and its conversion into alternate low-carbon footprint bio-energies and value-added products enforcing a zero-carbon circular bioeconomy is the major focus of this review.

Changing transport planning objectives during the Covid-19 lockdowns: Actions taken and lessons learned for enhancing sustainable urban mobility planning

Whilst there is research on how Covid-19 impacted travel demand and transport business, little attention has been paid on how Covid-19 has affected authorities' transport planning priorities and the actions taken to protect the public while travelling. This paper attempts to shed light on: a) how the transport planning priorities changed during the Covid-19 lockdowns in 2020/2021, and b) how the planning phases of the Sustainable Urban Mobility Plan (SUMP) framework can be strengthened to support a more resilient emergency planning environment. To address these questions, an online questionnaire was designed followed up by personal interviews from selected European cities. Data collection took place in November and December 2020, when most European countries were in lockdowns. Thirteen public authorities participated in the online questionnaire, while nine out of them were further interviewed. A mixed methods approach is used to analyse the quantitative and qualitative data and bring the results together to assess how SUMP priorities have changed. The results showed that the priority planning objectives were different in the period during the 2020/2021 lockdowns compared to the period before that. Public transport system planning was a priority in both periods, while planning for shared mobility and Mobility as a Service was further prioritised in the 2020/21 lockdowns. The main reasons for prioritising specific planning objectives were to secure public health, minimise environmental impact, support economic recovery and address social equity. The changes in the priority of planning objectives were also diverse between smaller and larger urban areas. Most of the actions adopted to accommodate the prioritised planning objectives were already defined before Covid-19, indicating that the lockdowns have acted as an accelerator of specific existing planning objectives.

Social Work and the Next Frontier of Racial Justice: Using COVID-19 as a Vehicle for Healing

The COVID-19 pandemic has put the United States and the world into a state of uncertainty. Before the onset of the coronavirus, awareness of health disparities across cities in the United States was questionable at best. As the world continues to grapple with the fallout of the pandemic and the response to it, several states and developed and developing countries created and implemented response efforts that were used as a guide, which social workers are most qualified to address but have not been a focus on a national nor international stage. This commentary focuses on two American states - Texas and Ohio as well as other global countries, and their responses that gained worldwide attention related to healthcare accessibility, service provision, and the role social workers should play moving forward and beyond the pandemic.

Dwelling in times of COVID-19: An analysis on habitability and environmental factors of Spanish housing

In the wake of the SARS-CoV-2 pandemic, promoted by the World Health Organization (WHO), governments urged people to stay at home. For this reason, practically all human activity took place inside the houses. The research question established if housing quality responded to people’s needs in the context of confinement. Specifically, the purpose was to taxonomize the dwelling stock occupied by confined households during the first COVID-19 wave in Spain, as well as to deepen in features and subjective perceptions on Indoor Environmental Quality (IEQ). As an exploratory study, an online questionnaire was disseminated in the Spring of 2020, obtaining 1,673 valid responses. A descriptive statistical analysis included sociodemographic, territorial and housing variables, related to indoor environmental quality, the availability of outdoor spaces, and the prospects for changes in. Also, a logistic regression stablished multivariate relations for the dependent variable “general dwelling satisfaction”. The results associated urban habitat, tenancy regime, higher incomes, and fewer cohabitants, with worse perceived IEQ, and lack of own outdoor space. Same variables showed relations with people’s desire for domestic changes. In conclusion, it is remarkable the determining role of housing design for dwellers’ satisfaction, especially in uncertain times like COVID-19 pandemic. This not only conditioned the different ways of inhabiting and occupying dwellings, but also the people’s capacity to face lockdown. The built environment, the habitat, and households’ circumstances also influenced. The latter did on people's perception of their experience, and how they lived and expressed it. Additionally, resilient building design and renovation opportunities were identified.

Terminated local subsidy on electric vehicle adoption during the COVID-19 pandemic: The case of Chongqing City

Chongqing, one of the four municipalities directly under the Central Government in China, terminated local subsidies for electric vehicles (EVs) on June 26, 2019. Shortly after the termination, EV adoption in China was affected by the coronavirus disease (COVID-19) pandemic. However, little research studies on whether the terminated local subsidy has a lasting impact on EV adoption, especially during the pandemic. Using EV adoption data from Chongqing and 44 other cities in China, this study aims to fill this gap by first proposing a new method to estimate the unobservable data of the treated unit in the preintervention periods to obtain accurate results. This study then also estimates unobservable data for more conservative results. The findings show that the terminated subsidy has had a significant positive impact on EV adoption during the COVID-19 pandemic compared to the situation where a local subsidy was never provided. The results show that in Chongqing, during the first five months of the pandemic, terminated local subsidy helped reduce the loss of EV adoption by approximately 3141 units when accurately estimated, and approximately 1696 units when more conservatively estimated. These findings help to understand the role of subsidies both during implementation and after their termination.

A review about COVID-19 in the MENA region: environmental concerns and machine learning applications

Coronavirus disease 2019 (COVID-19) has delayed global economic growth, which has affected the economic life globally. On the one hand, numerous elements in the environment impact the transmission of this new coronavirus. Every country in the Middle East and North Africa (MENA) area has a different population density, air quality and contaminants, and water- and land-related conditions, all of which influence coronavirus transmission. The World Health Organization (WHO) has advocated fast evaluations to guide policymakers with timely evidence to respond to the situation. This review makes four unique contributions. One, many data about the transmission of the new coronavirus in various sorts of settings to provide clear answers to the current dispute over the virus's transmission were reviewed. Two, highlight the most significant application of machine learning to forecast and diagnose severe acute respiratory syndrome coronavirus (SARS-CoV-2). Three, our insights provide timely and accurate information along with compelling suggestions and methodical directions for investigators. Four, the present study provides decision-makers and community leaders with information on the effectiveness of environmental controls for COVID-19 dissemination.

Label-free and portable field-effect sensor for monitoring RT-LAMP products to detect SARS-CoV-2 in wastewater

The COVID-19 pandemic caused by the coronavirus SARS-CoV-2 has proven the need for developing reliable and affordable technologies to detect pathogens. Particularly, the detecting the genome in wastewater could be an indicator of the transmission rate to alert on new outbreaks. However, wastewater-based epidemiology remains a technological challenge to develop affordable technologies for sensing pathogens. In this work, we introduce a label-free and portable field-effect transistor (FET)-based sensor to detect N and ORF1ab genes of the SARS-CoV-2 genome. Our sensor integrates the reverse transcription loop-mediated isothermal amplification (RT-LAMP) reaction as a cost-effective molecular detection exhibiting high specificity. The detection relies upon pH changes, due to the RT-LAMP reaction products, which are detected through a simple, but effective, extended-gate FET sensor (EGFET). We evaluate the proposed device by measuring real wastewater samples to detect the presence of SARS-CoV-2 genome, achieving a limit of detection of 0.31 × 10⁻³ ng/μL for end-point measurement. Moreover, we find the ability of the sensor to perform real-time-like analysis, showing that the RT-LAMP reaction provides a good response after 15 min for concentrations as low as 0.37 ng/μL. Hence, we show that our EGFET sensor offers a powerful tool to detect the presence of the SARS-CoV-2 genome with a naked-eye method, in a straightforward way than the conventional molecular methods for wastewater analysis.

Assessment of COVID-19 impact on sustainable development goals indicators in Egypt using fuzzy analytic hierarchy process

COVID-19 significantly influences the Sustainable Development Goals (SDGs) in both developed and developing countries. Within the 2030 agenda, Egypt is likely to face enormous negative ramifications from the virus spread. As a result, efficient control of the adverse repercussions of this virus is critical to achieving this objective. This research assesses indicators of specific SDGs in reflecting COVID-19 impact by conducting several questionnaire surveys among experts in Egypt. The scope of this research is limited to addressing poverty alleviation (SDG1), hunger abatement (SDG2), healthcare promotion (SDG3), sustainable economic growth (SDG8), and climate change mitigation (SDG13). The indicators are prioritized using the relative importance index, weighted aggregated sum product assessment technique for order preference by similarity to an ideal solution, and fuzzy analytic hierarchy process. The rankings are finally aggregated using an approach based on the half-quadratic theory. The results reveal that the most significant indicators in reflecting the COVID-19 impact are the share of population living below the international poverty line, undernourishment prevalence, official health sector support, annual gross domestic product per capita growth rate, and number of disaster deaths for SDG1, SDG2, SDG3, SDG8, and SDG13, respectively. Recognizing and ranking the indicators could help decision-makers understand the behavior of SDG indicators in light of COVID-19. The research findings could assist policymakers in making informed decisions to reduce the pandemic effects and sustain achieving SDGs by 2030.

An empirical analysis of the impact of gender inequality and sex ratios at birth on China's economic growth

The contribution of women to China's economic growth and development cannot be overemphasized. Women play important social, economic, and productive roles in any economy. China remains one of the countries in the world with severe gender inequality and sex ratio at birth (SRB) imbalance. Severe gender inequality and disenfranchisement of girls with abnormally high sex ratios at birth reflect deep-rooted sexism and adversely affect girls' development. For China to achieve economic growth, women should not be ignored and marginalized so that they can contribute to the country's growth, but the sex ratio at birth needs to be lowered because only women can contribute to growth. Thus, this study empirically predicts an asymmetric relationship between gender inequality, sex ratio at birth and economic growth, using NARDL model over the period 1980-2020. The NARDL results show that increases in gender inequality and sex ratio at birth significantly reduce economic growth in both the short and long term, while reductions in gender inequality and sex ratio at birth significantly boost economic growth in both the short and long term. Moreover, the results show the significant contribution of female labor force participation and female education (secondary and higher education) to economic growth. However, infant mortality rate significantly reduced economic growth. Strategically, the study recommends equal opportunities for women in employment, education, health, economics, and politics to reduce gender disparities and thereby promote sustainable economic growth in China. Moreover, policymakers should introduce new population policy to stabilize the sex ratio at birth, thereby promoting China's long-term economic growth.

Preliminary quantitative assessment of the multidimensional impact of the COVID-19 pandemic on Sustainable Development Goals

The intersectoral impacts of the COVID-19 pandemic on humanity raises concerns about its implications for sustainable development. Here, we examine a global quantitative impact of COVID-19 pandemic on Sustainable Development Goals (SDGs) across all 17 goals using 65 proxy indicators across 72 countries collected from April 2020 to February 2021. Our data-driven analysis indicated that adverse impacts of the pandemic have been particularly concerned on gender equality (Goal 5), affordable and clean energy (Goal 7), decent work and economic growth (Goal 8), sustainable cities and communities (Goal 11), and responsible consumption and production (Goal 12) with global scores estimated to be -0.38, -0.21, -0.28, -0.22 and -0.16, respectively. Country income level was a variable that strongly differentiates the responses to the pandemic (e.g., lower incomes had 14 negative goals compared to 11 and 4 negative goals assigned to middle- and high-income countries, respectively). However, Goals 5 and 8 were highly impacted worldwide regardless of income status. Furthermore, countries that had already higher performance in SDGs were less impacted by the pandemic, highlighting the importance of progress on the SDGs in increasing societal resilience to pandemics. The findings provide insights into the reinforcement of recovery policies (e.g., protecting vulnerable groups and transitioning to a green economy) and a basis for a quantitative discussion on the sectors to be prioritized.

Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases

During the last two years, the emergence of SARS-CoV-2 has led to millions of deaths worldwide, with a devastating socio-economic impact on a global scale. The scientific community's focus has recently shifted towards the association of the T cell immunological repertoire with COVID-19 progression and severity, by utilising T cell receptor sequencing (TCR-Seq) assays. The Multiplexed Identification of T cell Receptor Antigen (MIRA) dataset, which is a subset of the immunoACCESS study, provides thousands of TCRs that can specifically recognise SARS-CoV-2 epitopes. Our study proposes a novel Machine Learning (ML)-assisted approach for analysing TCR-Seq data from the antigens' point of view, with the ability to unveil key antigens that can accurately distinguish between MIRA COVID-19-convalescent and healthy individuals based on differences in the triggered immune response. Some SARS-CoV-2 antigens were found to exhibit equal levels of recognition by MIRA TCRs in both convalescent and healthy cohorts, leading to the assumption of putative cross-reactivity between SARS-CoV-2 and other infectious agents. This hypothesis was tested by combining MIRA with other public TCR profiling repositories that host assays and sequencing data concerning a plethora of pathogens. Our study provides evidence regarding putative cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and diseases, with M. tuberculosis and Influenza virus exhibiting the highest levels of cross-reactivity. These results can potentially shift the emphasis of immunological studies towards an increased application of TCR profiling assays that have the potential to uncover key mechanisms of cell-mediated immune response against pathogens and diseases.

Analysis of the Lockdown Effects on the Economy, Environment, and COVID-19 Spread: Lesson Learnt from a Global Pandemic in 2020

Lockdown policies have been implemented to reduce COVID-19 transmission worldwide. However, the shutdown of activities has resulted in large economic losses, and it has been widely reported that lockdown measures have resulted in improved air quality. Therefore, many previous studies have attempted to investigate the impacts of the COVID-19-induced lockdowns on the economy, environment, and COVID-19 spread. Nevertheless, the heterogeneity among countries worldwide in the economic, environmental, and public health aspects and the spatial effects of decomposition have not been well investigated in the existing related literature. In this study, based on the cross-sectional data of 158 countries in 2020 and the proposed nonlinear simultaneous spatial econometric models, we investigate the nonlinear and spatial impacts of the COVID-19-induced lockdowns on the economy, environment, and COVID-19 spread. The findings show that lockdowns have had statistically significant negative economic impacts and beneficial environmental consequences but no effect on COVID-19 spread. Noteworthily, this study also found the length of lockdown periods to affect the three domains of interest differently, with a piece of empirical evidence that the imposition of lockdowns for more than 31 days a year could result in economic impairments but contribute to environmental improvements. Lockdowns were shown to have substantially reduced PM2.5 not only in the countries that imposed the measures but also indirectly in the neighboring countries as a spatial spillover effect.

Modeling the impact of the COVID‐19 outbreak on environment, health sector and energy market

The global outbreak of COVID‐19 disease had a significant impact on the entire globe. Such a notable public health event can be seen as a “black swan” that brings unpredictable and unusual forces into the economic context and that it could typically lead to a chain of adverse reactions and market disruptions. Hence, the purpose of this study is to examine how COVID‐19 affects the environment, health, and the oil and energy markets. To achieve this objective, we used daily data for several measures that refer to the environment, health, and oil and energy, for the first wave of the COVID‐19 pandemic (December 31, 2019–May 22, 2020). The variable integration mix led to the approach of the ARDL model, and the Granger causality test was also employed. These empirical techniques allowed us to examine the cointegration between variables and causal relationships. The econometric results of the ARDL models exhibited that the global new cases and new deaths of COVID‐19 have short and long‐term effects on the environment, the health sector, the oil, and energy measures. However, no significant causal connection was found between the pandemic and the environment, the health sector, or the oil and energy industry, according to the Granger causality test. The uniqueness of current approach consists in the investigation of pandemic impact on the health, environment, oil, and energy sector by applying the ARDL model that permits the analysis of cointegration both in the long run and in the short term. This study provides important insights for investors and policy makers.

The effects of COVID-19 transmission on environmental sustainability and human health: Paving the way to ensure its sustainable management

The transmission dynamics and health risks of coronavirus disease 2019 (COVID-19) pandemic are inextricably linked to ineract with environment, climate, air pollution, and meteorological conditions. The spread of COVID-19 infection can thus perturb the 'planetary health' and livelihood by exerting impacts on the temporal and spatial variabilities of environmental pollution. Prioritization of COVID-19 by the health-care sector has been posing a serious threat to economic progress while undermining the efforts to meet the United Nations' Sustainable Development Goals (SDGs) for environmental sustainability. Here, we review the multifaceted effects of COVID-19 with respect to environmental quality, climatic variables, SDGs, energy resilience, and sustainability programs. It is well perceived that COVID-19 may have long-lasting and profound effects on socio-economic systems, food security, livelihoods, and the 'nexus' indicators. To seek for the solution of these problems, consensus can be drawn to establish and ensure a sound health-care system, a sustainable environment, and a circular bioeconomy. A holistic analysis of COVID-19's effects on multiple sectors should help develop nature-based solutions, cleaner technologies, and green economic recovery plans to help maintain environmental sustainability, ecosystem resilience, and planetary health.

Sustainable development goals under threat? Multidimensional impact of COVID-19 on our planet and society outweigh short term global pollution reduction

The Sustainable Development Goals (SDGs) call on all nations to accomplish 17 broad global development goals by 2030. However, the COVID-19 pandemic presents a challenging period in human history, causing large-scale impacts on society and the environment as governments shift priorities and divert funding in response to this pandemic. Through a literature survey and data acquirement from various international organizations (e.g. United Nations and European Space Agency), this manuscript is intended to provide critical insights into the impacts of the COVID-19 pandemic on the SDGs. We briefly describe this pandemic's positive and short-term effects on the environment, followed by a critical evaluation of its potential long-term impacts on the environment, society, and the SDGs. On the basis of COVID-19 effects, the SDGs are classified into three categories: directly-affected SDGs, indirectly-affected SDGs, and a stand-alone category. The COVID-19-induced lockdowns and restrictions resulted in a short-term decline in environmental pollution and greenhouse gases (GHG) emissions, providing valuable data for climate advocates and researchers. These positive impacts were essentially temporary due to the synchronized global response to the pandemic. The halted focus on the progress of the SDGs greatly impacts the global green transition to a healthy and sustainable world. COVID-19 threatens to impede the progress toward a prosperous, environment-friendly, and sustainable global development in multiple ways. These multi-dimensional threats have been critically evaluated, along with a description of potential solutions to curtail the adverse effects of COVID-19 on the SDGs. Considering the limited data regarding the impacts of the pandemic on the SDGs, diverse collaborative studies at the regional and global levels are recommended.

Modeling COVID-19 effects on SDGs using system dynamics in Egypt

The coronavirus disease 2019 (COVID-19) poses a significant threat to achieving the Sustainable Development Goals (SDGs). To address this challenge, a thorough examination of the pandemic's influence on four SDGs in Egypt is presented in a system dynamic model. The addressed goals are related to no poverty (SDG 1), zero hunger (SDG 2), decent work and economic growth (SDG 8), and climate action (SDG 13). The model is simulated over 35 years extending from 2015 to 2050. Furthermore, a web-based interactive learning environment is developed to analyze the interdependencies among public health activities and study the impacts of possible intervention countermeasures or prevention policies. Indicators including poverty line, food insecurity, gross domestic product (GDP) growth rate, and greenhouse gas (GHG) emissions are evaluated to track Egypt's performance in relation to SDGs 1, 2, 8, and 13. According to the simulation model, the poverty line will continue to decline until it reaches around 16% by 2050. According to the significant governmental efforts to follow its vision of 2030, Egypt can achieve a decreasing percentage of food insecurity, reaching 3% in 2030, and this percentage will continue to decrease until it reaches full sufficiency by 2050. The GDP growth rate will rise every year until it reaches 13.71% in 2050. With respect to climate, GHG emissions are predicted to fall to roughly 97 Mt CO2-equivalents by 2050. This approach revitalizes debates about the achievement of SDGs amid the crisis and acts as a powerful tool that aids decision-makers in identifying leverage points to avoid the long-term negative repercussions of the crisis on the economy, people, and environment.

Perovskite Solar Cells: A Review of the Recent Advances

Perovskite solar cells (PSC) have been identified as a game-changer in the world of photovoltaics. This is owing to their rapid development in performance efficiency, increasing from 3.5% to 25.8% in a decade. Further advantages of PSCs include low fabrication costs and high tunability compared to conventional silicon-based solar cells. This paper reviews existing literature to discuss the structural and fundamental features of PSCs that have resulted in significant performance gains. Key electronic and optical properties include high electron mobility (800 cm2/Vs), long diffusion wavelength (>1 μm), and high absorption coefficient (105 cm−1). Synthesis methods of PSCs are considered, with solution-based manufacturing being the most cost-effective and common industrial method. Furthermore, this review identifies the issues impeding PSCs from large-scale commercialisation and the actions needed to resolve them. The main issue is stability as PSCs are particularly vulnerable to moisture, caused by the inherently weak bonds in the perovskite structure. Scalability of manufacturing is also a big issue as the spin-coating technique used for most laboratory-scale tests is not appropriate for large-scale production. This highlights the need for a transition to manufacturing techniques that are compatible with roll-to-roll processing to achieve high throughput. Finally, this review discusses future innovations, with the development of more environmentally friendly lead-free PSCs and high-efficiency multi-junction cells. Overall, this review provides a critical evaluation of the advances, opportunities and challenges of PSCs.

Sustainable Economic Development, Digital Payment, and Consumer Demand: Evidence from China

In this era, the global COVID-19 pandemic has hit the economy hard. In the context of great challenges to sustainable economic development, it is of great practical significance to study how digital payment can promote consumer demand and sustainable economic development. From the perspective of sustainable economic development, this paper selects panel data of various provinces in China from 2011 to 2020 to test the correlation between digital payment and consumer demand by constructing econometric models and selecting relevant indicators, so as to reveal the impact of digital payment on consumer demand and sustainable economic development. Research shows that: (1) in the context of the COVID-19 pandemic, digital payments play a special and very important role in promoting household consumption and sustainable economic development; (2) the empirical results show that digital payment has a significant positive impact on consumer demand, which indicates that digital payment has an obvious promotion effect on consumer demand; (3) further research shows that the impact of digital payment on consumer demand has obvious heterogeneity. From the perspective of regional differences, digital payment has a significant positive impact on consumer demand in the eastern and western regions, while the impact is not obvious in the northeast and central regions, even though it also has a positive impact. From the perspective of urban-rural differences, digital payment has a significant impact on consumer demand in both urban and rural areas, and this impact is greater in rural areas than in urban areas. However, from the perspective of development stage, the stage characteristics of digital payment's impact on consumer demand in each region are not obvious, which may be caused by the short sample range. In addition, this paper also puts forward relevant suggestions for other countries to learn from.

Comfort Analysis of Hafnium (Hf) Doped ZnO Coated Self-Cleaning Glazing for Energy-Efficient Fenestration Application

To attain a comfortable building interior, building windows play a crucial role. Because of the transparent nature of the window, it allows heat loss and gain and daylight. Thus, they are one of the most crucial parts of the building envelope that have a significant contribution to the overall building energy consumption. The presence of dust particles on a window can change the entering light spectrum and creates viewing issues. Thus, self-cleaning glazing is now one of the most interesting research topics. However, aside from the self-cleaning properties, there are other properties that are nominated as glazing factors and are imperative for considering self-cleaning glazing materials. In this work, for the first time, Hf-doped ZnO was investigated as self-cleaning glazing and its glazing factors were evaluated. These outcomes show that the various percentages of ZnO doping with Hf improved the glazing factors, making it a suitable glazing candidate for the cold-dominated climate.

The Effects of Barriers and Freight Vehicle Restrictions on Logistics Costs: A Comparison before and during the COVID-19 Pandemic in Brazil

The COVID-19 pandemic exposed the fragility of the supply chains, in which the production, distribution, and transportation systems were disrupted, permanently or temporarily. Despite the enormous influences the pandemic had on transportation systems, no study has yet assessed the extent to which the freight restrictions and barriers influenced logistics costs after the start of the COVID-19 outbreak. However, understanding them is necessary to guide and stimulate public policies that intend to optimize the effects of freight restrictions on logistics costs. Thus, this paper assesses and compares the effects that such barriers in urban freight transport, and the restrictions imposed on freight vehicles, had on logistics costs before and during the pandemic. We surveyed more than 120 logistics professionals in Brazil in both 2018 and 2020/2021, making comparisons using models that were assessed using partial least squares structural equation modelling. The findings suggest that barriers and freight restrictions influenced the logistics costs positively, but such influences reduced during the pandemic. Being aware of the barriers and restrictions that influence logistics costs and of how the pandemic increased the effects of some restrictions is essential for managers to be able make timely and appropriate decisions and to plan and implement initiatives to prevent or reduce costs.

Quantifying the impacts of COVID-19 on Sustainable Development Goals using machine learning models

The COVID-19 pandemic has posed severe threats to global sustainable development. However, a comprehensive quantitative assessment of the impacts of COVID-19 on Sustainable Development Goals (SDGs) is still lacking. This research quantified the post-COVID-19 SDG progress from 2020 to 2024 using projected GDP growth and population and machine learning models including support vector machine, random forest, and extreme gradient boosting. The results show that the overall SDG performance declined by 7.7% in 2020 at the global scale, with 12 socioeconomic SDG performance decreasing by 3.0–22.3% and 4 environmental SDG performance increasing by 1.6–9.2%. By 2024, the progress of 12 SDGs will lag behind for one to eight years compared to their pre-COVID-19 trajectories, while extra time will be gained for 4 environment-related SDGs. Furthermore, the pandemic will cause more impacts on countries in emerging markets and developing economies than those on advanced economies, and the latter will recover more quickly to be closer to their pre-COVID-19 trajectories by 2024. Post-COVID-19 economic recovery should emphasize in areas that can help decouple economic growth from negative environmental impacts. The results can help government and non-state stakeholders identify critical areas for targeted policy to resume and speed up the progress to achieve SDGs by 2030.

Sustainable Development Goals in the COVID-19 Pandemic: A Narrative Review

The present narrative review aimed to analyze the impact of the COVID-19 pandemic on the sustainable development goals (SDGS). This information would allow a better comprehension of the actual state of the SDGS and a more efficient programming in future interventions. To achieve the objective of the study, a consensual and critical review was carried out using both primary sources, such as scientific articles, and secondary sources, such as bibliographic indexes, web pages and databases. The Sustainable Development Goals are a global call to action to end poverty, close the gender gap, protect the planet, and improve the lives of people around the world. In 2015, the United Nations General Assembly adopted 17 goals as part of the 2030 Agenda for Sustainable Development, which sets out a plan to achieve the goals in 15 years. However, the COVID-19 pandemic crisis has been a turning point in the achievement of these goals, due to all its consequences at the political, economic, and socio-cultural levels. This review can be used as a guide for future research and reviews in order to understand the status of each of the SDGs and what actions have been taken and proposed in the aftermath of the pandemic in recent years.

Recovery Measures for the Tourism Industry in Andalusia: Residents as Tourist Consumers

The pandemic caused by the coronavirus continues to test barriers around the world. In this sense, the tourism industry has become the sector most affected by the crisis with more than 900 million euros in losses. Recovery will require a great effort, especially in countries where the sector accounts for a large share of the economy and employment. This study analyzes the perceptions and proposals of the residents of the autonomous community of Andalusia. A total of 658 surveys were conducted during the closure. A quantitative and qualitative thematic analysis was carried out using SPSS and NVivo Pro programs. The findings provide significant insights into the economic recovery of society after the pandemic. The Andalusians have opted for local tourism so that the residents become the consumers of the tourist products of their territory. The deployment of new technologies and marketing campaigns should provide the basic strategies for structural changes and innovations. The residents demand a united Europe and disagree with the statements of some political leaders. The conclusions have practical and theoretical implications for tourist destinations.

The Impact of Digital Economy on the Economic Growth and the Development Strategies in the post-COVID-19 Era: Evidence From Countries Along the "Belt and Road"

The digital economy is considered as an effective measure to mitigate the negative economic impact of the Corona Virus Disease 2019 (COVID-19) epidemic. However, few studies evaluated the role of digital economy on the economic growth of countries along the "Belt and Road" and the impact of COVID-19 on their digital industries. This study constructed a comprehensive evaluation index system and applied a panel data regression model to empirically analyze the impact of digital economy on the economic growth of countries along the "Belt and Road" before COVID-19. Then, a Global Trade Analysis Project (GTAP) model was used to examine the impact of COVID-19 on their digital industries and trade pattern. Our results show that although there is an obvious regional imbalance in the digital economy development in countries along the "Belt and Road", the digital economy has a significantly positive effect on their economic growth. The main impact mechanism is through promoting industrial structure upgrading, the total employment and restructuring of employment. Furthermore, COVID-19 has generally boosted the demand for the digital industries, and the impact from the demand side is much larger than that from the supply side. Specifically, the digital industries in Armenia, Israel, Latvia and Estonia have shown great growth potential during the epidemic. On the contrast, COVID-19 has brought adverse impacts to the digital industries in Ukraine, Egypt, Turkey, and the Philippines. The development strategies are proposed to bridge the "digital divide" of countries along the "Belt and Road," and to strengthen the driving effect of the digital economy on industrial upgrading, employment and trade in the post-COVID-19 era.

Impacts of COVID-19 on Sustainable Development Goals and effective approaches to maneuver them in the post-pandemic environment

In the pursuit of constructing a sustainable world for all through the instrumental seventeen Sustainable Development Goals, the COVID-19 pandemic emerged and affected the efforts concentrated on these goals. Therefore, there is a pressing need to analyze the extent of the impact that unfolded from the pandemic on each Sustainable Development Goal and further to direct the post-pandemic situation to accelerate the progress in every goal. Besides, there exists a knowledge gap in understanding the Sustainable Development Goals and its interaction with each goal through synergic and trade-off effects. To address the aforementioned imperative problems, this study is formulated to perform an impact assessment as well as to provide direction in the post-pandemic environment to effectively progress towards the Sustainable Development Goals by using a hybrid qualitative and quantitative framework. A detailed investigation is carried out to examine the pandemic impacts in every goal, and a quantified impact analysis is performed in terms of the targets of the Sustainable Development Goals with the aid of ranking methodology. The results indicate that SDG 1 and SDG 8 are the most impacted goal. To provide deeper perspectives into the Sustainable Development Goals, a critical analysis of the targets and indicators is performed to characterize the goals from their elemental point of view, such as nature of goals, depending factors, locus of the goal, and Sustainable Development Goal interactions. Further, a novel parameter, the degree of randomness, is proposed whose application in environmental research is immense. The impact on each goal and impact interaction between all the SDGs are also mapped, through which the dynamics of Sustainable Development Goal interactions is elaborated. In context with the post-pandemic scenario, the strategies to achieve the Sustainable Development Goals with environmental focus are presented with prioritization factor that supports quick recovery. The introduced prioritization factor is formulated by employing a multi-criteria analysis methodology. In addition, the fundamental elements of SDGs are built upon one another to frame an optimized and effective approach to achieving the SDGs in the post-pandemic environment. Despite the strategies, a conceptual framework to align the business practices with the SDGs is propounded. This study deep down would provide a unique perspective to the research community and would impart deeper knowledge in connection with sustainability, while the solutions framed would steer the policy and decision-makers.

Recent Advances in Renewable Energy and Clean Energy

Energy generation from conventional energy sources, such as oil, coal, and gas, produces adverse environmental pollutants, e [...]

Analyzing and visualizing global research trends on COVID-19 linked to sustainable development goals

This study is intended to afford a comprehensive overview of the implications of COVID-19 on progress toward achieving the sustainable development goals (SDGs) set out in the United Nations (UN) 2030 Agenda and the state of related research activities on COVID-19 linked to the SDGs. Bibliometric techniques and visual mapping are proposed as methodological tools to better approach the objectives of the present work. This includes: retrieving related publications from Scopus database, investigating the trends and growth trajectories of research works, and analyzing the scenarios post-COVID-19 either optimistic or pessimistic outlooks. The national and international contributions and collaboration toward this theme of research are further analyzed at countries, institutions, and sources levels. This analysis indicates that research works conducted on the impacts of COVID-19 on the achievement of the SDGs are still in the immaturity level. The global research productivity on this topic was just 160 documents (0.19% of total global research productivity in all fields of science with relevance to COVID-19). The implications of COVID-19 on good health and well-being, SDG-3, have attracted considerable attention. It is followed by SDG-13 that concerned with climate changes. The post-COVID-19 scenarios showed deep and justified worries in relation to achieving the SDGs by 2030. This study figures the major issues debated in the literature with respect to COVID-19 and its implications on the SDGs. The study, furthermore, attempts to assess the required actions to advance the SDGs post-COVID-19.

Understanding regional mobility resilience and its relationship with regional culture during the COVID-19 pandemic: A pathogen-stress theory perspective

The global COVID-19 pandemic has severely impacted the passenger flow. Facing the same pandemic, various regions differ in the resilience of population mobility due to differences in the regional cultural. This study uses mobile big data to quantifies regional mobility resilience of 358 cities in China. Study results reveal the differences in regional mobility resilience of cities through spatial autocorrelation analysis, and verify the effects of regional culture on mobility resilience using a panel logit regression model based on pathogen-stress theory. Spatial heterogeneity and autocorrelation in the regional mobility resilience of Chinese cities are identified through spatial analysis, which are manifested by various hot spots over time. Moreover, the panel regression results indicate that the COVID-19 pandemic has a significant negative effect on regional mobility resilience; and that the negative effect of COVID-19 on regional mobility resilience is amplified in the cities with high degrees of dialect diversity, while it is weakened in the cities with high degrees of cultural tightness (which have strict norms and punishments for deviance). This study provides theoretical implications for mobility resilience in the context of COVID-19 and advances the pathogen-stress theory. Study findings also provide practical recommendations for regions to enhance regional mobility resilience under the challenges of future public health crisis events.

Global COVID-19 pandemic trends and their relationship with meteorological variables, air pollutants and socioeconomic aspects

Meteorological variables, air pollutants, and socioeconomic factors are associated with COVID-19 transmission. However, it is unclear what impact their interactions have on COVID-19 transmission, whether their impact on COVID-19 transmission is linear or non-linear, and where the inflexion points are. This study examined 1) the spatial and temporal trends in COVID-19 monthly infection rate of new confirmed cases per 100,000 people (R_n) in 188 countries/regions worldwide from March to November 2020; 2) the linear correlation between meteorological variables (temperature (T), rainfall (R), wind speed (WS), relative humidity (RH), air pressure (AP)), air pollutants (nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), ozone (O₃)) and socioeconomic aspects (population density (PD), gross domestic product per capita (GDP), domestic general government health expenditure per capita (GHE)) and R_n, and 3) the interaction and non-linear effects of the different variables on R_n, based on GeoDetector and Boosted regression tree. The results showed that the global R_n had was spatially clustered, and the average R_n increased From March to November 2020. Global R_n was negatively correlated with meteorological variables (T, R, WS, AP) and positively correlated with air pollutants (NO₂, SO₂, O₃) and socioeconomic aspects (GDP, GHE). The interaction of SO₂ and O₃, SO₂ and RH, and O₃ and T strongly affected R_n. The variables effect on COVID-19 transmission was non-linear, with one or more inflexion points. The findings of this work can provide a basis for developing a global response to COVID-19 for global sustainable development.