A sustainable trend in COVID-19 research: An environmental perspective DOI Creative Commons
Meysam Vadiati,

Leyla Ghasemi,

Saeideh Samani

et al.

Frontiers in Environmental Science, Journal Year: 2023, Volume and Issue: 11

Published: March 29, 2023

Coronavirus disease 2019 (COVID-19) has spread across the globe producing hundreds of thousands deaths, shutting down economies, closing borders and causing havoc on an unprecedented scale. Its potent effects have earned attention researchers in different fields worldwide. Among them, authors from countries published numerous research articles based environmental concepts COVID-19. The environment is considered essential receptor COVID-19 pandemic, it academically significant to look into publications follow pathway hot topics upcoming trends studies. Reviewing literature can therefore provide valuable information regarding strengths weaknesses facing considering viewpoint. present study categorizes understanding caused by COVID-19-related papers Scopus metadata 2020 2021. VOSviewer a promising bibliometric tool used analyze with keywords “COVID-19*” “Environment.” Then, narrative evaluation utilized delineate most interesting topics. Co-occurrence analysis applied this research, which further characterizes thematic clusters. mainly focused four central cluster concepts: air pollution, epidemiology virus transmission, water wastewater, policy. It also reveals that policy gained worldwide interest, main keyword “management” includes like waste management, sustainability, governance, ecosystem, climate change. Although these could appear other policy-related studies, importance pandemic requires such comprehensive research. fourth involves governance management concerns encountered during pandemic. Mapping clusters will pave way for view future potential ideas studies better. scope needs perspective reviewed recommended, expand vital role value sciences alerting, observing, prediction all In words, trend would shift qualitative perspectives quantitative ones.

Language: Английский

The impact of regional banks on environmental pollution: Evidence from China's city commercial banks DOI
Yang Chen, Liang Cheng, Chien‐Chiang Lee

et al.

Energy Economics, Journal Year: 2021, Volume and Issue: 102, P. 105492 - 105492

Published: July 30, 2021

Language: Английский

Citations

115
Towards Achieving Sustainability in the BRICS Economies: The Role of Renewable Energy Consumption and Economic Risk DOI Creative Commons
Opeoluwa Seun Ojekemi, Mehmet Ağa, Cosimo Magazzino

et al.

Energies, Journal Year: 2023, Volume and Issue: 16(14), P. 5287 - 5287

Published: July 10, 2023

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

Language: Английский

Citations

56
A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality DOI Creative Commons
Cosimo Magazzino, Marco Mele, Mario Coccia

et al.

Epidemiology and Infection, Journal Year: 2022, Volume and Issue: 150

Published: Jan. 1, 2022

The coronavirus disease 2019 (COVID-19), with new variants, continues to be a constant pandemic threat that is generating socio-economic and health issues in manifold countries. principal goal of this study develop machine learning experiment assess the effects vaccination on fatality rate COVID-19 pandemic. Data from 192 countries are analysed explain phenomena under study. This algorithm selected two targets: number deaths rate. Results suggest that, based respective plan, turnout participation campaign, doses administered, suddenly have reduction precisely at point where cut effect generated neural network. result significant for international scientific community. It would demonstrate effective impact campaign COVID-19, whatever country considered. In fact, once has started (for vaccines require booster, we refer least first dose), antibody response people seems prevent probability death related COVID-19. short, certain point, collapses increasing administered. All these results here can help decisions policymakers prepare optimal strategies, plans, lessen negative crisis socioeconomic systems.

Language: Английский

Citations

62
Exploring the spatio-temporal evolution of economic resilience in Chinese cities during the COVID-19 crisis DOI
Tong Cheng,

Yonghua Zhao,

Chunjiang Zhao

et al.

Sustainable Cities and Society, Journal Year: 2022, Volume and Issue: 84, P. 103997 - 103997

Published: June 17, 2022

Language: Английский

Citations

48
Investigating the nexus between energy, socio-economic factors and environmental pollution: A geo-spatial multi regression approach DOI
Uzair Aslam Bhatti, Hao Tang, Asad Khan

et al.

Gondwana Research, Journal Year: 2024, Volume and Issue: 130, P. 308 - 325

Published: March 1, 2024

Language: Английский

Citations

12
Supervised Machine Learning Approaches for Predicting Key Pollutants and for the Sustainable Enhancement of Urban Air Quality: A Systematic Review DOI Open Access
Ismail Essamlali, Hasna Nhaila, Mohamed El Khaïli

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(3), P. 976 - 976

Published: Jan. 23, 2024

Urban air pollution is a pressing global issue driven by factors such as swift urbanization, population expansion, and heightened industrial activities. To address this challenge, the integration of Machine Learning (ML) into smart cities presents promising avenue. Our article offers comprehensive insights recent advancements in quality research, employing PRISMA method cornerstone for reviewing process, while simultaneously exploring application frequently employed ML methodologies. Focusing on supervised learning algorithms, study meticulously analyzes data, elucidating their unique benefits challenges. These techniques, including LSTM (Long Short-Term Memory), RF (Random Forest), ANN (Artificial Neural Networks), SVR (Support Vector Regression), are instrumental our quest cleaner, healthier urban environments. By accurately predicting key pollutants particulate matter (PM), nitrogen oxides (NOx), carbon monoxide (CO), ozone (O3), these methods offer tangible solutions society. They enable informed decision-making planners policymakers, leading to proactive, sustainable strategies combat pollution. As result, well-being health populations significantly improved. In revised abstract, importance context explicitly emphasized, underlining role improving environments enhancing populations.

Language: Английский

Citations

11
Using an Artificial Neural Networks Experiment to Assess the Links among Financial Development and Growth in Agriculture DOI Open Access
Cosimo Magazzino, Marco Mele, Fabio Gaetano Santeramo

et al.

Sustainability, Journal Year: 2021, Volume and Issue: 13(5), P. 2828 - 2828

Published: March 5, 2021

Financial development, productivity, and growth are interconnected, but the direction of causality remains unclear. The relevance these linkages is likely different for developing developed economies, yet comparative cross-country studies scant. paper analyses relationship among credit access, output productivity in agricultural sector a large set countries, over period 2000–2012, using an Artificial Neural Networks approach. Empirical findings show that three variables influence each other reciprocally, although marked differences exist groups countries. role access more prominent OECD countries less important with lower level economic de-elopement. Our analysis allows us to highlight specific effects stimulating development sector: significantly affects production, whereas it also has impact on productivity.

Language: Английский

Citations

49
Tourism subindustry level environmental impacts in the US DOI

Chao Xiong,

Asif Khan, Sughra Bibi

et al.

Current Issues in Tourism, Journal Year: 2022, Volume and Issue: 26(6), P. 903 - 921

Published: March 6, 2022

Trends indicate that the tourism and hospitality (TH) industry is significantly contributing to socio-economic conditions of economies worldwide. However, TH-led economic development attained at cost environmental pollution. This research explores four TH subindustries' impacts on greenhouse gas (GHG) emissions air pollutants in US. We also considered energy consumption, growth, globalization normalize impacts. The ARDL bounds test approach applied a quarterly (2005-2019) time-series data analyze findings uncovered food drink places (FSDP) contribute higher GHG (CO2, CH4, N2O) long-run than rest subindustries. Compared other subsectors, accommodation (AC) sector contributed (CO, NH3, NOx, SO2, VOC, PM2.5). All subindustries positively consumption; however, FSDP, amusement, gambling, recreation (AGR) consume levels. Economic growth has mixed pollutants. Interestingly, shows negative Granger causality results show AC, AGR, performing arts sports cause PM2.5. Key implications policy initiatives are provided.

Language: Английский

Citations

37
Save lives or save livelihoods? A cross-country analysis of COVID-19 pandemic and economic growth DOI Open Access
Qu Feng, Guiying Laura Wu, Mengying Yuan

et al.

Journal of Economic Behavior & Organization, Journal Year: 2022, Volume and Issue: 197, P. 221 - 256

Published: March 10, 2022

Language: Английский

Citations

31
Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania DOI Creative Commons
Youness El Mghouchi, Mihaela Tinca Udriștioiu, Hasan Yıldızhan

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(5), P. 1532 - 1532

Published: Feb. 27, 2024

Inadequate air quality has adverse impacts on human well-being and contributes to the progression of climate change, leading fluctuations in temperature. Therefore, gaining a localized comprehension interplay between variations pollution holds great significance alleviating health repercussions pollution. This study uses holistic approach make predictions multivariate modelling. It investigates associations meteorological factors, encompassing temperature, relative humidity, pressure, three particulate matter concentrations (PM10, PM2.5, PM1), correlation PM noise levels, volatile organic compounds, carbon dioxide emissions. Five hybrid machine learning models were employed predict then Air Quality Index (AQI). Twelve sensors evenly distributed Craiova City, Romania, provided dataset for five months (22 September 2021–17 February 2022). The transmitted data each minute. prediction accuracy was evaluated results revealed that, general, coefficient determination (R2) values exceeded 0.96 (interval confidence is 0.95) and, most instances, approached 0.99. Relative humidity emerged as least influential variable concentrations, while accurate achieved by combining pressure with PM10 (less than 10 µm diameter) exhibited notable PM2.5 2.5 moderate PM1 1 diameter). Nevertheless, other findings indicated that not strongly related NOISE, CO2, VOC, these last variables should be combined another enhance accuracy. Ultimately, this established novel relationships predicting AQI based effective combinations predictor identified.

Language: Английский

Citations

8