The impact of regional banks on environmental pollution: Evidence from China's city commercial banks

Energy Economics, Год журнала: 2021, Номер 102, С. 105492 - 105492

Опубликована: Июль 30, 2021

Язык: Английский

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Towards Achieving Sustainability in the BRICS Economies: The Role of Renewable Energy Consumption and Economic Risk

Energies, Год журнала: 2023, Номер 16(14), С. 5287 - 5287

Опубликована: Июль 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.

Язык: Английский

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A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality

Epidemiology and Infection, Год журнала: 2022, Номер 150

Опубликована: Янв. 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.

Язык: Английский

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Exploring the spatio-temporal evolution of economic resilience in Chinese cities during the COVID-19 crisis

Sustainable Cities and Society, Год журнала: 2022, Номер 84, С. 103997 - 103997

Опубликована: Июнь 17, 2022

Язык: Английский

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Investigating the nexus between energy, socio-economic factors and environmental pollution: A geo-spatial multi regression approach

Gondwana Research, Год журнала: 2024, Номер 130, С. 308 - 325

Опубликована: Март 1, 2024

Язык: Английский

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Supervised Machine Learning Approaches for Predicting Key Pollutants and for the Sustainable Enhancement of Urban Air Quality: A Systematic Review

Sustainability, Год журнала: 2024, Номер 16(3), С. 976 - 976

Опубликована: Янв. 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.

Язык: Английский

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Using an Artificial Neural Networks Experiment to Assess the Links among Financial Development and Growth in Agriculture

Sustainability, Год журнала: 2021, Номер 13(5), С. 2828 - 2828

Опубликована: Март 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.

Язык: Английский

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Tourism subindustry level environmental impacts in the US

Current Issues in Tourism, Год журнала: 2022, Номер 26(6), С. 903 - 921

Опубликована: Март 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.

Язык: Английский

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Save lives or save livelihoods? A cross-country analysis of COVID-19 pandemic and economic growth

Journal of Economic Behavior & Organization, Год журнала: 2022, Номер 197, С. 221 - 256

Опубликована: Март 10, 2022

Язык: Английский

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Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania

Sensors, Год журнала: 2024, Номер 24(5), С. 1532 - 1532

Опубликована: Фев. 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.

Язык: Английский

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