An analysis of renewable energy consumption efficiency in terms of greenhouse gas production in selected European countries

Rozenský, Ladislav; Hájek, Miroslav; Vrba, Zdeněk; Pokorný, Richard; Hansen, Justin Michael; Lípa, Jan

doi:10.15376/biores.15.4.7714-7729

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…This research will discuss the application of Artificial Intelligence (AI) in disaster management with a focus on the effectiveness analysis of the AIR-Protection platform in detecting surrounding air quality. To achieve this goal, the study develops a new model by using AI as an independent variable framework and AIR-Protection as a dependent variable [9], [10], [11]. Additionally, the research involves a questionnaire given to 50 human resource managers working in the IT sector [12].…”

Section: Introductionmentioning

confidence: 99%

Image-based Air Quality Prediction using Convolutional Neural Networks and Machine Learning

Hardini,

Riza Chakim,

Magdalena

et al. 2023

ATT

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Air quality has become a major public concern due to the significant threat posed by air pollution to human health, and rapid and efficient monitoring of air quality is crucial for pollution control and human health. In this paper, deep learning and image-based models are proposed to estimate air quality. To evaluate the level of air quality, the model collects feature information from landscape photos taken by mobile cameras. To analyze public perception of air quality, researchers collected questionnaire data from 257 people. The Smartpls method allows for structural analysis to determine the influence of each variable on other variables and the extent of their contribution to the final variable of overall perception of air quality. This study aims to develop a novel approach for air quality prediction using image-based data and machine learning techniques. The research used convolutional neural networks to extract features from images and predict the air quality index. The study was conducted using a dataset obtained from a network of air quality sensors across the city. The results of the study showed that the proposed approach can provide accurate air quality predictions compared to the traditional methods. The developed model was able to capture the complex relationships between air quality and environmental factors, such as temperature and humidity. The implications of the study suggest that image-based air quality prediction can be a powerful tool for improving public health and reducing the impact of air pollution. The study's findings hold promise for a healthier future by facilitating more effective pollution management and improved air quality regulation. The study's primary novelty lies in its approach to air quality prediction by deploying convolutional neural networks to extract image features for predicting air quality indices. This application of advanced machine learning techniques to image-based data for air quality estimation marks a significant advancement.

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Section: Introductionmentioning

confidence: 99%

Image-based Air Quality Prediction using Convolutional Neural Networks and Machine Learning

Hardini,

Riza Chakim,

Magdalena

et al. 2023

ATT

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The faster the increase in renewable energy use, the faster the decrease in carbon emissions?

Akan,

Işık,

Gündüz

2024

Environ Dev Sustain

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The annual average near-surface global temperature will temporarily exceed 1.5 °C between 2023 and 2027. If this level is irrevocably exceeded, the functions of marine biodiversity, fisheries, and ecosystems may no longer be maintained. In light of this fact, this paper examines whether a shock-like (rapid and substantial) increase in the proportion of renewable energy sources could result in a precipitous reduction in greenhouse gas emissions. In order to accomplish this, the paper analyzes the magnitude and direction of the asymmetric and symmetric impacts of renewables on CO2 emissions, which represent shock-like and ordinary changes, respectively. Second, the paper examines whether there is a functional complementarity between major renewables in reducing CO2 emissions, and whether this complementarity increases when renewables experience asymmetric shocks. The conclusion of the paper is that the negative impact of renewables on CO2 emissions either (i) remains unchanged when they experience asymmetric shocks or (ii) increases by 60–260 percent or 100 percent, respectively, when they experience these shocks simultaneously or separately, respectively. The paper makes two recommendations to prevent the persistent exceeding of 1.5 °C. First, policymakers should intensify their efforts to rapidly and substantially increase renewable energy consumption in total. Second, they should accomplish this increase in all renewable sources in a balanced fashion so as to maximize their carbon-reduction effects by functionalizing their green complementarities.

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An analysis of renewable energy consumption efficiency in terms of greenhouse gas production in selected European countries

Cited by 2 publications

References 13 publications

Image-based Air Quality Prediction using Convolutional Neural Networks and Machine Learning

Image-based Air Quality Prediction using Convolutional Neural Networks and Machine Learning

The faster the increase in renewable energy use, the faster the decrease in carbon emissions?

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