English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This study provides a comprehensive time series analysis of dailyindustry-specific, country-wise CO$_2$ emissions from January 2019 to February2023. The research focuses on the Power, Industry, Ground Transport, DomesticAviation, and International Aviation sectors in European countries (EU27 & UK,Italy, Germany, Spain) and India, utilizing near-real-time activity data fromthe Carbon Monitor research initiative. To identify regular emission patterns,the data from the year 2020 is excluded due to the disruptive effects caused bythe COVID-19 pandemic. The study then performs a principal component analysis(PCA) to determine the key contributors to CO$_2$ emissions. The analysisreveals that the Power, Industry, and Ground Transport sectors account for asignificant portion of the variance in the dataset. A 7-day moving averageddataset is employed for further analysis to facilitate robust predictions. Thisdataset captures both short-term and long-term trends and enhances the qualityof the data for prediction purposes. The study utilizes Long Short-Term Memory(LSTM) models on the 7-day moving averaged dataset to effectively predictemissions and provide insights for policy decisions, mitigation strategies, andclimate change efforts. During the training phase, the stability andconvergence of the LSTM models are ensured, which guarantees their reliabilityin the testing phase. The evaluation of the loss function indicates thisreliability. The model achieves high efficiency, as demonstrated by $R^2$values ranging from 0.8242 to 0.995 for various countries and sectors.Furthermore, there is a proposal for utilizing scandium andboron/aluminium-based thin films as exceptionally efficient materials forcapturing CO$_2$ (with a binding energy range from -3.0 to -3.5 eV). Thesematerials are shown to surpass the affinity of graphene and boron nitridesheets in this regard.

Forecasting, capturing and activation of carbon-dioxide (CO$_2$): Integration of Time Series Analysis, Machine Learning, and Material Design