  This study provides a comprehensive time series analysis of daily
industry-specific, country-wise CO$_2$ emissions from January 2019 to February
2023. The research focuses on the Power, Industry, Ground Transport, Domestic
Aviation, and International Aviation sectors in European countries (EU27 & UK,
Italy, Germany, Spain) and India, utilizing near-real-time activity data from
the Carbon Monitor research initiative. To identify regular emission patterns,
the data from the year 2020 is excluded due to the disruptive effects caused by
the COVID-19 pandemic. The study then performs a principal component analysis
(PCA) to determine the key contributors to CO$_2$ emissions. The analysis
reveals that the Power, Industry, and Ground Transport sectors account for a
significant portion of the variance in the dataset. A 7-day moving averaged
dataset is employed for further analysis to facilitate robust predictions. This
dataset captures both short-term and long-term trends and enhances the quality
of the data for prediction purposes. The study utilizes Long Short-Term Memory
(LSTM) models on the 7-day moving averaged dataset to effectively predict
emissions and provide insights for policy decisions, mitigation strategies, and
climate change efforts. During the training phase, the stability and
convergence of the LSTM models are ensured, which guarantees their reliability
in the testing phase. The evaluation of the loss function indicates this
reliability. 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 and
boron/aluminium-based thin films as exceptionally efficient materials for
capturing CO$_2$ (with a binding energy range from -3.0 to -3.5 eV). These
materials are shown to surpass the affinity of graphene and boron nitride
sheets in this regard.
