… CO 2 emissions for these three countries. To do so, we use an advanced methodology in
Machine Learning … The Causal Direction from Dependency (D2C) algorithm set CO 2 emissions …

… of machine learning algorithms in predicting CO 2 emissions using the factors discussed.
Machine learning … In addition, the trends and relation between CO 2 emissions and various …

… CO 2 mitigation solutions are vital. New technologies can … and machine learning have
contributed to CO 2 emissions reduction in … in the construction industry to mitigate CO 2 emissions. …

… the total CO 2 emissions and the percentage of CO 2 emissions from different … CO 2
emissions by multiplying the total CO 2 emissions (in kt units) by the percentage of CO 2 emissions …

… all machine learning algorithms, R 2 values are varying between 0.8639 and 0.9235, and
RMSE is smaller than 5 × 10 6 tons for CO 2 emission … demand and CO 2 emission arising from …

… However, quantifying city-level CO 2 emissions is generally a difficult task due to lacking or
… data and machine learning methods to estimate and predict city-level CO 2 emissions across …

… , a machine learning algorithm is developed to forecast the CO 2 emission intensities in
European electrical power grids distinguishing between average and marginal emissions. The …

… nexus among CO 2 emissions, … Machine Learning experiment. Comparing the results of the
two approaches, we conclude that economic growth causes energy use and CO 2 emissions. …

… CO 2 emissions by giving independent variables at the same time domain. Because the
released data of production-based CO 2 emissions … ) by corresponding emission factors by the …

… machine learning algorithms play a pivotal role in various domains, including the profiling of
carbon dioxide (CO 2 ) emissions. This … with an evaluation of the learning models entailing …