Predicting carbon dioxide emissions in the United States of America using machine learning algorithms.

Carbon dioxide (CO2) emissions result from human activities like burning fossil fuels. CO2 is a greenhouse gas, contributing to global warming and climate change. Efforts to reduce CO2 emissions include transitioning to renewable energy. Monitoring and reducing CO2 emissions are crucial for mitigating climate change. Strategies include energy efficiency and renewable energy adoption. In the past few decades, several nations have experienced air pollution and environmental difficulties because of carbon dioxide (CO2) emissions. One of the most crucial methods for regulating and maximizing CO2 emission reductions is precise forecasting. Four machine learning algorithms with high forecasting precision and low data requirements were developed in this study to estimate CO2 emissions in the United States (US). Data from a dataset covering the years 1973/01 to 2022/07 that included information on different energy sources that had an impact on CO2 emissions were examined. Then, four algorithms performed the CO2 emissions forecast from the layer recurrent neural network with 10 nodes (L-RNN), a feed-forward neural network with 10 nodes (FFNN), a convolutional neural network with two layers with 10 and 5 filters (CNN1), and convolutional neural network with two layers and with 50 and 25 filters (CNN2) models. Each algorithm's forecast accuracy was assessed using eight indicators. The three preprocessing techniques used are (1) without any processing techniques, (2) processed using max-min normalization technique, and (3) processed using max-min normalization technique and decomposed by variation mode decomposition (VMD) technique with 7 intrinsic mode functions and 1000 iterations. The latter with L-RNN algorithm gave a high accuracy between the forecasting and actual values. The results of CO2 emissions from 2011/05 to 2022/07 have been forecasted, and the L-RNN algorithm had the highest forecast accuracy. The L-RNN model has the lowest value of 1.187028078, 135.5668592, and 11.64331822 for MAPE, MSE, and RMSE, respectively. The L-RNN model provides precise and timely forecasts that can help formulate plans to reduce carbon emissions and contribute to a more sustainable future. Moreover, the results of this investigation can improve our comprehension of the dynamics of carbon dioxide emissions, resulting in better-informed environmental policies and initiatives targeted at lowering carbon emissions.

Mathematical Modeling and Optimization of Highly Efficient Nontoxic All-Inorganic CsSnGeI3-Based Perovskite Solar Cells with Oxide and Kesterite Charge Transport Layers.

Despite exceptional optoelectronic properties and rapidly increasing efficiency of perovskite solar cells (PSCs), the issues of toxicity and device instability have hampered the commercialization of this renewable energy technology. Lead (Pb) being the main culprit creates major environmental risks and therefore must be replaced with a nontoxic material such as tin (Sn), germanium (Ge), etc. Moreover, replacing organic cations in the perovskite's ABX3 structure with inorganic ones like cesium (Cs) helps aid the stability issues. This study uses six different kesterite-based hole transport layers (HTLs) and three different metal oxide-based electron transport layers (ETLs) to numerically simulate and optimize all-inorganic CsSnGeI3 PSCs. Metal oxide ETLs are used in this study due to their large band gap, while kesterite HTLs are used due to their excellent conductive properties. All of the simulations are performed under standard testing conditions. A total of 18 novel planar (n-i-p) PSCs are modeled by the combination of various charge transport layers (CTLs), and the device optimization was done to enhance the power conversion efficiencies (PCEs) of the PSCs. Furthermore, the effect of CTLs on the energy band alignment, electric field, quantum efficiency, light absorption, and recombination rate is analyzed. Additionally, a detailed analysis of the impact of defect density (Nt), interface defects (ETL/Perv, Perv/HTL), temperature, and work function on the functionality of 18 different CsSnGeI3-based PSCs is conducted. The simulation findings demonstrate that SnO2/CsSnGeI3/CNTS is the most efficient optimized PSC among all of the simulated structures, with a PCE of 27.33%, Jsc of 28.04 mA/cm2, FF of 85%, and Voc of 1.14 V.