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.

Towards poverty alleviation in developing countries: An empirical study of the impact of land tenure reforms in Kati, Mali.

Post-colonial land tenure reforms in emerging countries have partly aimed at poverty reduction through equitable land access. However, the poverty rate keeps rising in rural and peri-urban settings in Sub-Saharan Africa dominated by agricultural activities. This article reviews land tenure reforms in Mali, from the year 2000 to 2017 regarding poverty alleviation and evaluates their impacts on indigenous smallholder farmers, using multiple linear and logistic regression models and local experts' elicitations. The results indicate that the advent of land titles as the only definitive evidence of land ownership, following the reforms, has generally weakened customary land management. Smallholder farmers face several barriers to obtaining land titles, limiting equity in land access and security. This has paved way for land markets marred by irregularities and resulted in colossal loss of agricultural lands, which are the main source of rural livelihood. Thus, the reforms have not yielded the intended poverty reduction outcomes. The study recommends that land transfers must be authorised by a single institution, represented at the various administrative levels, which issues an authentic and incorruptible document using appropriate technology. Moreover, since pro-poor provisions in the reforms usually lack implementing decrees in Mali, political will is key to achieving equitable land access and security.