Tackling future global emissions of carbon dioxide is a daunting task. Different black box models have been used to determine the trajectories of CO 2 emissions and other carbon stocks. Trajectories are important because climate modelers use them to project future climate under higher atmospheric CO 2 concentrations. In this paper, fully connected two-layer feed-forward neural network with tangent activation function that comes with hidden neurons as well as linear output neurons was used. The study applied classical nonlinear least squares algorithm such as LM (Levenberg-Marquardt), to predict potential emissions of selected emerging economies. Building the model on the basis of input variables such as crop production, livestock production, trade imports, trade exports, economic growth, renewable and nonrenewable energy consumption. These variables are considered to affect the ecosystems of high rising economic power states. The main idea is to ensure that emerging economies have a clear understanding of expected future emissions so that appropriate measures can be implemented to mitigate its impact. Data for the analysis were obtained from 1971 to 2013 from World Development Indicators and FAOSTAT database. Results indicate an achievement of training performance at epoch 11 when the value of the MSE (Mean Square Error) is 0.0003345 which indicates that the model errors are less than 0.05. Hence, the study concluded that the applied model is capable of predicting potential carbon dioxide emissions in emerging economies with the greatest precision.
The study scrutinized correlation between electricity production, trade, economic growth, industrialization and carbon dioxide emissions in Ghana. Our study disaggregated trade into export and import to spell out distinctive and individual variable contribution to emissions in Ghana. In an attempt to investigate, the study used time-series data set of World Development Indicators from 1971 to 2014. By means of Autoregressive Distributed Lag (ARDL) cointegrating technique, study established that variables are co-integrated and have long-run equilibrium relationship. Results of long-term effect of explanatory variables on carbon dioxide emissions indicated that 1% each increase of economic growth and industrialization, will cause an increase of emissions by 16.9% and 79% individually whiles each increase of 1% of electricity production, trade exports, trade imports, will cause a decrease in carbon dioxide emissions by 80.3%, 27.7% and 4.1% correspondingly. In the pursuit of carbon emissions' mitigation and achievement of Sustainable Development Goal (SDG) 13, Ghana need to increase electricity production and trade exports.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.