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Article
Author(s)
Kingsley Appiah1, 2, Jianguo Du1, Rhoda Appah3 and Daniel Quacoe1
Full-Text PDF XML 567 Views
DOI:10.17265/2162-5298/2018.08.003
Affiliation(s)
1. School of Management, Jiangsu University, Zhenjiang 212013, China
2. Accountancy Department, Kumasi Technical University, Kumasi 854, , Ghana
3. Administration, Community Special Vocational School, Deduako-Kumasi, Ghana
ABSTRACT
Tackling future
global emissions of carbon dioxide is a daunting task. Different black box
models have been used to determine the trajectories of CO2 emissions
and other carbon stocks. Trajectories are important because climate modelers
use them to project future climate under higher atmospheric CO2 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.
KEYWORDS
Carbon dioxide emissions, ANN (Artificial Neural Network), LM, Emerging economies.
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