TY - JOUR
T1 - Energy use, carbon dioxide emissions, GDP, industrialization, financial development, and population, a causal nexus in Sri Lanka
T2 - with a subsequent prediction of energy use using neural network
AU - Asumadu-Sarkodie, Samuel
AU - Owusu, Phebe Asantewaa
PY - 2016/9/20
Y1 - 2016/9/20
N2 - The study examines the causal relationship between energy use, carbon dioxide emissions, GDP, industrialization, financial development, and population from 1971 to 2012 in Sri Lanka, using the ARDL regression analysis and a subsequent prediction of energy use using neural network. There was evidence of a long-run equilibrium relationship running from carbon dioxide emissions, GDP, industrialization, financial development, and population to energy use. The Granger causality test shows a unidirectional causality running from carbon dioxide emissions to energy use and a bidirectional causality between industrialization and energy use. The overall predicted EUSE from 1971 to 2012 has a mean absolute percentage error of 1.97%. Evidence from the neural network shows that the statistical coefficient of R-square for both training and validation is 98% and 99% with a corresponding Root mean square Error of 11.11 and 6.10, respectively.
AB - The study examines the causal relationship between energy use, carbon dioxide emissions, GDP, industrialization, financial development, and population from 1971 to 2012 in Sri Lanka, using the ARDL regression analysis and a subsequent prediction of energy use using neural network. There was evidence of a long-run equilibrium relationship running from carbon dioxide emissions, GDP, industrialization, financial development, and population to energy use. The Granger causality test shows a unidirectional causality running from carbon dioxide emissions to energy use and a bidirectional causality between industrialization and energy use. The overall predicted EUSE from 1971 to 2012 has a mean absolute percentage error of 1.97%. Evidence from the neural network shows that the statistical coefficient of R-square for both training and validation is 98% and 99% with a corresponding Root mean square Error of 11.11 and 6.10, respectively.
KW - Carbon dioxide emissions
KW - Econometrics
KW - Energy economics
KW - Neural network
KW - Sri Lanka
UR - http://www.scopus.com/inward/record.url?scp=84992521746&partnerID=8YFLogxK
U2 - 10.1080/15567249.2016.1217285
DO - 10.1080/15567249.2016.1217285
M3 - Article
AN - SCOPUS:84992521746
SN - 1556-7249
VL - 11
SP - 889
EP - 899
JO - Energy Sources, Part B: Economics, Planning and Policy
JF - Energy Sources, Part B: Economics, Planning and Policy
IS - 9
ER -