Open AccessModeling the impacts of constant price GDP and population on CO2 emissions using Cobb-Douglas model and ant colony optimization algorithm
Author(s) -
Sukono Sukono,
Betty Subartini,
Priscila Thalia,
Sudradjat Supian,
Eman Lesmana,
Ruly Budiono,
Hafizan Juahir
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/621/1/012015
Subject(s) - gross domestic product , per capita , cobb–douglas production function , econometrics , population , estimator , economics , ant colony optimization algorithms , cobb , constant (computer programming) , production (economics) , mathematics , statistics , mathematical optimization , macroeconomics , computer science , biology , genetics , demography , sociology , programming language
The per capita Gross Domestic Product (GDP) measures a country’s economic growth. Increasing GDP is a dream of all countries, but generally, GDP increases often have a negative impact with increasing CO 2 emissions. This paper intends to model the impact of GDP growth based on constant prices and the population in increasing CO 2 emissions in Indonesia. Modeling is done by using Cobb-Douglas model production function, where parameter estimation is done by using ant colony optimization algorithm. Furthermore, model estimators are used for forecasting CO 2 emission concentrations. The results of the analysis show that the impact of GDP based on constant prices and population significantly follows the Cobb-Douglas model of production, with the coefficient of elasticity is 0.819405999 and 0.834930855, respectively. The value of determination was obtained at 97.4%, indicating that the correlation between GDP at constant prices and population with increasing CO 2 emissions in air is very strong. Estimator model obtained has a level of accuracy for forecasting is 0.98478981 or 98.4798981%. Thus, the model estimator obtained is able to describe the actual data pattern.