Modelling the contribution of CO2 emissions from fuel used on total CO2 emissions at power generation in Indonesia

Padjadjaran Earth Dialogues, International Symposium on Geophysical Issues

Budiono, Ruly^1 ; Juahir, Hafizan^2 ; Mamat, Mustafa^3 ; Sukono^4 ; Supian, Sudradjat^4 ; Nurzaman, Muhamad^1

Department of Biology, Universitas Padjadjaran, Jalan Raya Bandung-Sumedang Km 21, Jatinangor, Sumedang

45363, Indonesia^1

East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak, Trengganu, Kuala Terengganu

21300, Malaysia^2

Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Gong Badak, Trengganu, Kuala Terengganu

21300, Malaysia^3

Department of Mathematics, Universitas Padjadjaran, Jalan Raya Bandung-Sumedang Km 21, Jatinangor, Sumedang

45363, Indonesia^4

关键词: CO2 emissions;    Electricity production;    Estimated model;    Gas generation;    Mean absolute error;    Multiple linear regression models;    Multiple linear regressions;    Ordinary least square methods;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/311/1/012079/pdf
DOI  :  10.1088/1755-1315/311/1/012079

来源: IOP

【 摘 要 】

For electricity production, humans use fossil fuels, such as coal, fuel oil, diesel oil, and gas. The use of these fuels is a major source of greenhouse gas emissions, especially carbon dioxide (CO2) gases. In this paper, we have proposed the model of CO2 emissions from the fuel used to total CO2, in electricity production in Indonesia. Modelling is done by using multiple linear regression, while the parameter estimation is done by using ordinary least square method (OLS). The data used are CO2 emissions in million tonnes of greenhouse gas generations for the period of 2000-2015. Based on the results of the analysis, it can be seen that the contribution of CO2 emissions from the fuel used, to total CO2 emissions in electricity production significantly follows the multiple linear regression model. The total of CO2 emission prediction by using estimated model estimator, yields mean absolute error (MAPE) of 0.01026323. These concludes that the prediction data is very close to the actual data.