Considering multiple factors to forecast CO2 emissions: A hybrid multivariable grey forecasting and genetic programming approach

Chun-Cheng Lin, Rou Xuan He, Wan Yu Liu*

*此作品的通信作者

研究成果: Article同行評審

19 引文 斯高帕斯(Scopus)

摘要

Development of technology and economy is often accompanied by surging usage of fossil fuels. Global warming could speed up air pollution and cause floods and droughts, not only affecting the safety of human beings, but also causing drastic economic changes. Therefore, the trend of carbon dioxide emissions and the factors affecting growth of emissions have drawn a lot of attention in all countries in the world. Related studies have investigated many factors that affect carbon emissions such as fuel consumption, transport emissions, and national population. However, most of previous studies on forecasting carbon emissions hardly considered more than two factors. In addition, conventional statistical methods of forecasting carbon emissions usually require some assumptions and limitations such as normal distribution and large dataset. Consequently, this study proposes a two-stage forecasting approach consisting of multivariable grey forecasting model and genetic programming. The multivariable grey forecasting model at the first stage enjoys the advantage of introducing multiple factors into the forecasting model, and can accurately make prediction with only four or more samples. However, grey forecasting may perform worse when the data is nonlinear. To overcome this problem, the second stage is to adopt genetic programming to establish the error correction model to reduce the prediction error. To evaluating performance of the proposed approach, the carbon dioxide emissions in Taiwan from 2000 to 2015 are forecasted and analyzed. Experimental comparison on various combinations of multiple factors shows that the proposed forecasting approach has higher accuracy than previous approaches.

原文English
文章編號3432
期刊Energies
11
發行號12
DOIs
出版狀態Published - 1 12月 2018

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