Considering Multiple Factors to Forecast CO 2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach

Chun-Cheng Lin, Rou-Xuan He and Wan-Yu Liu
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Chun-Cheng Lin: Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu 300, Taiwan
Rou-Xuan He: Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu 300, Taiwan

Energies, 2018, vol. 11, issue 12, 1-25

Abstract: 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.

Keywords: CO 2 emission factors; CO 2 emission forecasting; multivariable grey forecasting model; genetic programming (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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