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Energies 2018, 11(12), 3432; https://doi.org/10.3390/en11123432

Considering Multiple Factors to Forecast CO2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach

1
Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu 300, Taiwan
2
Department of Forestry, National Chung Hsing University, Taichung 402, Taiwan
3
Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung 402, Taiwan
*
Author to whom correspondence should be addressed.
Received: 23 October 2018 / Revised: 1 December 2018 / Accepted: 5 December 2018 / Published: 7 December 2018
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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. View Full-Text
Keywords: CO2 emission factors; CO2 emission forecasting; multivariable grey forecasting model; genetic programming CO2 emission factors; CO2 emission forecasting; multivariable grey forecasting model; genetic programming
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Lin, C.-C.; He, R.-X.; Liu, W.-Y. Considering Multiple Factors to Forecast CO2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach. Energies 2018, 11, 3432.

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