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

• Published in: Energies (Basel) Vol. 11; no. 12; p. 3432
• Main Authors: Lin, Chun-Cheng, He, Rou-Xuan, Liu, Wan-Yu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2018
• Subjects: Air pollution; Alternative energy; Carbon dioxide; Climate change; CO2 emission factors; CO2 emission forecasting; Conflicts of interest; Drought; Economic change; Economic forecasting; Economic growth; Emissions; Energy consumption; Energy resources; Error correction; Forecasting; Foreign investment; Genetic algorithms; genetic programming; Global warming; Laboratories; Mathematical models; multivariable grey forecasting model; Normal distribution; Statistical analysis; Statistical methods; Turkey; United States > US; China; BRICS countries; Taiwan; Brazil; Russia; India; Pacific Northwest
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en11123432

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.