Employing a Fuzzy-Based Grey Modeling Procedure to Forecast China’s Sulfur Dioxide Emissions

Che-Jung Chang, Guiping Li, Shao-Qing Zhang and Kun-Peng Yu
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Che-Jung Chang: TSL Business School, Quanzhou Normal University, No. 398, Donghai Street, Quanzhou 362000, China
Guiping Li: Department of Management Science and Engineering, Business School, Ningbo University, No. 818, Fenghua Road, Ningbo 315211, China
Shao-Qing Zhang: TSL Business School, Quanzhou Normal University, No. 398, Donghai Street, Quanzhou 362000, China
Kun-Peng Yu: TSL Business School, Quanzhou Normal University, No. 398, Donghai Street, Quanzhou 362000, China

IJERPH, 2019, vol. 16, issue 14, 1-10

Abstract: Effective determination of trends in sulfur dioxide emissions facilitates national efforts to draft an appropriate policy that aims to lower sulfur dioxide emissions, which is essential for reducing atmospheric pollution. However, to reflect the current situation, a favorable emission reduction policy should be based on updated information. Various forecasting methods have been developed, but their applications are often limited by insufficient data. Grey system theory is one potential approach for analyzing small data sets. In this study, an improved modeling procedure based on the grey system theory and the mega-trend-diffusion technique is proposed to forecast sulfur dioxide emissions in China. Compared with the results obtained by the support vector regression and the radial basis function network, the experimental results indicate that the proposed procedure can effectively handle forecasting problems involving small data sets. In addition, the forecast predicts a steady decline in China’s sulfur dioxide emissions. These findings can be used by the Chinese government to determine whether its current policy to reduce sulfur dioxide emissions is appropriate.

Keywords: Grey system theory; small-data-set; forecasting; sulfur dioxide; emission (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
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