TSL Business School, Quanzhou Normal University, No. 398, Donghai Street, Quanzhou 362000, China.
Fujian University Engineering Research Center of Cloud Computing, Internet of Things and E-Commerce Intelligence, No. 398, Donghai Street, Quanzhou 362000, China.
Int J Environ Res Public Health. 2019 Jul 13;16(14):2504. doi: 10.3390/ijerph16142504.
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.
有效确定二氧化硫排放趋势有助于国家制定适当的政策，旨在降低二氧化硫排放，这对于减少大气污染至关重要。然而，为了反映当前的情况，有利的减排政策应该基于最新的信息。已经开发了各种预测方法，但它们的应用往往受到数据不足的限制。灰色系统理论是分析小数据集的一种潜在方法。在本研究中，提出了一种基于灰色系统理论和巨趋势扩散技术的改进建模程序，用于预测中国的二氧化硫排放。与支持向量回归和径向基函数网络的结果相比，实验结果表明，所提出的程序可以有效地处理涉及小数据集的预测问题。此外，预测结果表明中国的二氧化硫排放量将呈稳步下降趋势。这些发现可被中国政府用于判断其目前减少二氧化硫排放的政策是否合适。
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