College of Management, Research Institute of Business Analytics and Supply Chain Management, Shenzhen University, Shenzhen, 518060, China.
Donahue Graduate School of Business, Duquesne University, 820 Rockwell Hall, 600 Forbes Avenue, Pittsburg, PA, USA.
Environ Sci Pollut Res Int. 2021 Apr;28(14):17891-17912. doi: 10.1007/s11356-020-11418-3. Epub 2021 Jan 6.
Emission forecasting is vital for policy-making and emission reduction goals globally. This research aimed to perform an accurate model for forecasting and assessing CO emissions and the production of renewable electricity for the top two countries contributing to these emissions, the USA and China. In this study, we employed three novel advanced mathematical grey models: optimized discrete grey model (ODGM), nonhomogeneous discrete grey model (NDGM), and variable speed and adaptive structure grey model (VSSGM) to estimate the future trends of CO emissions and renewable electricity production. These breakthrough models added value in this field of research by reducing uncertainty surrounding ambiguity and numerical ranges and improving accuracy in assessments by using small samples and imperfect information. The findings showed that, by 2026, China's electricity production based on renewable sources would be higher than that of the USA. We find CO emissions in a downward trend, with more significant reductions in the USA than in China by the year 2026. The contributions of this study are the application of novel VSSGM and the use of synthetic relative growth rate modeling for predicting the overall growth of CO emissions and the production of renewable electricity in analyzed countries. The originality of this study lies in proposing a novel synthetic doubling time model to compute how long it will take, for China and the USA, to reduce their CO emissions and doubling their increase in renewable electricity production.
排放预测对于全球的政策制定和减排目标至关重要。本研究旨在为排放量最大的两个国家（美国和中国）建立一个准确的模型，用于预测和评估 CO 排放和可再生电力的生产。在这项研究中，我们采用了三种新颖的先进数学灰色模型：优化离散灰色模型（ODGM）、非齐次离散灰色模型（NDGM）和变速自适应结构灰色模型（VSSGM），以估计 CO 排放和可再生电力生产的未来趋势。这些突破性的模型通过减少不确定性和数值范围的模糊性，以及利用小样本和不完善的信息提高评估的准确性，为该研究领域增加了价值。研究结果表明，到 2026 年，中国的可再生能源发电量将高于美国。我们发现 CO 排放量呈下降趋势，到 2026 年，美国的降幅将大于中国。本研究的贡献在于应用了新颖的 VSSGM，并使用综合相对增长率模型来预测分析国家的 CO 排放总量增长和可再生电力生产。本研究的新颖之处在于提出了一种新的综合倍增时间模型，以计算中国和美国减少 CO 排放和增加可再生电力生产所需的时间。
