Assessment and prediction of environmental sustainability: novel grey models comparative analysis of China vs. the USA
Abstract
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.