Zhigeng Fang

TL;DR: In this article, two univariate models, ARIMA (the autoregressive integrated moving average) model and GM(1,1) model, are used to forecast China's primary energy consumption.

TL;DR: In this paper, the authors examined the relationship between energy consumption, urban population, economic growth and CO2 emissions in the BRICS countries (i.e., Brazil, Russia, India, China, and South Africa) by using a novel multi-variable grey model.

TL;DR: In this article, the operation axiom, operation rules of general grey numbers and a new algebraic system for general grey number are built based on the kernel and the degree of greyness of grey numbers.

TL;DR: The results of practical numerical examples demonstrate that the GM(1,1) with fractional order accumulation provides very remarkable predication performance compared with the traditional GM( 1,1), which is proved theoretically that the initial condition is not utilized, and the simulative value is convex and increased or is the decreased and concave when the actual sequence is nonnegative increased.

TL;DR: Wang et al. as mentioned in this paper divided the history of Chinese economy into four periods and examined the relation between Chinese energy consumption and economic growth by applying grey incidence analysis, which is one of the most important methods of grey system theory which can be applied to solve the problems with small samples.