Considering the speedy growth of industrialization and urbanization in China and the continued rise of coal consumption, this paper identifies factors that have impacted coal consumption in 1985–2011. After extracting the core factors, the Bayesian vector autoregressive forecast model is constructed, with variables that include coal consumption, the gross value of industrial output, and the downstream industry output (cement, crude steel, and thermal power). The impulse response function and variance decomposition are applied to portray the dynamic correlations between coal consumption and economic variables. Then for analyzing structural changes of coal consumption, the exponential smoothing model is also established, based on division of seven sectors. The results show that the structure of coal consumption underwent significant changes during the past 30 years. Consumption of both household sector and transport, storage, and post sectors continues to decline; consumption of wholesale and retail trade and hotels and catering services sectors presents a fluctuating and improving trend; and consumption of industry sector is still high. The gross value of industrial output and the downstream industry output have been promoting coal consumption growth for a long time. In 2015 and 2020, total coal demand is expected to reach 2746.27 and 4041.68 million tons of standard coal in China.
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