Abstract
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.
Highlights
As the basic source of energy, coal has facilitated the rapid development of the Chinese economy and has had a positive effect on the stability of the market economy
The results of this paper suggested that total energy consumption should increase to 2173 MtCE in 2010, an annual growth rate of 3.8%, which is slightly slower than the average rate in the past decade due to structural changes in the Chinese economy
A positive shock on thermal power output brings positive response to coal consumption, which has weak intensity and stable tendency. These results indicate that the development of downstream industries drives an increase in demand for coal, whose longterm relation is in the same direction
Summary
As the basic source of energy, coal has facilitated the rapid development of the Chinese economy and has had a positive effect on the stability of the market economy. Scholars have established a variety of energy demand forecasting models for different countries and regions as well as different kinds of energy Traditional methods such as time series, regression, econometric, decomposition, unit root test and cointegration, ARIMA (autoregressive integrated moving average), and input-output, as well as soft computing techniques such as fuzzy logic, GA (genetic algorithm), and ANN (artificial neural networks) are being extensively used for demand side management. ACO (ant colony), and PSO (particle swarm optimization) are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL (acronym for MARKet ALlocation) and LEAP (long-range energy alternatives planning model) are being used at the national and regional level for energy demand management [1]
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