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Coal Consumption Reduction in Shandong Province: A Dynamic Vector Autoregression Model

doi: 10.3390/su8090871
Coal consumption and carbon dioxide emissions from coal combustion in China are attracting increasing attention worldwide. Between 1990 and 2013, the coal consumption in Shandong Province increased by approximately 5.29 times. Meanwhile, the proportion of coal consumption of Shandong Province to China rose from 7.6% to 10.8%, and to the world, it rose from 1.8% to 5.5%. Identifying the drivers of coal consumption in Shandong Province is vital for developing effective environmental policies. This paper uses the Vector Autoregression model to analyze the influencing factors of coal consumption in Shandong Province. The results show that industrialization plays a dominant role in increasing coal consumption. Conversely, coal efficiency is the key factor to curtailing coal consumption. Although there is a rebound effect of coal efficiency in the short term, from a long-term perspective, coal efficiency will reduce coal consumption gradually. Both economic growth and urbanization have a significant effect on coal consumption in Shandong Province. In addition, the substitution effect of oil to coal has not yet met expectations. These findings are important for relevant authorities in Shandong in developing appropriate policies to halt the growth of coal consumption.
- Yuncheng University China (People's Republic of)
- North University of China China (People's Republic of)
- Yuncheng University China (People's Republic of)
coal consumption, Environmental effects of industries and plants, Shandong Province, TJ807-830, TD194-195, Renewable energy sources, Environmental sciences, STIRPAT model, impulse response functions, vector autoregression model, coal consumption; STIRPAT model; vector autoregression model; impulse response functions; Shandong Province, GE1-350
coal consumption, Environmental effects of industries and plants, Shandong Province, TJ807-830, TD194-195, Renewable energy sources, Environmental sciences, STIRPAT model, impulse response functions, vector autoregression model, coal consumption; STIRPAT model; vector autoregression model; impulse response functions; Shandong Province, GE1-350
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