Heterogeneity and connection in the spatial–temporal evolution trend of China’s energy consumption at provincial level

Abstract

Due to insufficient consideration of the provincial heterogeneity and connection in energy consumption, some “one-size-fits-all” energy policies in China are inefficient and ineffective. To support formulating more targeted energy policies, this article systematically investigates the spatial–temporal evolution trend of China’s energy consumption at the provincial level by developing an integrated prediction model which involves ARIMA, buffer-operator GM (1,1), spatial autocorrelation analysis, Monte Carlo Stochastic Sampling, and Social Carbon Cost (SCC) method. The results show that: (1) Most provinces can achieve peak energy consumption by 2030 with provincial heterogeneity further expanded. For provinces with saturated energy demand such as Liaoning and Jilin, energy efficiency should be further improved to reduce peak energy demand. For some inland provinces, especially Hunan, Jiangxi and Hebei, severe electricity shortage may occur if transforming energy consumption structure too fast; (2) Spatial clustering characteristics of main types of energy consumption will gradually disappear. Coal consumption, however, will remain High-High clustering feature in northern China, implying inappropriateness of blind “coal removal” policy. Southern provinces should emphasize on developing primary electricity. Coastal provinces should enhance diversity of the petroleum supply system; and (3) “coal-dominated” provinces of Shandong, Shanxi and Hebei will have large gaps in carbon emission quotas, while Sichuan, Ningxia and Guangxi will have quota surplus. Provincial SCC will vary greatly in 2025 ($300 million-$60 billion) with the top five provinces accounting for 38% of the total SCC. Establishing carbon emission trading system can weaken provincial differences and improve the economic efficiency of emission reduction.