Abstract
Given the binding provincial goals of energy intensity reduction and total energy consumption control in China, the main purpose of this study is to analyze the regional disparities of energy consumption from the perspectives of energy consumption per capita (EP) and energy intensity (EI), as well as to propose differentiated energy conservation policies. In doing so, quantile regression and regression-based Shapley value decomposition are performed in the case of 30 provinces in China during 2000–2015. The results of quantile regression specify that the impact of each determinant on EP differs distinctly at different quantiles. Income has a positive effect on EP, conversely, industrial structure, population density and transportation infrastructure have negative effects on EP. Similarly, the effect of each influencing factor on EI presents distinct dynamic varying process at different quantiles. Industrial structure, FDI and technological progress have significantly negative effects on EI, while energy mix has a positive effect on EI. Furthermore, based on the results of median regression, the assessment of contributions of individual variables to regional disparities of energy consumption per capita and energy intensity (i.e., EPD and EID) is conducted by the Shapley value decomposition method. It is found that inequality in income level is the most important reason for EPD and its annual average contribution rate is 70%. In addition, differences in population density play an important role in explaining EPD, while the inequality in transportation infrastructure contributes little to EPD. By contrast, EID is mainly due to differences in technological progress, whose annual average contribution rate is up to 46%. Following technological progress, the inequalities of FDI and energy mix are also important factors accounting for EID. On the whole, the contribution of industrial structure or regional factors is always small. Then, this study explores the provincial energy-saving development path based on the actual conditions of all provinces.
Highlights
In recent years, climate change and air pollution have been increasingly prominent, which may be mainly attributable to substantial energy consumption
The result shows industrial structure, Foreign direct investment (FDI) and technological progress have negative effects on energy intensity (EI), while energy mix has a positive effect on EI
In order to identify the contributions of individual determinants to EPD and EID measured by Gini, GE0 and GE1 obtained in Section 4.2, the Shapley value method proposed by Shorrocks [39] is the only alternative
Summary
Climate change and air pollution have been increasingly prominent, which may be mainly attributable to substantial energy consumption. The increase in energy consumption is regarded as an inevitable cost of economic growth [1]. Since the economic reforms in 1978, China’s economy has entered a sharp booming. China has exceeded the United States in energy consumption and became the largest energy consumer in 2010 [2]. With China’s primary energy consumption growing at over 5.3% per annum during 2005–2015, China accounted for 23% of world total primary energy consumption in 2016, with the value of 3053 million tons oil equivalent. What’s more, China has remained the largest growth market for energy for the last 16 years [3].
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