What Contributes to Regional Disparities of Energy Consumption in China? Evidence from Quantile Regression-Shapley Decomposition Approach

Feng Dong, Bolin Yu, Jixiong Zhang
2018 Sustainability  
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
more » ... s 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. Outlook 2035 [4], the share of China's energy demand will increase from 23% in 2015 to 26% in 2035. In addition, environmental deterioration has been increasingly prominent due to extensive economic development in the past decades. As the byproducts of energy consumption, a mushrooming number of Greenhouse gases (GHG) have posed a serious threat to environment because of the greenhouse effect. In 2007, China surpassed the United States and became the largest carbon emitter in the world [5] . Against the background of China's new normal, it is supposed to realize the importance and urgency of energy saving and consumption reduction. Because of large population and energy consumption, the per capita environment capacity is small in China; consequently, China is confronted with an urgent dilemma of resources and environment. Energy consumption per unit of GDP (also called energy intensity) reflects energy utilization efficiency in the process of economic growth, which is also the measurement of low-carbon economy and an important indicator of China's mitigation commitment. During the 13th Five-Year Plan period 2016-2020, China aims to decrease energy intensity by 15% for the whole economy [6] . Nevertheless, the decline in energy intensity is found to result in an increase in energy consumption [7] . The rebound effect of energy resource suggests the improvement of energy efficiency may lead to an increase in energy consumption [8, 9] . It is not advisable to use no increase in total energy consumption as a measurement of sustainability [10] . Therefore, we can see China has formulated the dual control targets of energy intensity and total energy consumption [11] . Thus, every province has been allocated its own burden in terms of energy intensity reduction and energy consumption increment (see Table A1 ). These mandatory targets aim to save energy resources, reduce pollutants and greenhouse gas emissions from the source and promote changes in economic development patterns. In fact, a striking feature of energy use in China is that there are significant regional differences in terms of total energy consumption and energy intensity (hereafter, EI). For one thing, energy consumption is unevenly distributed, for example, Shandong had the highest level of energy consumption with the value of 37945 tons of coal equivalent in 2015, while the least energy consumption was recorded in Hainan with merely 1938 tons of coal equivalent. For another, on the whole, EI in Eastern China is distinctly lower than that in Central and Western China. Specially, EI was the lowest in Beijing (0.51 tons per 10,000 yuan) in 2015 and slightly larger level of EI was reported by Guangdong (0.69) and Jiangsu (0.71). However, Ningxia had the maximum EI of 2.96, which was almost six times larger than that of Beijing. Thus, designing energy saving policies requires knowledge of interprovincial inequalities of both energy intensity and energy consumption. Recently, there are an increasing number of energy-related inequality studies, which involve cross-country inequalities of per capita carbon emissions [12] , energy intensity [13, 14] , energy consumption per capita [15] and ecological footprint per capita [16, 17] . These studies specify intensity indicator and per capita indicator are commonly used in inequality research. Duro et al. [15] apply a Theil index decomposition to inequality in energy consumption per capita and a variance decomposition to inequality in energy intensity levels among 16 OECD countries. Teixido-Figueras and Duro [18] perform regression-based inequality decomposition to investigate the contributions of determinants to the inequality among countries in natural resource consumption, which is measured by ecological footprint per capita. As interprovincial carbon inequality can be defined as the inequality of per capita carbon emissions among different provinces [19] , in the present study, interprovincial energy consumption inequality is defined as the inequality of energy consumption per capita (hereafter, EP) among different provinces. In comparison to aggregate energy consumption closely correlated with regional size, EP in different provinces is more comparable. Moreover, ignorance of province size (by population) may overestimate the real inequality performance. Specifically, provincial aggregated data set (i.e., aggregated energy consumption) will conceal intra-provincial heterogeneity in terms of energy consumption at individual level and residents' energy welfare changes. For example, energy consumption of Shandong is about seven times larger than that of Ningxia in 2015, while Ningxia's EP is twice larger than that of Shandong. It is assumed that inter-national patterns of inequality in energy consumption per capita/energy intensity would largely be paralleled on inter-provincial scale. Investigating interprovincial disparities Sustainability 2018, 10, 1806 3 of 26 of EP and EI (hereafter, EPD and EID) can provide important information for energy consumption projection and energy policy making. Thus, it is of great practical significance to study EPD and EID with respect to their formation mechanisms. Given the abovementioned, several important questions naturally arise. First, how can the interprovincial differences in EP/EI be measured accurately? Second, how can the heterogeneity of EP and EI be understood among different areas? In other words, why does EP or EI vary across different regions? Third, how can the contributions of individual determinants of EP (EI) to EPD (EID) be quantified? Fourth, with the knowledge of the causes of regional differences in EP and EI, what policies should be oriented to narrow the gaps of EP and EI among different regions? This study intends to extend the literature by resolving the four aforementioned issues. In doing so, we merge two traditions in energy-related literature: the analyses of determinants and the measure of inequalities. Accordingly, an integrated framework mainly developed in the income inequality literature is to be performed in a sample of 30 provinces in China during 2000-2015. Specifically, in terms of regression equations, the inequality in EI or EP is decomposed by the Shapley value decomposition method. In this study, quantile regression serves as the econometric model in this research and the results of median regression (at the 50% quantile) are specified as the estimated equations for inequality decomposition. As shown in Figure 1 , an integrated framework provides the clear research path of this study. Given that the provincial goals with respect to the integration of energy intensity reduction and energy consumption control, this research aims to extend the literature by finding the reasons for the disparities of EP and EI among provinces and ways to diminish these differences. Thus, in the case of 30 provinces in China, this study performs quantile regression and regression-based Shapley value decomposition developed in the income inequality literature. On the one hand, the quantile regression models for EP and EI are established, respectively. On the other hand, the estimated equations through median regression are selected for Shapley value decomposition. Accordingly, the contribution of each determinant to EPD or EID is got. In addition, provincial energy-saving development path is explored based on the actual situations of EP and EI in 2015. In the final part of the study, we present the conclusions and policy implications accordingly. Sustainability 2018, 10, x FOR PEER REVIEW 3 of 25
doi:10.3390/su10061806 fatcat:xv26wa67wzhjxcsnmicnhoequy