第七届青年地学论坛

Despite abundant energy resources in total amount, China is faced with energy scarcity in terms of per capita available volume. China’s energy consumption per capita is only about half that of the world average (Fan et al., 2020). Furthermore, the highly unequal distribution of energy resources and population in space makes energy scarcity in some regions even worse. In recent decades, along with growing population and rapidly developing economy, the energy demand in China is rising, resulting in increasing pressure on available energy supply. Therefore, given a limited volume of energy resources and increasing energy demand in China, a relatively equal final energy use distribution among different regions is necessary to ensure an equitable and just distribution of benefits that result from energy use. From the perspective of inequality, this study seeks to provide a comprehensive image of the provincial-level embodied energy (EE), that is, the energy embodied in local final demand, and a detailed decomposition of driving forces of EE inequality in China. We quantify and compare the degrees of the inequality in economic level, industrialization level, energy resources availability, direct energy consumption, embodied energy intensity, and EE in terms of final demand types and economic sectors. Using inequality decomposition techniques, we specify the contribution of between-region effect and within-region effect to the overall inequality. We also identify possible driving forces behind the inequality of the total, sectoral, consumption- and investment-driven EEs.
We first apply the well-known method of input-output analysis (IOA) to calculate the amount of energy embodied in local final demand based on the input-output tables in 30 Chinese provinces. The IOA analysis indicates that Inner Mongolia has the highest embodied energy intensity (1.20E+11 J/10000 yuan), in comparison to the lowest embodied energy intensity in Guangdong (5.13E+09 J/10000 yuan). As for per capita EE, Zhejiang has the highest volume (7.73E+11 J), in contrast to the lowest per capita EE in Hubei (2.90E+10 J). The Theil index (T) was then used to measure the inequality of provincial EE. The total provincial EE has the Theil index equal to 0.33, indicating high inequality in final energy use in China. The per capita GDP is more approaching to the equality with the Theil index equal to 0.09. The total provincial EE presents more equality in comparison to the energy resources availability (Theil index=0.99), implying that the overall trend of provincial-level embodied energy transfer in China is from energy abundant provinces to energy scarce provinces. The Theil index of the agricultural, industrial, and tertiary EEs are 0.28, 0.34, and 0.41, respectively. The agricultural EE presents the least inequality while the EE in the tertiary sector presents the highest inequality. The degree of inequality in industrial EE is close to that in the total EE.
Following the method developed by a previous study (Sun et al., 2017), we resort to the decomposition method (T=T_inter+T_intra) to examine possible drivers of the inequality in the EE. In this study, provinces are classified into three groups (low-level, middle-level, and high-level groups) according to a specific natural or socio-economic factor. Similarly, for each classification, T_inter can be viewed as an indication of the inequality due to a specific natural or socio-economic factor. For each factor, there are many possibilities of classifying provinces into three groups by changing the breakpoints where the low-level, middle-level, and high-level classes are separated. Decompositions of inequality are implemented for all the classification possibilities, and the highest T_inter is used to indicate inequality that can be attributed to a specific natural or socio-economic factor. If T_inter is greater than T_intra, it implies that inequality due to the EE difference between sub-classes dominates that due to variations within sub-classes, hence the factor is regarded as a significant driver for EE inequality. We consider economic level (indicated by per capita GDP), industrialization level (indicated by industrial GDP/total GDP), energy resources availability (indicated by per capita energy exploitation), direct energy consumption (indicated by per capita energy consumption), embodied energy intensity, and regional difference as possible drivers that lead to difference in EEs in varied provinces. According to the decomposition results, the embodied energy intensity is the most influential factor that leads to inequality in the total EE with its D_b (D_b=T_inter/T) equal to 76.41%. The energy resources availability and direct energy consumption also contribute to explain inequality in the total EE with T_inter over T_intra. The economic level, industrialization level, and regional difference seem to exert minor effects on total EE inequality with T_inter below T_intra.
The main factors that drive inequality in the consumption- and investment-driven EEs are quite similar to those in the total EE. Inequality in consumption- and investment-driven EEs can be related to the energy resources availability (D_b=74.35% and 63.33% for the consumption- and investment-driven EEs, respectively), embodied energy intensity (D_b=69.42% and 76.83%), and direct energy consumption (D_b=57.50% and 51.95%), but not economic level, industrialization level, and regional difference. The main factors that drive inequality in the sectoral EEs are not necessarily the same as those in the consumption- and investment-driven EEs. In the agricultural EE, which represents the least inequality in all the sectoral EEs, the inequality can be associated with energy resources availability (D_b=67.92%) and embodied energy intensity in agriculture (Db= 75.68%), but is less linked to the regional difference, economic level, industrialization level, and direct energy consumption with T_inter below T_intra. The drivers for inequality in industrial and tertiary EEs are similar. Inequality in EEs of both sectors can be related to the energy resources availability (D_b=64.63% and 76.55% for the industrial and tertiary EEs, respectively), embodied energy intensity in industry and service, respectively (D_b=54.20% and 52.69%), and direct energy consumption (D_b=71.32% and 79.23%), but not economic level, industrialization level, and regional difference.
This inequality analysis can aid in gaining a better understanding of energy challenges and drivers behind them in China. Furthermore, the results can guide policy inferences for the relief of interprovincial energy imbalance in China, which will benefit the mitigation of energy scarcity faced by a great proportion of population.