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Abstract
The scientific evaluation of trends in China’s future energy demands is highly important. Using provincial-level panel data from 1995 to 2015, we studied the relationships between the economic aggregate, the development of energy-intensive industries and energy demand from the perspective of changes in the proportion of energy-intensive industries in the national economy. We find that economic aggregate affects energy demand through energy-intensive industries and that changes in the economic structure are the key factor for change in energy demand. This means that China’s future energy demand will be much lower than that contained in forecasts that did not consider this factor. Comprehensively promoting green-tech development and strengthening the regulation of energy-extensive industries will be one of the key options for realizing China’s objective of controlling total energy consumption.
Notes
1 See Han Jun, “Energy Demand Analysis Methods.”
2 Lin Boqiang, “Structural Change, Efficiency Improvement and Energy Demand Prediction: A Case Study of China’s Electric Power Industry.”
3 Lin Weibin and Su Jian, “Economic Growth, Structural Change and Electric Power Consumption: Why Is It That Economic Growth and Electric Power Consumption Are Not Synchronized?”.
4 Zhao Jinwen and Fan Jitao, “Empirical Research on the Inherent Relationship between Economic Growth and Energy Consumption in China.”
5 Sun Han and Cheng Jinhua, “Prediction and Analysis of China’s Energy Demands in the Course of Industrialization and Urbanization.”
6 See Zhi-Fu Mi et al., “Potential Impact of Industry Structure on Energy Consumption and CO2 Emissions: A Case Study of Beijing,” pp. 455-462; Lin Boqiang, “Structural Change, Efficiency Improvement and Energy Demand Prediction: A Case Study of China’s Electric Power Industry.”
7 See P.K. Adom, W. Bekoe and S. Akoena, “Modelling Aggregate Domestic Electricity Demand in Ghana: An Autoregressive Distributed Lag Bounds Cointegration Approach,” pp. 530-537; Han Zhiyong, Wei Yiming and Fan Ying, “China’s Energy Intensity and Characteristics of Structural Changes in the Economy”; He Xiaoping, Liu Xiying and Lin Yanping, “China’s Electricity Demand Forecasting in the Urbanization Process.”
8 See B. Hofman and K. Labar, “Structural Change and Energy Use: Evidence from China’s Provinces.”
9 See F. Kahrl, D. Roland-Holst and D. Zilberman, “Past as Prologue? Understanding Energy Use in Post-2002 China,” pp. 759-771; H. Liao, Y. Fan and Y.M. Wei, “What Induced China’s Energy Intensity to Fluctuate: 1997-2006?”, pp. 4640-4649; D.H. Rosen and T. Houser, “What Drives China’s Demand for Energy (and What It Means for the Rest of Us),” in C. Fred Bergsten et al., eds., The China Balance Sheet in 2007 and Beyond.
10 See Lu Zhengnan, “Empirical Analysis of the Impact of Industry Structural Adjustment on China’s Energy Consumption”; Shi Dan and Zhang Jinlong, “The Impact of Industry Structural Changes on Energy Consumption.”
11 See B.W. Ang, “Decomposition Methodology in Industrial Energy Demand Analysis,” pp. 1081-1095; Kerui Du and Boqiang Lin, “Understanding the Rapid Growth of China’s Energy Consumption: A Comprehensive Decomposition Framework,” pp. 570-577.
12 The six major energy-intensive industries are the raw chemical materials and chemical product manufacturing industry; the non-metallic mineral product manufacturing industry; the ferrous metal smelting and calendering industry; the non-ferrous metal smelting and calendering industry; the petroleum, coking and nuclear fuel processing industry; and the power and heating production and supply industry. See National Bureau of Statistics of China, Statistical Report on National Economic and Social Development 2010.
13 Zhang Xun and Xu Jianguo, “Recalculating China’s Rate of Return on Capital.”
14 Bai Chong’en and Zhang Qiong, “Return on Capital in China and the Influencing Factors.”
15 In this paper, return on capital is the long-term return on capital in the broader sense calculated on the basis of researchers’ estimations of capital stock at the macro-level. Relative to the micro-level return on capital, this indicator gives better coverage of the changes in the whole economy and hence has the advantage of being more comprehensive. Although enterprise profits in secondary industry and the output of energy-intensive products increased in 2017, this short-term fluctuation does not conflict with the decline in long-term return on capital described in this paper. Short-term fluctuations are an objective phenomenon.
16 Although the development of energy-intensive industries presents an inverted U-shaped trend, we should also realize that the turning point may be the result of the influence of the economic cycle. Changes in trends and cyclical changes may coexist; they do not conflict.
17 See K.B. Medlock III and R. Soligo, “Economic Development and End-Use Energy Demand,” pp. 77-105.
18 This paper uses physical rather than monetary variables for the following reasons: 1) We wish to eliminate the impact of energy price fluctuations on products; 2) We only have the output data for energy-intensive industries from 2001 to 2011, and some of the value-added data are missing.
19 Tibet and Hainan are not included in this empirical study for the following reasons: 1) A large part of some of the two provinces’ variables are missing; 2) Their energy demand accounts for a very small proportion of China’s total energy demand.
20 See Vo Xuan-Vinh, “Net Private Capital Flows and Economic Growth: The Case of Emerging Asian Economies,” pp. 3135-3146.
21 We believe that our choice of the two-period lagged GDP per capita as the instrumental variable is more appropriate than the one-period lagged value. In the present circumstances, investment is an important source of economic development. However, due to the investment cycle or “outdated accounts” and other reasons, its role is often delayed. To some extent, this indicates that the impact of GDP per capita on energy use has a certain time lag, while one-period lagged GDP per capita directly affects current-period energy consumption. To some extent, using the two-period lagged value can help mitigate problems related to disturbance. See Chen Qiang, Advanced Econometrics and Stata Application.
22 D. Li, J. Chen and J. Gao, “Non-Parametric Time-Varying Coefficient Panel Data Models with Fixed Effects,” pp. 387-408.
23 The results can be obtained from the authors upon request.
24 International Futures, “Population Forecast for China,” http://www.ifs.du.edu/ifs/frm_CountryProfile.aspx?Country=CN, accessed April 10, 2017.
25 IEA, World Energy Outlook 2016.
26 Lin Boqiang, Wei Weixian and Li Pidong, “China’s Long-run Coal Demand: Impacts and Policy Choice.”