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Convergence analysis of China’s energy intensity at the industrial sector level

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Abstract

China’s industrial energy consumption accounted for approximately 70% of national energy demand in the past four decades. Regarding energy demand and environmental pollution, success in controlling energy demand and reducing energy intensity for industrial sectors in China would play a crucial role for the country’s sustainable growth problems. To formulate targeted energy plans, the features and characters of China’s industrial energy intensity should be carefully evaluated. In this study, a carefully designed econometric model that considers different technological factors including indigenous R&D and technology spillovers from foreign direct investment and trade under a united framework is applied to investigate the β-convergence characteristics for China’s industrial energy intensity by employing a panel dataset covering China’s 34 industrial sectors over 2000–2010. The results verify the existence of β-convergence in industrial energy intensity during the sample period. For the industrial sectors overall and the light industrial sectors, the empirical results indicate that indigenous R&D and technology spillovers from FDI and imports are beneficial in curbing energy intensity. However, technology spillover through exports makes it harder to reduce energy intensity. In addition, not all technological factors have played a significant role in reducing energy intensity for the heavy industrial sectors.

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Notes

  1. The CH-LP framework originates from the famous publications including Coe and Helpman (1995) (referenced as CH) and Van Pottelsberghe de la Potterie and Lichtenberg (1998) (referenced as LP). It is then commonly employed to measure the technology spillovers coming from FDI and trade in the international academic.

  2. For the openness, FDI is denoted by China’s actual use of foreign capital. The data on China’s imports with related countries (regions) are calculated at the customs of the countries (regions) of origin, and the data on China’s exports are calculated at the customs of the countries (regions) of destination. Since Hong Kong is one of the largest entrepot trade centers all over the world, a huge amount of China’s commodities exported to Hong Kong is enroute; therefore, the data on China’s exports to Hong Kong should be adjusted by deleting the amount of re-exports. Here, in line with Huang et al. (2017b), for simplicity, we assume that China’s exports to Hong Kong are the same to China’s imports from Hong Kong.

  3. The code and definition for the 34 industrial sectors are shown in the Appendix.

  4. The classification for the heavy industrial sectors and light industrial sectors can also be found in the Appendix.

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Authors and Affiliations

  1. School of Economics, Southwestern University of Finance and Economics, Chengdu, 611130, China

    Junbing Huang, Xuemei Zheng, Ailun Wang & Xiaochen Cai

Authors
  1. Junbing Huang
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  2. Xuemei Zheng
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  3. Ailun Wang
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  4. Xiaochen Cai
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Correspondence to Junbing Huang.

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Responsible editor: Muhammad Shahbaz

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Highlights

• The convergence of energy intensity for Chinese industrial sectors is investigated.

• There is evidence that β-convergence exists.

• Indigenous R&D activities play a key role in reducing China’s industrial energy intensity.

• Technology spillovers coming from openness are beneficial for industrial energy intensity reduction except for the export.

Appendix

Appendix

Table 5
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Huang, J., Zheng, X., Wang, A. et al. Convergence analysis of China’s energy intensity at the industrial sector level. Environ Sci Pollut Res 26, 7730–7742 (2019). https://doi.org/10.1007/s11356-018-3994-7

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  • DOI: https://doi.org/10.1007/s11356-018-3994-7

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