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Directed Technical Change, Capital Intensity Increase and Energy Transition: Evidence from China

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  • Dong Wang
  • Amin Mugera
  • Ben White

Abstract

This paper analyses the causes of China’s energy transition since 1978 when the economic reform policy was launched. We aim to determine if increasing capital intensity in the Chinese economy is driving a shift in the energy mix towards modern energy sources, such as solar electricity. The empirical investigation is based on national level time series data from 1978 to 2015. The results of a Granger Causality test show that increasing capital intensity causes transition to modern energy in the long run, but not vice versa. The impulse-response analysis, based on the Johanson cointegration and vector error correction model (VECM), verifies that capital intensity determines energy transition in the long-run and the adjustment period to an exogenous shock from capital intensity is around five years. This is in line with China’s National Five-year Plans which often introduce major shifts in energy and industrial policy. We conclude China’s energy transition is driven by capital deepening and biased technical change towards capital-intensive modern energy in the long run. The rate of change is increased by exogenous investment shocks partly as a result of policy initiatives introduced by China’s Five Year Plans.

Suggested Citation

  • Dong Wang & Amin Mugera & Ben White, 2019. "Directed Technical Change, Capital Intensity Increase and Energy Transition: Evidence from China," The Energy Journal, , vol. 40(1_suppl), pages 277-296, June.
    • Handle: RePEc:sae:enejou:v:40:y:2019:i:1_suppl:p:277-296
    DOI: 10.5547/01956574.40.SI1.dwan
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    References listed on IDEAS

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    Cited by:

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    3. Dong Wang & Chunlai Chen & Christopher Findlay & Jikun Huang & Justin Yifu Lin & Abedullah & Mohammad Jahangir Alam & Abid Hussain & Nunung Nuryartono & Tahlim Sudaryanto & David Shearer, 2025. "Stage Segmentation of Rural Transformation and Comparisons Among Bangladesh, China, Indonesia, and Pakistan: Combining Machine Learning and New Structural Economics to Facilitate International Agricultural Development and Policy Design," Asia and the Pacific Policy Studies, Wiley Blackwell, vol. 12(2), May.

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