4.7 Article

An application of a double bootstrap to investigate the effects of technological progress on total-factor energy consumption performance in China

Journal

ENERGY
Volume 128, Issue -, Pages 575-585

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2017.04.044

Keywords

Energy consumption performance; Technological progress; Non-radial DEA model; Double boot-strapping; China

Funding

  1. Collaborative Innovation Center for Energy Economics and Energy Policy [1260-Z0210011]
  2. Ministry of Education [0610-X1016004]
  3. Beijing Finance Project [PXM2016-178216-000010]

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This paper proposes a total-factor energy consumption performance index (TEPI) for measuring China's energy efficiency across 30 provinces during the period 1997 to 2012. The TEPI is derived by solving an improved non-radial data envelopment analysis (DEA) model, which is based on an energy distance function. The production possibility set is constructed by combining the super-efficiency and sequential DEA models to avoid discriminating power problem and technical regress. In order to explore the impacts of technological progress on TEPI and perform statistical inferences on the results, a two-stage double bootstrap approach is adopted. The important findings are that China's energy technology innovation produces a negative effect on TEPI, while technology import and imitative innovation produce positive effects on TEPI. Thus, the main contribution of TEPI improvement is technology import. These conclusions imply that technology import especially foreign direct investment (FDI) is important for imitative innovation and can improve China's energy efficiency. In the long run, as the technical level of China approaches to the frontier, energy technology innovation and its wide adoption become a sus-tained way to improve energy efficiency. Therefore, it is urgent for China to introduce measures such as technology translation and spillover policies as well as energy pricing reforms to support energy technology innovation. (C) 2017 Elsevier Ltd. All rights reserved.

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