4.8 Article

Impact of energy conservation policies on the green productivity in China's manufacturing sector: Evidence from a three-stage DEA model

Journal

APPLIED ENERGY
Volume 168, Issue -, Pages 351-363

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2016.01.104

Keywords

Energy conservation policy; Green productivity; Malmquist-Luenberger productivity index; Three-stage DEA model; China

Funding

  1. Xiamen University - Newcastle University Joint Strategic Partnership Fund
  2. Grant for Collaborative Innovation Center for Energy Economics and Energy Policy [1260-Z0210011]
  3. Xiamen University Flourish Plan Special Funding [1260-Y07200]
  4. China Sustainable Energy Program [G-1506-23315]

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This study introduces an improved Malmquist-Luenberger productivity index to measure the green productivity growth of China's manufacturing sector during the 11th Five-Year Period (2006-2010). A three-stage data envelopment analysis model is adopted to measure the effects of government measures on green productivity growth. The main results are: (i) the average value of the Malmquist productivity index is 1.045 and the average value of the Malmquist-Luenberger productivity index accounting for CO2 emissions is 1.027. This indicates imply that the relatively higher values of the former are at the expense of substantial energy usage and CO2 emissions; (ii) China's energy-saving policies and measures, such as mass promotion and adoption of energy-saving technology, closure and elimination of obsolete production capacity, and reduction of over-capacity are important for green development; (iii) after eliminating the effects of environmental influences and statistical noise on output slacks, the adjusted green productivity changes are smaller while the adjusted technical changes are larger than the corresponding initial levels; (iv) the energy conservation policies implemented in China's manufacturing sector are far from the optimal level, and more stringent enforcement would be conducive for green productivity growth in the manufacturing sector. (C) 2016 Elsevier Ltd. All rights reserved.

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