Energy industry investment influences total factor productivity of energy exploitation: A biased technical change analysis

JOURNAL OF CLEANER PRODUCTION(2019)

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摘要
Technical progress plays a critical role in sustaining a clean world through energy efficiency improvement. The optimal energy industry investment not only reduces the cost for energy industry but also avoids environmental degradation via over-investment. This study addresses both technical progress and energy industry investment. We present a novel measuring framework for the Malmquist total factor productivity index (MI) of energy exploitation, and accordingly analyze the MI of China's 30 provinces between 2006 and 2016. Based on the efficiency measurement results, we further examine the nonlinear impact of energy industry investment on the MI under different types of biased technical change. The results show that the growth rate of MI is 1.92%. The growth rates of MI's two decomposition indices, technical efficiency and technical change, are 0.63% and 1.59%, respectively, indicating that the MI rise is primarily ascribed to technical change. The mean values of the output-biased technological change, the input-biased technological change and the magnitude of technological change are 1.0443, 1.0066 and 0.9723, respectively, indicating that of the three, output-biased technological change is the most significant contributor to technical change. The variation of these technical changes in different provinces provides information that could help in designing an effective energy policy to increase efficiency for a specific province. According to the threshold estimates of energy industry investment on the MI, the effect is significant within the range greater than the first threshold value. We find that the MI increases by 0.0001 when energy industry investment increases by 100,000,000 yuan. (C) 2019 Elsevier Ltd. All rights reserved.
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关键词
Energy industry investment,Malmquist total factor productivity index of energy exploitation,Biased technical change,Panel threshold model
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