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Investment efficiency of the new energy industry in China

descriptionPublicationkeyboard_double_arrow_right Article , Journal 01 Feb 2018 United Kingdom Publisher:Elsevier BVJournal:Energy Economics, volume 70, pages 536-544 (issn: 0140-9883,

Authors: Zeng S; Jiang C; Ma C; Su B;

doi: 10.1016/j.eneco.2017.12.023

handle: 2164/12948

Investment efficiency of the new energy industry in China

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Abstract

This paper evaluates the investment efficiency of the new energy industry in China and investigates factors that explain variations in investment efficiency across firms and over time. Applying a four-stage semi-parametric DEA analysis framework to a sample of listed new energy firms over the period 2012-2015, we find that the overall investment efficiency of the new energy industry is relatively low, with an average total technical efficiency of 44%, pure technical efficiency of 48%, and scale efficiency of 90%. We also find that new energy firms’ investment efficiency is affected by both macroeconomic conditions and firm-specific characteristics. Our results are robust and have significant implications for policy makers and firm managers.

Country

United Kingdom

Related Organizations

Newcastle University
United Kingdom
Beijing University of Technology
China (People's Republic of)
National University of Singapore
Singapore
Middlesex University
United Kingdom
Middlesex University
United Kingdom

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Keywords

China, HG Finance, 330, Supplementary Data, Semi-parametric DEA analysis, Investment efficiency, HG, 300, New energy industry

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	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	133
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	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%