Proceedings of the 2016 2nd International Conference on Social Science and Technology Education (ICSSTE 2016)

Study of the Power Grid Enterprise Performance Based on Data Envelopment Analysis

Authors
Shuguo Zhang, Jingjie Niu
Corresponding Author
Shuguo Zhang
Available Online May 2016.
DOI
10.2991/icsste-16.2016.196How to use a DOI?
Keywords
DEA, power grid enterprises, investment and outcome
Abstract

In this paper, data envelopment technology is applied to estimate the comprehensive technical efficiency of power grid enterprises and study its pure technical efficiency and scale efficiency. The calculation result is analyzed. Through the analysis of the actual operation results of the input redundancy and output deficiency, this result is associated with the local GNP data. The reasonable value of the input and output of the power grid enterprises to achieve the best performance is obtained by the prediction of the future local GNP values.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 2nd International Conference on Social Science and Technology Education (ICSSTE 2016)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
May 2016
ISBN
978-94-6252-177-3
ISSN
2352-5398
DOI
10.2991/icsste-16.2016.196How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Shuguo Zhang
AU  - Jingjie Niu
PY  - 2016/05
DA  - 2016/05
TI  - Study of the Power Grid Enterprise Performance Based on Data Envelopment Analysis
BT  - Proceedings of the 2016 2nd International Conference on Social Science and Technology Education (ICSSTE 2016)
PB  - Atlantis Press
SP  - 1076
EP  - 1082
SN  - 2352-5398
UR  - https://doi.org/10.2991/icsste-16.2016.196
DO  - 10.2991/icsste-16.2016.196
ID  - Zhang2016/05
ER  -