Research on Electricity Consumption and Economic Growth Based on Variable Coefficient Panel Data Regression

DOI

10.2991/seiem-16.2016.126How to use a DOI?

Keywords

Variable coefficient, Panel data, Economic growth, Electricity consumption

Abstract

With the rapid economic development, China has become the largest power consumer,An accurate grasp of the relationship between electricity consumption and economic growth, the sustainable development of the economy and the development of electric energy policy will have a great help. This paper adopts the variable coefficient panel data model, on the base of panel data of electricity consumption and economic growth in 30 provinces in China from 1995 to 2013, panel unit root test and panel cointegration test are carried out. Then the model test is carried out. Combined with the results of the analysis of China's various s regions of the relationship between power consumption and economic growth, the results show that the power consumption in China has a great influence on electricity consumption in the central region and a relatively small trend in the eastern and western regions. In terms of the level of economic development, the degree of influence of the developed and underdeveloped areas is also different. And put forward corresponding countermeasures to the results, which provides a strong basis for the establishment of energy-saving society in China. Innovation is the use of variable coefficient panel model can effectively analyze the impact of electricity consumption on the size of economic growth.

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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TY  - CONF
AU  - Peng Lv
AU  - Jun-chao Wang
PY  - 2016/12
DA  - 2016/12
TI  - Research on Electricity Consumption and Economic Growth Based on Variable Coefficient Panel Data Regression
BT  - Proceedings of the 2016 International Seminar on Education Innovation and Economic Management (SEIEM 2016)
PB  - Atlantis Press
SP  - 492
EP  - 495
SN  - 2352-5398
UR  - https://doi.org/10.2991/seiem-16.2016.126
DO  - 10.2991/seiem-16.2016.126
ID  - Lv2016/12
ER  -