Total factor energy efficiency in regions of China: An empirical analysis on SBM-DEA model with undesired generation
Due to the imbalance of regional development and the different energy efficiency of different regions in China, it is necessary to measure the total factor energy efficiency of various economic zones and get the actual situation of each region. The paper uses SBM-DEA Model considering undesired gene...
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doaj-65c800e406494e7d9de81ee852efb2572020-11-25T02:26:17ZengElsevierJournal of King Saud University: Science1018-36472020-04-0132319251931Total factor energy efficiency in regions of China: An empirical analysis on SBM-DEA model with undesired generationYu Shang0Haibin Liu1Yue Lv2Management School, China University of Mining & Technology (beijing), Beijing 100083, ChinaManagement School, China University of Mining & Technology (beijing), Beijing 100083, ChinaManagement School, China University of Mining & Technology (beijing), Beijing 100083, ChinaDue to the imbalance of regional development and the different energy efficiency of different regions in China, it is necessary to measure the total factor energy efficiency of various economic zones and get the actual situation of each region. The paper uses SBM-DEA Model considering undesired generations to measure the total factor energy efficiency in different regions of China. When analyzing the situation of multiple inputs and multiple outputs, the paper will adopt a decision making-unit that measures multiple inputs and outputs. Thirty provinces and municipalities are divided into eight economic zones by using the State Council’s division method. The average annual total factor energy measurement value in China from 2005 to 2016 is 0.4559 under the consideration of environmental constraints. With the existing technology and the constant investment scale, there is still a 50% increase in this value. This provides a theoretical upside for the further transformation and upgrading of China’s energy production capacity and the reform of the supply side. Then it uses Moran index to get the spatial correlation of TFEE separately. It shows that there is a significant spatial positive correlation of China’s total factor energy efficiency. The conclusion is that China’s total factor energy efficiency has not increased with economic growth, and the regional gap is large, and there is room for improvement of 50%. It also shows that there is a positive spatial correlation among regional TFEE values. That is, high TFEE value in certain area could promote the value of surrounding provinces, indicating that China’s current economic growth is still dominated by energy consumption, and China is also in the middle and late stages of industrialization. Keywords: SBM-DEA Model, Total factor energy efficiency, Moran index, Spatial correlation testhttp://www.sciencedirect.com/science/article/pii/S1018364720300355 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yu Shang Haibin Liu Yue Lv |
spellingShingle |
Yu Shang Haibin Liu Yue Lv Total factor energy efficiency in regions of China: An empirical analysis on SBM-DEA model with undesired generation Journal of King Saud University: Science |
author_facet |
Yu Shang Haibin Liu Yue Lv |
author_sort |
Yu Shang |
title |
Total factor energy efficiency in regions of China: An empirical analysis on SBM-DEA model with undesired generation |
title_short |
Total factor energy efficiency in regions of China: An empirical analysis on SBM-DEA model with undesired generation |
title_full |
Total factor energy efficiency in regions of China: An empirical analysis on SBM-DEA model with undesired generation |
title_fullStr |
Total factor energy efficiency in regions of China: An empirical analysis on SBM-DEA model with undesired generation |
title_full_unstemmed |
Total factor energy efficiency in regions of China: An empirical analysis on SBM-DEA model with undesired generation |
title_sort |
total factor energy efficiency in regions of china: an empirical analysis on sbm-dea model with undesired generation |
publisher |
Elsevier |
series |
Journal of King Saud University: Science |
issn |
1018-3647 |
publishDate |
2020-04-01 |
description |
Due to the imbalance of regional development and the different energy efficiency of different regions in China, it is necessary to measure the total factor energy efficiency of various economic zones and get the actual situation of each region. The paper uses SBM-DEA Model considering undesired generations to measure the total factor energy efficiency in different regions of China. When analyzing the situation of multiple inputs and multiple outputs, the paper will adopt a decision making-unit that measures multiple inputs and outputs. Thirty provinces and municipalities are divided into eight economic zones by using the State Council’s division method. The average annual total factor energy measurement value in China from 2005 to 2016 is 0.4559 under the consideration of environmental constraints. With the existing technology and the constant investment scale, there is still a 50% increase in this value. This provides a theoretical upside for the further transformation and upgrading of China’s energy production capacity and the reform of the supply side. Then it uses Moran index to get the spatial correlation of TFEE separately. It shows that there is a significant spatial positive correlation of China’s total factor energy efficiency. The conclusion is that China’s total factor energy efficiency has not increased with economic growth, and the regional gap is large, and there is room for improvement of 50%. It also shows that there is a positive spatial correlation among regional TFEE values. That is, high TFEE value in certain area could promote the value of surrounding provinces, indicating that China’s current economic growth is still dominated by energy consumption, and China is also in the middle and late stages of industrialization. Keywords: SBM-DEA Model, Total factor energy efficiency, Moran index, Spatial correlation test |
url |
http://www.sciencedirect.com/science/article/pii/S1018364720300355 |
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