The Total-Factor Energy Efficiency of Regions in China: Based on Three-Stage SBM Model
Abstract
:1. Introduction
2. Methodology of Models
2.1. Three-Stage SBM Model
2.2. TFEE Evaluation and Deconstruction Model
3. Empirical Results
3.1. Variables
- Capital stock (K): Epitaxial expanding reproduction is the main growth mode of China’s city. This is mainly because capital input is a driving force for the economic growth of city. At present, most scholars selected capital stock as an indicator for measuring capital input (Hu and Wang, 2006 [6]; Honma and Hu, 2008 [7]). However, the methods to measure capital stock, for example the perpetual inventory method, may involve the utilization rate of capital or the depreciation rate of fixed assets, which are unavailable. Thus, we set physical capital stock as capital investment indicator and set fixed total investment as specific indicator. We estimate the capital stock of each city based on the “Perpetual Inventory law”. Firstly, according to the following formula, the based capital stock of each city in 2001 is estimated.
- Labor (L): As the city is a labor-intensive place, the development level and the competitiveness of city depend largely on the quality and quantity of labor. According to some research achievement, such as Hu and Wang (2006) [6] and Honma and Hu (2008) [7], we use the total number of the employed population in the current period as labor input indicator.
- Energy investment (E): Because China has not comprehensively collected data of city energy consumption, we must get the data of energy investment another way. We choose annual electricity energy consumption as energy investment indicator, as electricity demand of GDP elasticity is very close to the total energy, and the power consumption data that computer readout are more accurate, electricity energy could more accurately represent the overall energy efficiency situation of China.
- Economy output (Y): Currently, most scholars consider the economic index to be energy-utilization outputs, where the economic index measures the services provided by energy utilization with market price. Based on the features of city and its energy consumption, we use the real GDP of cities as economy output indicator.
- Non-consensual output (SO2): For the same reason as above, the non-consensual indicator measures the services provided by energy utilization with physical units. We use sulfur dioxide emission of cities as a non-consensual output indicator.
- Environment variables: At present, there are many studies on the factors impacting TFEE. Based on these studies (Chen et al., 2015 [29]; Liu et al., 2014a [38]; 2014b [39]), we choose five external environment factors. Industry structure: In cities with a developed industry, the market is relatively standard and effective competition could be fully realized, thereby stimulating each enterprise to improve their own productivity. Technology level: Cohen and Levinthal (1989) [41] propose that technology investment could strength the ability to absorb information, promote the transfer of knowledge and improve innovation. Advanced technology also promotes the development of renewable energy, such as solar power and wind power, which has higher efficiency and less environmental side effect. Therefore, technology level is the key factor in increasing energy efficiency. Infrastructure: The city with well-developed infrastructure could make resource allocation more reasonable and the agglomeration and scale effects stronger. Government intervention: As Chinese government is now trying its best to reduce waste and low-efficiency energy, if in a city, the government is putting reducing energy waste as its main job, then usually the energy efficiency of this city will be higher. Energy endowment: The energy endowment has a significant influence on energy efficiency. At present, energy endowment in most Chinese cities depends on raw coal, petroleum, diesel and electrical power. However, there are some differences in the energy level of different cities. Compared with coal, electrical power is high-efficiency energy [29]. Special indicators are: (1) output value of the second industry/regional total product (IN/GDP); (2) foreign direct investment/fixed total investment (FDI/I); (3) cargo volume/(highway mileage+railway mileage) (T/H); (4) expenditure/regional total product (CZ/GDP); and (5) the number of employees in mining industry/the total number of employees (CJ/TW).
3.2. First Stage: Evaluation of TFEE and District Differences
3.3. Second Stage: The Environment Impact of TFEE and Reasons of Differences
3.4. Third Stage: Structure Decomposition of TFEE and Promotion Paths
4. Conclusions
Author Contributions
Conflicts of Interest
References
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National | Eastern | Central | Western | North-Eastern | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
TFFE | ME | EE | TFFE | ME | EE | TFFE | ME | EE | TFFE | ME | EE | TFFE | ME | EE | |
2001 | 0.54 | 0.67 | 0.81 | 0.58 | 0.66 | 0.88 | 0.48 | 0.64 | 0.75 | 0.57 | 0.69 | 0.82 | 0.48 | 0.66 | 0.72 |
2002 | 0.57 | 0.70 | 0.82 | 0.61 | 0.71 | 0.86 | 0.51 | 0.66 | 0.78 | 0.61 | 0.70 | 0.87 | 0.50 | 0.73 | 0.68 |
2003 | 0.55 | 0.75 | 0.74 | 0.59 | 0.79 | 0.74 | 0.49 | 0.73 | 0.67 | 0.60 | 0.71 | 0.84 | 0.49 | 0.79 | 0.62 |
2004 | 0.54 | 0.72 | 0.74 | 0.55 | 0.75 | 0.74 | 0.48 | 0.71 | 0.67 | 0.59 | 0.69 | 0.84 | 0.50 | 0.75 | 0.68 |
2005 | 0.54 | 0.72 | 0.74 | 0.55 | 0.75 | 0.74 | 0.48 | 0.71 | 0.67 | 0.59 | 0.69 | 0.85 | 0.50 | 0.75 | 0.68 |
2006 | 0.57 | 0.71 | 0.71 | 0.61 | 0.72 | 0.85 | 0.52 | 0.70 | 0.74 | 0.60 | 0.70 | 0.86 | 0.54 | 0.75 | 0.72 |
2007 | 0.56 | 0.74 | 0.75 | 0.60 | 0.75 | 0.80 | 0.49 | 0.71 | 0.69 | 0.57 | 0.73 | 0.78 | 0.55 | 0.77 | 0.71 |
2008 | 0.56 | 0.79 | 0.71 | 0.61 | 0.79 | 0.78 | 0.50 | 0.77 | 0.64 | 0.56 | 0.78 | 0.71 | 0.57 | 0.83 | 0.68 |
2009 | 0.57 | 0.71 | 0.71 | 0.62 | 0.71 | 0.88 | 0.51 | 0.68 | 0.76 | 0.56 | 0.73 | 0.77 | 0.57 | 0.74 | 0.77 |
2010 | 0.66 | 0.71 | 0.92 | 0.70 | 0.75 | 0.93 | 0.60 | 0.70 | 0.86 | 0.63 | 0.74 | 0.85 | 0.67 | 0.71 | 0.95 |
2011 | 0.60 | 0.68 | 0.87 | 0.67 | 0.73 | 0.92 | 0.54 | 0.66 | 0.81 | 0.59 | 0.68 | 0.86 | 0.57 | 0.63 | 0.91 |
2012 | 0.58 | 0.79 | 0.73 | 0.62 | 0.81 | 0.76 | 0.54 | 0.78 | 0.70 | 0.57 | 0.77 | 0.74 | 0.56 | 0.77 | 0.72 |
mean | 0.57 | 0.72 | 0.77 | 0.61 | 0.74 | 0.82 | 0.51 | 0.70 | 0.73 | 0.59 | 0.72 | 0.82 | 0.54 | 0.74 | 0.74 |
N | 276 | 87 | 79 | 76 | 34 |
2001 | 2005 | 2010 | 2012 | |
---|---|---|---|---|
Top 10 | Tianjin, Shenyang, Changchun, Shanghai, Wuxi, Hangzhou, Guangzhou, Shenzhen, Dongguan, Chengdu | Changchun, Shanghai, Wuxi, Suzhou, Hangzhou, Guangzhou, Shenzhen, Foshan, Chongqing, Dongguan | Beijing, Shenyang, Shanghai, Suzhou, Changsha, Guangzhou, Shenzhen, Foshan, Dongguan, Chongqing | Beijing, Tianjing, Shanghai, Suzhou, Qingdao, Changsha, Guangzhou, Shenzhen, Foshan, Chongqing |
Bottom 10 | Cangzhou, Yingkou, Fuxin, Shizuishan, Yinchuan, Tongchuan, Xingtai, Xining, Puyang, Jiaozuo | Fuxin, Changzhi, Shangqiu, Tongchuan, Yingkou, Hegang, Puyang, Xining, Jiaozuo, Yinchuan | Puyang, Anyang, Xinxiang, Fuxin, Zhoukou, Jiaozuo, Nanyang, Guiyang, Yingkou, Shangqiu | Handan, Yingkou, Guangyuan, Anyang, Jiaxing, Puyang, Nanyang, Guiyang, Jiaozuo, Shangqiu |
Frontier Cities | 33 | 27 | 33 | 36 |
Year | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Moran’s I | 0.112 | 0.148 | 0.14 | 0.104 | 0.086 | 0.101 | 0.089 | 0.081 | 0.088 | 0.177 | 0.135 | 0.114 |
Z | 4.823 | 6.305 | 5.968 | 4.49 | 3.746 | 4.361 | 3.87 | 3.526 | 3.827 | 7.543 | 5.785 | 4.917 |
Variable/Model | Capital Slack Variable | Labor Slack Variable | Energy Slack Variable | |||
---|---|---|---|---|---|---|
SAR | SEM | SAR | SEM | SAR | SEM | |
IN/GDP | −1.7 | −1.7 | −0.07 | −0.05 | 2.92 | 2.17 |
(5.05) *** | (5.03) *** | (0.566) *** | (0.42) | (10.61) *** | (5.46) *** | |
FDI/I | −0.01 | −0.01 | −0.01 | −0.01 | −0.01 | −0.01 |
(3.04) *** | (3.09) *** | (0.32) | (0.28) | (2.52)* | (2.28) * | |
H/T | 0.47 | 0.55 *** | 0.02 | 0.09 | −0.27 | −0.46 |
(5.15) *** | (6.07) *** | (0.65) | (3.11) *** | (3.72) *** | (4.90) *** | |
CZ/GDP | 0.33 | 0.65 | 0.44 | 0.55 | 0.66 | 0.37 |
(0.56) | (1.13) | (2.94) ** | (3.57) *** | (1.43) | (0.73) | |
CJ/TW | 1.13 | 1.13 | 0.22 | 0.13 | 0.78 | 1.09 |
(3.37) *** | (3.37) *** | (1.31) | (0.77) | (2.83) *** | (1.92) | |
λ/ρ | 0.35 | 0.04 | 0.61 | 0.34 | 0.64 | 0.6 |
(3.10) *** | (0.28) | (8.37) *** | (2.71) ** | (9.46) *** | (7.77) *** | |
R2 | 0.52 | 0.5 | 0.52 | 0.51 | 0.59 | 0.58 |
logL | −7355.14 | −7350.19 | −1277.15 | −1270.45 | −5112.59 | −5171.03 |
Model | SAR | SEM | SAR |
2001 | 2005 | 2010 | 2012 | |
---|---|---|---|---|
Top 10 | Tianjin, Shenyang, Changchun, Shanghai, Wuxi, Hangzhou, Guangzhou, Shenzhen, Dongguan, Chengdu | Changchun, Shanghai, Wuxi, Suzhou, Hangzhou, Guangzhou, Shenzhen, Foshan, Chongqing, Dongguan | Beijing, Shenyang, Shanghai, Suzhou, Changsha, Guangzhou, Shenzhen, Foshan, Dongguan, Chongqing | Beijing, Tianjing, Shanghai, Suzhou, Qingdao, Changsha, Guangzhou, Shenzhen, Foshan, Chongqing |
Bottom 10 | Cangzhou, Yingkou, Fuxin, Shizuishan, Yinchuan, Tongchuan, Xingtai, Xining, Puyang, Jiaozuo | Fuxin, Changzhi, Shangqiu, Tongchuan, Yingkou, Hegang, Puyang, Xining, Jiaozuo, Yinchuan | Puyang, Anyang, Xinxiang, Fuxin, Zhoukou, Jiaozuo, Nanyang, Guiyang, Yingkou, Shangqiu | Handan, Yingkou, Guangyuan, Anyang, Jiaxing, Puyang, Nanyang, Guiyang, Jiaozuo, Shangqiu |
Frontier Cities | 33 | 27 | 33 | 36 |
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Huang, H.; Wang, T. The Total-Factor Energy Efficiency of Regions in China: Based on Three-Stage SBM Model. Sustainability 2017, 9, 1664. https://doi.org/10.3390/su9091664
Huang H, Wang T. The Total-Factor Energy Efficiency of Regions in China: Based on Three-Stage SBM Model. Sustainability. 2017; 9(9):1664. https://doi.org/10.3390/su9091664
Chicago/Turabian StyleHuang, Haifeng, and Tao Wang. 2017. "The Total-Factor Energy Efficiency of Regions in China: Based on Three-Stage SBM Model" Sustainability 9, no. 9: 1664. https://doi.org/10.3390/su9091664