As mentioned before, the paper uses the super-efficiency SBM model to measure the production efficiency of thermal power plants, and the specific results are shown in Table 3. Due to the length of the paper, only certain years of the measurement results are displayed. In accordance with the general practice of existing research and the classification standards of the National Bureau of Statistics, the paper divides different cities into eastern, central, and western region. Here the eastern region of China includes: Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan, Liaoning; The central region of China includes: Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan, Heilongjiang, Jilin; The western region of China includes: Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang.
In general, a distinct divergence is observed in the production efficiency of thermal power across different provinces in China. This means the disparity between provinces with superior production efficiency and those lagging behind is widening over time. From the perspective of regional distribution, the eastern region always leads; The west is not far behind; The central region has the lowest. The eastern region of China encompasses China's most economically advanced provinces, including Beijing, Shanghai, and Jiangsu. The robust economic growth in this area necessitates a substantial power supply, thereby enhancing the efficiency of power generation equipment.
The main reason why the western region can surpass the central region of China is that the average efficiency of the west has been significantly improved due to Qinghai and Xinjiang. Qinghai benefits from its small number of employees, with the lowest number of employees per year among all provinces; Xinjiang benefits from high capacity utilization rate, with the average operation time of thermal power in 2020 being 5260 hours, while the national average operation time in the same year was 4211 hours. The higher operation time leads to higher capacity utilization rate (average operation time/total possible operation time) in Xinjiang. These characteristics also allow Qinghai and Xinjiang to rank among the forefront of production efficiency.
Besides, Fujian's production efficiency surpasses that of other eastern provinces is mainly because of its lower carbon emissions with unit electricity generated.
For provinces with lower production efficiency, a common characteristic is that the average operation time of thermal power is low and always less than 4000 hours. Among these provinces, both Yunnan and Sichuan have abundant renewable energy resources and fully utilize hydropower to guarantee power supply, while Jilin and Liaoning have a large energy input and consume more energy compared to cities like Shanghai under similar power generation conditions.
Table 3
Production Efficiency of Thermal Power Plants in Various Provinces from 2008 to 2020
Region | Province | 2008 | 2010 | 2012 | 2014 | 2016 | 2018 | 2020 | Mean | Rank |
Eastern | Beijing | 1.06 | 0.95 | 1.09 | 1.07 | 1.07 | 1.21 | 1.47 | 1.12 | 3 |
| Tianjin | 0.82 | 0.72 | 0.81 | 0.65 | 0.63 | 0.64 | 0.72 | 0.69 | 22 |
| Hebei | 0.85 | 0.85 | 0.82 | 0.79 | 0.75 | 1.01 | 0.78 | 0.80 | 16 |
| Shandong | 1.01 | 0.71 | 0.66 | 0.69 | 1.03 | 1.04 | 1.07 | 0.89 | 10 |
| Liaoning | 0.83 | 0.65 | 0.68 | 0.56 | 0.52 | 0.48 | 0.62 | 0.59 | 28 |
| Shanghai | 1.02 | 1.08 | 1.01 | 1.00 | 1.00 | 0.67 | 0.80 | 0.94 | 8 |
| Jiangsu | 1.09 | 1.09 | 1.13 | 1.09 | 1.08 | 1.04 | 1.00 | 1.07 | 4 |
| Zhejiang | 1.01 | 1.02 | 1.00 | 1.01 | 0.89 | 0.56 | 0.89 | 0.92 | 9 |
| Fujian | 1.01 | 1.00 | 1.09 | 1.21 | 1.96 | 1.09 | 1.11 | 1.15 | 2 |
| Guangdong | 1.08 | 1.08 | 1.09 | 1.07 | 1.01 | 0.78 | 1.04 | 1.00 | 6 |
| Hainan | 1.08 | 1.04 | 1.01 | 1.04 | 1.02 | 1.06 | 1.04 | 1.04 | 5 |
Central | Shanxi | 0.86 | 0.83 | 0.80 | 0.76 | 0.55 | 0.53 | 0.78 | 0.70 | 21 |
| Jilin | 0.75 | 0.54 | 0.58 | 0.49 | 0.46 | 0.47 | 0.58 | 0.54 | 29 |
| Heilongjiang | 0.82 | 0.75 | 0.78 | 0.72 | 0.64 | 0.66 | 0.76 | 0.70 | 20 |
| Anhui | 0.77 | 0.84 | 1.00 | 1.00 | 0.88 | 0.59 | 1.01 | 0.88 | 12 |
| Jiangxi | 0.69 | 0.68 | 0.75 | 0.77 | 0.79 | 1.02 | 1.02 | 0.82 | 15 |
| Henan | 0.69 | 0.69 | 0.67 | 0.64 | 0.54 | 0.44 | 0.65 | 0.60 | 27 |
| Hubei | 0.70 | 0.71 | 0.77 | 0.70 | 0.63 | 0.66 | 0.77 | 0.72 | 19 |
| Hunan | 0.66 | 0.70 | 0.73 | 0.63 | 0.52 | 0.60 | 0.70 | 0.65 | 24 |
Western | Inner Mongolia | 0.72 | 0.70 | 0.71 | 0.72 | 0.67 | 1.04 | 1.06 | 0.79 | 17 |
| Chongqing | 0.66 | 1.08 | 0.72 | 0.62 | 0.53 | 0.59 | 0.67 | 0.73 | 18 |
| Sichuan | 0.48 | 0.56 | 0.59 | 0.49 | 0.39 | 0.41 | 0.48 | 0.49 | 30 |
| Guangxi | 0.81 | 1.01 | 1.00 | 0.85 | 0.57 | 0.73 | 1.03 | 0.89 | 11 |
| Guizhou | 0.85 | 1.01 | 0.89 | 0.79 | 1.00 | 0.63 | 0.75 | 0.84 | 14 |
| Yunnan | 0.69 | 0.74 | 0.67 | 0.58 | 0.51 | 0.46 | 0.54 | 0.61 | 26 |
| Shaanxi | 1.01 | 0.62 | 0.74 | 0.66 | 0.55 | 0.56 | 0.92 | 0.66 | 23 |
| Gansu | 0.81 | 0.67 | 0.70 | 0.60 | 0.53 | 0.56 | 0.66 | 0.62 | 25 |
| Qinghai | 1.87 | 2.00 | 2.10 | 2.11 | 1.91 | 2.07 | 2.24 | 2.05 | 1 |
| Ningxia | 1.02 | 0.69 | 1.03 | 1.05 | 0.72 | 0.71 | 0.79 | 0.87 | 13 |
| Xinjiang | 0.66 | 0.63 | 0.73 | 1.27 | 1.14 | 1.33 | 1.18 | 0.99 | 7 |
Eastern | | 0.99 | 0.93 | 0.94 | 0.93 | 1.00 | 0.87 | 0.96 | 0.93 | |
Central | | 0.74 | 0.72 | 0.76 | 0.71 | 0.63 | 0.62 | 0.78 | 0.70 | |
Western | | 0.87 | 0.88 | 0.90 | 0.89 | 0.77 | 0.83 | 0.94 | 0.87 | |
China | | 0.88 | 0.85 | 0.88 | 0.85 | 0.82 | 0.79 | 0.90 | 0.85 | |
Note: Due to length constraints, the measurement results of some years are not shown. |
Figure 4 (A) shows the changes in the average production efficiency of various regions from 2008 to 2020. Between 2015 and 2019, the average production efficiency showed significant fluctuations, similar to the results of Meng et al. (2023). In 2019, the average production efficiency of the west even surpassed that of the east, mainly due to a significant decline in the production efficiency of Hebei province in 2019. In that year, while the installed capacity of thermal power in Hebei increased, the thermal power generation actually reduced, leading to a drop in Hebei's production efficiency. This suggests that during the transition and upgrading process of constructing more environmentally friendly thermal power plants to replace high-polluting ones, there would be significant fluctuations in production efficiency.
In addition, as shown in the figure, H1 is confirmed. Due to stricter environmental regulation in eastern provinces, these provinces will focus on researching green technologies rather than production technologies. Production efficiency in China's central and western regions has been progressively advancing, drawing closer to the eastern region, with a significant catch-up effect.
Figure 4 Trends in Production Efficiency and Total Factor Energy Efficiency
The paper further measures the TFEE of each province from the perspective of energy conservation. The calculation results are shown in Table 4, where only the calculation results of some years are shown due to length constraints.
From the table, it can be seen that the TFEE of each province is generally high, and the average TFEE of each region shows "East > Central > West". The top cities are Beijing, Hainan, Fujian, Shandong, Hubei and the bottom cities are Heilongjiang, Chongqing, Yunnan, Jilin, Sichuan.
The reason why the TFEE of the east maintains a high level is that the eastern region covers developed areas and has the strictest environmental regulations. In the energy utilization rate, Qinghai and Xinjiang also no longer have a significant advantage, failing to make the average TFEE of the west surpass that of the central.
Figure 4 (B) shows the changes in the average TFEE of various regions from 2008 to 2020. The TFEE is relatively stable overall, with slight fluctuations between 2015 and 2019. From the figure, it can be seen that the average TFEE of the central region is gradually approaching that of the east, and there is still a great difference between the west and the east. The TFEE in western region has been gradually declining, lagging behind the eastern and central regions. Therefore, H2 is not valid.
Table 4
Total Factor Energy Efficiency of Thermal Power Industry in Each Province from 2008 to 2020
Region | Province | 2008 | 2010 | 2012 | 2014 | 2016 | 2018 | 2020 | Mean | Rank |
Eastern | Beijing | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1 |
| Tianjin | 0.95 | 0.99 | 0.83 | 0.81 | 0.69 | 0.83 | 0.83 | 0.85 | 20 |
| Hebei | 0.89 | 0.92 | 0.89 | 0.95 | 0.92 | 1.00 | 1.00 | 0.93 | 11 |
| Shandong | 1.00 | 0.96 | 0.94 | 1.00 | 0.96 | 1.00 | 1.00 | 0.98 | 4 |
| Liaoning | 0.87 | 0.87 | 0.81 | 0.86 | 0.83 | 0.89 | 1.00 | 0.87 | 18 |
| Shanghai | 1.00 | 1.00 | 1.00 | 1.00 | 0.43 | 0.85 | 0.85 | 0.80 | 23 |
| Jiangsu | 1.00 | 1.00 | 1.00 | 1.00 | 0.75 | 1.00 | 1.00 | 0.97 | 8 |
| Zhejiang | 1.00 | 1.00 | 1.00 | 1.00 | 0.95 | 1.00 | 1.00 | 0.99 | 3 |
| Fujian | 1.00 | 0.99 | 1.00 | 1.00 | 1.00 | 0.67 | 1.00 | 0.91 | 13 |
| Guangdong | 1.00 | 1.00 | 0.98 | 1.00 | 0.73 | 1.00 | 1.00 | 0.97 | 6 |
| Hainan | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1 |
Central | Shanxi | 0.90 | 0.92 | 0.84 | 0.90 | 0.88 | 0.88 | 1.00 | 0.90 | 14 |
| Jilin | 0.80 | 0.82 | 0.68 | 0.64 | 0.50 | 0.62 | 0.65 | 0.67 | 27 |
| Heilongjiang | 0.76 | 0.74 | 0.75 | 0.81 | 0.33 | 0.68 | 0.70 | 0.62 | 30 |
| Anhui | 0.94 | 0.96 | 0.94 | 1.00 | 1.00 | 1.00 | 1.00 | 0.97 | 7 |
| Jiangxi | 0.81 | 0.94 | 0.83 | 1.00 | 0.53 | 0.74 | 1.00 | 0.82 | 22 |
| Henan | 0.91 | 0.95 | 0.92 | 0.95 | 0.93 | 1.00 | 1.00 | 0.95 | 9 |
| Hubei | 0.97 | 1.00 | 1.00 | 1.00 | 0.69 | 1.00 | 1.00 | 0.97 | 5 |
| Hunan | 0.85 | 0.93 | 0.94 | 1.00 | 0.70 | 0.94 | 0.95 | 0.89 | 16 |
Western | Inner Mongolia | 0.78 | 0.80 | 0.74 | 0.77 | 0.74 | 0.78 | 0.86 | 0.77 | 25 |
| Chongqing | 0.67 | 1.00 | 0.66 | 0.57 | 0.39 | 0.56 | 0.55 | 0.65 | 28 |
| Sichuan | 0.78 | 0.89 | 0.86 | 0.80 | 0.63 | 0.47 | 0.64 | 0.71 | 26 |
| Guangxi | 0.97 | 1.00 | 1.00 | 1.00 | 0.61 | 0.98 | 1.00 | 0.87 | 17 |
| Guizhou | 0.87 | 0.92 | 0.83 | 0.93 | 0.65 | 0.85 | 0.92 | 0.86 | 19 |
| Yunnan | 0.72 | 0.82 | 0.69 | 0.56 | 0.78 | 0.54 | 0.32 | 0.65 | 29 |
| Shaanxi | 1.00 | 0.94 | 0.90 | 0.95 | 0.93 | 1.00 | 1.00 | 0.95 | 10 |
| Gansu | 0.90 | 0.95 | 0.87 | 0.92 | 0.60 | 0.77 | 0.79 | 0.80 | 24 |
| Qinghai | 0.80 | 0.77 | 0.77 | 0.80 | 0.87 | 0.89 | 0.87 | 0.83 | 21 |
| Ningxia | 1.00 | 0.98 | 0.92 | 0.83 | 0.53 | 0.96 | 0.94 | 0.91 | 12 |
| Xinjiang | 0.76 | 0.86 | 0.82 | 1.00 | 1.00 | 1.00 | 0.84 | 0.90 | 15 |
Eastern | | 0.97 | 0.98 | 0.95 | 0.96 | 0.84 | 0.93 | 0.97 | 0.93 | |
Central | | 0.87 | 0.91 | 0.86 | 0.91 | 0.70 | 0.86 | 0.91 | 0.85 | |
Western | | 0.84 | 0.90 | 0.82 | 0.83 | 0.70 | 0.80 | 0.79 | 0.81 | |
China | | 0.90 | 0.93 | 0.88 | 0.90 | 0.75 | 0.86 | 0.89 | 0.87 | |
Note: Due to length constraints, the measurement results of some years are not shown. |