Power shortage index for 218 Chinese cities
- Contributors: Dongmei Guo, Qin Li, Peng Liu, Xunpeng Shi, Jian Yu
- Abstract: This paper uses the text analysis method to construct a city-level power shortage index. We selected daily newspapers from 218 prefecture-level city as the data source and used a combination of selected high-frequency words with expert investigation to screen out basic terms related to power shortage. The following 20 keywords were identified: peak scheduling management, power generation, waste heat power generation, off-peak, grid disconnection, power rationing, switching off, orderly power consumption, tripping, peak avoidance, disconnection, plant power consumption, staggered peak avoidance, pull road, accident electricity, overload, transferring power supply, security of electricity, power rationing, and load transfer.
Mr. Jian Yu and his collaborators present a power shortage index to characterize the city-level power outages for 218 Chinese cities from 2001 to 2017. Please cite the following papers when using this data:
- Guo, D., Li, Q., Liu, P., Shi, X., Yu, J., 2023. Power shortage and firm performance: Evidence from a Chinese city power shortage index, Energy Economics, Vol. 119, No.106593.
With the authorization of Professor Yu Jian from the Central University of Finance and Economics, CnOpenData established a display area and data index for this data to facilitate scholars’ browsing.
For data download, please click Power shortage index for 218 Chinese cities .
Data Application Guide
Visual representation of the word frequencies of power shortage keywords
Time interval
2001-2017
Field display
| Economic policy uncertainty (EPU) index |
|---|
| city |
| year |
| power shortage index with 5 keywords |
| power shortage index with 20 keywords |
| planned power shortage index |
| unplanned power shortage index |
Sample data
| province_code | province_name | year | China's provincial EPU index |
|---|---|---|---|
| 11 | Beijing | 2017 | 68.1627655 |
| 12 | Tianjin | 2017 | 139.3325806 |
| 13 | Hebei | 2017 | 79.15668488 |
| 14 | Shanxi | 2017 | 189.1946869 |
| 15 | Inner Mongolia | 2017 | 76.54681396 |
| 21 | Liaoning | 2017 | 91.38005066 |
| 22 | Jilin | 2017 | 89.98912048 |
| 23 | Heilongjiang | 2017 | 7.519604683 |
| 31 | Shanghai | 2017 | 80.68917847 |
| 32 | Jiangsu | 2017 | 47.75826263 |
| 33 | Zhejiang | 2017 | 97.14289093 |
| 34 | Anhui | 2017 | 111.6440353 |
| 35 | Fujian | 2017 | 160.7241058 |
| 36 | Jiangxi | 2017 | 102.082283 |
| 37 | Shandong | 2017 | 78.49134827 |
| 41 | Henan | 2017 | 96.05151367 |
| 42 | Hubei | 2017 | 99.25975037 |
| 43 | Hunan | 2017 | 54.22574615 |
| 44 | Guangdong | 2017 | 56.38425064 |
| 45 | Guangxi | 2017 | 118.8451614 |
| 46 | Hainan | 2017 | 57.19842529 |
| 50 | Chongqing | 2017 | 107.0997543 |
| 51 | Sichuan | 2017 | 118.5892792 |
| 52 | Guizhou | 2017 | 90.86172485 |
| 53 | Yunnan | 2017 | 38.87081146 |
| 54 | Tibet | 2017 | 163.8305817 |
| 61 | Shaanxi | 2017 | 99.39268494 |
| 62 | Gansu | 2017 | 137.8933868 |
| 63 | Qinghai | 2017 | 79.26584625 |
| 64 | Ningxia | 2017 | 102.7975769 |
| 65 | Xinjiang | 2017 | 46.38435364 |
| 11 | Beijing | 2016 | 47.85101318 |
| 12 | Tianjin | 2016 | 143.1006927 |
| 13 | Hebei | 2016 | 115.6779938 |
| 14 | Shanxi | 2016 | 98.37980652 |
| 15 | Inner Mongolia | 2016 | 108.1842651 |
| 21 | Liaoning | 2016 | 80.5438385 |
| 22 | Jilin | 2016 | 104.2610016 |
| 23 | Heilongjiang | 2016 | 81.40262604 |
| 31 | Shanghai | 2016 | 128.4771423 |
| 32 | Jiangsu | 2016 | 43.71399307 |
| 33 | Zhejiang | 2016 | 90.4801178 |
| 34 | Anhui | 2016 | 125.6614151 |
| 35 | Fujian | 2016 | 94.90132904 |
| 36 | Jiangxi | 2016 | 113.8047638 |
| 37 | Shandong | 2016 | 55.01879883 |
| 41 | Henan | 2016 | 86.23591614 |
| 42 | Hubei | 2016 | 139.5249634 |
| 43 | Hunan | 2016 | 58.70393753 |
| 44 | Guangdong | 2016 | 57.20541382 |
References
- Guo, D., Li, Q., Liu, P., Shi, X., Yu, J., 2023. Power shortage and firm performance: Evidence from a Chinese city power shortage index, Energy Economics, Vol. 119, No.106593.
Data update frequency
Updated from time to time