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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

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