Constructing enterprise pollution index and monitoring analysis using electricity big data
Electricity big data, as a new data source, can provide real-time and continuous data on enterprise energy consumption and production activities. However, existing methods have not taken into account the impact of electricity big data on the pollution index of enterprises, resulting in incomplete da...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of XPU
2024-12-01
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| Series: | Xi'an Gongcheng Daxue xuebao |
| Subjects: | |
| Online Access: | http://journal.xpu.edu.cn/en/#/digest?ArticleID=1521 |
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| Summary: | Electricity big data, as a new data source, can provide real-time and continuous data on enterprise energy consumption and production activities. However, existing methods have not taken into account the impact of electricity big data on the pollution index of enterprises, resulting in incomplete data used in research that cannot fully reflect the pollution behavior of enterprises. To this end, a new method for analyzing enterprise pollution index was constructed using power big data in the article. Based on the electricity source and pollutant emission structure of the enterprise, a pollution evaluation index system for the enterprise was constructed, and the importance scale and weight coefficient of each index were determined. The comprehensive index score was calculated using the entropy weight method, which reflected the risk or impact level of the enterprise in terms of pollutant emissions, environmental impact, and resource utilization. The results show that there are certain differences in the pollution index among different enterprises, with the highest pollution index of 0.14 in the chemical industry, and lower pollution indices of 0.04 in the beverage manufacturing and rubber manufacturing industries. The proposed method can effectively display the differences in pollution indices among different enterprises and has reference value. |
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| ISSN: | 1674-649X |