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 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.

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2024-12-01
DOI
https://doi.org/10.13338/j.issn.1674-649x.2024.06.011
Primary Topic
Wheat and Barley Genetics and Pathology
Type
article
Field-Weighted Citation Impact
0.00
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article

Constructing enterprise pollution index and monitoring analysis using electricity big data

SHILAP Revista de lepidopterología
Wheat and Barley Genetics and Pathology
article

Constructing enterprise pollution index and monitoring analysis using electricity big data

article en

Abstract

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.

SHILAP Revista de lepidopterología
Openalex Percentile: Top 36%
Wheat and Barley Genetics and Pathology
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