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Real-Time Carbon Emissions Monitoring of High-Energy-Consumption Enterprises in Guangxi Based on Electricity Big Data

Author

Listed:
  • Chunli Zhou

    (Guangxi Power Grid Co., Ltd., Nanning 530023, China)

  • Xiqiao Lin

    (Guangxi Power Grid Co., Ltd., Nanning 530023, China)

  • Renhao Wang

    (School of Applied Economics, Renmin University of China, Beijing 100872, China)

  • Bowei Song

    (School of Applied Economics, Renmin University of China, Beijing 100872, China)

Abstract

Real-time carbon emissions monitoring at the enterprise level is a crucial tool in shifting macrolevel carbon peak and carbon neutrality plans toward micro-level implementations. This study extends the existing CO 2 emissions accounting framework to enterprise emissions monitoring. We analyze the correlation mechanism between electricity consumption and CO 2 emissions by industries, calculate the electricity–CO 2 coefficients, and finally model an enterprise-level real-time carbon emissions monitoring method based on electricity big data. Taking Guangxi region as a sample, the results show that (1) the proportion of electricity-related emissions is on the rising stage in Guangxi, with 441 g CO 2 /KWh emitted from electricity consumption in 2020, (2) the carbon emissions from the energy-intensive industries account for over 70% of the whole society, and they all have high electricity–CO 2 coefficients, far exceeding the industry average of 1129 g/kWh, and (3) the monitoring method is applied to 1338 enterprises from over 40 industries. The emission characteristics reflect the regional and industrial heterogeneity. This enterprise-level monitoring method aims to optimize the carbon emissions calculation method toward higher temporal and spatial resolutions, so as to provide an important numerical basis for promoting carbon emission reduction and sustainable development.

Suggested Citation

  • Chunli Zhou & Xiqiao Lin & Renhao Wang & Bowei Song, 2023. "Real-Time Carbon Emissions Monitoring of High-Energy-Consumption Enterprises in Guangxi Based on Electricity Big Data," Energies, MDPI, vol. 16(13), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5124-:d:1185699
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    References listed on IDEAS

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