Monitoring the enterprise carbon emissions using electricity big data: A case study of Beijing

Abstract

Enterprises are major sources of anthropogenic carbon emissions, and high-quality data on enterprise carbon emissions are prerequisites for climate abatement policies and actions. However, most of the existing data are more than one-year lagging, easily manipulated, and concentrated at the industrial or regional level. To bridge these gaps, this study develops a monitoring approach for enterprise carbon emissions by combining electricity big data, bottom-up emission accounting model, and network model. The proposed approach has then been applied to monitor the real-time carbon emissions of 0.81 million enterprises in Beijing. Our major findings are that: (1) Owing to a large amount of embodied carbon emissions from electricity inflow, Beijing's electricity-related CO2 emissions are 73.57 million tonnes in 2020, constituting 55% of its total. (2) The CO2 emissions per kWh of electricity consumed in Beijing is 645.26 g, whose top three traceable contributors are Hebei (206.71 g), Shanxi (142.21 g), and Beijing itself (133.07 g). (3) The average monitoring error of enterprise carbon emissions is less than 7%, proving the effectiveness of the proposed approach using electricity big data.