High frequency measurement of carbon emissions based on power big data: A case study of Chinese Qinghai province

In this paper, we build the electricity‑carbon model with techniques of frequency transformation, and statistical modeling. With macro statistical data released by Statistics Bureau of Qinghai Province and high-frequency power big data provided by State Grid Qinghai Electric Power Company, based on the electricity‑carbon model we apply the inventory method to measure the monthly carbon emissions of Qinghai Province and its prefectural-level cities, as well as its industry, construction, and other five industries. Additionally, we apply the same method to measure the emission reduction contribution of green power by using the data of proportion of “green power”. The results show that first there is a “double imbalanced” phenomenon for the distribution of Qinghai's carbon emissions, which means that the distribution of carbon emissions of Qinghai's prefectural-level cities is imbalanced and the distribution of carbon emissions of Qinghai's industries is also imbalanced. Second, the emission reduction effect of “green power” is significant. And the quantity of its reduction accounts for 54 % of Qinghai's total emission. Third, in comparison with the seven institutions' relative error rate which is about 7 % for measuring China's carbon emissions, our results are reliable. [Display omitted] •We create an electricity-carbon model for high-frequency, multi-dimensional measurement of provincial carbon emissions.•The emission reduction effect of green power from the provincial aspect is initially estimated.•Our method has been employed by National Development and Reform Commission of China in 2023.