Research on high-precision accounting methods for regional carbon emissions under the peak carbon target

Author:

Liu Nian,Tang Bao,Dong Zheng,Zhang Rui,Zhao Xiaofeng

Abstract

In recent years, the level of digitalization and intelligence in the energy sector has profoundly changed the management mode of the energy sector. However, in the process of promoting the digital transformation of the energy sector, we also face some difficulties. One of the main manifestations is the lack of energy data sharing, and the difficulty of realizing the convergence and integration of various types of energy data. In addition, there is insufficient mastery of customer-side energy use data and a lag in the perception of customer demand, which requires the improvement and enhancement of energy efficiency diagnosis and energy saving services. At the same time, we have not yet established advanced digital technology monitoring means to grasp the user’s energy use and carbon emissions in real time. Therefore, this paper proposes the study of high-precision accounting methods for regional carbon emissions under the carbon peak goal. Based on the energy consumption and production activities of each region, a greenhouse gas emission inventory is compiled, so that carbon emissions can be accurately calculated. The high-precision accounting method of carbon emissions is analyzed, and by establishing a more complete database and model, and constructing a regional online monitoring system under the peak carbon target, we aim to improve the efficiency of data acquisition and processing, as well as how to combine carbon emission accounting with the peak carbon target, in order to provide support for the formulation of a more effective emission reduction policy. The testing experiments after flow and CO2 concentration measurements show that the accuracy of the experiments on regional carbon emissions is as high as 97.5%.

Publisher

EDP Sciences

Reference15 articles.

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2. Jinbei Fu, Mengnan Li, Weida Xu et al. Study on analyzing CO_(2) emissions from power plants based on multi-source data such as satellite remote sensing[J/OL]. China Environmental Science, 1–12[2023-11-29].

3. Wang Ying, Wang Franklin Eddie. Simulation and Selection of Green Residential Development Paths under the Carbon Peak Goal--Taking Xi'an City as an Example[J]. Productivity Research, 2023,(09):94–99.

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