基于电力大数据的碳排放测算技术初探OA

Energy activities are the primary sources of carbon dioxide emissions.Accurate and near real-time monitoring of carbon emissions from energy activities is fundamental for governments and enterprises to grasp the total carbon emissions and formulate carbon management strategies.However,existing accounting methods are insufficient for high-frequency carbon emission calculations,whereas direct measurement methods necessitate additional equipment to be installed.Consequently,low-cost and high-frequency carbon emission estimation techniques remain unavailable.To address this issue,by using the correlation between electricity consumption and energy activities,this paper proposes an estimation technology for carbon emissions from energy activities based on power big data(abbreviated as"electricity-based carbon emission estimation").Firstly,the methodological framework for"electricity-based carbon emission estimation"technology is proposed.From a data-centric perspective,a detailed investigation and analysis on related foundational data are conducted,and the potential application scope of existing data is explored.Secondly,a time-series based"electricity-based carbon emission estimation"model applicable at regional,industrial,and enterprise levels is constructed,followed by an evaluation of its effectiveness using actual data from China.The analysis results demonstrate that the proposed method can significantly improve the timeliness of carbon emission estimation.Finally,the future research and potential application directions for the"electricity-based carbon emission estimation"technology are prospected.