基于灰色预测模型的管道公司碳排放峰值预测

Prediction of peak carbon emission of pipeline company based on grey prediction model

对于我国实现碳达峰碳中和的重大战略目标来说,油气管道行业的碳达峰情况至关重要。为了帮助企业研究适用于管道公司的碳减排管控措施,提高碳排放管理水平,完成公司碳达峰目标,采用基于灰色预测—偏最小二乘组合模型对X公司碳排放进行了预测。首先,多种排放因素都会导致碳排放量发生改变,采用偏最小二乘法对影响因素进行回归建模不仅可以将碳排放量造成影响的因素考虑在内,也可以克服共线性对结果的影响;其次,考虑到时间较远的数据对结果的影响不大,对每年的数据赋予权重,以提高结果精度;最后,利用建立的基于灰色预测—偏最小二乘组合模型,以X公司2017—2022年的数据为例,对X公司2023—2027年的碳排放量进行了预测。

In order to deal with the global climate change and achieve the major strategic goal of carbon peak carbon neutralization,the carbon peak situation of the oil and gas pipeline industry plays a vital role.In order to help enterprises to study carbon emission reduction control measures applicable to pipeline companies,we improve the level of carbon emission management,and achieve the company carbon peak target.In this paper,the combination model of grey prediction and partial least square is used to predict the carbon emission of X company.A variety of emission factors will lead to changes in carbon emissions.Partial least square method is used to model the influencing factors,taking into account the factors affecting carbon emissions,while overcoming the impact of collinearity on the results.Then,considering that the long-time data has little impact on the results,weight is given to the annual data to improve the accuracy of the results.Finally,using the combination model based on grey prediction and partial least squares and taking the data of X Company from 2017 to 2022 as an example,the carbon emission from 2023 to 2027 is predicted.