Research on the Impact of Carbon Trading Market on Electricity Emission Reduction Based on GM-BP Model

Y. Hu, Yuanjie Xu, Tiantian Ye
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引用次数: 1

Abstract

In order to achieve energy conservation and emission reduction goals, China has included "carbon peak" and "carbon neutrality" in its national strategy. Electricity is the industry with the largest carbon emissions in China, and active efforts to reduce electricity emissions have had a significant positive impact on the achievement of the "dual carbon" goal. Carbon emissions trading plays an important role in promoting the large-scale optimization of energy allocation in the power industry across the country. At present, reducing carbon emissions from electricity is still focused on technological upgrading and the promotion of new energy. This article conducts an in-depth study on the counter-control of indicator analysis and forecasting methods starting from the carbon trading market. Use the grey relational model to explore the correlation between the carbon trading market and electricity carbon emission reduction. Combined with the results of the electricity carbon emission prediction model based on the BP (back propagation) neural network, it provides a reference basis and reasonable suggestions for the rapid realization of the "dual carbon" goal.
基于GM-BP模型的碳交易市场对电力减排的影响研究
为实现节能减排目标,中国将“碳峰值”和“碳中和”纳入国家战略。电力是中国碳排放量最大的行业,积极减少电力排放对实现“双碳”目标产生了显著的积极影响。碳排放权交易在推动全国电力行业大规模优化能源配置方面发挥着重要作用。目前,减少电力碳排放的重点仍然是技术升级和新能源的推广。本文从碳交易市场入手,对指标分析与预测方法的逆控制进行了深入研究。运用灰色关联模型探讨碳交易市场与电力碳减排之间的关系。结合基于BP(反向传播)神经网络的电力碳排放预测模型结果,为快速实现“双碳”目标提供参考依据和合理建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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