Scholarlink

<p>Differential information is a core principle in grey system theory. However, traditional grey multivariate models often overlook the variations between feature sequences and related factor sequences. This oversight leads to a loss of critical information and weakens the model’s predictive accuracy. Firstly, to fully utilize differential information, this paper proposes the multivariate heterogeneous cumulative grey prediction model. Secondly, with the introduction of the implementation program for the synergistic reduction of pollution and carbon emissions, North China has stepped up its efforts to combat atmospheric pollution and reduce carbon emissions. There is spatial correlation of air quality in the region. Therefore, the paper proposes the spatiotemporal multivariate heterogeneous cumulative grey model. Finally, the spatiotemporal multivariate heterogeneous cumulative grey model is applied to air quality prediction in North China. This model enables analysis of both future air quality trends in each province and the overall regional air quality under different carbon emission rates. These insights offer robust theoretical and empirical support for developing future synergy policies on pollution and carbon reduction.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Heterogeneous spatiotemporal forecasting the impact of carbon policies on air quality

  • 期刊论文

  • 发表日期：

    2025年9月29日

查看全文
  • Li Yongtong,
  • Wang Yonghua,
  • Sun Tianwei,
  • Yan Chen

摘要

Differential information is a core principle in grey system theory. However, traditional grey multivariate models often overlook the variations between feature sequences and related factor sequences. This oversight leads to a loss of critical information and weakens the model’s predictive accuracy. Firstly, to fully utilize differential information, this paper proposes the multivariate heterogeneous cumulative grey prediction model. Secondly, with the introduction of the implementation program for the synergistic reduction of pollution and carbon emissions, North China has stepped up its efforts to combat atmospheric pollution and reduce carbon emissions. There is spatial correlation of air quality in the region. Therefore, the paper proposes the spatiotemporal multivariate heterogeneous cumulative grey model. Finally, the spatiotemporal multivariate heterogeneous cumulative grey model is applied to air quality prediction in North China. This model enables analysis of both future air quality trends in each province and the overall regional air quality under different carbon emission rates. These insights offer robust theoretical and empirical support for developing future synergy policies on pollution and carbon reduction.