Carbon dioxide (CO) emissions, the primary greenhouse effect catalyst, command global attention due to associated environmental challenges. Urgent carbon reduction is imperative, particularly with scholarly discourse emphasizing the criticality of peak emissions and carbon neutrality. Accurate CO emission prediction holds immense importance for shaping effective management policies aimed at emission reduction and environmental mitigation. This study introduces an enhanced multivariable grey prediction model (AGMC(1,N)), utilizing the particle swarm optimization (PSO) algorithm based on artificial intelligence to determine its optimal order. Rigorous analysis, including a disturbance bound classification discussion, validates the superior stability and outstanding predictive capability of the AGMC(1,N) model, as exemplified in a detailed case study. Applying the AGMC(1,N) model to forecast CO emissions in the Beijing-Tianjin-Hebei region and Shanxi Province reveals a correlation between energy, primary and secondary industry growth, GDP per capita, and increased emissions, while rising urbanization and natural gas consumption correlate with emission decline. The study concludes with actionable proposals derived from predictive insights, providing valuable support for decision-making by management departments focused on emission reduction.

中文翻译：

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利用相邻累积多变量灰色模型预测二氧化碳排放量


二氧化碳（CO）排放是温室效应的主要催化剂，由于相关的环境挑战而引起全球关注。紧迫的碳减排势在必行，特别是在学术讨论强调峰值排放和碳中和的重要性的情况下。准确的二氧化碳排放预测对于制定旨在减少排放和缓解环境影响的有效管理政策至关重要。本研究引入了一种增强型多变量灰色预测模型（AGMC（1，N）），利用基于人工智能的粒子群优化（PSO）算法来确定其最优阶数。严格的分析（包括扰动界限分类讨论）验证了 AGMC(1,N) 模型的卓越稳定性和出色的预测能力，如详细的案例研究所示。应用 AGMC(1,N) 模型预测京津冀地区和山西省的二氧化碳排放量，揭示了能源、第一和第二产业增长、人均 GDP 和排放增加之间的相关性，同时城市化和天然气的增长消费量与排放量下降相关。该研究最后提出了基于预测见解的可行建议，为管理部门的减排决策提供了宝贵的支持。