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对机器学习在碳排放评估研究中的应用进行审查:预测优化和驱动因素选择预测优化

Chen Zhao¹, Min Zhang¹, Jiandong Bai¹

¹College of Environmental Science and Engineering, Nankai University, #38, Tongyan Road, Haihe Education Park, Jinnan District, Tianjin 300350, China.

The Science of the total environment

|June 3, 2025

概括

机器学习 (ML) 模型对于预测碳排放至关重要,决策树是最常见的. 未来的研究应侧重于人口因素和数据标准化,以更好地缓解气候变化.

科学领域:

环境科学环境科学
计算机科学计算机科学
气候变化研究气候变化研究

背景情况:

全球越来越关注温室效应,需要有效的碳排放预测模型.
机器学习 (ML) 为分析气候变化研究中复杂数据集提供了强大的工具.
碳核算是理解和减轻环境影响的关键领域.

研究的目的:

系统地审查碳核算中的机器学习应用.
综合研究进展,确定知识差距,并提出未来的研究方向.
通过ML提供一个框架,通过ML推进碳核算.

主要方法:

对126篇同行评审论文的系统文献综述.
对碳核算应用的机器学习方法的分析.
确定碳排放预测中的关键决定因素和趋势.

主要成果:

单一模型预测框架占主导地位,基于决策树的ML模型最为普遍.
能源类型和消费结构是碳排放的关键决定因素.
人口因素 (人口动态,老龄化) 与经济驱动因素一起显得至关重要.

结论:

机器学习在预测准确性和碳核算效率方面具有优势.
挑战包括数据标准化,互操作性和开放访问共享.
未来的工作需要跨学科的合作,以推进碳核算研究和减缓气候变化的努力.

关键词:

人工智能的人工智能是人工智能.预测碳排放的预测驾驶因子选驾驶因子选驾驶因子选机器学习机器学习预测模型的预测模型.

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