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实现中国的二氧化碳减排目标:来自混合PPA-PPR预测模型的见解

Xiaohong Yu¹, Haiyan Xu², Jun Yin³

¹College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; College of Business and Economics, Shanghai Business School, Shanghai, 200235, China.

Journal of environmental management

|November 23, 2024

概括

中国中国中国中国.

科学领域:

环境科学环境科学
气候变化建模模型
数据科学数据科学数据科学

背景情况:

中国是世界上最大的二氧化碳 (CO2) 排放国.
国家计划的目标是达到二氧化碳排放峰值和碳中和.
对二氧化碳排放的准确预测对于政策评估至关重要.

研究的目的:

提高二氧化碳排放预测模型的准确性和可靠性.
评估中国在当前趋势下实现二氧化碳减排目标的可行性.
为了比较一种新的混合模型与既有机器学习技术.

主要方法:

开发了一种混合投影追逐回归 (PPR) 和寄生虫掠食优化算法 (PPA) 模型 (PPA-PPR).
利用了从1965年到2017年的时间序列二氧化碳排放数据.
该PPA-PPR模型的性能与BPNN,RF,SVM,GM,TDGM和LSTM模型进行了基准测试.

主要成果:

与其他模型相比,PPA-PPR模型表现出卓越的稳定性和准确性.
在验证数据上达到1.19%的平均绝对百分比误差 (MAPE),在预测数据 (2018-2021) 上达到1.44%.
预测表明,如果目前的发展趋势持续下去,中国将无法实现2030年二氧化碳排放峰值和减排目标.

结论:

PPA-PPR混合模型在二氧化碳排放预测准确性方面取得了显著的改进.
在没有政策调整的情况下,中国面临着实现2030年二氧化碳减排目标的挑战.
未来的研究可以探索时间序列分解,以进一步完善模型性能.

关键词:

二氧化碳 (CO2) 的排放量.环境建模环境建模来自中国的证据.预测预测预测预测机器学习 (ML) 是指机器学习.预测追踪回归 (PPR) 的方法

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