基于PSO-ELM综合预测模型的碳排放预测和脱分析:来自中国重庆市的证据
Bo Liu1, Haodong Chang2, Yan Li3
1College of Management Science, Chengdu University of Technology, Chengdu, 610059, China.
概括
重庆重庆是一座城市.
科学领域:
- 环境科学 环境科学
- 气候变化研究 气候变化研究
- 能源经济学 能源经济学
背景情况:
- 中国的第十四个五年计划强调了碳峰值和碳中和目标.
- 准确的碳排放预测对于实现这些"双碳"目标至关重要.
- 传统模型面临着数据更新缓慢和预测准确度低的挑战.
研究的目的:
- 分析影响重庆碳排放的关键因素.
- 预测重庆在第十四个五年计划期间的碳排放趋势.
- 评估碳排放的新型综合预测模型.
主要方法:
- 灰色相关法用于确定关键排放因素 (煤炭,石油,天然气消耗).
- 单一预测模型 (GM(1,1),山坡回归,BP,WOA-BP) 用于初始安装和预测.
- 粒子集群优化-极端学习机器 (PSO-ELM) 作为一个综合预测模型.
- 基于重庆政策文件进行预测的场景指标.
主要成果:
- 重庆的碳排放量呈现上升趋势,但增长速度正在放缓.
- 从1998年到2025年,碳排放与GDP之间的脱状态较弱.
- 与单个模型相比,PSO-ELM模型显示出更高的预测准确性和稳定性.
结论:
- 公共服务局-ELM模型为碳排放预测提供了一种改进的方法.
- 研究为重庆的低碳发展战略提供了宝贵的见解.
- 调查结果支持制定政策,以实现中国的"双碳"目标.
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