工业碳排放效率预测和碳排放减少策略基于多目标粒子群集优化-反向传播:从区域集群的角度来看
Hongtao Jiang1, Jian Yin1, Danqi Wei2
1Center for China Western Modernization, Guizhou University of Finance and Economics, Guiyang, Guizhou 550025, China; College of Big Data Application and Economic, Guizhou University of Finance and Economics, Guiyang, Guizhou 550025, China; Key Laboratory of Green Fintech, Guiyang 550025, China.
The Science of the total environment
|October 12, 2023
概括
这项研究预测了中国城市未来的工业碳排放效率 (ICEE),使用了新的机器学习框架. 大多数城市都显示出改善的潜力,突出地区低碳发展战略.
科学领域:
- 环境科学 环境科学
- 经济学 经济学 经济学
- 数据科学数据科学数据科学
背景情况:
- 全球气候变化需要减少工业碳排放以实现可持续发展.
- 工业碳排放效率 (ICEE) 是评估环境绩效和低碳转型的一个关键指标.
- 了解IEEE的区域差异对于有针对性的减排战略至关重要.
研究的目的:
- 开发一个框架来预测未来的IEEE使用地理空间限制和多目标机器学习.
- 根据ICEE水平对中国城市进行区域化,并分析未来的趋势.
- 确定驱动力,并提出适合不同地区的碳减排战略.
主要方法:
- 计算了285个中国城市的ICEE,使用超高效率的基于宽松的衡量标准.
- 通过空间约束的多变量聚类,将城市分为四个IEEE地区.
- 在ICEE预测中使用多目标粒子群优化-BP (MOPSO-BP) 模型.
- 使用地理探测器和多尺度地理加权回归来分析驱动力.
主要成果:
- 大多数城市具有低或中等ICEE;超高效的城市集中在东部沿海地区.
- 与传统的BP和PSO-BP模型相比,MOPSO-BP模型表现出优异的预测性能.
- 在接下来的十年里,在大多数地区 (不包括农业生产区) 存在显著的ICEE改进潜力.
- 宏观经济和微观经济因素促进IEEE,而城市建设的影响不同;信息技术显示空间异质性.
结论:
- 开发的框架为管理和规划IEEE提供了可靠的方法.
- 区域化战略对于有效的工业碳排放减少至关重要.
- 结果为未来的区域低碳发展道路提供了建设性的建议.
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