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空间时间模式的演变和从城市地区的需求侧角度对电气碳排放的定量预测
Ying Tian1,2, Hui Cao3,4, Dapeng Yan1,2
1The State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University, Beilin District, Xi'an, 710049, Shaanxi, People's Republic of China.
Scientific reports
|July 11, 2025
概括
分析中国广东的城市电气碳排放,揭示了关键的时空模式. 一个新的框架准确地预测了排放,帮助低碳转型战略.
科学领域:
- 环境科学 环境科学
- 气候变化研究 气候变化研究
- 城市规划 城市规划
背景情况:
- 全球气候变化需要了解城市碳排放,以实现有效的低碳转型.
- 发达的沿海地区,如中国广东,在管理电气碳足迹方面面临着独特的挑战.
- 准确的时空分析和排放预测对于区域可持续性至关重要.
研究的目的:
- 开发一个综合框架来分析城市需求侧电气碳排放的时空模式.
- 提高发达沿海地区碳排放预测的准确性.
- 确定碳排放的驱动因素和空间差异化.
主要方法:
- 利用来自3000个配电网络站的高频数据 (2018年5月至9月).
- 集成的每月,每日和每小时的时间序列与标准偏差圆和Kriging空间插值.
- 采用了逻辑平均分数指数 (LMDI) 分析和具有特征工程的双向门式反复单位 (Bi-GRU) 模型.
主要成果:
- 确定了碳排放引力中心向西南-东北的显著迁移轨迹.
- 观察到的空间差异化,其特点是中心城市聚集和外围分散.
- 金融和税收成为主要的积极驱动因素,工业产出和商业消费显示规模依赖的影响.
- 使用Bi-GRU模型预测碳排放波动的准确率达到了82.83%.
结论:
- 综合时空分析框架有效地揭示了排放动态和空间特征.
- 数据驱动的预测模型提供了碳排放波动的准确预测.
- 这些发现为制定城市地区有针对性的减排政策提供了方法论支持.
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