在2013-2021年期间,中国的县级每月CO2排放量
Ming Gao1, Chaofan Tu2, Miaomiao Liu1
1School of Public Administration, Southwestern University of Finance and Economics, Chengdu, China.
Scientific data
|July 14, 2025
概括
这项研究使用新型混合模型改进了中国的二氧化碳 (CO2) 排放估计. 它利用多源数据进行更准确的县级二氧化碳预测,支持"双碳"战略.
科学领域:
- 环境科学 环境科学
- 地球科学 地球科学 地球科学
- 数据科学数据科学数据科学
背景情况:
- 在中国,县级二氧化碳排放估计经常使用自上而下的方法.
- 之前的研究依赖于单一的夜间光亮度,假设与二氧化碳排放有统一的相关性,这往往是不准确的.
- 忽略了CO2排放量和不同地区的光强度之间的异质关系.
研究的目的:
- 开发一种更准确的方法来估计中国的县级二氧化碳排放.
- 构建用于二氧化碳排放预测的仪器变量精细数据集.
- 支持实现中国县级"双碳"战略.
主要方法:
- 使用改进的夜间照明数据,人类定居点数据和社会经济指标构建了一个数据集.
- 开发了一个混合回归算法,结合了深度神经网络和CatBoost.
- 产生的仪器变量与二氧化碳排放的线性关系比总夜间亮度更强.
主要成果:
- 从2013年到2021年,估计中国在县级的每月二氧化碳排放量.
- 与传统方法相比,新的仪器变量与二氧化碳排放的相关性更强.
- 混合模型提供了更准确,更细化的二氧化碳排放数据.
结论:
- 开发的方法为县级二氧化碳排放估计提供了更强大的方法.
- 这种方法可以推广到全球其他地区,以进行类似的环境监测.
- 这些发现为评估中国实现"双碳"目标的进展提供了坚实的基础.
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