在2013-2019年期间,在中国的多个规模上通过增强的人口光指数对CO2排放量进行分类,并通过增强的人口光指数对其趋势进行表征
Bin Guo1, Tingting Xie1, Wencai Zhang2
1College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China.
The Science of the total environment
|September 24, 2023
概括
这项研究开发了使用夜间灯光和人口密度的增强人口光指数 (RPNTL),以提高碳排放 (CE) 估计准确度. 该RPNTL方法显著提高了CE预测,特别是在农村地区,有助于碳控制战略.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 地理空间分析是什么
背景情况:
- 关于碳排放 (CE) 的全球关注需要准确的数据来缓解气候变化.
- 现有的CE数据集存在粗略的空间分辨率和低精度,阻碍了有效的碳管理.
- 准确的CE数据对于实现碳峰值和碳中和目标至关重要.
研究的目的:
- 为改进碳排放估计制定一个增强的人口-光指数 (RPNTL).
- 区分和分析城市,工业和农村部门的碳排放.
- 研究碳排放的时空动态,并为控制策略提供信息.
主要方法:
- 整合夜间照明数字号码 (DN) 值 (VIIRS/NPP) 和人口密度,以创建RPNTL.
- 通常最小平方 (OLS),地理加权回归 (GWR),时间加权回归 (TWR) 和地理和时间加权回归 (GTWR) 模型的应用.
- 使用热点分析,趋势分析和标准偏差圆的CE数据和时空分析的拉斯特化.
主要成果:
- 地理和时间加权回归 (GTWR) 模型在CE估计中表现出卓越的表现.
- 增强的RPNTL实现了较高的确定系数 (R2 = 0.95) 与夜间光 (NTL) 单独 (R2 = 0.92) 相比.
- 在农村的CE预测中观察到显著的改善 (R2从0.76增加到0.81).
结论:
- RPNTL,特别是与GTWR分析时,为估计碳排放提供了更准确的方法.
- 时空分析揭示了中国 (2013-2019年) 不同地区和部门的不同CE趋势和重心转移.
- 这些发现为有针对性的碳排放控制政策和决策提供了有价值的数据驱动的见解.
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