Scholarlink

<p>Atmospheric CO<sub>2</sub>-based top-down approaches enable objective evaluation of climate mitigation efforts but face dual constraints: sparse monitoring limits spatial resolution, while emission heterogeneity hampers downscaling. To enhance downscaling accuracy, we present a hybrid training method integrating multi-resolution inverse fluxes—national-scale coarse grids with fine-scale grids (Shanxi/Jiangsu). Experiments show hybrid training outperforms conventional approaches, increasing <i>R</i>² from 0.56 to 0.61 with 2.7%-area fine grids and reducing prediction biases compared to data fusion without high-resolution inputs while vastly exceeding nearest-neighbor interpolation (<i>R</i>² = 0.39). By combining gap-filled CO/NO<sub>2</sub> columns, nighttime lights, population density, vegetation indices, and meteorological data, we downscaled national 45 km eight-day CO<sub>2</sub> fluxes to daily 10 km resolution. The derived dataset reveals emission inequities: top 20% cities contribute more than 50% of national emissions, exposing regional capacity disparities. This framework leverages expanding CO<sub>2</sub> monitoring networks to progressively refine spatiotemporal resolution, enabling city-level verification of mitigation actions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Downscaling top-down CO2 emissions and sinks in China empowered by hybrid training

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月23日

查看全文
  • Junting Zhong,
  • Deying Wang,
  • Lifeng Guo,
  • Changhong Miao,
  • Da Zhang,
  • Fei Yu,
  • Weihua Pan,
  • Fugang Li,
  • Bo Peng,
  • Lichun Li,
  • Lei Ren,
  • Lingyun Zhu,
  • Yan Chen,
  • Chongyuan Wu,
  • Jiaying Li,
  • Xiliang Zhang,
  • Xiaoye Zhang

摘要

Atmospheric CO2-based top-down approaches enable objective evaluation of climate mitigation efforts but face dual constraints: sparse monitoring limits spatial resolution, while emission heterogeneity hampers downscaling. To enhance downscaling accuracy, we present a hybrid training method integrating multi-resolution inverse fluxes—national-scale coarse grids with fine-scale grids (Shanxi/Jiangsu). Experiments show hybrid training outperforms conventional approaches, increasing R² from 0.56 to 0.61 with 2.7%-area fine grids and reducing prediction biases compared to data fusion without high-resolution inputs while vastly exceeding nearest-neighbor interpolation (R² = 0.39). By combining gap-filled CO/NO2 columns, nighttime lights, population density, vegetation indices, and meteorological data, we downscaled national 45 km eight-day CO2 fluxes to daily 10 km resolution. The derived dataset reveals emission inequities: top 20% cities contribute more than 50% of national emissions, exposing regional capacity disparities. This framework leverages expanding CO2 monitoring networks to progressively refine spatiotemporal resolution, enabling city-level verification of mitigation actions.