Gaughan Andrea E, Oda Tomohiro, Sorichetta Alessandro, Stevens Forrest R, Bondarenko Maksym, Bun Rostyslav, Krauser Laura, Yetman Greg, Nghiem Son V
University of Louisville, Department of Geography and Geosciences, Louisville, KY, United States of America.
WorldPop, School of Geography and Environmental Science, University of Southampton, United Kingdom.
IOP Conf Ser Mater Sci Eng. 2019;1(9):1-14. doi: 10.1088/2515-7620/ab3d91. Epub 2019 Sep 11.
Tracking spatiotemporal changes in GHG emissions is key to successful implementation of the United Nations Framework Convention on Climate Change (UNFCCC). And while emission inventories often provide a robust tool to track emission trends at the country level, subnational emission estimates are often not reported or reports vary in robustness as the estimates are often dependent on the spatial modeling approach and ancillary data used to disaggregate the emission inventories. Assessing the errors and uncertainties of the subnational emission estimates is fundamentally challenging due to the lack of physical measurements at the subnational level. To begin addressing the current performance of modeled gridded CO emissions, this study compares two common proxies used to disaggregate CO emission estimates. We use a known gridded CO model based on satellite-observed nighttime light (NTL) data (Open Source Data Inventory for Anthropogenic CO, ODIAC) and a gridded population dataset driven by a set of ancillary geospatial data. We examine the association at multiple spatial scales of these two datasets for three countries in Southeast Asia: Vietnam, Cambodia and Laos and characterize the spatiotemporal similarities and differences for 2000, 2005, and 2010. We specifically highlight areas of potential uncertainty in the ODIAC model, which relies on the single use of NTL data for disaggregation of the non-point emissions estimates. Results show, over time, how a NTL-based emissions disaggregation tends to concentrate CO estimates in different ways than population-based estimates at the subnational level. We discuss important considerations in the disconnect between the two modeled datasets and argue that the spatial differences between data products can be useful to identify areas affected by the errors and uncertainties associated with the NTL-based downscaling in a region with uneven urbanization rates.
追踪温室气体排放的时空变化是成功实施《联合国气候变化框架公约》（UNFCCC）的关键。虽然排放清单通常为追踪国家层面的排放趋势提供了一个强大的工具，但次国家层面的排放估计往往未被报告，或者报告的稳健性各不相同，因为这些估计通常依赖于用于分解排放清单的空间建模方法和辅助数据。由于次国家层面缺乏实地测量，评估次国家排放估计的误差和不确定性从根本上来说具有挑战性。为了开始解决模拟网格化一氧化碳排放的当前性能问题，本研究比较了用于分解一氧化碳排放估计的两种常见代理指标。我们使用基于卫星观测夜间灯光（NTL）数据的已知网格化一氧化碳模型（人为一氧化碳开源数据清单，ODIAC）和由一组辅助地理空间数据驱动的网格化人口数据集。我们研究了东南亚三个国家（越南、柬埔寨和老挝）这两个数据集在多个空间尺度上的关联，并描述了2000年、2005年和2010年的时空异同。我们特别强调了ODIAC模型中潜在不确定性的领域，该模型仅使用NTL数据来分解非点源排放估计。结果表明，随着时间的推移，基于NTL的排放分解在次国家层面上往往以与基于人口的估计不同的方式集中一氧化碳估计值。我们讨论了两个模拟数据集之间脱节的重要考虑因素，并认为数据产品之间的空间差异有助于识别在城市化率不均衡的地区受基于NTL的降尺度相关误差和不确定性影响的区域。
