Emission inventories (EIs) are the fundamental tool to monitor compliance with greenhouse gas (GHG) emissions and emission reduction commitments. Inventory accounting guidelines provide the best practices to help EI compilers across different countries and regions make comparable, national emission estimates regardless of differences in data availability. However, there are a variety of sources of error and uncertainty that originate beyond what the inventory guidelines can define. Spatially explicit EIs, which are a key product for atmospheric modeling applications, are often developed for research purposes and there are no specific guidelines to achieve spatial emission estimates. The errors and uncertainties associated with the spatial estimates are unique to the approaches employed and are often difficult to assess. This study compares the global, high-resolution (1 km), fossil fuel, carbon dioxide (CO₂), gridded EI Open-source Data Inventory for Anthropogenic CO₂ (ODIAC) with the multi-resolution, spatially explicit bottom-up EI geoinformation technologies, spatio-temporal approaches, and full carbon account for improving the accuracy of GHG inventories (GESAPU) over the domain of Poland. By taking full advantage of the data granularity that bottom-up EI offers, this study characterized the potential biases in spatial disaggregation by emission sector (point and non-point emissions) across different scales (national, subnational/regional, and urban policy-relevant scales) and identified the root causes. While two EIs are in agreement in total and sectoral emissions (2.2% for the total emissions), the emission spatial patterns showed large differences (10~100% relative differences at 1 km) especially at the urban-rural transitioning areas (90–100%). We however found that the agreement of emissions over urban areas is surprisingly good compared with the estimates previously reported for US cities. This paper also discusses the use of spatially explicit EIs for climate mitigation applications beyond the common use in atmospheric modeling. We conclude with a discussion of current and future challenges of EIs in support of successful implementation of GHG emission monitoring and mitigation activity under the Paris Climate Agreement from the United Nations Framework Convention on Climate Change (UNFCCC) 21st Conference of the Parties (COP21). We highlight the importance of capacity building for EI development and coordinated research efforts of EI, atmospheric observations, and modeling to overcome the challenges.

中文翻译：

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网格化二氧化碳排放清单中的错误和不确定性

排放清单（EI）是监测温室气体（GHG）排放和减排承诺的合规性的基本工具。清单会计准则提供了最佳实践，可帮助不同国家和地区的EI编制者进行可比较的国家排放估算，而与数据可用性的差异无关。但是，有许多错误和不确定性来源，这些错误和不确定性源于清单指南无法定义的范围。空间显式EI是大气建模应用程序的关键产品，通常是出于研究目的而开发的，并且没有实现空间排放估算的具体准则。与空间估计有关的误差和不确定性对于所采用的方法是唯一的，并且通常难以评估。₂），人为产生的CO _2的网格化EI开源数据清单（ODIAC）具有多分辨率，空间明确的自下而上的EI地理信息技术，时空方法和全碳，可提高波兰范围内的温室气体清单（GESAPU）的准确性。通过充分利用自下而上的EI提供的数据粒度，本研究描述了不同规模（国家，地方以下/区域和城市相关政策）的排放部门（点排放和非点排放）的空间分解潜在偏差。规模），并找出根本原因。尽管两个EI的总排放量和部门排放量一致（占总排放量的2.2％），但是排放空间模式显示出很大的差异（在1 km处相对差异为10〜100％），尤其是在城乡过渡地区（90-100） ％）。但是，我们发现，与之前针对美国城市报告的估算值相比，城市地区的排放量协议出奇的好。本文还讨论了将空间显式EI用于气候缓解应用的方法，而不是大气建模中的常用方法。最后，我们讨论了为支持成功实施《联合国气候变化框架公约》（UNFCCC）第21次缔约方会议（COP21）在《巴黎气候协定》下实施的温室气体排放监测和减缓活动而开展的EI的当前和未来挑战。我们强调了能力建设对于EI开发的重要性，以及协调EI，大气观测和建模以克服挑战的研究工作的重要性。