提高碳排放效率（CEE）对于减少CO 2排放至关重要。大多数关于CO 2排放的研究都是在国家和行业尺度上进行的，城市尺度的研究仍然需要包括在内。为了收集更加一致的市县尺度CO 2排放数据，训练麻雀搜索神经网络来拟合能源消耗CO 2本研究中夜间灯光的排放。此外，运用SBM-DEA模型和​​空间计量技术，从全要素投入角度考察了长三角地区27个城市2000—2020年的CEE值。调查结果表明，中东欧的总体趋势不稳定且不平衡。长三角地区的CEE值从2000年的0.720下降到2020年的0.628，这意味着长三角地区资本和劳动力投入过剩，经济产出不足。碳效率低值区主要集中在长三角西部地区，即安徽省地区。上海、无锡和苏州的碳效率值较高，分别为1.21、2.08和1.00，是碳效率的典范城市。而其余城市都有不同程度的效率损失。以池州—嘉兴为中线，长三角中东欧格局呈现“中间低、两端高”的状态，中东欧重心总体南移。此外，中东欧冷区集中在皖南地区，形成以池州、安庆、宣城为集群、向外扩散的低效率区。总体而言，本文显着缩小了碳核算研究的空间尺度，研究结果可应用于定制碳减排政策的制定。中东欧冷区集中在皖南地区，形成以池州、安庆、宣城为集群、向外扩散的低效率区。总体而言，本文显着缩小了碳核算研究的空间尺度，研究结果可应用于定制碳减排政策的制定。中东欧冷区集中在皖南地区，形成以池州、安庆、宣城为集群、向外扩散的低效率区。总体而言，本文显着缩小了碳核算研究的空间尺度，研究结果可应用于定制碳减排政策的制定。

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Improving carbon emission efficiency(CEE) is crucial to reducing CO2 emissions. Most studies on CO2 emission are conducted at national and industrial scales, and city-scale studies still need to be included. In order to collect more consistent city- and county-scale CO2 emission data, the sparrow search neural network is trained to fit the energy consumption CO2 emissions with nighttime light in this study. Additionally, using the SBM-DEA model and spatial econometric techniques, the CEE values of 27 cities in the Yangtze River Delta region (YRDR) from 2000 to 2020 were examined from the perspective of total factor inputs. The findings demonstrate that CEE's general trend is erratic and uneven. The CEE value of the YRDR decreases from 0.720 in 2000 to 0.628 by 2020, which means that the YRDR has redundant capital and labour inputs and insufficient economic output. The low value carbon efficiency areas are mainly concentrated in the western part of the YRDR, i.e. the Anhui Province region. Shanghai, Wuxi and Suzhou have high carbon efficiency values of 1.21, 2.08 and 1.00 respectively, and are exemplary cities in terms of carbon efficiency, while the rest of the cities have varying degrees of efficiency loss. Taking Chizhou-Jiaxing as the middle line, the CEE pattern in the YRDR presents a state of “low in the middle and high at each end,” and center of gravity for CEE generally shifts southward. Additionally, the cold-spot areas of CEE are concentrated in the southern part of Anhui Province, and develop a low-efficiency zone with Chizhou, Anqing, and Xuancheng as clusters and spreading outwards. Overall, this paper significantly narrows the spatial scale of carbon accounting studies and the findings can be applied to the formulation of customized carbon reduction policies.