463 citations as recorded by crossref.

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32. A global carbon assimilation system using a modified ensemble Kalman filter S. Zhang et al. 10.5194/gmd-8-805-2015
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38. A comparison of estimates of global carbon dioxide emissions from fossil carbon sources R. Andrew 10.5194/essd-12-1437-2020
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44. Consistent regional fluxes of CH4 and CO2 inferred from GOSAT proxy XCH4 : XCO2 retrievals, 2010–2014 L. Feng et al. 10.5194/acp-17-4781-2017
45. Removing traffic emissions from CO2 time series measured at a tall tower using mobile measurements and transport modeling A. Schmidt et al. 10.1016/j.atmosenv.2014.08.006
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49. A Novel Approach for Predicting Anthropogenic CO2 Emissions Using Machine Learning Based on Clustering of the CO2 Concentration Z. Ji et al. 10.3390/atmos15030323
50. Theoretical assessment of the ability of the MicroCarb satellite city-scan observing mode to estimate urban CO2 emissions K. Wu et al. 10.5194/amt-16-581-2023
51. Integrating remote sensing with OpenStreetMap data for comprehensive scene understanding through multi-modal self-supervised learning L. Bai et al. 10.1016/j.rse.2024.114573
52. The Impact of Foreign Direct Investment on Green Technology Innovation: Evidence from the Threshold Effect of Absorptive Capacity L. Ge et al. 10.1080/10630732.2024.2385116
53. Measuring the synergy of air pollution and CO2 emission in Chinese urban agglomerations: an evaluation from the aggregate impact and correlation perspectives Y. Guan et al. 10.1007/s00477-024-02705-3
54. Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation Y. Fu et al. 10.3390/rs13193880
55. Carbon saving potential of urban parks due to heat mitigation in Yangtze River Economic Belt M. Chen et al. 10.1016/j.jclepro.2022.135713
56. Experimental Assessments of Anthropogenic Emissions of Nitrogen Oxides from the Territory of St. Petersburg Based on Data from Long-Term Mobile Measurements D. Ionov et al. 10.1134/S0001433824700154
57. Constraining emission estimates of carbon monoxide using a perturbed emissions ensemble with observations: a focus on Beijing L. Yuan et al. 10.1007/s11869-021-01041-7
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60. Simultaneous shipborne measurements of CO2, CH4 and CO and their application to improving greenhouse-gas flux estimates in Australia B. Bukosa et al. 10.5194/acp-19-7055-2019
61. Large Chinese land carbon sink estimated from atmospheric carbon dioxide data J. Wang et al. 10.1038/s41586-020-2849-9
62. Paths to low-carbon development in China: The role of government environmental target constraints T. Bai et al. 10.24136/oc.2023.034
63. Are ICT and CO2 emissions always a win-win situation? Evidence from universal telecommunication service in China X. Zhang et al. 10.1016/j.jclepro.2023.139262
64. Spatial-Temporal Patterns and Driving Factors of Logistics Carbon Emissions: Case Study of Yangtze River Delta in China E. Zhu et al. 10.1177/03611981241242068
65. Detecting regional patterns of changing CO 2 flux in Alaska N. Parazoo et al. 10.1073/pnas.1601085113
66. CTDAS-Lagrange v1.0: a high-resolution data assimilation system for regional carbon dioxide observations W. He et al. 10.5194/gmd-11-3515-2018
67. Geostationary Emission Explorer for Europe (G3E): mission concept and initial performance assessment A. Butz et al. 10.5194/amt-8-4719-2015
68. Can we evaluate a fine-grained emission model using high-resolution atmospheric transport modelling and regional fossil fuel CO₂ observations? F. Vogel et al. 10.3402/tellusb.v65i0.18681
69. Variation patterns and driving factors of regional atmospheric CO2 anomalies in China Y. Fu et al. 10.1007/s11356-021-17139-5
70. Spatiotemporal variations of urban CO2 emissions in China: A multiscale perspective K. Shi et al. 10.1016/j.apenergy.2017.11.042
71. Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors Z. Lv et al. 10.3390/atmos11030231
72. Identifying industrial heat sources using time-series of the VIIRS Nightfire product with an object-oriented approach Y. Liu et al. 10.1016/j.rse.2017.10.019
73. GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY O. Danylo et al. 10.23939/istcgcap2015.01.131
74. Impacts of different biomass burning emission inventories: Simulations of atmospheric CO2 concentrations based on GEOS-Chem M. Su et al. 10.1016/j.scitotenv.2023.162825
75. Future Scenarios of Urban Nighttime Lights: A Method for Global Cities and Its Application to Urban Expansion and Carbon Emission Estimation M. Kii et al. 10.3390/rs16061018
76. Evaluating the Mutual Relationship between IPAT/Kaya Identity Index and ODIAC-Based GOSAT Fossil-Fuel CO2 Flux: Potential and Constraints in Utilizing Decomposed Variables Y. Hwang et al. 10.3390/ijerph17165976
77. Inter-annual variability of summertime CO 2 exchange in Northern Eurasia inferred from GOSAT XCO 2 M. Ishizawa et al. 10.1088/1748-9326/11/10/105001
78. All urban areas’ energy use data across 640 districts in India for the year 2011 K. Tong et al. 10.1038/s41597-021-00853-7
79. Wintertime CO2, CH4, and CO Emissions Estimation for the Washington, DC–Baltimore Metropolitan Area Using an Inverse Modeling Technique I. Lopez-Coto et al. 10.1021/acs.est.9b06619
80. Improving the joint estimation of CO2 and surface carbon fluxes using a constrained ensemble Kalman filter in COLA (v1.0) Z. Liu et al. 10.5194/gmd-15-5511-2022
81. Unintended consequences: China's pairing assistance policy and carbon emissions in administrative border areas -evidence from China X. Yu et al. 10.1016/j.jenvman.2025.124301
82. Near-real-time estimation of fossil fuel CO2 emissions from China based on atmospheric observations on Hateruma and Yonaguni Islands, Japan Y. Tohjima et al. 10.1186/s40645-023-00542-6
83. The Relationship Between Three-Dimensional Spatial Structure and CO2 Emission of Urban Agglomerations Based on CNN-RF Modeling: A Case Study in East China B. Pan et al. 10.3390/su16177623
84. Net CO2 fossil fuel emissions of Tokyo estimated directly from measurements of the Tsukuba TCCON site and radiosondes A. Babenhauserheide et al. 10.5194/amt-13-2697-2020
85. Monitoring gas flaring in Texas using time-series sentinel-2 MSI and landsat-8 OLI images W. Wu et al. 10.1016/j.jag.2022.103075
86. Advanced method for compiling a high-resolution gridded anthropogenic CO 2 emission inventory at a regional scale M. Xu et al. 10.1080/10095020.2024.2425182
87. A novel method for spatial allocation of volatile chemical products emissions: A case study of the Pearl River Delta Z. Cai et al. 10.1016/j.atmosenv.2023.120119
88. Mitigating geolocation errors in nighttime light satellite data and global CO2 emission gridded data V. Kinakh et al. 10.23939/mmc2021.02.304
89. Estimating CO2 emissions for 108 000 European cities D. Moran et al. 10.5194/essd-14-845-2022
90. How does extreme heat affect carbon emission intensity? Evidence from county-level data in China L. Jiang et al. 10.1016/j.econmod.2024.106814
91. Unintended environmental gains: The impact of China–Europe Railway Express on carbon dioxide emissions in China P. He et al. 10.1016/j.tranpol.2024.05.014
92. Influence of emission inventory resolution on the modeled spatio-temporal distribution of air pollutants in Buenos Aires, Argentina, using WRF-Chem A. López-Noreña et al. 10.1016/j.atmosenv.2021.118839
93. Isentropic transport and the seasonal cycle amplitude of CO2 E. Barnes et al. 10.1002/2016JD025109
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100. Spatiotemporal dynamic decoupling states of eco-environmental quality and land-use carbon emissions: A case study of Qingdao City, China Y. Yang & H. Li 10.1016/j.ecoinf.2023.101992
101. A global coupled Eulerian-Lagrangian model and 1 × 1 km CO2 surface flux dataset for high-resolution atmospheric CO2 transport simulations A. Ganshin et al. 10.5194/gmd-5-231-2012
102. A New Method for Top-Down Inversion Estimation of Carbon Dioxide Flux Based on Deep Learning H. Wang et al. 10.3390/rs16193694
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114. Humans and biodiversity: population and demographic trends in the hotspots J. Williams 10.1007/s11111-012-0175-3
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