413 citations as recorded by crossref.

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3. Experimental Estimates of Integral Anthropogenic CO2 Emissions in the City of St. Petersburg Y. Timofeyev et al. 10.1134/S0001433822030100
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5. The Carbon Cycle of Southeast Australia During 2019–2020: Drought, Fires, and Subsequent Recovery B. Byrne et al. 10.1029/2021AV000469
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7. A synthesis of carbon dioxide emissions from fossil-fuel combustion R. Andres et al. 10.5194/bg-9-1845-2012
8. Simulation of contribution of continental anthropogenic sources to variations in the CO2 concentration during winter period on Hateruma Island A. Ganshin et al. 10.1134/S1024856013010089
9. Evaluating the Ability of the Pre-Launch TanSat-2 Satellite to Quantify Urban CO2 Emissions K. Wu et al. 10.3390/rs15204904
10. Imputing missing data in non-renewable empower time series from night-time lights observations L. Neri et al. 10.1016/j.ecolind.2017.08.040
11. Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data Y. Sun et al. 10.3390/rs12182916
12. Development of a regional carbon assimilation system and its application for estimating fossil fuel carbon emissions in the Yangtze River Delta, China Z. Zhang et al. 10.1016/j.scitotenv.2024.177720
13. EU Net-Zero Policy Achievement Assessment in Selected Members through Automated Forecasting Algorithms C. Tudor & R. Sova 10.3390/ijgi11040232
14. Anthropogenic Heat Release: Estimation of Global Distribution and Possible Climate Effect B. CHEN et al. 10.2151/jmsj.2014-A10
15. Constraining Urban CO2 Emissions Using Mobile Observations from a Light Rail Public Transit Platform D. Mallia et al. 10.1021/acs.est.0c04388
16. Research on the emission reduction effects of carbon trading mechanism on power industry: plant-level evidence from China Y. Han et al. 10.1108/IJCCSM-06-2022-0074
17. Monitoring spatiotemporal characteristics of land-use carbon emissions and their driving mechanisms in the Yellow River Delta: A grid-scale analysis Y. Yang & H. Li 10.1016/j.envres.2022.114151
18. How to accurately assess the spatial distribution of energy CO2 emissions? Based on POI and NPP-VIIRS comparison X. Zhang et al. 10.1016/j.jclepro.2023.136656
19. High-resolution mapping of carbon dioxide emissions in Guizhou Province and its scale effects C. Zeng et al. 10.1038/s41598-024-71836-y
20. A simple method based on the thermal anomaly index to detect industrial heat sources H. Xia et al. 10.1016/j.jag.2018.08.003
21. Spatiotemporal evolution and multi-scale coupling effects of land-use carbon emissions and ecological environmental quality X. Zhang et al. 10.1016/j.scitotenv.2024.171149
22. Origin, realization path and key scientific issues of carbon neutrality: Climate change and sustainable urbanization M. CHEN et al. 10.31497/zrzyxb.20220509
23. North American CO<sub>2</sub> exchange: inter-comparison of modeled estimates with results from a fine-scale atmospheric inversion S. Gourdji et al. 10.5194/bg-9-457-2012
24. Brightness of Nighttime Lights as a Proxy for Freight Traffic: A Case Study of China J. Tian et al. 10.1109/JSTARS.2013.2258892
25. Effects of urban forms on CO2 emissions in China from a multi-perspective analysis K. Shi et al. 10.1016/j.jenvman.2020.110300
26. A global carbon assimilation system using a modified ensemble Kalman filter S. Zhang et al. 10.5194/gmd-8-805-2015
27. Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China Z. He et al. 10.3390/rs12040718
28. Impact of COVID-19 on the Spatio-temporal Distribution of CO2 Emission Y. Han et al. 10.1051/e3sconf/202339302006
29. Near-real-time global gridded daily CO2 emissions X. Dou et al. 10.1016/j.xinn.2021.100182
30. Spatial distributions of <i>X</i><sub>CO<sub>2</sub></sub> seasonal cycle amplitude and phase over northern high-latitude regions N. Jacobs et al. 10.5194/acp-21-16661-2021
31. A comparison of estimates of global carbon dioxide emissions from fossil carbon sources R. Andrew 10.5194/essd-12-1437-2020
32. Quantification of Fossil Fuel CO2 Emissions on the Building/Street Scale for a Large U.S. City K. Gurney et al. 10.1021/es3011282
33. Optimizing 4 years of CO2 biospheric fluxes from OCO-2 and in situ data in TM5: fire emissions from GFED and inferred from MOPITT CO data H. Peiro et al. 10.5194/acp-22-15817-2022
34. VIIRS night-time lights C. Elvidge et al. 10.1080/01431161.2017.1342050
35. Direct space‐based observations of anthropogenic CO2 emission areas from OCO‐2 J. Hakkarainen et al. 10.1002/2016GL070885
36. A decadal inversion of CO₂ using the Global Eulerian–Lagrangian Coupled Atmospheric model (GELCA): sensitivity to the ground-based observation network T. Shirai et al. 10.1080/16000889.2017.1291158
37. Consistent regional fluxes of CH<sub>4</sub> and CO<sub>2</sub> inferred from GOSAT proxy XCH<sub>4</sub> : XCO<sub>2</sub> retrievals, 2010–2014 L. Feng et al. 10.5194/acp-17-4781-2017
38. 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
39. Forecasting China’s GDP at the pixel level using nighttime lights time series and population images N. Zhao et al. 10.1080/15481603.2016.1276705
40. Inverse Modeling of CO₂ Fluxes Using GOSAT Data and Multi-Year Ground-Based Observations T. Saeki et al. 10.2151/sola.2013-011
41. Toward Accurate, Policy-Relevant Fossil Fuel CO2 Emission Landscapes K. Gurney et al. 10.1021/acs.est.0c01175
42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. CO2 annual and semiannual cycles from multiple satellite retrievals and models X. Jiang et al. 10.1002/2014EA000045
52. Anthropogenic Methane Emission and Its Partitioning for the Yangtze River Delta Region of China C. Hu et al. 10.1029/2018JG004850
53. Simultaneous shipborne measurements of CO<sub>2</sub>, CH<sub>4</sub> and CO and their application to improving greenhouse-gas flux estimates in Australia B. Bukosa et al. 10.5194/acp-19-7055-2019
54. Large Chinese land carbon sink estimated from atmospheric carbon dioxide data J. Wang et al. 10.1038/s41586-020-2849-9
55. Paths to low-carbon development in China: The role of government environmental target constraints T. Bai et al. 10.24136/oc.2023.034
56. 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
57. 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
58. Detecting regional patterns of changing CO 2 flux in Alaska N. Parazoo et al. 10.1073/pnas.1601085113
59. 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
60. Geostationary Emission Explorer for Europe (G3E): mission concept and initial performance assessment A. Butz et al. 10.5194/amt-8-4719-2015
61. 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
62. Variation patterns and driving factors of regional atmospheric CO2 anomalies in China Y. Fu et al. 10.1007/s11356-021-17139-5
63. Spatiotemporal variations of urban CO2 emissions in China: A multiscale perspective K. Shi et al. 10.1016/j.apenergy.2017.11.042
64. Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors Z. Lv et al. 10.3390/atmos11030231
65. 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
66. GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY O. Danylo et al. 10.23939/istcgcap2015.01.131
67. 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
68. 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
69. 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
70. 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
71. 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
72. 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
73. 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
74. 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
75. 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
76. Net CO<sub>2</sub> 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
77. 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
78. 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
79. 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
80. Mitigating geolocation errors in nighttime light satellite data and global CO2 emission gridded data V. Kinakh et al. 10.23939/mmc2021.02.304
81. Estimating CO2 emissions for 108 000 European cities D. Moran et al. 10.5194/essd-14-845-2022
82. 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
83. 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
84. 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
85. Isentropic transport and the seasonal cycle amplitude of CO2 E. Barnes et al. 10.1002/2016JD025109
86. Comparing GOSAT observations of localized CO2 enhancements by large emitters with inventory‐based estimates R. Janardanan et al. 10.1002/2016GL067843
87. Four decades of hydrological response to vegetation dynamics and anthropogenic factors in the Three-North Region of China and Mongolia D. Li et al. 10.1016/j.scitotenv.2022.159546
88. Spatiotemporal Dynamics of Land Use Carbon Balance and Its Response to Urbanization: A Case of the Yangtze River Economic Belt X. Jiang et al. 10.3390/land14010041
89. Spaceborne detection of XCO2 enhancement induced by Australian mega-bushfires J. Wang et al. 10.1088/1748-9326/abc846
90. The CO<sub>2</sub> integral emission by the megacity of St Petersburg as quantified from ground-based FTIR measurements combined with dispersion modelling D. Ionov et al. 10.5194/acp-21-10939-2021
91. Consistent weekly cycles of atmospheric NO2, CO, and CO2 in a North American megacity from ground-based, mountaintop, and satellite measurements H. Wang et al. 10.1016/j.atmosenv.2021.118809
92. 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
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94. A New Method for Top-Down Inversion Estimation of Carbon Dioxide Flux Based on Deep Learning H. Wang et al. 10.3390/rs16193694
95. High resolution carbon emissions simulation and spatial heterogeneity analysis based on big data in Nanjing City, China X. Chuai & J. Feng 10.1016/j.scitotenv.2019.05.138
96. Variability of Atmospheric CO2 Over the Arctic Ocean: Insights From the O‐Buoy Chemical Observing Network K. Graham et al. 10.1029/2022JD036437
97. Soil respiration–driven CO 2 pulses dominate Australia’s flux variability E. Metz et al. 10.1126/science.add7833
98. Network design for quantifying urban CO<sub>2</sub> emissions: assessing trade-offs between precision and network density A. Turner et al. 10.5194/acp-16-13465-2016
99. Contrasting Patterns of Urban Expansion in Colombia, Ecuador, Peru, and Bolivia Between 1992 and 2009 N. Álvarez-Berríos et al. 10.1007/s13280-012-0344-8
100. Sensitivity of simulated CO<sub>2</sub> concentration to sub-annual variations in fossil fuel CO<sub>2</sub> emissions X. Zhang et al. 10.5194/acp-16-1907-2016
101. Differential Spatiotemporal Patterns of CO2 Emissions in Eastern China’s Urban Agglomerations from NPP/VIIRS Nighttime Light Data Based on a Neural Network Algorithm L. Zhou et al. 10.3390/rs15020404
102. Exploring the nexus of urban form, transport, environment and health in large-scale urban studies: A state-of-the-art scoping review G. Dyer et al. 10.1016/j.envres.2024.119324
103. Top-down estimate of surface flux in the Los Angeles Basin using a mesoscale inverse modeling technique: assessing anthropogenic emissions of CO, NO<sub>x</sub> and CO<sub>2</sub> and their impacts J. Brioude et al. 10.5194/acp-13-3661-2013
104. Humans and biodiversity: population and demographic trends in the hotspots J. Williams 10.1007/s11111-012-0175-3
105. Effects of 3D urban morphology on CO2 emissions using machine learning: Towards spatially tailored low-carbon strategies in Central Wuhan, China P. Tian et al. 10.1016/j.uclim.2024.102122
106. Synergistic effects of heat and carbon on sustainable urban development: Case study of the Wuhan Urban Agglomeration X. Zhou et al. 10.1016/j.jclepro.2023.138971
107. Site selection and effects of background towers on urban CO2 estimates: A case study from central downtown Zhengzhou in China G. Ren et al. 10.1016/j.envres.2024.120169
108. Bayesian inverse estimation of urban CO2 emissions: Results from a synthetic data simulation over Salt Lake City, UT L. Kunik et al. 10.1525/elementa.375
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111. CDIAC-FF: global and national CO<sub>2</sub> emissions from fossil fuel combustion and cement manufacture: 1751–2017 D. Gilfillan & G. Marland 10.5194/essd-13-1667-2021
112. Evaluating China's fossil-fuel CO<sub>2</sub> emissions from a comprehensive dataset of nine inventories P. Han et al. 10.5194/acp-20-11371-2020
113. Next-Generation Digital Ecosystem for Climate Data Mining and Knowledge Discovery: A Review of Digital Data Collection Technologies A. Hsu et al. 10.3389/fdata.2020.00029
114. Measuring Greenhouse Gas Emissions from Point Sources with Mobile Systems M. Cai et al. 10.3390/atmos13081249
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116. The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network S. Crowell et al. 10.5194/acp-19-9797-2019
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131. Atmospheric CO2 Observations Reveal Strong Correlation Between Regional Net Biospheric Carbon Uptake and Solar‐Induced Chlorophyll Fluorescence Y. Shiga et al. 10.1002/2017GL076630
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