439 citations as recorded by crossref.

1. Spatio-Temporal Variations and Influencing Factors of Country-Level Carbon Emissions for Northeast China Based on VIIRS Nighttime Lighting Data G. Xu et al. 10.3390/ijerph20010829
2. EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012 G. Janssens-Maenhout et al. 10.5194/essd-11-959-2019
3. Experimental Estimates of Integral Anthropogenic CO2 Emissions in the City of St. Petersburg Y. Timofeyev et al. 10.1134/S0001433822030100
4. MetaCity: Data-driven sustainable development of complex cities Y. Zhang et al. 10.1016/j.xinn.2024.100775
5. The Carbon Cycle of Southeast Australia During 2019–2020: Drought, Fires, and Subsequent Recovery B. Byrne et al. 10.1029/2021AV000469
6. Comparison of the data‐driven top‐down and bottom‐up global terrestrial CO2 exchanges: GOSAT CO2 inversion and empirical eddy flux upscaling M. Kondo et al. 10.1002/2014JG002866
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. Spatial heterogeneity in the impact of urban spatial structure on carbon emissions and storage in coastal region: a case study of Samarinda Metropolitan Area, Indonesia A. Hasanah et al. 10.1007/s10668-025-06110-w
14. Decoding urban emissions: the overlooked impact of commercial and public service zones across regions and seasons Z. Yi et al. 10.1080/15481603.2025.2492971
15. EU Net-Zero Policy Achievement Assessment in Selected Members through Automated Forecasting Algorithms C. Tudor & R. Sova 10.3390/ijgi11040232
16. Anthropogenic Heat Release: Estimation of Global Distribution and Possible Climate Effect B. CHEN et al. 10.2151/jmsj.2014-A10
17. Constraining Urban CO2 Emissions Using Mobile Observations from a Light Rail Public Transit Platform D. Mallia et al. 10.1021/acs.est.0c04388
18. 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
19. 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
20. 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
21. High-resolution mapping of carbon dioxide emissions in Guizhou Province and its scale effects C. Zeng et al. 10.1038/s41598-024-71836-y
22. 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
23. 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
24. Pioneering Micro-Scale Mapping of Urban CO Emissions from Fossil Fuels with GIS L. Khodakarami 10.32604/rig.2024.050908
25. Origin, realization path and key scientific issues of carbon neutrality: Climate change and sustainable urbanization M. CHEN et al. 10.31497/zrzyxb.20220509
26. North American CO2exchange: inter-comparison of modeled estimates with results from a fine-scale atmospheric inversion S. Gourdji et al. 10.5194/bg-9-457-2012
27. Brightness of Nighttime Lights as a Proxy for Freight Traffic: A Case Study of China J. Tian et al. 10.1109/JSTARS.2013.2258892
28. Effects of urban forms on CO2 emissions in China from a multi-perspective analysis K. Shi et al. 10.1016/j.jenvman.2020.110300
29. A global carbon assimilation system using a modified ensemble Kalman filter S. Zhang et al. 10.5194/gmd-8-805-2015
30. Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China Z. He et al. 10.3390/rs12040718
31. Impact of COVID-19 on the Spatio-temporal Distribution of CO2 Emission Y. Han et al. 10.1051/e3sconf/202339302006
32. Near-real-time global gridded daily CO2 emissions X. Dou et al. 10.1016/j.xinn.2021.100182
33. Spatial distributions of XCO2 seasonal cycle amplitude and phase over northern high-latitude regions N. Jacobs et al. 10.5194/acp-21-16661-2021
34. A comparison of estimates of global carbon dioxide emissions from fossil carbon sources R. Andrew 10.5194/essd-12-1437-2020
35. Quantification of Fossil Fuel CO2 Emissions on the Building/Street Scale for a Large U.S. City K. Gurney et al. 10.1021/es3011282
36. 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
37. VIIRS night-time lights C. Elvidge et al. 10.1080/01431161.2017.1342050
38. Direct space‐based observations of anthropogenic CO2 emission areas from OCO‐2 J. Hakkarainen et al. 10.1002/2016GL070885
39. 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
40. 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
41. 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
42. 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
43. Inverse Modeling of CO₂ Fluxes Using GOSAT Data and Multi-Year Ground-Based Observations T. Saeki et al. 10.2151/sola.2013-011
44. Toward Accurate, Policy-Relevant Fossil Fuel CO2 Emission Landscapes K. Gurney et al. 10.1021/acs.est.0c01175
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. CO2 annual and semiannual cycles from multiple satellite retrievals and models X. Jiang et al. 10.1002/2014EA000045
55. Anthropogenic Methane Emission and Its Partitioning for the Yangtze River Delta Region of China C. Hu et al. 10.1029/2018JG004850
56. 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
57. Large Chinese land carbon sink estimated from atmospheric carbon dioxide data J. Wang et al. 10.1038/s41586-020-2849-9
58. Paths to low-carbon development in China: The role of government environmental target constraints T. Bai et al. 10.24136/oc.2023.034
59. 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
60. 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
61. Detecting regional patterns of changing CO 2 flux in Alaska N. Parazoo et al. 10.1073/pnas.1601085113
62. 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
63. Geostationary Emission Explorer for Europe (G3E): mission concept and initial performance assessment A. Butz et al. 10.5194/amt-8-4719-2015
64. 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
65. Variation patterns and driving factors of regional atmospheric CO2 anomalies in China Y. Fu et al. 10.1007/s11356-021-17139-5
66. Spatiotemporal variations of urban CO2 emissions in China: A multiscale perspective K. Shi et al. 10.1016/j.apenergy.2017.11.042
67. Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors Z. Lv et al. 10.3390/atmos11030231
68. 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
69. GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY O. Danylo et al. 10.23939/istcgcap2015.01.131
70. 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
71. 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
72. 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
73. 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
74. 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
75. 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
76. 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
77. 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
78. 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
79. 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
80. 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
81. 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
82. 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
83. 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
84. Mitigating geolocation errors in nighttime light satellite data and global CO2 emission gridded data V. Kinakh et al. 10.23939/mmc2021.02.304
85. Estimating CO2 emissions for 108 000 European cities D. Moran et al. 10.5194/essd-14-845-2022
86. 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
87. 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
88. 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
89. Isentropic transport and the seasonal cycle amplitude of CO2 E. Barnes et al. 10.1002/2016JD025109
90. Comparing GOSAT observations of localized CO2 enhancements by large emitters with inventory‐based estimates R. Janardanan et al. 10.1002/2016GL067843
91. 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
92. 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
93. Spaceborne detection of XCO2 enhancement induced by Australian mega-bushfires J. Wang et al. 10.1088/1748-9326/abc846
94. The CO2 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
95. 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
96. 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
97. 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
98. A New Method for Top-Down Inversion Estimation of Carbon Dioxide Flux Based on Deep Learning H. Wang et al. 10.3390/rs16193694
99. 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
100. Variability of Atmospheric CO2 Over the Arctic Ocean: Insights From the O‐Buoy Chemical Observing Network K. Graham et al. 10.1029/2022JD036437
101. Soil respiration–driven CO 2 pulses dominate Australia’s flux variability E. Metz et al. 10.1126/science.add7833
102. Local energy zone for urban carbon emissions studies – Accounting, characterization and prediction R. Wang & C. Ren 10.1016/j.apenergy.2025.125865
103. 净零排放对中国大气二氧化碳浓度的影响——基于GEOS-Chem模型的理想模拟 景. 谭 et al. 10.1360/N072024-0163
104. Network design for quantifying urban CO2 emissions: assessing trade-offs between precision and network density A. Turner et al. 10.5194/acp-16-13465-2016
105. 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
106. Sensitivity of simulated CO2 concentration to sub-annual variations in fossil fuel CO2 emissions X. Zhang et al. 10.5194/acp-16-1907-2016
107. 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
108. 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
109. Top-down estimate of surface flux in the Los Angeles Basin using a mesoscale inverse modeling technique: assessing anthropogenic emissions of CO, NOx and CO2 and their impacts J. Brioude et al. 10.5194/acp-13-3661-2013
110. Humans and biodiversity: population and demographic trends in the hotspots J. Williams 10.1007/s11111-012-0175-3
111. 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
112. 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
113. 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
114. 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
115. Vista-LA: Mapping methane-emitting infrastructure in the Los Angeles megacity V. Carranza et al. 10.5194/essd-10-653-2018
116. High-resolution atmospheric emission inventory of the argentine energy sector. Comparison with edgar global emission database S. Puliafito et al. 10.1016/j.heliyon.2017.e00489
117. CDIAC-FF: global and national CO2 emissions from fossil fuel combustion and cement manufacture: 1751–2017 D. Gilfillan & G. Marland 10.5194/essd-13-1667-2021
118. Evaluating China's fossil-fuel CO2 emissions from a comprehensive dataset of nine inventories P. Han et al. 10.5194/acp-20-11371-2020
119. 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
120. Measuring Greenhouse Gas Emissions from Point Sources with Mobile Systems M. Cai et al. 10.3390/atmos13081249
121. Can weather variables and electricity demand predict carbon emissions allowances prices? Evidence from the first three phases of the EU ETS M. Eslahi & P. Mazza 10.1016/j.ecolecon.2023.107985
122. Mapping Spatiotemporal Dynamic Changes in Urban CO2 Emissions in China by Using the Machine Learning Method and Geospatial Big Data W. Guo et al. 10.3390/rs17040611
123. Assessment of the Impacts of Different Carbon Sources and Sinks on Atmospheric CO2 Concentrations Based on GEOS-Chem G. Qu et al. 10.3390/rs17061009
124. 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
125. The Open-source Data Inventory for Anthropogenic CO2, version 2016 (ODIAC2016): a global monthly fossil fuel CO2 gridded emissions data product for tracer transport simulations and surface flux inversions T. Oda et al. 10.5194/essd-10-87-2018
126. Monitoring of Atmospheric Carbon Dioxide over Pakistan Using Satellite Dataset N. An et al. 10.3390/rs14225882
127. TanSat Mission Achievements: from Scientific Driving to Preliminary Observations Y. LIU et al. 10.11728/cjss2018.05.627
128. Identifying local anthropogenic CO2 emissions with satellite retrievals: a case study in South Korea C. Shim et al. 10.1080/01431161.2018.1523585
129. Potential of Spaceborne Lidar Measurements of Carbon Dioxide and Methane Emissions from Strong Point Sources C. Kiemle et al. 10.3390/rs9111137
130. Carbon Emission Status and Regional Differences of China: High-Resolution Estimation of Spatially Explicit Carbon Emissions at the Prefecture Level J. Guo et al. 10.3390/land14020291
131. DMSP-OLS Radiance Calibrated Nighttime Lights Time Series with Intercalibration F. Hsu et al. 10.3390/rs70201855
132. Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal P. Palmer et al. 10.1038/s41467-019-11097-w
133. Characteristics of atmospheric CO2 fluxes and the estimation of their potential sources around Boseong Standard Weather Observatory (BSWO) C. Park et al. 10.1016/j.atmosenv.2021.118340
134. Monitoring and Forecasting XCO2 Using OCO-2 Satellite Data and Deep Learning K. Lee & K. Kim 10.5572/KOSAE.2024.40.5.572
135. Spatial Downscaling of NPP/VIIRS DNB Nighttime Light Data Based on Deep Learning W. Xu et al. 10.1109/JSTARS.2024.3454093
136. On the impact of granularity of space-based urban CO2 emissions in urban atmospheric inversions: A case study for Indianapolis, IN T. Oda et al. 10.1525/elementa.146
137. Anthropogenic emission inventories in China: a review M. Li et al. 10.1093/nsr/nwx150
138. A global map of emission clumps for future monitoring of fossil fuel CO2 emissions from space Y. Wang et al. 10.5194/essd-11-687-2019
139. Simulation of variability in atmospheric carbon dioxide using a global coupled Eulerian – Lagrangian transport model Y. Koyama et al. 10.5194/gmd-4-317-2011
140. Atmospheric CO2 Observations Reveal Strong Correlation Between Regional Net Biospheric Carbon Uptake and Solar‐Induced Chlorophyll Fluorescence Y. Shiga et al. 10.1002/2017GL076630
141. An improved nightlight-based method for modeling urban CO2 emissions J. Han et al. 10.1016/j.envsoft.2018.05.008
142. A statistical approach for isolating fossil fuel emissions in atmospheric inverse problems V. Yadav et al. 10.1002/2016JD025642
143. Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm C. O'Dell et al. 10.5194/amt-11-6539-2018
144. Drivers of column-average CO2 variability at Southern Hemispheric Total Carbon Column Observing Network sites N. Deutscher et al. 10.5194/acp-14-9883-2014
145. Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China B. Yu et al. 10.1109/JSTARS.2015.2399416
146. On the impact of urbanisation on CO2 emissions M. Luqman et al. 10.1038/s42949-023-00084-2
147. Monthly, global emissions of carbon dioxide from fossil fuel consumption R. Andres et al. 10.1111/j.1600-0889.2011.00530.x
148. Regional CO2 flux estimates for 2009–2010 based on GOSAT and ground-based CO2 observations S. Maksyutov et al. 10.5194/acp-13-9351-2013
149. Potential of European 14CO2 observation network to estimate the fossil fuel CO2 emissions via atmospheric inversions Y. Wang et al. 10.5194/acp-18-4229-2018
150. 基于中国大气反演系统的卫星<bold>CO</bold><sub><bold>2</bold></sub>数据同化对全球碳收支的评估 哲. 金 et al. 10.1360/N072022-0123
151. Comprehensive evaluation of land-use carbon emissions integrating social network analysis and a zone-based machine learning approach H. Fan et al. 10.1016/j.eiar.2024.107775
152. Characterizing Carbon Emissions and the Associations with Socio-Economic Development in Chinese Cities Z. Shen & L. Xin 10.3390/ijerph192113786
153. Tropical methane emissions explain large fraction of recent changes in global atmospheric methane growth rate L. Feng et al. 10.1038/s41467-022-28989-z
154. A method for estimating localized CO2 emissions from co-located satellite XCO2 and NO2 images B. Fuentes Andrade et al. 10.5194/amt-17-1145-2024
155. Multi-year observations reveal a larger than expected autumn respiration signal across northeast Eurasia B. Byrne et al. 10.5194/bg-19-4779-2022
156. The spatiotemporal evolution and impact mechanism of energy consumption carbon emissions in China from 2010 to 2020 by integrating multisource remote sensing data M. Wang et al. 10.1016/j.jenvman.2023.119054
157. Improving resolution of a spatial air pollution inventory with a statistical inference approach J. Horabik & Z. Nahorski 10.1007/s10584-013-1029-4
158. Potential improvements in global carbon flux estimates from a network of laser heterodyne radiometer measurements of column carbon dioxide P. Palmer et al. 10.5194/amt-12-2579-2019
159. Sensitivity of simulated CO2 concentration to regridding of global fossil fuel CO2 emissions X. Zhang et al. 10.5194/gmd-7-2867-2014
160. Improving accuracy of economic estimations with VIIRS DNB image products N. Zhao et al. 10.1080/01431161.2017.1331060
161. High-resolution mapping of combustion processes and implications for CO2 emissions R. Wang et al. 10.5194/acp-13-5189-2013
162. Greenhouse gas observation network design for Africa A. Nickless et al. 10.1080/16000889.2020.1824486
163. NO2 emissions from oil refineries in the Mississippi Delta M. Filonchyk & M. Peterson 10.1016/j.scitotenv.2023.165569
164. Large Uncertainties in Urban‐Scale Carbon Emissions C. Gately & L. Hutyra 10.1002/2017JD027359
165. A Cluster of CO2 Change Characteristics with GOSAT Observations for Viewing the Spatial Pattern of CO2 Emission and Absorption D. Liu et al. 10.3390/atmos6111695
166. Relationships between CO₂ Flux Estimated by Inverse Analysis and Land Surface Elements in South America and Africa K. MABUCHI et al. 10.2151/jmsj.2016-021
167. A spatial uncertainty metric for anthropogenic CO2emissions D. Woodard et al. 10.1080/20430779.2014.1000793
168. Changes in remotely sensed Forel-Ule Index for the coastal seas of Japan, 2013–2023 L. Zhu et al. 10.1007/s12145-024-01507-z
169. A meta-analysis for the nighttime light remote sensing data applied in urban research: Key topics, hotspot study areas and new trends B. Dong et al. 10.1016/j.srs.2024.100186
170. Spatial allocation of anthropogenic carbon dioxide emission statistics data fusing multi-source data based on Bayesian network J. Tao & X. Kong 10.1038/s41598-021-93456-6
171. Spatio-temporal dynamics of human-induced carbon emissions in Southeast Asia (1992–2022) based on nighttime light C. Huang et al. 10.1016/j.eehl.2025.100150
172. Study on the spatialization of anthropogenic carbon emissions in China based on SVR-ZSSR M. Liu et al. 10.1038/s41598-023-28462-x
173. Considerable role of urban functional form in low-carbon city development T. Lan et al. 10.1016/j.jclepro.2023.136256
174. Constraining Fossil Fuel CO2 Emissions From Urban Area Using OCO‐2 Observations of Total Column CO2 X. Ye et al. 10.1029/2019JD030528
175. Construction and Application of a Regional Kilometer-Scale Carbon Source and Sink Assimilation Inversion System (CCMVS-R) L. Guo et al. 10.1016/j.eng.2023.02.017
176. Adjoint of the global Eulerian–Lagrangian coupled atmospheric transport model (A-GELCA v1.0): development and validation D. Belikov et al. 10.5194/gmd-9-749-2016
177. Ongoing CO2 monitoring verify CO2 emissions and sinks in China during 2018–2021 J. Zhong et al. 10.1016/j.scib.2023.08.039
178. A Multiscale Evaluation of the Coupling Relationship between Urban Land and Carbon Emissions: A Case Study of Chongqing, China C. Li et al. 10.3390/ijerph17103416
179. High‐resolution atmospheric inversion of urban CO2 emissions during the dormant season of the Indianapolis Flux Experiment (INFLUX) T. Lauvaux et al. 10.1002/2015JD024473
180. 基于便携式EM27/SUN光谱仪的点源碳排放监测方法——以合肥皖能发电厂为例 李. Li Chao et al. 10.3788/AOS241235
181. 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. 10.31857/S0002351524020115
182. Province-level fossil fuel CO2 emission estimates for China based on seven inventories P. Han et al. 10.1016/j.jclepro.2020.123377
183. Potential remote forcing of North Atlantic SST tripole anomalies on the seesaw haze intensity between late winter months in the North China plain: A case study J. Wang et al. 10.1002/asl.1170
184. Large scale synthesis of Mo2C nanoparticle incorporated carbon nanosheet (Mo2C–C) for enhanced hydrogen evolution reaction A. Mondal et al. 10.1016/j.ijhydene.2019.09.051
185. Validation of WRF-Chem Model and CAMS Performance in Estimating Near-Surface Atmospheric CO2 Mixing Ratio in the Area of Saint Petersburg (Russia) G. Nerobelov et al. 10.3390/atmos12030387
186. The First Global Carbon Dioxide Flux Map Derived from TanSat Measurements D. Yang et al. 10.1007/s00376-021-1179-7
187. Potentially underestimated gas flaring activities—a new approach to detect combustion using machine learning and NASA’s Black Marble product suite S. Chakraborty et al. 10.1088/1748-9326/acb6a7
188. Modelling monthly-gridded carbon emissions based on nighttime light data R. Wan et al. 10.1016/j.jenvman.2024.120391
189. Estimation of virtual water contained in international trade products using nighttime imagery N. Zhao & E. Samson 10.1016/j.jag.2012.02.002
190. Social–Ecological Factors and Ecosystem Service Trade-Offs/Synergies in Vegetation Change Zones of Qilian Mountain National Park During 2000–2020 X. Yang et al. 10.3390/rs17081402
191. Can removing regional barriers reduce carbon emissions in border regions? Quasi-natural experiments from China X. Yu & K. Wan 10.1007/s10644-025-09863-6
192. Mapping a High-Resolution Anthropogenic CO2 Emissions Inventory at City-Level Using Point-Line-Area Method S. Liu et al. 10.1007/s41810-024-00265-1
193. High-resolution inventory of technologies, activities, and emissions of coal-fired power plants in China from 1990 to 2010 F. Liu et al. 10.5194/acp-15-13299-2015
194. Spatiotemporal dynamics of CO2 emissions from central heating supply in the North China Plain over 2012–2016 due to natural gas usage Y. Cui et al. 10.1016/j.apenergy.2019.03.060
195. Assessing nighttime lights for mapping the urban areas of 50 cities across the globe H. Bagan et al. 10.1177/2399808317752926
196. A Lagrangian approach towards extracting signals of urban CO2 emissions from satellite observations of atmospheric column CO2 (XCO2): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”) D. Wu et al. 10.5194/gmd-11-4843-2018
197. Spatial modeling of micro‐scale carbon dioxide sources and sinks in urban environments: A novel approach to quantify urban impacts on global warming L. Khodakarami 10.1002/ghg.2273
198. Satellites capture socioeconomic disruptions during the 2022 full-scale war in Ukraine I. Ialongo et al. 10.1038/s41598-023-42118-w
199. Improving Nighttime Light Imagery With Location-Based Social Media Data N. Zhao et al. 10.1109/TGRS.2018.2871788
200. Constraining sector-specific CO2 and CH4 emissions in the US S. Miller & A. Michalak 10.5194/acp-17-3963-2017
201. Retrieval anthropogenic CO2 emissions from OCO-2 and comparison with gridded emission inventories C. Jin et al. 10.1016/j.jclepro.2024.141418
202. Urban macro-level impact factors on Direct CO2 Emissions of urban residents in China J. Zhang et al. 10.1016/j.enbuild.2015.08.011
203. Estimating Socio-economic Indicators Through Nighttime Lights: From DMSP/OLS to Suomi NPP/VIIRS-DNB T. Nakaya 10.3169/itej.72.569
204. Addressing Measurement Error Bias in GDP with Nighttime Lights and an Application to Infant Mortality with Chinese County Data X. Chen 10.1177/0081175016654737
205. Estimation of anthropogenic CO2 emissions at different scales for assessing SDG indicators: Method and application Y. Hua et al. 10.1016/j.jclepro.2023.137547
206. Cropland Carbon Uptake Delayed and Reduced by 2019 Midwest Floods Y. Yin et al. 10.1029/2019AV000140
207. Estimation of the Distribution of Global Anthropogenic Heat Flux C. Bing & S. Guang-Yu 10.1080/16742834.2012.11446974
208. Data Processing and Analysis Approach to Retrieve Carbon Dioxide Weighted-Column Mixing Ratio and 2-<inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math> </inline-formula>m Reflectance With an Airborne Laser Absorption Spectrometer J. Jacob et al. 10.1109/TGRS.2018.2863711
209. Estimating regional greenhouse gas fluxes: an uncertainty analysis of planetary boundary layer techniques and bottom-up inventories X. Zhang et al. 10.5194/acp-14-10705-2014
210. How do CO2 emissions and efficiencies vary in Chinese cities? Spatial variation and driving factors in 2007 Y. Tian & W. Zhou 10.1016/j.scitotenv.2019.04.239
211. A Modeling Framework of Atmospheric CO2 in the Mediterranean Marseille Coastal City Area, France B. Nathan et al. 10.3390/atmos15101193
212. Spatiotemporal comparative analysis of three carbon emission inventories in mainland China L. Zheng et al. 10.1016/j.apr.2025.102417
213. Impact of Prior Terrestrial Carbon Fluxes on Simulations of Atmospheric CO2 Concentrations Y. Fu et al. 10.1029/2021JD034794
214. Improved spatial representation of a highly resolved emission inventory in China: evidence from TROPOMI measurements N. Wu et al. 10.1088/1748-9326/ac175f
215. Decadal variations in CO2 during agricultural seasons in India and role of management as sustainable approach A. Singh et al. 10.1016/j.eti.2022.102498
216. Effects of Emission Variability on Atmospheric CO2 Concentrations in Mainland China W. Lu et al. 10.3390/rs17050814
217. Gridded uncertainty in fossil fuel carbon dioxide emission maps, a CDIAC example R. Andres et al. 10.5194/acp-16-14979-2016
218. An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO2 Retrievals D. Roten et al. 10.1029/2020EA001343
219. The Orbiting Carbon Observatory-2 early science investigations of regional carbon dioxide fluxes A. Eldering et al. 10.1126/science.aam5745
220. Assessment of thermal power plant CO2 emissions quantification performance and uncertainty of measurements by ground-based remote sensing C. Li et al. 10.1016/j.envpol.2024.124886
221. A cluster-based method to map urban area from DMSP/OLS nightlights Y. Zhou et al. 10.1016/j.rse.2014.03.004
222. A Comparative Analysis of Anthropogenic CO2 Emissions at City Level Using OCO‐2 Observations: A Global Perspective P. Fu et al. 10.1029/2019EF001282
223. Sensitivity of the simulated CO2 concentration to inter-annual variations of its sources and sinks over East Asia Y. Fu et al. 10.1016/j.accre.2020.03.001
224. The OCO-3 mission: measurement objectives and expected performance based on 1 year of simulated data A. Eldering et al. 10.5194/amt-12-2341-2019
225. Estimation of carbon emissions in various clustered regions of China based on OCO-2 satellite XCO2 data and random forest modelling Y. Tan et al. 10.1016/j.atmosenv.2024.120860
226. A Thermodynamic Geography: Night-Time Satellite Imagery as a Proxy Measure of Emergy L. Coscieme et al. 10.1007/s13280-013-0468-5
227. Neighborhood Emission Mapping Operation (NEMO): A 1-km anthropogenic emission dataset in the United States S. Ma & D. Tong 10.1038/s41597-022-01790-9
228. Study of the footprints of short-term variation in XCO2 observed by TCCON sites using NIES and FLEXPART atmospheric transport models D. Belikov et al. 10.5194/acp-17-143-2017
229. Assessment of the NOх integral emission from the St.Petersburg megacity by means of mobile DOAS measurements combined with dispersion modelling D. Ionov et al. 10.1016/j.apr.2022.101598
230. Mapping high-resolution energy consumption CO2 emissions in China by integrating nighttime lights and point source locations M. Wang et al. 10.1016/j.scitotenv.2023.165829
231. Far-field biogenic and anthropogenic emissions as a dominant source of variability in local urban carbon budgets: A global high-resolution model study with implications for satellite remote sensing A. Schuh et al. 10.1016/j.rse.2021.112473
232. Exploring dynamics relationship between carbon emissions and eco-environmental quality in Samarinda Metropolitan Area: A spatiotemporal approach A. Hasanah & J. Wu 10.1016/j.scitotenv.2024.172188
233. Predicting European cities’ climate mitigation performance using machine learning A. Hsu et al. 10.1038/s41467-022-35108-5
234. Optimization of a prognostic biosphere model for terrestrial biomass and atmospheric CO2 variability M. Saito et al. 10.5194/gmd-7-1829-2014
235. High-resolution spatial distribution of greenhouse gas emissions in the residential sector O. Danylo et al. 10.1007/s11027-019-9846-z
236. A Coupled CH4, CO and CO2 Simulation for Improved Chemical Source Modeling B. Bukosa et al. 10.3390/atmos14050764
237. Spatiotemporal Evolution and Tapio Decoupling Analysis of Energy-Related Carbon Emissions Using Nighttime Light Data: A Quantitative Case Study at the City Scale in Northeast China B. Liu & J. Lv 10.3390/en17194795
238. Spatio-temporal modeling of satellite-observed CO2 columns in China using deep learning Z. He et al. 10.1016/j.jag.2024.103859
239. Recent research quantifying anthropogenic CO2emissions at the street scale within the urban domain K. Gurney 10.1080/17583004.2014.986849
240. County-Level Spatiotemporal Dynamics and Driving Mechanisms of Carbon Emissions in the Pearl River Delta Urban Agglomeration, China F. Wang et al. 10.3390/land13111829
241. Spatio-temporal pattern evolution of carbon emissions at the city-county-town scale in Fujian Province based on DMSP/OLS and NPP/VIIRS nighttime light data Y. Zheng et al. 10.1016/j.jclepro.2024.140958
242. A new space-borne perspective of crop productivity variations over the US Corn Belt P. Somkuti et al. 10.1016/j.agrformet.2019.107826
243. Assumptions about prior fossil fuel inventories impact our ability to estimate posterior net CO2 fluxes that are needed for verifying national inventories T. Oda et al. 10.1088/1748-9326/ad059b
244. Short-term reduction of regional enhancement of atmospheric CO2 in China during the first COVID-19 pandemic period S. Sim et al. 10.1088/1748-9326/ac507d
245. Can Mixed Land Use Reduce CO2 Emissions? A Case Study of 268 Chinese Cities Q. Li et al. 10.3390/su142215117
246. Sectoral carbon emission prediction and spatial modeling framework: A local climate zone-based case study of the Guangdong-Hong Kong-Macao Greater Bay Area R. Wang et al. 10.1016/j.scs.2024.105756
247. Global carbon emission spatial pattern in 2030 under INDCs: using a gridding approach based on population and urbanization L. Tao et al. 10.1108/IJCCSM-04-2021-0038
248. Examining partial-column density retrieval of lower-tropospheric CO2 from GOSAT target observations over global megacities A. Kuze et al. 10.1016/j.rse.2022.112966
249. Impact of spatial proxies on the representation of bottom-up emission inventories: A satellite-based analysis G. Geng et al. 10.5194/acp-17-4131-2017
250. A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon F. Santos et al. 10.1371/journal.pone.0226224
251. Assessing the Effectiveness of an Urban CO2 Monitoring Network over the Paris Region through the COVID-19 Lockdown Natural Experiment J. Lian et al. 10.1021/acs.est.1c04973
252. Earth observation technology’s alignment with OHCHR indicators for strengthening human rights breach investigations and adjudication S. Rapach et al. 10.1016/j.scijus.2024.09.006
253. A new global carbon flux estimation methodology by assimilation of both in situ and satellite CO2 observations W. Su et al. 10.1038/s41612-024-00824-w
254. Explore the spatial pattern of carbon emissions in urban functional zones: a case study of Pudong, Shanghai, China E. Zhu et al. 10.1007/s11356-023-31149-5
255. Top‐Down Constraints on Anthropogenic CO2 Emissions Within an Agricultural‐Urban Landscape C. Hu et al. 10.1029/2017JD027881
256. Towards space based verification of CO2 emissions from strong localized sources: fossil fuel power plant emissions as seen by a CarbonSat constellation V. Velazco et al. 10.5194/amt-4-2809-2011
257. Evaluating national and subnational CO2 mitigation goals in China’s thirteenth five-year plan from satellite observations G. Pan et al. 10.1016/j.envint.2021.106771
258. Spatialization of Chinese R-410A emissions from the room air-conditioning sector P. Wu et al. 10.1007/s10668-022-02264-z
259. Spatial variation in household consumption-based carbon emission inventories for 1200 Japanese cities K. Kanemoto et al. 10.1088/1748-9326/abc045
260. Diurnal, weekly, seasonal, and spatial variabilities in carbon dioxide flux in different urban landscapes in Sakai, Japan M. Ueyama & T. Ando 10.5194/acp-16-14727-2016
261. How does urbanization affect CO2 emissions of central heating systems in China? An assessment of natural gas transition policy based on nighttime light data W. Zhang et al. 10.1016/j.jclepro.2020.123188
262. Particulate matter-attributable mortality and relationships with carbon dioxide in 250 urban areas worldwide S. Anenberg et al. 10.1038/s41598-019-48057-9
263. Analysis of CO2, CH4, and CO surface and column concentrations observed at Réunion Island by assessing WRF-Chem simulations S. Callewaert et al. 10.5194/acp-22-7763-2022
264. Optimizing the Spatial Resolution for Urban CO2 Flux Studies Using the Shannon Entropy J. Liang et al. 10.3390/atmos8050090
265. Analyzing the impact of three-dimensional building structure on CO2 emissions based on random forest regression J. Lin et al. 10.1016/j.energy.2021.121502
266. Urban-focused satellite CO2 observations from the Orbiting Carbon Observatory-3: A first look at the Los Angeles megacity M. Kiel et al. 10.1016/j.rse.2021.112314
267. Shedding Light on Agricultural Transitions, Dragon Fruit Cultivation, and Electrification in Southern Vietnam Using Mixed Methods L. Krauser et al. 10.1080/24694452.2021.1940825
268. On the Ability of Space‐Based Passive and Active Remote Sensing Observations of CO2 to Detect Flux Perturbations to the Carbon Cycle S. Crowell et al. 10.1002/2017JD027836
269. Global enhanced vegetation photosynthesis in urban environment and its drivers revealed by satellite solar-induced chlorophyll fluorescence data S. Wang et al. 10.1016/j.agrformet.2023.109622
270. Enhanced regional terrestrial carbon uptake over Korea revealed by atmospheric CO2 measurements from 1999 to 2017 J. Yun et al. 10.1111/gcb.15061
271. Errors and uncertainties in a gridded carbon dioxide emissions inventory T. Oda et al. 10.1007/s11027-019-09877-2
272. Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources R. Bun et al. 10.1007/s11027-018-9791-2
273. Public data openness and carbon emission reduction in administrative boundary areas X. Yu & K. Wan 10.1007/s00168-025-01374-6
274. Investigating sources of variability and error in simulations of carbon dioxide in an urban region C. Martin et al. 10.1016/j.atmosenv.2018.11.013
275. Tweets or nighttime lights: Comparison for preeminence in estimating socioeconomic factors N. Zhao et al. 10.1016/j.isprsjprs.2018.08.018
276. Constraint of satellite CO2 retrieval on the global carbon cycle from a Chinese atmospheric inversion system Z. Jin et al. 10.1007/s11430-022-1036-7
277. Comparison of Global Downscaled Versus Bottom‐Up Fossil Fuel CO2 Emissions at the Urban Scale in Four U.S. Urban Areas K. Gurney et al. 10.1029/2018JD028859
278. The Orbiting Carbon Observatory-2 (OCO-2) and in situ CO2 data suggest a larger seasonal amplitude of the terrestrial carbon cycle compared to many dynamic global vegetation models R. Lei et al. 10.1016/j.rse.2024.114326
279. Assessing China's Scope 2 CO2 emissions and mitigation pace from space G. Pan & Y. Xu 10.1016/j.atmosenv.2023.119906
280. A CO2–Δ14CO2 inversion setup for estimating European fossil CO2 emissions C. Gómez-Ortiz et al. 10.5194/acp-25-397-2025
281. Spatial Heterogeneity of Combined Factors Affecting Vegetation Greenness Change in the Yangtze River Economic Belt from 2000 to 2020 C. Peng et al. 10.3390/rs15245693
282. Impact of Urban Growth on Air Quality in Indian Cities Using Hierarchical Bayesian Approach P. Misra et al. 10.3390/atmos10090517
283. The Impact of Urbanization Growth Patterns on Carbon Dioxide Emissions: Evidence from Guizhou, West of China C. Zeng et al. 10.3390/land11081211
284. Anthropogenic CO2 emissions assessment of Nile Delta using XCO2 and SIF data from OCO-2 satellite A. Shekhar et al. 10.1088/1748-9326/ab9cfe
285. Analysis of spatial and temporal carbon emission efficiency in Yangtze River Delta city cluster — Based on nighttime lighting data and machine learning Q. Sun et al. 10.1016/j.eiar.2023.107232
286. Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach Y. Liu et al. 10.1016/j.envpol.2017.12.070
287. Estimates of CO2 Anthropogenic Emission from the Megacity St. Petersburg Y. Timofeyev et al. 10.1134/S1028334X20090184
288. Uncertainty in gridded CO2 emissions estimates S. Hogue et al. 10.1002/2015EF000343
289. Evaluating nighttime lights and population distribution as proxies for mapping anthropogenic CO2emission in Vietnam, Cambodia and Laos A. Gaughan et al. 10.1088/2515-7620/ab3d91
290. Estimating enhancement ratios of nitrogen dioxide, carbon monoxide and carbon dioxide using satellite observations C. MacDonald et al. 10.5194/acp-23-3493-2023
291. Mapping carbon–thermal environments for comprehending real-time scenarios C. Srivastava & A. Bharat 10.1007/s11600-024-01387-3
292. Generating the Nighttime Light of the Human Settlements by Identifying Periodic Components from DMSP/OLS Satellite Imagery H. Letu et al. 10.1021/acs.est.5b02471
293. Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives M. Zhao et al. 10.3390/rs11171971
294. Estimating regional fluxes of CO2 and CH4 using space-borne observations of XCH4: XCO2 A. Fraser et al. 10.5194/acp-14-12883-2014
295. Column-averaged CO2 concentrations in the subarctic from GOSAT retrievals and NIES transport model simulations D. Belikov et al. 10.1016/j.polar.2014.02.002
296. Projecting the future fine-resolution carbon dioxide emissions under the shared socioeconomic pathways for carbon peak evaluation D. Ding et al. 10.1016/j.apenergy.2024.123240
297. High-resolution carbon neutrality mapping and a heterogeneity analysis for China's two typical megalopolises M. Xia et al. 10.1016/j.uclim.2023.101488
298. On the Benefit of GOSAT Observations to the Estimation of Regional CO₂ Fluxes H. Takagi et al. 10.2151/sola.2011-041
299. Refined estimate of China's CO2 emissions in spatiotemporal distributions M. Liu et al. 10.5194/acp-13-10873-2013
300. Evaluating the Causal Relations between the Kaya Identity Index and ODIAC-Based Fossil Fuel CO2 Flux Y. Hwang et al. 10.3390/en13226009
301. Fossil fuel CO2 emissions over metropolitan areas from space: A multi-model analysis of OCO-2 data over Lahore, Pakistan R. Lei et al. 10.1016/j.rse.2021.112625
302. NiO hollow microspheres as efficient bifunctional electrocatalysts for Overall Water-Splitting A. Mondal et al. 10.1016/j.ijhydene.2018.06.139
303. Spatiotemporal association of carbon dioxide emissions in China's urban agglomerations Y. Qian et al. 10.1016/j.jenvman.2022.116109
304. A comparison of five high-resolution spatially-explicit, fossil-fuel, carbon dioxide emission inventories for the United States M. Hutchins et al. 10.1007/s11027-016-9709-9
305. Toward a satellite-based monitoring system for urban CO2 emissions in support of global collective climate mitigation actions T. Wilmot et al. 10.1088/1748-9326/ad6017
306. The Indianapolis Flux Experiment (INFLUX): A test-bed for developing urban greenhouse gas emission measurements K. Davis et al. 10.1525/elementa.188
307. An Annual “Urban Core-Suburban-Rural” Triad Structure Dataset for China From 1992 to 2021 B. Xiong et al. 10.1109/JSTARS.2023.3341390
308. Spatiotemporal evolution of carbon emissions and influencing factors in county-level based on nighttime lighting data: a case study in Huaihai economic zone core city Z. Wu et al. 10.1080/13467581.2024.2373827
309. Monthly trends of methane emissions in Los Angeles from 2011 to 2015 inferred by CLARS-FTS observations C. Wong et al. 10.5194/acp-16-13121-2016
310. Simulating estimation of California fossil fuel and biosphere carbon dioxide exchanges combining in situ tower and satellite column observations M. Fischer et al. 10.1002/2016JD025617
311. Remote Sensing Monitoring and Analysis of Spatiotemporal Changes in China’s Anthropogenic Carbon Emissions Based on XCO2 Data Y. Wang et al. 10.3390/rs15123207
312. Heterogeneity study on mechanisms influencing carbon emission intensity at the county level in the Yangtze River Delta urban Agglomeration: A perspective on main functional areas Y. Guo et al. 10.1016/j.ecolind.2024.111597
313. Plant responses to volcanically elevated CO2 in two Costa Rican forests R. Bogue et al. 10.5194/bg-16-1343-2019
314. Four years of global carbon cycle observed from the Orbiting Carbon Observatory 2 (OCO-2) version 9 and in situ data and comparison to OCO-2 version 7 H. Peiro et al. 10.5194/acp-22-1097-2022
315. Quantifying uncertainties in nighttime light retrievals from Suomi-NPP and NOAA-20 VIIRS Day/Night Band data Z. Wang et al. 10.1016/j.rse.2021.112557
316. Quantifying the trends and affecting factors of CO2 emissions under different urban development patterns: An econometric study on the Yangtze river economic belt in China X. He et al. 10.1016/j.scs.2024.105443
317. Quantification of SO2 and CO2 emission rates from coal-fired power plants in the Korean peninsula via airborne measurements J. Kim et al. 10.1016/j.scitotenv.2025.179430
318. Social capital, household income and carbon dioxide emissions: A multicountry analysis J. Imbulana Arachchi & S. Managi 10.1016/j.eiar.2022.106838
319. Modeling multi-type urban landscape dynamics along the horizontal and vertical dimensions J. He et al. 10.1016/j.landurbplan.2023.104683
320. Bias-correcting carbon fluxes derived from land-surface satellite data for retrospective and near-real-time assimilation systems B. Weir et al. 10.5194/acp-21-9609-2021
321. The added value of satellite observations of methane forunderstanding the contemporary methane budget P. Palmer et al. 10.1098/rsta.2021.0106
322. City-level carbon emissions accounting and differentiation integrated nighttime light and city attributes Y. Zhou et al. 10.1016/j.resconrec.2022.106337
323. Spatial and temporal variation in energy-based carbon dioxide emissions and their predictions at city scale in future, China Y. Xie et al. 10.1016/j.psep.2024.11.032
324. Analysis of Dynamic Evolution and Spatial-Temporal Heterogeneity of Carbon Emissions at County Level along “The Belt and Road”—A Case Study of Northwest China S. Sun et al. 10.3390/ijerph192013405
325. Recent variations in soil moisture use efficiency (SMUE) and its influence factors in Asian drylands H. Hao et al. 10.1016/j.jclepro.2022.133860
326. Southern California megacity CO2, CH4, and CO flux estimates using ground- and space-based remote sensing and a Lagrangian model J. Hedelius et al. 10.5194/acp-18-16271-2018
327. A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion J. Ray et al. 10.5194/gmd-8-1259-2015
328. Estimation of observation errors for large-scale atmospheric inversion of CO₂ emissions from fossil fuel combustion Y. Wang et al. 10.1080/16000889.2017.1325723
329. A Study on Bio-Diesel and Jet Fuel Blending for the Production of Renewable Aviation Fuel R. El-Maghraby 10.4028/www.scientific.net/MSF.1008.231
330. Evaluating Anthropogenic CO2 Bottom-Up Emission Inventories Using Satellite Observations from GOSAT and OCO-2 S. Zhang et al. 10.3390/rs14195024
331. Assessing fossil fuel CO 2 emissions in California using atmospheric observations and models H. Graven et al. 10.1088/1748-9326/aabd43
332. An inversion method for estimating strong point carbon dioxide emissions using a differential absorption Lidar T. Shi et al. 10.1016/j.jclepro.2020.122434
333. Spatial Cross-Correlation of GOSAT CO2 Concentration with Repeated Heat Wave-Induced Photosynthetic Inhibition in Europe from 2009 to 2017 Y. Hwang et al. 10.3390/rs14184536
334. Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data H. Xiao et al. 10.1016/j.apenergy.2018.09.200
335. Implications of Emission Sources and Biosphere Exchange on Temporal Variations of CO2 and δ13C Using Continuous Atmospheric Measurements at Shadnagar (India) M. Pathakoti et al. 10.1029/2022JD036472
336. Impact of Urban Surface Characteristics and Socio-Economic Variables on the Spatial Variation of Land Surface Temperature in Lagos City, Nigeria D. Dissanayake et al. 10.3390/su11010025
337. Detection of fossil-fuel CO2 plummet in China due to COVID-19 by observation at Hateruma Y. Tohjima et al. 10.1038/s41598-020-75763-6
338. Exploring the Spatial Relationship between Urban Vitality and Urban Carbon Emissions H. Yang et al. 10.3390/rs15082173
339. Remote Spectrometric Measurements of Atmospheric Nitrogen Dioxide near Urban Thermal Power Plants D. Ionov & M. Makarova 10.1134/S1024856024701288
340. Temporal changes in the emissions of CH4 and CO from China estimated from CH4 / CO2 and CO / CO2 correlations observed at Hateruma Island Y. Tohjima et al. 10.5194/acp-14-1663-2014
341. Synergistic governance of urban heat islands, energy consumption, carbon emissions, and air pollution in China: Evidence from a Spatial Durbin Model T. Wu & Z. Qiao 10.1016/j.envpol.2025.126025
342. Population density regulation may mitigate the imbalance between anthropogenic carbon emissions and vegetation carbon sequestration D. Liang et al. 10.1016/j.scs.2023.104502
343. Space-based detection of missing sulfur dioxide sources of global air pollution C. McLinden et al. 10.1038/ngeo2724
344. Gridded estimates of CO2 emissions: uncertainty as a function of grid size S. Hogue et al. 10.1007/s11027-017-9770-z
345. Designing an Atmospheric Monitoring Network to Verify National CO2 Emissions S. Sim et al. 10.1007/s13143-023-00343-3
346. Spatiotemporal Investigation of Near-Surface CO2 and Its Affecting Factors Over Asia F. Mustafa et al. 10.1109/TGRS.2022.3178125
347. Detecting the Responses of CO2 Column Abundances to Anthropogenic Emissions from Satellite Observations of GOSAT and OCO-2 M. Sheng et al. 10.3390/rs13173524
348. National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the global stocktake B. Byrne et al. 10.5194/essd-15-963-2023
349. Long-term urban carbon dioxide observations reveal spatial and temporal dynamics related to urban characteristics and growth L. Mitchell et al. 10.1073/pnas.1702393115
350. Remote sensing and social sensing for socioeconomic systems: A comparison study between nighttime lights and location-based social media at the 500 m spatial resolution N. Zhao et al. 10.1016/j.jag.2020.102058
351. Estimation of global CO2 fluxes using ground-based and satellite (GOSAT) observation data with empirical orthogonal functions R. Zhuravlev et al. 10.1134/S1024856013060158
352. Did Polycentric and Compact Structure Reduce Carbon Emissions? A Spatial Panel Data Analysis of 286 Chinese Cities from 2002 to 2019 K. Zhu et al. 10.3390/land11020185
353. Resolution dependence of uncertainties in gridded emission inventories: a case study in Hebei, China B. Zheng et al. 10.5194/acp-17-921-2017
354. Estimates of CO2 Emissions and Uptake by the Water Surface near St. Petersburg Megalopolis G. Nerobelov & Y. Timofeyev 10.1134/S1024856021050158
355. Global anthropogenic CO2 emissions and uncertainties as a prior for Earth system modelling and data assimilation M. Choulga et al. 10.5194/essd-13-5311-2021
356. Improved Constraints on Northern Extratropical CO2 Fluxes Obtained by Combining Surface‐Based and Space‐Based Atmospheric CO2 Measurements B. Byrne et al. 10.1029/2019JD032029
357. Enhanced electrocatalytic overall alkaline water splitting induced by interfacial electron coupling of Mn3O4 nano-cube@CeO2/γ-FeOOH nanosheet hetero-structure D. Ghosh et al. 10.1039/D4TA05336B
358. Mapping the Carbon Footprint of Nations K. Kanemoto et al. 10.1021/acs.est.6b03227
359. Statistical characterization of urban CO2 emission signals observed by commercial airliner measurements T. Umezawa et al. 10.1038/s41598-020-64769-9
360. Separation of biospheric and fossil fuel fluxes of CO2 by atmospheric inversion of CO2 and 14CO2 measurements: Observation System Simulations S. Basu et al. 10.5194/acp-16-5665-2016
361. Mass-conserving tracer transport modelling on a reduced latitude-longitude grid with NIES-TM D. Belikov et al. 10.5194/gmd-4-207-2011
362. Diurnal variations and source apportionment of ozone at the summit of Mount Huang, a rural site in Eastern China J. Gao et al. 10.1016/j.envpol.2016.11.031
363. Development of a unit-based industrial emission inventory in the Beijing–Tianjin–Hebei region and resulting improvement in air quality modeling H. Zheng et al. 10.5194/acp-19-3447-2019
364. A decade of CO2 flux measured by the eddy covariance method including the COVID-19 pandemic period in an urban center in Sakai, Japan M. Ueyama & T. Takano 10.1016/j.envpol.2022.119210
365. Atmospheric CO2 in the megacity Hangzhou, China: Urban-suburban differences, sources and impact factors Y. Chen et al. 10.1016/j.scitotenv.2024.171635
366. Spaceborne detection of localized carbon dioxide sources F. Schwandner et al. 10.1126/science.aam5782
367. A multiresolution spatial parameterization for the estimation of fossil-fuel carbon dioxide emissions via atmospheric inversions J. Ray et al. 10.5194/gmd-7-1901-2014
368. Tracking unaccounted greenhouse gas emissions due to the war in Ukraine since 2022 R. Bun et al. 10.1016/j.scitotenv.2024.169879
369. Unravelling urban carbon dynamics: a multi-source data study on Nanchang city's carbon dioxide emissions L. Yao et al. 10.1080/17538947.2025.2491819
370. The impact of transport model differences on CO2 surface flux estimates from OCO-2 retrievals of column average CO2 S. Basu et al. 10.5194/acp-18-7189-2018
371. Advancing Regional–Scale Spatio–Temporal Dynamics of FFCO2 Emissions in Great Bay Area J. Zhao et al. 10.3390/rs16132354
372. Nonlinear mechanisms of CO2 emissions in growing and shrinking cities: An empirical study on integrated effects of aging and industrial structure in Japan X. He et al. 10.1016/j.jclepro.2024.142665
373. The impacts of fossil fuel emission uncertainties and accounting for 3-D chemical CO2 production on inverse natural carbon flux estimates from satellite and in situ data J. Wang et al. 10.1088/1748-9326/ab9795
374. Automated detection of atmospheric NO2 plumes from satellite data: a tool to help infer anthropogenic combustion emissions D. Finch et al. 10.5194/amt-15-721-2022
375. Determination of the emission rates of CO2 point sources with airborne lidar S. Wolff et al. 10.5194/amt-14-2717-2021
376. Retrieving the Vertical Profile of Greenhouse Gas Using Fourier Transform Spectrometer (FTS) - Part I: Carbon Dioxide (CO2) M. Kim et al. 10.5572/KOSAE.2024.40.3.349
377. A comparison of CO2 emissions from the national greenhouse gases inventory report with CO2 emissions from the global inverse models and the regional WRF-Chem in the Korean Peninsula Y. Cho et al. 10.1007/s11027-024-10195-5
378. Assimilation of OCO-2 retrievals with WRF-Chem/DART: A case study for the Midwestern United States Q. Zhang et al. 10.1016/j.atmosenv.2020.118106
379. Exploring the Spatiotemporal Dynamics of CO2 Emissions through a Combination of Nighttime Light and MODIS NDVI Data Y. Li et al. 10.3390/su151713143
380. Optimizing Urban Form to Enhance Dispersion of Carbon Emissions: A Case Study of Hangzhou S. Sun & L. Xu 10.3390/buildings14082478
381. The effects of economic and political integration on power plants’ carbon emissions in the post-soviet transition nations A. Jorgenson et al. 10.1088/1748-9326/aa650b
382. Terrestrial ecosystem carbon flux estimated using GOSAT and OCO-2 XCO2 retrievals H. Wang et al. 10.5194/acp-19-12067-2019
383. Distinguishing Anthropogenic CO2 Emissions From Different Energy Intensive Industrial Sources Using OCO‐2 Observations: A Case Study in Northern China S. Wang et al. 10.1029/2018JD029005
384. Comparing a global high-resolution downscaled fossil fuel CO2 emission dataset to local inventory-based estimates over 14 global cities J. Chen et al. 10.1186/s13021-020-00146-3
385. Using atmospheric trace gas vertical profiles to evaluate model fluxes: a case study of Arctic-CAP observations and GEOS simulations for the ABoVE domain C. Sweeney et al. 10.5194/acp-22-6347-2022
386. Technical note: A high-resolution inverse modelling technique for estimating surface CO2 fluxes based on the NIES-TM–FLEXPART coupled transport model and its adjoint S. Maksyutov et al. 10.5194/acp-21-1245-2021
387. Investigating the effect of carbon leakage on the environmental Kuznets curve using luminosity data A. Steinkraus 10.1017/S1355770X17000249
388. Estimating the Carbon Emissions of Remotely Sensed Energy-Intensive Industries Using VIIRS Thermal Anomaly-Derived Industrial Heat Sources and Auxiliary Data X. Kong et al. 10.3390/rs14122901
389. Mapping spatio-temporal changes of Chinese electric power consumption using night-time imagery N. Zhao et al. 10.1080/01431161.2012.684076
390. On-Orbit Relative Radiometric Calibration of the Night-Time Sensor of the LuoJia1-01 Satellite G. Zhang et al. 10.3390/s18124225
391. WOMBAT v1.0: a fully Bayesian global flux-inversion framework A. Zammit-Mangion et al. 10.5194/gmd-15-45-2022
392. Estimating CO2 (carbon dioxide) emissions at urban scales by DMSP/OLS (Defense Meteorological Satellite Program's Operational Linescan System) nighttime light imagery: Methodological challenges and a case study for China L. Meng et al. 10.1016/j.energy.2014.04.103
393. Estimates of European uptake of CO2 inferred from GOSAT XCO2 retrievals: sensitivity to measurement bias inside and outside Europe L. Feng et al. 10.5194/acp-16-1289-2016
394. Spatiotemporal variations in urban CO2 flux with land-use types in Seoul C. Park et al. 10.1186/s13021-022-00206-w
395. Understanding China's CO2 emission drivers: Insights from random forest analysis and remote sensing data Q. Lei et al. 10.1016/j.heliyon.2024.e29086
396. An atmospheric inversion over the city of Cape Town: sensitivity analyses A. Nickless et al. 10.5194/acp-19-7789-2019
397. The Effect of GOSAT Observations on Estimates of Net CO₂ Flux in Semi-Arid Regions of the Southern Hemisphere M. Kondo et al. 10.2151/sola.2016-037
398. Estimating Global Anthropogenic CO2 Gridded Emissions Using a Data-Driven Stacked Random Forest Regression Model Y. Zhang et al. 10.3390/rs14163899
399. High Pressure Supercritical Carbon Dioxide Separation from its Mixture with Nitrogen at Different Temperatures R. El-Maghraby et al. 10.4028/www.scientific.net/MSF.1008.1
400. A Data-Driven Assessment of Biosphere-Atmosphere Interaction Impact on Seasonal Cycle Patterns of XCO2 Using GOSAT and MODIS Observations Z. He et al. 10.3390/rs9030251
401. Tracking city CO2 emissions from space using a high-resolution inverse modelling approach: a case study for Berlin, Germany D. Pillai et al. 10.5194/acp-16-9591-2016
402. Regional-Scale Estimation of Electric Power and Power Plant CO2 Emissions Using Defense Meteorological Satellite Program Operational Linescan System Nighttime Satellite Data H. Letu et al. 10.1021/ez500093s
403. High-resolution temporal and spatial evolution of carbon emissions from building operations in Beijing J. Wang et al. 10.1016/j.jclepro.2022.134272
404. The potential of CO2 satellite monitoring for climate governance: A review G. Pan et al. 10.1016/j.jenvman.2020.111423
405. High-resolution spatiotemporal patterns of China’s FFCO2 emissions under the impact of LUCC from 2000 to 2015 J. Zhao et al. 10.1088/1748-9326/ab6edc
406. Examining CO2 Model Observation Residuals Using ACT‐America Data T. Gerken et al. 10.1029/2020JD034481
407. Using a combination of nighttime light and MODIS data to estimate spatiotemporal patterns of CO2 emissions at multiple scales W. Guo et al. 10.1016/j.scitotenv.2022.157630
408. Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation K. Hu et al. 10.3390/rs16183394
409. Spatiotemporal Evolution and Influencing Factors of Carbon Footprint in Yangtze River Economic Belt Z. Shao et al. 10.3390/land14030641
410. Spatial modelling of street-level carbon emissions with multi-source open data: A case study of Guangzhou Y. Zheng et al. 10.1016/j.uclim.2024.101974
411. Mapping Global Fossil Fuel Combustion CO2 Emissions at High Resolution by Integrating Nightlight, Population Density, and Traffic Network Data J. Ou et al. 10.1109/JSTARS.2015.2476347
412. Provincial localization framework for SDGs in China: Enhancing support for sustainable governance W. Song et al. 10.1016/j.apgeog.2024.103505
413. Co-Response of Atmospheric NO2 and CO2 Concentrations from Satellites Observations of Anthropogenic CO2 Emissions for Assessing the Synergistic Effects of Pollution and Carbon Reduction K. Guo et al. 10.3390/rs17050739
414. Application of DMSP/OLS Nighttime Light Images: A Meta-Analysis and a Systematic Literature Review Q. Huang et al. 10.3390/rs6086844
415. Regional uncertainty of GOSAT XCO2 retrievals in China: quantification and attribution N. Bie et al. 10.5194/amt-11-1251-2018
416. Dark Times: nighttime satellite imagery as a detector of regional disparity and the geography of conflict L. Coscieme et al. 10.1080/15481603.2016.1260676
417. Exploiting OMI NO2 satellite observations to infer fossil-fuel CO2 emissions from U.S. megacities D. Goldberg et al. 10.1016/j.scitotenv.2019.133805
418. Nighttime Lights and Population Migration: Revisiting Classic Demographic Perspectives with an Analysis of Recent European Data X. Chen 10.3390/rs12010169
419. A Vector Map of Carbon Emission Based on Point-Line-Area Carbon Emission Classified Allocation Method H. Liu et al. 10.3390/su122310058
420. A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling N. Charkovska et al. 10.1007/s11027-018-9836-6
421. How and why did fossil fuel use change in Fukushima Prefecture before and after the Great East Japan Earthquake? R. Cong et al. 10.1016/j.egyr.2021.12.046
422. Quantification of urban atmospheric boundary layer greenhouse gas dry mole fraction enhancements in the dormant season: Results from the Indianapolis Flux Experiment (INFLUX) N. Miles et al. 10.1525/elementa.127
423. Spatiotemporal Patterns and Drivers of the Carbon Budget in the Yangtze River Delta Region, China Q. Fu et al. 10.3390/land11081230
424. A city-level comparison of fossil-fuel and industry processes-induced CO2 emissions over the Beijing-Tianjin-Hebei region from eight emission inventories P. Han et al. 10.1186/s13021-020-00163-2
425. Poverty alleviation and local environmental degradation: An empirical analysis in Colombia D. Malerba 10.1016/j.worlddev.2019.104776
426. Impact of net-zero emissions on atmospheric CO2 concentration in China: Ideal simulations based on the GEOS-Chem model J. Tan et al. 10.1007/s11430-024-1502-y
427. Power generation structural transformation, carbon emissions, air pollution and economic development: a monthly data analysis for 278 cities in China G. Zhang et al. 10.1080/00036846.2025.2497562
428. The digital revolution's environmental paradox: Exploring the synergistic effects of pollution and carbon reduction via industrial metamorphosis and displacement Z. Li et al. 10.1016/j.techfore.2024.123528
429. A new global gridded data set of CO2 emissions from fossil fuel combustion: Methodology and evaluation P. Rayner et al. 10.1029/2009JD013439
430. Spatial effects of carbon dioxide emissions from residential energy consumption: A county-level study using enhanced nocturnal lighting H. Lu & G. Liu 10.1016/j.apenergy.2014.06.036
431. Doomed to fail? A call to reform global climate governance and greenhouse gas inventories K. Herman 10.1007/s10784-024-09637-x
432. Evaluation of various observing systems for the global monitoring of CO2 surface fluxes K. Hungershoefer et al. 10.5194/acp-10-10503-2010
433. Creating a Global Grid of Distributed Fossil Fuel CO2 Emissions from Nighttime Satellite Imagery T. Ghosh et al. 10.3390/en3121895
434. Urban CO2 emissions in China: Spatial boundary and performance comparison B. Cai & L. Zhang 10.1016/j.enpol.2013.10.072
435. Estimating spatiotemporal variations of city-level energy-related CO2 emissions: An improved disaggregating model based on vegetation adjusted nighttime light data X. Liu et al. 10.1016/j.jclepro.2017.12.197
436. Developing a high-resolution emission inventory tool for low-carbon city management using hybrid method – A pilot test in high-density Hong Kong M. Cai et al. 10.1016/j.enbuild.2020.110376
437. Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data J. Ou et al. 10.1371/journal.pone.0138310
438. A remote sensing technique for global monitoring of power plant CO2emissions from space and related applications H. Bovensmann et al. 10.5194/amt-3-781-2010
439. Analysis of the Economic Ripple Effect of the United States on the World due to Future Climate Change Z. Zhang et al. 10.1029/2018EF000839