376 citations as recorded by crossref.

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20. What role does digital finance play in low-carbon development? Evidence from five major urban agglomerations in China J. Wu et al. 10.1016/j.jenvman.2023.118060
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22. Biomass Resources and Emission Reduction Potential of Agricultural and Livestock Residues in Mainland China from 2013 to 2022 K. Luo et al. 10.3390/su16156460
23. Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager D. Wang et al. 10.3390/app14062450
24. Spatially Non-Stationary Response of Carbon Emissions to Urbanization in Han River Ecological Economic Belt, China W. Li et al. 10.3390/ijerph20010363
25. The impact of industry-favoring land allocation strategy on urban carbon emissions: a city-level empirical study in China Y. Qi et al. 10.1007/s10668-024-04550-4
26. 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
27. Analyzing nitrogen oxides to carbon dioxide emission ratios from space: A case study of Matimba Power Station in South Africa J. Hakkarainen et al. 10.1016/j.aeaoa.2021.100110
28. 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
29. 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
30. Can we use atmospheric CO2 measurements to verify emission trends reported by cities? Lessons from a 6-year atmospheric inversion over Paris J. Lian et al. 10.5194/acp-23-8823-2023
31. Is dense or sprawl growth more prone to heat-related health risks? Spatial regression-based study in Delhi, India S. Pramanik et al. 10.1016/j.scs.2022.103808
32. Carbon Monitor, a near-real-time daily dataset of global CO2 emission from fossil fuel and cement production Z. Liu et al. 10.1038/s41597-020-00708-7
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34. 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
35. Soil respiration–driven CO 2 pulses dominate Australia’s flux variability E. Metz et al. 10.1126/science.add7833
36. 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
37. 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
38. 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
39. Modeling fuel-, vehicle-type-, and age-specific CO2 emissions from global on-road vehicles in 1970–2020 L. Yan et al. 10.5194/essd-16-4497-2024
40. 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
41. 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
42. Scaling of Chinese urban CO2 emissions and multiple dimensions of city size C. Yang & S. Zhao 10.1016/j.scitotenv.2022.159502
43. The impact of urban configuration types on urban heat islands, air pollution, CO2 emissions, and mortality in Europe: a data science approach T. Iungman et al. 10.1016/S2542-5196(24)00120-7
44. 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
45. Role of space station instruments for improving tropical carbon flux estimates using atmospheric data P. Palmer et al. 10.1038/s41526-022-00231-6
46. Lagrangian inversion of anthropogenic CO2 emissions from Beijing using differential column measurements K. Che et al. 10.1088/1748-9326/ac7477
47. 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
48. The effect of digital technology on residential and non-residential carbon emission Z. Pu et al. 10.1016/j.iref.2024.103495
49. Proactive visual prediction auditing the green eco-safety through backcasting approach booster by grey recruitment priority conceptual framework A. Abed et al. 10.1016/j.heliyon.2022.e11729
50. Mapping the spatial-temporal changes in energy consumption-related carbon emissions in the Beijing-Tianjin-Hebei region via nighttime light data X. Zhang et al. 10.1016/j.scs.2023.104476
51. Daily CO2 Emission Reduction Indicates the Control of Activities to Contain COVID-19 in China R. Wang et al. 10.1016/j.xinn.2020.100062
52. Contributions of China's terrestrial ecosystem carbon uptakes to offsetting CO2 emissions under different scenarios over 2001–2060 M. Xu et al. 10.1016/j.gloplacha.2024.104485
53. Current status, future prediction and offset potential of fossil fuel CO2 emissions in China J. Cao et al. 10.1016/j.jclepro.2023.139207
54. Detection of Chinese Spring Festival in Beijing using in-situ CO2 observations and atmospheric inversion Z. Liu et al. 10.1016/j.atmosenv.2024.120446
55. Estimating power plant CO2 emission using OCO-2 XCO2 and high resolution WRF-Chem simulations T. Zheng et al. 10.1088/1748-9326/ab25ae
56. 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
57. Estimating the marginal effects of plot-scale socio-economic and spatial morphological factors on carbon emissions from non-linear perspective C. Zhao & H. Jiao 10.1016/j.jclepro.2024.144318
58. China’s ambitious low-carbon goals require fostering city-level renewable energy transitions G. Yang et al. 10.1016/j.isci.2023.106263
59. Designing an Atmospheric Monitoring Network to Verify National CO2 Emissions S. Sim et al. 10.1007/s13143-023-00343-3
60. Exploring the trends of research: a bibliometric analysis of global ship emission estimation practices K. Mohiuddin et al. 10.1007/s40722-024-00341-1
61. Spatiotemporal association of carbon dioxide emissions in China's urban agglomerations Y. Qian et al. 10.1016/j.jenvman.2022.116109
62. Impact of radiative forcing of spatially varying CO2 concentrations on net primary production J. Peng et al. 10.3389/feart.2022.953605
63. Population, GDP, and Carbon Emissions as Revealed by SNPP-VIIRS Nighttime Light Data in China With Different Scales K. Shi et al. 10.1109/LGRS.2022.3195266
64. Exploring the effect of urban spatial development pattern on carbon dioxide emissions in China: A socioeconomic density distribution approach based on remotely sensed nighttime light data S. Liu et al. 10.1016/j.compenvurbsys.2022.101847
65. The impact and channel effects of Yangtze River’s great protection strategy on carbon emissions: quasi-experimental evidence from China Q. He et al. 10.1007/s10668-024-04692-5
66. Estimating CO2 Emissions from Large Scale Coal-Fired Power Plants Using OCO-2 Observations and Emission Inventories Y. Hu & Y. Shi 10.3390/atmos12070811
67. National CO2budgets (2015–2020) inferred from atmospheric CO2observations in support of the global stocktake B. Byrne et al. 10.5194/essd-15-963-2023
68. City-level carbon emissions accounting and differentiation integrated nighttime light and city attributes Y. Zhou et al. 10.1016/j.resconrec.2022.106337
69. 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
70. Assimilation of atmospheric CO2 observations from space can support national CO2 emission inventories T. Kaminski et al. 10.1088/1748-9326/ac3cea
71. Multi-source data assessment and multi-factor analysis of urban carbon emissions: A case study of the Pearl River Basin, China B. Zhang et al. 10.1016/j.uclim.2023.101653
72. 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
73. 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
74. 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
75. Understanding elevated CO2 concentrations in East Asia relative to the global mean during boreal spring on the slow and interannual timescales S. Yeh et al. 10.1016/j.scitotenv.2023.166098
76. Mapping carbon–thermal environments for comprehending real-time scenarios C. Srivastava & A. Bharat 10.1007/s11600-024-01387-3
77. A top-down estimation of subnational CO2 budget using a global high-resolution inverse model with data from regional surface networks L. Nayagam et al. 10.1088/1748-9326/ad0f74
78. Research on regional carbon emission scenario simulation based on GA-BP-KDE under uncertain conditions K. Pan et al. 10.1016/j.apr.2024.102375
79. Quantifying strong point sources emissions of CO2 using spaceborne LiDAR: Method development and potential analysis T. Shi et al. 10.1016/j.enconman.2023.117346
80. Evaluating the Ability of the Pre-Launch TanSat-2 Satellite to Quantify Urban CO2 Emissions K. Wu et al. 10.3390/rs15204904
81. 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
82. Constraining Sector‐Specific CO2 Fluxes Using Space‐Based XCO2 Observations Over the Los Angeles Basin D. Roten et al. 10.1029/2023GL104376
83. Linking global terrestrial CO<sub>2</sub> fluxes and environmental drivers: inferences from the Orbiting Carbon Observatory 2 satellite and terrestrial biospheric models Z. Chen et al. 10.5194/acp-21-6663-2021
84. Five years of variability in the global carbon cycle: comparing an estimate from the Orbiting Carbon Observatory-2 and process-based models Z. Chen et al. 10.1088/1748-9326/abfac1
85. Reconciliation of asynchronous satellite-based NO2 and XCO2 enhancements with mesoscale modeling over two urban landscapes R. Lei et al. 10.1016/j.rse.2022.113241
86. Global and regional carbon budget for 2015–2020 inferred from OCO-2 based on an ensemble Kalman filter coupled with GEOS-Chem Y. Kong et al. 10.5194/acp-22-10769-2022
87. Absolute Contribution of the Non-Uniform Spatial Distribution of Atmospheric CO2 to Net Primary Production through CO2-Radiative Forcing J. Peng et al. 10.3390/su131910897
88. Anthropogenic Osmium in Macroalgae from Tokyo Bay Reveals Widespread Contamination from Municipal Solid Waste A. Sproson et al. 10.1021/acs.est.0c01602
89. 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
90. Impacts of urbanization on insect herbivory and plant defences in oak trees X. Moreira et al. 10.1111/oik.05497
91. The Carbon Cycle of Southeast Australia During 2019–2020: Drought, Fires, and Subsequent Recovery B. Byrne et al. 10.1029/2021AV000469
92. Southern California megacity CO<sub>2</sub>, CH<sub>4</sub>, 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
93. Automated detection of atmospheric NO<sub>2</sub> plumes from satellite data: a tool to help infer anthropogenic combustion emissions D. Finch et al. 10.5194/amt-15-721-2022
94. Statistical characterization of urban CO2 emission signals observed by commercial airliner measurements T. Umezawa et al. 10.1038/s41598-020-64769-9
95. Mapping contiguous XCO2 by machine learning and analyzing the spatio-temporal variation in China from 2003 to 2019 M. Zhang & G. Liu 10.1016/j.scitotenv.2022.159588
96. Towards monitoring localized CO<sub>2</sub> emissions from space: co-located regional CO<sub>2</sub> and NO<sub>2</sub> enhancements observed by the OCO-2 and S5P satellites M. Reuter et al. 10.5194/acp-19-9371-2019
97. What Are the Different Measures of Mobility Telling Us About Surface Transportation CO2 Emissions During the COVID‐19 Pandemic? J. Gensheimer et al. 10.1029/2021JD034664
98. Monitoring of Atmospheric Carbon Dioxide over Pakistan Using Satellite Dataset N. An et al. 10.3390/rs14225882
99. Do Environmental Regulations Facilitate a Low-Carbon Transformation in China’s Resource-Based Cities? W. Xie et al. 10.3390/ijerph20054502
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102. 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
103. Using Space‐Based CO2 and NO2 Observations to Estimate Urban CO2 Emissions E. Yang et al. 10.1029/2022JD037736
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108. Uncertainty and sensitivity analysis of modeling plant CO2 exchange in the built environment P. Li & Z. Wang 10.1016/j.buildenv.2020.107539
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114. Unveiling dynamic change, driving factors and recommended practices in sustainable development of county-scale within Qinghai province, China T. Ma et al. 10.1007/s10668-024-05569-3
115. Evaluating Global Atmospheric Inversions of Terrestrial Net Ecosystem Exchange CO2 Over North America on Seasonal and Sub‐Continental Scales Y. Cui et al. 10.1029/2022GL100147
116. Detection of Anthropogenic CO2 Emission Signatures with TanSat CO2 and with Copernicus Sentinel-5 Precursor (S5P) NO2 Measurements: First Results D. Yang et al. 10.1007/s00376-022-2237-5
117. A comparison of estimates of global carbon dioxide emissions from fossil carbon sources R. Andrew 10.5194/essd-12-1437-2020
118. 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
119. Optical diagnostic study of internal and external EGR combined with oxygenated fuels of n-butanol, PODE3 and DMC to optimize the combustion process of FT synthetic diesel W. Sun et al. 10.1016/j.fuel.2023.129390
120. Effects of urban forms on CO2 emissions in China from a multi-perspective analysis K. Shi et al. 10.1016/j.jenvman.2020.110300
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122. 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
123. 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
124. Spatiotemporal Distribution of Carbon Sink Indicators—NPP and Its Driving Analysis in Ordos City, China L. Zhu et al. 10.3390/app13116457
125. 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
126. Dynamic evolutionary characteristics and influence mechanisms of carbon emission intensity in counties of the Yangtze River Delta, China Z. Ma et al. 10.1007/s11356-023-30392-0
127. 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
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130. Timely estimates of India's annual and monthly fossil CO<sub>2</sub> emissions R. Andrew 10.5194/essd-12-2411-2020
131. Country-scale trends in air pollution and fossil fuel CO2 emissions during 2001–2018: confronting the roles of national policies and economic growth R. Lei et al. 10.1088/1748-9326/abc9e1
132. A methodology to constrain carbon dioxide emissions from coal-fired power plants using satellite observations of co-emitted nitrogen dioxide F. Liu et al. 10.5194/acp-20-99-2020
133. 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
134. Prediction of Satellite-Based Column CO2 Concentration by Combining Emission Inventory and LULC Information S. Bhattacharjee & J. Chen 10.1109/TGRS.2020.2985047
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138. 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
139. 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
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142. Impact of El Niño Southern Oscillation on CO2 and Solar‐Induced Fluorescence Over the Indo‐Pacific Region R. Albright et al. 10.1029/2023EA003126
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145. 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
146. CH4 and CO emission estimates for megacities: deriving enhancement ratios of CO2, CH4, and CO from GOSAT-2 observations H. Ohyama et al. 10.1088/1748-9326/ad89e0
147. Real-Time Carbon Emissions Monitoring of High-Energy-Consumption Enterprises in Guangxi Based on Electricity Big Data C. Zhou et al. 10.3390/en16135124
148. Spatiotemporal analysis of atmospheric methane concentrations and key influencing factors using machine learning in the Middle East (2010–2021) S. Mousavi 10.1016/j.rsase.2024.101406
149. Water Stress Dominates 21st‐Century Tropical Land Carbon Uptake P. Levine et al. 10.1029/2023GB007702
150. High‐Resolution Lagrangian Inverse Modeling of CO2 Emissions Over the Paris Region During the First 2020 Lockdown Period K. Nalini et al. 10.1029/2021JD036032
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153. Quantifying the Impact and Importance of Natural, Economic, and Mining Activities on Environmental Quality Using the PIE-Engine Cloud Platform: A Case Study of Seven Typical Mining Cities in China J. Zeng et al. 10.3390/su16041447
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166. Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO): global and European emission temporal profile maps for atmospheric chemistry modelling M. Guevara et al. 10.5194/essd-13-367-2021
167. Carbon peak prediction for differentiated cities from a low-carbon perspective: Key factors, scenario analysis, and low-carbon pathways K. Pan et al. 10.1016/j.ecolind.2024.112629
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