174 citations as recorded by crossref.

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8. A Monte Carlo Method for Quantifying Uncertainties in the Official Greenhouse Gas Emission Factors Database of Costa Rica G. Molina-Castro https://doi.org/10.3389/fenvs.2022.896256
9. Energy Based Greenhouse Gas Inventory of Port Operations: A Case Study from Lampung, Indonesia W. Utami et al. https://doi.org/10.64780/jeims.v1i2.11
10. Enhancing the Feed Efficiency of Crop Residues in Ruminants – A Comprehensive Review M. Kamal et al. https://doi.org/10.2478/aoas-2024-0081
11. High-resolution inversion of methane emissions over Europe using the Community Inversion Framework and FLEXPART A. Mengistu et al. https://doi.org/10.5194/acp-26-12295-2026
12. Global nitrous oxide budget (1980–2020) H. Tian et al. https://doi.org/10.5194/essd-16-2543-2024
13. India leads in emission intensity per GDP: Insights from the gridded emission inventory for residential, road transport, and energy sectors V. Velamuri et al. https://doi.org/10.1016/j.jes.2024.10.015
14. Global nitrous oxide emissions from livestock manure during 1890–2020: An IPCC tier 2 inventory L. Zhang et al. https://doi.org/10.1111/gcb.17303
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18. Observing CO2 emissions from global key coal-fired power plants based on Orbiting carbon Observatory-3 satellite and improved Gaussian plume model Z. Zhou et al. https://doi.org/10.1016/j.jclepro.2025.145945
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23. On the utility of simultaneous CO2 and CO observations from GOSAT-2: examining global urban emissions and their socioeconomic characteristics M. Matsuda et al. https://doi.org/10.1088/2515-7620/ae9134
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25. Automation and decarbonization: the role of industrial robots in China W. Zhao & K. Liu https://doi.org/10.1080/17538963.2026.2632455
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27. Retrieving the Vertical Profile of Greenhouse Gas Using Fourier Transform Spectrometer (FTS) - Part II: Methane (CH4) M. Kim et al. https://doi.org/10.5572/KOSAE.2024.40.3.361
28. Tracking air pollution and CO2 emissions in 13,189 urban areas worldwide using large geospatial datasets S. Kim et al. https://doi.org/10.1038/s43247-025-02270-9
29. Modelling wetland methane emission estimates by leveraging new observations of anthropogenic point-source plumes A. Campbell et al. https://doi.org/10.5194/amt-19-5603-2026
30. Study on carbon emission characteristics and its influencing factors of energy consumption in Sichuan Province, China Q. Li et al. https://doi.org/10.3389/fenvs.2024.1414730
31. Evaluation of atmospheric sulfur dioxide simulated with the EMAC (version 2.55) Chemistry–Climate Model using satellite and ground-based observations I. Makroum et al. https://doi.org/10.5194/gmd-19-447-2026
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35. Efficacy of the CO Tracer Technique in Partitioning Biogenic and Anthropogenic Atmospheric CO2 Signals in the Humid Subtropical Eastern Highland Rim City of Cookeville, Tennessee W. Gichuhi & L. Gamage https://doi.org/10.3390/atmos14020208
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44. Magnitude and seasonal variation of N2O and CH4 emissions over a mixed agriculture-urban region X. Tong et al. https://doi.org/10.1016/j.agrformet.2023.109433
45. Estimating CO2 emissions for 108 000 European cities D. Moran et al. https://doi.org/10.5194/essd-14-845-2022
46. An inter-comparison of inverse models for estimating European CH4 emissions E. Ioannidis et al. https://doi.org/10.5194/essd-18-167-2026
47. Mobile Measurement of CO2 and CH4 on Ring Roads and Expressways in Xi’an, China: Spatial Patterns and Influencing Factors F. Wei et al. https://doi.org/10.3390/atmos16121351
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49. The booming non-food bioeconomy drives large share of global land-use emissions S. Wang et al. https://doi.org/10.1016/j.gloenvcha.2023.102760
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53. The spatial impacts of the circular economy on carbon intensity - new evidence from the super-efficient SBM-DEA model P. Chen https://doi.org/10.1177/0958305X221125125
54. High-resolution inversion of urban methane emissions in the Chengdu–Chongqing economic circle using ground-based observations and a dynamic error Bayesian framework Z. Xia et al. https://doi.org/10.1016/j.jclepro.2026.148229
55. Mapping High-Resolution Carbon Emission Spatial Distribution Combined with Carbon Satellite and Muti-Source Data L. Cui et al. https://doi.org/10.3390/rs17173125
56. A comprehensive and synthetic dataset for global, regional, and national greenhouse gas emissions by sector 1970–2018 with an extension to 2019 J. Minx et al. https://doi.org/10.5194/essd-13-5213-2021
57. Trends in atmospheric methane concentrations since 1990 were driven and modified by anthropogenic emissions R. Skeie et al. https://doi.org/10.1038/s43247-023-00969-1
58. Heterogeneous drivers of decarbonization in the global power sector X. Peng et al. https://doi.org/10.1088/1748-9326/add9b1
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60. Air Pollution Monitoring, and Modelling: An Overview F. Tahir Bahadur et al. https://doi.org/10.1080/15275922.2023.2297437
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64. Industrial-source heavy metal(loid) pollution pressure on China's farmland soils: Enterprise-level assessment and spatial regionalization Y. Guan et al. https://doi.org/10.1016/j.jhazmat.2026.143640
65. Deriving regional and point source nitrogen oxides emissions in China from TROPOMI using the directional derivative approach with nonlinear chemical lifetime fitting L. Chen et al. https://doi.org/10.5194/essd-18-2749-2026
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67. Implications of ozone transport on air quality in the Sichuan Basin, China Y. Zhang et al. https://doi.org/10.1007/s11356-024-33991-7
68. Validation of the 2021 Emission Inventory for Cuenca, Ecuador, Through Weather and Air Quality Modeling in the Framework of WRF-Chem R. Parra et al. https://doi.org/10.3390/atmos17060569
69. Review of methane emission source tracing methods in oilfield regions Y. Liu et al. https://doi.org/10.1016/j.jgsce.2025.205708
70. Identifying a Country’s Freight Transport-Intensive Economic Sectors and Their Logistics Emissions—Method Development and Exemplary Evaluation with Austria P. Miklautsch et al. https://doi.org/10.3390/su142215050
71. The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2019 A. Petrescu et al. https://doi.org/10.5194/essd-15-1197-2023
72. Global warming will largely increase waste treatment CH4 emissions in Chinese megacities: insight from the first city-scale CH4 concentration observation network in Hangzhou, China C. Hu et al. https://doi.org/10.5194/acp-23-4501-2023
73. Mapping spatio-temporal changes of energy consumption-related NOx emissions at grid scale in the Pearl River Delta region using nighttime light data X. Li et al. https://doi.org/10.1016/j.sftr.2025.101535
74. Effects of Emission Variability on Atmospheric CO2 Concentrations in Mainland China W. Lu et al. https://doi.org/10.3390/rs17050814
75. Biomass burning contributions to Mexico City’s atmospheric CO2 estimated using a multi-isotope approach M. Villarreal-Brito et al. https://doi.org/10.1016/j.jes.2026.03.048
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78. Spatiotemporal and sectoral dynamics of air pollutants and greenhouse gas emissions in Nigeria (1970–2023) D. Omokpariola https://doi.org/10.1007/s44274-026-00821-1
79. Identifying Urban CO2 Marginal Abatement Costs Under Alternative Reference Technologies: Evidence from 278 Chinese Cities Q. Xiao et al. https://doi.org/10.3390/su18189354
80. Quantification of SO2 and CO2 emission rates from coal-fired power plants in the Korean peninsula via airborne measurements J. Kim et al. https://doi.org/10.1016/j.scitotenv.2025.179430
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88. Global Covenant of Mayors, a dataset of greenhouse gas emissions for 6200 cities in Europe and the Southern Mediterranean countries A. Kona et al. https://doi.org/10.5194/essd-13-3551-2021
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90. Evaluation of the coupled high-resolution atmospheric chemistry model system MECO(n) using in situ and MAX-DOAS NO2 measurements V. Kumar et al. https://doi.org/10.5194/amt-14-5241-2021
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110. CH4 emissions from Northern Europe wetlands: compared data assimilation approaches G. Monteil et al. https://doi.org/10.5194/acp-25-14251-2025
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115. Trends and drivers of anthropogenic NO emissions in China since 2020 H. Li et al. https://doi.org/10.1016/j.ese.2024.100425
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135. Six years of continuous carbon isotope composition measurements of methane in Heidelberg (Germany) – a study of source contributions and comparison to emission inventories A. Hoheisel & M. Schmidt https://doi.org/10.5194/acp-24-2951-2024
136. Identifying Major CO2 and CH4 Emission Sources with the STILT Model in the Seoul Metropolitan Area During Winter 2024, Including the ASIA-AQ Campaign H. Choi et al. https://doi.org/10.1007/s13143-025-00428-1
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