Articles | Volume 14, issue 2
https://doi.org/10.5194/essd-14-845-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-845-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
23 Feb 2022
Data description article |
| 23 Feb 2022
| 23 Feb 2022
Estimating CO2 emissions for 108 000 European cities
Daniel Moran
CORRESPONDING AUTHOR
Industrial Ecology Programme, Department of Energy and Process
Engineering, Norwegian University of Science and Technology, Trondheim,
Norway
Peter-Paul Pichler
Department of Social Metabolism and Impacts, Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
Heran Zheng
Industrial Ecology Programme, Department of Energy and Process
Engineering, Norwegian University of Science and Technology, Trondheim,
Norway
Helene Muri
Industrial Ecology Programme, Department of Energy and Process
Engineering, Norwegian University of Science and Technology, Trondheim,
Norway
Jan Klenner
Industrial Ecology Programme, Department of Energy and Process
Engineering, Norwegian University of Science and Technology, Trondheim,
Norway
Diogo Kramel
Industrial Ecology Programme, Department of Energy and Process
Engineering, Norwegian University of Science and Technology, Trondheim,
Norway
Johannes Többen
Department of Social Metabolism and Impacts, Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
Helga Weisz
Department of Social Metabolism and Impacts, Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
Thomas Wiedmann
Sustainability Assessment Program, School of Civil and Environmental
Engineering, UNSW Sydney, Australia
Annemie Wyckmans
Faculty for Architecture and Design, Norwegian University of Science and
Technology, Trondheim, Norway
Anders Hammer Strømman
Industrial Ecology Programme, Department of Energy and Process
Engineering, Norwegian University of Science and Technology, Trondheim,
Norway
Kevin R. Gurney
School of Informatics, Computing, and Cyber Systems, Northern Arizona
University, Flagstaff, AZ, USA
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Total article views: 8,000 (including HTML, PDF, and XML)
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Cited
22 citations as recorded by crossref.
- Optimizing Spatial Scales for Evaluating High-Resolution CO2 Fossil Fuel Emissions: Multi-Source Data and Machine Learning Approach Y. Fang et al. https://doi.org/10.3390/su17209009
- The increasing emission divide between cities of the Global North and Global South: Towards adjustable mitigation scenarios at the city level Y. Jabareen https://doi.org/10.1177/00420980231152846
- Tracing CO2 emissions across megacity landscapes: beyond citywide totals to structural heterogeneity and mitigation Y. Zhu et al. https://doi.org/10.1016/j.ese.2025.100602
- Key predictors of greenhouse gas emissions for cities committing to mitigate and adapt to climate change C. Franco et al. https://doi.org/10.1016/j.cities.2023.104342
- Key Predictors of Greenhouse Gas Emissions for Cities Committing to Mitigate and Adapt to Climate Change C. Franco et al. https://doi.org/10.2139/ssrn.4183235
- Rethinking urban riparian ecosystems as a frontline strategy to counter climate change: mapping 60 years of carbon sequestration evolution in Padua, Italy F. Peroni et al. https://doi.org/10.3389/fclim.2023.1235886
- Predicting European cities’ climate mitigation performance using machine learning A. Hsu et al. https://doi.org/10.1038/s41467-022-35108-5
- Uncovering divergences and potential gaps in local greenhouse gases emissions accounting and aggregation V. Palermo et al. https://doi.org/10.1016/j.crsust.2024.100263
- How green can it be? A methodology for calculating green roof retrofit potential in Valencia M. Zayas-Orihuela et al. https://doi.org/10.1016/j.enbuild.2024.114893
- Can green roofs decarbonize the cities? An artificial vision-driven approach to calculating their potential T. Pino-Gallardo et al. https://doi.org/10.1016/j.scs.2025.106672
- Prediction of greenhouse gas emissions for cities and local municipalities monitoring their advances to mitigate and adapt to climate change C. Franco et al. https://doi.org/10.1016/j.scs.2022.104114
- Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method Y. Wang et al. https://doi.org/10.5194/essd-15-3597-2023
- Machine learning estimates for G20 subnational urban GHG emissions from 2000–2020 Y. Yu et al. https://doi.org/10.1038/s41597-026-06691-9
- Urban emissions and land use efficiency scenarios for avoiding increments of global warming Ş. Kılkış https://doi.org/10.1016/j.energy.2024.132174
- Environmental monitoring in European regions using the sustainable development goals (SDG) framework P. Nakhle et al. https://doi.org/10.1016/j.indic.2023.100332
- Green methanol production from photovoltaics in Europe D. Rodriguez-Pastor et al. https://doi.org/10.1016/j.renene.2025.123751
- Urban Scaling Functions: Emission, Pollution and Health C. La Porta & S. Zapperi https://doi.org/10.1007/s11524-024-00888-2
- Cities: Allocating climate change responsibilities at planetary scale M. Hachaichi https://doi.org/10.1016/j.uclim.2022.101329
- Kuznets and the cities: Urban level EKC evidence from Europe M. Rizzati et al. https://doi.org/10.1016/j.ecolind.2023.110143
- An open-source regionalization approach for subnational EEMRIO insights and proof-of-concept application to EU steel circularity A. Kortleve et al. https://doi.org/10.1007/s44498-026-00024-0
- Why Local Governments Set Climate Targets: Effects of City Size and Political Costs K. Eisenack https://doi.org/10.1007/s10640-024-00919-1
- A nationwide integrated demand–supply framework for analyzing urban carbon footprints and mitigation potential in Israel I. Megira et al. https://doi.org/10.1038/s42949-025-00289-7
22 citations as recorded by crossref.
- Optimizing Spatial Scales for Evaluating High-Resolution CO2 Fossil Fuel Emissions: Multi-Source Data and Machine Learning Approach Y. Fang et al. https://doi.org/10.3390/su17209009
- The increasing emission divide between cities of the Global North and Global South: Towards adjustable mitigation scenarios at the city level Y. Jabareen https://doi.org/10.1177/00420980231152846
- Tracing CO2 emissions across megacity landscapes: beyond citywide totals to structural heterogeneity and mitigation Y. Zhu et al. https://doi.org/10.1016/j.ese.2025.100602
- Key predictors of greenhouse gas emissions for cities committing to mitigate and adapt to climate change C. Franco et al. https://doi.org/10.1016/j.cities.2023.104342
- Key Predictors of Greenhouse Gas Emissions for Cities Committing to Mitigate and Adapt to Climate Change C. Franco et al. https://doi.org/10.2139/ssrn.4183235
- Rethinking urban riparian ecosystems as a frontline strategy to counter climate change: mapping 60 years of carbon sequestration evolution in Padua, Italy F. Peroni et al. https://doi.org/10.3389/fclim.2023.1235886
- Predicting European cities’ climate mitigation performance using machine learning A. Hsu et al. https://doi.org/10.1038/s41467-022-35108-5
- Uncovering divergences and potential gaps in local greenhouse gases emissions accounting and aggregation V. Palermo et al. https://doi.org/10.1016/j.crsust.2024.100263
- How green can it be? A methodology for calculating green roof retrofit potential in Valencia M. Zayas-Orihuela et al. https://doi.org/10.1016/j.enbuild.2024.114893
- Can green roofs decarbonize the cities? An artificial vision-driven approach to calculating their potential T. Pino-Gallardo et al. https://doi.org/10.1016/j.scs.2025.106672
- Prediction of greenhouse gas emissions for cities and local municipalities monitoring their advances to mitigate and adapt to climate change C. Franco et al. https://doi.org/10.1016/j.scs.2022.104114
- Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method Y. Wang et al. https://doi.org/10.5194/essd-15-3597-2023
- Machine learning estimates for G20 subnational urban GHG emissions from 2000–2020 Y. Yu et al. https://doi.org/10.1038/s41597-026-06691-9
- Urban emissions and land use efficiency scenarios for avoiding increments of global warming Ş. Kılkış https://doi.org/10.1016/j.energy.2024.132174
- Environmental monitoring in European regions using the sustainable development goals (SDG) framework P. Nakhle et al. https://doi.org/10.1016/j.indic.2023.100332
- Green methanol production from photovoltaics in Europe D. Rodriguez-Pastor et al. https://doi.org/10.1016/j.renene.2025.123751
- Urban Scaling Functions: Emission, Pollution and Health C. La Porta & S. Zapperi https://doi.org/10.1007/s11524-024-00888-2
- Cities: Allocating climate change responsibilities at planetary scale M. Hachaichi https://doi.org/10.1016/j.uclim.2022.101329
- Kuznets and the cities: Urban level EKC evidence from Europe M. Rizzati et al. https://doi.org/10.1016/j.ecolind.2023.110143
- An open-source regionalization approach for subnational EEMRIO insights and proof-of-concept application to EU steel circularity A. Kortleve et al. https://doi.org/10.1007/s44498-026-00024-0
- Why Local Governments Set Climate Targets: Effects of City Size and Political Costs K. Eisenack https://doi.org/10.1007/s10640-024-00919-1
- A nationwide integrated demand–supply framework for analyzing urban carbon footprints and mitigation potential in Israel I. Megira et al. https://doi.org/10.1038/s42949-025-00289-7
Saved (final revised paper)
Latest update: 01 Jun 2026
Short summary
This paper presents the modeling methods used for the website https://openghgmap.net, which provides estimates of CO2 emissions for 108 000 European cities.
This paper presents the modeling methods used for the website https://openghgmap.net, which...
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