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.
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
Related authors
No articles found.
Jean-Francois Lamarque, Pierre Friedlingstein, Brian Osias, Steve Strongin, Venkatramani Balaji, Kevin W. Bowman, Josep G. Canadell, Philippe Ciais, Heidi Cullen, Kenneth J. Davis, Scott C. Doney, Kevin R. Gurney, Alicia R. Karspeck, Charles D. Koven, Galen McKinley, Glen P. Peters, Julia Pongratz, Britt Stephens, and Colm Sweeney
EGUsphere, https://doi.org/10.5194/egusphere-2025-6457, https://doi.org/10.5194/egusphere-2025-6457, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
This Perspective highlights requirements to scale the carbon credit market and enable the growth in climate solutions funded through such market. The requirements are on the understanding of the value of the proposed carbon credit projects, and on the availability of a verification system. This verification becomes particularly relevant as the carbon credit market scales to significant impacts on CO2 (or other greenhouse gases), such that attribution to collective actions can be identified.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
Short summary
Short summary
This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Ruben Urraca, Greet Janssens-Maenhout, Nicolás Álamos, Lucas Berna-Peña, Monica Crippa, Sabine Darras, Stijn Dellaert, Hugo Denier van der Gon, Mark Dowell, Nadine Gobron, Claire Granier, Giacomo Grassi, Marc Guevara, Diego Guizzardi, Kevin Gurney, Nicolás Huneeus, Sekou Keita, Jeroen Kuenen, Ana Lopez-Noreña, Enrique Puliafito, Geoffrey Roest, Simone Rossi, Antonin Soulie, and Antoon Visschedijk
Earth Syst. Sci. Data, 16, 501–523, https://doi.org/10.5194/essd-16-501-2024, https://doi.org/10.5194/essd-16-501-2024, 2024
Short summary
Short summary
CoCO2-MOSAIC 1.0 is a global mosaic of regional bottom-up inventories providing gridded (0.1×0.1) monthly emissions of anthropogenic CO2. Regional inventories include country-specific information and finer spatial resolution than global inventories. CoCO2-MOSAIC provides harmonized access to these datasets and can be considered as a regionally accepted reference to assess the quality of global inventories, as done in the current paper.
Seyed Vahid Mousavi, Khalil Karami, Simone Tilmes, Helene Muri, Lili Xia, and Abolfazl Rezaei
Atmos. Chem. Phys., 23, 10677–10695, https://doi.org/10.5194/acp-23-10677-2023, https://doi.org/10.5194/acp-23-10677-2023, 2023
Short summary
Short summary
Understanding atmospheric dust changes in the Middle East and North Africa (MENA) region under future climate scenarios is essential. By injecting sulfate aerosols into the stratosphere, stratospheric aerosol injection (SAI) geoengineering reflects some of the incoming sunlight back to space. This study shows that the MENA region would experience lower dust concentration under both SAI and RCP8.5 scenarios compared to the current climate (CTL) by the end of the century.
Daniele Visioni, Ben Kravitz, Alan Robock, Simone Tilmes, Jim Haywood, Olivier Boucher, Mark Lawrence, Peter Irvine, Ulrike Niemeier, Lili Xia, Gabriel Chiodo, Chris Lennard, Shingo Watanabe, John C. Moore, and Helene Muri
Atmos. Chem. Phys., 23, 5149–5176, https://doi.org/10.5194/acp-23-5149-2023, https://doi.org/10.5194/acp-23-5149-2023, 2023
Short summary
Short summary
Geoengineering indicates methods aiming to reduce the temperature of the planet by means of reflecting back a part of the incoming radiation before it reaches the surface or allowing more of the planetary radiation to escape into space. It aims to produce modelling experiments that are easy to reproduce and compare with different climate models, in order to understand the potential impacts of these techniques. Here we assess its past successes and failures and talk about its future.
Mengdie Xie, John C. Moore, Liyun Zhao, Michael Wolovick, and Helene Muri
Atmos. Chem. Phys., 22, 4581–4597, https://doi.org/10.5194/acp-22-4581-2022, https://doi.org/10.5194/acp-22-4581-2022, 2022
Short summary
Short summary
We use data from six Earth system models to estimate Atlantic meridional overturning circulation (AMOC) changes and its drivers under four different solar geoengineering methods. Solar dimming seems relatively more effective than marine cloud brightening or stratospheric aerosol injection at reversing greenhouse-gas-driven declines in AMOC. Geoengineering-induced AMOC amelioration is due to better maintenance of air–sea temperature differences and reduced loss of Arctic summer sea ice.
Hanna Lee, Helene Muri, Altug Ekici, Jerry Tjiputra, and Jörg Schwinger
Earth Syst. Dynam., 12, 313–326, https://doi.org/10.5194/esd-12-313-2021, https://doi.org/10.5194/esd-12-313-2021, 2021
Short summary
Short summary
We assess how three different geoengineering methods using aerosol affect land ecosystem carbon storage. Changes in temperature and precipitation play a large role in vegetation carbon uptake and storage, but our results show that increased levels of CO2 also play a considerable role. We show that there are unforeseen regional consequences under geoengineering applications, and these consequences should be taken into account in future climate policies before implementing them.
Cited articles
Andres, R. J., Marland, G., Fung, I., and Matthews, E.: A 1∘ × 1∘ distribution of carbon dioxide emissions from fossil
fuel consumption and cement manufacture, 1950–1990, Global Biogeochem. Cy., 10, 419–429, https://doi.org/10.1029/96GB01523, 1996.
Andres, R. J., Boden, T. A., and Higdon, D. M.: Gridded uncertainty in fossil fuel carbon dioxide emission maps, a CDIAC example, Atmos. Chem. Phys., 16, 14979–14995, https://doi.org/10.5194/acp-16-14979-2016, 2016a.
Andres, R. J., Boden, T. A., and Marland, G.: Monthly Fossil-Fuel CO2
Emissions: Mass of Emissions Gridded by One Degree Latitude by One Degree
Longitude, ESS-DIVE [data set], https://doi.org/10.3334/CDIAC/ffe.MonthlyMass.2016, 2016b.
Asefi-Najafabady, S., Rayner, P. J., Gurney, K. R., McRobert, A., Song, Y.,
Coltin, K., Huang, J., Elvidge, C., and Baugh, K.: A multiyear, global
gridded fossil fuel CO2 emission data product: Evaluation and analysis of
results, J. Geophys. Res.-Atmos., 119, 10213–10231,
https://doi.org/10.1002/2013JD021296, 2014.
Baiocchi, G., Creutzig, F., Minx, J., and Pichler, P.-P.: A spatial typology of human settlements and their CO2 emissions in England, Global Environmental Change, 34, 13–21, https://doi.org/10.1016/j.gloenvcha.2015.06.001, 2015.
Basu, S., Lehman, S. J., Miller, J. B., Andrews, A. E., Sweeney, C., Gurney,
K. R., Xu, X., Southon, J., and Tans, P. P.: Estimating US fossil fuel CO2
emissions from measurements of 14C in atmospheric CO2, P.
Natl. Acad. Sci. USA, 117, 13300–13307, https://doi.org/10.1073/pnas.1919032117, 2020.
Baur, A. H., Lauf, S., Förster, M., and Kleinschmit, B.: Estimating greenhouse gas emissions of European cities – Modeling emissions with only one spatial and one socioeconomic variable, Sci. Total Environ., 520, 49–58, https://doi.org/10.1016/j.scitotenv.2015.03.030, 2015.
Bun, R., Hamal, K., Gusti, M., and Bun, A.: Spatial GHG inventory at the regional level: accounting for uncertainty, Climatic Change, 103, 227–244, https://doi.org/10.1007/s10584-010-9907-5, 2010.
Bun, R., Nahorski, Z., Horabik-Pyzel, J., Danylo, O., See, L., Charkovska,
N., Topylko, P., Halushchak, M., Lesiv, M., Valakh, M., and Kinakh, V.:
Development of a high-resolution spatial inventory of greenhouse gas
emissions for Poland from stationary and mobile sources, Mitig.
Adapt. Strat. Gl., 24, 853–880, https://doi.org/10.1007/s11027-018-9791-2,
2019.
Chen, G., Shan, Y., Hu, Y., Tong, K., Wiedmann, T., Ramaswami, A., Guan, D., Shi, L., and Wang, Y.: Review on City-Level Carbon Accounting, Environ. Sci. Technol., 53, 5545–5558, https://doi.org/10.1021/acs.est.8b07071, 2019a.
Chen, S., Liu, Z., Chen, B., Zhu, F., Fath, B. D., Liang, S., Su, M., and Yang, J.: Dynamic carbon emission linkages across boundaries, Earth's Future, 7, 197–209, https://doi.org/10.1029/2018EF000811, 2019b.
Crippa, M., Oreggioni, G., Guizzardi, D., Muntean, M., Schaaf, E., Lo Vullo, E., Solazzo, E., Monforti-Ferrario, F., Olivier, J., and Vignati, E.: Fossil CO2 and GHG emissions of all world countries, Publications Office of the European Union, Luxembourg, EUR 29849 EN JRC117610, https://doi.org/10.2760/687800, 2019.
Crippa, M., Solazzo, E., Huang, G., Guizzardi, D., Koffi, E., Muntean, M.,
Schieberle, C., Friedrich, R., and Janssens-Maenhout, G.: High resolution
temporal profiles in the Emissions Database for Global Atmospheric Research,
Sci. Data, 7, 121, https://doi.org/10.1038/s41597-020-0462-2, 2020.
Davis, K. J., Deng, A., Lauvaux, T., Miles, N. L., Richardson, S. J.,
Sarmiento, D. P., Gurney, K. R., Hardesty, R. M., Bonin, T. A., Brewer, W.
A., Lamb, B. K., Shepson, P. B., Harvey, R. M., Cambaliza, M. O., Sweeney,
C., Turnbull, J. C., Whetstone, J., and Karion, A.: The Indianapolis Flux
Experiment (INFLUX): A test-bed for developing urban greenhouse gas emission
measurements, Elementa, 5, 21, https://doi.org/10.1525/elementa.188, 2017.
Dijkstra, E. W.: A note on two problems in connexion with graphs, Numer.
Math., 1, 269–271, https://doi.org/10.1007/BF01386390, 1959.
Douglas, D. H. and Peucker, T. K.: ALGORITHMS FOR THE REDUCTION OF THE
NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE,
Cartographica: The International Journal for Geographic Information and
Geovisualization, 10, 112–122, https://doi.org/10.3138/FM57-6770-U75U-7727, 1973.
Elguindi, N., Granier, C., Stavrakou, T., Darras, S., Bauwens, M., Cao, H.,
Chen, C., Denier van der Gon, H. A. C., Dubovik, O., Fu, T. M., Henze, D.
K., Jiang, Z., Keita, S., Kuenen, J. J. P., Kurokawa, J., Liousse, C.,
Miyazaki, K., Müller, J.-F., Qu, Z., Solmon, F., and Zheng, B.:
Intercomparison of Magnitudes and Trends in Anthropogenic Surface Emissions
From Bottom-Up Inventories, Top-Down Estimates, and Emission Scenarios,
Earth's Future, 8, e2020EF001520, https://doi.org/10.1029/2020EF001520, 2020.
Fong, W. K., Sotos, M., Doust, M., Schultz, S., Marques, A., and Deng-Beck,
C.: Global Protocol for Community-Scale Greenhouse Gas Emission Inventories,
WRI, C40 Cities, and ICLEI, available at: http://www.ghgprotocol.org/city-accounting (last access: 1 January 2022), 2016.
Fu, M., Kelly, J. A., and Clinch, J. P.: Estimating annual average daily traffic and transport emissions for a national road network: A bottom-up methodology for both nationally-aggregated and spatially-disaggregated results, J. Transp. Geogr., 58, 186–195, https://doi.org/10.1016/j.jtrangeo.2016.12.002, 2017.
Gately, C. K. and Hutyra, L. R.: CMS: CO2 Emissions from Fossil Fuels Combustion, ACES Inventory for Northeastern USA [data set], https://doi.org/10.3334/ORNLDAAC/1501, 2018.
Gaughan, A. E., Oda, T., Sorichetta, A., Stevens, F. R., Bondarenko, M.,
Bun, R., Krauser, L., Yetman, G., and Nghiem, S. V.: Evaluating nighttime
lights and population distribution as proxies for mapping anthropogenic CO2
emission in Vietnam, Cambodia and Laos, Environmental Research
Communications, 1, 091006, https://doi.org/10.1088/2515-7620/ab3d91, 2019.
Ghosh, S., Mueller, K., Prasad, K., and Whetstone, J.: Accounting for
Transport Error in Inversions: An Urban Synthetic Data Experiment, Earth and
Space Science, 8, e2020EA001272, https://doi.org/10.1029/2020EA001272, 2021.
Grassi, G., House, J., Kurz, W. A., Cescatti, A., Houghton, R. A., Peters,
G. P., Sanz, M. J., Viñas, R. A., Alkama, R., Arneth, A., Bondeau, A.,
Dentener, F., Fader, M., Federici, S., Friedlingstein, P., Jain, A. K.,
Kato, E., Koven, C. D., Lee, D., Nabel, J. E. M. S., Nassikas, A. A.,
Perugini, L., Rossi, S., Sitch, S., Viovy, N., Wiltshire, A., and Zaehle,
S.: Reconciling global-model estimates and country reporting of
anthropogenic forest CO2 sinks, Nat. Clim. Change, 8, 914–920,
https://doi.org/10.1038/s41558-018-0283-x, 2018.
Gurney, K. R., Mendoza, D. L., Zhou, Y., Fischer, M. L., Miller, C. C., Geethakumar, S., and de la Rue du Can, S.: High Resolution Fossil Fuel Combustion CO2 Emission Fluxes for the United States, Environ. Sci. Technol., 43, 5535–5541, https://doi.org/10.1021/es900806c, 2009.
Gurney, K. R., Razlivanov, I., Song, Y., Zhou, Y., Benes, B., and Abdul-Massih, M.: Quantification of Fossil Fuel CO2 Emissions on the Building/Street Scale for a Large U.S. City, Environ. Sci. Technol., 46, 12194–12202, https://doi.org/10.1021/es3011282, 2012.
Gurney, K. R., Patarasuk, R., Liang, J., Song, Y., O'Keeffe, D., Rao, P., Whetstone, J. R., Duren, R. M., Eldering, A., and Miller, C.: The Hestia fossil fuel CO2 emissions data product for the Los Angeles megacity (Hestia-LA), Earth Syst. Sci. Data, 11, 1309–1335, https://doi.org/10.5194/essd-11-1309-2019, 2019.
Gurney, K. R., Song, Y., Liang, J., and Roest, G.: Toward Accurate, Policy-Relevant Fossil Fuel CO2 Emission Landscapes, Environ. Sci. Technol., 54, 9896–9907, https://doi.org/10.1021/acs.est.0c01175, 2020a.
Gurney, K. R., Liang, J., Patarasuk, R., Song, Y., Huang, J., and Roest, G.: The Vulcan Version 3.0 High-Resolution Fossil Fuel CO2 Emissions for the United States, J. Geophys. Res.-Atmos., 125, e2020JD032974, https://doi.org/10.1029/2020JD032974, 2020b.
Haberl, H., Wiedenhofer, D., Schug, F., Frantz, D., Virág, D., Plutzar, C., Gruhler, K., Lederer, J., Schiller, G., Fishman, T., Lanau, M., Gattringer, A., Kemper, T., Liu, G., Tanikawa, H., van der Linden, S., and Hostert, P.: High-Resolution Maps of Material Stocks in Buildings and Infrastructures in Austria and Germany, Environ. Sci. Technol., 55, 3368–3379, https://doi.org/10.1021/acs.est.0c05642, 2021.
Harris, S., Weinzettel, J., Bigano, A., and Källmén, A.: Low carbon
cities in 2050? GHG emissions of European cities using production-based and
consumption-based emission accounting methods, J. Clean. Prod., 248, 119206, https://doi.org/10.1016/j.jclepro.2019.119206, 2020.
Hecht, R., Kunze, C., and Hahmann, S.: Measuring Completeness of Building
Footprints in OpenStreetMap over Space and Time, ISPRS Int. Geo-Inf., 2, 1066–1091, https://doi.org/10.3390/ijgi2041066, 2013.
Heinonen, J., Ottelin, J., Ala-Mantila, S., Wiedmann, T., Clarke, J., and Junnila, S.: Spatial consumption-based carbon footprint assessments – A review of recent developments in the field, J. Clean. Prod., 256, 120335, https://doi.org/10.1016/j.jclepro.2020.120335, 2020.
Hogue, S., Marland, E., Andres, R. J., Marland, G., and Woodard, D.: Uncertainty in gridded CO2 emissions estimates, Earth's Future, 4, 225–239, https://doi.org/10.1002/2015EF000343, 2016.
Hsu, Y.-K., VanCuren, T., Park, S., Jakober, C., Herner, J., FitzGibbon, M., Blake, D. R., and Parrish, D. D.: Methane emissions inventory verification in southern California, Atmos. Environ., 44, 1–7, https://doi.org/10.1016/j.atmosenv.2009.10.002, 2010.
Hutchins, M. G., Colby, J. D., Marland, G., and Marland, E.: A comparison of five high-resolution spatially-explicit, fossil-fuel, carbon dioxide emission inventories for the United States, Mitig. Adapt. Strat. Gl., 22, 947–972, https://doi.org/10.1007/s11027-016-9709-9, 2017.
IPCC: Guidelines for National Greenhouse Gas Inventories, vol. 4, chap. 4, IGES, Toyko, available at: https://www.ipcc-nggip.iges.or.jp/public/2006gl/ (last access: 1 January 2022), 2006.
Jones, M. W., Andrew, R. M., Peters, G. P., Janssens-Maenhout, G., De-Gol, A. J., Ciais, P., Patra, P. K., Chevallier, F., and Le Quéré, C.: Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories 1959–2018, Scientific Data, 8, 2, https://doi.org/10.1038/s41597-020-00779-6, 2021.
Kim, J., Shusterman, A. A., Lieschke, K. J., Newman, C., and Cohen, R. C.: The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors, Atmos. Meas. Tech., 11, 1937–1946, https://doi.org/10.5194/amt-11-1937-2018, 2018.
Kona, A., Monforti-Ferrario, F., Bertoldi, P., Baldi, M. G., Kakoulaki, G., Vetters, N., Thiel, C., Melica, G., Lo Vullo, E., Sgobbi, A., Ahlgren, C., and Posnic, B.: Global Covenant of Mayors, a dataset of greenhouse gas emissions for 6200 cities in Europe and the Southern Mediterranean countries, Earth Syst. Sci. Data, 13, 3551–3564, https://doi.org/10.5194/essd-13-3551-2021, 2021.
Kramel, D., Muri, H., Kim, Y., Lonka, R., Nielsen, J. B., Ringvold, A. L.,
Bouman, E. A., Steen, S., and Strømman, A. H.: Global Shipping Emissions
from a Well-to-Wake Perspective: The MariTEAM Model, Environ. Sci.
Technol., 55, 15040–15050, https://doi.org/10.1021/acs.est.1c03937, 2021.
Kurokawa, J., Ohara, T., Morikawa, T., Hanayama, S., Janssens-Maenhout, G., Fukui, T., Kawashima, K., and Akimoto, H.: Emissions of air pollutants and greenhouse gases over Asian regions during 2000–2008: Regional Emission inventory in ASia (REAS) version 2, Atmos. Chem. Phys., 13, 11019–11058, https://doi.org/10.5194/acp-13-11019-2013, 2013.
Lauvaux, T., Gurney, K. R., Miles, N. L., Davis, K. J., Richardson, S. J.,
Deng, A., Nathan, B. J., Oda, T., Wang, J. A., Hutyra, L., and Turnbull, J.:
Policy-Relevant Assessment of Urban CO2 Emissions, Environ. Sci. Technol., 54, 10237–10245, https://doi.org/10.1021/acs.est.0c00343,
2020.
Liu, Z., Wang, F., Tang, Z., and Tang, J.: Predictions and driving factors of production-based CO2 emissions in Beijing, China, Sustain. Cities Soc., 53, 101909, https://doi.org/10.1016/j.scs.2019.101909, 2020a.
Liu, Z., Ciais, P., Deng, Z., Lei, R., Davis, S. J., Feng, S., Zheng, B.,
Cui, D., Dou, X., Zhu, B., Guo, R., Ke, P., Sun, T., Lu, C., He, P., Wang,
Y., Yue, X., Wang, Y., Lei, Y., Zhou, H., Cai, Z., Wu, Y., Guo, R., Han, T.,
Xue, J., Boucher, O., Boucher, E., Chevallier, F., Tanaka, K., Wei, Y.,
Zhong, H., Kang, C., Zhang, N., Chen, B., Xi, F., Liu, M., Bréon, F.-M.,
Lu, Y., Zhang, Q., Guan, D., Gong, P., Kammen, D. M., He, K., and
Schellnhuber, H. J.: Near-real-time monitoring of global CO2 emissions
reveals the effects of the COVID-19 pandemic, Nat. Commun., 11,
5172, https://doi.org/10.1038/s41467-020-18922-7, 2020b.
Long, Z., Zhang, Z., Liang, S., Chen, X., Ding, B., Wang, B., Chen, Y., Sun,
Y., Li, S., and Yang, T.: Spatially explicit carbon emissions at the county
scale, Resources, Conservation and Recycling, 173, 105706, https://doi.org/10.1016/j.resconrec.2021.105706, 2021.
Mallia, D. V., Mitchell, L. E., Kunik, L., Fasoli, B., Bares, R., Gurney, K.
R., Mendoza, D. L., and Lin, J. C.: Constraining Urban CO2 Emissions Using
Mobile Observations from a Light Rail Public Transit Platform, Environ.
Sci. Technol., 54, 15613–15621, https://doi.org/10.1021/acs.est.0c04388, 2020.
Maurice, L. Q., Hockstad, L., Höhne, N., Hupe, J., Lee, D. S., and
Rypdal, K.: Chapter 2.3: Mobile Combustion. Section 6: Civil aviation, in:
2006 IPCC Guidelines for National Greenhouse Gas Inventories, 56–74, available at: https://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html (last access: 1 January 2022), 2006.
Meng, L., Graus, W., Worrell, E., and Huang, B.: 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, Energy, 71, 468–478,
https://doi.org/10.1016/j.energy.2014.04.103, 2014.
Milojevic-Dupont, N., Hans, N., Kaack, L. H., Zumwald, M., Andrieux, F., de Barros Soares, D., Lohrey, S., Pichler, P.-P., and Creutzig, F.: Learning from urban form to predict building heights, PLOS ONE, 15, e0242010, https://doi.org/10.1371/journal.pone.0242010, 2020.
Minx, J., Baiocchi, G., Wiedmann, T., Barrett, J., Creutzig, F., Feng, K., Frster, M., Pichler, P.-P., Weisz, H., and Hubacek, K.: Carbon footprints of cities and other human settlements in the UK, Environ. Res. Lett., 8, 35039, https://doi.org/10.1088/1748-9326/8/3/035039, 2013.
Moran, D.: OpenGHGMap – Europe – CO2 Emissions in 108,000 European Cities
(2018_20210907a), Zenodo [data set],
https://doi.org/10.5281/zenodo.5482480, 2021.
Moran, D. D., Kanemoto, K., Jiborn, M., Wood, R., Többen, J., Seto, K. C., Többen, J., and Seto, K. C.: Carbon footprints of 13 000 cities, Environ. Res. Lett., 13, 064041, https://doi.org/10.1088/1748-9326/aac72a, 2018.
Mueller, K. L., Lauvaux, T., Gurney, K. R., Roest, G., Ghosh, S., Gourdji,
S. M., Karion, A., DeCola, P., and Whetstone, J.: An emerging GHG estimation
approach can help cities achieve their climate and sustainability goals,
Environ. Res. Lett., 16, 084003, https://doi.org/10.1088/1748-9326/ac0f25, 2021.
Nangini, C., Peregon, A., Ciais, P., Weddige, U., Vogel, F., Wang, J., Bron,
F.-M., Bachra, S., Wang, Y., Gurney, K., Yamagata, Y., Appleby, K.,
Telahoun, S., Canadell, J. G., Grbler, A., Dhakal, S., and Creutzig, F.: A
global dataset of CO2 emissions and ancillary data related to emissions for
343 cities, Scientific Data, 6, 180280, https://doi.org/10.1038/sdata.2018.280, 2019.
NASA OCO-2 Mission Homepage: Homepage, available at: https://www.nasa.gov/mission_pages/oco2/index.html, last
access: 23 August 2021.
Nassar, R., Napier-Linton, L., Gurney, K. R., Andres, R. J., Oda, T., Vogel,
F. R., and Deng, F.: Improving the temporal and spatial distribution of CO2
emissions from global fossil fuel emission data sets, J. Geophys. Res.-Atmos., 118, 917–933, https://doi.org/10.1029/2012JD018196,
2013.
Neumann, K., Elbersen, B. S., Verburg, P. H., Staritsky, I., Pérez-Soba, M., de Vries, W., and Rienks, W. A.: Modelling the spatial distribution of livestock in Europe, Landscape Ecol., 24, 1207, https://doi.org/10.1007/s10980-009-9357-5, 2009.
Oda, T. and Maksyutov, S.: A very high-resolution (1 km × 1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights, Atmos. Chem. Phys., 11, 543–556, https://doi.org/10.5194/acp-11-543-2011, 2011.
Oda, T., Maksyutov, S., and Andres, R. J.: 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, Earth Syst. Sci. Data, 10, 87–107, https://doi.org/10.5194/essd-10-87-2018, 2018.
Osses, M., Rojas, N., Ibarra, C., Valdebenito, V., Laengle, I., Pantoja, N., Osses, D., Basoa, K., Tolvett, S., Huneeus, N., Gallardo, L., and Gómez, B.: High-definition spatial distribution maps of on-road transport exhaust emissions in Chile, 1990–2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-218, in review, 2021.
Ott, L., Sellers, P. J., Schimel, D., Moore III, B., O'Dell, C., Crowell,
S., Kawa, S. R., Pawson, S., Chatterjee, A., Baker, D. F., and Schuh, A. E.:
NASA's Carbon Cycle OSSE Initiative – Informing future space-based observing
strategies through advanced modeling and data assimilation, American
Geophysical Union, Fall Meeting 2017, New Orleans, 11–17 Dec 2017, abstract #GC51C-0817, available at: https://ui.adsabs.harvard.edu/abs/2017AGUFMGC51C0817O, (last access: 1 January 2022), 2017.
Patarasuk, R., Gurney, K., O'Keeffe, D., Song, Y., Huang, J., Rao, P., Buchert, M., Lin, J. C., Mendoza, D., and Ehleringer, J. R.: Urban high-resolution fossil fuel CO2 emissions quantification and exploration of emission drivers for potential policy applications, Urban Ecosyst., 19, 1013–1039, https://doi.org/10.1007/s11252-016-0553-1, 2016.
Peled, Y. and Fishman, T.: Estimation and mapping of the material stocks of buildings of Europe: a novel nighttime lights-based approach, Resour. Conserv. Recy., 169, 105509, https://doi.org/10.1016/j.resconrec.2021.105509, 2021.
Petrescu, A. M. R., Peters, G. P., Janssens-Maenhout, G., Ciais, P., Tubiello, F. N., Grassi, G., Nabuurs, G.-J., Leip, A., Carmona-Garcia, G., Winiwarter, W., Höglund-Isaksson, L., Günther, D., Solazzo, E., Kiesow, A., Bastos, A., Pongratz, J., Nabel, J. E. M. S., Conchedda, G., Pilli, R., Andrew, R. M., Schelhaas, M.-J., and Dolman, A. J.: European anthropogenic AFOLU greenhouse gas emissions: a review and benchmark data, Earth Syst. Sci. Data, 12, 961–1001, https://doi.org/10.5194/essd-12-961-2020, 2020.
Plant, G., Kort, E. A., Floerchinger, C., Gvakharia, A., Vimont, I., and Sweeney, C.: Large fugitive methane emissions from urban centers along the US East Coast, Geophys. Res. Lett., 46, 8500–8507, https://doi.org/10.1029/2019GL082635, 2019.
Rafiq, T., Duren, R. M., Thorpe, A. K., Foster, K., Patarsuk, R., Miller, C. E., and Hopkins, F. M.: Attribution of methane point source emissions using airborne imaging spectroscopy and the Vista-California methane infrastructure dataset, Environ. Res. Lett., 15, 124001, https://doi.org/10.1088/1748-9326/ab9af8, 2020.
Ramaswami, A. and Chavez, A.: What metrics best reflect the energy and
carbon intensity of cities? Insights from theory and modeling of 20 US
cities, Environ. Res. Lett., 8, 035011,
https://doi.org/10.1088/1748-9326/8/3/035011, 2013.
Ramaswami, A., Tong, K., Canadell, J. G., Jackson, R. B., Stokes, E., Dhakal, S., Finch, M., Jittrapirom, P., Singh, N., Yamagata, Y., Yewdall, E., Yona, L., and Seto, K. C.: Carbon analytics for net-zero emissions sustainable cities, Nature Sustainability, 4, 460–463, https://doi.org/10.1038/s41893-021-00715-5, 2021.
Ramer, U.: An iterative procedure for the polygonal approximation of plane
curves, Comput. Vision Graph., 1, 244–256, https://doi.org/10.1016/S0146-664X(72)80017-0, 1972.
Rayner, P. J., Raupach, M. R., Paget, M., Peylin, P., and Koffi, E.: A new
global gridded data set of CO2 emissions from fossil fuel combustion:
Methodology and evaluation, J. Geophys. Res., 115, D19306, https://doi.org/10.1029/2009JD013439, 2010.
Roest, G. S., Gurney, K. R., Miller, S. M., and Liang, J.: Informing urban climate planning with high resolution data: the Hestia fossil fuel CO2 emissions for Baltimore, Maryland, Carbon Balance and Management, 15, 22, https://doi.org/10.1186/s13021-020-00157-0, 2020.
Shan, Y., Guan, D., Liu, J., Mi, Z., Liu, Z., Liu, J., Schroeder, H., Cai,
B., Chen, Y., Shao, S., and Zhang, Q.: Methodology and applications of city
level CO2 emission accounts in China, J. Clean. Prod., 161,
1215–1225, https://doi.org/10.1016/j.jclepro.2017.06.075, 2017.
Shan, Y., Guan, D., Hubacek, K., Zheng, B., Davis, S. J., Jia, L., Liu, J.,
Liu, Z., Fromer, N., Mi, Z., Meng, J., Deng, X., Li, Y., Lin, J., Schroeder,
H., Weisz, H., and Schellnhuber, H. J.: City-level climate change mitigation
in China, Science Advances, 4, 10, https://doi.org/10.1126/sciadv.aaq0390, 2018.
Solazzo, E., Crippa, M., Guizzardi, D., Muntean, M., Choulga, M., and Janssens-Maenhout, G.: Uncertainties in the Emissions Database for Global Atmospheric Research (EDGAR) emission inventory of greenhouse gases, Atmos. Chem. Phys., 21, 5655–5683, https://doi.org/10.5194/acp-21-5655-2021, 2021.
Townsend-Small, A., Tyler, S. C., Pataki, D. E., Xu, X., and Christensen, L. E.: Isotopic measurements of atmospheric methane in Los Angeles, California, USA: Influence of “fugitive” fossil fuel emissions, J. Geophys. Res.-Atmos., 117, D07308, https://doi.org/10.1029/2011JD016826, 2012.
Turnbull, J. C., Karion, A., Davis, K. J., Lauvaux, T., Miles, N. L.,
Richardson, S. J., Sweeney, C., McKain, K., Lehman, S. J., Gurney, K. R.,
Patarasuk, R., Liang, J., Shepson, P. B., Heimburger, A., Harvey, R., and
Whetstone, J.: Synthesis of Urban CO2 Emission Estimates from Multiple
Methods from the Indianapolis Flux Project (INFLUX), Environ. Sci. Technol., 53, 287–295, https://doi.org/10.1021/acs.est.8b05552, 2019.
Wang, R., Tao, S., Ciais, P., Shen, H. Z., Huang, Y., Chen, H., Shen, G. F., Wang, B., Li, W., Zhang, Y. Y., Lu, Y., Zhu, D., Chen, Y. C., Liu, X. P., Wang, W. T., Wang, X. L., Liu, W. X., Li, B. G., and Piao, S. L.: High-resolution mapping of combustion processes and implications for CO2 emissions, Atmos. Chem. Phys., 13, 5189–5203, https://doi.org/10.5194/acp-13-5189-2013, 2013.
Wang, S., Liu, X., Zhou, C., Hu, J., and Ou, J.: Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities, Appl. Energ., 185, 189–200, https://doi.org/10.1016/j.apenergy.2016.10.052, 2017.
Wennberg, P. O., Mui, W., Wunch, D., Kort, E. A., Blake, D. R., Atlas, E. L., Santoni, G. W., Wofsy, S. C., Diskin, G. S., Jeong, S., and Fischer, M. L.: On the Sources of Methane to the Los Angeles Atmosphere, Environ. Sci. Technol., 46, 9282–9289, https://doi.org/10.1021/es301138y, 2012.
Whetstone, J. R.: Advances in urban greenhouse gas flux quantification: The
Indianapolis Flux Experiment (INFLUX), Elementa: Science of the
Anthropocene, 6, 24, https://doi.org/10.1525/elementa.282, 2018.
Wiedmann, T., Chen, G., Owen, A., Lenzen, M., Doust, M., Barrett, J., and Steele, K.: Three-scope carbon emission inventories of global cities, J. Ind. Ecol., 25, 735–750, https://doi.org/10.1111/jiec.13063, 2021.
Woodard, D., Branham, M., Buckingham, G., Hogue, S., Hutchins, M., Gosky, R., Marland, G., and Marland, E.: A spatial uncertainty metric for anthropogenic CO2 emissions, Greenhouse Gas Measurement and Management, 4, 139–160, https://doi.org/10.1080/20430779.2014.1000793, 2014.
WRI, C40, and ICLEI: Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC) – An Accounting and Reporting Standard for Cities v1.1, World Resources Institute, C40 Cities Climate Leadership Group and ICLEI Local Governments for Sustainability, 2014.
Wu, D., Lin, J. C., Oda, T., and Kort, E. A.: Space-based quantification of
per capita CO2 emissions from cities, Environ. Res. Lett., 15,
035004, https://doi.org/10.1088/1748-9326/ab68eb, 2020.
Yanto, J. and Liem, R. P.: Aircraft fuel burn performance study: A
data-enhanced modeling approach, Transport. Res. D-Tr. E., 65, 574–595, https://doi.org/10.1016/j.trd.2018.09.014, 2018.
Zheng, B., Cheng, J., Geng, G., Wang, X., Li, M., Shi, Q., Qi, J., Lei, Y.,
Zhang, Q., and He, K.: Mapping anthropogenic emissions in China at 1 km
spatial resolution and its application in air quality modeling, Sci.
Bull., 66, 612–620, https://doi.org/10.1016/j.scib.2020.12.008, 2021a.
Zheng, H., Többen, J., Dietzenbacher, E., Moran, D., Meng, J., Wang, D., and Guan, D.: Entropy-based Chinese city-level MRIO table framework, Econ. Syst. Res., 1–26, https://doi.org/10.1080/09535314.2021.1932764, 2021b.
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...
Altmetrics
Final-revised paper
Preprint