Gridded Carbon Footprint

Gridded estimate of carbon footprints for 189 countries in 2013

  • Source: NTNU/LU/Shinshu/Yale


The Gridded Carbon Footprint dataset shows a gridded estimate of absolute and per capita carbon emissions in the year 2013 for 189 countries. The results were produced by the Gridded Global Model of Carbon Footprints (GGMCF) model at a spatial resolution of 250 meters. GGMCF is based on previously available data for carbon footprint (CF), population, and household spending. The model uses urban vs. rural consumption patterns and purchasing power as the main indicators of per capita footprint. Higher purchasing power generally increases the chance of participating in activities that lead to more carbon emissions.

The dataset was created by the Norwegian University of Science and Technology (NTNU) in collaboration with Shinshu University, Yale, and Lund University. Most cities, towns, and rural areas around the world have no carbon footprint data. This dataset provides the first internationally comparable CF estimates for many regions of the world. The results presented here can help develop strategies to reduce carbon footprint. Additionally, this dataset can be used by national governments to locate small regions with disproportionally high emissions rates.


The dataset was created in four distinct steps:

  1. National CFs of consumption (CFn) data were taken from the Eora multi-region input-output (MRIO) database for the year 2015. This data included 189 countries and accounted for close to 100% of global CO₂ emission.

  2. For the EU, UK, USA, Japan, and China, existing subnational CF models were available from a variety of publications. These models were used to separate national CF of consumption data into subnational/regional data CFr. The size of regions ranged from ZIP codes to provinces. In steps 3 and 4 these subnational regions are treated the same as countries. Regions included the disaggregated subnational regions along with countries that are not disaggregated.

  3. Within each region, the CFr was further disaggregated into urban and rural residents utilizing urban vs. rural population data and urban vs. rural expenditure pattern data from the World Bank, Eurostat, and US BEA. This data included 113 countries and accounted for 81% of global CO₂ production for the year 2015.

  4. The data was subsequently disaggregated to produce CFs of grid cells using gridded population maps and gridded income data. First, the urban and rural grid cells were identified using the Global Human Settlement Layer Settlement Model Grid (GHS-SMOD). Then the CF for each region was disaggregated across the grid cells based on the combined purchasing power of the population living within each grid cell. Carbon footprints are usually directly related to income and purchasing power. The population in each cell was multiplied by the mean purchasing power at that location to calculate the overall purchasing power per grid cell.

For the full documentation, please see the source methodology.


Excerpts of this description page were taken from the source metadata. Resource Watch shows only a subset of the dataset. For access to the full dataset and additional information, click on the “Learn more” button.

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Formal name

Global Gridded Model of Carbon Footprints (GGMCF)


  • For 76 countries (a mixture of developed and developing countries, driving 19% of global CO₂ emissions) no comparative expenditure data were available. In these countries all households were assumed to have a national average expenditure pattern.

  • While many of the urban areas with the highest CF are in countries with high carbon footprints, 41 of the top 200 (e.g. Dhaka, Cairo, Lima) are in countries where total and per capita emissions are low (e.g. Bangladesh, Egypt, Peru). In these urban areas, population and affluence combine to drive footprints at a similar scale as counterparts in the highest income countries.

Suggested citation

Moran, D., Kanemoto K; Jiborn, M., Wood, R., Többen, J., and Seto, K.C. (2018) Carbon footprints of 13,000 cities. Environmental Research Letters DOI: 10.1088/1748-9326/aac72a. Accessed through Resource Watch, (date).


Norwegian University of Science and Technology (NTNU)
Shinshu University (Shinshu)
Lund University (LU)
Yale University (Yale)

Geographic coverage


Spatial resolution

250 m

Date of content



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