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Doughnut Data Portraits: Indicator Library
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A database tool to explore thousands of indicators and targets from 30+ existing Doughnut Data Portraits
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Welcome to Doughnut Economics Action Lab's (DEAL's) Doughnut Data Portraits Indicator Library. The primary aim for DEAL in compiling this Indicator Library is to provide a tool that supports prospective analysts and practitioners in the process of creating their own Data Portrait by seeing what other initiatives have done. The database will be updated on an ongoing basis. The Indicator Library should be seen as a global compendium or repository of indicators; please note the quantitative results are not directly comparable across places.
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The information in this Indicator Library spreadsheet groups five ‘tabs’ located at the bottom of the screen into two sections:
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1. Indicator Library. This section includes the main database as well as interactive and dynamic charts that visualise summary information. There are two tabs in this section:

- Database. This tab contains the core database of indicators and targets, structured according to the ‘four lenses’ methodology, and including more than 30 existing Doughnut Data Portraits. The database has more than 2,000 rows and it has been organised to be used with 'filters' which allow users to search the database by column headings, such as by place, by lens, and/or by dimension. For example, users can filter the data to explore indicators related to a specific dimension across places, such as food or climate change, and/or to zoom in to an individual place, such as Grenoble or Melbourne, among other possibilities.

- Charts. This tab contains dynamic visualisations of the indicator-specific information that has been filtered by users in the Database tab. These charts are updated whenever the information selected in the Database filters changes. Other charts, such as maps of geographical spread by city and country are also available.
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2. Supplementary Materials. This section includes additional materials that are relevant to using the database along with a template for ‘downscaling’ planetary boundaries to sub-national levels using an income-based approach. There are three tabs in this section:

- Column descriptions. This tab provides a description and an illustrative example for each of the column headings included in the Indicator Library (i.e. ‘Database’ tab).

- Concordance tables and charts. This tab provides a record of the Portrait-specific harmonisation and concordance process that was used to rename any applicable dimension labels in existing Portraits to ensure consistency with the dimension labels used in DEAL's guidance. Labels that were harmonised are indicated by coloured cells (red for the non-matching labels used in source publications and green for the renamed labels following DEAL’s dimension names). Additional summary charts and concordance information by dimension are also included.

- Global-ecological template. This tab provides a quantitative template for the global-ecological lens that includes the main calculations used to downscale national environmental footprints and planetary boundaries using an income-based approach, with Amsterdam's data shown as an example. Although this template is not directly related to the Indicator Library, it is included as a tool that practitioners creating their own Data Portraits may find useful to adapt in order to estimate consumption-based footprints (with respect to downscaled per capita boundaries) for their places.
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Visit the Indicator Library's landing page on the DEAL Platform for additional details and overview, including an Excel-based version, instructions on how to use filters, and where to go next.
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The Doughnut Data Portraits Indicator Library has been compiled by Joel Petterson and Andrew Fanning. Please send any comments or suggestions to Andrew Fanning ( andrew@doughnuteconomics.org ). We hope this tool is useful for you, and look forward to your feedback.
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Suggested citation: Petterson, J and Fanning, AL (2025). Doughnut Data Portrait Indicator Library (v1.0). Doughnut Economics Action Lab, Oxford. https://doi.org/10.64981/BOSY3928
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SOURCES
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For additional details and relevant information on the specific Data Portrait results and information compiled in this Indicator Library, please refer to the source publications listed below.
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Doughnut Data Portraits included in this Indicator LibrarySource Publications
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AmsterdamRaworth, K et al. (2020). The Amsterdam city doughnut: A tool for transformative action. Amsterdam, Netherlands.
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Bannau Brycheiniog (Brecon Beacons)Lucocq, H et al. (2023). Park Doughnut. Brecon Beacons, United Kingdom.
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BarcelonaVentayol, I et al. (2023). Barcelona City Dònut. Barcelona, Spain: Barcelona City Council.
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Birmingham-LadywoodCivic Square (2022). A Neighbourhood Doughnut Portrait. Birmingham, United Kingdom.
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Brussels v2.0Dethier, F et al. (2023). Doughnut Portrait of the Brussels-Capital Region. Brussels, Belgium.
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CaliforniaAritza, A et al. (2024). The California Doughnut Snapshot. California, United States of America.
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Cergy PontoiseSeidl, G et al. (2025). La donut el la CACP. Cergy Pontoise, France.
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CopenhagenGregersen, S et al. (2024). Københavns Doughnut 2024. Copenhagen, Denmark.
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CornwallTurner, R et al. (2020) Towards a Sustainable Economy: State of the Doughnut. Cornwall, United Kingdom: University of Exeter.
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EdinburghZecca, F (2020). The Edinburgh City Portrait: downscaling the Doughnut economic model to the city of Edinburgh. Edinburgh, United Kingdom.
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El MonteEntramada (2021). Portrait of the City: El Monte. El Monte, Chile
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GenevaGilloots, C et al. (2022). Greater Geneva City Portrait. Geneva, Switzerland: University of Lausanne.
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GlasgowHjelmskog, A et al. (2023). Thriving Glasgow Portrait Report. Glasgow, United Kingdom.
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GrenobleLemeur, N et al (2025). Doughnut Data Portrait of Grenoble - A 360° diagnosis of the place. Grenoble, France.
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LausanneGilloots, C et al. (2023). The UNIL Donut: A Navigation Tool for Ecological and Social Transition. Lausanne, Switzerland: University of Lausanne
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LeedsChatterton, P et al. (2022) The first Leeds Doughnut City Portrait: towards a safe and thriving city for all. Leeds, United Kingdom: Climate Action Leeds.
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LondonEboli, C et al. (2022). Doughnut Economics in London: A City Portrait and Call to Action. London, United Kingdom.
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MiddlesbroughImai, E et al. (2023). Data portrait of Middlesbrough. Middlesbrough, United Kingdom.
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NanaimoCity of Nanaimo (2024). Nanaimo Monitoring Strategy. Nanaimo, Canada
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OxfordshireBrown, D. et al. (2024). An Oxfordshire Doughnut Economics Project. Oxfordshire, United Kingdom.
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PortlandThriving Cities Initiative (2019). Four Lenses of Portland's City Portrait. Portland, United States of America
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Regen MelbourneRickards, L et al. (2024). Greater Melbourne City Portrait. Melbourne, Australia.
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Rhein Kreis NeussNELA et al. (2025). A donut for the Rhein-Kreis-Neuss. Rhein-Kreis-Neuss, Germany.
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RigaBreuil, G et al. (2025). Riga’s Doughnut City Portrait. Riga, Latvia: Riga Energy Agency.
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SeattleLee, H et al. (2025). Doughnut Economics Seattle City Portrait. Seattle, United States of America
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South Africa - National and nine provinces The Green Connection (2022). Safe and Just Operating Space in South Africa: National and Provincial Doughnuts. South Africa.
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South Africa - Saldanha-BayFourie, M and Selomane, O (2022). Doughnut Economics Indicators for Saldanha Bay Municipality. Saldanha Bay, South Africa: The Green Connection.
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Tampere (Pirkanmaa region)Pokkinen, K. (2021). Utilization of doughnut model in sustainable development: city portrait of The Tampere Region. Tampere, Finland.
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TomelillaBergh, R et al. (2024). City portrait of Tomelilla. Tomelilla, Sweden
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WellingtonMalandain, H et al. (2022) Wellington City Doughnut Draft Portrait 2022. Wellington, New Zealand: Wellington City Council
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YerevanMinasyan, N et al. (2022). Yerevan embraces “Doughnut mindset”. Yerevan, Armenia
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