What you eat matters as much as where you eat it: Threefold effect of distance.

A new analysis of 3,500 US cities reveals that the carbon “hoofprint” of meat can vary more than threefold depending on where it’s produced—and where it’s eaten.

By Emma Bryce in Anthropocene

October 24, 2025

A new study on U.S. meat consumption suggests we’ve been missing a crucial piece of the carbon footprint puzzle: where our meat actually comes from.

Researchers found that a person’s “hoofprint,” or meat-related carbon emissions, can vary more than threefold depending on the city they live in, even though Americans eat roughly the same amount of meat nationwide. The difference, they say, lies far beyond city limits.

The work highlights the “interconnectedness between urban and rural spaces,” says Benjamin Goldstein, assistant professor of environment and sustainability at the University of Michigan and lead author of the new paper.

Goldstein and his team analyzed beef, pork, and chicken consumption in more than 3,500 U.S. cities and towns, tracing each supply chain back to the farms where the animals were raised. They modeled pollution impacts at every stage—from animal feed and housing to manure management, meat processing, and transportation.

Beef sourced from grazing systems, for instance, tended to carry higher emissions than feedlot beef because grass-fed cattle produce more methane.

The results reveal a vast, tangled web linking cities to hundreds of rural counties, each with distinct farming methods and distances that dramatically shape the carbon intensity of meat. Beef sourced from grazing systems, for instance, tended to carry higher emissions than feedlot beef because grass-fed cattle produce more methane. Meanwhile, cities sourcing beef from dairy regions—where cows are culled at the end of life—saw lower emissions, since they require fewer resources than cows that are raised purposefully for meat. Methane-capture technology on farms made a major difference, too.

Recommended Reading:

What drives consumers to more sustainable meat? Ironically, it isn’t telling them it’s sustainable.

While beef generally dominated emissions, in some cities pork had a higher impact—but in 386 cities, pork had a smaller footprint than even chicken. Transport distances also played a role in determining each city’s per capita hoofprint.

The range was striking. In Richmond, Missouri, residents had the largest individual meat impact, emitting 1,731 kilograms of CO₂ equivalent per person—more than three times higher than in Houghton, Michigan, which had just 500 kilograms. Goldstein notes that 50,000 Americans live in cities where their meat emissions rival those from home energy use.

cities sourcing beef from dairy regions—where cows are culled at the end of life—saw lower emissions, since they require fewer resources than cows that are raised purposefully for meat

New York City, however, tells a different story: despite consuming more meat overall, its per-capita footprint is lower thanks to sourcing from less carbon-intensive regions.

The takeaway, says Goldstein, is that cutting emissions isn’t just about eating less meat—it’s about knowing where it comes from. Cities could shrink their hoofprints by up to 51% through smarter sourcing, reducing food waste, and shifting from high- to lower-intensity meats.

Transport distances also played a role in determining each city’s per capita hoofprint.

Goldstein et. al. “The carbon hoofprint of cities is shaped by geography and production in the livestock supply chain.” Nature Climate Change. 2025.

---

Abstract

Meat consumed in cities is largely produced in rural regions. Supply chain opacity and complexity hinder understanding of (and the ability to address) the distributed impacts of urban meat consumption on rural communities and environments. Here we combine supply chain models with spatial carbon accounting to quantify and map the GHG emissions from beef, chicken and pork consumption—the carbon hoofprint—for all 3,531 cities in the contiguous USA. This carbon hoofprint totals 329 MtCO₂e, equivalent to emissions from US at-home fossil fuel combustion. Surprising differences in the carbon intensity of meat-producing regions explain variation in per capita hoofprints between cities (500–1,731 kgCO₂e). Demand-side measures such as reducing food waste and dietary shifts (for example, more chicken, less beef) could halve emissions. Our modelling highlights reduction strategies across the supply chain and provides a basis to address the transboundary impacts of cities.

Main

Food, in particular meat and dairy products, represents a substantial portion of the GHG footprint of a city^1,2. Reducing these emissions is difficult because agricultural supply chains are geographically dispersed and have many processes and inputs (for example, fertilizer, irrigation and fuel)^3,4. Although the linkages between cities and supply regions for meat, produce and other transboundary flows are well-conceptualized in the literature, empirically connecting them remains rudimentary and underdeveloped^5,6,7. This has ramifications in terms of understanding and addressing how consumption in one region (for example, the city) affects environmental and socioeconomic conditions in producing regions (for example, rural communities).

The predominant method when quantifying the GHG footprint of cities (and corporations) is the spend-based approach, which relies on broad, industry or national averages for products and processes. Given that the environmental and socioeconomic impacts associated with food and other land-derived products are geographically diverse, this reliance is highly problematic. Studies have attempted to identify the food supply region (foodshed⁷) of a city, but these efforts lack sufficient precision: simply considering ‘meat’ as a whole rather than differentiating between beef, pork or chicken each of which has distinct production geographies and impacts^8,9,10. The few studies that have tried to nuance this broad categorization use labour-intensive surveys that lack geographic specificity^11,12 (Supplementary Note 1gives a review of relevant literature).

More granular mapping of urban–rural linkages is necessary to enable cities (and citizens) to address the spatially diffuse impacts generated by resource consumption^5,13. To date, we have lacked a scalable, high-resolution method to capture subnational flows and impacts of meat and other products consumed in cities.

Two obstacles have hindered development of such an approach. First, data linking production to consumption are scarce or proprietary^14,15. Open data, such as freight flow surveys encompass broad industry sectors (for example, animal feeds, meat and milled grain products)⁸, seldom scale to urban areas¹⁶ nor do they cover several supply-chain stages. Second, even if linkages can be identified, spatially explicit data on the environmental intensity of production are usually only available for nations¹⁷, regions^18,19 or other large geographies.

We overcome these obstacles using the food-system supply-chain sustainability (FoodS³) model²⁰. This mass-balanced supply–demand model reconstructs meat supply chains (including losses) for 93% of the US population by linking 3,143 feed, livestock and poultry producing counties and 3,531 meat-consuming cities (census-designated ‘urban areas’) (Methods). Owing to its high urbanization rate (~85%) and status as the world’s second largest producer²¹ and per capita consumer²² of meat, the USA makes a compelling case. Using a scalable spatially resolved approach, this research comprehensively tracks commodity flows between cities and their hinterlands across several supply-chain stages. Where existing methods trade geographic specificity (in sourcing or impacts) for scalability, this modelling effort sacrifices neither.

This research provides a vivid picture of the production geographies of meat and animal feed—the meatshed—of US cities. We combine reconstructed supply chains with location-specific GHG estimates of feed, livestock production and primary processing (cradle-to-processing gate) to calculate the GHG intensity of producing beef, chicken and pork for each city. This is then paired with consumption estimates to calculate the annual embodied GHGs from beef, chicken and pork consumption—the carbon hoofprint—for every US city.

Results reveal that cities source meat and animal feed from distinct geographies with varying GHG production intensities. This generates striking differences in hoofprints, which are impossible to capture using traditional carbon accounting methods for cities. Despite comparable meat consumption, per capita hoofprints vary by a factor of three between cities. The spatial granularity of our results also highlights unexpected pathways to reduce GHGs associated with diet (for example, consuming pork rather than chicken in certain cities).

Scenarios show that cities can reduce the hoofprint by 14–51% by implementing strategies to reduce edible food loss and by promoting dietary shifts from beef to poultry. This is a cost-effective decarbonization lever for the estimated 60 million Americans living in cities, where the hoofprint rivals GHGs from household energy consumption. Supply-side strategies, such as silvopastoral systems, can further reduce the hoofprint. Our modelling framework can be expanded to other commodities and geographies, ultimately helping researchers to better conceptualize, map and measure the co-evolution of urban and rural spaces. Reducing the environmental impacts from the consumption of meat (and other products) in cities will ultimately require changing both production and consumption. Our model aids this transition by providing a way to map, measure and mitigate the transboundary impacts of cities on an increasingly urban planet.

References

1. C40 Good Food Cities Declaration: How Cities Are Achieving the Planetary Health Diet for All (C40 Cities, 2022); https://www.c40.org/wp-content/uploads/2022/02/C40-Good-Food-Cities-Declaration_Public-progress-report_Feb-2022.pdf
2. Plant Based Treaty Endorsers: Cities, Towns & Regions (Plant Based Treaty, 2025); https://plantbasedtreaty.org/cities/
3. Seto, K. C. & Ramankutty, N. Hidden linkages between urbanization and food systems. Science 352, 943–945 (2016).Article CAS Google Scholar 
4. Goldstein, B., Birkved, M., Fernández, J. & Hauschild, M. Surveying the environmental footprint of urban food consumption. J. Ind. Ecol. 21, 151–165 (2017).Article CAS Google Scholar 
5. Seto, K. C. et al. Urban land teleconnections and sustainability. Proc. Natl Acad. Sci. USA 109, 7687–7692 (2012).Article CAS Google Scholar 
6. Brenner, N. & Schmid, C. Towards a new epistemology of the urban?. City 19, 151–182 (2015).Article Google Scholar 
7. Schreiber, K., Hickey, G. M., Metson, G. S., Robinson, B. E. & MacDonald, G. K. Quantifying the foodshed: a systematic review of urban food flow and local food self-sufficiency research. Environ. Res. Lett. 16, 023003 (2021).Article Google Scholar 
8. Karakoc, D. B., Wang, J. & Konar, M. Food flows between counties in the United States from 2007 to 2017. Environ. Res. Lett. 17, 034035 (2022).Article Google Scholar 
9. Karakoc, D. B., Konar, M., Puma, M. J. & Varshney, L. R. Structural chokepoints determine the resilience of agri-food supply chains in the United States. Nat. Food4, 607–615 (2023).Article Google Scholar 
10. Sikiru, A. et al. Methane emissions in cattle production: biology, measurement and mitigation strategies in smallholder farmer systems. Environ. Dev. Sustain.https://doi.org/10.1007/s10668-024-04939-1 (2024).Article Google Scholar 
11. Boyer, D., Sarkar, J. & Ramaswami, A. Diets, food miles, and environmental sustainability of urban food systems: analysis of nine Indian cities. Earth’s Future 7, 911–922 (2019).Article Google Scholar 
12. Ramaswami, A. et al. An urban systems framework to assess the trans-boundary food–energy–water nexus: implementation in Delhi, India. Environ. Res. Lett. 12, 025008 (2017).Article Google Scholar 
13. Hubacek, K., Feng, K., Minx, J. C., Pfister, S. & Zhou, N. Teleconnecting consumption to environmental impacts at multiple spatial scales. J. Ind. Ecol. 18, 7–9 (2014).Article Google Scholar 
14. Pichler, A. et al. Building an alliance to map global supply networks. Science 382, 270–272 (2023).Article CAS Google Scholar 
15. Goldstein, B. & Newell, J. P. Why academics should study the supply chains of individual corporations. J. Ind. Ecol. 23, 1316–1327 (2019).Article Google Scholar 
16. Ao, Y. Z., Siddik, M. A. B., Konar, M. & Marston, L. T. Watersheds and infrastructure providing food, energy, and water to US cities. Earth’s Future 12, e2023EF004258 (2024).Article Google Scholar 
17. Lenzen, M., Moran, D., Kanemoto, K. & Geschke, A. Building Eora: a global multi-region input–output database at high country and sector resolution. Econ. Syst. Res. 25, 20–49 (2013).Article Google Scholar 
18. Zheng, H. et al. Chinese provincial multi-regional input–output database for 2012, 2015, and 2017. Sci. Data 8, 244 (2021).Article Google Scholar 
19. Wernet, G. et al. The ecoinvent database version 3 (part I): overview and methodology. Int. J. Life Cycle Assess. 21, 1218–1230 (2016).Article Google Scholar 
20. Smith, T. M. et al. Subnational mobility and consumption-based environmental accounting of US corn in animal protein and ethanol supply chains. Proc. Natl Acad. Sci. USA 114, E7891–E7899 (2017).Article CAS Google Scholar 
21. Production—Beef (USDA FAS, 2024); https://fas.usda.gov/data/production/commodity/0111000 (2024).
22. OECD-FAO Agricultural Outlook 2024–2033 (OECD, 2024); https://www.oecd.org/en/publications/2024/07/oecd-fao-agricultural-outlook-2024-2033_e173f332.html
23. Friedlingstein, P. et al. Global carbon budget 2023. Earth Syst. Sci. Data 15, 5301–5369 (2023).Article Google Scholar 
24. Greenhouse Gas Inventory Data Explorer (EPA, 2023); https://cfpub.epa.gov/ghgdata/inventoryexplorer/
25. Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987–992 (2018).Article CAS Google Scholar 
26. Eshel, G., Shepon, A., Makov, T. & Milo, R. Land, irrigation water, greenhouse gas, and reactive nitrogen burdens of meat, eggs, and dairy production in the United States. Proc. Natl Acad. Sci. USA 111, 11996–12001 (2014).Article CAS Google Scholar 
27. Gilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data 5, 180227 (2018).Article Google Scholar 
28. Robinson, T. P. et al. Mapping the global distribution of livestock. PLoS ONE 9, e96084 (2014).Article Google Scholar 
29. Crespi, J. M. & Saitone, T. L. Are cattle markets the last frontier? Vertical coordination in animal-based procurement markets. Annu. Rev. Resour. Econ. 10, 207–227 (2018).Article Google Scholar 
30. Schwartzkopf-Genswein, K. S. et al. Road transport of cattle, swine and poultry in North America and its impact on animal welfare, carcass and meat quality: a review. Meat Sci. 92, 227–243 (2012).Article CAS Google Scholar 
31. Pelton, R. E. O. et al. Greenhouse gas emissions in US beef production can be reduced by up to 30% with the adoption of selected mitigation measures. Nat. Food 5, 787–797 (2024).Article CAS Google Scholar 
32. Goldstein, B., Moses, R., Sammons, N. & Birkved, M. Potential to curb the environmental burdens of American beef consumption using a novel plant-based beef substitute. PLoS ONE 12, e0189029 (2017).Article Google Scholar 
33. Plant-Powered Carbon Challenge (City of New York, 2024).
34. Hawkes, C., Harris, J. & Gillespie, S. Changing diets: urbanization and the nutrition transition. In 2017 Global Food Policy Report 34–41 (IFPRI, 2017).
35. Cohen, N. & Ilieva, R. T. Expanding the boundaries of food policy: the turn to equity in New York City. Food Policy 103, 102012 (2021).Article Google Scholar 
36. López Cifuentes, M., Freyer, B., Sonnino, R. & Fiala, V. Embedding sustainable diets into urban food strategies: a multi-actor approach. Geoforum 122, 11–21 (2021).Article Google Scholar 
37. Willett, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).Article Google Scholar 
38. Godfray, H. C. J. et al. Meat consumption, health, and the environment. Sciencehttps://doi.org/10.1126/science.aam5324 (2018).Article Google Scholar 
39. Puigdueta, I., Aguilera, E., Cruz, J. L., Iglesias, A. & Sanz-Cobena, A. Urban agriculture may change food consumption towards low carbon diets. Glob. Food Secur. 28, 100507 (2021).Article Google Scholar 
40. Vaqué, L. G. French and Italian food waste legislation. Eur. Food Feed Law Rev. 12, 224–233 (2017).Google Scholar 
41. Protz, A. Nijmegen becomes third Dutch city to ban meat ads. Plant Based Newshttps://plantbasednews.org/lifestyle/nijmegen-ban-meat-ads-embraces-plant-based-treaty/ (3 April 2025).
42. Nutrient Upcycling Alliance (OP2B, 2022); https://op2b.org/wp-content/uploads/2022/03/Yara.pdf
43. National Emissions Inventory (EPA, 2025); https://www.epa.gov/air-emissions-inventories/national-emissions-inventory-nei
44. Domingo, N. G. G. et al. Air quality-related health damages of food. Proc. Natl Acad. Sci. USA 118, e2013637118 (2021).Article CAS Google Scholar 
45. Godar, J., Suavet, C., Gardner, T. A., Dawkins, E. & Meyfroidt, P. Balancing detail and scale in assessing transparency to improve the governance of agricultural commodity supply chains. Environ. Res. Lett. 11, 035015 (2016).Article Google Scholar 
46. Global Metals and Mining (S&P Global, 2025); https://www.spglobal.com/market-intelligence/en/solutions/products/resources/capital-iq-pro-metals
47. Forest Inventory and Analysis (USDA, 2025); https://research.fs.usda.gov/programs/fia
48. Consumer Expenditure Surveys—Tables (US Bureau of Labor Statistics, 2024).
49. Ramaswami, A. et al. Carbon analytics for net-zero emissions sustainable cities. Nat. Sustain. 4, 460–463 (2021).Article Google Scholar 
50. National Health and Nutrition Examination Survey (Centers for Disease Control and Prevention, 2020).
51. Food Intakes Converted to Retail Commodities (USDA, 2019).
52. Goldstein, B. P., Gounaridis, D., Newell, J. P., Pelton, R. & Schmitt, J. Do we accurately measure what we consume? Environ. Res. Lett. 19, 084006 (2024).Article Google Scholar 
53. Archer, E., Hand, G. A. & Blair, S. N. Validity of U.S. nutritional surveillance: national health and nutrition examination survey caloric energy intake data, 1971–2010. PLoS ONE https://doi.org/10.1371/journal.pone.0076632 (2013).
54. Ahluwalia, N., Dwyer, J., Terry, A., Moshfegh, A. & Johnson, C. Update on NHANES dietary data: focus on collection, release, analytical considerations, and uses to inform public policy. Adv. Nutr. 7, 121–134 (2016).Article Google Scholar 
55. American Community Survey Demographic and Housing Estimates (US Census Bureau, 2024).
56. Brauman, K. A. et al. Unique water scarcity footprints and water risks in US meat and ethanol supply chains identified via subnational commodity flows. Environ. Res. Lett. 15, 105018 (2020).Article CAS Google Scholar 
57. Pelton, R. E. O. et al. Land use leverage points to reduce GHG emissions in U.S. agricultural supply chains. Environ. Res. Lett. 16, 115002 (2021).Article CAS Google Scholar 
58. Livestock Slaughter 2019 Summary (USDA, 2020); https://usda.library.cornell.edu/concern/publications/r207tp32d
59. Livestock and Meat Domestic Data (USDA, 2024); https://www.ers.usda.gov/data-products/livestock-and-meat-domestic-data/
60. Buzby, J. C., Bentley, J. T., Padera, B., Campuzano, J. & Ammon, C. Updated Supermarket Shrink Estimates for Fresh Foods and Their Implications for ERS Loss-Adjusted Food Availability Data (USDA, 2016).
61. Pelton, R. Improving Sustainability Management Decisions with Modified Life Cycle Assessment Methods (Univ. Minnesota Twin Cities, 2016).
62. Global Agricultural Trade System (GATS) (USDA, 2024); https://apps.fas.usda.gov/gats/default.aspx?publish=1
63. Food and Agriculture Data (FAO, 2024); https://www.fao.org/faostat/en/#home
64. Jones, C. & Kammen, D. M. Spatial distribution of U.S. household carbon footprints reveals suburbanization undermines greenhouse gas benefits of urban population density. Environ. Sci. Technol. 48, 895–902 (2014).Article CAS Google Scholar 
65. New York City Household Consumption-Based GHG Emissions Inventory (NYC Mayor’s Office of Climate & Environmental Justice, 2025); https://www.nyc.gov/content/climate/pages/reports-and-publications/new-york-city-household-consumption-based-ghg-emissions-inventory
66. City of Los Angeles 2022 Community Greenhouse Gas Inventory Report (City of Los Angeles, 2024); https://clkrep.lacity.org/onlinedocs/2022/22-1402_rpt_BOS_02-14-24.pdf
67. Goldstein, B. The carbon hoofprint of cities. Supplementary data and code for ‘The carbon hoofprint of cities’ in Nature Climate Change. OSFhttps://doi.org/10.17605/OSF.IO/6EXPC (2025).