ArticlePublisher preview available

Changes in global food consumption increase GHG emissions despite efficiency gains along global supply chains

June 2023
Nature Food 4(6)

DOI:10.1038/s43016-023-00768-z

Authors:

Yanxian Li

University of Groningen

Honglin Zhong

University of Maryland, College Park

Yuli Shan

University of Birmingham

Ye Hang

Nanjing University of Aeronautics and Astronautics

Show all 7 authorsHide

Greenhouse gas (GHG) emissions related to food consumption complement production-based or territorial accounts by capturing carbon leaked through trade. Here we evaluate global consumption-based food emissions between 2000 and 2019 and underlying drivers using a physical trade flow approach and structural decomposition analysis. In 2019, emissions throughout global food supply chains reached 30 ±9% of anthropogenic GHG emissions, largely triggered by beef and dairy consumption in rapidly developing countries—while per capita emissions in developed countries with a high percentage of animal-based food declined. Emissions outsourced through international food trade dominated by beef and oil crops increased by ~1 Gt CO2 equivalent, mainly driven by increased imports by developing countries. Population growth and per capita demand increase were key drivers to the global emissions increase (+30% and +19%, respectively) while decreasing emissions intensity from land-use activities was the major factor to offset emissions growth (−39%). Climate change mitigation may depend on incentivizing consumer and producer choices to reduce emissions-intensive food products.

GHG emissions throughout global supply chains from consumption of food products by country in 2000 and 2019 a, b, The background map shows the level of consumption-based emissions at the country scale in 2000 (a) and 2019 (b). The pie chart shows the fraction of consumption-based emissions of animal-based and plant-based food products in 2000 (a) and 2019 (b), and the size represents the total emissions for 18 countries/regions. AUS: Australia; BRA: Brazil; CAN: Canada; CHN: China; ROEA: rest of East Asia; EE: Eastern Europe; IND: India; IDN: Indonesia; JPN: Japan; ROLAC: rest of Latin America and the Caribbean; NENA: Near East and North Africa; ROO: rest of Oceania; RUS: Russia; ROSA: rest of South Asia; ROSEA: rest of Southeast Asia; SSA: sub-Saharan Africa; USA: United States; WE: Western Europe. Details for the division and scope of 18 countries/regions are shown in Supplementary Tables 3 and 4. Base map layer: World Countries (http://tapiquen-sig.jimdo.com). Carlos Efraín Porto Tapiquén. Orogénesis Soluciones Geográficas. Porlamar, Venezuela 2015. Based on shapes from Environmental Systems Research Institute. Source data

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Per capita GHG emissions of food consumption by country in 2000 and 2019 a, b, The background map shows the level of per capita consumption-based emissions at the country scale in 2000 (a) and 2019 (b). The pie chart shows the fraction of average consumption-based emissions of animal-based and plant-based food products per person in 2000 (a) and 2019 (b), and the size represents per capita emissions of 18 countries/regions. Abbreviations of 18 countries/regions and the source of the base map are listed in Fig. 1 caption. Source data

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GHG emissions embodied in domestic supply and international trade of food of major countries in 2000, 2010 and 2019 a, Ratio of domestic GHG emissions to total embodied emissions of food consumption by 18 major countries. Domestic GHG emissions refer to the emissions embodied in domestic food supply within a national territory including emissions from all food products, animal-based and plant-based food products (from left to right). b, GHG emissions embodied in food imports and exports of 18 major countries. The circles represent net imports or exports of emissions from food consumption. Source data

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Patterns of emissions flows embodied in trade a–c, Patterns of emissions flows embodied in international trade of all types of (a), animal-based (b) and plant-based (c) food product among and within 18 countries/regions in 2000 and 2019 (unit: Mt CO2-eq). Width of the lines represents the volumes of emissions embodied in trade from exporter to importer, and the colour is the same as the exporter. Flows cover more than 90% of total emissions embodied in international bilateral trade annually as small flows are not shown here. Numbers in parentheses in each sub-plot represent the ratio of emissions embodied in trade to total consumption-based emissions. Abbreviations of 18 countries/regions are shown in Fig. 1 caption. Source data

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Contributions of five driving factors to changes in GHG emissions from food consumption a–s, Contributions of five driving factors to changes in GHG emissions from food consumption of the global (a) and 18 countries/regions (b–s) between 2000 and 2019. The grey bars indicate total emissions. The coloured bars represent the absolute contribution (positive or negative) of different driving factors to the changes in global and national/regional emissions in every period. Source data

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Nature Food | Volume 4 | June 2023 | 483–495 483

nature food

https://doi.org/10.1038/s43016-023-00768-z

Article

Changes in global food consumption

increase GHG emissions despite efficiency

gains along global supply chains

Yanxian Li 1, Honglin Zhong2,3, Yuli Shan 4 , Ye Hang 1,5, Dan Wang 1,

Yannan Zhou 1,6 & Klaus Hubacek 1

Greenhouse gas (GHG) emissions related to food consumption complement

production-based or territorial accounts by capturing carbon leaked

through trade. Here we evaluate global consumption-based food emissions

between 2000 and 2019 and underlying drivers using a physical trade

ow approach and structural decomposition analysis. In 2019, emissions

throughout global food supply chains reached 30 ±9% of anthropogenic

GHG emissions, largely triggered by beef and dairy consumption in

rapidly developing countries—while per capita emissions in developed

countries with a high percentage of animal-based food declined. Emissions

outsourced through international food trade dominated by beef and oil

crops increased by ~1 Gt CO2 equivalent, mainly driven by increased imports

by developing countries. Population growth and per capita demand

increase were key drivers to the global emissions increase (+30% and +19%,

respectively) while decreasing emissions intensity from land-use activities

was the major factor to oset emissions growth (−39%). Climate change

mitigation may depend on incentivizing consumer and producer choices to

reduce emissions-intensive food products.

The agrifood system drives global land-use, agricultural and other

beyond-farm activities and contributes to about one-third of global

anthropogenic greenhouse gas (GHG) emissions

1–3

. The United Nations

projects that an additional 70% of the current food demand will be

needed to feed the world’s estimated population of 9.1 billion by 2050

(ref. 4). Population growth, expansion of food production and an

increase in animal-based diets are likely to further increase emissions

and squeeze the global carbon budget

5,6

. Thus, mitigating emissions

at every stage of food supply chains from production to consumption

is crucial to limit global warming6–8.

Production-based emissions (PBE) or territorial emissions are

based on emissions from production (including exports) within a

region9. Previous studies1,2,10,11 have quantified global GHG emissions

from food production based on global food-related emissions inven-

tories (for example, FAOSTAT, provided by the Food and Agriculture

Organization of the United Nations (https://www.fao.org/faostat/

en/#data/); and EDGAR-Food, provided by EDGAR—Emissions Data-

base for Global Atmospheric Research (https://edgar.jrc.ec.europa.

eu/edgar_food)). However, food products are increasingly traded

internationally through global supply chains, and geographically

Received: 4 November 2022

Accepted: 9 May 2023

Published online: 15 June 2023

Check for updates

1Integrated Research on Energy, Environment and Society (IREES), Energy and Sustainability Research Institute Groningen (ESRIG), University of

Groningen, Groningen, the Netherlands. 2Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining, China. 3Institute of Blue and

Green Development, Weihai Institute of Interdisciplinary Research, Shandong University, Weihai, China. 4School of Geography, Earth and Environmental

Sciences, University of Birmingham, Birmingham, UK. 5College of Economics and Management & Research Centre for Soft Energy Science, Nanjing

University of Aeronautics and Astronautics, Nanjing, China. 6Business School, University of Shanghai for Science and Technology, Shanghai, China.

e-mail: y.shan@bham.ac.uk; k.hubacek@rug.nl

Content courtesy of Springer Nature, terms of use apply. Rights reserved

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