@article{f95bd30451574229beb7e0ef01fc544b,
title = "Changes in global food consumption increase GHG emissions despite efficiency gains along global supply chains",
abstract = "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.",
author = "Yanxian Li and Honglin Zhong and Yuli Shan and Ye Hang and Dan Wang and Yannan Zhou and Klaus Hubacek",
note = "Funding Information: We thank T. Kastner for providing the code for the PTF approach. We thank the support from Greenpeace Germany for the initial data analysis, modelling and discussions as part of the project {\textquoteleft}Outsourced Environmental Degradation of the EU{\textquoteright}. This research is also supported by the National Natural Science Foundation of China (72243004, 72174111), the Shandong Natural Science Foundation of China (ZR2021MG013), the major programme of the National Social Science Foundation of China (21ZDA065), the United Kingdom Research and Innovation (UoB Policy Support Fund PSF-16). For the purpose of open access, a CC BY public copyright licence is applied to any Author Accepted Manuscript arising from this submission. Y.L., Y.H., D.W. and Y.Z. acknowledge the funding support by the China Scholarship Council Ph.D. programme. Funding Information: We thank T. Kastner for providing the code for the PTF approach. We thank the support from Greenpeace Germany for the initial data analysis, modelling and discussions as part of the project {\textquoteleft}Outsourced Environmental Degradation of the EU{\textquoteright}. This research is also supported by the National Natural Science Foundation of China (72243004, 72174111), the Shandong Natural Science Foundation of China (ZR2021MG013), the major programme of the National Social Science Foundation of China (21ZDA065), the United Kingdom Research and Innovation (UoB Policy Support Fund PSF-16). For the purpose of open access, a CC BY public copyright licence is applied to any Author Accepted Manuscript arising from this submission. Y.L., Y.H., D.W. and Y.Z. acknowledge the funding support by the China Scholarship Council Ph.D. programme. Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive licence to Springer Nature Limited.",
year = "2023",
month = jun,
doi = "10.1038/s43016-023-00768-z",
language = "English",
volume = "4",
pages = "483--495",
journal = "Nature Food",
issn = "2662-1355",
publisher = "Springer Nature",
number = "6",
}