GHG mitigation strategies on China’s diverse dish consumption are key to meet the Paris Agreement targets

Combatting climate change depends on demand-side mitigation strategies related to food, which is in turn contingent on explicit estimation and management of dish-level emissions. Here, on the basis of a bottom-up integrated emissions framework, we first estimate the greenhouse gas emissions of 540 dishes from 36 cuisines using data from over 800,488 restaurants in China’s provincial capital cities. By mining residents’ dietary preferences, we then design various dietary change strategies to explicitly link food emissions to the Paris Agreement pledges. The results show that China’s food system greenhouse gas emissions were approximately 4.64 GtCO2eq in 2020, accounting for 37% of total emissions, with average per-dish emissions of 8.44 kgCO2eq. Current emission patterns of food consumption in China may not be consistent with the attainment of the 1.5 °C and 2 °C climate targets, but transitioning towards low-emission cuisines and dishes could change that by reducing emissions by 38–69%.

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Data availability

The data supporting the findings of this study are available within the paper and Supplementary Information. The observed dish consumption data of restaurants in the representative cities are derived from https://www.dianping.com/. The data of food ingredient type, weight and cooking time for dishes from the recipes ‘The Cookbook China’ 27 , ‘A Bite of China’ 28 and https://www.meishij.net/. China’s population projections are taken from https://databank.worldbank.org/source/population-estimates-and-projections. The emission factors of food ingredients are provided by Poore and Nemecek (2018) entitled ‘Reducing foods environmental impacts through producers and consumers’ (https://doi.org/10.1126/science.aaq0216). Residents’ per capita consumption of food ingredients data are from the National Bureau of Statistics of China, available at https://www.stats.gov.cn/. All necessary data in this study could be accessed via GitHub at https://github.com/naturefood/NF_Dish-consumption-data. Source data are provided with this paper.

Code availability

The data collecting and processing are conducted in Microsoft Excel 2020, Python (v.3.9) and Eureqa (v.1.24.0), while Tableau (v.10.5) is used to visualize the results. More details on codes are available from the corresponding authors upon request.

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Acknowledgements

We acknowledge funding from the National Natural Science Foundation of China (72325008, 72101051, 71988101 and 72293565), the National Key Research and Development Program of China (2020YFA0608603) and the Fundamental Research Funds for the Central Universities in China.

Author information

Authors and Affiliations

  1. School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian, China Xian Yang & Qian Gao
  2. School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China Hongbo Duan & Shouyang Wang
  3. PanshiData Technology, Shenyang, China Muhua Zhu
  4. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China Shouyang Wang
  1. Xian Yang