# A global strategy to mitigate the environmental impact of China’s ruminant consumption boom

## Abstract

Rising demand for ruminant meat and dairy products in developing countries is expected to double anthropogenic greenhouse gas and ammonia emissions from livestock by 2050. Mitigation strategies are urgently needed to meet demand while minimizing environmental impacts. Here, we develop scenarios for mitigating emissions under local vs global supply policies using data from 308 livestock farms across mainland China, where emissions intensities are ~50% higher than those in developed nations. Intensification of domestic production and globalized expansion through increased trade result in reductions in global emissions by nearly 30% over a business-as-usual scenario, but at the expense of trading partners absorbing the associated negative externalities of environmental degradation. Only adoption of a mixed strategy combining global best-practice in sustainable intensification of domestic production, with increased green-source trading as a short-term coping strategy, can meet 2050 demand while minimizing the local and global environmental footprint of China’s ruminant consumption boom.

## Introduction

Ruminant production is one of the major contributors to global environmental degradation1,2,3,4. Beef, mutton, and milk production contributes 80% of total greenhouse gas (GHG) emissions2 and 75% of ammonia (NH3) emissions5 in the livestock sector. Globally, the GHG emissions from ruminant production have caused US$679 billion in damage costs to ecosystems, and US$13 billion in damage costs to human health2,6,7. The NH3 emissions from ruminant production have substantial negative effects on the local environment, such as atmospheric haze and nitrogen deposition, leading to human health impacts and eutrophication5,8,9. For example, in the United States the damage costs of NH3 emissions from the production of livestock export products are estimated to be even higher than the net market value of the exported food10,11.

The global consumption of ruminant products has been rising dramatically in the past two decades. Half of the global ruminant meat demand and two thirds of global milk demand are predicted to come from developing nations by 2050, especially China and India1,12. Without effective action in developing nations, rising demand for ruminant products is likely to push the global environment close to or beyond a sustainable threshold (the planetary boundary: refs. 13,14,15). Given the increasing globalization of trade and environmental damage16,17, the mitigation strategies adopted in developing nations will have an important effect on the livelihoods and welfare of both developing and developed nations.

China is arguably the most important new consumer market for ruminant products18,19, and consumption is increasing rapidly20. However, the burgeoning demand for ruminant products in China has been met with relatively little regard for environmental impacts thus far. Certainly, over the past 3 years, the Chinese government has adopted a range of policies aimed at reducing livestock pollution, but only a few of these have been specifically targeted at ruminant production21,22. Similarly, in other developing nations, policy changes to mitigate the environmental impacts of ruminant production have also been slow in coming. For instance, it was not until 2009 that Brazil issued public policies and interventions in beef and soy supply chains to slow Amazon deforestation23, and India only recently developed policy on manure management to reduce GHG emissions in the dairy sector24. The key problem for developing nations, and for the world, remains the relatively neglected connection between ruminant consumption and environmental degradation.

Here we evaluate policy options for meeting the demand for ruminant products in China, while minimizing local and global GHG and NH3 emissions to 2050. We first develop a dynamic model to analyze the GHG (CH4, N2O, CO2) and NH3 emissions along ruminant production chains (feed crop planting, primary feed processing, completed feed processing, livestock rearing, and livestock product processing; Supplementary Figs 1, 2; Supplementary Tables 1-10) using both mass balance assessment and life cycle assessment. These assessments are based on a field survey of 308 ruminant farms (beef cattle, dairy cattle, and sheep) across all 31 provinces of mainland China, and extensive literature review. We then develop a series of scenarios for mitigating emissions under a range of local vs global supply policies. After comparing the local and transferred emissions among these scenarios, we find that a mixed strategy combining global best practice in sustainable intensification of domestic production with increased green-source trading with low emission nations can produce the greatest reduction in global emissions over a business-as-usual scenario.

## Results

### Booming consumption of ruminant products

The consumption of ruminant meat increased exponentially in China from the early 1990s (Fig. 1a) and dairy products increased from the early 2000s (Fig. 1b) when per capita incomes began to rise. As a consequence, the proportion of ruminant meat in total dietary meat consumption (pork, poultry, and ruminant meat) has increased substantially, from 6% to 14%, over the past three decades (Supplementary Fig. 3). Despite the magnitude of the boom, national consumption figures are still lower than global averages (by 2012, China only consumed 14% of world ruminant meat and 7% of dairy products supply, with its ca 20% of global population), and there is significant capacity for further growth (per capita consumption of ruminant meat and milk in China is only 21% and 13%, respectively, of values for the USA in 2012). Combining several methods considering historical consumption patterns and income elasticity (Supplementary Table 11), we project that the demand for ruminant meat and dairy products in China will reach 17.8 and 77.8 kilogram per person per year by 2050; which is still only 48% and 30% of current USA values for ruminant meat and milk consumption, respectively. As a result, the total demand for ruminant products is predicted to double by 2050 (Fig. 1a, b).

### Scenario analyses

We calculated local and global GHG and NH3 emissions across the whole life cycle of the ruminant production process to 2050. We considered a reference scenario (business-as-usual) and six contrasting scenarios of variation in domestic production versus international trade modes (as detailed in Supplementary Table 16). For comparative purposes, we assumed that in all scenarios China’s demand for ruminant products in 2050 was fixed at the levels determined in the forecasting analysis above; i.e. 24.7 Mt for ruminant meat and 107.8 Mt for dairy products. Technology improvements included an increase in productivity and decrease in GHG and NH3 emissions intensities. We also assumed that the projected increase in the production of ruminant products relative to 2012 would be realized through industrial production systems. In addition, the Chinese government clearly promoted the grain to feed program in 2015, i.e., changing maize grains to silage or other forage in the cold regions of northern China (Supplementary Discussion). Hence, we assumed that the future expansion of feed fields would predominantly occur in existing farmland instead of forests, so that there would be no large increase in GHG emissions resulting from land use changes. Consequently, in the scenario settings, GHG emissions from land use changes were not a component that was incorporated in this study. For incorporation of dynamic change among competing land uses, see discussion in Supplementary Discussion.

We only considered international trade, while ignoring domestic redistribution among provinces within China. The GHG and NH3 emissions are those included in the full production chain and transportation of goods and products, but excluding the emissions from energy used for freezer storage due to lack of data (see parameters in Supplementary Tables 14, 15). In scenario calculation, we also considered the decreasing of emission intensities in major exporting nations due to technology improvement. Based on the time series of CO2-eq emissions for beef meat and milk production of 1961–2016 from the FAOSTAT database (Agri-Environmental Indicators-Emission intensities), we estimated the emissions intensities by 2050 (Supplementary Table 32). Due to the lack of historical data on NH3 emissions intensities, we assumed that the deceasing ratio of NH3 emission intensities of the major exporting nations is the same as that of the GHG emissions. Taking New Zealand as an example, the GHG emissions intensity of milk production in 2050 is projected to decrease by nearly 13%, so the NH3 emissions intensity of milk in 2050 is assumed to decrease by 13% (see Supplementary Table 33). For soybean and alfalfa, we assumed that emissions intensities increased by 30% (Supplementary Tables 34-35). We used data on the proportion of importing nations and imports of various types of products in 2012 from FAOSTAT (Supplementary Table 15) for future import calculation. All international transportation was assumed to be via shipping, with distance data obtained from Distance Netpas 3.2 (https://netpas.net/products/product_detail_DT_CN.php). The loss ratios during transportation were set to 8% for milk, 5% for ruminant meat, and 3% for livestock feed.

### Life cycle compilation and analysis

The GHG and NH3 emissions intensities of beef and milk in exporting nations were collected from literature sources. To find candidate publications, we searched for papers reporting LCAs for soybean, alfalfa, maize, liquid milk and beef meat using Web of Knowledge, Google Scholar, and PubMed. We chose all published LCAs that detailed the system boundaries of the study and that included and delimited the full cradle to product portion of the food/crop lifecycle of potential GHG and NH3 emissions, including emissions from pre-farm activities such as fertilizer production, but excluding emissions from land-use change (Supplementary Table 38). If the studies analyzed production from cradle to farm gate, we recalculated the results to our system boundary. For consistency, we chose the studies in which the allocation method was protein allocation (or we recalculated based on the available allocation information provided). In order to better compare the emissions between different food groups, we calculated emissions per kilogram. The functional unit of dairy cattle is liquid fresh milk (protein content 3.2%). The final product of beef cattle is carcass meat (protein content 20%). We recalculated the GHG emissions based on the coefficients of IPCC 201458. Because few data were available on NH3 emissions from the full production chain, we recalculated NH3 emissions from cattle breeding based on the proportions of each component of total emissions measured across the whole production chain reported by59 Thomassen et al. (Supplementary Table 39).

### Uncertainty analysis

Uncertainties associated with the calculated emissions were estimated using a Monte Carlo simulation (10,000 runs). The major uncertainty came from the emission factors collected from the published literature. Another important uncertainty source arises from the activity data used in this study. Input variables related to emission factors, cattle population, productivity of cattle, fertilizer use in feed crop cultivation, and some model parameters have been assumed to vary and have been randomly sampled. The medians and 95 percent confidence intervals for these parameters were calculated and used to characterize the uncertainties associated with the GHG and NH3 emissions intensities. The probability distribution functions and uncertainty values assumed for the GHG and NH3 emission model parameters and data input variables are summarized in Supplementary Table 30 and Supplementary Table 31 of the Supplementary Information. Taking the industrial milk production system as an example, the Monte Carlo simulation showed that GHG and NH3 emissions intensities had an uncertainty range of 0.95–1.84 kg CO2-eq per kg milk and 0.0049–0.0167 kg NH3 per kg milk, respectively.

## Data availability

The authors declare that all other data supporting the findings of this study are available within the article and its Supplementary Information files, or are available from the corresponding author upon reasonable request.

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## Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 31670329, 31470463, and 31770434), and the Zhejiang Provincial Natural Science Foundation of China (Grant Nos LY17G030030). We thank W. Yang, D. Liu, B. Xu, and M. Mikkilä for their comments on an earlier version of the manuscript and assistance during manuscript revision. We thank C.C. Huang, C.B. Zhang, S.Y. Li, Ri-e Bu, X.P. Ge, M. Chang, Y. Geng, W.J. Han, R.H. Xu, C.D. Fang, B. Luo, Z.Y. Zhao, C.C. Yu, M.M. Shi, K.D. Zhu, and T. Zhou for supporting the field work. We thank J.X. Liu for consultation on dairy cow breeding.

## Author information

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### Contributions

J.C., Y.G. and Y.Y.D. designed the study. Y.Y.D. carried out the surveys and data analyses, and wrote the paper. G.F.Y., Y.R., X.F., K.X.P., S.L.L., X.W. and Z.L.Q. participated in the surveys and contributed to the analyses and interpretation. Y.Y.D., J.C., Y.G., and R.K.D. wrote the paper with contributions from Y.M., L.A.M., M.H., S.X.C., X.Z.L., F.M., and C.H.P. Y.Y.D. and G.F.Y. drew the figures.

### Corresponding authors

Correspondence to Jie Chang or Raphael K. Didham.

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### Cite this article

Du, Y., Ge, Y., Ren, Y. et al. A global strategy to mitigate the environmental impact of China’s ruminant consumption boom. Nat Commun 9, 4133 (2018). https://doi.org/10.1038/s41467-018-06381-0

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