Abstract
Greenhouse gas emissions from meat and dairy production are often highly uncertain; these emissions are typically estimated using inventory-based, ‘bottom-up’ models, which contain uncertainties that are difficult to quantify. Modeled emissions estimates can be corroborated using atmospheric measurements—taken above and downwind of animal production regions—to produce ‘top-down’ emissions estimates. Top-down and bottom-up estimates of animal methane show good agreement when considering global emissions. However, in the US, where animal production is predominantly highly intensified with confined feeding operations, animal methane emissions may be 39%-90% higher than bottom-up models predict (expressed as mean differences across studies). Animal emissions may grow in the future as meat and dairy demand increases in developing countries. We examine East and Southeast Asia as a test case, where emissions from increased meat and dairy production are expected to be offset by improved efficiency from intensive methods. We adjust the share of direct emissions projected to come from intensive systems by the intensities derived from US top-down estimates. We find that region-wide emissions from meat and milk production could reach 1.52 (1.41–1.62) GtCO2eq by 2050, an amount 21% (13%-29%) higher than previously predicted. Therefore, intensification may not be as effective in mitigating emissions in developing countries as is commonly assumed.
Generated Summary
This topical review examines the potential for underestimation of methane emissions from intensively raised animals, particularly in the context of sustainable development goals. The study employs a multi-faceted approach, combining a review of existing literature on emission estimation methods, a comparison of bottom-up and top-down emissions data, and a case study focusing on the implications of intensification in East and Southeast Asia (ESA). The core methodology involves analyzing discrepancies between different emissions estimation techniques to assess the accuracy of current models and projecting future emissions based on different intensification scenarios. The research aims to highlight the uncertainties associated with livestock emissions, particularly concerning intensive farming practices, and to evaluate the effectiveness of intensification strategies in mitigating greenhouse gas (GHG) emissions.
Key Findings & Statistics
- Global meat consumption is expected to increase by 50% in the coming decades (FAO 2018a).
- Animal agriculture represents 15.6% of total annual greenhouse gas (GHG) emissions globally (FAO 2017 using a 100 year global warming potential (GWP100) for non-CO2 GHGs).
- In the US, animal methane emissions may be 39%-90% higher than bottom-up models predict (expressed as mean differences across studies).
- Region-wide emissions from meat and milk production could reach 1.52 (1.41–1.62) GtCO2eq by 2050, an amount 21% (13%-29%) higher than previously predicted.
- Global animal methane emissions may be only slightly higher than the bottom-up models predict about 5% higher and within the margin of error (e.g. Turner et al 2015).
- At least four top-down estimates of the contiguous US, representative of emissions occurring over at least one full year, indicate that direct animal methane emissions are 39%-90% higher than bottom-up models predict (Miller et al 2013, Wecht et al 2014, Turner et al 2015).
- Of five regional top-down studies, four found significantly higher methane emissions from areas of confined animal production than the bottom-up models suggest (Jeong et al 2016, Chen et al 2018, Desjardins et al 2018, Yu et al 2021).
- If the share of direct GHG emissions projected to come from intensive systems is scaled in proportion with top-down US estimates (scaled up by 65%, the average inferred from top-down estimates, provided in table 1), future emissions per unit of meat and dairy will not decrease as much as FAO bottom-up estimates predict (figure 2(C)). Multiplying this emission intensity by total consumption, we find that the ESA region could reach 1.52 (1.41-1.62 total range) GtCO2eq by 2050, an amount 21% (13%-29%) higher than previously predicted (figure 2(D)).
Other Important Findings
- Greenhouse gas emissions from meat and dairy production are often highly uncertain; these emissions are typically estimated using inventory-based, ‘bottom-up’ models, which contain uncertainties that are difficult to quantify.
- Modeled emissions estimates can be corroborated using atmospheric measurements—taken above and downwind of animal production regions—to produce ‘top-down’ emissions estimates.
- Animal emissions may grow in the future as meat and dairy demand increases in developing countries.
- Therefore, intensification may not be as effective in mitigating emissions in developing countries as is commonly assumed.
- The majority of global agricultural emissions are estimated to come from lower efficiency, pastoral systems (Steinfeld et al 2006).
- The benefits of improved feed requirements are commonly assumed to include fewer GHG emissions: both lower indirect emissions from feed production and grazing, and fewer direct emissions from wastes like manure, urine, and belches, per unit of food produced.
- Bottom-up estimates of methane are created by tallying up populations of each type of animal, known as activity data. These data are then multiplied by emissions factors—estimates of gas emitted by each animal daily or each operation annually.
- Researchers have therefore recommended intensifying existing extensive and pastoral systems to reduce GHG emissions (e.g. Steinfeld et al 2006, Thornton and Herrero 2010, Swain et al 2018).
- The discrepancies between bottom-up and top-down estimates in the US and Canada imply that models may under-predict the emissions intensity of animals raised in intensive, predominantly confined systems.
- High methane emissions may also stem from animal disease. Infections in animals are widespread in intensive production systems; approximately one quarter of US dairy cows have mastitis.
- If the share of direct GHG emissions projected to come from intensive systems is scaled in proportion with top-down US estimates, future emissions per unit of meat and dairy will not decrease as much as FAO bottom-up estimates predict.
- Indirect emissions account for approximately 40%-50% of animal agriculture’s emissions.
- Our prospective analysis demonstrates that animal emissions in the ESA region may not plateau in by 2030, as has previously been projected.
Limitations Noted in the Document
- The study acknowledges that bottom-up emissions inventories may underestimate uncertainties, and these are often difficult to compare and scrutinize.
- The uncertainty ranges in bottom-up estimates are possibly too small, and often do not reflect uncertainties arising from model structure and parameter selection.
- The level of disaggregation or detail in emissions inventories also determines how finely top-down emissions estimates can be used to distinguish among source types.
- Top-down atmospheric estimates rely on wind observations and models with their own errors.
- These atmospheric transport errors are different from the errors in bottom-up models.
- The current body of top-down estimates is insufficient to understand precisely why bottom-up models may underpredict animal emissions.
- The specific metabolic processes and source categories that can fully explain the discrepancy in North American emissions are still unknown.
- This case study is limited in terms of the confidence with which we can project and scale such differences.
- Uncertainties are not reported by FAO in their ESA regional emissions through 2050, which is a common tendency in bottom-up emissions estimates and projections.
Conclusion
The review underscores the critical need to re-evaluate the methodology for estimating and projecting GHG emissions from animal agriculture, particularly in intensive production systems. The consistent underestimation of methane emissions by bottom-up models in the United States, compared to top-down atmospheric measurements, highlights significant uncertainties. “The discrepancies between bottom-up and top-down estimates in the US and Canada imply that models may under-predict the emissions intensity of animals raised in intensive, predominantly confined systems.” This discrepancy raises concerns about the accuracy of emission inventories and the reliability of projections used for climate change mitigation strategies. “This pattern furthermore suggests that a greater proportion of total global methane emissions from animals is coming from intensive systems than is routinely reported.” The study’s findings suggest that the benefits of intensification might be overestimated, especially in regions with growing demand. The case study in ESA, demonstrates that the projected emissions reductions due to intensification may be less substantial than anticipated. “If the share of direct GHG emissions projected to come from intensive systems is scaled in proportion with top-down US estimates, future emissions per unit of meat and dairy will not decrease as much as FAO bottom-up estimates predict.” The document emphasizes that it is crucial to recognize the limits of intensification for sustainable development. While intensification is often presented as a solution, it is not without its drawbacks. The study notes that “Policies that incentivize landless and industrialized animal production methods risk amplifying other public health and environmental harms.” The study underscores that socio-environmental benefits can be realized by providing resources in areas of pastoral production for intensification practices that do not involve confinement to similar levels of intensive US ‘landless’ systems. To achieve sustainable development goals, a comprehensive approach is required, that considers both supply and demand. “Managing both supply and demand of animal-sourced foods must be considered within efforts to reform and develop food systems sustainably while mitigating climate change.” This includes dietary changes, public-private partnerships, and adjustments to taxation and subsidy structures. The study concludes that effective strategies must ensure that intensification strategies maximize co-benefits and constrain potential externalities. The results call for a reassessment of how we measure and forecast agricultural emissions, as well as re-evaluating the assumptions underlying mitigation strategies. “Despite this uncertainty, our illustrative example of ESA demonstrates that widescale adaptation of intensive production methods in low- and middle-income countries may have limited benefits to GHG emissions, especially if demand continues to grow.” The review provides a cautionary note, urging a more nuanced and comprehensive approach to agricultural emissions, development, and climate change mitigation.