The Center for Biological Diversity (CBD) is promoting a campaign with a target of 50% reduction in meat and dairy consumption. To support this campaign, CBD is interested in quantifying the changes in greenhouse gas emissions (GHGE) based on a number of diet scenarios projected to 2030. What would be the net change in diet-related GHGE if diet patterns remained as they are today? What if US dietary intake followed long-term projections made by USDA-ERS? What would be the net savings in diet-related GHGE from a 50% reduction in meat and dairy consumption? Finally, what would be the net savings in diet-related GHGE from a 90% reduction in beef consumption alongside a 50% reduction in all other meat and dairy categories?
The following offers a brief description of how we propose to quantify these changes.
The USDA’s Loss Adjusted Food Availability (LAFA) data series offers a top-down approximation of the daily food intake of the US population. The data series measures food supplies of over 200 commodities moving from production through marketing channels for domestic consumption; adjustments are made for food losses (spoilage, plate waste) at retail and consumer levels to more closely approximate intake. This food availability is then divided by the population to provide an estimate of average per capita intake. These intakes are presented at the commodity level, not the “as consumed” food product level: e.g., wheat, corn, beef, rather than bread, corn syrup, hamburger. This has advantages for linking to environmental impact such as GHGE because most of the available environmental impact data is at the commodity level. However, it also means that only GHGE associated with production (farm-level and basic commodity processing like flours and oils) can be traced: estimates will not include emissions associated with secondary processing, packaging, distribution, retailing or consumption (home storage and preparation).
This proposal utilizes LAFA as the starting point in defining the US diet. After calculating GHGE associated with that defined diet as the baseline, a series of projection scenarios to 2030 are built to demonstrate changes in diet-related GHGE under increases and decreases in meat and milk consumption. These scenarios are built to offer defensible “what if” quantifications of the emission impacts of differing future trajectories.
An additional component to this analysis will be based on simulating the results of the different scenarios using a bottom-up approach. For this, we would rely on our existing modeling of National Health and Nutrition Examination Survey (NHANES). Previously we connected our emission factor database (dataFIELD) to all foods listed in the 24-hour recall dietary data used with the 2005-2010 NHANES. We would update this to the most recent NHANES dataset (2015-16). Calculating the scenarios using this additional approach has several advantages to exclusive use of the LAFA approach, including:
• It would provide a second set of estimates that could be used with the LAFA estimates. This bracketing would provide a stronger evidence base for public messaging.
• Because the bottom-up approach is based on nationally representative data, it provides a population distribution to the single point mean estimate for each scenario result.
• Additional scenarios could be tested, based on given percentages of the population making changes.
• Scenario changes at the mean using LAFA data could be compared to existing consumption data. This would give an indication of the percentage of the population that currently eat at the desired endpoint (50% of mean), and could lend a measure of optimism to the public messaging.
• Substitutions made at the individual level can be checked for nutritional correspondence to show that the scenario changes do not jeopardize nutrition (most likely will improve).