New research database for evidence-based meat reduction
A comprehensive collection of meat reduction research is waiting for researchers, advocates, and funders to unlock its potential
Summary author: Elisa Autric1.
This post introduces research conducted by Ben Stevenson, Jacob R. Peacock, Julia Fabienne Sandkühler, Jessica E. Hope, Constanza Arévalo, Joanna E. Anderson, Maya B. Mathur, and the many volunteers who helped screen sources for eligibility2. For a more comprehensive summary, visit Rethink Priorities’ website. The full database and methodology details are available at osf.io/dnu58.
The scattered science problem
If you’re an advocate, researcher, or funder seeking effective meat reduction interventions, you’ve likely encountered a familiar challenge. The literature presents a fragmented landscape: studies scattered across environmental science, medicine, psychology, and animal advocacy research. Academic papers intermix with gray literature from nonprofits and NGOs. Research focuses range from the effectiveness of leaflets to the optimization of menu design and the impacts of educational campaigns.
This fragmentation creates a significant barrier to evidence synthesis, and the most dedicated advocates and researchers struggle to get a complete picture of what actually works. While hundreds of billions of animals are raised in industrial agriculture systems each year, we end up with blind spots, duplicated efforts, and missed opportunities to learn from the most effective approaches.
Introducing a comprehensive database of meat reduction interventions
After identifying and screening over 8,600 unique sources, we recently released a database containing 413 sources investigating interventions to reduce meat and animal product consumption. Unlike previous reviews that often had restrictive criteria (focusing only on adults, only randomized trials, or only specific types of interventions), we cast the widest possible net.
This database is different from others previously released on this topic:
Minimal restrictions, maximum scope: We included any intervention designed to reduce meat consumption across any human population, using any study design that could provide useful evidence. While other reviews might focus only on educational interventions or exclude non-randomized studies, we recognized that breakthrough insights often come from unexpected places.
Real-world relevance: We included actual consumption data, self-reported consumption, and even hypothetical choice scenarios. Some databases exclude choice architecture studies that occur in campus cafeterias or grocery stores because they’re not randomized controlled trials—but these natural experiments may have greater external validity than lab studies.
Gray literature: A significant portion of meat reduction research is conducted outside traditional academic journals, often published by nonprofits and advocacy organizations. We ensured that we captured this often-overlooked evidence.
What you can do with this database
The database represents a foundation for future research. The systematic screening work has been completed, but significant analytical opportunities remain.
Complete the coding work
We developed a detailed framework for extracting key information from each source; however, we were unable to complete the coding due to resource constraints. This creates a valuable opportunity for researchers to build upon our systematic foundation.
Doing so, they could filter through subsets of the data and answer questions such as:
Which types of interventions show the strongest effects across different populations?
How do educational approaches compare to choice architecture changes?
What factors predict whether an intervention will have lasting effects?
Which studies have the strongest methodological designs for testing causality?
Conduct targeted literature reviews
The database opens up possibilities for focused meta-analyses and systematic reviews on specific intervention types. We had originally planned reviews on classroom interventions, increasing the availability of plant-based options, and menu design—but additional research directions could extend well beyond our initial scope.
Build on existing work that draws on our database
The database has already proven valuable—two research teams used our database as a foundation for their own analyses.
Green and co-authors (2024) conducted a meta-analysis that revealed existing interventions have quite small effects, concluding that meaningfully reducing meat consumption remains “an unsolved problem.”
Sleegers and co-authors (2025) built the Library of Interventions for Meat Elimination (LIME), an interactive platform that lets users explore studies and calculate meta-analytic effect sizes.
Next steps
The database and codebook are freely available on the Open Science Framework at osf.io/dnu58. Many sources are open access, and for those that aren’t, we’ve provided stable links that can often be accessed through academic libraries.
If you have questions about this work, you can reach out to Jacob Peacock, who coordinated the project from its inception.
Whether you’re a researcher planning your next study, an advocate designing a campaign, or a funder evaluating grant applications, this database offers an unprecedented foundation for evidence-based meat reduction decision-making.
Acknowledgements
Thank you to Urszula Zarosa for reviewing this post and Shane Coburn for copyediting.
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Disclosure: This piece was drafted by Elisa Autric with support from an AI assistant (in line with our AI use policy). Our team reviewed it for accuracy, clarity, and tone. We use this approach to produce high-quality work while optimizing our resources.
List of volunteers who helped screen the sources for eligibility (listed alphabetically by surname): Tara A., Hasan Alparslan Bayrak, Elena Bräu, Kenneth Diao, AnnaLise Hoopes, Steven Mai, Karan Makkar, Emily MacNintch, Dung Nguyen, John Nyabwari Nyambane, Sada Rice, Sian Richards, Tapinder Sadu, Elena Schaller, Ranjan Sharangpani, Ethan Terrill, Timea Tarczy, Sofia Estefania Vera Verduzco, and Geoffrey Yip.