The Vegan Report Nothing Seems to Work | Science of Activism
Oct 29, 2025
Seth Ariel Green is a researcher at Stanford's Humane and Sustainable Food Lab, focused on reducing meat consumption. He discusses the significance of meta-analysis in summarizing diverse studies on meat consumption, revealing only small overall effects from various interventions. Seth highlights that choice architecture yielded the best results, while psychology-based interventions showed inconsistency. He notes the challenges of engaging with animal welfare messaging and reveals intriguing findings from a randomized menu trial on plant-based options. Green emphasizes the need for continued testing in this complex arena.
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Meta-Analysis Can Mask Incompatibility
- Meta-analysis combines many studies to estimate a literature-wide effect rather than rely on single trials.
- But averaging conceptually different interventions or outcomes can produce results that are hard to interpret.
Use Strict Inclusion Criteria
- Only include high-quality studies that measure actual consumption to get meaningful meta-analytic estimates.
- Require sufficient sample sizes and randomized controlled designs with delayed outcome measurement.
Effects On Meat Intake Are Very Small
- The meta-analysis found only tiny average effects overall (Cohen's D ≈ 0.07), implying a few percentage points change at best.
- This led to the paper's conclusion that meaningfully reducing meat consumption is still an unsolved problem.
