ICML 2025

What Can Large Language Models Do for Sustainable Food?
Anna T. Thomas 1 Adam Yee 2 Andrew Mayne 1 Maya B. Mathur 1 Dan Jurafsky 1 Kristina Gligori´c 1
Abstract
Food systems are responsible for a third of human-
caused greenhouse gas emissions. We investigate
what Large Language Models (LLMs) can con-
tribute to reducing the environmental impacts of
food production. We define a typology of design
and prediction tasks based on the sustainable food
literature and collaboration with domain experts,
and evaluate six LLMs on four tasks in our typol-
ogy. For example, for a sustainable protein design
task, food science experts estimated that collabo-
ration with an LLM can reduce time spent by 45%
on average, compared to 22% for collaboration
with another expert human food scientist. How-
ever, for a sustainable menu design task, LLMs
produce suboptimal solutions when instructed to
consider both human satisfaction and climate im-
pacts. We propose a general framework for in-
tegrating LLMs with combinatorial optimization
to improve reasoning capabilities. Our approach
decreases emissions of food choices by 79% in
a hypothetical restaurant while maintaining par-
ticipants’ satisfaction with their set of choices.
Our results demonstrate LLMs’ potential, sup-
ported by optimization techniques, to accelerate
sustainable food development and adoption. Our
code is available at https://github.com/
thomasat/llms-sustainable-food.
1. Introduction
Global food production practices are a leading contributor to
climate change, deforestation, water pollution, biodiversity
loss, antibiotic resistance, and zoonotic disease transmis-
sion (Allan et al., 2023; Hopwood et al., 2020; Scherer et al.,
2019). Given the disproportionate harms of animal agricul-
ture in particular, which accounts for 57% of food systems
emissions, a number of companies have formed to develop
1Stanford University 2Umai Works. Correspondence to: Anna
T. Thomas <thomasat@stanford.edu>, Kristina Gligori´c <glig-
oric...