Environment

Greener Than Humans? Environmental Attitudes in Large Language Models

Stefanie Kunkel, Tilman Hartwig, Marcus Voss, Emma K. Schütt, Angelika Gellrich

Featured June 5, 2026

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Simply

Large language models often sound more environmentally friendly than the average person, but they are easily swayed by the user's tone, making them unreliable for objective sustainability advice.

In depth

The paper introduces a sustainability-oriented benchmark to evaluate how Large Language Models (LLMs) perceive environmental issues. By comparing model outputs against established human survey data, the authors demonstrate that LLMs exhibit a pro-environmental bias but remain highly susceptible to sycophancy and persona-based manipulation, which complicates their reliability in real-world sustainability decision-making.

Key Takeaways

1
LLMs consistently demonstrate higher levels of environmental cognition and affect compared to the average German population.
2
There is no systematic correlation between model metadata (size, origin, release context) and the sustainability-oriented responses provided.
3
Models exhibit significant contextual sensitivity, mirroring user-specified ideological positions through sycophantic shifts.

Conceptual Flow

HIGH LEVEL

Methodology

The researchers created a test to see how models answer questions about nature and then compared those answers to real people.

Survey Questions

Large Language Models

Compare model answers to human survey data

Environmental Attitude Score

Results

The models were found to be very sensitive to the user's role, often changing their answers to match what the user seems to want.

Neutral Prompt

Persona Prompt

Measure shift in attitude score

Sycophancy Metric