BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving

Tianyao Shi; Yi Ding

doi:10.18653/2026.findings-emnlp.1207

BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving

Correct Metadata for

Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account. Once you create that issue, the correction will be reviewed by a staff member.

⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>. You may use ... for bold, ... for italic, ... for underline, <sc>...</sc> for small-caps, <tt>...<tt> for typewriter text, <url>...</url> for URLs, <a href=...> for hyperlinks, and <par/> for paragraph breaks.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

Large language model (LLM) serving creates environmental impacts beyond carbon and water, including ecosystem damage through biodiversity-related pathways. We present BIRDS, a framework for Biodiversity Impact of Request-Driven LLM Serving. BIRDS defines request-level functional units, quantifies operational and embodied biodiversity impact, and introduces Quality-Normalized Biodiversity Impact (QNBI) to jointly analyze ecological impact and response quality. Across diverse workloads, models, GPUs, and regions, BIRDS reveals that reveals that biodiversity impact accumulates at scale and exposes quality-aware serving tradeoffs. The code is available at https://github.com/TianyaoShi/BIRDS.

Anthology ID:: 2026.findings-emnlp.1207
Volume:: Findings of the Association for Computational Linguistics: EMNLP 2026
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:: Findings
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 25476–25498
Language:
URL:: https://aclanthology.org/2026.findings-emnlp.1207/
DOI:: 10.18653/2026.findings-emnlp.1207
Bibkey:
Cite (ACL):: Tianyao Shi and Yi Ding. 2026. BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 25476–25498, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving (Shi & Ding, Findings 2026)
Copy Citation:
PDF:: https://aclanthology.org/2026.findings-emnlp.1207.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{shi-ding-2026-birds,
    title = "{BIRDS}: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving",
    author = "Shi, Tianyao  and
      Ding, Yi",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-emnlp.1207/",
    doi = "10.18653/2026.findings-emnlp.1207",
    pages = "25476--25498",
    ISBN = "979-8-89176-462-0",
    abstract = "Large language model (LLM) serving creates environmental impacts beyond carbon and water, including ecosystem damage through biodiversity-related pathways. We present BIRDS, a framework for Biodiversity Impact of Request-Driven LLM Serving. BIRDS defines request-level functional units, quantifies operational and embodied biodiversity impact, and introduces Quality-Normalized Biodiversity Impact (QNBI) to jointly analyze ecological impact and response quality. Across diverse workloads, models, GPUs, and regions, BIRDS reveals that reveals that biodiversity impact accumulates at scale and exposes quality-aware serving tradeoffs. The code is available at https://github.com/TianyaoShi/BIRDS."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="shi-ding-2026-birds">
    <titleInfo>
        <title>BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Tianyao</namePart>
        <namePart type="family">Shi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yi</namePart>
        <namePart type="family">Ding</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2026</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Hung-Yi</namePart>
            <namePart type="family">Lee</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sunipa</namePart>
            <namePart type="family">Dev</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jessy</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Desmond</namePart>
            <namePart type="family">Elliott</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Budapest, Hungary</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-462-0</identifier>
    </relatedItem>
    <abstract>Large language model (LLM) serving creates environmental impacts beyond carbon and water, including ecosystem damage through biodiversity-related pathways. We present BIRDS, a framework for Biodiversity Impact of Request-Driven LLM Serving. BIRDS defines request-level functional units, quantifies operational and embodied biodiversity impact, and introduces Quality-Normalized Biodiversity Impact (QNBI) to jointly analyze ecological impact and response quality. Across diverse workloads, models, GPUs, and regions, BIRDS reveals that reveals that biodiversity impact accumulates at scale and exposes quality-aware serving tradeoffs. The code is available at https://github.com/TianyaoShi/BIRDS.</abstract>
    <identifier type="citekey">shi-ding-2026-birds</identifier>
    <identifier type="doi">10.18653/2026.findings-emnlp.1207</identifier>
    <location>
        <url>https://aclanthology.org/2026.findings-emnlp.1207/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>25476</start>
            <end>25498</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving
%A Shi, Tianyao
%A Ding, Yi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F shi-ding-2026-birds
%X Large language model (LLM) serving creates environmental impacts beyond carbon and water, including ecosystem damage through biodiversity-related pathways. We present BIRDS, a framework for Biodiversity Impact of Request-Driven LLM Serving. BIRDS defines request-level functional units, quantifies operational and embodied biodiversity impact, and introduces Quality-Normalized Biodiversity Impact (QNBI) to jointly analyze ecological impact and response quality. Across diverse workloads, models, GPUs, and regions, BIRDS reveals that reveals that biodiversity impact accumulates at scale and exposes quality-aware serving tradeoffs. The code is available at https://github.com/TianyaoShi/BIRDS.
%R 10.18653/2026.findings-emnlp.1207
%U https://aclanthology.org/2026.findings-emnlp.1207/
%U https://doi.org/10.18653/2026.findings-emnlp.1207
%P 25476-25498

Download as File

Markdown (Informal)

[BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving](https://aclanthology.org/2026.findings-emnlp.1207/) (Shi & Ding, Findings 2026)

BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving (Shi & Ding, Findings 2026)

ACL

Tianyao Shi and Yi Ding. 2026. BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 25476–25498, Budapest, Hungary. Association for Computational Linguistics.