# Towards a multi-criteria evaluation of the environmental footprint of generative ai services

Adrien Berthelot, Eddy Caron, Mathilde Jay, Laurent Lefèvre

► To cite this version:

Adrien Berthelot, Eddy Caron, Mathilde Jay, Laurent Lefèvre. Towards a multi-criteria evaluation of the environmental footprint of generative ai services. ICT4S 2024 - International Conference on Information and Communications Technology for Sustainability, Jun 2024, Stockholm, Sweden. pp.1-1, 2024. [hal-04586653](https://hal-04586653)

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Submitted on 24 May 2024

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# TOWARDS A MULTI-CRITERIA EVALUATION OF THE ENVIRONMENTAL FOOTPRINT OF GENERATIVE AI SERVICES

Adrien Berthelot¹,², Eddy Caron¹, Mathilde Jay¹,³ and Laurent Lefèvre¹

¹Univ. Lyon 1 UCBL, EnsL, CNRS, Inria, LIP. Lyon, France

²OCTO Technology. Paris, France

³Univ. Grenoble Alpes, Grenoble INP, LIG. Grenoble, France

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## Introduction

Generative AI represents a new stage in digital transformation through its many applications. Unfortunately, by accelerating the growth of digital technology, Gen-Al is contributing to the **multiple environmental damages** caused by its sector.

We propose to study not only the impact of developing a model but also that of its deployment and use as a **service**. The figure at the left shows what we consider the standard structure of a Gen-Al service. Our methodology (presented in the diagram below) is applied and validated on an AI service based on **Stable Diffusion**, an open-source text-to-image generative deep-learning model.

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

## Training electricity cost

**Replication** is the most reliable method to estimate the electricity consumption. But for training, it would too expensive.

Our solution: replicate a fraction of it and use **linear regression to extrapolate** the results.

The figure below shows the difference between the methods used to estimate the electricity consumption. It leads to a **better understanding of electricity consumption**.

<figure><img src="image_10.png" /></figure>

## Environmental footprint of the service

<table><thead><tr><th>Functional Unit (FU)</th><th>Abiotic Depletion Potential (kg.Sb.eq)</th><th>Warming Potential (kg.CO2.eq)</th><th>Primary Energy (MJ)</th></tr></thead><tbody><tr><td>FU1 - Average single use of service</td><td>6.72e-8</td><td>7.84e-3</td><td>2.02e-1</td></tr><tr><td>FU2 - A year of service</td><td>4.64</td><td>3.60e+5</td><td>8.93e+6</td></tr></tbody></table>

Impact distributions for FU1 and FU2

<figure><img src="image_11.png" /></figure>

Environmental impact of Stable Diffusion for FU1 and FU2

## Sensitivity to the utilization rate and the scope of training

### 1. Average utilization rate (AUR) of data center equipment

* AUR = percentage of time in the equipment lifespan during which it is actively used, as opposed to when it is either idle or on standby.

* Crucial to the allocation for cost-heavy data center equipment.

* Impact remains small if the AUR is higher than **20%** (is that reasonable?)

### 2. Scope of training phases taken into account.

* Diagram below shows hierarchical **fine-tuning layers** in training. Our standard scenario (S) only includes versions v1.1 and v1.2.

* We consider versions up to v1.5 in a full legacy scenario (L). Figure on the right shows the additional impact due to these additional versions.

Potential layers of training for a Gen-Al service

**Current boundaries:**

* Fondation model (v1)

* Sub-version (v1.1, v1.2, etc.)

Deployment of the service with training on company data

<figure><img src="image_12.png" /></figure>

AWS Bedrock

Extra-training phase, such as "fine tuning", at various scales:

* User profil

* User session

Comparison between the standard scenario (S) and the full legacy scenario (L) for FU2

<figure><img src="image_13.png" /></figure>

## Conclusions

* Generative AI offers digital services that are particularly costly in environmental terms.

* The total footprint is not concentrated in a single part and a single impact.

* A large proportion of GHG emissions can be avoided, but this will **not be enough**.

* The transformation of data centers induced by the multiplication of these services will generate numerous impacts of 2e and 3e order.

* More than generative AI as a technology, it is the rapid, growing, uncontrolled **deployment** of it as a service that represents a problem for our environment.

<figure><img src="image_14.png" /></figure>

Poster based on: Adrien Berthelot, Eddy Caron, Mathilde Jay et Laurent Lefèvre. Estimating the environmental impact of Generative-Al services using an LCA-based methodology. Procedia CIRP, vol. 122, pages 707-712, May 2024.

https://www.sciencedirect.com/science/article/pii/S2212827124001173

Contacts: adrien.berthelot@octo.com, mathilde.jay@univ-grenoble-alpes.fr, laurent.lefevre@ens-lyon.fr, eddy.caron@ens-lyon.fr

<figure><img src="image_15.png" /></figure>
# TOWARDS A MULTI-CRITERIA EVALUATION OF THE ENVIRONMENTAL FOOTPRINT OF GENERATIVE AI SERVICES

Adrien Berthelot¹,², Eddy Caron¹, Mathilde Jay¹,³ and Laurent Lefèvre¹

¹Univ. Lyon 1 UCBL, EnsL, CNRS, Inria, LIP. Lyon, France

²OCTO Technology. Paris, France

³Univ. Grenoble Alpes, Grenoble INP, LIG. Grenoble, France

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

## Introduction

Generative AI represents a new stage in digital transformation through its many applications. Unfortunately, by accelerating the growth of digital technology, Gen-Al is contributing to the **multiple environmental damages** caused by its sector.

We propose to study not only the impact of developing a model but also that of its deployment and use as a **service**. The figure at the left shows what we consider the standard structure of a Gen-Al service. Our methodology (presented in the diagram below) is applied and validated on an AI service based on **Stable Diffusion**, an open-source text-to-image generative deep-learning model.

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

## Environmental footprint of the service

<table><thead><tr><th>Functional Unit (FU)</th><th>Abiotic Depletion Potential (kg.Sb.eq)</th><th>Warming Potential (kg.CO2.eq)</th><th>Primary Energy (MJ)</th></tr></thead><tbody><tr><td>FU1 - Average single use of service</td><td>6.72e-8</td><td>7.84e-3</td><td>2.02e-1</td></tr><tr><td>FU2 - A year of service</td><td>4.64</td><td>3.60e+5</td><td>8.93e+6</td></tr></tbody></table>

Impact distributions for FU1 and FU2

Environmental impact of Stable Diffusion for FU1 and FU2

<figure><img src="image_11.png" /></figure>

## Sensitivity to the utilization rate and the scope of training

### 1. Average utilization rate (AUR) of data center equipment

* AUR = percentage of time in the equipment lifespan during which it is actively used, as opposed to when it is either idle or on standby.

* Crucial to the allocation for cost-heavy data center equipment.

* Impact remains small if the AUR is higher than **20%** (is that reasonable?)

### 2. Scope of training phases taken into account.

* Diagram below shows hierarchical **fine-tuning layers** in training. Our standard scenario (S) only includes versions v1.1 and v1.2.

* We consider versions up to v1.5 in a full legacy scenario (L). Figure on the right shows the additional impact due to these additional versions.

Potential layers of training for a Gen-Al service

**Current boundaries:**

* Fondation model (v1)

* Sub-version (v1.1, v1.2, etc.)

Deployment of the service with training on company data

<figure><img src="image_12.png" /></figure>

AWS Bedrock

Extra-training phase, such as "fine tuning", at various scales:

* User profil

* User session

Comparison between the standard scenario (S) and the full legacy scenario (L) for FU2

<figure><img src="image_13.png" /></figure>

## Conclusions

* Generative AI offers digital services that are particularly costly in environmental terms.

* The total footprint is not concentrated in a single part and a single impact.

* A large proportion of GHG emissions can be avoided, but this will **not be enough**.

* The transformation of data centers induced by the multiplication of these services will generate numerous impacts of 2e and 3e order.

* More than generative AI as a technology, it is the rapid, growing, uncontrolled **deployment** of it as a service that represents a problem for our environment.

<figure><img src="image_14.png" /></figure>

Poster based on: Adrien Berthelot, Eddy Caron, Mathilde Jay et Laurent Lefèvre. Estimating the environmental impact of Generative-Al services using an LCA-based methodology. Procedia CIRP, vol. 122, pages 707-712, May 2024.

https://www.sciencedirect.com/science/article/pii/S2212827124001173

Contacts: adrien.berthelot@octo.com, mathilde.jay@univ-grenoble-alpes.fr, laurent.lefevre@ens-lyon.fr, eddy.caron@ens-lyon.fr

<figure><img src="image_15.png" /></figure>