# From Attributional to Consequential LCA: Which Theoretical Framework for Assessing AI's Environmental Impacts?

David Ekchajzer, Adrien Berthelot, Jacques Combaz, Daniel Schien

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David Ekchajzer, Adrien Berthelot, Jacques Combaz, Daniel Schien. From Attributional to Consequential LCA: Which Theoretical Framework for Assessing AI's Environmental Impacts?. 2026. hal-05512793v2

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# From Attributional to Consequential LCA: Which Theoretical Framework for Assessing AI's Environmental Impacts?

David Ekchajzer<fnref n="1" />,<fnref n="2" />*, Adrien Berthelot<fnref n="3" />*, Jacques Combaz<fnref n="4" />, Daniel Schien<fnref n="2" />

## Abstract

The current hype surrounding generative AI is amplifying the environmental footprint of Information and Communication Technologies (ICT). In response, research into 'green AI' or 'AI for green' has surged. One area of research in this field involves assessing the environmental impacts of AI services. However, studies often lack explicit epistemological and methodological framing, which hinders the quality of their results and sometimes makes them inconsistent. In response to the absence of appropriate framing, the present study draws upon the life cycle assessment (LCA) literature to propose three consistent methodological frameworks, based on their respective epistemological and methodological principles: Attributional LCA, Short-Term Consequential LCA, and Long-Term Consequential LCA. Each approach is examined in terms of its ethical foundations, mathematical logic, and practical implications for defining system boundaries, handling fixed and dynamic costs, and accounting for indirect effects. These approaches are then applied to a common use case for a generative AI service. By clarifying these epistemic and methodological principles, we aim to enhance environmental assessment practices within the sustainable AI literature and improve our understanding of the environmental impacts of this technology.

## 1 Introduction

Over the past decade, artificial intelligence (AI) has evolved from being confined to specialists to becoming a common practice. Technologies like transformer architectures, diffusion models or large language models (LLMs) have turned once-experimental capabilities into mass-market digital services available on demand. All of these numerous services are revolving around the umbrella term Generative AI (GenAI) [1] which can be identified as the main driver of current changes in the Information and Communication Technologies (ICT) industry. With this shift, questions about the environmental implications of AI have acquired a new urgency both in the academic literature and the industry. Environmental assessment of AI service and systems has been a key approach shaping the academic literature on the topic since 2019. Environmental assessment can be theorized as a process designed to link the ecosphere where impacts, e.g. resource depletion or pollution, occur, to the technosphere, all "human creations, arrayed across the surface of our planet as well as orbiting above it" [2] (e.g. roads, buildings, vehicles, machinery, computers, etc.). Digital and AI services are processes that take place in the technosphere. As

with many other technospheric processes, establishing a link between their existence and modifications in the ecosphere is not straightforward, and hence needs to be properly justified.

The literature on sustainability and AI and more generally IT systems lacks justification for the methodological and epistemological choices that allow AI services to be linked to the environment. This is particularly problematic in a context where environmental assessment yields figures intended to inform decision-making about the sustainability of AI.

In this paper, we address those challenges of assessing the environmental impacts of AI services by clarifying how different epistemological and methodological approaches shape both the scope and interpretation of results. These questions sit at the intersection of Life-Cycle Assessment (LCA) and ICT for Sustainability (ICT4S), two fields that have each advanced in recent years yet still face challenges when applied to digital services. Our central argument is that meaningful evaluation requires explicit and consistent choices about framing and that these choices must align with the purpose of the assessment in order to establish the validity and relevance of the environmental figures produced.

<footnote n="1">¹Univ Evry, IMT-BS, LITEM, Université Paris-Saclay, Paris, France</footnote>

<footnote n="2">²Hubblo, Châtillon, France</footnote>

<footnote n="3">³University of Bristol, UK</footnote>

<footnote n="4">⁴Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG</footnote>

<footnote>*Authors contributed equally to this paper.</footnote>
To this end, we draw on the LCA literature to define three distinct approaches: Attributional LCA (ALCA), Short term Consequential LCA (ST-CLCA), and Long term Consequential LCA (LT-CLCA). Each approach is examined in terms of its ethical foundations, mathematical logic, and practical implications for defining system boundaries, handling fixed and dynamic costs, and accounting for indirect effects. To make these distinctions tangible, we apply qualitatively the three approaches to a generative AI service, illustrating how epistemological and methodological choices influence which processes are included, how impacts are allocated, and what causal relationships are considered. This comparative analysis reveals not only the strengths and limitations of each framework but also the risks of misinterpretation when results are extrapolated beyond their intended context.

The remainder of this paper is organized as follows. We begin in Section 2 by situating our work within the evolution of AI and sustainability literature, highlighting the challenges that arise when assessing digital services. Building on this foundation, Section 3 introduces the three methodological framework: ALCA, ST-CLCA, and LT-CLCA, and explains their ethical principles, mathematical constraints, and rules for defining service boundaries. In Section 4 these approaches are applied to a generative AI service to illustrate how methodological choices shape the assessment of environmental impacts. Section 5 then discusses the role of causal vision in attributional approaches and normative vision in consequential approaches, as well as the notion of time in the three approaches studied, before highlighting practical implications for the assessment of AI services. Finally, Section 6 concludes by synthesizing the main insights, acknowledging limitations, and outlining priorities for future research.

# 2 Background

## 2.1 Environmental assessment of AI system in the literature

The literature on the assessment of environmental impacts of AI has evolved significantly over the past few years, progressively encompassing a broader range of concerns. Initial studies focused primarily on the direct energy consumption of machine learning (ML) models, particularly the electricity required for training [3]. This was soon followed by analyses of the carbon emissions associated with the energy consumption of the ML process [4]. As the field matured, researchers began applying life cycle assessment (LCA) methodologies to ML, offering a more comprehensive view that includes upstream

and downstream impacts as well as other impacts criteria than greenhouse gas emissions such as metal depletion or water use [5, 6, 7]. More recently, assessments have emerged, evaluating AI as digital services expanding again the scope [8] by focusing not only on the ML cost linked to AI but also impacts like the rising cost of AI inference in these systems [9].

In addition to the consideration of this direct effects, sometimes referred has first-order effects, calls has been made in the literature to study indirect environmental impacts of AI [10, 11], sometime referred as second and third order effects [12]. We consider direct effects to be the environmental impacts caused by the life cycle of the digital equipment and infrastructure necessary for the operation of digital services, from the extraction of raw materials to the end of life. Indirect effects, on the other hand, correspond to changes in the quantity or nature of any other activity following the deployment of digitisation processes that lead to increased or reduced impacts. This shift in the literature aligns with earlier work on the indirect effects of ICT more broadly, which highlighted how digital technologies can influence environmental outcomes through behavioural, economic, and systemic changes [13, 14, 15, 16]. These indirect impacts are particularly relevant in the context of AI, where the deployment of intelligent systems can reshape consumption patterns, infrastructure requirements, and decision-making processes, thereby generating complex and often unintended environmental consequences.

## 2.2 A problem of framing

If a growing body of research is now using environmental assessment in the AI for sustainability literature with a diversity of practices, we identify a clear lack of epistemological and methodological framing in this literature. This issue is exacerbated by industry's lack of transparency, which lead to the production of many third-party estimates that are insufficiently contextualised [17]. The framing of an evaluation should be able to respond to the following questions:

**Why should an assessment be conducted?** The reasons for conducting assessments are not always clear in the literature. In particular, are they to determine the ecological responsibilities of AI systems throughout their own lifecycle or to define the effects induced by the deployment of such systems? Although these objectives may seem similar, we will see later that they are not as they are grounded in different ethical perspectives.

**What should we assess?** In particular how to define the boundaries of the system under study, determine what is or isn't included? For example,
should AI model training be included or not when
assessing the impacts of an inference?

**How should we assess it?** What calculation rules must be followed during the assessment process and how should they be justified? In the context of AI and ICT systems, where infrastructure is shared by many services, how should the impacts of these enabling infrastructures be accounted for?

Establishing an explicit framing is crucial, as it will serve to shape both the results of an environmental assessment and its subsequent interpretation. Furthermore, the utilisation of an inconsistent framing approach has the potential to engender misinformation. A notable illustration of this phenomenon is evidenced when calculating the potential of AI services to reduce emissions by comparing the carbon footprint of a human to that of an AI service [18], without acknowledging the fact that the emissions associated with human activities will in reality not be avoided [19]. These questions are all the more important in a context where the results will serve in decision-making process.

The surge in AI use and investment creates an
emergency to integrate environmental assessment
in decision-making. However, this emergency high-
lights the lack of standardized framework for assess-
ing potential environmental cost and benefits of AI.
This situation relates more broadly to the current
state of research at the intersection of sustainabil-
ity and ICT, or ICT4S, a field in which researchers
are still in progress in their capacities to accurately
assess environmental cost and benefits of ICT ser-
vices. But more crucially, the methodological issues
around the framing of sustainability assessment for
AI connects directly to the more general problem of
environmental assessment of ICT services.

Research questions addressed in this paper are the
following:

**What** consistent framings can be employed
to assess the environmental impacts of AI
services and systems ?

How to implement them in the context of a
specific AI services ?

Drawing interdisciplinarity on both the ICT4S and LCA literatures, we propose a coherent framework that allows quantitative approaches to be theoretically grounded while proposing concrete solutions or avenues to the main specificities of each approach in the case of digital AI services.

3 Methods

## 3.1 Three assessment approaches

The LCA literature has discussed these methodolog-
ical issues since the origins of the field [20, 21], dis-
tinguishing between attributional and consequential
methods [22], the latter being considered more ap-
propriate for change-oriented analysis [23]. While
the attributional methodology is still subject to some
debate in the field, the academic debate on conseq-
quential approaches is even more open [24, 25], as
several approaches coexist [26, 27]. In particular, their
implementation in the context of services is almost
non-existent, especially when it comes to digital ser-
vices.

All conceptions rely on a realist ontology that posits the existence of physical processes and environmental mechanisms ontologically independent of the analyst. However, attributional and consequential approaches can be seen as different epistemological approaches as they are considering different criteria to make a knowledge valid when studying the logic behind the linkage between ecosphere and technosphere. In the following discussion, we propose to use a classification of three methodological approaches drawn from the LCA literature, each of which can be used to discuss the sustainability of ICT services, in particular AI services. The proposed methodological approaches are attributional (ALCA), short-term consequential (ST-LCA) and long-term consequential (LT-LCA). Existing methodologies and methods used in the literature on AI or ICT for sustainability can be fitted into this conceptual framework detailed in Figure 1.

As there is no theoretical nor practical consensus
on the LCA literature in particular when talking
about consequential approaches the proposed the-
oretical definition and practical approach are based
on specific LCA authors' views and conceptions with
a logical consistency. The selection of these views
is justified by their relevance for the analysis of ICT
services, particularly AI services.

The attributional, long-term consequential and
short-term consequential approaches each have dif-
ferent characteristics, which we explain below:
Ethic Linking processes taking place in the techno-
sphere to phenomenon taking place in the ecosphere
is grounded in ethical preferences, e.g. determining
if an action, such as using, deploying or modifying a
digital service, is good or right [27, 23]. Thus, to en-
sure their coherence, each approach follows a specific
ethical perspective.

**Mathematics** Linking the technosphere to the eco-
sphere involves mathematical operations. The valid-
ity of these operations depends on the ethical prefer-
<figure><img src="image_2.png" /><figcaption>**Figure 1:** *Theoretical legacy of main assessment methodologies applied to ICT*</figcaption></figure>

ences of the selected approach. Thus, each approach is governed by mathematical rules to ensure coherence.

**Service system** The service system (SS), sometimes more generally referred as Product-Service System (PSS), is defined as comprising all processes involved in the provision of a service, whether directly or indirectly. Deciding which processes to include is not straightforward, as it is at least partly subjective and conventional, and depends on the ethical preferences of the chosen approach [27]. Each approach therefore has its own rules relating to process selection. This is a crucial stake, as defining the assessed SS implies defining the perimeter of the assessment. Moreover, the definition of the SS is difficult in the case of digital services as they dynamically share multiple large mutualised infrastructures, e.g. networks, as pointed by [28].

**Handling multifunctionality** A multifunctional process occurs when a process involved in the lifecycle of a service produces other functions. Multifunctionality presents challenges in environmental assessment, as it is difficult to determine the impact of a specific function produced by a multifunctional process. While most ICT infrastructure processes could be considered multifunctional, they fall under the category of combined production [24], where the production of co-product A can vary independently of the production of co-product B. This makes it possible to break down the production process into smaller processes without major issues. In the more problematic case of joint production [29], we could cite the production of heat by data centres, which is entirely dependent on the production of computing or storage services. How should the impacts of a data centre be associated with both heat generation and

computing and storage services? Each approach has its own rules for handling this type of situation.

**Handling fixed costs** A more recurring problem, is the handling of fixed costs, i.e. the initial impacts that occur regardless of whether the process is instantiated or not. In the context of digital services, fixed costs are a recurring problem as the infrastructures and devices involved have usually important fixed costs. For example, AI inferences performed in data centres have fixed impacts (e.g. construction, idle power and equipment impacts) that are not dependent on the number of inferences performed in a short term vision. Considering these fixed costs is not straightforward; each approach has its own rules for dealing with them. To summarize the different approach toward fixed and dynamic costs, we will use Figure 2. In this figure, we represent a theoretical SS. Each column represents a production unit, e.g. a server. Each unit of the SS has fixed cost, i.e. its embodied footprint and its fixed power consumption, and a dynamic cost, i.e. its marginal electric consumption rising until it reaches full capacity. Each time the demand is growing over a certain threshold, a new unit is added to the SS, this unit starts with a theoretical null dynamic consumption, as it is waiting to handle the first new demand. Note that each new production unit may differ from the previous one, particularly with regard to fixed and marginal impacts per unit of production. Here, the units of production are considered to have decreasing impact over time thanks to technological improvements.

## 3.2 Attributionnal LCA

*"The attributional approach attempts to provide information on what portion of global burdens can be associated*
<figure><img src="image_3.png" /><figcaption>Figure 2: Handling fix vs. dynamic impacts</figcaption></figure>

with a product (and its life cycle)." [30]

**Ethic** Attributional approaches mostly follows a rule-based or deontological ethics, whereby actions is considered good "to the extend that they support good systems" defined by a specific set of normative rules [23]. In an environmental impact assessment context, an action is considered good or bad depending on the volume of environmental impact associated with it <fnref n="1" />. In the case of attributional methods, the rules take the form of mathematical and perimeter constraints, as well as allocation rules.

**Mathematics** Attributional approaches must adhere to an additivity rule, whereby the total impact of the system is equal to the sum of its constituent parts' impacts [26]. This strong mathematical constraint enables the impacts of a system to be shared among all its parts. For example, a data centre could implement an attributional approach to allocate a proportion of its impacts to each of its customers, such that the

total impact of the customers is equal to the total impact of the data centre. This rule is often interpreted as a linearity rule, whereby the impacts of the system are distributed across its parts in a linear manner. However, even if linearity appears to be the easiest way to attribute impacts, other rules, such as an affine or logarithmic distribution, could be used, provided that the integral of the distribution function is equal to one.

**Service system** In the attributional approach, the service system integrates all processes linked by flows to the service, thus, the service system is constructed as a chain of processes linked by physical and service flows. This chain encompasses processes that occurred before and after the service's existence.

**Handling multifunctionality** To handle multifunctionality, attributional approaches typically employ partitioning methods (or allocation) [26]. In this approach, the impact of a process shared by several products or services is divided according to a shared criterion applicable to both products. For example, if a data centre proposes virtual machines (VMs) and recovers waste heat for a heating network, the impacts could be allocated entirely to the VM processes, on the basis that it is the main product. Alternatively,

<footnote n="1">¹ For example, a demand for low carbon electricity can be considered good in an attributional context since it "supports" this type of electricity generation even if it does not displace fossil fuel power generation in practice due to market or production constraints.</footnote>
the impacts could be allocated using a monetary ra-
tio, which would distribute the impacts between the
VMs and heat, based on the revenue share of each
co-product. This monetary ratio is referred to as an
allocation key. Alternatively, 'system expansion' [31]
can be used, as discussed in the next section. To the
best of our knowledge, this technique has never been
used in the context of an ALCA assessment of AI
or ICT services, except for assessing the end of life
phase's impacts of digital equipments [32].

**Handling fixed costs** In ALCA, the fixed costs of ex-
isting infrastructure are usually spread evenly across
each unit of output produced by the infrastructure
through an allocation procedure. In Figure 2, we
allocate the impacts of the existing infrastructures
composed of units 1 and 2 depending on the share
of the assessed demand to the total capacity of pro-
duction of both units. The division yield the average
impact per unit of production. For example, the fixed
impacts of a server serving multiple VMs could be
split based on the virtual CPU cores (vCPUs) allo-
cated to each VM, as described in [33]. Here, the
vCPU is the allocation key.

**ALCA in the ICT4S and AI4S literature** Today, most
studies rely on attributional frameworks, often with-
out stating this explicitly. The main approach consists
of identifying the processes involved in the digital
service, assessing their impact, and allocating this
impact to either a unit of service or the entire service
over a given period of time, e.g. 1 year as recom-
mended by the ITU standard for ICT services [34].
Papers mentioned in the previous section fit this ap-
proach: the LCAs of the BLOOM AI model [6], Stable
Diffusion [35] and AI training on Nvidia A100 [36],
all rely on this attributional framework. In the ICT4S
community, the specific case of network impacts as-
sessment is worth mentioning. This has been a key
concern in the literature. Traditionally, data con-
sumption on a network, e.g. downloading a 1 GB file,
is assessed by allocating the impacts of the whole
network proportionally to the amount of data con-
sumed. This implies a linear relationship between
data consumption and environmental impacts.

## 3.3 Short-term & Long-term Consequential LCA

"The consequential approach attempts to provide in-
formation on the environmental burdens that occur,
directly or indirectly, as a consequence of a decision (usu-
ally represented by changes in demand for a product)" [30]

**Ethic** Consequential approaches follow a con-
sequential, or teleological, ethic, whereby an action
is considered good to the extends that it lead to
positive outcomes [23, 27]. In an environmental

impact assessment, the outcome of an action is
considered positive or negative depending on its
environmental consequences.

**Mathematics** This has no mathematical implications, as causal relationships can be modelled using a variety of mathematical models depending on the type of relationship.

**Service system** In the consequential approach, the service system integrates all processes linked by a cause-and-effect relationship. The nature of these relationships is open. They can be physical, energetic, or service-related, as well as economic, sociological, psychological... This cause-and-effect chain encompasses processes that occur only after the action considered has taken place, since the service cannot affect processes that have already happened.

In CLCA approaches, the inclusion of a process
is linked to the definition of a reference scenario,
which defines what would happen without the con-
sidered action (also called counterfactual scenario).
A process is included to the extent that it would
not happen or would not have been transformed
without the existence of the action. The impacts of
a service is thus the difference between the impact
of the technosphere with the service deployed (the
service scenario) and its impact without the service
(the reference scenario). In practice, all parts of the
technosphere that are not modified by the service
are disregarded, since they yield the same impact
in both scenarios. Note that, due to the substantial
cause-and-effect relationships, a greater proportion
of the technosphere could potentially be included
compared to the service systems considered in attri-
butional approaches.

**Handling multifunctionality** To handle multifunc-
tionality, consequential approaches rely on system
expansion, meaning the scope of the study is ex-
panded to include co-products [26]. For example, if
a data centre produces virtual machines and recov-
ers waste heat for a heating network, both products
should be included in the product system. The ques-
tion then becomes what the effects of heat generation
would be compared to a reference scenario without
this generation of heat. Depending on the context,
objectives and type of effects considered, many dif-
ferent approaches could be implemented. One way
to address the effects is to take a market perspective
and follow a substitution approach. This approach
assumes that heat production will avoid heat gen-
eration by another source, typically fossil fuel heat
generation. Thus, the use of a VM would also lead
to avoided impacts.

**Handling fixed costs** In the context of the consequen-
tial approach, we differentiate between two methods:
short-term (ST-LCA) and long-term (LT-LCA), as pic-
tured in Figure 2. Although these two approaches are defined by their relationship to time frames, the difference between them is actually defined from an economical perspective and related to the ability of infrastructure to adapt to changing demand [37]. In the short-term approach, infrastructures capacity is considered unaffected by changing demand. In other words, we only consider the 'short-term marginal' effects on existing capacities. Conversely, long-term approaches consider infrastructure capacity changes. In other words, we consider the 'long-term marginal' effects on existing and additional capacities deployed in response to increased demand. For example, in the case of additional electricity consumption, a short-term approach would only account for the additional resources consumed by the existing power plant meeting this demand (e.g. coal in a coal power plant), whereas a long-term approach would also account for additional resources (e.g. the building of a new coal power plant) [37]. Note that the additional capacity may be of a different nature to the existing capacity. For example, in the case of electricity consumption, new capacity could come from renewable sources (e.g. solar panels) or in the case of data centre, new data centre might implement more efficient cooling systems.

**Short-term CLCA in the ICT4S and AI4S literature**

Research in computer science, with the aim of improving the energy efficiency of software, has often implicitly adopted a partial short-term consequential framework. The focus is therefore on the impact of a change on the total or dynamic electricity consumption of a system, sometimes characterising this energy consumption in terms of its impact on global warming. A characteristic field of research for this approach is *carbon-aware* or *energy-aware computing*. It consists of temporarily or geographically shifting tasks to be executed on a system, often a cloud operator [38]. Although this field focuses mostly on short-term consequences, it sometimes incorporates embodied costs [39], typical of the ALCA approach, recognising its limitations [40] and calling for a long-term perspective on the modelling of the impacts of electricity consumption [41].

Outside *carbon-aware computing*, several studies subscribe to the short-term approach by taking into account both dynamic and passive consumption variations, like in distributed and networks systems research [42, 43].

In the context of AI services, studies using the ST-CLCA framework are logically focused on the usage phase [44], or more precisely, the server-side inference process [9]. These studies aim to examine the energy efficiency of different AI services or different models responding to the same functional unit.

**Long-term CLCA in the ICT4S and AI4S literature**

Studying the long-term direct environmental effects of AI and IT systems would involve examining how changes in the quantity and nature of digital infrastructure are affected by changes such as increased demand or new offerings. Although few studies have adopted long-term consequential approaches, notable research conducted at LaBRI in Bordeaux, France, examined the long-term effects of demand modifications on telecommunication networks and their environmental impacts [45, 46]. This work was published as a direct criticism of attributional approaches for investigating additional demand to telecommunication networks [47].

If few research have use consequential approaches for studying the direct impacts of IT and AI service, research on their indirect impacts have developed approaches that rely on consequential thinking. Indeed, studying the consequences of deploying IT or AI solution such as rebound effects [11] rely per definition on a consequential thinking. This literature has almost never explicitly stated that it subscribes to long-term consequentialist approaches. We could cite the research on the taxonomy of environmental effects of digital services which tries to map the consequential effects brought by IT solutions [48]. We can also cite the more recent approaches on net impacts studies which tries to quantitatively describe those effects in comparison to a reference scenario [49].

## 3.4 Use-case

To illustrate the approaches described above, we will each apply each of them to the same use case. We have chosen Stable Diffusion as an example, a service based on a generative AI model capable of generating images from textual input. Several similar services have emerged in recent years, including DALL-E and Midjourney, which, like Stable Diffusion, appeared in 2022. There is also a pre-existing literature on attributive LCA of text-to-image services, such as [6, 35, 8]. For each methodological approach, we propose to implement our own methodology qualitatively in the context of generative AI to determine the service system. It is not the intention of these methodologies to reflect what would be the unique methodological implementations for each approach, but rather to serve as illustrations and concrete proposals on which others can draw.

We have identified two relevant functional units (FU) for this type of system, which will be used to demonstrate the three different approaches:

* FU1: Sending the user an image generated from a prompt written by the user.

* FU2: Operate the service for one year.
We will consider a simplified version of the AI service, where the service has only one function defined by FU1. FU2 consists of all the satisfied FU1 during a year.

We chose these two FUs because there are common questions when discussing of the impact of digital technologies, e.g. "What is the cost of generating a picture?" or "What is the footprint of this services?", and therefore will be useful to highlight the importance of framing in our problem. Moreover, FU2 is the recommended FU by the ITU [34] to assess environmental impact of ICT services.

# 4 Results

## 4.1 Attributional

We propose using a flow diagram to qualitatively define the service system. This representation provides an overview of the processes linked by physical and service flows. When a process is multifunctional or serves multiple clients, we specify the allocation key chosen to identify the share of the process's impact assigned to the functional unit.

If we take as example work like [35], the service system required to fulfil FU1 is determined as follows.

An inference relies on the process of using an end user terminal. This process is multifunctional to the extent that it could be used to serve other functions than prompt writing and reading. To partition the impacts among those different functions we chose to allocate them by time spent satisfying FU1 to the total use time. This normative rule is justified by the fact that an increase demand in time spent will induce additional electricity consumption.

The inference request is then transmitted through the telecommunication network, e.g. mobile or fixed-line. Since the infrastructure is shared between users we need to partition its impacts on all its lifecycle among those usages. We chose to allocate them by volume of transferred data for FU1 to the total volume of data transferred through these networks. This normative rule is justified by the fact that an increase demand in time transferred will be responsible of the scaling of network infrastructures.

The inference request is then handled by a web-server cluster hosted in a data centre. Since the infrastructure is shared between users we need to partition its impacts of all its lifecycle among those usages. We chose to allocate them to the total the number of satisfied queries by the server in a year. This normative rule is justified by the fact that an increase demand in request will induce a need for addition web servers to be deployed.

The inference is then processed by the AI model hosted in a GPU cluster also in a data centre. Since the infrastructure is shared between users we need to partition its impacts on all its lifecycle among those usages. We chose to allocate by the active GPU active time satisfying FU1 to the total active GPU active time. This normative rule is justified by the fact that an increase demand in request will induce an increase need for GPU time which will eventually lead to the deployment of additional GPU.

Last, the inference is possible because the model was previously trained. This training shall be included as it is linked by a service flow with the functional unit. The training will serve all inferences so we need to partition its impacts on all its lifecycle among those usages. There are many potential allocation keys, such as the number of images or tokens generated by the model. Which allocation key is best is a topic of ongoing debate in the GreenAI literature [50, 51, 9, 8]. For each process involved in the fulfilling of the service digital equipments and the consumption of electricity are involved.

The electricity assessment should include the total electrical consumption of the equipment to guarantee the additivity rule (i.e. that the sum of the electricity consumed by the equipment is equal to the sum of the electricity associated with all the functions it fulfils). We therefore account for idle consumption, fixed consumption and dynamic or marginal consumption. The impact of this electricity consumption is then determined by identifying the average electricity mix used to respond to this demand. In this attributional approach, we would use the electricity mix at the location and, if possible, the time at which it was consumed.

The impact of deployed equipment should be considered by looking at average resource and energy consumption that happened prior the functional unit: extraction, manufacturing, distribution, installation and also after the functional unit: decommissioning (end of life).

**Analysis** The main challenges when conducting such attributional studies lies in the right definition of the service system and allocation keys in order to avoid double counting and deliver meaningful description of the system under assessment. The choice of allocation procedures is the subject of intense debate in the LCA community. Even though there exist attempts to provide general guidelines to facilitate this process, the recommended procedures should not be considered better per se but as solutions suitable to specific contexts. We therefore recommend to properly justify allocation choices in relation to the characteristics of the system under study and the objective of the study, as we did briefly above.
<figure><img src="image_4.png" /><figcaption>Figure 3: Flow diagram of ALCA</figcaption></figure>

ever, the allocation keys chosen in our example are based on simple rules that correspond to traditional allocation methods for ICT systems described in the literature, and these should be improved.

More specifically, a difficult issue to address regarding AI services is the integration of the impacts of training the AI model. This is a research and development cost that enables the service to be delivered. However, this training is often continuous and progressive throughout the year. This leads to the problem of allocating the training costs or amortising the model training costs. It also raises questions about how specific this development cost is to this service, i.e. whether it could be used by others, and how to estimate the number of inferences beyond the defined timeframe. These issues of allocation and inclusion, although complex to address [19], can be tackled through the gradual standardisation of practices. This will be aided, on the one hand, by advances in standards [52] and the homogenisation of service systems.

The attributional approach should not be interpreted as strictly change-oriented; rather, it is a normative framework that enables responsibilities to be distributed across a value/supply chain. In this approach, therefore, FU1 does not cause impact X; rather, it is considered responsible for impact X within the chosen normative framework of the study — a framework that could or should become standard. Consequently, there is no a priori reason to assume that an ALCA is indicative of changes in environmental impacts brought about by the functional unit, although this may be the case in specific cases. Therefore, practitioners should have at least a qualitative understanding of the system's dynamics to avoid making counterproductive decisions.

## 4.2 Consequential short term

In this short term consequential approach, we propose using a consequence tree as proposed in [53, 54] to qualitatively define the service system. This representation provides an overview of consequences induced by the realisation of the considered action (the root of the tree) to the final environmental impacts (the leaves of the tree). Considering the same service as before, the service system is required to fulfil FU1 is determined using Figure 4. The reference scenario chosen for this study is a technosphere without this additional demand.

Inferences involve additional consumption by the user terminal, the network and the data centre infrastructure. This induced demand results in increased electrical consumption for each of these infrastructures and their equipment. As shown in Figure 2, it is necessary to differentiate between fixed and dynamic or marginal consumption. As we are in a short term approach, we only consider the dynamic consumption induced by our additional demand. This is justified by the fact that this fixed consumption would still occur in our reference scenario (no additional demand). The same justification applies to not integrating the embodied impacts of the equipment and infrastructure. However, if the additional demand is significant and/or recurrent, it may contribute to scaling up infrastructures, thereby eventually increasing fixed consumption and embodied impacts. Nevertheless, we do not consider such effects in short-term approaches, assuming that existing capacities can accommodate existing demand either by the provision of non-allocated existing resources (our case) or alternatively by the displacement of existing demand in case of fully constrained supply.

This additional demand for electricity will lead to extra production by the 'short-term marginal supplier' [37]. In the short term approach, the marginal supplier is usually the one with the capacity to adapt to this demand, which is often the least efficient sup-
<figure><img src="image_5.png" /><figcaption>**Figure 4:** *Short-term CLCA consequence tree for FU1*</figcaption></figure>

plier using older technologies [55]. In the case of the electricity market, short-term supply is defined by very specific rules (e.g. merit order [56]) that depend on the electrical grid. Defining the "real" marginal supplier in particular in the electricity market is a very difficult if not impossible task [40, 57].

The impact of additional production depends on the identified marginal supplier. If the marginal supplier is a fossil fuel power plant, the impacts would be that of burning fossil fuels. Alternatively, if the marginal supplier is a renewable power plant whose production was curtailed in the reference scenario, the marginal impact will be zero.

**Analysis** In short term approaches, most processes included in the IT service system are linked to the dynamic electrical consumption of IT equipment and, to a lesser extent, the potential resources extracted as a direct consequence of electricity consumption and production. Short-term impacts also include water used for cooling IT and electrical infrastructures, which we have not been mentioned here as a simplification.

The electricity consumption that is accounted for is often referred to in the literature as marginal consumption. This requires access to marginal measurements, which are often difficult to obtain [57]. Defining the marginal effect of additional electrical consumption on water depletion is even more challenging, as these effects are strongly context dependent (e.g. they are influenced by the type of cooling, other workloads, the weather, etc.), not only computation dependent.

The short term consequential approach is truly change oriented. However, as it does not consider the impacts of changing demand on infrastructure or the indirect effects of service deployment, it provides a limited view of the environmental impacts of AI services. Therefore, this approach is only relevant when the objective is to identify technical optimizations and when the nature and function of the service cannot be questioned. Anyone conducting a study using this approach should acknowledge these limitations and justify its use in relation to the objec-

tives of the study. They should also have at least a qualitative understanding of the long-term effects of generalising the change under investigation.

## 4.3 Consequential long term

In the long-term consequential approach, we propose using a consequence tree to qualitatively define the service system. Considering the same service as before, the service system required to fulfil FU2 is determined using Figure 7. The reference scenario chosen for this study is a technosphere without the AI model.

We distinguish between direct and indirect effects, as these have been discussed in different terms in the ICT4S literature.

**Directs effects** As in the short-term perspective, only resources resulting from the implementation of the considered action, i.e., the deployment of AI services in our context, are taken into account. However, in the long-term approach it is considered that the deployment of AI services may not only lead to changes in the level of use of existing infrastructure, but also contribute to the transformation of production capacity. In C-LCA literature, an economic approach analysing market response is generally chosen [55]. The way in which market reactions to an increase (respectively decrease) in demand are described depends on the characteristics of the market and demand. [55] proposes a number of rules to take this increase (respectively decrease) into account. However, to our knowledge, no study has addressed changes in demand in the specific context of digital services.

As part of our AI model, deployment involves reserving additional GPUs. As these GPUs already exist at the time of reservation, only the marginal impact of using this equipment is taken into account, as in the short-term approach. However, reserving additional GPUs will indicate to the market that there is a need for more computing power, which will lead to new GPUs being produced - either because the additional demand as reach a the safety capac-
<figure><img src="image_6.png" /><figcaption>**Figure 5:** Relative share of installed computing power from NVIDIA AI chips, sorted by generation type, figure and data from [58]</figcaption></figure>

ity threshold or because the additional demand has displaced an existing demand. It is the impact of these new cards that will be considered in the long-term approach, rather than the cards already in use. This distinction is important because the new cards may not be technologically equivalent. In fact, the mix of graphics card technologies on the market is rapidly evolving in favour of the latest, more efficient architectures, as depicted on Figure 5.

Conversely, the deployment of the AI model does not, in principle, imply any additional demand for user terminals. On the one hand, the terminal market is already saturated<fnref n="2" /> and, on the other hand, the production capacity of smartphones is not affected by FU2, which does not contribute to accelerating the renewal of terminals. Therefore, only short-term effects need to be taken into account, i.e. the additional marginal demand for electricity.

In LT-CLCA, it is the long-term marginal electricity supplier that needs to be accounted for. Unlike short-term marginal modelling, this approach allows to account for changes in electricity production capacity induced by additional, **sustained demand** <fnref n="3" />. This additional demand is interpreted as a market signal that triggers investments in new production capacity or the non-decommissioning of existing capacity in the event of declining demand. As the technologies deployed in response to this signal generally differ from the average electricity mix, the overall mix changes. In practice, since electricity production is usually modelled in the background system, long-term marginal electricity impact factors — such as those provided in the ecoinvent consequential database — can be used [59].

There are no multifunctional processes in the foreground that would require system expansion. However, there are processes with significant fixed im-

<figure><img src="image_7.png" /><figcaption>**Figure 6:** Schematic example of production growth with threshold effects</figcaption></figure>

pacts. For instance, the data centre hosting the GPUs used by the model also host other GPUs and IT equipment. To account for the impacts of building construction, it must be considered whether the additional demand is sufficient to trigger the construction of a new building. In a “threshold” approach, the fixed impacts of such building can be attributed entirely to the functional unit (light and dark orange in Figure 2). However, infrastructure deployment is generally driven by accumulated demand. When the functional unit is small relative to the total capacity of the process, it becomes difficult to assign the construction of the building to a single unit. Even when it can be demonstrated that it triggers new infrastructure deployment, assigning all the fixed impacts to a single functional unit can also be considered unfair: the rest of the demand is also necessary for such an event to happen, making it equally ‘responsible’. To address this, the threshold effect can be linearized (like in 6) by assigning share of the fixed impacts of the construction of a new data centre (dark orange only in Figure 2).

**Indirect effects** The deployment of a digital service not only brings about changes in its own life cycle, but also in that of other activities. These are referred to as indirect effects or sometimes second- and third-order effects [48]. In the context of an LT-CLA, these effects can be taken into account insofar as the approach allows for the integration of changes in the quantity and nature of processes brought about by the considered action.

By its capacity to generate images, StableDiffusion is able to substitute the creation of images by a graphic designers. The environmental consequences of such substitution are not straightforward as it does not substitute all activities conducted by a graphic

<footnote n="2">² One could imagine a widespread purchase of AI-specific hardware, but this is not the case today</footnote>

<footnote n="3">³ Since capacity are not elastic, the additional demand needs to be sustained in order to induce new capacity production.</footnote>
designer contrary to what was suggested by [18] and criticized in [19]. In a long-term consequential approach, only the marginal resources directly involved by the image creation would be substitute. The avoided impacts of this resources shall be assessed following a long-term marginal approach.

Since Mid-journey allows for the generation of images in less costly way than a graphic designer it likely to behave has a typical case of rebound effect [60]. Economic gain could be reallocated to the generation of additional images leading to direct rebound or could be allocate to new impactful activities leading to indirect rebound.

Finally, we could argue that, in the long term, StableDiffusion could bring about systemic transformation and structural economic changes that could lead to environmental burden [13, 48]. They could lead to avoided or additional long-term marginal impacts. Investigating these changes is beyond the scope of this paper. As these effects may be of a different nature, their quantification would draw on different disciplines. However, as with any consequential effect, their quantification is always derived from comparing the impacts of the system under study with the action implemented and a reference scenario in which the action hasn't been. The assessment of the changes, or effects, between the two scenarios should be consistent with a long-term marginal approach.

**Analysis** In a LT-CLCA, the direct effects of delivering the service and all sub-processes involved shall be considered through a long-term consequential approach. While a lot of work has been done on consequential data for electricity or industrial production, this has not been the case for digital equipment and infrastructure, making it difficult, if not impossible, to maintain a consequential approach throughout a long-term study on AI or IT services. In this case, attributional data could be used to approximate the consequential data. We do not yet know the effects that this could have on the interpretation on this kind of processes.

In addition, supply and demand mechanisms in the digital sector are extremely complex. For example, we previously considered in this paper that GPU production was driven by GPU reservations. However, this is not typically the case, as GPUs are actually driven by investment linked to performative demand projections. Therefore, it is simplistic to assume that additional production and related impacts are only caused by additional demand.

With regard to indirect effects, it is not straightforward to delineate the types of causal relationship to include in the service system. The LCA literature provides limited guidance, as most publications adopting a consequential approach have con-

centrated on goods. As a result, the emphasis has largely been placed on the impacts associated with product manufacturing rather than on those arising from product use. In the context of services, and in particular AI-based and digital services, which can induce substantial systemic transformations [48], both dimensions — production and use — are of critical importance. Moreover, the effects under consideration in the C-LCA literature frequently pertain to the microeconomic domain, encompassing phenomena such as complementarity, substitution, and rebound effects. However, the documented environmental implications of AI extend well beyond the relatively narrow framework of microeconomic transformations [11]. Consequently, the investigation of the long-term impacts of AI services requires the integration of additional causal relationships, including those operating at macroeconomic, sociological, political, or psychological levels. For instance, the democratic risks associated with AI-based image generation may induce environmental burdens. Yet, it is difficult — if not impossible — to quantitatively incorporate such higher-order Known unknowns effects [13] into standard assessment frameworks. Maintaining a mixed-methods combining qualitative and quantitative approaches thus appears indispensable for preserving a genuinely systemic perspective [49].

# 5 Discussion

## 5.1 On the influence of consequential thinking on ALCA

Although we have distinguished between deontological ethics (rule-based), which is associated with attributional approaches, and teleological ethics (consequentialist), which is associated with consequentialist approaches, these two approaches are, in fact, intertwined [27]. In particular, the rules of the deontological approach often stem from consequentialist thinking. For example, not running a red light is a rule that aims to avoid the negative consequences of an accident. In attributional approaches, allocations, even if they are normative, is one of the ethical rules that may be influenced by a consequential thinking. The ISO standard recommends that the chosen allocation key reflects "physical causality" [61, 62]. In other words, an allocation key should ideally represent the physical consequences of implementing the functional unit. In practice, the causality between the processes investigated and their impacts are hard to determine, even more in the ICT sector.

For multifunctional processes involving user terminals, we opted for time-based allocation. However,
<figure><img src="image_8.png" /><figcaption>Figure 7: Consequence tree for long-term CLCA</figcaption></figure>

the physical causality between the process and its em-
bodied impacts is more closely linked to the effects
of exogenous obsolescence than to the time spent.

For network usage, a process with significant fixed
costs, we opted for a volume-based allocation. How-
ever, the causality between the process of transferring
bits and its impact is also linked to demand spikes
and regulations [45]. This is why volume-based al-
locations are highly criticised on the grounds that
they are not consistent in consequential terms. An
alternative proposed by [63] is the power model. A
model for the energy consumption of network data
transfer which respects the rules of the attributional
approach, particularly the additivity rules. However,
The power model better reflects the causal effects of
data consumption by using a distinctive allocation
key for fixed costs (number of subscribers) and vari-
able costs (data transfer). More recently, Schien et al.
[64] further developed this idea of allocating fixed
network costs based on a causal understanding of
the modelled phenomena by integrating peak effects.

The implications are that the ALCA cannot be
viewed as purely rule-based or artificial [27]. There-
fore, modelling choices, particularly allocation keys,
should be justified in terms of both the study's objec-
tives and their relevance to the reality being studied.

5.2 On the influence of normativity on
CLCA

It may be tempting to consider CLCA to be less nor-
mative than ALCA. While there may be several rules
that comply with ALCA norms and produce differ-
ent results, a CLCA would ideally represent all the
environmental consequences of an action, leaving
in theory no room for conventions and guided by a
single principle: the assessment should be as close as
possible to the "real" causal links between an action
and its environmental consequences. In theory these
links could, be verified or falsified through empirical
results [27, 65]. In practice, however, some experi-
ments are impossible in practice (e.g. randomized
control trials at a worldwide economic scale). There-
fore, it is often impossible to determine "the" exact
causal links with confidence. Furthermore, the conse-
quences of implementing the AI solution are always
considered in comparison to a reference scenario. As
these scenarios cannot coexist, their creation cannot
be guided purely by 'realism' and will always include
normative aspects.

Thus, in practice, CLCA relies on practical rules
based on heuristics, such as those proposed by Wei-
dema, which are based on micro-economic thinking
and can constitute a form of normativity when fol-
lowed in a practical context. Therefore, researchers
and practitioners in AI and ICT for sustainability
should develop, test and confirm heuristics based on
empirical results in order to facilitate the develop-
ment of these approaches in the sector.

5.3 Time consideration

It is sometimes understood that ALCA and CLCA approaches deal with different time periods, the former being a study on past action and the latter on prospective actions. This confusion stems from the lack of distinction between when the action is performed and when its impacts are studied.

CLCA can study actions happening *ex ante*, *ex post*, or *midway*, but only considers the impacts occurring after the action. ALCA can also be conducted *ex ante*, *ex post*, or *midway*, but will consider impacts that happen both prior to and after the considered action.

Moreover, if we introduce a distinction between
short-term and long-term CLCA, the difference does
not directly link with time consideration. ST-CLCA
could study the impact of an action on a longer time
frame than a LT-CLCA. What is important is the
relation to the infrastructure, to the definition of the
service system. LT-CLCA considers the system as
dynamic, while it is static for ALCA and ST-CLCA.

5.4 On the predominance of reductionism
in Green AI

The main literature reviews on the field known as "Green AI" describe a field focused on the energy efficiency of workloads, i.e. training or inference phases [66, 67]. While various studies described do not explicitly mention their approaches as we have defined them, they are similar to a short term framework close to an ST-CLCA. Most studies compare the dynamic power consumption of different algorithms for the same task, without attention to infrastructure and its footprint. While assessing the footprint of AI services using ST-LCA can provide valuable insights, much of the Green AI research field seems to be implicitly blocked by this approach. As [68] defines it "The term Green AI refers to AI research that yields novel results while taking into account the computational cost, encouraging a reduction in resources spent". While the state-of-the-art does take into account part of the computational costs associated with AI technologies, encouraging the reduction of resource expenditure will be difficult to achieve without moving away from this short-term approach.

To go beyond this narrow framing, ALCA could
be employed to understand the dependencies of AI
services to polluting infrastructures and value chain
broader than electricity consumption. However, most

existing ALCA in the "Green AI" literature tends to fo-
cus on individual usages focusing on the footprint of
a user's individual action, e.g. 'What is the footprint
of an exchange with an LLM?'. There is a pressing
demand for an individualised response to the ques-
tion of the consequences of using AI services. This
narrow framing is also found in industry [69, 70] and
public information [71, 72]. This creates an incentive
to include functional units such as FU1 in the design
of studies, as we have done. Instead, research should
focus on providing a more systemic account of the
environmental impact of AI services and consider the
environmental burden of systems as a whole. This
involves using FUs like FU2 to assess and report on
the impacts of global AI systems.

However, a more systemic account should also in-
vestigate the transformation brought about by AI in
terms of its direct effects on digital infrastructures
and systems, and its indirect effects on other sectors.
It should also consider its (in)compatibility with en-
vironmental transition plans. This involves taking a
LT-CLA perspectives applied to the deployment of
AI systems in specific or global contexts. The recent,
unstabilised concept of ‘frugal AI’ could be devel-
oped in this direction. This concept has appeared in
the literature [73, 74] and goes beyond the notion of
energy efficiency. A recent standard even ties the no-
tion of frugal AI to the assessment of indirect impacts
as well as infrastructure costs [52].

5.5 Relevance of each approaches in AI
services contexts

Our main recommendation, in line with the main
contribution of this article, is to clearly define the
methodological framework for the evaluation. This
involves choosing a framework that is consistent with
the objectives and resources of the study. Table 1
summarises the consistent use of each approach for
each type of stakeholder.

**Power efficiency design and other design decisions:**
We are referring, in a broad sense, to the people who
define the technical aspects of the AI service, e.g. soft-
ware developers, cloud engineers and UX designers.
In this case, the ALCA makes it possible to assess the
distribution of the service's impacts and thus priori-
tise them<fnref n="4" />. For example, which is responsible for the
most impacts: training or inference? The ST-CLCA
is more appropriate for specific contexts, such as
choosing an algorithm based on energy consumption
criteria or selecting a load management policy for the
service. Finally, LT-CLCA can estimate the impacts
of design or technical choices concerning the service

<footnote n="4">⁴ Considering that relevant allocations keys have been used</footnote>
<table><caption>Table 1: Summary of each method's relevance in different context</caption><thead><tr><th>Stakeholder</th><th>ALCA</th><th>ST-CLCA</th><th>LT-CLCA</th></tr></thead><tbody><tr><td>Designer</td><td>Prioritise the eco-design of the part of an AI system that has the greatest impact</td><td>Manage the direct consequences of technical choices in terms of energy efficiency</td><td>Manage the direct and indirect effects of design and technical choices</td></tr><tr><td>Individual consumer/citizen</td><td>Choose from two LLM options with eco-labels</td><td>Focus on the narrow consequences of performing a prompt</td><td>Highlight the effects of collective decisions relating to AI services</td></tr><tr><td>Policymaker</td><td>Establish a normative reporting framework</td><td>Not relevant</td><td>Understand direct and indirect effects of developing an AI and define coherent policy to achieve sustainability goals</td></tr><tr><td>Organisation</td><td>Standardized reporting</td><td>Comparative studies in specific contexts</td><td>Understand direct and indirect effects of developing or integrating AI and monitoring long-term strategies</td></tr></tbody></table>

on future user or service resource demand (e.g. GPU purchases), as well as managing indirect effects.

**Consumer/citizen orientation:** We consider the capabilities and responsibilities of individuals in consumer or citizen roles. In this context, the ALCA can be used to label products, helping consumers to choose the model that best meets their environmental criteria and reduces their personal carbon footprint, for example. ST-LCA could be used to estimate the short-term marginal environmental impact of a specific action, such as making a single request to an LLM service. This answers the question, 'What are the immediate impacts induced by my single prompt ?' within a narrow ethical framework. Finally, LT-LCA could be used by citizens to highlight the effects of collective decisions regarding AI services, such as the avoided impacts induced by boycotting AI services.

**Policymaking:** We consider here the design of potential environmental regulations for AI services. The ALCA can be used to establish a normative reporting framework for the environmental impact of LLMs, encouraging AI developers to adopt more sustainable practices. LT-CLCA is relevant for understanding the long-term direct and indirect effects of the development of AI system while establishing ways to achieve sustainability objectives in line with broader frameworks, such as forward-looking scenarios and national objectives. Finally, ST-CLCA's focus on the short term means it does not correspond to the traditional scope and challenges of policymaking.

**Organisation:** We consider here an organisation such as a private company, public administration or association. At this level, ALCA is relevant for standardised reports such as annual greenhouse gas reports. As with policy-making, LT-CLCA is useful for understanding the long-term direct and indirect effects of developing an AI system within a company. It can also be used to establish and monitor long-term strategies (e.g. net-zero trajectories at an organisational level). Finally, in limited contexts, ST-CLCA can provide insight into specific decisions that do not affect the organisation's long-term strategy.

Note that when ALCA is used for reporting or labelling, the evaluation must adhere to coherent norms, such as a similar allocation procedure. The details will be the subject of scientific and political negotiations.

# 6 Conclusion

The rapid proliferation of AI services and the infrastructure that supports them makes integrating environmental assessment into decision-making both urgent and complex. Current practices in the fields of 'Green AI' often take a narrow approach without justifying this methodological or epistemological choice. However, as discussed, environmental assessment involves making ethical choices which fundamentally shapes how impacts of systems are associated with
the environment. As we have demonstrated, this has an important impact on how the results should be interpreted, and can sometimes lead to misinformation regarding the direct and indirect impacts of AI systems.

By defining and comparing three distinct assessment approaches — ALCA, ST-CLCA and LT-CLCA — through the use case of a Gen-AI service, we have proposed a framework that can be used to situate existing and future approaches within a consistent epistemological and methodological framework for environmental accounting of AI systems.

We have demonstrated that the ST-CLCA category is currently one of the most prevalent frameworks for discussing environmental issues surrounding AI. This de facto choice do not accurately represents the two main topics most often overlooked in analyses of AI services: infrastructure and indirect effects. The ALCA approach could be used to better understand the dependencies of AI services on polluting infrastructures. However, only LT-CLCA, the least frequently used method, can provide insight into the transformations brought about by the deployment of GenAI, in terms of both direct impacts on ICT infrastructures and indirect impacts.

## Acknowledgements

This work was funded by UKRI EPSRC through award EP/X038858/1 and by the ANRT (CIFRE N°2023/0363).

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