Concept · 1 paper · updated 2026-11-08

Life cycle assessment for AI services

Judging an AI service by its whole life cycle, from raw materials through production, use and disposal, rather than by training or use-phase energy alone. The method compares an AI-enhanced application with a reference application, impact by impact. One position paper proposes it, with no experiments or code, so the page has a single source.

sustainabilitylife-cycle-assessmentenvironmental-impact

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AI-compiled overview, updated 2026-11-08, not yet reviewed by a human. Every claim cites its source; check it when a detail matters.

Overview

Life cycle assessment (LCA) adds up the environmental flows of a product or service across its stages: raw material acquisition, production, use and end of life 2110.11822 §3.2, tab. 1. The question it answers for AI is whether an AI-enhanced application has a smaller environmental footprint than the same application without AI, once the AI's own impacts are counted 2110.11822 §4.1, eq. 1.

The shared idea is a difference. Each system is assessed over the same functional unit, and the result is the change in each impact criterion. A positive change means the AI-enhanced application causes more impact 2110.11822 eq. 1. The paper does not state this sign convention in words; it follows from how the difference is defined.

Variants

Paired comparison with a reference application

This is the method the paper proposes. Two systems are assessed: a reference application without AI, and the AI-enhanced application, whose assessment includes the AI service 2110.11822 §4. The per-criterion difference is the net impact 2110.11822 §4.1, eq. 1. The paper's worked example is heating one square metre to 20 °C for a year 2110.11822 §2.3. The AI-enhanced case it discusses is a building whose AI saves heating energy by tracking occupancy 2110.11822 §4.1. Card: Unraveling the Hidden Environmental Impacts of AI Solutions for Environment.

Consequential assessment at large scale

The paper says that a large deployment needs a consequential LCA, which also counts knock-on effects such as new mines or extra fossil-fuel generation 2110.11822 §5.4. The source card records only this statement, with no procedure or numbers for it, so treat it as an open direction rather than a method 2110.11822 §5.4.

When to use which

Situation	Use	Why
You can name a reference application that does the same job without AI	Paired comparison	Only the difference shows whether the AI gives a net gain 2110.11822 §4.1, eq. 1
You have only the energy of training or running the model	Do not report a net gain	The paper says energy alone misses production and end-of-life impacts 2110.11822 §2.1, §3.1
The deployment is large enough to change supply chains or energy markets	Consequential assessment, as a direction	The attributional comparison may miss knock-on effects 2110.11822 §5.4
You need one score to rank options	Keep impact criteria separate	The paper argues against collapsing criteria into one number and rejects monetary value units 2110.11822 §4.1
You want the benefit to be stated in a sales or policy claim	Show the full comparison and its inputs	The paper says a sound LCA shows technical potential only; real-world effects can differ 2110.11822 §5.3

Implementation sketch

The steps follow the paper's method 2110.11822 §3.2, §4. Impact criteria and the functional unit are inputs the user must supply.

Pseudocode

# Net life-cycle impact of an AI-enhanced application  [arxiv:2110.11822 eq. 1]
# Input:  reference app m1, AI-enhanced app m2, d impact criteria
# Output: delta, one value per criterion; positive means more impact

def net_impact(m1, m2, criteria):
    # Assess both systems over the same functional unit  [§2.3]
    # Scope each tier: terminals, network, data centre or server  [§3.2]
    lca_m1 = life_cycle_assessment(m1, criteria)
    lca_m2 = life_cycle_assessment(m2, criteria)
    # The assessment of m2 includes the AI service's own impacts  [§4.1]
    # Keep one value per criterion; do not collapse them  [§4.1]
    return lca_m2 - lca_m1

Shared equipment needs an allocation rule. The paper suggests execution time for production and end-of-life of shared servers, and says the exact key is not stated 2110.11822 §3.2. The static use-phase share in its example is 1/n for n concurrent programs 2110.11822 §3.2.

Pseudocode

# Share of a device's static use-phase energy charged to one program  [§3.2]
# Input:  static_energy (J), n_concurrent (int, >= 1)
# Output: energy charged to this program (J)

def static_share(static_energy, n_concurrent):
    # Split evenly: each of n concurrent programs takes 1/n  [§3.2]
    return static_energy / n_concurrent

Contradictions

None known. The page rests on one paper, and that paper reports no experiments whose results could conflict with another's.

Papers

Unraveling the Hidden Environmental Impacts of AI Solutions for Environment: proposes a life-cycle comparison of an AI-enhanced application with a reference application, and reviews AI-for-Green papers for environmental evaluation.

Source and license

Compiled from the papers listed on this page, all published under CC BY, CC BY-SA or CC0, with citations. See each paper's page for its authors and license.