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# Modelling diverse futures of AI and the climate

Jo Walton, Iliana Depounti, Romain Jacob, Kat Kelly

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Jo Walton, Iliana Depounti, Romain Jacob, Kat Kelly. Modelling diverse futures of AI and the climate. 2026. hal-05513842

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# Modelling diverse futures of AI and the climate

Jo Walton, Iliana Depounti, Romain Jacob, Kat Kelly *

February 2026

## Abstract

How should recent models of interactions between AI and the climate be understood from the perspective of policy design and public accountability? We take as our case study Stern et al. (2025), a study that projects that 2022-2035 global carbon savings enabled by artificial intelligence will exceed AI's own energy-related emissions. The study methodologically advances earlier estimates, combining expert elicitation with historical technology-adoption data across three domains, energy, food and transport, and concluding that AI is likely to be a net climate benefit, contingent on active and effective public governance. Although we concur with the recommendation of active and effective public governance, we challenge the framing of 'net climate benefit / cost.' This framing risks fostering an impression of AI as broadly self-offsetting, in ways that may obscure the conditions under which net climate benefits fail to materialise. Similarly, it risks obscuring crucial distinctions between diverse AI systems with diverse infrastructural requirements. Stern et al.'s accounting boundary, focused on electricity use while excluding embodied and enabled emissions, is moreover potentially overly narrow. However, in this response, we use this to reinforce our position that AI's net carbon impact is deeply uncertain, rather than to propose 'correct' boundaries. Against the background of deep uncertainty, modelling has the potential to help identify where, when, under what constraints, and with what uncertainties particular AI applications deliver genuine decarbonisation benefits, especially if deployed as an explicitly imaginative, democratic practice for generating multiple, radically different future pathways. Contrary to current practice, a robust approach to climate governance requires open data, explicit communication of deep uncertainty, and inclusion of a broad range of stakeholders.

## 1 Introduction

AI is often described as having both environmental benefits and costs, and there is growing interest in quantifying these and weighing them against each other. To explore this 'net impact' framing, we focus on an exemplary recent study, Stern et al. (2025), which projects that AI's carbon savings will substantially outweigh its carbon costs by 2035. [15] Published in *Nature*, this study represents a methodological advance on existing corporate grey literature (e.g. PwC
and Microsoft 2019, Baidu and IDC 2021, BCG and Google 2023), by combin-
ing expert elicitation with historical technology-adoption data to estimate net
impact across three domains: solar and wind power, meat and dairy, and light
road vehicles. [13] [3]

For the study’s authors, because the avoided emissions projected in even
just these three domains (3.2-5.4 Gt CO2e per year) exceed AI’s own total
energy-based emissions (0.4–1.6 GtCO2e)—and because it is reasonable to as-
sume similar patterns may hold elsewhere—AI will be a net climate benefit.
They conclude that “the case for using AI for the climate transition is not only
strong but imperative.” [15]

1.1 Policy relevance

Such research may come as a relief to leading AI companies that have been
unable to deliver on their own decarbonisation strategies, and are increasingly
offering the argument that secondary climate benefits—i.e. delivered outside of
the companies’ own value chains—diminishes the case for significantly stronger
public governance of AI’s climate impacts.

By contrast, Stern et al. actually emphasise that the net climate benefits
which they describe are not inevitable in any market-led scenario. Rather,
such benefits presuppose active public governance: AI is unlikely to accelerate
low-carbon solutions unless AI is deliberately directed toward accelerating low-
carbon solutions. Stern et al. do not go into specifics, but example measures
might be spread across AI-specific law and regulation, energy-system regulation,
environmental planning and permitting requirements for data centres, corporate
climate-disclosure rules, public procurement policies, and related tools.

We agree with Stern et al.’s key recommendation on active governance. Nev-
ertheless, we argue that the study also undermines this key recommendation,
by assigning AI as a whole a ‘net carbon impact.’ This framing risks implying
that AI is effectively self-offsetting in carbon terms. It also encourages a view
of AI as a coherent, unified object, rather than a policy domain made up of
heterogeneous systems and practices, many of whose trends are contingently
connected, and are linkable or separable through policy intervention.

Identifying robust ways to disaggregate ‘AI’ as a broad component of pol-
icy analysis is essential. Policymakers and other stakeholders need evidence
and decision tools which differentiate between different classes of AI-related
activities – likely comprising, for example, different architectures of digital sys-
tems, different model sizes and usage patterns, different classes of AI use, and
different material infrastructural requirements — and which build in and com-
municate uncertainty effectively. Such granular distinctions are preconditions
for any evidence-based governance and credible public accountability around AI
in relation to the climate.
## 1.2 Transparency and participation

More strikingly than most emergent technologies, AI is already deeply politi-cised, entangled with disputes over social and economic power, environmental limits, control over information flows, the conditions of democratic oversight, and geopolitical rivalry. We hold that modelling work on AI and climate is an area where norms of transparency can — and should — be pushed signifi-cantly further, in support of more participatory governance, grounded in clearly evidenced climate value.

Stern et al. provide the strongest example to date of what such work cur-rently looks like, and their methodological openness represents a clear advance on earlier corporate grey literature. At the same time, opportunities remain to strengthen both the scope of disclosure and the clarity of communication, and to work actively to include relevant audiences from outside of the field. Expert elicitation, in particular, is neither prominently foregrounded nor described in detail in Stern et al.'s study; the crucial question of who is being involved in shaping AI's future is therefore obscured.

We recognise that methodologies underpinning the study are standard prac-tice within technology adoption modelling. However, as detailed in later sec-tions, some additional issues emerge in relation to the model's sectoral assump-tions (both in renewable energy and alternative proteins), which would benefit from clarification and further research.

Unclear citation lineages also make it difficult to independently assess several claims with significant emissions implications. Later in this paper we question the model assumption that AI can increase the load factor of solar and wind by up to 20%, as a significant example.

## 1.3 Deep uncertainty

Overall, we propose a change of course for modelling AI-climate interactions. Such modelling should begin not from the question “Is AI a net climate benefit?” but from the recognition of *deep uncertainty* in the decision theory sense. By this we mean that crucial model features — such as boundaries of key drivers, and relationships between them, probability distributions, and/or how to weight and value alternative outcomes — cannot be robustly specified.

AI is a heterogeneous technological ensemble whose climate implications de-pend on myriad uncertain factors including linkages across multiple non-linear systems. At the global level, analysts do not agree on how to specify what counts as ‘AI’ for carbon modelling purposes, nor what should count as AI-indexed carbon emissions or savings. Similarly, at the level of individual AI systems, despite a wealth of empirical studies, carbon estimate methodologies remain fragmented and ad hoc, with inconsistency on market-based vs. location based carbon accounting, and whether and how to include indirect and enabled emissions, rebound effects, and demand substitution. Fragmentation also re-flects legal barriers, where proprietary data limit scrutiny, as well as linguistic, cultural, and national divides, with constrained access from a UK standpoint
to research from major East and South Asian AI hubs, particularly China.

Under such conditions of deep uncertainty, modelling should not centre on
developing rival estimations of a global net figure, but should rather evaluate
robustness across many competing, structurally-distinct representations of the
world, seeking to clarify where, when, and under what constraints and with
what uncertainties, specific AI interventions enable genuine decarbonisation.
Crucially, modelling has the potential to become far more participatory than it
has been to date, creating spaces where genuinely distinct futures are fleshed
out, in order to rival the narrow set of visions promoted by industry and, to
date, largely endorsed by policymakers.

Going even further, greater inclusion of societal stakeholders, including the voices of NGOs and civil society, grassroots anti-AI and AI democratisation movements, trade unions, demographics most exposed to AI impacts, green tech practitioners, and critical technology scholars and practitioners capable of catalysing alternative imaginaries, would likewise enhance the credibility and value of modelling at the intersection of AI and the climate. Against this background, we now turn to our main Stern et. al case study.

2 Communication of model assumptions and methodology

2.1 Assumption of rapid scaling

At the centre of Stern et al.’s approach is an adoption model for three potentially
high-impact solutions—renewables integration (solar, wind, storage), alternative
proteins (AP), and light road vehicles—constructed from enabling-condition
scores (affordability, attractiveness, accessibility), sector-specific weights, and
logistic adoption functions. Because the method searches for weights that gen-
erate sector-plausible S-curves, the modelling architecture presupposes rapid
adoption over the next decade. If the structure of the model rules out “no
take-off” or “stall” pathways, then the resulting adoption dynamics should be
understood as a structural modelling assumption, and prominently communi-
cated as such.

The model uses Linear Programming (LP) to search for plausible combi-
nations of attribute weights (i.e. how important each of the three enabling
conditions is). These weights must produce maturity scores that, when entered
into a logistic function (which includes growth, inflection point, etc.), generate
a realistic adoption curve (roughly S-shaped). The logic is grounded in his-
torical patterns: Stern et al. draw on datasets like the Historical Adoption of
Technology (HATCH) and FAOSTAT to identify typical values for the growth
rate (g) and the maturity score at the inflection point (M*) based on previous
technologies in each sector. [12] [6]

The use of logistic S-curves is a well-established convention in energy-transition
and techno-economic modelling, reflecting many historical cases where diffusion
accelerated and later saturated. Nonetheless, whether this assumption is ap-
propriate for each of the AI-enabled climate solutions, including post-inflection
plateau and 2035 value (e.g. 33% or 50% in the case of AP), remains open to
debate. While the study consistently refers to “potential” savings, this qualifier
does not fully convey that the adoption dynamics are structurally constrained
to be monotonic and to include inflection points. Making this more explicit —
in terms accessible to readers without modelling expertise, which would include
most policymakers — would support interpretation of the results, including
tailoring to specific policy decisions.

Historical datasets such as HATCH contain a variety of adoption pathways, including linear, multiphase, and plateauing patterns, not only S-curves. Greater transparency about how HATCH and FAOSTAT informed the parameter choices would allow users of this research to verify the empirical grounding of the assumptions. The analysis could also have been accompanied by sensitivity tests for slower-than-expected or non-sigmoidal adoption pathways.

## 2.2 Expert elicitation and transparency

Expert elicitation is not mentioned in the abstract or earlier sections of Stern et al., and—given the paper’s framing for a broad audience including policymakers—it may not be obvious that substantial informed subjective judgment is involved in constructing the underlying model. The adoption model’s attribute weights—and the assumed trajectories of the enabling conditions through to 2035 — are sourced from expert judgment (informed by prior Systemiq work), before being shaped by heuristics derived from historical technology-adoption patterns. “For attractiveness and accessibility, the impact of AI was assessed qualitatively. However, for affordability, we assessed the impact quantitatively because the definitions involved time to price parity.” In other words, core parameters driving the steepness and timing of the S-curves are elicited from experts.

Expert elicitation is also a well-established methodology, and there is nothing inherently inappropriate about its use here. Expert elicitation can be a useful methodology for problems in which no reliable empirical data exist, where the relevant processes are emergent or poorly characterised, and/or where the variables of interest lie in the future and cannot yet be observed. It provides a structured way to capture informed judgement about uncertain parameters, especially when those parameters cannot be resolved through measurement or extrapolation alone.

However, again this raises important questions about the details of the methodology, its uncertainties, and transparency and communication. Was a formal elicitation method followed? Who was understood to be an expert, and on what basis? What were their disciplinary backgrounds, and did they have any conflicts of interest? Given the small pre-existing literature and rapidly evolving material picture, might any experts have relied on sources which were out-of-date and/or reflected conflicts-of-interest, such as the PwC or BCG reports noted above? Were techniques like snowball sampling used (where experts nominate others), and if so, what was done to ensure a legitimate diversity of
views? Were there any outlier opinions, and how were these handled?

There are many reference points here for robust expert elicitation, for example, the Classical Model (Cooke's Method), which uses performance-based weighting of experts' judgments, or the IDEA protocol (Investigate, Discuss, Estimate, Aggregate), which encourages structured, iterative consensus-building; other recognised frameworks include Delphi, SHELF, and the MRC Reference Protocol, which offer guidance on how to frame questions, reduce bias, aggregate divergent views, and document assumptions transparently. These frameworks stress the importance of rigour, transparency, and diversity in expert selection—along with calibration techniques to assess the reliability of participants' judgments.

The paper's central quantitative claims require explicit visibility of expert-elicitation design, including sample composition, and conflict-of-interest management, and ideally calibration, aggregation, and sensitivity analysis to alternative weightings and trajectories. Without these—at a minimum, more explicit discussion of what it means to omit them—the headline result is underdetermined. It also makes it difficult to situate the model within the broader landscape of societal stakeholders, including civil society voices critical of the current direction of AI policy.

# 3 Boundary definitions

## 3.1 Background

It is not possible to make a “like-for-like” comparison between AI’s costs and benefits without contestable methodological commitments and/or value judgments. Even if analysis is restricted to “CO2e emissions” as costs, and avoided and removed “CO2e emissions” as benefits, these remain categorically different quantities, with different relationships to counterfactual world-states. This raises the risk of forcing distinct quantities into a shared metric in ways that obscure their attendant uncertainties. Nevertheless, in some domains, such heterogeneity has been translated into a common currency (e.g. as in ecosystem-services approaches to valuing nature) to inform decision-making.

Stern et al. implicitly recognise these challenges, and aim to proceed with conservative assumptions, to avoid overestimating the net carbon benefits of AI. In particular, they seek to avoid two symmetrical risks: (a) defining AI's carbon emissions potential too narrowly, and/or (b) defining AI's carbon mitigation potential too broadly. Their study therefore includes reflections on how the costs boundary might have been narrower, and how the benefits boundary might have been broader. In this section we discuss those reflections, and suggest that the costs scope is actually narrower than their framing suggests, i.e. there are costs which may plausibly have been included, which were excluded. More speculatively, we suggest that the benefits scope may (in two respects) be broader than their framing suggests. We do not seek to propose 'correct' boundaries, but rather argue that these considerations indicate deep uncertainty.
## 3.2 Making narrow costs appear broad

To avoid defining AI's carbon emissions too narrowly, Stern et al. rely primarily on data-centre electricity-demand scenarios combined with IEA grid-intensity trajectories, emphasising that these ranges encompass “all AI activities” – even though a narrower focus on climate-aligned uses in the three focal sectors might be seen as a legitimate alternative. Some studies have chosen even narrower scopes, e.g. considering the inference phase of AI systems but not their training phases, [12] [5] or vice-versa. The study's use of location-based emissions factors, rather than market-based accounting, also avoids the artificially low estimates that can arise from clean-energy certificates, representing offsetting by questionably-additional clean energy.

However, broadness and narrowness are necessarily relative. Stern et al.'s cost boundary may be broad compared with those used in the two corporate white papers cited as precedents, but when situated within the wider literature on digital sustainability, the boundary appears narrow. For example, emission categories commonly mentioned in the literature, but omitted from Stern et al.'s analysis, include:

* Networks and devices (operational energy beyond data centres).

* Embodied carbon of data-centres, networks, and devices (manufacture, transport and installation, end-of-life disposal).

* Enabled emissions: including AI-driven optimisation of fossil fuel extraction, extending asset lifetimes, lowering breakeven prices, and expanding economically recoverable reserves; AI-driven optimisation of carbon-intensive activities; the consumer-oriented outputs of AI search and chatbots.

Devices include not only e.g. user devices like laptops and phones, but also sensor networks, control systems, etc. foundational to many AI-powered optimization interventions. In addition to these omissions, the boundary excludes non-carbon environmental impacts such as water use for cooling or the mining of tech metals. A further category of more speculative costs can also be articulated—notably mitigation deterrence, including the potential for AI-centred narratives to delay or displace proven near-term decarbonisation by encouraging confidence in technological breakthroughs whose delivery and timelines remain uncertain (cf. research on CO2 removal and climate delay).

## 3.3 Making broad benefits appear narrow

### 3.3.1 Isolating AI within complex non-linear drivers

On the benefits side, the situation is less clear-cut. To avoid defining benefits too broadly, Stern et al. restrict their mitigation estimates to a small set of potentially high-impact AI-enabled climate solutions. The chosen solutions are connected with relatively mature technologies (solar and wind, APs, EVs), and
more emergent and speculative technologies are excluded. Stern et al. mention they have omitted “the impact of AI on accelerating capital deployment or supporting better policymaking, which are key levers to speeding up the climate transition.” [15] For a more comprehensive list of possible AI decarbonisation opportunities, see for example the ADViCE hub, funded by the UK government.[4] Stern et al. also remark on “positive rebound effects,” whose omission implies that the benefits scope might have been broadened through modeling compounding, intersectoral and systemic impacts.

Narrowness and broadness are relative terms. While the study explains why the selected benefits boundary may be judged narrow, we identify two respects in which it can also be read as broad. We recognise that these claims are more contestable than the case developed in the previous section, but we present them in the interests of advancing the discussion, and in support of our recognition of deep uncertainty.

### 3.3.2 What should go in a 'BAU' baseline?

The crucial question here is the definition of AI and of BAU. When AI is involved in some larger decarbonising process, total impact can sometimes be attributed implicitly or explicitly to AI itself, with little effort to isolate the technology's specific marginal impact. This is evident, for example, in PwC and Microsoft (2019) and BCG and Google (2023). Stern et al. represent an advance on this grey literature: they explicitly frame AI as an accelerator rather than an independent causal driver, and constrain its potential benefits accordingly.

However, a key issue that remains unresolved is the definition of “AI” itself. The distinction between an “AI” scenario and a Business As Usual (BAU) scenario is conceptually challenging, since – depending on the definition of AI selected – BAU plausibly already includes extensive use of AI. Indeed, histories of “AI” often begin with the advent of modern computing in the mid-20th century, and the term “AI” has frequently been described as a marketing label or socio-cultural category rather than a cleanly delimited technical class.

This definitional ambiguity creates a problem for modelling and governance. How are projected “with AI” scenarios distinguished from “no AI” scenarios, and is it appropriate to characterise the latter as “Business as Usual”? Recent advances in deep learning blur into longer-standing forms of automation, data analytics, and algorithmic decision-making. If AI is not consistently distinguished from adjacent digital technologies, the BAU counterfactual risks misleading policymakers and other decision-makers. For example, the modelled benefits attributed to AI may in fact reflect a broader bundle of efficiency gains that could plausibly have occurred without frontier AI scaling and extensive data-centre build-out—that is, without the developments that the headline claim that “AI is a net climate benefit” is plausibly used to justify.

This appears to be an open problem. We suggest two promising starting points for future modelling. In climate modelling and the economics of innovation, one common approach is to construct baselines on an “existing technology only” basis, and then represent emerging technologies as additional options or
separate scenarios layered on top of this baseline. By analogy, a methodologically coherent way to handle AI would be to treat established techniques and digital infrastructure as part of the existing landscape, and define “AI” for scenario purposes as the more recent wave of frontier systems (for example, post-2020 model classes) and their likely future iterations. To avoid confusion, this would need a new designation, i.e. “Frontier AI” or “Emerging AI.”

Another promising approach would be to emphasise the material infrastructure of AI (processing power of chips, build-out of data centres, etc.). Some estimates put data centre growth globally at 17–18% per year in recent years. On this approach, when the net climate impact of AI is discussed, the operative definition of “AI” is not exhausted by techniques or model architectures, but by the material commitments those techniques entail. In principle, a small model may be more carbon-intensive than a large one (for example, if it is embedded in consumer software and serves many more queries). Using material infrastructure in this way might provide a way of boundarying the relevant “AI” for evaluative purposes: the relevant AI is whatever set of computational practices requires and drives this infrastructural expansion. Though the story is of course complicated by running frontier AI systems on older infrastructure as well, this nonetheless might be an appealing approach to keep the materiality of AI in clear view. Again, to avoid confusion, this would need a new designation, rather than simply “AI,” e.g. “Build-Out-Dependent AI.” This would also permit cross-referencing with governance models, e.g. policy support for all traditional AI regardless of use case, but policy support for BODAI only in climate critical use cases.

Stern et al. do not explicitly confirm either of these approaches, and it is possible that the definition of AI was left to experts consulted to develop their own perspectives on. In Stern et al.’s study, because AI is not precisely defined, the benefits boundary may be broader than it looks, because it may be absorbing gains that do not depend on the phenomenon whose costs are being counted (frontier-scale AI).

If the BAU baseline treats computational methods as fixed, while the “AI” scenario assigns a range of system improvements to AI, including those that could plausibly be achieved without frontier scaling, then the benefits boundary is broader than its framing suggests. The BAU counterfactual implicitly treats these gains as contingent on commercial, general-purpose AI growth, whereas several could arise from domain-bounded tools that do not require the expansive infrastructure assumed in the main scenario. In a later section, we develop a little further an alternative research direction, which would permit computational innovations for climate that plausibly persist under a materially constrained, non-scaling trajectory.

### 3.3.3 Second-order mitigation as “positive rebound”?

Stern et al. use the term “rebound effects” in a way that departs from standard usage in the AI-and-climate literature, where rebound effects refer to offsets to expected emissions savings that arise when efficiency improvements reduce costs
or resource requirements and thereby stimulate additional consumption. Stern et al. note that the prevailing focus on these kind of effects, which they term “negative rebound effects,” may not capture the full range of possible system responses, and suggest that out-of-scope “positive rebound effects” may make the benefits larger than modelled. Two distinct, related claims may be implicitly at work here. One is balance: the omission of rebound effects may be thought to parallel the omission of what Stern et al. term “positive rebound effects.” The other is a claim of conservativeness: the omission of “positive rebound effects” keeps the scope of benefits narrow.

However, “positive rebound effect” is somewhat difficult to make sense of. Our usage, which we believe to be conventional despite some variations (and which is consistent with Kaack et al. 2022, cited by Stern et al.) is as follows: enabled emissions should be understood as a broad class of emissions increases that arise because an AI system expands or intensifies a carbon-intensive activity (or mitigates its shrinkage or de-intensification). Enabled emissions include increases driven by induced demand, where AI lowers cost and therefore stimulates higher usage. Induced demand sits within the classic family of rebound effects, which are always “negative” in Stern et al.’s sense, and describe the behavioural and systemic responses that partially or fully offset expected emissions savings following an efficiency improvement. Jevons Paradox represents an extreme case of rebound, in which efficiency gains lead to an overall rise in total consumption. Substitution effects (consumption switching from one good to another because of relative cost or other changes) are conceptually distinct from induced demand, but hard to disentangle in practice: in some cases they may be two different lenses on the same phenomenon.

It’s also worth briefly noting that enabled emissions are not restricted to cost-mediated responses. They also include capability-mediated expansion, e.g. where AI makes a carbon-intensive activity newly possible, more accurate, safe, scalable, or otherwise improved in ways that would not occur through price elasticity alone. In practice, enabled emissions therefore encompass not only rebound effects, including induced demand, but also the additional emissions that flow from AI’s extension of the feasible frontier of extractive or energy-intensive practices.

With their use of “positive rebound effects” Stern et al. are capturing a distinction between two different kinds of mitigation effects, particularly from the view of the data and methods necessary to model them. A clearer terminology, and one that avoids confusion with the established rebound literature, could distinguish between first-order enabled mitigation and second-order enabled mitigation. First-order enabled mitigation and second-order enabled mitigation might be distinguished in detail on a case-by-case basis; in the present case first-order enabled mitigation refers to directly modelled emissions reductions within Stern et al.’s three selected sectors, whereas second-order enabled mitigation refers to wider system-level effects—such as the example provided that energy saved through AI-optimisation might be redeployed into additional mitigation efforts such as innovative carbon removal. Second-order enabled mitigation should not be confused with any kind of rebound effect.
Distinguishing them in this way allows the General Equilibrium tractabil-
ity of second-order mitigation to be made explicit, and avoids importing the
established empirical grounding of the rebound framework into an analytically
distinct domain. This is a legitimate conceptual distinction, but the rebound
vocabulary is not the best vehicle for it. The “positive” effects Stern et al.
mention arise through system-level spillovers, indirect complementarities, and
broader structural interactions that require different modelling tools and rest
on different evidentiary foundations than classic rebound mechanisms.

Describing second-order enabled mitigation as “positive rebound” introduces a terminologically misleading symmetry with negative rebound effects. It implies a parallel between (a) price-mediated rebound mechanisms that reliably erode expected mitigation, and (b) contingent, higher-order interactions that may enhance it. A study would be presumed to have fulfilled one criteria of boundary equivalence if it either included both or omitted both. Treating these as equivalent omissions helps to frame the study’s “net-impact” boundary decisions as balanced. In fact, the two categories rest on fundamentally different types of evidence and pose very different modelling challenges.

3.3.4 Boundaries: Deep Uncertainty

In their modelling, Stern et al. include (on the cost side) only direct electricity-
related emissions from AI use, without including any downstream carbon im-
pacts. Against this the study attributes to AI (on the benefit side) the large
projected mitigation from accelerated transitions in food (alternative proteins
instead of meat and dairy), transport (EVs instead of petrol vehicles), and en-
ergy systems (wind and solar instead of fossils).

Taken together, the analyses above could suggest that Stern et al.’s cost boundary should be widened and/or benefits boundary tightened until the two are proportionate. Intuitively, it may seem “unfair” to count only the electricity used to power AI systems on the cost side while allowing broad, behaviourally mediated pathways—such as AI-nudged demand for meat substitutes (see next section)—to appear on the benefits side. Our analysis also might be taken to imply that carbon costs and benefits of AI can be made more comparable if the benefits are indexed to material infrastructure in the same way as the costs are. I.e. it could involve a set of graduated infrastructural baselines—defined by a total global compute growth (or degrowth) pathway, pathways distributing across different tasks, and assumptions about spillovers—against which AI-enabled mitigation is estimated. Such an approach would allow the modelling to track more closely the physical conditions that make AI possible, and would place costs and benefits within a more coherent evaluative frame.

We have some sympathy for these approaches but we take a different posi-
tion. We strongly question the usefulness of any net calculation in the absence
of clearly defined users of such a calculation. Under conditions of deep uncer-
tainty—and in a political environment where competing interests seek to in-
strumentalise AI and climate narratives—the value of a “potential net impact”
number is far from clear. It is not obvious who the putative users of such a num-
ber is, or what decisions it is meant to support. The hypothetical policymaker who is willing to deploy AI for climate mitigation but is hesitating primarily due to concerns about its carbon footprint—and who would be materially reassured by a single global net figure aggregating diverse AI systems—is difficult to identify. In practice, climate-conscious policymakers confront specific applications, sectoral transitions, procurement choices, and regulatory levers—domains where differentiated, context-specific analysis is far more relevant. Even assuming the existence of the climate-conscious policymaker who is skeptical of the climate benefits of AI and/or concerned about the emissions associated with its use, it is hard to see why they would rely on estimated net figures. A net figure implicitly frames AI as self-offsetting, when in practice policymakers and practitioners should be concerned with encouraging particular uses and constraining others.

Once a degree of methodological consensus is established—whatever its philosophical underpinnings—this will allow AI systems to be analysed on broadly comparable terms. A shared set of conventions will make it possible to evaluate different AI interventions using appropriately similar boundary conditions, counterfactual conventions, and perhaps reporting formats. This will help with many practical tasks: benchmarking interventions within a sector, comparing alternative models or architectures, identifying high-impact design decisions, or assessing whether an AI deployment delivers the benefits it claims. Such methodological convergence should be balanced with a degree of methodological pluralism, and consensus should always be subject to revision and improvement. This is unlikely, however, to make the question of AI’s net carbon impact on the global scale more tractable.

## 3.4 Sector-specific slow-take-off and no-take-off scenarios

### 3.4.1 Model assumptions in solar and wind

Worldwide, solar PV usage increased rapidly in the last decade, evolving from a niche market to a mainstream electricity source. Projecting the next ten years, the study appears to attribute up to 20% load-factor improvement for wind and solar to AI-enabled forecasting and grid optimisation, translating into 1.8 GtCO₂e annual savings by 2035. This would be highly transformative for global energy systems, on the order of 5% of projected energy-related emissions in 2035 under a typical policy scenario (e.g. 35 Gt).

Even allowing for additional mechanisms not discussed in the paper, such as AI-assisted wake-steering layout optimisation, a 20% load-factor gain appears optimistic, and the study provides inadequate discussion and evidence for this assumption. One likely explanation is that several distinct effects have been grouped under load-factor improvement, without being explicitly mentioned in the study. Some, such as reduced curtailment or improved storage dispatch, may raise effective utilisation without improving load factor itself, making the attribution potentially misleading.

According to the technical annex, the model derives the 20% assumption from a NREL (2023) study. We have been unable to locate this study, as the
reference given by Stern et al. is to an AI vendor blog (Datategy) posted in
early 2024. The actual claim on this blog post is that “AI can improve the
efficiency of wind turbines by up to 20%”, not that AI can increase the load
factor of solar and wind by up to 20%, as Stern et al. state. The blog post
only offers “Study by the National Renewable Energy Laboratory (NREL)” as
its source. No further sources are cited in relation to solar.

For wind, the Datategy post does not specify how it defines “efficiency” or
how this relates to load factor. The wording “up to 20%” leaves the distri-
bution ambiguous, i.e. whether a 20% improvement is common or an outlier.
Operational gains in wind and solar plants (for example yaw control, wake steer-
ing, or predictive maintenance) can reduce avoidable losses, but load factor is
principally shaped by geographic conditions, the underlying resource (wind dis-
tribution or solar irradiance), and the characteristics of installed technology.
We would not expect incremental operational gains to produce proportionate
increases in load factor.

The NREL study cited by Datategy cannot be identified from the information provided, so it is not possible to assess the study directly or confirm whether Datategy's interpretation is accurate. Assuming the date is correct, the closest publicly accessible match is a 2023 conference paper by Glaws et al., which underpins a 2024 *Nature Energy* article. [7]. [9] That work finds that co-optimising plant layouts with wake steering can reduce land requirements by an average of 18 percent. If this is the study on which the “20 percent” claim is based, it does not correspond to a 20% increase in load factor.

Another 20% figure appears in Stern et al. in relation to wind, but this
relates only to a 2019 study which is not about increasing energy production: a
model recommended optimal hourly delivery commitments to the grid a day in
advance, increasing economic value by 20%, compared to the baseline scenario
of no time-based commitments. [8]

Although AI is described as enabling ‘up to 20%’ improvements, this upper
bound is treated as a fixed effect size. The modelling does not incorporate
lower-end estimates or parameter uncertainty for the power sector, so the 1.8
GtCO₂e figure reflects a single optimistic assumption rather than a band of
possible outcomes.

The significant question is perhaps: can AI credibly improve the load factor
of solar PV and wind by up to 20% and save 1.8 GtCO2e per year by 2035?
To date we have been unable to find robust evidence with which to assess this
claim, and we welcome responses and correspondence on this point.

We also note that the language is grammatically equivocal, and a possible
alternative interpretation of the claim is that “the efficiency of renewable energy
systems” may be improved by up to 20%, although this would be a vague claim
and unsupported by the cited source, so we do not discuss it here.

3.5 Model assumptions in food

As with solar and wind, Stern et al.’s treatment of alternative proteins (APs)
rests on a modelling architecture partly inherited from Systemiq’s “tipping-
points” framework. [16] Systemiq supplies the three enabling conditions—affordability, attractiveness, accessibility—together with the assumption that rapid, S-curve adoption becomes possible once these conditions reach threshold levels. Against this background, the study projects that AI could raise AP adoption from 8–14% (BAU) to 18–33% (ambitious AI), and further to 27–50% (highly ambitious) by 2035. It attributes this mainly to AI improving taste and texture via protein discovery. At the upper end of 50%, this could comprise 3.0 GtCO2e of avoided emissions per year, accounting for the majority of the net 5.4 Gt CO2e upper estimate. The width of the total range (3.2-5.4) is also almost entirely by the AP uncertainty (0.9-3.0), with a tiny contribution from the 0.5–0.6 EV spread.

Systemiq’s earlier research rates APs as “amber” (0) on all three enabling conditions, noting that APs accounted for roughly 2% of the combined animal-plus-alternative protein market in 2020. The phrasing is loose—APs are 0% of the animal protein market by definition—but the intended point is that APs remain niche. Systemiq’s focus, and Stern et al.’s by extension, is on meat substitutes such as burgers, sausages, and mince made from soy, pea, fungal protein, or similar ingredients. Systemiq’s earlier research lists familiar barriers: APs are visible in fast-food chains and supermarkets but not yet easily available everywhere. Target conditions—cost parity, comparable taste/texture, health concerns addressed, wide availability—are framed as necessary to reach a tipping point.

However, even meeting those criteria does not guarantee a tipping point. Habit, identity, tradition, social signalling and status associated with “real meat,” food cultures, logistics, and incumbent agricultural interests can affect the pace of uptake, even when substitutes taste good and are affordable. Stern et al. briefly note some of these factors: “traditional meat and dairy will likely continue being consumed in some regions due to cultural and religious traditions and buying or producing APs will be impossible in isolated, rural areas which lack the access to the required technologies. Quantifying the extent to which these factors will act as a barrier to dietary shifts is challenging” (Technical Annex). Conversely, similar cultural dynamics (media, cooking shows, recipe culture, school dinners) are sites of leverage with potential to accelerate uptake. [16] Of course, actual dietary shifts are rarely one-to-one swaps. Many people who reduce meat intake do so through entirely different meal patterns—lentils, chickpeas, tofu, tempeh, grains—not via engineered substitutes. Numerous low-carbon protein sources are not “meat-like” at all. The focus on substitutes may persist because it more elegantly supports a tipping-point narrative: if shoppers can replace beef mince with pea-protein mince without changing shopping or cooking routines, uptake might, in theory, accelerate rapidly.

The citation trail for APs is better than that for wind and solar, but still has limitations. Systemiq’s *Breakthrough Effect* acknowledges that diffusion of new social norms is speculative and that AP adoption may remain linear rather than exponential if reinforcing feedback loops fail to materialise, but this does not appear to have been reflected in Stern et al. Tracing back the sources that underpin Systemiq’s expectations of rapid AP growth—including two BCG
reports—there is little methodological and data transparency. In these BCG reports, parity and inflection points are not clearly distinguished; pathways also seem to build in assumptions about social norms without examining them. [1] [2] It is worth noting that BCG's base case of 11% adoption in 2035 aligns with Stern et al.'s BAU of 8–14%, but BCG's upside case, incorporating aggressively supportive policy and technological step-change, is significantly less at 22%.

At the upper end, Stern et al.'s claim is striking: AI-enabled improvements lift replacement of meat by alternative proteins by up to 50% from a BAU case of 8–14%. It is unclear whether such adoption trajectories represent plausible futures or simply optimistic priors. A credible adoption narrative or model would integrate techno-market dynamics with sociocultural research. Why people shift diets varies: cost, health, ethics, religion, identity, availability, sociability, taste, tradition, livelihood structures, and regional food histories. In pastoralist economies, for example, meat is entangled with culture, labour, and social reproduction; taste parity may plausibly fail to drive substitution. Stern et al. also briefly mention nudging consumer behaviour with AI: again, more detail is needed, but this could plausibly include signposting and positioning in physical space (supermarkets) or digital space (priority and prominence in food ordering apps); such interventions raise legal, regulatory, and ethical questions, including compliance with consumer-protection law, limits on behavioural manipulation, obligations around transparency and consent, and emerging restrictions on personalised recommender systems under data-protection and platform-regulation regimes.

## 3.6 Model assumptions in mobility

We do not discuss these in detail. The mobility contribution is split between (i) AI-enabled shared mobility reducing kilometres travelled and (ii) AI-assisted EV adoption via cheaper batteries and better charging-infrastructure siting. Both effects are plausible; both face well-documented countervailing dynamics. Shared-mobility gains can be undone by induced demand and convenience effects, and is (like APs) dependent on complex cultural factors; EV adoption is strongly policy-driven (standards, incentives, infrastructure) and globally uneven. The study could potentially clarify how rebound and induced travel are handled, and how much of the claimed EV effect is specifically AI-dependent versus attributable to learning curves, scale, and policy. As with power and food, there are opportunities for careful boundary work around the “AI vs. no-AI baseline,” to avoid crediting AI for changes that would likely occur under strong policy regardless.
# 4 Net impact framing

## 4.1 Other net impact framings

Stern et al. (2025) situates itself in relation to prior corporate grey literature that popularised optimistic “net carbon savings” figures (PwC and Microsoft 2019, BCG and Google 2023). The paper ties adoption trajectories to S-curves modelled using historical data on technological diffusion, with estimated impacts based on expert elicitation. It also places useful emphasis on public governance and clearly signals several limitations.

However, we question the usefulness of retaining the “net carbon savings” framing popularised by earlier corporate papers. In some domains translating heterogeneous phenomena into a common currency and netting has practical use; in the present case, it collapses distinctions between AI uses with plausible decarbonisation value and AI uses that are irrelevant or actively harmful. It also tacitly treats the costs of scaling one part of the AI ecosystem as necessary preconditions for benefits elsewhere, without providing evidence for such coupling. There are plausible mechanisms for such coupling—capital allocation dynamics, supply-chain learning, and cross-domain spillovers in methods and infrastructure—but these merit targeted empirical study and nuanced modelling rather than assumption. Overall, such a net impact approach creates the risk of classifying AI as categorically ‘self-offsetting’ by licensing a formal or informal ‘amortisation’ of the supposed net benefit across all AI infrastructure and systems.

Stern et al. are of course far from alone in adopting such a framing. For example, the “net carbon impact” framing is also used by the IEA in its 2025 report: “The adoption of existing AI applications in end-use sectors could lead to 1 400 Mt of CO2 emissions reductions in 2035 in the Widespread Adoption Case. This does not include any breakthrough discoveries that may emerge thanks to AI in the next decade.” [10] The IEA acknowledges deep uncertainty in relation to potential AI-powered solutions, and proposes a Widespread Adoption Case, which “considers only existing AI-led interventions informed by real-world case studies that can be scaled to the sectoral level.” The IEA’s use of a “net carbon impact” lens in its 2025 Electricity 2025 assessment is, in one sense, institutionally appropriate: as a global energy watchdog, the agency is expected to produce harmonised, scenario-based estimates of system-level impacts, even when the task involves substantial epistemic uncertainty. Projecting whole-system consequences of emerging technologies is within its remit, and the Widespread Adoption Case—restricted to interventions with documented, scalable use cases—represents a defensible attempt to bound such speculation. At the same time, the IEA’s estimates illustrate the limitations of the net-impact style of accounting. Even when carefully caveated, framing AI in aggregate risks obscuring the heterogeneity of applications, and entrenching the assumption that climate-positive uses and climate-negative uses belong to a single integrated technological package.

Similarly, PwC have recently done modelling to suggest "the additional en-
ergy use in data centres would be roughly offset by the energy savings realised in the rest of the economy" and that "emissions would be 0.3% to 1.9% lower with widespread AI adoption than without it." This is based on the key assumptions (stated but not evidenced or discussed) that for every percentage-point increase in share of AI-enabled business activity, there is a 0.4 percentage-point increase in energy efficiency of data centres, and a 0.1 percentage-point increase in energy efficiency outside data centres. It does not appear to include rebound effects. It also does not disaggregate sectors or AI use cases. PwC's three economic scenarios (Trust-Based Transformation, Tense Transition, and Turbulent Times) indicate how AI is being positioned as a technological fix to deliver climate mitigation: in the upside scenario, "the economic benefits of AI would exceed the stranded asset costs associated with decarbonisation," the middle scenario sees "AI gains and climate costs largely cancelling each other out" and in the downside scenario "AI takes away more work than it creates."

Situating Stern et al. alongside corporate white papers and the IEA underscores the broader methodological challenge: how to characterise AI's climate consequences without collapsing the distinctions that most matter for governance. Stern et al., despite its methodological advances on earlier work, risks reinforcing an industry-led narrative in which speculative benefits are used to downplay environmental costs and to legitimise market-driven priorities that are poorly aligned with the climate transition. Such a study offers itself as an easy reference point for AI industry advocacy that aims to dismiss legitimate and well-evidenced concerns over the complex influence of big tech on global energy transition. This risk is heightened as large technology companies position themselves as climate policy actors, for example through their extensive involvement in the ongoing revision of the GHG Protocol.

Net carbon impact claims must be read in historical context. Around 2020, several major tech firms pledged to reach “net zero” by specific dates and stay within stated carbon budgets. Many of those pledges have already been broken. Rather than acknowledge failure, sustainability communications are redrawing the boundaries of what is being counted. Instead of emphasising their own Scope 1–3 emissions — where performance is poor, and robust rules like the GHG Protocol and SBTi make it harder to present the numbers in a favourable light — they now emphasise projections of AI’s overall climate impact, a category for which no common standard exists and which no one agrees how to measure. Public debate often treats “AI” as if it were one thing, a discrete technology to be supported or resisted. In this context of deep uncertainty and polarised debate, confident claims can take on undue agency. Even the most prominent and transparent methodological limitations often do not travel with headline findings, let alone buried methodological disclosures. It is very easy to shift from the carefully-scoped claim of AI’s energy-related carbon emissions only, to a claim about AI’s total carbon emissions (perhaps assumed by audiences to include embodied and enabled emissions); indeed this is what two of the authors do when presenting a compressed version for the World Economic Forum, which cannot find space for the nuance: “we project that AI could add 0.4–1.6 GtCO2e annually by 2035.” [14] There is plenty of precedent in this respect; for example,
Baidu and IDC's 2021 claim that AI will comprise 70% of ICT-driven avoided emissions by 2035 is typically misunderstood (in English language sources) as the claim that AI will be responsible for 70% of total avoided emissions.

Even if a study like Stern et al. offers a careful estimate of AI's potential net climate impact over the next decade, we must still ask who and what such a number is for. Policymaker tools—standards, incentives, procurement, permits, reporting requirements etc.—do not map neatly onto a global net-impact metric. Companies, meanwhile, have their own strategic and competitive pressures, and system-level climate accounting is rarely something they can operationalise in day-to-day decisions, even when they take environmental commitments seriously. Where numbers of this kind often have their greatest influence is in the atmosphere around AI. They help create a sense that the climate implications have been weighed up, that experts have done the work, and that uncertainty is being appropriately managed.

It is also worth asking what kind of AI is being discussed. For example, one AI use case in wind energy is a narrow domain Graph Neural Network, orders of magnitude smaller and simpler than well-known modern LLMs, serving fast, purpose-built predictions. This informs one of the great unasked questions of Stern et al. and other works exploring AI's 'net carbon impact' – Not: “Can AI-powered emissions savings offset an arbitrary, lightly regulated expansion of AI capacity?” But rather: “What level of data centre capacity is actually needed to meet our climate-related AI goals?”

To their credit, Stern et al. are clear that AI is not the sole driver of the mitigation pathways they analyse, and isolate the incremental change attributable to AI. The innovation opportunity cost, however, complicates the counterfactual. In multi-factor contexts such as alternative protein adoption, AI may shift the gradients of the S-curve, but only in interaction with other factors such as consumer norms, regulatory design, supply-chain logistics, geopolitics, cultural expectations, political lobbying by meat and dairy, and others. Under a materially constrained “no AI” or “climate-critical-AI-only” baseline, gains attributed to AI may still arise through other channels: domain-specific innovation, behavioural interventions, or computational tools that may be considered “AI” but do not require the expansive infrastructure and training regimes assumed in the main scenario.

A closely related risk is that of unrealistically raising the expectations of policymakers and publics about innovation-driven net carbon savings in the coming decades. In this sense, AI should also be contextualised within debates around Negative Emissions Technologies; Carbon Capture and Storage (CCS) was repeatedly projected as a major wedge in emissions reduction pathways, but has drastically failed to scale (one high-profile example, the Kemper “clean coal” plant in Mississippi, was meant to capture 65% of CO₂e from coal gasification, but collapsed after billions in overruns); more recently hopes have been invested in Bioenergy with Carbon Capture and Storage (BECCS) and Direct Air Capture (DAC). CCS, BECCS and DAC were written into climate models at gigatonne scales long before they were technically, economically, or politically viable. In reality, deployment has so far remained orders of magnitude lower.
Those inflated projections nonetheless served a purpose: they arguably helped justify weaker near-term action by assuming that large removals would arrive later. The parallel is not, of course, that AI is a negative emissions technology – but NETs offer some indication of the risk of a premature welcome of technological solutions. Have we learned the lessons of BECCS?

## 4.2 Qualitative scenarios and public imaginaries

Stern et al.'s recommendations, and their overall framing and headline results, stand in tension. The call for active regulation and governance is important, yet the claim that AI will be a net carbon benefit provides an easy soundbite for industry actors to downplay costs, exaggerate benefits, evade scrutiny, and delay, dilute, or capture regulation. Such an appeal to an abstract “net” that conceals the uneven and contested realities of AI’s climate impacts is evident, for example, in an opinion piece “Net zero needs AI — five actions to realize its promise” (2025) by Amy Luers, Global Director of Sustainability Science at Microsoft, also published in *Nature*, which cites Stern et al. in support. Luers writes that “without artificial-intelligence technologies, balancing human-caused greenhouse-gas emissions with carbon removals by 2050 is out of reach.[11] No-one is seriously imagining a world in which all forms of artificial intelligence disappear. The line functions as reassurance: it suggests that as long as AI exists, the possibility of meeting climate goals remains open, and it aligns criticism of AI (without specifying the content of the criticism) with abandoning climate goals.

Here we suggest a possible research direction. Models such as Stern et al., if carefully framed, have the potential to spark public debate about the kinds of futures worth pursuing, including the kinds of AI (if any) present in those futures. At present, however, the comparison of the BAU scenario with an AI scenario offers primarily a quantitative contrast, without much qualitative differentiation. To complement the somewhat all-or-nothing “BAU vs. AI” analysis, qualitatively diverse scenarios could be used. One informative scenario, close to the current “BAU” scenario, might be a “climate-critical-AI-only” scenario. Under such a scenario, commercially driven scaling pauses, while well-established computational methods continue under public-interest governance for clearly defined climate tasks, using prudently controlled computational resources. Such a scenario could assume capping aggregate training compute, limiting model classes to lighter architectures, restricting retraining cadence, confining deployment to research HPCs or regulated operational settings with relatively small auditable energy and carbon budgets, while also allocating appropriate resources for riskier but potentially high-impact research.

We are not here proposing this as a policy recommendation, but as a modelling concept, from which recommendations could be derived. Other scenarios could incorporate greater decentralisation of AI infrastructure, greater democratic and participatory checks and controls, and different levels of regulatory alignment with social and ecological goals such as the SDGs, as well as other relevant characteristics sourced through participatory co-production involving
a diverse range of interested parties. Scenarios of this sort would acknowledge
that AI is already part of digital practice, and difficult to perfectly separate from
wider digital infrastructures. At the same time, such scenarios could still delin-
eate meaningful alternatives to the current, lightly regulated and commercially
driven trajectory.

It is not obvious that any scenario must be privileged as a baseline; alter-
natively, a set of realistic graduated ‘baselines’ could be presented, retaining
already embedded computational tools but placing them under governance ar-
rangements that constrain their scaling to varying degrees, and directing them
to varying degrees to climate-related research. This might offer greater delibera-
tive possibilities and methodological transparency than a “no AI” BAU scenario
that is an emergent artefact of an expert elicitation process.

## 4.3 Conclusion

S-curves are commonly used in technological-adoption modelling, but the as-
sumption that each climate solution will experience a rapid acceleration and
reach its inflection point within the next decade is a strong one; users of the re-
search deserve a clear and prominent statement of this expected timing, because
it shapes every downstream estimate of AI-enabled mitigation.

Expert elicitation is a legitimate modelling strategy, which only needs to
be presented with full transparency about who the experts are, how they were
selected, how judgments were aggregated and calibrated, and ideally how al-
ternative weightings would change results. In this study, those elements are
either obscured or delegated to antecedent work with limited methodological
disclosure.

The stated potential savings are large, but the cited evidentiary base for po-
tential savings is thin, and in one case quickly leads to a citational dead-end. AI
is definitionally vague. The specification of the BAU scenario is largely hidden
within the expert elicitation work, and there are opportunities for a greater va-
riety of constrained-AI, low-AI, and mission-led-AI scenarios, and greater imag-
inative diversity as regards governance arrangements. Framing the analysis as a
global net climate impact implies deep uncertainty, because there is no consensus
methodological basis for commensurating the costs and benefits of AI either for
individual AI systems nor at the global level. The choice to include only direct
electricity-related emissions while excluding embodied and enabled emissions
produces a boundary that many would regard as overly narrow. Reasonable
analyses will disagree on where these boundaries should lie, but the central is-
sue is that headline results are already being interpreted as if they reflect a much
broader emissions scope. The net-impact framing also risks reinforcing a nar-
rative of AI as self-offsetting, which does not support informed decision-making
about specific interventions to deploy remaining carbon budgets to align with
IPCC and Paris-agreement pathways.

Comprehensive attempts to model environmental, economic and social im-
pacts of AI are not necessarily without merit, if they are appropriately framed,
methodologically sound and transparent, and sensitive (as far as possible) to
the risk that they are misinterpreted or deliberately misused. Methodologies which collapse diverse impacts into common metrics do exist in practice. Nor are such methodologies always misguided. In practice, policymakers, practitioners, investors, and other actors often need summary indicators to compare options, set priorities, or track progress. Composite frameworks can help to discipline decision-making, highlight trade-offs, and make highly complex systems tractable enough to govern. In some contexts, they may be indispensable. However, instead of asking whether AI, “in the round,” is good or bad for the climate, it is crucial to acknowledge deep uncertainty, evaluate concrete applications in context, with attention to who benefits, who bears costs and experiences harms. Diverse, qualitatively distinct scenarios offer better deliberative opportunities, and better opportunities for democratic governance, than comparison with an opaque and potentially unrealistic BAU ‘baseline’ abstraction. Such evaluation must also significantly push the envelope in terms of methodological and data transparency around AI’s costs and benefits, should be paired with diverse credible pathways to govern the scale and direction of AI deployment, and should be developed with meaningful involvement from a far wider set of societal stakeholders.

## References

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* Author Contributions

This article was primarily researched and written by Walton, supported by research by Kelly. Romain and Depounti provided additional review. Additional reviewers have been consulted, and further co-authors may be confirmed prior to full publication.

### 4.3.1 Funding

Work on this article was funded by the project Sustainable AI Futures, from the Bridging Responsible AI Divides (BRAID) programme of UKRI funding. Grant number UKRI569.

### 4.3.2 Conflict of Interest

No conflicts of interest to declare.