arxiv: 2604.26539 · v1 · submitted 2026-04-29 · 💻 cs.CY

Recognition: unknown

Counting own goals: High-level assessment of the economic relationship between the ICT and the Oil and Gas sectors and its environmental implications

B\'eatrice Dromard, Gauthier Roussilhe, Srinjoy Mitra

Pith reviewed 2026-05-07 12:39 UTC · model grok-4.3

classification 💻 cs.CY

keywords ICT sectorOil and GasInput-output analysisFinancial flowsAdded emissionsDigitalizationRenewable energyGenerative AI

0 comments

The pith

On average 2% of ICT financial flows go to oil and gas, with more than four dollars to O&G for every dollar to renewables and nuclear in 2022.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper applies input-output analysis to economic data spanning 2000 to 2022 to map the financial ties between the ICT sector and the oil and gas industry. It documents that a steady share of ICT spending supports O&G operations, growing in absolute scale as the ICT sector expands, while flows to renewable and nuclear energy remain far smaller. The work classifies specific digital activities within O&G to support future assessments and includes two case studies that estimate the emissions added by those digital tools. It also traces early connections between GPU hardware and O&G investments as context for today's generative AI growth. A sympathetic reader would care because the findings shift attention from ICT's direct footprint to the emissions enabled when digital technologies improve the efficiency or reach of carbon-intensive extraction and production.

Core claim

Input-output tables show that ICT directs on average 2% of its annual financial flows toward the oil and gas sector, and that in 2022 this ratio reached more than four dollars to O&G for every one dollar to renewable and nuclear energy. The authors supply a classification of digital activities in O&G, estimate added emissions in two concrete case studies of oil operations, and examine causal links between the historical success of GPU technology and its early ties to the O&G sector, thereby providing analytical elements for quantifying the net emissions consequences of digitalizing fossil-fuel activities.

What carries the argument

Input-output analysis of inter-sector financial flows, supplemented by a classification of digital activities within oil and gas operations.

If this is right

Absolute ICT spending on O&G products and services has increased substantially with the sector's growth.
Digital tools applied to O&G activities produce net added emissions in the studied cases.
Environmental accounting for ICT must include enabling effects on carbon-intensive sectors rather than only direct or avoided emissions.
The historical entanglement between GPU technology and O&G investments continues to shape current AI hardware trajectories.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same input-output approach could be extended to other high-emission sectors such as agriculture or heavy transport to identify comparable digital multipliers.
If the observed flow ratios persist, accelerating digitalization in energy systems may improve fossil-fuel operations more than it accelerates the shift to low-carbon alternatives.
Long-term tracking of these inter-sector flows offers a measurable indicator of how quickly ICT decouples from legacy energy infrastructure.

Load-bearing premise

The input-output tables and the chosen classification of digital activities in O&G accurately reflect real economic relationships without major data gaps or unaccounted offsetting effects from 2000 to 2022.

What would settle it

Granular transaction data or updated input-output tables for the same period showing the average ICT-to-O&G flow below 1.5% or the 2022 ratio below three-to-one would falsify the central quantitative claim.

Figures

Figures reproduced from arXiv: 2604.26539 by B\'eatrice Dromard, Gauthier Roussilhe, Srinjoy Mitra.

**Figure 1.** Figure 1: Global ICT sector inputs to global O&G sector, in 2022, in millions of Euros view at source ↗

**Figure 2.** Figure 2: Evolution of O&G and ICT shares across O&G and view at source ↗

**Figure 4.** Figure 4: Evolution of global ICT sector inputs to global O&G view at source ↗

**Figure 5.** Figure 5: Biggest national ICT sectors to global O&G sector, in 2022, in millions of Euros, in absolute value view at source ↗

**Figure 6.** Figure 6: Evolution of main ICT national contributions (2022) view at source ↗

read the original abstract

The ICT sector has been one of the most successful and fastest-growing industry in history. While the environmental issue in this sector has mainly been addressed by assessing its footprint and, to a lesser extent, its avoided emissions or net impacts, the additional emissions from the digitalization of carbon-intensive activities, such as the Oil and Gas (O&G) sector, have rarely been discussed. By doing so, we have forgotten to count the own goals conceded over more than 20 years in the troubled relationship between the ICT and the O&G sector. Using input-output analysis and economic data ranging from 2000 to 2022, we observe that on average 2% of the annual financial flows from the ICT sector are directed towards the Oil and Gas sector. Considering the significant growth of the ICT sector during this time, O&G companies now spends a massive amount on ICT products in absolute terms. It also appears that in 2022, for each dollar going from the ICT sector to the renewable and nuclear energy industry, more than $4 go to the O&G industry. In addition, we also provide a classification of digital activities in the O&G sector to facilitate environmental assessments and present two case studies estimating potential added emissions from the digitalization of oil activities. Finally, looking at the immense growth in generative AI, we provide an exploration of causal links between the current success of GPU technology and its intricate early relationship with the O&G sector. This article lays the groundwork for defining the nature of the relationship between ICT and O&G, which predates the current hype surrounding generative AI. We provide the analytical elements needed to begin estimating the added emissions from the digitalisation of O&G.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper quantifies ICT-to-O&G financial flows from 2000-2022 via input-output tables and flags potential added emissions from digitalization, but the methods and data details are too thin to support the headline ratios.

read the letter

The core point is that ICT has directed a steady share of its spending toward oil and gas over two decades, with 2022 flows showing more than four times as much going to O&G as to renewables and nuclear combined. The authors treat these flows as a proxy for indirect emissions enabled by digital tools in extraction and exploration. They also supply a classification of digital activities in O&G plus two brief case studies and a short historical note on GPUs and the sector. That is the new ground they cover. The work is useful for pulling together existing input-output data into a time series focused on this one relationship and for sketching how digitalization might increase rather than reduce emissions in carbon-intensive industries. The case studies and the GPU angle give readers concrete examples to think about. The framing is straightforward and avoids overclaiming net impacts. The main weaknesses sit in the execution. No specific input-output tables or vintages are named, no emission factors or conversion steps are shown, and there are no uncertainty ranges, sensitivity checks, or robustness tests around the 2 percent average or the 4:1 ratio. The custom classification of digital activities inside O&G is introduced without evidence that it maps cleanly onto the industry codes in the tables, which is a common source of error in this kind of work. The jump from monetary flows to added emissions also leaves out offsetting effects and data gaps over the full period. These are fixable but currently leave the quantitative claims hard to evaluate. Readers who follow ICT footprint studies or cross-sector climate accounting will find the paper worth a look as an early mapping exercise. It is not ready for citation on its own numbers, but the topic is relevant enough that a serious editor should send it for peer review once the authors add the missing data sources, mapping details, and basic checks.

Referee Report

2 major / 2 minor

Summary. The paper applies input-output analysis to economic data spanning 2000–2022 to quantify financial flows from the ICT sector to Oil & Gas (O&G), reporting an average 2% of annual ICT flows directed to O&G and a 2022 ratio of more than 4:1 in favor of O&G over renewable/nuclear energy. It introduces a custom classification of digital activities within O&G, presents two case studies estimating added emissions from digitalization of oil activities, and explores historical links between GPU technology and O&G that may relate to current generative AI growth.

Significance. If the core flow estimates and classification hold under scrutiny, the work identifies an under-examined channel of ICT-enabled emissions via support for carbon-intensive sectors, supplying a classification and case-study framework that could support more granular net-impact assessments of digitalization. The long time series and explicit comparison to renewables add a temporal dimension that is currently missing from most ICT footprint studies.

major comments (2)

[Classification of digital activities in O&G] The section presenting the custom classification of digital activities in O&G (and its application to extract intermediate consumption from the input-output tables): the manuscript does not supply an explicit concordance table mapping the new categories onto the industry codes or sub-sectors used in the source IO tables. Because the headline 2% average and 4:1 ratio are derived directly from these extracted flows, any misalignment between the custom classification and the underlying table aggregation directly affects the central quantitative claims.
[Results on financial flows] The results section reporting the 2% average flow and 2022 ratio (and the associated tables or figures): no uncertainty ranges, sensitivity tests to alternative sector aggregations, or robustness checks against different IO table vintages are provided. Given the known coarseness of digital-service categories in 2000–2022 IO tables, the absence of these checks leaves the reported ratios vulnerable to material revision.

minor comments (2)

[Abstract and Methods] The abstract and methods description state the use of input-output analysis and the 2000–2022 range but do not name the specific IO database(s), country coverage, or emission conversion factors employed; these details should be added for reproducibility.
[Case studies] The two case studies on added emissions would benefit from a short table listing the key assumptions (activity levels, emission intensities, digitalization penetration rates) so readers can assess transferability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below and outline the revisions we will implement.

read point-by-point responses

Referee: [Classification of digital activities in O&G] The section presenting the custom classification of digital activities in O&G (and its application to extract intermediate consumption from the input-output tables): the manuscript does not supply an explicit concordance table mapping the new categories onto the industry codes or sub-sectors used in the source IO tables. Because the headline 2% average and 4:1 ratio are derived directly from these extracted flows, any misalignment between the custom classification and the underlying table aggregation directly affects the central quantitative claims.

Authors: We agree that an explicit concordance table is necessary to ensure transparency and allow verification of the flow extraction. In the revised manuscript we will add a detailed concordance table that maps each custom digital activity category in the O&G sector to the precise industry codes and sub-sectors used in the source input-output tables. This will directly support the reported 2% average and 4:1 ratio by clarifying the aggregation steps. revision: yes
Referee: [Results on financial flows] The results section reporting the 2% average flow and 2022 ratio (and the associated tables or figures): no uncertainty ranges, sensitivity tests to alternative sector aggregations, or robustness checks against different IO table vintages are provided. Given the known coarseness of digital-service categories in 2000–2022 IO tables, the absence of these checks leaves the reported ratios vulnerable to material revision.

Authors: We acknowledge that the coarseness of digital-service categories in the available IO tables makes robustness checks important. In the revised manuscript we will add uncertainty ranges for the headline figures, sensitivity tests to alternative sector aggregations, and comparisons across different IO table vintages. These additions will quantify the stability of the 2% average and 2022 ratio under plausible variations in classification. revision: yes

Circularity Check

0 steps flagged

No circularity: claims derived from external input-output tables and data

full rationale

The paper applies standard input-output analysis to external economic datasets (2000-2022) to compute observed financial flows (average 2% ICT to O&G) and ratios (2022 >4:1 vs renewables/nuclear). The provided classification of digital activities in O&G is an auxiliary contribution for future assessments and does not enter the flow calculations as a definitional or fitted input. No equations reduce results to inputs by construction, no self-citation load-bearing premises, and no fitted parameters renamed as predictions. The derivation chain remains independent of the paper's own outputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The analysis rests on standard input-output tables and the assumption that observed financial flows translate into added emissions via digitalization; no free parameters or invented entities are apparent from the abstract.

axioms (1)

domain assumption Input-output analysis tables accurately represent inter-sector financial flows for the studied period
The paper applies this standard economic method to derive the 2% average and 2022 ratio.

pith-pipeline@v0.9.0 · 5624 in / 1335 out tokens · 55639 ms · 2026-05-07T12:39:20.545475+00:00 · methodology

discussion (0)

Reference graph

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