The world wide web of carbon: Toward a relational footprinting of information and communications technology's climate impacts
Bibliographic record
Abstract
The climate impacts of the information and communications technology sector—and Big Data especially—is a topic of growing public and industry concern, though attempts to quantify its carbon footprint have produced contradictory results. Some studies argue that information and communications technology's global carbon footprint is set to rise dramatically in the coming years, requiring urgent regulation and sectoral degrowth. Others argue that information and communications technology's growth is largely decoupled from its carbon emissions, and so provides valuable climate solutions and a model for other industries. This article assesses these debates, arguing that, due to data frictions and incommensurate study designs, the question is likely to remain irresolvable at the global scale. We present six methodological factors that drive this impasse: fraught access to industry data, bottom-up vs. top-down assessments, system boundaries, geographic averaging, functional units, and energy efficiencies. In response, we propose an alternative approach that reframes the question in spatial and situated terms: A relational footprinting that demarcates particular relationships between elements—geographic, technical, and social—within broader information and communications technology infrastructures. Illustrating this model with one of the global Internet's most overlooked components—subsea telecommunication cables—we propose that information and communications technology futures would be best charted not only in terms of quantified total energy use, but in specifying the geographical and technical parts of the network that are the least carbon-intensive, and which can therefore provide opportunities for both carbon reductions and a renewed infrastructural politics. In parallel to the politics of (de)growth, we must also consider different network forms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".