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Record W4412186947 · doi:10.5194/esd-16-979-2025

Delineating the technosphere: definition, categorization, and characteristics

2025· article· en· W4412186947 on OpenAlexafffund
Eric D. Galbraith, Abdullah-Al- Faisal, Tanya Matitia, William Fajzel, Ian Hatton, Helmut Haberl, Fridolin Krausmann, Dominik Wiedenhofer

Bibliographic record

VenueEarth System Dynamics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInnovation, Sustainability, Human-Machine Systems
Canadian institutionsMcGill University
FundersH2020 European Research CouncilAustrian Science FundNatural Sciences and Engineering Research Council of CanadaHorizon 2020 Framework ProgrammeCanada Research Chairs
KeywordsCategorizationComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract. The global assemblage of human-created buildings, infrastructure, machinery, and other artifacts has been called the “technosphere”, and it plays a major role in the present-day dynamics of the Earth system. The technosphere enables the rapid extraction of natural resources and the combustion of fossil fuels, impacting biodiversity and causing climate change while generating copious amounts of waste materials. At the same time, the technosphere supports humans in many ways, including the provision of food, shelter, transportation, and long-distance communication, and it is the main component of material wealth. Despite its importance, Earth system science has been slow to explicitly incorporate the technosphere as an integrated part of its conceptual and quantitative frameworks. Here we propose a refined definition of the technosphere, intended to assist in developing functional integration with other Earth system spheres as well as social sciences. We also suggest a categorization system for the things that make up the technosphere based on how their end uses support human motivations. Given the formal definition and resolved categorization, we delineate basic attributes of the technosphere, including its mass distribution among categories and across the Earth surface, and discuss its first-order temporal dynamics. In particular, of the 1-trillion-tonne technosphere mass, we estimate that roughly one-half is buildings and one-third transportation infrastructure, both of which we map globally at 1° resolution. Movable entities, mostly composed of vehicles, vessels, and machinery, account for less than 2 % of the total technosphere mass yet are comparable to the biomass of all animals on Earth. We show that reconstructions of the technosphere since 1900 are consistent with an autocatalytic process, resulting in exponential growth with a long-run increase of > 3 % yr−1, equivalent to a 20-year doubling time. Building a stronger quantitative understanding of the technosphere can help to better integrate it within Earth system science while bridging natural and social sciences to support physically plausible pathways towards sustainability and human wellbeing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0110.013
Science and technology studies0.0010.007
Scholarly communication0.0070.008
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.008
GPT teacher head0.266
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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