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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

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