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Record W7146382172

Trends of the Zero Carbon Cities in Japan

2021· article· en· W7146382172 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Sustainable Development
Canadian institutionsnot available
FundersMedical Research CouncilCentre National de la Recherche ScientifiqueIslamic Development BankMinistry of EnvironmentIndo-French Centre for the Promotion of Advanced ResearchIndian Institute of Technology DelhiInternational Science and Technology CenterDeutsche ForschungsgemeinschaftInternational Development Research CentreNational Applied Research Laboratories
KeywordsGreenhouse gasZero (linguistics)Global warmingPopulationCarbon fibersLimit (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

The Paris Agreement sets the goal to limit the global warming to well below 2 °C and preferably 1.5 °C. The recent IPCC report warned that 1.5 °C-warming may occur much earlier than expected. To meet the 1.5 °C target, global emissions of greenhouse gases (GHGs) should be net zero by 2050 or earlier. Increasing number of countries, local governments, and private companies are committing for the 1.5 °C target worldwide. In Japan, this zero carbon movement was initiate by several local governments in 2019, followed by the national government’s commitment in 2020. Now, over 400 local governments, representing nearly 90% of the national population in Japan, announced themselves as the “Zero Carbon City” under the national framework (as of July 30th 2021). This article illustrates the rapidly increasing trends of the Zero Carbon Cities with the overview of emission and energy status in Japan, and analyzes the triggering and supporting elements including the new development of national policies and strategies to ensure the implementation of zero carbon measures at local level as well as to create social and economic co-benefit to the local regions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.016
GPT teacher head0.233
Teacher spread0.217 · 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