Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".