Current status and bibliometric analysis of carbon emission reduction pathway research based on artificial intelligence and Web of science platforms
Bibliographic record
Abstract
Globally, tackling climate change and promoting low-carbon development has become a universally accepted course of action.self20century90Since the 1990s, human understanding of climate change has gradually deepened, and a scientific theoretical system and systematic governance framework have been constructed.In order to deeply understand the research status and development stage of carbon emission reduction path, this paper systematically combs and analyzes the relevant literature.This study was selected based on the Web of Science core collection database 2006 Year to 2023 Published by global scholars 8679 the English documents were used as research samples, and CiteSpace software was used to conduct an in-depth visual analysis of the number of published papers, the group of core authors, the distribution of research institutions, published journals, keywords and key areas of research.The results reveal that research in the field of carbon emissions in1991Before 200 years, it was still in its infancy, and then the number of literature increased year by year.The main research institutions are distributed in China, the United States and the United Kingdom, and the research in this field involves environmental science, engineering, environmental science, economics and other disciplines.In addition, climate change and energy development and rational utilization are the two major research hotspots in this field.The study results indicate that: (1) During the study period, the research of carbon emission reduction path received high attention, and the publication volume of relevant literature continued to increase and the growth rate increased significantly.(2) Through the analysis of the author group, it is found that a clear core author group has not been formed in the field of carbon emission reduction path.(3) The main research forces are concentrated in China, the United Kingdom, the United States and other countries, among which 13 research institutions including the Chinese Academy of Sciences and Tsinghua University have shown significant research ability and influence, and the cooperation between the research institutions and ✉Corresponding author.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.181 | 0.235 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".