Global greenhouse gas emissions in the 21st century: Complex network, driver pattern and economy-based interaction
Bibliographic record
Abstract
Achieving a reduction in global greenhouse gas (GHG) emissions requires collaborative efforts from the international community; however, a comprehensive understanding of the spatiotemporal characteristics (i.e., complex emission networks and driver patterns) and the mutual influence of gross domestic product (GDP) and GHG emissions remains limited at a global level in the 21st century, which is not conducive to forming a consensus in global climate change negotiations and formulating relevant policies. To fill these gaps, this study comprehensively analyzes the complex network and driver pattern of GHG emissions, as well as the corresponding mutual influence with GDP for 185 countries during 2000–2021, based on social network analysis, the logarithmic Divisia decomposition approach, and panel vector autoregression model at global and regional levels. The results indicate that significant heterogeneity and inequality exist in terms of GHG emissions among regions and countries in different geographical areas and economic income levels. Additionally, GDP per capita and GHG emission intensity are the largest positive and negative drivers, respectively, affecting the increase in global GHG emissions. Furthermore, key countries, such as Germany and Canada, that could serve as coordinating bridges to strengthen collaboration in the global emission network are identified. This study highlights the need to encourage key participants in the emission network and foster international cooperation in governance, energy technology, and economic investment to address climate change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".