Governing Climate Change at the G20 Rome and UN Glasgow Summits and Beyond
Bibliographic record
Abstract
How and why does the Group of 20 (G20) work, both alone and together with the United Nations (UN), to advance the effective global governance of climate change, especially in 2021 and beyond? G20 summit performance on climate change has increased since 2008 as measured by the six major dimensions of governance, but not by the results in net emissions reduced. G20 efforts to spur performance at subsequent UN climate summits has varied, from substantial at G20 Pittsburgh for UN Copenhagen in 2009, to limited at G20 Antalya for UN Paris in 2015, and to strong at G20 Rome for UN Glasgow in 2021. G20 efforts have been spurred by the physical climate shockactivated vulnerabilities experienced by G20 members in the lead-up to G20 and UN summits, especially from escalating extreme weather events, but have been constrained by diversionary shocks from finance in 2008–09, terrorism and migration in 2015, and COVID-19 in 2020–21. Also important were the personal commitments of, and domestic political support within, G20 and UN summit hosts, especially regarding the G20 and UN summits uniquely chaired by Group of 7 (G7) members Italy and the United Kingdom in 2021. Yet, the unprecedented combined G20-UN supply of global climate governance in 2021 fell even further behind the proliferating global demand to control climate change. To close the gap, the G20 should invite the heads of the major multilateral environmental organizations to participate in G20 summits, hold more environment ministers’ meetings each year, and mount an annual climate-focused summit at the UN General Assembly.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".