Sustainability: Greenhouse Gas Protocol and Global GHG Emissions’ Status and Trends
Bibliographic record
Abstract
Climate change is the biggest health threat to humanity, profoundly impacting the UN Sustainable Development Goals (SDGs), demanding urgent attention and action. This study presents the evolution and domains of the ESG concept, along with the importance of sustainability reporting. The widely accepted standards for sustainability reporting have been enumerated. The Greenhouse Gas (GHG) Protocol has been discussed in detail, including Scope 1 (direct), Scope 2 (indirect), and Scope 3 (value chain) GHG emissions, along with Scope 4 (avoided emissions); and setting GHG targets. The SBTi criteria and recommendations for near-term and net-zero targets for GHG coverage of the seven GHGs covered by UNFCCC, Kyoto Protocol, and GHG Protocol are also presented. The analysis of GHG emissions shows a significant increase since the start of the 21st century, rising from 36.18 to 52.96 Giga tons of CO2 equivalent (46.41%) between 2000 and 2023. In 2023, around 62.73% of global GHG emissions came from the top six contributors: China (30.10%), the USA (11.25%), India (7.8%), the EU27 (6.08%), Russia (5.05%), and Brazil (2.45%). The CAGR of GHG emissions with 1990 as the base year is negative for the EU27 (-1.25%), Japan (-0.71%), Russia (-0.42%, and the USA (-0.12%) against the global CAGR of +1.47%. Overall, emissions increased in 2023 compared to 2022 for the top contributors, except for the USA (-1.41%), the EU27 (-7.48%), and Japan (-6.01%). Further research is needed to assess progress toward climate change targets and to implement emission reduction strategies across all sectors in every country.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".