What drives greenhouse gas emissions? An international scoping review of academic studies in 2010–2019
Bibliographic record
Abstract
Abstract Greenhouse gas (GHG) emissions have increased globally 10% in the last decade, but there is a large variation in emissions trajectories by country. Understanding the main drivers of recent changes in GHG emissions is important to guide effective climate action. Using a narrative scoping review of academic literature, we access 648 abstracts and review 30 studies to identify statistically significant independent variables that were associated with GHG emissions nationally and multinationally (i.e., in country groupings) during or overlapping the period 2010–2019. We describe the findings in terms of potential reasons for the positive or negative associations, outline the strength of associations relative to other variables within the same study, and compare the associations to findings in other studies. We find that population, energy consumption, and gross domestic product (GDP) per capita are the most common independent variables associated with increases in GHG emissions, whereas the square of GDP per capita and renewable energy production are associated with GHG reductions. We assign GHG drivers to seven categories: economic, energy, demographic, technology innovation, transportation, policy, and others. We conclude by discussing implications for future research and climate policy.
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 | 0.021 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.043 | 0.046 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".