New perspectives on temperate inland wetlands as natural climate solutions under different CO2-equivalent metrics
Bibliographic record
Abstract
There is debate about the use of wetlands as natural climate solutions due to their ability to act as a “double-edged sword” with respect to climate impacts by both sequestering CO 2 while emitting CH 4 . Here, we used a process-based greenhouse gas (GHG) perturbation model to simulate wetland radiative forcing and temperature change associated with wetland state conversion over 500 years based on empirical carbon flux measurements, and CO 2 -equivalent (CO 2 -e.q.) metrics to assess the net flux of GHGs from wetlands on a comparable basis. Three CO 2 -e.q. metrics were used to describe the relative radiative impact of CO 2 and CH 4 —the conventional global warming potential (GWP) that looks at pulse GHG emissions over a fixed timeframe, the sustained-flux GWP (SGWP) that looks at sustained GHG emissions over a fixed timeframe, and GWP* that explicitly accounts for changes in the radiative forcing of CH 4 over time (initially more potent but then diminishing after about a decade)—against model-derived mean temperature profiles. GWP* most closely estimated the mean temperature profiles associated with net wetland GHG emissions. Using the GWP*, intact wetlands serve as net CO 2 -e.q. carbon sinks and deliver net cooling effects on the climate. Prioritizing the conservation of intact wetlands is a cost-effective approach with immediate climate benefits that align with the Paris Agreement and the Intergovernmental Panel on Climate Change timeline of net-zero GHG emissions by 2050. Restoration of wetlands also has immediate climate benefits (reduced warming), but with the majority of climate benefits (cooling) occurring over longer timescales, making it an effective short and long-term natural climate solution with additional co-benefits.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".