Chapter G2 Carbon emissions from land use and land-cover change
Bibliographic record
Abstract
Abstract. The net flux of carbon from land use and land-cover change (LULCC) is significant in the global carbon budget but uncertain, not only because of uncertainties in rates of deforestation and forestation, but also because of uncertainties in the carbon density of the lands actually undergoing change. Furthermore, there are differences in approaches used to determine the flux that introduce variability into estimates in ways that are difficult to evaluate, and there are forms of management not considered in many of the analyses. Thirteen recent estimates of net carbon emissions from LULCC are summarized here. All analyses consider changes in the area of agricultural lands (croplands and pastures). Some consider, also, forest management (wood harvest, shifting cultivation). None of them includes the emissions from the degradation of tropical peatlands. The net flux of carbon from LULCC is not the same as "emissions from deforestation", although the terms are used interchangeably in the literature. Means and standard deviations for annual emissions are 1.14 ± 0.23 and 1.13 ± 0.23 Pg C yr−1 (1 Pg = 1015 g carbon) for the 1980s and 1990s, respectively. Four studies also consider the period 2000–2009, and the mean and standard deviations for these four are 1.14 ± 0.39, 1.17 ± 0.32, and 1.10 ± 0.11 Pg C yr−1 for the three decades. For the period 1990–2009 the mean global emissions from LULCC are 1.14 ± 0.18 Pg C yr−1. The errors are smaller than previously estimated, as they do not represent the range of error around each result, but rather the standard deviation across the mean of the 13 estimates. Errors that result from data uncertainty and an incomplete understanding of all the processes affecting the net flux of carbon from LULCC have not been systematically evaluated but are likely to be on the order of ±0.5 Pg C yr−1.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.132 | 0.026 |
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".