Assessing the carbon sink of afforestation with the Carbon Budget Model at the country level: an example for Italy
Bibliographic record
Abstract
In the context of the Kyoto Protocol, the mandatory accounting of Afforestation and Reforestation (AR) activities requires estimating the forest carbon (C) stock changes for any direct human-induced expansion of forest since 1990. We used the Carbon Budget Model (CBM) to estimate C stock changes and emissions from fires on AR lands at country level. Italy was chosen because it has one of the highest annual rates of AR in Europe and the same model was recently applied to Italy’s forest management area. We considered the time period 1990-2020 with two case studies reflecting different average annual rates of AR: 78 kha yr-1, based on the 2013 Italian National Inventory Report (NIR, official estimates), and 28 kha yr-1, based on the Italian Land Use Inventory System (IUTI estimates). We compared these two different AR rates with eight regional forest inventories and three independent local studies. The average annual C stock change estimated by CBM, excluding harvest or natural disturbances, was equal to 1738 Gg C yr-1 (official estimates) and 630 Gg C yr-1 (IUTI estimates). Results for the official estimates are consistent with the estimates reported by Italy to the KP for the period 2008-2010; for 2011 our estimates are about 20% higher than the country’s data, probably due to different assumptions on the fire disturbances, the AR rate and the dead wood and litter pools. Furthermore, our analysis suggests that: (i) the impact on the AR sink of different assumptions of species composition is small; (ii) the amount of harvest provided by AR has been negligible for the past (< 3%) and is expected to be small in the near future (up to 8% in 2020); (iii) forest fires up to 2011 had a small impact on the AR sink (on average, < 100 Gg C yr-1). Finally the comparison of the historical AR rates reported by NIR and IUTI with other independent sources gives mixed results: the regional inventories support the AR rates reported by the NIR, while some local studies suggest AR rates somehow intermediate between NIR and IUTI. In conclusion, this study suggests that the CBM can be applied at country level to estimate the C stock changes resulting from AR, including the effect of harvest and fires, though only a comparison with results based on direct field measurements could verify the model’s capability to estimate the real C stock change.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".