Spatiotemporal Distribution and Driving Factors of Forest Biomass Carbon Storage in China: 1977–2013
Bibliographic record
Abstract
Increasing forest vegetation is important for carbon dynamics and to maintain the ecological and environmental balance in China. However, there is little understanding of how socioeconomic factors affect forest biomass carbon storage (FBCS). Here, we used continuous functions for biomass expansion factors and China’s seven completed forest inventories to estimate the changes in FBCS for 31 provinces in mainland China between 1977 and 2013. We developed a model that decomposes the contribution of the different socioeconomic factors driving FBCS. We found China’s FBCS increased from 4972 TgC (1 Tg = 1012g) in 1977–1981 to 7435 TgC in 2009–2013, with a mean growth of 77 TgC/a, and the average forest carbon density increased from 36.0 to 38.9Mg/ha (1 Mg = 106g), mainly due to the arbor forest contribution. Among the seven regions in China, the southwestern region currently accounts for the highest proportion (37.3%) of national FBCS, followed by northeastern (19.7%), northern (12.5%) and eastern region (10.8%). The main socio-economic factors affecting FBCS were forest land dependence, industrial structure and economic development level. Optimizing forest type and age structure, improving forest productivity, and strengthening forest management are feasible options to further increase China’s FBCS.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".