MétaCan
Menu
Back to cohort
Record W4410184464 · doi:10.1111/gcb.70219

Role of Forest Carbon Change in Shaping Future Land Use and Land Cover Change

2025· article· en· W4410184464 on OpenAlexafffundabout
Meng Luo, Xin Zhao, Dalei Hao, Ben Bond‐Lamberty, Adam Daigneault, Pralit Patel, Sian Kou‐Giesbrecht, Christopher Reyer, Hamid Dashti, Min Chen

Bibliographic record

VenueGlobal Change Biology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaScience Mission DirectorateNuclear Safety and Security CommissionNational Aeronautics and Space Administration
KeywordsEnvironmental scienceLand use, land-use change and forestryGlobal changeClimate changeBiogeochemical cycleSoil carbonLand useLand coverCarbon cycleCarbon sequestrationVegetation (pathology)Atmospheric carbon cycleGreenhouse gasAgroforestryEcologyEcosystemSoil scienceSoil waterCarbon dioxide

Abstract

fetched live from OpenAlex

ABSTRACT Global change, particularly the changes in atmospheric CO2 concentration, climatic variables, and nitrogen deposition, has been widely recognized and examined to have worldwide impacts on forest carbon. However, its influence on forest area required to meet the demand for timber and carbon storage and subsequent land use and land cover change (LULCC) is rarely studied. This study explores the role of global change‐driven forest carbon change in shaping future global LULCC projections and investigates underlying drivers. We incorporated the global change impacts on forest carbon from the Canadian Land Surface Scheme Including Biogeochemical Cycles model simulations (driven by meteorological forcing projections from two Earth system models [ESMs]) into the Global Change Analysis Model, under three combinations of shared socioeconomic pathways and representative concentration pathways (SSP126, SSP370, and SSP585). Including forest carbon change decreases the projected expansion of managed forest and managed pasture, reduces the loss of unmanaged pastures and forests, and provides more cropland. The relative change in managed forest by 2100 is −4.0%, −21.7%, and −31.9%, under SSP126, SSP370, and SSP585, respectively, when forest carbon change is considered. CO2 fertilization is the dominant driver, increasing forest vegetation and soil carbon by 37% and 4.1%, and leading to 78.6% of the total area with a change in land use types by 2100 under SSP585. In comparison, climate change reduces forest vegetation and soil carbon by −3.5% and −0.8%, influencing 23.9% of the total area with a change in land use types by 2100 under SSP585, while nitrogen deposition has minor impacts. Using meteorological forcing data from two ESMs leads to similar impacts of forest carbon change on LULCC in terms of sign and trend but different magnitudes. This study highlights the large impact of forest carbon change on shaping future LULCC dynamics and the critical role of CO2 fertilization.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.772

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.035
GPT teacher head0.271
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueGlobal Change BiologySame topicForest Management and PolicyFrench-language works237,207