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Record W4223634747 · doi:10.1088/1748-9326/ac661b

Net carbon accounting and reporting are a barrier to understanding the mitigation value of forest protection in developed countries

2022· article· en· W4223634747 on OpenAlexaboutno aff
Brendan Mackey, William R. Moomaw, David B. Lindenmayer, Heather Keith

Bibliographic record

VenueEnvironmental Research Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
FundersRockefeller Brothers Fund
KeywordsGreenhouse gasDeforestation (computer science)Climate change mitigationNatural resource economicsLoggingClimate changeCarbon accountingIllegal loggingBusinessEnvironmental scienceEnvironmental protectionEnvironmental resource managementGeographyForestryEconomics

Abstract

fetched live from OpenAlex

Abstract Meeting the Paris Agreement global warming target requires deep and rapid cuts in CO 2 emissions as well as removals from the atmosphere into land sinks, especially forests. While international climate policy in the land sector does now recognize forest protection as a mitigation strategy, it is not receiving sufficient attention in developed countries even though they experience emissions from deforestation as well as from logging of managed forests. Current national greenhouse gas inventories obscure the mitigation potential of forest protection through net carbon accounting between the fossil fuel and the land sectors as well as within the different categories of the land. This prevents decision-makers in national governments, the private sector and civil society having access to all the science-based evidence needed to evaluate the merits of all mitigation strategies. The consequences of net carbon accounting for global policy were investigated by examining annual inventory reports of four high forest cover developed countries (Australia, Canada, USA, and Russia). Net accounting between sectors makes a major contribution to meeting nationally determined contributions with removals in Forest Land offsetting between 14% and 38% of the fossil fuel emissions for these countries. Analysis of reports for Australia at a sub-national level revealed that the State of Tasmania delivered negative emissions due to a change in forest management—a large and rapid drop in native forest logging—resulting in a mitigation benefit of ∼22 Mt CO 2 -e yr –1 over the reported period 2011/12–2018/19. This is the kind of outcome required globally to meet the Paris Agreement temperature goal. All CO 2 emissions from, and atmospheric removals into, forest ecosystem carbon stocks now matter and should be counted and credited to achieve the deep and rapid cuts in emissions needed over the coming decades. Accounting and reporting systems therefore need to show gains and losses of carbon stocks in each reservoir. Changing forest management in naturally regenerating forests to avoid emissions from harvesting and enabling forest regrowth is an effective mitigation strategy that can rapidly reduce anthropogenic emissions from the forest sector and simultaneously increase removals of CO 2 from the atmosphere.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.054
GPT teacher head0.294
Teacher spread0.240 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations35
Published2022
Admission routes1
Has abstractyes

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