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Record W2563210553 · doi:10.5194/bg-14-2387-2017

The European forest sector: past and future carbon budget and fluxes under different management scenarios

2017· article· en· W2563210553 on OpenAlexaff
Roberto Pilli, Giacomo Grassi, Werner A. Kurz, Giulia Fiorese, Alessandro Cescatti

Bibliographic record

VenueBiogeosciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsEnvironmental sciencePrimary productionClimate changeCarbon sinkGreenhouse gasForest managementBiomass (ecology)ChronosequenceProductivityAfforestationAtmospheric sciencesAgroforestryEcologyEcosystemBiology

Abstract

fetched live from OpenAlex

Abstract. The comprehensive analysis of carbon stocks and fluxes of managed European forests is a prerequisite to quantify their role in biomass production and climate change mitigation. We applied the Carbon Budget Model (CBM) to 26 European countries, parameterized with country information on the historical forest age structure, management practices, harvest regimes and the main natural disturbances. We modeled the C stocks for the five forest pools plus harvested wood products (HWPs) and the fluxes among these pools from 2000 to 2030. The aim is to quantify, using a consistent modeling framework for all 26 countries, the main C fluxes as affected by land-use changes, natural disturbances and forest management and to assess the impact of specific harvest and afforestation scenarios after 2012 on the mitigation potential of the EU forest sector. Substitution effects and the possible impacts of climate are not included in this analysis. Results show that for the historical period from 2000 to 2012 the net primary productivity (NPP) of the forest pools at the EU level is on average equal to 639 Tg C yr−1. The losses are dominated by heterotrophic respiration (409 Tg C yr−1) and removals (110 Tg C yr−1), with direct fire emissions being only 1 Tg C yr−1, leading to a net carbon stock change (i.e., sink) of 110 Tg C yr−1. Fellings also transferred 28 Tg C yr−1 of harvest residues from biomass to dead organic matter pools. The average annual net sector exchange (NSE) of the forest system, i.e., the carbon stock changes in the forest pools including HWP, equals a sink of 122 Tg C yr−1 (i.e., about 19 % of the NPP) for the historical period, and in 2030 it reaches 126, 101 and 151 Tg C yr−1, assuming constant, increasing (+20 %) and decreasing (−20 %) scenarios, respectively, of both harvest and afforestation rates compared to the historical period. Under the constant harvest rate scenario, our findings show an incipient aging process of the forests existing in 1990: although NPP increases (+7 %), heterotrophic respiration increases at a greater rate (+13 %) and this leads to a decrease in the sink in the forest pools (−6 %) in 2030 compared to the historical period. By comparing the evolution of the biomass as a function of the NPP (i.e., the turnover time) for each country, we highlighted at least three groups of countries and turnover times. This means that, contrary to the assumptions proposed by other authors, this relationship cannot be assumed as a constant for all the EU countries, but specific conditions, such as the harvest rate, the current age structure and the forest composition, may contribute to the country-specific evolution of biomass stocks. The detailed picture of the C fluxes condensed in this study, and their evolution under different harvest scenarios, may represent both a benchmark for similar studies and a basis for broader analyses (e.g., including substitution effects of wood) on the mitigation potential of the EU forest sector.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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.

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

Citations61
Published2017
Admission routes1
Has abstractyes

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