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Enregistrement W2057659133 · doi:10.1088/1748-9326/2/4/045021

Tropical deforestation and greenhouse gas emissions

2007· article· en· W2057659133 sur OpenAlexaboutno aff
Holly K. Gibbs, Martin Herold

Notice bibliographique

RevueEnvironmental Research Letters · 2007
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueConservation, Biodiversity, and Resource Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGreenhouse gasUnited Nations Framework Convention on Climate ChangeDeforestation (computer science)Environmental scienceClimate changeGlobal warmingNatural resource economicsKyoto ProtocolConference of the partiesClearingCarbon sinkCarbon creditClimate change mitigationEnvironmental protectionBusinessEconomicsEcology

Résumé

récupéré en direct d'OpenAlex

Carbon emissions from tropical deforestation have long been recognized as a key component of the global carbon budget, and more recently of our global climate system. Tropical forest clearing accounts for roughly 20% of anthropogenic carbon emissions and destroys globally significant carbon sinks (IPCC 2007 ). Global climate policy initiatives are now being proposed to address these emissions and to more actively include developing countries in greenhouse gas mitigation (e.g. Santilli et al 2005 , Gullison et al 2007 ). In 2005, at the Conference of the Parties (COP) in Montreal, the United Nations Framework Convention on Climate Change (UNFCCC) launched a new initiative to assess the scientific and technical methods and issues for developing policy approaches and incentives to reduce emissions from deforestation and degradation (REDD) in developing countries (Gullison et al 2007 ). Over the last two years the methods and tools needed to estimate reductions in greenhouse gas emissions from deforestation have quickly evolved, as the scientific community responded to the UNFCCC policy needs. This focus issue highlights those advancements, covering some of the most important technical issues for measuring and monitoring emissions from deforestation and forest degradation and emphasizing immediately available methods and data, as well as future challenges. Elements for effective long-term implementation of a REDD mechanism related to both environmental and political concerns are discussed in Mollicone et al . Herold and Johns synthesize viewpoints of national parties to the UNFCCC on REDD and expand upon key issues for linking policy requirements and forest monitoring capabilities. In response to these expressed policy needs, they discuss a remote-sensing-based observation framework to start REDD implementation activities and build historical deforestation databases on the national level. Achard et al offer an assessment of remote sensing measurements across the world's tropical forests that can provide key consistency and prioritization for national-level efforts. Gibbs et al calculate a range of national-level forest carbon stock estimates that can be used immediately, and also review ground-based and remote sensing approaches to estimate national-level tropical carbon stocks with increased accuracy. These papers help illustrate that methodologies and tools are indeed available to estimate emissions from deforestation. Clearly, important technical challenges remain (e.g. quantifying degradation, assessing uncertainty, verification procedures, capacity building, and Landsat data continuity) but we now have a sufficient technical base to support REDD early actions and readiness mechanisms for building national monitoring systems. Thus, we enter the COP 13 in Bali, Indonesia with great hope for a more inclusive climate policy encompassing all countries and emissions sources from both land-use and energy sectors. Our understanding of tropical deforestation and carbon emissions is improving and with that, opportunities to conserve tropical forests and the host of ecosystem services they provide while also increasing revenue streams in developing countries through economic incentives to avoid deforestation and degradation. References Gullison R E et al 2007 Tropical forests and climate policy Science 316 985–6 Intergovernmental Panel on Climate Change (IPCC) 2007 Climate Change 2007: The Physical Science Basis: Summary for Policymakers http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-spm.pdf Santilli M et al 2005 Tropical deforestation and the Kyoto Protocol: an editorial essay Clim. Change 71 267–76 Focus on Tropical Deforestation and Greenhouse Gas Emissions Contents Pan-tropical monitoring of deforestation F Achard, R DeFries, H Eva, M Hansen, P Mayaux and H-J Stibig Monitoring and estimating tropical forest carbon stocks: making REDD a reality Holly K Gibbs, Sandra Brown, John O Niles and Jonathan A Foley Elements for the expected mechanisms on 'reduced emissions from deforestation and degradation, REDD' under UNFCCC D Mollicone, A Freibauer, E D Schulze, S Braatz, G Grassi and S Federici Linking requirements with capabilities for deforestation monitoring in the context of the UNFCCC-REDD process Martin Herold and Tracy Johns Reference scenarios for deforestation and forest degradation in support of REDD: a review of data and methods Lydia P Olander, Holly K Gibbs, Marc Steininger, Jennifer J Swenson and Brian C Murray Applying the conservativeness principle to REDD to deal with the uncertainties of the estimates Giacomo Grassi, Suvi Monni, Sandro Federici, Frederic Achard and Danilo Mollicone Identifying optimal areas for REDD intervention: East Kalimantan, Indonesia as a case study Nancy L Harris, Silvia Petrova, Fred Stolle and Sandra Brown A first map of tropical Africa's above-ground biomass derived from satellite imagery A Baccini, N Laporte, S J Goetz, M Sun and H Dong

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil0,035

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,001
Communication savante0,0020,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0100,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,024
Tête enseignante GPT0,265
Écart entre enseignants0,240 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations43
Publié2007
Routes d'admission1
Résumé présentoui

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