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Record W1631240603

Capabilities and deficiencies of terrestrial forest inventory systems in the assessment of forest degradation in the scope of REDD

2013· article· en· W1631240603 on OpenAlexaboutno aff
Daniel Plugge

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsDeforestation (computer science)Reducing emissions from deforestation and forest degradationUnited Nations Framework Convention on Climate ChangeClimate changeGlobal warmingEnvironmental resource managementSustainable forest managementForest degradationGreenhouse gasNatural resource economicsEnvironmental scienceHarmonizationBusinessForest managementClimate change mitigationEnvironmental protectionAgroforestryGeographyCarbon stockLand degradationKyoto ProtocolEcologyEconomicsAgriculture
DOInot available

Abstract

fetched live from OpenAlex

Despite the well-known importance to human society and environment, the deforestation and degradation of forests continues at an alarming rate. The many efforts made in the past to reduce the loss and destruction of forests, have had no significant impact on the negative development of forest areas. In the light of global climate change, the role of forests as the largest terrestrial carbon reservoir and in the global carbon cycle has gained relevance. A new mechanism to reduce the deforestation and degradation of tropical forests called REDD (Reducing Emissions from Deforestation and Forest Degradation) was introduced to the climate negotiations under the United Nations Framework Convention on Climate Change (UNFCCC) in Montreal in 2005. REDD aims to assign a monetary value to the carbon stored in forests and to reward developing countries for the reduction of their deforestation and forest degradation rates. Since the first submission, the debate on scientific, political and public levels has led to many valuable amendments to the initial proposal, including the integration of the conservation of forest carbon stocks, the sustainable management of forests and the enhancement of forest carbon stocks, nowadays termed REDD+. While the initial concept of REDD appears simple, its implementation requires the careful harmonization of a multitude of topics. The following comprehensive summary presents the major topics that need to be considered for a successful implementation of REDD+. One of these topics is the establishment of a Measuring, Reporting and Verification (MRV) system. The three articles that constitute this cumulative dissertation focus on the development and characteristics of a MRV system to assess changes in forest area and forest carbon stock. The first part of the comprehensive summary presents the thematic context of the three articles. This thematic context consists of the major topics associated with the implementation of REDD+. It introduces the initial concept of REDD and presents main terms and definitions. Then, the ‘Framework of REDD+’ focuses on the ‘Drivers of Deforestation and Forest Degradation’, the implementation of REDD+ as a ‘Phased Approach’, the ‘Financial Aspects of REDD+’, the possibilities and implications of setting ‘Forest reference (emission) levels’ and the ‘Safeguards’, concluded by explanations of ‘Additionality, Permanence and Leakage’. After this, the summary presents the ‘Core issues of REDD+ for the thesis’. These are ‘Deforestation’, ‘Forest Degradation’, ‘Measuring, Reporting and Verification (MRV) for REDD+’ and ‘Uncertainties’. These issues form the pillars on which the three articles of this cumulative dissertation are based. The second part of the comprehensive summary integrates the three articles into the thematic context. The first article focusses on the development and implementation of a combined inventory approach for REDD+. It demonstrates how reliable estimates of forest carbon stocks and its changes over time can be made with the introduced methodology. The second article analyses the influence of uncertainties and monitoring costs on the accountability of emission reductions. It shows that uncertainties may outweigh successful efforts in the reduction of emission and that the influence of uncertainties on the successful implementation of REDD+ is higher than the influence of the price paid per ton of carbon. The third article focuses on the influence of uncertainties at two points in time and different sizes of forest degradation areas on accountable emission reductions. It shows that rules for the propagation of uncertainties from one period to another are needed and that the size of forest degradation areas has a major impact on the accountability of emission reductions. Each article is presented with a short summary of the applied methods and gained results, followed by a discussion of the respective article in the thematic context, showing the implications and recommendations of the findings for the issues presented in the first part. On the basis of the articles and their discussion in the thematic context, specific conclusions on the capabilities and the deficiencies of terrestrial forest inventory systems in the assessment of forest degradation in the scope of REDD+ are drawn. The first part of the conclusions shows that terrestrial forest inventory techniques are capable of assessing and estimating forest degradation, and readily available for the implementation of a MRV system. The second part shows that there are deficiencies in the applicability of these techniques such as high costs or difficulties in assessing remote areas and that terrestrial forest inventories alone cannot provide the required level of completeness for a credible MRV system. The comprehensive summary is completed by a general conclusion on ways to overcome the remaining deficiencies. The complete versions of the three articles are attached in the Annex.

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)0.047
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.007
Science and technology studies0.0010.002
Scholarly communication0.0100.007
Open science0.0020.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.252
Teacher spread0.230 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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