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Record W7133026549

Global Warming and Production Constraints in the Nordic Countries : Carbon Farming as an opportunity for European Forestry

2024· dissertation· en· W7133026549 on OpenAlexaboutno aff
Sean Edward Cappone

Bibliographic record

VenueTrepo - Institutional Repository of Tampere University · 2024
Typedissertation
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasGlobal warmingSustainabilityProduction (economics)Climate changeCarbon priceCarbon creditCarbon taxAgriculture
DOInot available

Abstract

fetched live from OpenAlex

Forestry has been playing an important role in Nordic economies for centuries. With the dramatic acceleration of global warming, forests have increasingly played a strategic role not only as biomass sources but also as carbon sinks. What is slowing down the scale-up of reforestation programs and the sale of carbon credits from nature-based solutions? Why are not enough financial resources dedicated to nature-based technologies? Forests are generally considered temporary capture and storage solutions, while the market is looking in the direction of long-lasting or permanent storage technologies. Fraud and double-counting have jeopardised the Voluntary Carbon Market (VCM), reducing market acceptancy for nature-based credits. When it comes to decision-making, economies are under increasing pressure, as the “polluter pays” principle can undermine an already fragile system. Production constraints and emissions reductions are the two faces, of an increasingly petty coin under inflationary stress. The EU ETS system was designed to address a problem caused by the markets, through the markets. Its efficacy is still an open question with no clear answer. Taxonomy pressures have been increasing industrial sensitivity to the sustainability discussion, driving trends towards energy efficiency and emission reductions. In the last decades, in Europe, important GHG emission decrements have been successfully achieved, especially in the leading economies. Still, global emissions, in absolute terms, keep growing year after year. In 2023 we long-breached the tragic limit of 1.5°C. Oceans registered the highest average temperature on record while biodiversity keeps shrinking across the majority of the ecosystems. At the end of 2024, politics still fail, on a global scale, to address consistently the emergency. The COP29 ended up as an oil lobby gathering rather than a climate and biodiversity table. At the New Your Climate Week, the discussion was still stuck on the technicalities, instead of designing action plans. All this while greenwashing is widespread and hard to tackle. Insurance costs are spiking due to climate change and the decades-old financial forecasts are turning out to be too optimistic to hedge consistently properties, facilities and the food production sector. With the right technological and political tools, Afforestation, Reforestation and Proforestation may play a key role in supporting the global green transition by providing us with precious time. Forests are a powerful, scalable and financially sustainable alternative to expensive and slow-to-implement technology-based solutions. Biodiversity restoration through ARR solutions would help address the Kunming-Montreal agreement objectives. Innovative financial solutions joining strengths with sustainable forestry management can be crucial to the successful development of the Carbon Reduction and Carbon Farming framework from the EU. In this paper, we will present how project development protocols, if resilient and redundant enough, can play a crucial role in permanent carbon sequestration, ecosystem services restoration and independence from faulty legacy Validation and Verification Bodies. The proposed protocol provides solid guidelines for Project Developers while creating the grounds for financially sustainable long-term carbon pools. Innovative Forest Management practices will guarantee the local communities a stable regular source of high-quality biomass while generating an income from carbon sequestration and storage in permanent forests. Integration of modern validation and verification technologies and techniques, such as aerial and satellite LiDAR and thermal imagery, have increased in accessibility. This will have them playing a critical role in restoring credibility. Transparency, Traceability and Transmissibility of the information.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.634

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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