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Record W1558735001 · doi:10.24857/rgsa.v3i3.182

Conflitos Socioambientais envolvendo Projetos de Mecanismo de Desenvolvimento Limpo (MDL) na América Latina

2009· article· pt· W1558735001 on OpenAlexaff
Andréa Cardoso Ventura, José Celio Silveira Andrade

Bibliographic record

VenueRevista de Gestão Social e Ambiental · 2009
Typearticle
Languagept
FieldEnvironmental Science
TopicEnvironmental Sustainability and Education
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHumanitiesPolitical scienceChemistryPhysicsPhilosophy

Abstract

fetched live from OpenAlex

O Protocolo de Kyoto surge em 1997 como uma pretensa solução para o aquecimento global, Apenas um dos seus mecanismos permite a participação direta dos países em desenvolvimento: o Mecanismo de Desenvolvimento Limpo (MDL). A proposta deste mecanismo é que os países em desenvolvimento possam contribuir para a redução de gases do efeito estufa usando financiamento dos países desenvolvidos e, ao mesmo tempo, promovam o desenvolvimento sustentável. No entanto, não há um consenso os atores sociais envolvidos sobre a eficácia dos projetos MDL. Um número crescente de ONGs os critica, argumentando que não há contribuição para o meio ambiente global e para o desenvolvimento sustentável com o MDL. Este trabalho apresenta os resultados de uma investigação que analisou, através de um estudo de caso comparativo, dois diferentes projetos MDL na América Latina: o Projeto Plantar, no Brasil, e o Projeto Fray Bentos de Biomassa, no Uruguai. Os casos têm pelo menos um ponto comum: ambos envolvem conflitos socioambientais entre empresas privadas e ONGs sobre plantações de eucalipto em escala industrial. Através de revisão bibliográfica e documental, entrevistas com os principais atores envolvidos em cada caso, e da observação não participante, este artigo tenta analisar as principais semelhanças e diferenças entre estes conflitos. Observa-se que, não obstante as diferenças marcantes existentes, os casos são ligados em aspectos-chave, a exemplo da contestação ao modelo de desenvolvimento apoiado pelos projetos de MDL e da forma de contestação utilizada pelos integrantes do movimento social ambientalista de oposição.Palavras-chave: Conflitos socioambientais; Mecanismo de Desenvolvimento Limpo (MDL); América Latina.AbstractThe Kyoto Protocol comes up in 1997 as a supposed solution to global warming. Only one of its mechanisms allows direct participation of developing countries: the Clean Development Mechanism (CDM). The purpose of this mechanism is that developing countries can contribute to reduce Greenhouse Gas Emissions using funding from developed countries and, at the same time, promote sustainable development. However, there is not a consensus on CDM projects effectiveness among the social actors involved. A growing number of NGOs criticize them, arguing that there is not any contribution to the global environment and sustainable development with the CDM. This paper presents the results of an investigation that examined, through a comparative case study, two different CDM projects in Latin America: Plantar Project in Brazil and the Project Biomass Fray Bentos in Uruguay. The cases have at least one point in common: both involve social and environmental conflicts between private companies and NGOs on eucalyptus plantations in industrial scale. Through literature and documentary review, interviews with key actors involved in each case, and non-participant observation, this article attempts to analyze the similarities and differences between these conflicts. It is observed that, despite marked differences, the cases are linked to key aspects, such as the challenge to the development model supported by the CDM projects and the contestation methods used by the social environmentalist movement of opposition.Keywords: Social Environmental Conflicts; Clean Development Mechanisms (CDM); Latin America.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.276
Teacher spread0.263 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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