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Record W2517075421

Understanding conditions for cooperation when regulating common resources: Why is climate change so difficult to govern?

2014· dissertation· en· W2517075421 on OpenAlexaboutno aff
Bojana Arsenovic

Bibliographic record

VenueBIBSYS Brage (BIBSYS (Norway)) · 2014
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeComputer scienceEnvironmental resource managementBusinessEnvironmental scienceBiologyEcology
DOInot available

Abstract

fetched live from OpenAlex

International community has had little success in solving the problem of governing shared natural resources, of which the changing climate is the most prominent example. I wanted to know why. To answer this question I explored the conditions for achieving collective goals and providing for public goods, using the theory of collective action as a tool for analysis. First I examined collective action conditions in local settings, by looking into research conducted on local level natural resource management. Based on insights from the local settings I further investigated conditions for collective action in the international system. That I did by presenting the fundamental differences between the Montreal Protocol and the Kyoto Protocol, setting out an overview of existing international cooperation on climate change. Due to the anarchical nature of the international system with no central authority to enforce laws, agreements between states cannot be legally binding, which gives the states strong incentive to free-ride. In a system with economy based on competition where states are primarily interested in growth, the prospects for successful joint efforts to curb dangerous climate change seem rather gloomy.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.210
GPT teacher head0.297
Teacher spread0.087 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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