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Record W1482536848

A Tale of Three Signatories: Learning from the European Union, Japanese, and Canadian Kyoto Experiences in Crafting a Superior United States Climate Change Regime

2009· article· en· W1482536848 on OpenAlexaboutno aff
Andrew B. Schatz

Bibliographic record

VenueSSRN Electronic Journal · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Systems and Judicial Processes
Canadian institutionsnot available
Fundersnot available
KeywordsKyoto ProtocolGreenhouse gasEuropean unionClimate changeEmissions tradingClimate change mitigationRenewable energyPolitical scienceInternational tradeBusinessEconomyEngineeringEconomics
DOInot available

Abstract

fetched live from OpenAlex

As we enter the compliance years of the Kyoto Protocol, the world waits to see whether the first global effort at combating climate change will be deemed a success or failure. While there may be no clear answer to that question, an analysis of various nations’ efforts at compliance reveals a wide spectrum of approaches and obstacles to tackling this enormous challenge. This Article explores the varying successes and failures of three signatories - the European Union, Japan, and Canada - in meeting their obligations to reduce greenhouse gas emissions under the Kyoto Protocol. The author critically analyzes the European, Japanese, and Canadian experiences, applying the lessons learned to more effectively structure a superior U.S. climate change regime. Most significantly, the author concludes that the United States would be wise to follow Europe’s holistic model of economy-wide regulation, yet is wholly unprepared to implement an aggressive climate plan without transforming its economy first. Based on other nations’ experiences, this Article concludes that the United States should implement a greenhouse gas registry, establish stringent regulations capable of withstanding shifts in political currents, and auction rather than allocate emissions credits. To meet these challenges, the United States must upgrade its electric grid to support a renewable energy infrastructure, facilitate the growth of nuclear and natural gas co-generation power plants as a transitional alternative base-load power source to coal, and invest heavily in the technologies of tomorrow.

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)0.004
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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 designQualitative
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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