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Record W1605998974

Legal Preparedness for the Global Green Economy

2012· article· en· W1605998974 on OpenAlexaboutno aff
Markus W. Gehring

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
Fundersnot available
KeywordsPreparednessBusinessEconomyPolitical scienceEconomicsLaw
DOInot available

Abstract

fetched live from OpenAlex

As argued in the 2011 UN Green Economy Report, the world currently faces an important opportunity to move from current resource depleting, polluting and wasteful forms of economic growth to a new, low-carbon, sustainable green economy. New markets and industries are emerging, for clean renewable energy, environmental goods and services, local organic agriculture, and payment for ecosystem services. International economic law is evolving rapidly, and could foster rather than frustrate this shift. With more than 350 trade treaties, over 2,700 investment accords, new international disputes on resources, environment and development issues, and regimes like the UN Climate Convention Cancun Agreement or the UN Biodiversity Convention Nagoya Protocol adopting new economic instruments, it remains somewhat unclear how international rules can guide governments and markets. Canada, the EU and many developing country partners have committed to sustainable development globally. They are actively negotiating new Free Trade Agreements (FTAs) & Foreign Investment Protection Agreements (FIPAs). In preparation for the 2012 United Nations Conference on Sustainable Development (UN CSD), where countries will report their progress in developing policies and laws for the green economy, this paper aims to advance our international understanding of opportunities, challenges and also limits of law and policy instruments to support development of a global green economy. The project builds on my SSHRC research and outreach project for Sustainable Prosperity where I analyzed the international economic legal framework for market-based instruments (MBIs) supplemented by case studies of actual experiences with environmental pricing reform measures for low-carbon development, also forest ecosystems and biodiversity stewardship. I explained how the rules of WTO legal regime (including recent dispute settlement outcomes) have a profound impact on MBIs. I have also been awarded a SSHRC workshop grant and aim to share some of the workshop results with IUCN members in July. This research builds upon previous studies published in my recent work with Kluwer Law International- Sustainable Development in World Trade Law and in Sustainable Development in World Investment Law.

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)0.019
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0070.023
Scholarly communication0.0120.014
Open science0.0020.007
Research integrity0.0120.019
Insufficient payload (model declined to judge)0.0100.002

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 head0.026
GPT teacher head0.332
Teacher spread0.306 · 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 designTheoretical or conceptual
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
Published2012
Admission routes1
Has abstractyes

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