NET ZERO EMISSIONS AND FREE TRADE AGREEMENTS: EFFORTS AT INTEGRATING CLIMATE GOALS BY THE UNITED KINGDOM AND AUSTRALIA
Bibliographic record
Abstract
Abstract The negotiation of the free trade agreement (FTA) between Australia and the United Kingdom promised to integrate trade and climate policies. As a leader of the United Nations Framework Convention on Climate Change (UNFCCC) conference in Glasgow, the UK seemed well-placed to exert pressure on Australia, a country that was yet to embrace a target of net zero emissions by 2050. This article asks whether the FTA achieves this aim. It explains the link between trade liberalisation and climate change, referring to the scale and composition of economic activity and drawing upon examples from energy, agriculture, building and transportation sectors, as well as strategic factors. It provides an original analytical framework to assess the FTA's contributions to climate change goals, pointing to: (1) provisions to strengthen climate commitments, including net zero targets; (2) provisions to facilitate trade and investment in climate-related areas; and (3) provisions relating to enforcement and cooperation. It compares selected initiatives of other FTAs, including the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP), the European Union–Canada Comprehensive Economic and Trade Agreement (CETA), the UK–New Zealand FTA and the Singapore–Australia Green Economy Agreement. It reviews the FTA's negotiating process and its aftermath, including complaints about public participation. The article's conclusion that the FTA makes minimal contribution to climate change mitigation has implications for the broader quest for mutually supportive trade and climate policies, and, now that a net zero target has been legislated by the newly elected Australian Parliament, for the FTA's future implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".