Green Diplomacy at the Crossroads of International Law and International Relations
Bibliographic record
Abstract
This article examines the evolution of green diplomacy as a central instrument at the nexus of international law and international relations, highlighting its transformative role in contemporary global governance. While environmental diplomacy historically revolved around treaty negotiation and compliance, recent developments indicate a shift toward using environmental objectives as mechanisms of geopolitical influence, strategic cooperation, and economic leverage. Drawing on foundational legal instruments, including the UNFCCC, Kyoto Protocol, Paris Agreement, Montreal Protocol, CBD, CITES, UNCLOS, the Stockholm Declaration, and the 2030 Agenda, this study demonstrates how international law establishes the normative and institutional foundations of climate action, while diplomatic processes operationalize these commitments within political practice. Through integrated case studies, the article analyzes the dynamics of EU–China climate collaboration and competition, the United States’ withdrawal and return to the Paris Agreement, climate justice diplomacy led by Small Island Developing States, and the Carbon Border Adjustment Mechanism as a tool of green trade. The research reveals that green diplomacy now extends far beyond environmental protection, shaping global power structures, trade regimes, financial flows, and security agendas. Ultimately, the findings suggest that green diplomacy is emerging as a multidimensional governance framework capable of reconfiguring international relations in an era defined by climate urgency, technological transition, and heightened environmental interdependence.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.050 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".