Daya Tarik Kerjasama Perjanjian Iklim California-Quebec bagi Pemerintah Daerah di Amerika Utara
Bibliographic record
Abstract
Perjanjian karbon California-Quebec menunjukkan bagaimana mekanisme perdagangan karbon regional dapat memberikan manfaat bagi negara bagian dan provinsi. Kesepakatan ini menggabungkan rencana perdagangan emisi karbon Cap-and-Trade California dengan sistem Quebec untuk menciptakan pasar karbon gabungan terbesar di Amerika Utara. Pasar gabungan ini meningkatkan efisiensi sekaligus menurunkan biaya kebijakan bagi kedua pemerintah Quebec dan California. Artikel ini bertujuan untuk mengetahui sejauh mana kerjasama California-Quebec mampu menjadi model baru bagi pemerintah daerah di regional Amerika Utara dalam upaya mengimplementasikan kebijakan iklim regional melalui hubungan luar negeri. Penelitian ini menggunakan penelitian kualitatif dengan tipe analisis deskriptif. Dalam penelitian ini, digunakan teknik pengumpulan data sekunder dengan mengambil berbagai referensi yang relevan dan metode studi literatur. Penelitian ini menggunakan teori paradiplomasi transregional dan teori paradiplomasi hijau untuk menganalisis topik yang dibahas. Hasil dari penelitian menunjukkan bahwa keberhasilan Quebec dan California dalam menciptakan kestabilan harga karbon melalui perjanjian iklim mampu menarik negara lain di kawasan regional Amerika Utara untuk ikut berkontribusi dalam upaya mengatasi perubahan iklim. Kata Kunci: Amerika Utara, California, Quebec, Perubahan Iklim, Emisi Karbon.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.004 |
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".