Global Adaptation Law: Optimizing Legal Design for Multi-Level Public Goods after the Paris Agreement
Bibliographic record
Abstract
The increasingly severe and irreversible effects of climate change around the world make adaptation to a changing climate an immediate and urgent global priority, as the Paris Agreement on Climate Change acknowledged. Yet adaptation investment — to make communities and ecosystems more resilient to climate change — has been slow to materialize. Closing the finance gap and rising to the challenge of adaptation requires two conceptual shifts in how we think about adaptation law and governance. The first is that optimal adaptation is a public good, much like a healthy climate or safe streets. Everyone is better off in a resilient community that can thrive despite climate impacts, whether they contributed to resilience or not. This means adaptation investment will likely continue to be underprovided by the market in the absence of an effective legal regime. The second is that adaptation is not merely a local matter, though it is still largely treated as such. In several important scenarios, it will also be an international public good requiring international cooperation. Parties to the Paris Agreement seemingly recognized this when they described adaptation as a “global challenge” with “local, subnational, national, regional and international dimensions.” However, they did not consider what this means in practical terms for law and governance, and the literature is still largely silent on this issue. This Article seeks to move the analysis forward. It makes three principal contributions. First, building on economic analysis of collective action problems, externalities, and public goods, it develops a novel analytical framework to examine the adaptation challenge and similar cross-cutting legal issues. In particular, it re-conceptualizes climate adaptation as a multi-level public good — with domestic, transboundary, and global dimensions. Second, it explores the implications of this conceptual shift for institutional and legal design at each level of governance. It considers the efficacy of different market-based mechanisms (Coasean private contracting) and prescriptive regulation in the light of this framework and explores the distinctions between the domestic and the international realms. Third, it proposes a multi-level governance model that could help produce what I call “optimal adaptation” and identifies three priority areas for institution-building in the transboundary setting, which poses particular challenges for legal design. This framework will open avenues for more granular and critical study of the legal design and contracting required to rise to the challenge of multi-level public goods.
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".