Standards and SSOs in the contested widening and deepening of financial markets: The arrival of Green Municipal Bonds in Mexico City
Bibliographic record
Abstract
Particularly since the financial crisis of 2008, much has been written about the growing influence of finance in the development of cities in the global North. Today, financial markets also appear to be expanding southwards. A small but growing number of existing studies on the financialization of Southern cities helpfully explore how international investment changes urban development locally. Yet, they say less about the actors, procedures, and hurdles through which global market expansion is forged and expanded in the first place. In this paper, we examine the role of standards and standards-setting organizations in fostering market expansion and financial deepening. In particular, we highlight the efforts of standards-setting organizations in implementing a novel financial tool, green municipal bonds. Green municipal bonds are debt instruments that allow cities to raise capital through the issuance of bonds exclusively for investment in projects certified as sustainable. First employed by European, Canadian, and US cities, the paper traces the processes that led up to the issuance of these bonds in Mexico City, one of the first municipalities in the so-called ‘emerging markets’ to issue green municipal bonds. Our findings indicate that standards barely impacted project implementation; nevertheless, with the political efforts of standards-setting organizations, standards worked as vehicles through which infrastructures of markets, knowledge, and political support were built, legitimized, and secured. While these processes widened and deepened financialization, they also encountered challenges in terms of the long-term stabilization of these infrastructures and the upkeep of political support.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".