Climate-Environmental Governance in the Mexico Valley Metropolitan Area: Assessing Local Institutional Capacities in the Face of Current and Future Urban Metabolic Dynamics
Bibliographic record
Abstract
This paper focuses on the evaluation of local institutional capacities for advancing climate-environmental governance in the Mexico Valley Metropolitan Area (MVMA). It starts with a brief contextualization of the MVMA, followed by an estimation of current and tendential urban inflows and outflows by 2050 with the objective of delineating the challenges and potential implications ahead. Next, an assessment of local climate-environmental institutional capacities is offered. For that, the methodology and main outcomes of the so-called ICI-CLIMA index is presented for 2019. A qualitative discussion continues in order to assert the challenges and opportunities for advancing a coordinated urban agenda for sustainability and resilience. Such discussion has been enriched with documental and other type of information gathered through field research in all of the 76 municipalities that comprise the MVMA. The paper concludes that, in addition to the limited current climate-environmental local capacities, there is a mismatch between them and both the level of climate vulnerability officially identified and the environmental challenges currently facing. Therefore, for coping with a tendential scenario of increasing urban inflows and outflows and their associated climate-environmental implications, MVMA governments will have to improve their capacities while advancing, at all levels of government, the coordination of climate-environmental agendas, and of the later with urban planning and development agendas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".