Multi-Vector Approach to Cities’ Transition to Low-Carbon Emission Developments
Bibliographic record
Abstract
Globally, cities have made efforts to shift to low-carbon emission development (LED), amidst air pollution, greenhouse gas (GHG) emissions, and high temperature anomalies. However, the emphasis on cities to help shift the global economy to LED has been on a single individual sector approach operating in silos rather than the inter and intra-specific outcomes of multiple sectors. Thus, there are uncertainties of adopting suitable pathways for cities’ transition to LED, due largely to data paucity and policy incoherence, constrained further by barriers to integrating science, policy, and practice. Hence, the need for cities to take advantage of the benefits of multi-directional perspectives of multiple sectors acting together—the “multi-vector” approach, to confront key questions of climate compatible development (CCD) that support LED. Therefore, the paper extends the development narratives of the CCD approach to an “enhanced” climate compatible development (EnCCD) pathway with in-built questions and determinants to scope cities’ transition to LED. The EnCCD suggests that the standalone intersection between mitigation and development to deliver LED will not result in cities’ resilience unless (i) co-benefits, which are outcomes of mitigation and adaptation, and (ii) climate-resilient development, the product of adaptation and development, coevolved. Therefore, the EnCCD transforms the development policy focus of cities on separate, single-purpose sectors, such as energy or transport, into multi-sector portfolios having synergistic benefits of mitigation, adaptation, and development strategies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".