Bibliographic record
Abstract
With cities accounting for half the world's population today, and two-thirds of global energy demand, urbanization is exacting a serious toll on the environment. As rapid urban growth continues, energy use in cities and associated levels of greenhouse gas (GHG) emissions are projected to continue unabated; current projections indicate that approximately 70 percent of the world's population will live in cities by 2050, producing some 80 percent of the world's GHG emissions. Unfortunately, most of this urban growth will take place in developing countries, where the vast majority of people remain underserved by basic infrastructure service and where city authorities are under-resourced to shift current trajectories. Further, the developing regions of Africa and Asia are where the most rapid urbanization is taking place, and they are least able to cope with the uncertainties and extremities of climate impacts. The development and mainstreaming of energy-efficient and low-carbon urban pathways that curtail climate impacts without hampering the urban development agenda thus are essential to meeting such challenges. Reducing long-term energy use through efficiency also enhances energy security by decreasing dependence on imported and fossil fuel. In addition, lower energy costs free up a city's resources to improve or expand services while providing important local co-benefits, creating new jobs, enhancing competitiveness, improving air quality and health, and providing a better quality of life. The scope of the papers encapsulates all three urban contexts: new cities, expanding cities, and retrofitting existing cities. The range of policy-relevant conceptual tools and practices discussed during the sessions, and subsequently built upon in this volume, helps achieve a better understanding of leverage points for energy-efficiency interventions and helps catalyze solutions that will delink high levels of carbon-intensive energy use from urban growth without compromising local development priorities.
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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 | 0.030 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.034 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".