Energy Efficient Cities : Assessment Tools and Benchmarking Practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it