SSPCR 2025 - Smart and Sustainable Planning for Cities and Regions Conference
Bibliographic record
Abstract
For a decade the SSPCR International Conference has been shaping the future of urban and regional development. The 2025 edition brought together visionaries, researchers, policymakers, and stakeholders to explore sustainable and digital transformations in cities and regions worldwide. The Smart and Sustainable Planning for Cities and Regions (SSPCR) 2025 conference was held in Bolzano, Italy, from 9 to 12 December 2025, organized by Eurac Research at the NOI Techpark. The conference brought together the international scientific and professional community to discuss pathways for more sustainable, resilient, and inclusive cities and regions. SSPCR 2025 gathered 269 on-site participants from 47 countries and featured a rich programme including 7 thematic tracks, 16 special sessions, and 7 events. The discussions addressed the interconnected challenges of climate neutrality, energy transition, mobility systems, social justice, governance, circular economy, and the growing role of data and artificial intelligence in urban and regional planning. The seven thematic tracks covered key domains shaping contemporary planning debates: energy transition towards climate-neutral cities; adaptive regeneration strategies; sustainable mobility systems; just and inclusive urban governance; economics and valuation of the urban energy transition; circular economy approaches; and data- and AI-powered territories for planning and management.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 0.022 |
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".