Bibliographic record
Abstract
This policy paper examines how integrated urban policy, which co-ordinates policy action across sectors, levels of government, and stakeholders, can accelerate progress towards the SDGs in cities. As the 2030 Agenda deadline nears, cities across OECD countries show mixed results on the SDGs. While cities have improved on SDG 17 on partnerships for the goals and SDG 11 on sustainable cities and communities, significant gaps remain in areas linked to housing, transport, energy efficiency and climate action. This paper argues that National Urban Policies (NUPs) can help accelerate progress by aligning urban development with the SDGs. Drawing on OECD and G7 countries’ experience, the paper highlights seven priority fields where integrated urban policy can drive progress across multiple goals: affordable housing, sustainable transport, energy efficiency, digitalisation, urban regeneration, land-use planning, and green infrastructure. The paper also explores how governments can strengthen integrated urban policy by deepening multi-stakeholder engagement, ensuring public and private finance is secured and aligned, and maintaining continuous monitoring and evaluation of policies. The analysis and recommendations aim to support governments at all levels advance more integrated and effective urban policy to accelerate progress towards the SDGs and shape more resilient urban futures. The paper was prepared by the OECD to support the Italian Presidency of the G7 in 2024 and informed the discussions of the G7 Ministerial Meeting on Sustainable Urban Development in November 2024 in Rome, Italy. It also benefited from additional data, examples and reviews under the Canadian Presidency of the G7 in 2025.
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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".