Nexus SDG Intelligence. A meta-analysis of the world's Voluntary Local Reviews.

The world's Voluntary Local Reviews, read as one.

A structured meta-analysis of every published VLR, translated into comparable evidence about how cities set priorities, implement policy, and close the gap between what they promise and what they deliver.

450+

VLRs

60+

Countries

Regions

170,000+

Extracted items

From governance text to structured evidence.

A single VLR tells one city's story. Read together, across the full corpus, the same text becomes a comparable body of evidence about policy, challenges, and commitment at scale.

The Intelligence Engine reads each VLR and extracts three categories of signal from the same text: the challenges cities name, the commitments they make, and the policy actions they propose. Every extraction is tagged, classified, and traceable to its source.

Voluntary Local Review · City Example · 2026

4.3 Urban mobility and accessibility

Despite recent progress in integrated transport planning, the city continues to face significant constraints in the financing of public transport expansion⁰¹, particularly in the northern districts where ageing infrastructure limits service coverage.

To address this, the Plan commits to a 45% modal share for public transport by 2030⁰², supported by the extension of three metro lines and a new bus rapid transit corridor⁰³ along the eastern axis.

Implementation will be coordinated across the Mobility, Urban Planning, and Finance departments, with progress reported annually against a set of defined indicators.

p. 62

01 · Challenge p. 62

Financing constraints in public transport

Fiscal & Financial Infrastructure Regional · Northern districts

02 · Commitment p. 62

Public transport modal share target

Quantitative Target 45% by 2030 Concrete

03 · Policy Action p. 62

Metro extension and BRT corridor

Capital Investment SDG 11.2 Direct spending

A single paragraph becomes three classified extractions, each traceable to its source. Applied across 400+ VLRs, the same transformation becomes the body of evidence Nexus SDG Intelligence reads from.

Method

Three signals, one framework.

Each document is processed for challenges, commitments, and policy actions. Every extraction is classified, normalised, and mapped to SDG and New Urban Agenda references.

Output

Comparable evidence at scale.

Five analytical surfaces that let you read patterns across cities, regions, and time, with every pattern traceable back to its source paragraph.

What this is, and what it isn't.

Nexus SDG Intelligence is a structured reading of the full public corpus, turning what cities have already written into evidence that can be compared across geographies, frameworks, and time.

Every Voluntary Local Review in the collection has been processed by the Nexus Intelligence Engine, a pipeline combining natural language processing and large language models with a governance-specific classification framework. Each extracted item is categorised, mapped to the relevant Sustainable Development Goal and New Urban Agenda reference, and linked back to the paragraph it came from. Nothing in Nexus SDG Intelligence is ungrounded; every finding is traceable to source.

What Nexus SDG Intelligence shows is what cities themselves have said. Patterns, gaps, and silences are features of the corpus, not interpretations layered on top of it.

Nexus SDG Intelligence is updated as new VLRs are published. The classification framework is versioned and documented, and the underlying methodology is open for review by partner institutions on request.

The world's Voluntary Local Reviews, read as one.

From governance text to structured evidence.

What this is, and what it isn't.

Open Nexus SDG Intelligence.