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TRUVACE RECORD VERSION
record: TRV-2026-2731
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:16:01.336983Z
status: published
lens: trace
sector: climate
headline: A Meta-Analysis of Artificial Intelligence in the Built Environment: High-Efficacy Silos and Fragmented Ecosystems
dek: Cities face mounting pressures to deliver reliable, low-carbon services amid rapid urbanization and budget constraints. Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT) are widely promoted to automate operations and strengthen decision-support across the built environment; however, it remains unclear whether these interventions are both effective and systemically integrated across domains. We conducted a Preferred Reporting Items for Systematic Reviews (PR…
gain_title: A meta-analysis of 71 studies of AI, machine learning and IoT in urban infrastructure found a large pooled improvement in within-domain performance and sustainability outcomes (Hedges' g = 0.92), still positive at about 0.68–0.70 after bias adjustment.
problem_title: The same meta-analysis of 71 urban infrastructure studies found 91.5% of AI implementations operate at isolated or minimal data-sharing levels and only 1.4% achieve real-time multi-domain integration.
trace_subject: AI, machine learning and IoT interventions in urban infrastructure
gain_reading: A meta-analysis of 71 studies of AI, machine learning and IoT in urban infrastructure found a large pooled improvement in within-domain performance and sustainability outcomes (Hedges' g = 0.92), still positive at about 0.68–0.70 after bias adjustment.
gain_evidence: A random-effects model shows a large, pooled effect (Hedges’ g = 0.92; 95% CI: 0.78–1.06; p < 0.001) for within-domain performance/sustainability outcomes. | a conservative bias-adjusted estimate suggests a still-positive effect of g ≈ 0.68–0.70
problem_reading: The same meta-analysis of 71 urban infrastructure studies found 91.5% of AI implementations operate at isolated or minimal data-sharing levels and only 1.4% achieve real-time multi-domain integration.
problem_evidence: 91.5% of implementations operate at integration Levels 0–1 (isolated or minimal data sharing), and only 1.4% achieve real-time multi-domain integration (Level 3)
quick_read: A PRISMA-aligned systematic review and meta-analysis covering January 2015 to July 2025 screened 7432 records and kept 71 empirical studies of AI, machine learning, deep learning and IoT in urban infrastructure. Pooled across those studies, within-domain performance and sustainability outcomes improved by a large margin (Hedges' g = 0.92).

The gains rarely connect. 91.5% of implementations run at integration Levels 0–1, with isolated systems or minimal data sharing, and just 1.4% reach real-time multi-domain integration. The authors say this fragmentation prevents cross-domain synergies and the system-wide value cities want.

Publication bias is likely (Egger's test p = 0.03), and a bias-adjusted estimate puts the effect near g ≈ 0.68–0.70. The authors rate overall certainty of evidence as Moderate under GRADE because of heterogeneity and small-study effects.
limitation: Publication bias is likely and the studies are heterogeneous, so the authors rate overall certainty of evidence as only Moderate, with a bias-adjusted effect lower than the raw estimate.
tag: Dual reading
key_points: Searches of five open-access indices (MDPI, DOAJ, CORE, BASE, OpenAIRE) returned 7432 records, and 71 studies met the inclusion criteria for quantitative synthesis. | The random-effects pooled effect was Hedges' g = 0.92 (95% CI 0.78–1.06, p < 0.001) for within-domain performance and sustainability outcomes. | Egger's test (p = 0.03) points to likely publication bias; the adjusted estimate is about g ≈ 0.68–0.70, still positive. | 91.5% of implementations sit at integration Levels 0–1, with isolated or minimal data sharing. | Only 1.4% of implementations achieve real-time multi-domain integration (Level 3). | The authors recommend mandating open standards and APIs, setting up city-level data governance, funding Level-2/3 integration pilots and adopting cross-domain evaluation metrics.
rundown: The review followed PRISMA and covered empirical AI, ML, DL and IoT interventions in urban infrastructure published between January 2015 and July 2025. It drew on five open-access indices and reduced 7432 records to 71 studies for quantitative synthesis.

A random-effects model produced a large pooled effect of Hedges' g = 0.92 (95% CI 0.78–1.06). The authors call the picture a dual reality: high efficacy in silos but pervasive fragmentation across domains.

To turn local gains into system-wide value, the authors propose mandating open standards and APIs, city-level data governance, funding pilots at integration Levels 2 and 3, and evaluating across domains. They rate certainty of evidence Moderate under GRADE, citing heterogeneity and small-study effects, offset by the magnitude and consistency of benefits.
sources:
- peer_reviewed | Smart Cities | https://doi.org/10.3390/smartcities8050174 | 2025-10-15
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