aceModerate evidence
Published recordClimateThe Trace
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A Meta-Analysis of Artificial Intelligence in the Built Environment: High-Efficacy Silos and Fragmented Ecosystems
- Truvace record
- TRV-2026-2731raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
- sha256
- 4f7f49cc6ecd…check it
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…
65Gain side
How the two sides scoreClose call
62Problem side
The gain
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.
The problem
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.
A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.
What the scores mean
G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.
P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.
What this doesn’t fix
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.
Sources
1 on file- Smart Citiesdoi:10.3390/smartcities8050174
What the review found when it combined the studies
These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.
Within-domain performance and sustainability outcomes of AI interventions in urban infrastructure
Hedges' g 0.92 (95% CI 0.78 to 1.06)
71 studies · search to July 2025
The review’s own words
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.
Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results
Cite this record
v1Truvace Impact Record TRV-2026-2731, v1: “A Meta-Analysis of Artificial Intelligence in the Built Environment: High-Efficacy Silos and Fragmented Ecosystems.” Truvace, 2026-11-12. /record/TRV-2026-2731 (accessed on the day you copy it). sha256 4f7f49cc6ecd56f3…
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