Leveraging artificial intelligence to enable sustainable urban development through the creation of smart and environmentally friendly carbon-free cities.

Amina Salhi, Fahad Algarni, Rayan Alshamrani, Ashrf Althbiti, Atef Ismail, Basma M Hassan

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed, 1 pooled it

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

Sustainable smart sensing and AI-driven platforms for real-time detection and monitoring of mycotoxins across the food supply chain.Mycotoxin research · 2026
Pooled it

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Amina SalhiDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

Fahad AlgarniCollege of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia.

Rayan AlshamraniDepartment of Information Technology, College of Computers and Information Technology, Taif University, P.O.Box 11099, 21944, Taif, Saudi Arabia.

Ashrf AlthbitiDepartment of Information Technology, College of Computers and Information Technology, Taif University, P.O.Box 11099, 21944, Taif, Saudi Arabia.

Atef IsmailPhysics Department, Al-Azhar University, Asyut, 71524, Egypt.

Basma M HassanFaculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt. basma.mohamed@ai.kfs.edu.eg.

Funding

Princess Nourah Bint Abdulrahman University PNURSP2025R909

6 · The paper itself

Abstract

In an era of rapid urbanization and environmental degradation, sustainable urban development is imperative to ensure a high quality of life while reducing carbon footprints. This paper presents a comprehensive framework that leverages artificial intelligence to drive data-informed decisions in smart cities, ultimately aiming to create sustainable and carbon-free urban environments. Our approach integrates diverse datasets representative of key urban challenges including energy efficiency, air quality, infrastructure durability, and both residential and industrial energy consumption into a unified predictive modeling platform. Utilizing PyCaret's low-code machine learning (ML) library, we automated the training, selection, and evaluation of numerous regression models, with a particular focus on ensemble-based methods such as Extra Trees, CatBoost, and LightGBM. Rigorous benchmarking across six publicly available datasets demonstrated near-perfect predictive performance, with R

Indexed as

Carbon neutralityEnergy efficiencyPredictive modelingSmart citiesSustainable urban development

Identifiers

PMID41087390

DOI10.1038/s41598-025-16801-z

PMCPMC12521657

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.