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Sustainable Application of Artificial Intelligence and Machine Learning
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Sustainable Application of Artificial Intelligence and Machine Learning

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (30 June 2025) | Viewed by 6566

Special Issue Editor

Dr. Osama Sohaib

E-Mail Website
Guest Editor
School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia School of Business, American University of Ras Al Khaimah, Ras al Khaimah, United Arab Emirates
Interests: sustainable information systems; digital business; human-centered AI; applied machine learning; business analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on exploring the intersection of artificial intelligence (AI) and machine learning (ML) with sustainability and aims to investigate how AI and ML technologies can be harnessed to address various challenges related to sustainability, including, but not limited to, environmental protection, social equity, economic development, resource management, digital business, and information systems.

The overarching goal is to explore how AI and ML technologies can be strategically deployed to enhance the sustainability performance of businesses, optimize digital operations, and contribute to sustainable development goals.

This Special Issue seeks to provide a platform for researchers, practitioners, policymakers, and industry stakeholders to share their latest findings, insights, and experiences regarding the utilization of AI and ML technologies to advance sustainability goals. It seeks to foster interdisciplinary dialogue and collaboration, identify emerging trends and challenges, and showcase innovative solutions that contribute to the sustainable development agenda.

Dr. Osama Sohaib
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI) and machine learning (ML)
  • sustainability
  • sustainable development
  • digital business
  • information systems
  • sustainable business models
  • sustainable innovation
  • entrepreneurship
  • digital transformation
  • ethical AI
  • social implications
  • responsible governance
  • sustainable practices

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

19 pages, 4134 KiB  
Article
The Lithium-Ion Battery Temperature Field Prediction Model Based on CNN-Bi-LSTM-AM
by Boyu Wang, Zheying Chen, Puhan Zhang, Yong Deng and Bo Li
Sustainability 2025, 17(5), 2125; https://doi.org/10.3390/su17052125 - 1 Mar 2025
Viewed by 1513
Abstract
This study focuses on the internal temperature field of lithium-ion batteries, aiming to address the temperature variation issues arising from complex operating conditions in new energy batteries. To cope with unpredictable temperature fluctuations and long delay times, we propose an enhanced Convolutional Bidirectional [...] Read more.
This study focuses on the internal temperature field of lithium-ion batteries, aiming to address the temperature variation issues arising from complex operating conditions in new energy batteries. To cope with unpredictable temperature fluctuations and long delay times, we propose an enhanced Convolutional Bidirectional Long Short-Term Memory Neural Network (CNN-Bi-LSTM-AM) model for temperature field prediction. The model integrates CNN for spatial feature extraction, Bi-LSTM for capturing temporal characteristics, and an attention mechanism to enhance the identification of key time-series features. By simulating temperature variations through a lumped model and thermal runaway model, we generate temperature field data, which are then utilized by the deep learning model to effectively capture the complex nonlinear relationships between temperature, voltage, state of charge (SOC), insulation resistance, current, and the internal temperature field. Performance evaluation using accuracy metrics and validation under various environmental conditions demonstrates that the model improves prediction accuracy by 1.2–2.3% compared to traditional methods (e.g., ARIMA, LSTM) with only a slight increase in testing time. Comprehensive evaluations, including ablation studies, thermal runaway tests, and computational efficiency analysis, further validate the robustness and applicability of the model. Furthermore, this study contributes to the optimization of battery life and safety by enhancing the prediction accuracy of the internal temperature field, thereby reducing resource waste caused by battery performance degradation. The findings provide an innovative approach to advancing new energy battery technology, promoting its development toward greater safety, stability, and environmental sustainability, which aligns with global sustainable development goals. Full article
(This article belongs to the Special Issue Sustainable Application of Artificial Intelligence and Machine Learning)
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23 pages, 3036 KiB  
Article
Comparison of Vertex AI and Convolutional Neural Networks for Automatic Waste Sorting
by Jhonny Darwin Ortiz-Mata, Xiomara Jael Oleas-Vélez, Norma Alexandra Valencia-Castillo, Mónica del Rocío Villamar-Aveiga and David Elías Dáger-López
Sustainability 2025, 17(4), 1481; https://doi.org/10.3390/su17041481 - 11 Feb 2025
Cited by 1 | Viewed by 1593
Abstract
This study discusses the optimization of municipal solid waste management through the implementation of automated waste sorting systems, comparing two advanced artificial intelligence methodologies: Vertex AI and convolutional neural network (CNN) architectures, developed using TensorFlow. Automated solid waste classification is presented as an [...] Read more.
This study discusses the optimization of municipal solid waste management through the implementation of automated waste sorting systems, comparing two advanced artificial intelligence methodologies: Vertex AI and convolutional neural network (CNN) architectures, developed using TensorFlow. Automated solid waste classification is presented as an innovative technological approach that leverages advanced algorithms to accurately identify and segregate materials, addressing the inherent limitations of conventional sorting methods, such as high labor dependency, inaccuracies in material separation, and constrained scalability for processing large waste volumes. A system was designed for the classification of paper, plastic, and metal waste, integrating an Arduino Uno microcontroller, a Raspberry Pi, a high-resolution camera, and a robotic manipulator. The system was evaluated based on performance metrics including classification accuracy, response time, scalability, and implementation cost. The findings revealed that Xception achieved a flawless classification accuracy of 100% with an average processing time of 0.25 s, whereas Vertex AI, with an accuracy of 90% and a response time of 2 s, exceled in cloud scalability, making it ideal for resource-constrained environments. The findings highlight Xception’s superiority in high-precision applications and Vertex AI’s adaptability in scenarios demanding flexible deployment, advancing efficient and sustainable waste management solutions. Full article
(This article belongs to the Special Issue Sustainable Application of Artificial Intelligence and Machine Learning)
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17 pages, 6598 KiB  
Article
Enhancing Smart Grid Sustainability: Using Advanced Hybrid Machine Learning Techniques While Considering Multiple Influencing Factors for Imputing Missing Electric Load Data
by Zhiwen Hou and Jingrui Liu
Sustainability 2024, 16(18), 8092; https://doi.org/10.3390/su16188092 - 16 Sep 2024
Cited by 3 | Viewed by 2009
Abstract
Amidst the accelerating growth of intelligent power systems, the integrity of vast and complex datasets has become essential to promoting sustainable energy management, ensuring energy security, and supporting green living initiatives. This study introduces a novel hybrid machine learning model to address the [...] Read more.
Amidst the accelerating growth of intelligent power systems, the integrity of vast and complex datasets has become essential to promoting sustainable energy management, ensuring energy security, and supporting green living initiatives. This study introduces a novel hybrid machine learning model to address the critical issue of missing power load data—a problem that, if not managed effectively, can compromise the stability and sustainability of power grids. By integrating meteorological and temporal characteristics, the model enhances the precision of data imputation by combining random forest (RF), Spearman weighted k-nearest neighbors (SW-KNN), and Levenberg–Marquardt backpropagation (LM-BP) techniques. Additionally, a variance–covariance weighted method is used to dynamically adjust the model’s parameters to improve predictive accuracy. Tests on five metrics demonstrate that considering various correlated factors reduces errors by approximately 8–38%, and the hybrid modeling approach reduces predictive errors by 12–24% compared to single-model approaches. The proposed model not only ensures the resilience of power grid operations but also contributes to the broader goals of energy efficiency and environmental sustainability. Full article
(This article belongs to the Special Issue Sustainable Application of Artificial Intelligence and Machine Learning)
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