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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (28 April 2026) | Viewed by 28722

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Special Issue Editors

Dr. Hossein Bonakdari

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Guest Editor

Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Interests: water resources; hydrology; AI; climate change; sustainable development; time series; hydrological modelling; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Ebrahim Ghaderpour

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Guest Editor

Department of Earth Sciences & CERI Research Centre, Sapienza University of Rome, P.le Aldo Moro, 5, 00185 Rome, Italy
Interests: artificial intelligence; big data analytics; geology; hydrology; remote sensing; time series analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Silvio José Gumiere

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Guest Editor

Department of Soil and Agri-Food Engineering, Universite Laval, Québec, QC G1V 0A6, Canada
Interests: climate change; drought management; soil and water conservation; irrigation; hydrological modeling; surface hydrology; rainfall runoff modeling; hydraulics; numerical modeling; hydrology; hydrologic and water resource management; environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to a Special Issue titled "Advancing Sustainable Development Through Artificial Intelligence (AI)" in the Sustainability journal. As the world faces the pressing challenges of climate change, resource depletion, and social inequality, the role of AI in driving sustainable development has become increasingly significant. AI offers the potential to optimize resource usage, enhance decision-making processes, and build more resilient and equitable systems, making it a critical area of research in our journey toward a sustainable future.

This Special Issue aims to explore the intersection of AI and sustainable development, focusing on how AI technologies can be leveraged to address a broad range of applications while ensuring a cohesive collection of high-impact articles.

We welcome submissions on the following themes:

AI for Climate Change Mitigation and Adaptation
AI-driven Resource Management (e.g., Water, Energy, Agriculture)
Smart Cities and Sustainable Urban Planning
AI in Environmental Monitoring and Conservation
Ethical and Social Implications of AI in Sustainable Development
AI-based Decision Support Systems for Sustainable Practices
Integration of AI with IoT for Sustainable Solutions

We look forward to receiving your contributions and showcasing innovative research that advances the field of sustainable development through the application of AI.

Dr. Hossein Bonakdari
Dr. Ebrahim Ghaderpour
Dr. Silvio José Gumiere
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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)
sustainable development
climate change mitigation
resource management
environmental monitoring
smart cities
decision support systems (DSS)
Internet of Things (IoT)
ethical AI
resilient systems

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Published Papers (8 papers)

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Research

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20 pages, 3843 KB

Open AccessArticle

UAV-Assisted Pesticide Application in Potato Cultivation Under Waterlogged Soil Conditions: Orthophotomap-Based Monitoring and Field Assessment

by Andrey Ronzhin, Artem Ryabinov, Elena Shkodina, Anton Saveliev, Ekaterina Cherskikh and Aleksandra Figurek

Sustainability 2026, 18(9), 4567; https://doi.org/10.3390/su18094567 - 6 May 2026

Viewed by 375

Abstract

The operational limitations of ground-based machinery in waterlogged soils of northern regions often lead to missed agronomic treatments, resulting in substantial yield losses. This problem is important in potato production, where a delay in disease protection can quickly lead to loss of leaf mass and reduced yield. This article evaluates an integrated UAV-based approach for crop production in potato cultivation, encompassing aerial soil analysis, weed segmentation, targeted spraying, and yield prediction. A field experiment was designed using a developed GD-4 drone on a 300 m × 6 m test plot. Aerial photography was used to generate an orthophotomap for monitoring and planning pesticide applications. The UAV, operating at a 2-m altitude, achieved a 3-m spray swath, enabling complete plot coverage. Visual assessment confirmed superior plant health in the test plot compared to the control. Quantitative analysis revealed a yield of 39.06 t/ha in the test plot, a 15.2% increase over the control plot (33.91 t/ha), with a comparable percentage of marketable tubers (94.5% vs. 93.3%). The study concludes that UAV technology is a reliable means of remote sensing and offers an alternative for ensuring timely agricultural operations and enhancing yield in inaccessible terrains. Full article

(This article belongs to the Special Issue Advancing Sustainable Development Through Artificial Intelligence (AI))

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18 pages, 852 KB

Open AccessArticle

A Role for Artificial Intelligence (AI) in Qualitative Research? An Exploratory Analysis Examining New York City Residents’ Perceptions on Climate Change

by Nadav L. Sprague, Gabriella Y. Meltzer, Michelle L. Dandeneau, Daritza De Los Santos, Drew B. O’Neil, Andrew K. Kim, Alejandra Parisi, Shane Araujo, Christine C. Ekenga, Eva L. Siegel and Diana Hernández

Sustainability 2025, 17(23), 10459; https://doi.org/10.3390/su172310459 - 21 Nov 2025

Cited by 1 | Viewed by 1357

Abstract

As artificial intelligence (AI) advances, there is growing interest in leveraging this technology to enhance climate change research and responses. While AI has been applied in quantitative climate research, its role in qualitative research remains underdeveloped. Yet, qualitative inquiry is essential for understanding how individuals perceive and experience the effects of climate change. This study aimed to both (1) gain a deeper understanding of New York City residents’ perceptions and lived experiences of climate change and (2) evaluate the suitability of AI for analyzing qualitative data. Using StreetTalk, a qualitative method involving street-intercept video interviews and social media dissemination, research teams analyzed interview transcripts through four approaches: human-only, human-then-AI, AI-then-human, and AI-only. Co-authors were then provided with anonymized (blinded) versions of the final theme sets that they did not contribute to and evaluated them using a standardized rubric developed for this study. The AI-then-human approach produced the most comprehensive and contextually accurate results, yielding nine key themes: (1) personal responsibility and action, (2) community unity and support, (3) government and corporate responsibility, (4) concern for future generations, (5) climate change impact, (6) climate-related conspiracy theories, (7) low literacy around local climate change, (8) helplessness, and (9) competing interests around climate change. These findings provide valuable local perspectives to guide evidence-based strategies for climate mitigation and community engagement. This research also represents an initial step toward establishing best practices for integrating AI into qualitative data analysis. Full article

(This article belongs to the Special Issue Advancing Sustainable Development Through Artificial Intelligence (AI))

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20 pages, 4033 KB

Open AccessArticle

AI-Based Virtual Assistant for Solar Radiation Prediction and Improvement of Sustainable Energy Systems

by Tomás Gavilánez, Néstor Zamora, Josué Navarrete, Nino Vega and Gabriela Vergara

Sustainability 2025, 17(19), 8909; https://doi.org/10.3390/su17198909 - 8 Oct 2025

Viewed by 1449

Abstract

Advances in machine learning have improved the ability to predict critical environmental conditions, including solar radiation levels that, while essential for life, can pose serious risks to human health. In Ecuador, due to its geographical location and altitude, UV radiation reaches extreme levels. This study presents the development of a chatbot system driven by a hybrid artificial intelligence model, combining Random Forest, CatBoost, Gradient Boosting, and a 1D Convolutional Neural Network. The model was trained with meteorological data, optimized using hyperparameters (iterations: 500–1500, depth: 4–8, learning rate: 0.01–0.3), and evaluated through MAE, MSE, R², and F1-Score. The hybrid model achieved superior accuracy (MAE = 13.77 W/m², MSE = 849.96, R² = 0.98), outperforming traditional methods. A 15% error margin was observed without significantly affecting classification. The chatbot, implemented via Telegram and hosted on Heroku, provided real-time personalized alerts, demonstrating an effective, accessible, and scalable solution for health safety and environmental awareness. Furthermore, it facilitates decision-making in the efficient generation of renewable energy and supports a more sustainable energy transition. It offers a tool that strengthens the relationship between artificial intelligence and sustainability by providing a practical instrument for integrating clean energy and mitigating climate change. Full article

(This article belongs to the Special Issue Advancing Sustainable Development Through Artificial Intelligence (AI))

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28 pages, 33880 KB

Open AccessArticle

F-Segfomer: A Feature-Selection Approach for Land Resource Management on Unseen Domains

by Manh-Hung Nguyen and Chi-Cuong Vu

Sustainability 2025, 17(6), 2640; https://doi.org/10.3390/su17062640 - 17 Mar 2025

Cited by 1 | Viewed by 1503

Abstract

Satellite imagery segmentation is essential for effective land resource management. However, diverse geographical landscapes may limit segmentation accuracy in practical applications. To address these challenges, we propose the F-Segformer network, which incorporates a Variational Information Bottleneck (VIB) module to enhance feature selection within the SegFormer architecture. The VIB module serves as a feature selector, providing improved regularization, while SegFormer is well adapted to unseen domains. Combining these methods, our F-Segformer robustly enhanced segmentation performance in new regions that do not appear in the training process. Additionally, we employ Online Hard Example Mining (OHEM) to prioritize challenging samples during training, the setting helps with accelerating model convergence even with the co-trained VIB loss. Experimental results on the LoveDA dataset show that our method can achieve a comparable result to well-known domain-adaptation methods without using data from the target domain. In a practical scenario when the segmentation model is trained on a domain and tested on an unseen domain, our method shows a significant improvement. Last but not least, OHME helps the model converge three times faster than without OHME. Full article

(This article belongs to the Special Issue Advancing Sustainable Development Through Artificial Intelligence (AI))

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24 pages, 4972 KB

Open AccessArticle

NDMI-Derived Field-Scale Soil Moisture Prediction Using ERA5 and LSTM for Precision Agriculture

by Elham Koohikeradeh, Silvio Jose Gumiere and Hossein Bonakdari

Sustainability 2025, 17(6), 2399; https://doi.org/10.3390/su17062399 - 9 Mar 2025

Cited by 12 | Viewed by 5516

Abstract

Accurate soil moisture prediction is fundamental to precision agriculture, facilitating optimal irrigation scheduling, efficient water resource allocation, and enhanced crop productivity. This study employs a Long Short-Term Memory (LSTM) deep learning model, integrated with high-resolution ERA5 remote sensing data, to improve soil moisture estimation at the field scale. Soil moisture dynamics were analyzed across six commercial potato production sites in Quebec—Goulet, DBolduc, PBolduc, BNiquet, Lalancette, and Gou-new—over a five-year period. The model exhibited high predictive accuracy, with correlation coefficients (R) ranging from 0.991 to 0.998 and Nash–Sutcliffe efficiency (NSE) values reaching 0.996, indicating strong agreement between observed and predicted soil moisture variability. The Willmott index (WI) exceeded 0.995, reinforcing the model’s reliability. The integration of NDMI assessments further validated the predictions, demonstrating a strong correlation between NDMI values and LSTM-based soil moisture estimates. These findings confirm the effectiveness of deep learning in capturing spatiotemporal variations in soil moisture, underscoring the potential of AI-driven models for real-time soil moisture monitoring and irrigation optimization. This research study provides a scientifically robust framework for enhancing data-driven agricultural water management, promoting sustainable irrigation practices, and improving resilience to soil moisture variability in agricultural systems. Full article

(This article belongs to the Special Issue Advancing Sustainable Development Through Artificial Intelligence (AI))

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18 pages, 4642 KB

Open AccessArticle

Sustainable Operation Strategy for Wet Flue Gas Desulfurization at a Coal-Fired Power Plant via an Improved Many-Objective Optimization

by Jianfeng Huang, Zhuopeng Zeng, Fenglian Hong, Qianhua Yang, Feng Wu and Shitong Peng

Sustainability 2024, 16(19), 8521; https://doi.org/10.3390/su16198521 - 30 Sep 2024

Cited by 7 | Viewed by 3826

Abstract

Coal-fired power plants account for a large share of the power generation market in China. The mainstream method of desulfurization employed in the coal-fired power generation sector now is wet flue gas desulfurization. This process is known to have a high cost and be energy-/materially intensive. Due to the complicated desulfurization mechanism, it is challenging to improve the overall sustainability profile involving energy-, cost-, and resource-relevant objectives via traditional mechanistic models. As such, the present study formulated a data-driven many-objective model for the sustainability of the desulfurization process. We preprocessed the actual operation data collected from the desulfurization tower in a domestic ultra-supercritical coal-fired power plant with a 600 MW unit. The extreme random forest algorithm was adopted to approximate the objective functions as prediction models for four objectives, namely, desulfurization efficiency, unit power consumption, limestone supply, and unit operation cost. Three metrics were utilized to evaluate the performance of prediction. Then, we incorporated differential evolution and non-dominated sorting genetic algorithm-III to optimize the multiple parameters and obtain the Pareto front. The results indicated that the correlation coefficient (R2) values of the prediction models were greater than 0.97. Compared with the original operation condition, the operation under optimized parameters could improve the desulfurization efficiency by 0.25% on average and reduce energy, cost, and slurry consumption significantly. This study would help develop operation strategies to improve the sustainability of coal-fired power plants. Full article

(This article belongs to the Special Issue Advancing Sustainable Development Through Artificial Intelligence (AI))

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Review

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39 pages, 1190 KB

Open AccessReview

The Role of AI in Predictive Modelling for Sustainable Urban Development: Challenges and Opportunities

by Elda Cina, Ersin Elbasi, Gremina Elmazi and Zakwan AlArnaout

Sustainability 2025, 17(11), 5148; https://doi.org/10.3390/su17115148 - 3 Jun 2025

Cited by 17 | Viewed by 12055

Abstract

As urban populations continue to rise, cities face mounting challenges related to infrastructure strain, resource management, and environmental degradation. Sustainable urban development has emerged as a crucial strategy to balance economic growth, social equity, and environmental preservation. In this context, artificial intelligence offers transformative potential, particularly through predictive modeling, which enables data-driven decision making for more efficient and resilient urban planning. This paper explores the role of AI-powered predictive models in supporting sustainable urban development, focusing on key applications such as infrastructure optimization, energy management, environmental monitoring, and climate adaptation. The study reviews current practices and real-world examples, highlighting the benefits of predictive analytics in anticipating urban needs and mitigating future risks. It also discusses significant challenges, including data limitations, algorithmic bias, ethical concerns, and governance issues. The discussion emphasizes the importance of transparent, inclusive, and accountable AI frameworks to ensure equitable outcomes. In addition, the paper presents comparative insights from global smart city initiatives, illustrating how AI and IoT-based strategies are being applied in diverse urban contexts. By examining both the opportunities and limitations of AI in this domain, the paper offers insights into how cities can responsibly harness AI to advance sustainability goals. The findings underscore the need for interdisciplinary collaboration, ethical safeguards, and policy support to unlock AI’s full potential in shaping sustainable, smart cities. Full article

(This article belongs to the Special Issue Advancing Sustainable Development Through Artificial Intelligence (AI))

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Other

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33 pages, 4433 KB

Open AccessSystematic Review

How Can Large Language Models Drive Environmental Sustainability? A Systematic Scoping Review

by Xiaotong Su, Ting Liu, Patrick Pang, Yiming Taclis Luo and Dennis Wong

Sustainability 2026, 18(9), 4327; https://doi.org/10.3390/su18094327 - 27 Apr 2026

Viewed by 869

Abstract

Currently, Large Language Models (LLMs), exemplified by ChatGPT, are accelerating technological development across various domains, including the environmental domain, owing to their powerful text-generation and information-processing capabilities. With changes in global climate and environmental conditions, environmental sustainability has emerged as a major global challenge. Leveraging LLMs to advance environmental sustainability and mitigate current environmental problems is considered a valuable and effective approach. This study aims to systematically synthesize research progress and core challenges in current LLMs for promoting sustainability-related fields, and to comprehensively analyze the application contexts, impacts, and development potential of various LLMs within the environmental sector. Following the PRISMA-ScR guidelines, a comprehensive search was conducted across six databases: Web of Science (WOS), Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, and Google Scholar. A total of 20 articles were ultimately included for analysis. The findings indicate that LLMs play a positive role in maintaining environmental sustainability and promoting the low-carbon energy transition. The applications of LLMs span six core domains: the green transition, carbon emission management, air quality assessment, smart city operations, map analysis, and human cognition and behavioral observation. However, the training and operation of current LLMs consume considerable resources, which creates an inherent conflict with the goals of sustainable development. Future efforts must focus on developing a secure, equitable, and scalable LLM support system to advance environmental sustainability. This requires optimizing model energy efficiency and ensuring a balance between performance, reliability, and environmental impact. These endeavors are crucial for addressing environmental problems and guaranteeing the sustainable progression of LLMs across diverse environmental contexts. Full article

(This article belongs to the Special Issue Advancing Sustainable Development Through Artificial Intelligence (AI))

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