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Artificial Intelligence and Sustainable Development

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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 (10 March 2026) | Viewed by 21115

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Editors

Dr. Pierrick Bruneau

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Guest Editor

Trustworthy AI Group, Luxembourg Institute of Science and Technology, L-4362 Esch-sur-Alzette, Luxembourg
Interests: AI; machine learning; deep learning; Bayesian models
Special Issues, Collections and Topics in MDPI journals

Dr. Laura Cirrincione

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Guest Editor

Department of Engineering, University of Palermo, 90128 Palermo, Italy
Interests: smart solution and technologies for sustainable buildings; climate change resilience; environmental sustainability; sustainable urban environments; energy efficiency in buildings; dynamic building simulation; sustainable mobility; building energy efficiency; resilient buildings; urban environmental sustainability; urban energy resilience; urban environmental resilience; indoor and outdoor environmental quality; HVACs
Special Issues, Collections and Topics in MDPI journals

Dr. Gianluca Scaccianoce

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Guest Editor

Department of Engineering, University of Palermo, Viale delle Scienze Bld. 9, 90131 Palermo, Italy
Interests: dynamic building simulation; sustainable buildings; sustainable materials for the construction sector; innovative building envelope components; green roofs; building energy efficiency; indoor thermal comfort; lighting; acoustics; HVAC systems; urban energy efficiency; urban environmental sustainability; climate change resilient buildings; urban climate change resilience; urban energy resilience; outdoor environmental quality; atmospheric pollution; renewable energy sources; sustainable urban mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last decade, the advent of deep learning has enabled the development of groundbreaking AI systems and applications, profoundly impacting the economy and society.

This major technological breakthrough presents a significant opportunity to foster sustainable development, as illustrated by many recent contributions in AI-powered smart agriculture, urban development, circular economy, environmental monitoring, and sustainable energy systems. AI's transformative potential in these areas is highlighted, from optimizing resource use and improving efficiency to addressing global challenges like climate change, food security, energy interactions and social equity.

However, AI systems cannot be blindly adopted; they must be carefully evaluated for bias and safety, calling for guidelines in ethics and trustworthiness. Building and running AI systems come with high energy demands and reliance on rare elements, which have their own environmental impacts. Research must ensure these issues do not undermine AI's positive contributions. Additionally, the high costs of AI tools and training concentrate AI power in a few wealthy entities, potentially exacerbating global inequalities and limiting access to AI-driven solutions in lower-income regions. These challenges raise ethical concerns and necessitate governance frameworks to ensure AI's benefits are equitably distributed and aligned with the Sustainable Development Goals.

This Special Issue aims to collect high-quality research, including original research articles, reviews, and case studies, that explores the interplay between AI and sustainable development as described above. Topics will be considered of interest if they relate to the keywords listed below.

We look forward to receiving your contributions.

Dr. Pierrick Bruneau
Dr. Laura Cirrincione
Dr. Gianluca Scaccianoce
Guest Editors

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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-anonymized 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

sustainable AI governance
circular economy
social sustainability
ethical AI
trustworthy AI
sustainable development goals
AI-based building management systems
AI-based energy management systems
AI-based smart grid management
AI-based dynamic urban management
AI for cultural heritage

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Published Papers (10 papers)

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26 pages, 11041 KB

Open AccessArticle

Multi-Scale Attribution of Land Surface Temperature Driving Mechanisms in a Cold Region City: A Study on Spatial Non-Stationarity and Nonlinearity Based on XGBoost-SHAP

by Liang Qu, Rihan Hai, Kaihong Liang, Quanyi Zheng and Mengxiao Jin

Sustainability 2026, 18(9), 4451; https://doi.org/10.3390/su18094451 - 1 May 2026

Cited by 1 | Viewed by 573

Abstract

Accurately quantifying the driving mechanisms of land surface temperature (LST) is fundamental to developing climate-resilient urban strategies. However, traditional linear models often fail to capture the complex nonlinear interactions and spatial non-stationarity inherent in urban thermal environments, especially when hindered by multicollinearity among morphological indicators. This study proposes a multi-scale spatial explainability attribution framework by integrating an XGBoost machine learning model with SHAP (SHapley Additive Explanations) to decipher the thermal dynamics of Changchun, a representative cold-region city in China. Utilizing a 500 m grid-based dataset, we incorporated 3D urban morphology (BVD), land cover (NDVI, NDWI), and socioeconomic factors. The results indicate that the XGBoost model achieves superior predictive performance (R² = 0.694) compared to traditional OLS models. SHAP global attribution identified Building Volume Density (BVD) as the primary warming driver, as its three-dimensional volume creates “thermal traps” through radiation trapping and reduced ventilation. Notably, NDVI exhibits a significant nonlinear “cooling threshold effect” at 0.3, beyond which its mitigation efficiency stagnates or even reverses due to vegetation fragmentation and heat-induced physiological stress. Furthermore, spatial mapping reveals a distinct “sign reversal” in NDWI’s impact, reflecting the dualistic thermal regulation of water bodies across different urban–rural gradients. These findings suggest that urban thermal management strategies should shift from merely restricting 2D surface occupancy (e.g., Building Density) to a more sophisticated approach focused on precisely controlling 3D volume intensity (BVD). This study provides a “point-to-area” diagnostic tool supporting a transition to spatially targeted urban planning interventions. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainable Development)

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21 pages, 3561 KB

Open AccessArticle

A CLIP-Guided Multi-Objective Optimization Framework for Sustainable Design: Integrating Aesthetic Evaluation, Energy Efficiency, and Life Cycle Environmental Performance

by Hanwen Zhang, Myun Kim, Hao Hu and Yitong Wang

Sustainability 2026, 18(8), 4064; https://doi.org/10.3390/su18084064 - 19 Apr 2026

Viewed by 477

Abstract

Achieving sustainable design requires balancing environmental performance, resource efficiency, functional feasibility, and aesthetic acceptance throughout the product life cycle. However, traditional design approaches often struggle to quantitatively integrate subjective aesthetic evaluation with objective sustainability indicators such as energy consumption, carbon emissions, and material recyclability. To address this challenge, this study proposes a semantic-guided multi-objective optimization framework for sustainable design that integrates cross-modal aesthetic evaluation with life cycle environmental performance assessment. The proposed framework employs a Contrastive Language–Image Pre-training (CLIP)-based semantic evaluation mechanism to translate abstract sustainability and aesthetic concepts into quantifiable design features, enabling consistent assessment across diverse design solutions. These semantic features are further optimized using a multi-objective evolutionary optimization strategy to simultaneously minimize energy consumption and carbon emissions while maximizing material recovery and design quality. Life cycle environmental indicators derived from OpenLCA datasets are incorporated into the optimization process to ensure practical sustainability relevance. The experimental results demonstrate that the proposed framework achieves a superior performance compared with benchmark optimization methods. Specifically, carbon emission equivalents are reduced to as low as 12.3 kg CO₂e, material recovery rates exceed 92%, and total computational energy consumption is reduced by more than 40% relative to comparative models. In addition, the framework shows strong stability and convergence efficiency while maintaining a high aesthetic evaluation accuracy in high-quality design ranges. The findings indicate that the proposed approach provides an effective pathway for integrating aesthetic value with environmental responsibility in sustainable design practice. This framework supports low-carbon and resource-efficient product development and offers practical insights for sustainable manufacturing, circular design, and environmentally conscious innovation. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainable Development)

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22 pages, 891 KB

Open AccessArticle

Ensemble Learning with Systematic Hyperparameter Optimization for Urban-Bike-Sharing Demand Prediction

by Ivona Brajevic, Eva Tuba and Milan Tuba

Sustainability 2026, 18(8), 3766; https://doi.org/10.3390/su18083766 - 10 Apr 2026

Viewed by 600

Abstract

Bike sharing is an established component of urban mobility infrastructure, offering a low-emission alternative to motorized transport for short trips in cities worldwide. Accurate demand forecasting is essential for efficient system operation: it enables better bike redistribution, reduces user wait times, and lowers the operational costs associated with rebalancing. This study evaluated multiple ensemble strategies for hourly bike-sharing demand prediction, comparing bagging methods (Random Forest, Extra Trees), boosting methods (AdaBoost, Gradient Boosting Regressor, Histogram-based Gradient Boosting Regressor), and a Voting ensemble, while systematically investigating the impact of hyperparameter optimization. A repeated hold-out protocol was used, in which the dataset was randomly divided into 80% training and 20% test subsets across 10 random splits; 5-fold cross-validation was applied within each training fold exclusively for hyperparameter tuning, ensuring the test set remained unseen during model selection. Random Search and Bayesian Optimization were compared under identical budgets of 60 configurations per model. Results show that optimization substantially improves all models, with the most pronounced gains for AdaBoost (58% RMSE reduction) and Gradient Boosting Regressor (45% RMSE reduction). A Voting ensemble combining a Random Search-tuned Gradient Boosting Regressor and a Bayesian-optimized Histogram-based Gradient Boosting Regressor achieves the best overall performance (RMSE of 38.48, R² of 0.955) with the lowest variance among all repeated splits. Feature importance analysis confirms that hour of day and temperature are the dominant demand drivers, consistent with the operational patterns of urban bike-sharing systems. The performance difference between Random Search and Bayesian Optimization is negligible for most models, suggesting that well-designed search spaces allow simpler strategies to achieve competitive results. A controlled comparison conducted under identical experimental conditions shows that the Voting ensemble is statistically equivalent to XGBoost and nominally better than LightGBM, while CatBoost achieves a statistically significant advantage, highlighting it as a strong individual alternative. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainable Development)

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19 pages, 1090 KB

Open AccessArticle

Facilitating AI-Driven Sustainability: A Service-Oriented Architecture for Interoperable Environmental Data Access

by Babak Jalalzadeh Fard, Sadid A. Hasan and Jesse E. Bell

Sustainability 2026, 18(5), 2445; https://doi.org/10.3390/su18052445 - 3 Mar 2026

Cited by 2 | Viewed by 963

Abstract

Advances in artificial intelligence (AI), particularly agentic AI, have created opportunities to enhance global sustainability by improving the efficiency and accuracy of environmental monitoring and response systems. Agentic AIs autonomously plan and execute towards specific goals with minimal or no human intervention; however, accessing environmental data is challenging and requires expertise due to inherent fragmentation and the diversity of data formats. The Model Context Protocol (MCP) is an open standard that allows AI systems to securely access and interact with diverse software tools and data sources through unified interfaces, reducing the need for custom integrations while enabling more accurate, context-aware assistance. This study introduces WeatherInfo_MCP, an interface that provides the required expertise for AI agents to access National Weather Service (NWS) data. Built on a service-oriented architecture, the system uses a centralized engine to handle robust geocoding and data extraction while providing AI agents with simple, independent tools to retrieve weather data from the NWS API. The system was validated through 14 unit tests and 23 comprehensive protocol compliance tests against the MCP 2025-06-18 specification, achieving a 100% pass rate across all categories, demonstrating its reliability when working with AI agents. We also successfully tested our model alongside a memory MCP to showcase its performance in a multi-MCP environment. While in its earliest version, WeatherInfo_MCP connects to the NWS API, its modular design and compliance with software development and MCP standards facilitate immediate expansion to additional environmental data and tools. WeatherInfo_MCP is released as an open-source tool to support the sustainable development community, enabling broad adoption of AI agents for environmental use cases. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainable Development)

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15 pages, 2905 KB

Open AccessArticle

DeepWasteSort-SI-SSO: A Vision Transformer-Based Waste Image Classification Framework Optimized with Self Improved Sparrow Search Optimizer

by Nasser A. Alsadhan

Sustainability 2026, 18(4), 2080; https://doi.org/10.3390/su18042080 - 19 Feb 2026

Viewed by 464

Abstract

Automated waste classification is essential for improving recycling efficiency and supporting sustainable waste management systems. However, conventional convolutional neural network (CNN) approaches primarily focus on localized feature extraction, which may limit their ability to capture complex spatial relationships in heterogeneous waste materials. This study proposes DeepWasteSort-SI-SSO, a Vision Transformer (ViT)-based framework enhanced with a Self-Improved Sparrow Search Optimization (SI-SSO) strategy for hyperparameter tuning. The optimization process focuses on key training parameters, including learning rate, batch size, and dropout rate, to improve convergence stability and reduce the risk of suboptimal local minima. The framework was evaluated on a balanced four-class waste image dataset (paper, wood, food, and leaves; N = 4000) using a five-fold cross-validation protocol. Experimental results achieved an average accuracy of 95.5% (±0.007), a macro-averaged AUC-ROC of 0.975, and a Cohen’s Kappa coefficient of 0.938, indicating strong agreement between predicted and true labels. Comparative experiments against ResNet-50 and a baseline ViT configuration suggest that SI-SSO optimization improves performance stability with only a modest increase in computational cost. These findings highlight the potential of optimized Transformer-based approaches for automated waste image classification under controlled evaluation conditions. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainable Development)

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26 pages, 1881 KB

Open AccessArticle

How Does the Construction of New Generation of National AI Innovative Development Pilot Zones Affect Carbon Emissions Intensity? Empirical Evidence from China

by Lu Wang, Ziying Zhao, Xiaojun Xu, Xiaoli Wang and Yuting Wang

Sustainability 2025, 17(15), 6858; https://doi.org/10.3390/su17156858 - 28 Jul 2025

Cited by 4 | Viewed by 3402

Abstract

At a critical juncture in the global low-carbon transition, the role of artificial intelligence (AI) in facilitating low-carbon growth has become increasingly significant. To accelerate the integration of AI with socio-economic development, China has established National New Generation Artificial Intelligence Innovation and Development Pilot Zones (AIPZ). However, the specific impact of these zones on low-carbon development remains unclear. This study utilized panel data from 30 provinces in China from 2013 to 2022 and employed the multi-period difference-in-differences (DID) model and the spatial autoregressive difference-in-differences (SARDID) model to examine the carbon emissions reduction effects of the AIPZ policy and its spatial spillover effects. The findings revealed that the policy significantly reduced carbon emissions intensity (CEI) across provinces, with an average reduction effect of 6.9%. The analysis of the impact mechanism confirmed the key role of human, technological, and financial resources. Heterogeneity analysis indicated varying effects across regions, with more significant reductions in eastern and energy-rich areas. Further analysis using the SARDID model confirmed spatial spillover effects on CEI. This paper aims to enhance understanding of the relationship between AIPZ and CEI and provide empirical evidence for policymakers during the low-carbon transition. By exploring the potential of the AIPZ policy in emissions reduction, it proposes targeted strategies and implementation pathways for policymakers and industry participants to promote the sustainable development of China’s low-carbon economy. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainable Development)

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25 pages, 1830 KB

Open AccessArticle

Artificial Intelligence Adoption and Role of Energy Structure, Infrastructure, Financial Inclusions, and Carbon Emissions: Quantile Analysis of E-7 Nations

by Shanwen Gu and Adil Javed

Sustainability 2025, 17(13), 5920; https://doi.org/10.3390/su17135920 - 27 Jun 2025

Cited by 13 | Viewed by 3087

Abstract

The E-7 nations face significant challenges in harmonizing artificial intelligence (AI) adoption with sustainable economic and environmental goals. While AI holds transformative potential to revolutionize energy structures, modernize infrastructure, broaden financial inclusion, and reduce carbon emissions, its effective integration is frequently hindered by policy inertia, economic limitations, and long-standing institutional barriers. Using the multi-level perspective (MLP), this study employs the method of moments quantile regression (MMQREG) on panel data from 2004 to 2024 to investigate the determinants of artificial intelligence (AI) adoption, focusing on the roles of energy structure (ES), infrastructure (INFRA), financial inclusion (FI), economic growth (GDP), patent activity (Tpatent), population (TP), and carbon emissions (CE) across E-7 nations. The study findings reveal that economic growth and energy structure play a significant role in driving AI adoption, while inadequacies in infrastructure and limited financial inclusion significantly hinder AI progress. Additionally, the analysis reveals a positive relationship between AI adoption and CO₂ emissions, where early stages of technology uptake lead to increased emissions, but sustained integration eventually results in efficiency gains that help to reduce them. These findings underscore the need for E-7 nations to adopt targeted policies that modernize digital and physical infrastructure, broaden financial access, and expedite the transition to sustainable energy systems. This study offers actionable insights for policymakers to align digital innovation with sustainable development goals. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainable Development)

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23 pages, 3258 KB

Open AccessArticle

Trade-Off Between Energy Consumption and Three Configuration Parameters in Artificial Intelligence (AI) Training: Lessons for Environmental Policy

by Sri Ariyanti, Muhammad Suryanegara, Ajib Setyo Arifin, Amalia Irma Nurwidya and Nur Hayati

Sustainability 2025, 17(12), 5359; https://doi.org/10.3390/su17125359 - 10 Jun 2025

Cited by 7 | Viewed by 5794

Abstract

Rapid advancements in artificial intelligence (AI) have led to a substantial increase in energy consumption, particularly during the training phase of AI models. As AI adoption continues to grow, its environmental impact presents a significant challenge to the achievement of the United Nations’ Sustainable Development Goals (SDGs). This study examines how three key training configuration parameters—early-stopping epochs, training data size, and batch size—can be optimized to balance model accuracy and energy efficiency. Through a series of experimental simulations, we analyze the impact of each parameter on both energy consumption and model performance, offering insights that contribute to the development of environmental policies that are aligned with the SDGs. The results demonstrate strong potential for reducing energy usage without compromising model reliability. The results highlight three lessons: promoting early-stopping epochs as an energy-efficient practice, limiting training data size to enhance energy efficiency, and developing standardized guidelines for batch size optimization. The practical applicability of these three lessons is illustrated through the implementation of a smart building attendance system using facial recognition technology within an Ecocampus environment. This real-world application highlights how energy-conscious AI training configurations support sustainable urban innovation and contribute to climate action and environmentally responsible AI development. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainable Development)

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17 pages, 3247 KB

Open AccessArticle

A Fuzzy LARG Index for Assessing the Lean, Agile, Resilience, and Green Paradigms in Industrial Companies

by Abbas Al-Refaie and Natalija Lepkova

Sustainability 2025, 17(5), 1863; https://doi.org/10.3390/su17051863 - 22 Feb 2025

Cited by 7 | Viewed by 1963

Abstract

Today’s sharp competition has forced organizations to adopt effective improvement paradigms, including lean, agile, green, and resilience (LARG). However, an assessment tool is necessary to monitor the progress of LARG adoption and evaluate its effectiveness. This research, therefore, developed an index for assessing the effectiveness of LARG paradigms by evaluating their principles and practices with experts’ fuzzy evaluations. Initially, thorough research on LARG paradigms was conducted to determine the paradigm principles and their measures and prepare a comprehensive LARG survey. The Delphi method with four experts was used to rate item measures of LARG based on a five-point Likert scale. The principles and measures of each paradigm were represented by triangular fuzzy membership functions. Then, defuzzified values were obtained for each principle and set as inputs in the fuzzy inference system (FIS) to obtain a crisp value for each paradigm. Next, the defuzzified values of lean, agile, and green (LAG) were input in the FIS to obtain a crisp LAG index. Finally, the defuzzified values of the LAG and resilience (R) were measured and then inserted as inputs in the FIS to obtain a comprehensive defuzzified LARG value. The effectiveness of the proposed LARG framework was validated in pharmaceutical and chemical organizations. The results revealed that the LARG index is an effective tool for evaluating lean, agile, green, and resilience paradigms for both organizations. In conclusion, the LARG index provides valuable support to decision-makers in determining a business’s weaknesses and strengths and guides technical managers to possible improvement actions to enhance competitiveness and sustainability. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainable Development)

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26 pages, 6491 KB

Open AccessSystematic Review

A Systematic Review of Green and Sustainable AI: Taxonomy, Metrics, Challenges, and Open Research Directions

by Outmane Marmouzi, Ilham Oumaira and Mehdia Ajana El Khaddar

Sustainability 2026, 18(8), 4115; https://doi.org/10.3390/su18084115 - 21 Apr 2026

Cited by 1 | Viewed by 1719

Abstract

Due to the recent rapid development of artificial intelligence (AI) and its expanding impact on the planet, green and sustainable AI research has increasingly gained attention. This systematic literature review searches main databases, including Scopus, Web of Science, and Google Scholar, using an organized methodological approach. Following a thorough screening process, 49 final studies published between 2016 and 2026 are selected from an initial identification of 325 original records. We identify and analyze four key categories of sustainable AI practices: (1) model-level algorithmic efficiency, (2) hardware- and system-level optimization, (3) lifecycle- and data-centric approaches, and (4) operational and policy-level sustainability. We also highlight and explain four dimensions at the intersection of AI and environmentally responsible behavior: AI for sustainable applications’ development in industries, ethical considerations and accountability in using AI, and opportunities enabled by generative AI. We then combine existing taxonomies, evaluation metrics, and challenges to identify areas for improvement and suggest future research directions. Based on our analysis, we emphasize the need for interdisciplinary cooperation to facilitate responsible AI innovation and match it with global sustainable development goals (SDGs). We also highlight the importance of developing adequate frameworks along with precisely defined and standardized metrics to assess the environmental impact of AI. This review aims to encourage more responsible and environmentally friendly AI practices by providing a structured framework for researchers, educators, and professionals engaged in sustainable AI. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainable Development)

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