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Artificial Intelligence and Sustainable Development
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Artificial Intelligence and Sustainable Development

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

Deadline for manuscript submissions: 10 March 2026 | Viewed by 5318

Special Issue Editors

Dr. Pierrick Bruneau

E-Mail Website
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

E-Mail Website
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

E-Mail Website
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

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

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

Published Papers (4 papers)

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Research

26 pages, 1881 KB  
Article
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
Viewed by 972
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 [...] Read more.
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  
Article
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 2 | Viewed by 831
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 [...] Read more.
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  
Article
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 1 | Viewed by 1613
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’ [...] Read more.
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  
Article
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 2 | Viewed by 1046
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 [...] Read more.
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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