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AI for Sustainable Development: Applications and Impacts across Industries

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Products and Services".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 4401

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Special Issue Editors

Prof. Dr. Ahreum Hong

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Guest Editor

Graduate School of Technology Management, Kyung Hee University, Seoul, Republic of Korea
Interests: artificial intelligence in sustainable technology management; digital transformation and innovation in emerging markets; data-driven decision making for environmental sustainability

Prof. Dr. Yannan Li

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Guest Editor

Graduate School of Technology Management, Kyung Hee University, Seoul, Republic of Korea
Interests: AI and sustainable development; environmental policy and AI integration; human resource management and AI technologies

Special Issue Information

Dear Colleagues,

The Special Issue on “AI for Sustainable Development: Applications and Impacts across Industries” aims to explore how Artificial Intelligence (AI) is revolutionizing various sectors to support the achievement of sustainable development goals (SDGs). This issue invites research that examines the application of AI in promoting sustainability across diverse domains such as energy, agriculture, urban planning, and resource management. This Special Issue seeks to contribute to a deeper understanding of how AI can be harnessed to address global environmental and societal challenges by bridging the gap between AI innovation and sustainable practices. We encourage submissions that provide empirical evidence, methodological advancements, and theoretical perspectives on integrating AI technologies to foster sustainable development. The goal is to advance interdisciplinary research highlighting AI’s potential to drive progress toward a more sustainable future.

Prof. Dr. Ahreum Hong
Prof. Dr. Yannan Li
Guest Editors

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Keywords

artificial intelligence
AI applications
economics in AI
ethics in AI
educational AI
transportation AI
machine learning
domain-specific AI
interdisciplinary research

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Published Papers (7 papers)

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Research

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29 pages, 2056 KiB

Open AccessArticle

Effectiveness of Artificial Intelligence Practices in the Teaching of Social Sciences: A Multi-Complementary Research Approach on Pre-School Education

by Yunus Doğan, Veli Batdı, Yavuz Topkaya, Salman Özüpekçe and Hatun Vera Akşab

Sustainability 2025, 17(7), 3159; https://doi.org/10.3390/su17073159 (registering DOI) - 2 Apr 2025

Abstract

The aim of this study is to evaluate artificial intelligence applications in the preschool education level within the framework of the multi-complementary approach (McA). The McA is designed as a comprehensive approach that encompasses multiple analysis methods. In the first phase of the study, the pre-complementary knowledge process, meta-analysis, and meta-thematic analysis methods were used; in the post-complementary knowledge process, an experimental design with a control group and pre-test/post-test was applied. Finally, in the complementary knowledge phase, the findings of the first two phases were combined, providing an opportunity to evaluate the effectiveness of artificial intelligence applications in preschool education from a more comprehensive and broader perspective. The study provides information about the McA, and then the methodological process and findings of the research are presented in detail within this framework. After providing information about the McA, the methodological process and results of the study are presented step by step within this framework. A literature review based on document analysis in the context of social sciences and teaching in preschool education using artificial intelligence applications has shown that the application of artificial intelligence has positive and significant effects on both student performance and various variables supporting teaching. The complementary results favoring artificial intelligence applications encourage the increased use of such technologies in preschool education, promoting their more widespread and systematic use in the teaching environment. Full article

(This article belongs to the Special Issue AI for Sustainable Development: Applications and Impacts across Industries)

13 pages, 236 KiB

Open AccessArticle

Students’ Attitudes Towards AI and How They Perceive the Effectiveness of AI in Designing Video Games

by Sara Sáez-Velasco, Mario Alaguero-Rodríguez, Sonia Rodríguez-Cano and Vanesa Delgado-Benito

Sustainability 2025, 17(7), 3096; https://doi.org/10.3390/su17073096 - 31 Mar 2025

Viewed by 57

Abstract

The aim of this paper is to find out what the attitudes of higher education students in arts education are towards generative AI and how this relates to their use of it in their academic/professional practice. This is a case study and an exploratory, descriptive and correlational quantitative research study, the methodology of which allows us to determine the vision of the sample of participants in relation to the subject. The design consists of three phases: (1) students complete an Attitude Towards Artificial Intelligence (ATAI) scale; (2) they then create two sketches as a collage of images to be used as visual references for a future digital illustration, one using images from the internet and the other using a generative AI tool; and (3) finally, students complete a questionnaire on their perception after using the generative AI tool used in the activity. The results show significant relationships between attitudes towards AI and perceptions of its effectiveness, efficiency, creativity, and design autonomy. It seems that the attitude with which students approach AI tools is a determining factor when it comes to using them in design tasks and can contribute to quality education. Full article

(This article belongs to the Special Issue AI for Sustainable Development: Applications and Impacts across Industries)

29 pages, 4923 KiB

Open AccessArticle

Artificial Intelligence Applications in Primary Education: A Quantitatively Complemented Mixed-Meta-Method Study

by Yavuz Topkaya, Yunus Doğan, Veli Batdı and Sami Aydın

Sustainability 2025, 17(7), 3015; https://doi.org/10.3390/su17073015 - 28 Mar 2025

Viewed by 260

Abstract

In recent years, rapidly advancing technology has reshaped our world, holding the potential to transform social and economic structures. The United Nations’ Sustainable Development Goals (SDGs) provide a comprehensive roadmap that promotes not only economic growth but also social, environmental, and global sustainability. Meanwhile, artificial intelligence (AI) has emerged as a critical technology contributing to sustainable development by offering solutions to both social and economic challenges. One of the fundamental ideas is that education should always maintain a dynamic structure that supports sustainable development and fosters individuals equipped with sustainability skills. In this study, the impact of various variables related to AI applications in primary education at the elementary school level, in line with sustainable development goals, was evaluated using a mixed meta-method complemented with quantitative analyses. Within the framework of the mixed meta-method, a meta-analysis of data obtained from studies conducted between 2005 and 2025 was performed using the CMA program. The analysis determined a medium effect size of g = 0.51. To validate the meta-analysis results and enhance their content validity, a meta-thematic analysis was conducted, applying content analysis to identify themes and codes. In the final stage of this research, to further support the data obtained through the mixed meta-method, a set of evaluation form questions prepared within the Rasch measurement model framework was administered to primary school teachers. The collected data were analyzed using the FACETS program. The findings from the meta-analysis document review indicated that AI studies in primary education were most commonly applied in mathematics courses. During the meta-thematic analysis process, themes related to the impact of AI applications on learning environments, challenges encountered during implementation, and proposed solutions were identified. The Rasch measurement model process revealed that AI applications were widely used in science and mathematics curricula (FBP-4 and MP-2). Among the evaluators (raters), J2 was identified as the most lenient rater, while J11 was the strictest. When analyzing the AI-related items, the statement “I can help students prepare a presentation describing their surroundings using AI tools” (I17) was identified as the most challenging item, whereas “I understand how to effectively use AI applications in classroom activities” (I14) was found to be the easiest. The results of the analyses indicate that the obtained data are complementary and mutually supportive. The findings of this research are expected to serve as a guide for future studies and applications related to the topic, making significant contributions to the field. Full article

(This article belongs to the Special Issue AI for Sustainable Development: Applications and Impacts across Industries)

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25 pages, 954 KiB

Open AccessArticle

Navigating the Digital Frontier: Exploring the Dynamics of Customer–Brand Relationships Through AI Chatbots

by Zongwen Xia and Randall Shannon

Sustainability 2025, 17(5), 2173; https://doi.org/10.3390/su17052173 - 3 Mar 2025

Viewed by 554

Abstract

With the rapid advancement of artificial intelligence (AI), chatbots represent a transformative tool in digital customer engagement, reshaping customer–brand relationships. This paper explores AI chatbots on customer–brand interactions by analyzing key features, such as interaction, perceived enjoyment, customization, and problem-solving. Based on the Technology Acceptance Model (TAM), the research investigates how these attributes influence perceived ease of use, perceived usefulness, customer attitudes, and ultimately, customer–brand relationships. Adopting a mixed-methods approach, this study begins with qualitative interviews to identify key engagement factors, which then inform the design of a structured quantitative survey. The findings reveal that AI chatbot features significantly enhance customer perceptions, with ease of use and usefulness in shaping positive attitudes and strengthening brand connections. The research further underscores the role of AI-driven personalization in delivering sustainable customer engagement by optimizing digital interactions, reducing resource-intensive human support, and promoting long-term brand loyalty. By integrating TAM with customer–brand relationship theories, this study contributes to AI and sustainability research by highlighting how intelligent chatbots can facilitate responsible business practices, enhance operational efficiency, and promote digital sustainability through automation and resource optimization. The findings provide strategic insights for businesses seeking to design AI-driven chatbot systems that improve customer experience and align with sustainable digital transformation efforts. Full article

(This article belongs to the Special Issue AI for Sustainable Development: Applications and Impacts across Industries)

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24 pages, 1930 KiB

Open AccessArticle

The Impact of Rainfall on Water, Energy, Industry and Economic Growth—Based on Empirical Data from 29 Provinces in China

by Yuan Gao, Qiqi Xiao and Zhong Fang

Sustainability 2025, 17(1), 40; https://doi.org/10.3390/su17010040 - 25 Dec 2024

Cited by 1 | Viewed by 687

Abstract

Sustainable urban development requires good interaction between water, energy, infrastructure and socio-economic areas. In the context of more frequent heavy rainfall and flooding events, managing the subsystems within the city in an integrated manner and realizing sustainable development have become popular research topics. Based on the above analysis, this paper constructs a water, energy, industry and economic growth system. It also introduces rainfall as an exogenous variable into the model in order to simulate the process of interactions between subsystems within a city and achieve sustainable development. By measuring the dynamic changes and spatial distribution characteristics of the efficiency values of the total water–energy–industry and economic growth system and each subsystem in 29 provinces in China, the following conclusions are drawn: (1) Most of the provinces are in the situation of “high-efficiency–negative growth” or “low-efficiency–positive growth”, and the constraints for them to reach the state of “high efficiency–positive growth” are due to the water subsystem. (2) The low-efficiency provinces are mainly concentrated in the central region, and the spillover effect of the low-efficiency provinces on the neighboring regions is more notable than that of the high-efficiency provinces. (3) The addition of rainfall improves the total efficiency in most provinces, with the most obvious improvement in the efficiency of the water subsystem. (4) The efficiency value of the industry and economic growth subsystem is relatively less affected by the amount of rainfall, but excessive rainfall will also have a negative impact. Finally, relevant policy recommendations are made to inform the relevant government departments in formulating policies related to addressing climate change and achieving sustainable urban development. Full article

(This article belongs to the Special Issue AI for Sustainable Development: Applications and Impacts across Industries)

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24 pages, 6150 KiB

Open AccessArticle

Forecasting Maritime and Financial Market Trends: Leveraging CNN-LSTM Models for Sustainable Shipping and China’s Financial Market Integration

by Zihui Han, Xiangcheng Zhu and Zhenqing Su

Sustainability 2024, 16(22), 9853; https://doi.org/10.3390/su16229853 - 12 Nov 2024

Cited by 2 | Viewed by 1318

Abstract

With the acceleration of economic globalization, China’s financial market has emerged as a vital force in the global financial system. The Baltic Dry Index (BDI) and China Container Freight Index (CCFI) serve as key indicators of the shipping sector’s health, reflecting their sensitivity to shifts in China’s financial landscape. This study utilizes an innovative CNN-LSTM deep learning model to forecast the BDI and CCFI, using 25,974 daily data points from the Chinese financial market between 5 May 2015 and 30 November 2022. The model achieves high predictive accuracy across diverse samples, frequencies, and structural variations, with an R² of 97.2%, showcasing its robustness. Beyond its predictive strength, this research underscores the critical role of China’s financial market in advancing sustainable practices within the global shipping industry. By merging advanced analytics with sustainable shipping strategies, the findings offer stakeholders valuable tools for optimizing operations and investments, reducing emissions, and promoting long-term environmental sustainability in both sectors. Additionally, this study enhances the resilience and stability of financial and shipping ecosystems, laying the groundwork for an eco-friendly, efficient, and sustainable global logistics network in the digital era. Full article

(This article belongs to the Special Issue AI for Sustainable Development: Applications and Impacts across Industries)

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Other

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39 pages, 1745 KiB

Open AccessSystematic Review

Digital Twins, Extended Reality, and Artificial Intelligence in Manufacturing Reconfiguration: A Systematic Literature Review

by Anjela Mayer, Lucas Greif, Tim Markus Häußermann, Simon Otto, Kevin Kastner, Sleiman El Bobbou, Jean-Rémy Chardonnet, Julian Reichwald, Jürgen Fleischer and Jivka Ovtcharova

Sustainability 2025, 17(5), 2318; https://doi.org/10.3390/su17052318 - 6 Mar 2025

Viewed by 569

Abstract

This review draws on a systematic literature review and bibliometric analysis to examine how Digital Twins (DTs), Extended Reality (XR), and Artificial Intelligence (AI) support the reconfiguration of Cyber–Physical Systems (CPSs) in modern manufacturing. The review aims to provide an updated overview of these technologies’ roles in CPS reconfiguration, summarize best practices, and suggest future research directions. In a two-phase process, we first analyzed related work to assess the current state of assisted manufacturing reconfiguration and identify gaps in existing reviews. Based on these insights, an adapted PRISMA methodology was applied to screen 165 articles from the Scopus and Web of Science databases, focusing on those published between 2019 and 2025 addressing DT, XR, and AI integration in Reconfigurable Manufacturing Systems (RMSs). After applying the exclusion criteria, 38 articles were selected for final analysis. The findings highlight the individual and combined impact of DTs, XR, and AI on reconfiguration processes. DTs notably reduce reconfiguration time and improve system availability, AI enhances decision-making, and XR improves human–machine interactions. Despite these advancements, a research gap exists regarding the combined application of these technologies, indicating potential areas for future exploration. The reviewed studies recognized limitations, especially due to diverse study designs and methodologies that may introduce risks of bias, yet the review offers insight into the current DT, XR, and AI landscape in RMS and suggests areas for future research. Full article

(This article belongs to the Special Issue AI for Sustainable Development: Applications and Impacts across Industries)

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