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31 pages, 3373 KB

Open AccessArticle

Measuring AI Automation Exposure for Sustainable Workforce Transitions: A Two-Mechanism Decomposition

by Gang Peng

Sustainability 2026, 18(17), 9103; https://doi.org/10.3390/su18179103 - 4 Sep 2026

Viewed by 247

Abstract

Artificial intelligence (AI) automates work through two mechanisms: codification-based automation, which applies rule-based systems to structured tasks, and learning-based automation, which applies machine learning and generative AI to pattern- and language-intensive tasks. An account of task-based exposure must represent both, yet each existing [...] Read more.

Artificial intelligence (AI) automates work through two mechanisms: codification-based automation, which applies rule-based systems to structured tasks, and learning-based automation, which applies machine learning and generative AI to pattern- and language-intensive tasks. An account of task-based exposure must represent both, yet each existing measure captures a single mechanism or conflates the two. Using publicly available O*NET data, we decompose exposure into a Routine Task Intensity (RTI) measure for codification and a Machine Learning Capability Index (MLCI) for learning, combined into a composite AI Exposure Index (AEI). The two components correlate weakly, so no single index can stand in for both. Validating against O*NET’s incumbent-reported degree of automation across two periods (2011–2019 and 2020–2025), we find realized automation remains dominated by codification, while the learning channel registers only recently and faintly. The two components also interact: each predicts realized automation most strongly where the other is absent, so entering them jointly with their interaction outperforms either component alone and the composite. Because forecasts built on such indices inform where retraining and income support are directed, accurate mechanism attribution bears on decent work and inequality. The decomposition offers a more complete, transparent, and reproducible account of task-based exposure than any single-mechanism index. Full article

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

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29 pages, 1444 KB

Open AccessArticle

Artificial Intelligence and Sustainable Manufacturing Upgrading: Evidence from Manufacturing Export Technological Sophistication in Chinese Cities

by Minhui Li, Lufang Kang, Fang Yu, Wusong Zhou, Wenxuan Zhao and Jie Qiu

Sustainability 2026, 18(17), 8931; https://doi.org/10.3390/su18178931 - 1 Sep 2026

Viewed by 318

Abstract

Upgrading manufacturing exports is essential for sustainable economic development. It strengthens productivity, resilience, and industrial competitiveness as cost-based advantages weaken. Yet it remains unclear whether city-level AI ecosystems are associated with this transition. Using a balanced panel of 257 Chinese prefecture-level cities from [...] Read more.

Upgrading manufacturing exports is essential for sustainable economic development. It strengthens productivity, resilience, and industrial competitiveness as cost-based advantages weaken. Yet it remains unclear whether city-level AI ecosystems are associated with this transition. Using a balanced panel of 257 Chinese prefecture-level cities from 2010 to 2023, this study examines the relationship between city-level AI ecosystem development, proxied by the stock of AI-related enterprises, and manufacturing export technological sophistication. Two-way fixed-effects models are combined with robustness checks, supplementary lagged and city-trend specifications, channel-related association analysis, heterogeneity analysis, and panel threshold models. The results show a positive within-city association between AI ecosystem development and export technological sophistication. AI ecosystem development is also positively associated with technological innovation and entrepreneurial vitality, while the positive industrial-structure association is weaker and sensitive to the inference method. The association varies descriptively across city types and becomes more pronounced after the local AI ecosystem crosses estimated thresholds. The study advances existing research by shifting from single-technology or firm-adoption measures to a city-level ecosystem perspective and by linking that perspective to export-structure upgrading. Theoretically, it highlights the role of absorptive capacity and complementary local capabilities; practically, it supports differentiated policies that match AI infrastructure, skills, and manufacturing capabilities to cities’ development stages. Because the evidence is observational, the estimates are interpreted as conditional associations rather than definitive causal effects. Full article

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

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43 pages, 1629 KB

Open AccessArticle

Artificial Intelligence Utilization and Perceived Firm Performance in Chinese Logistics Firms: The Roles of Innovation Capability and Logistics Efficiency

by Chenghao Shang and Changone Kim

Sustainability 2026, 18(15), 7525; https://doi.org/10.3390/su18157525 - 23 Jul 2026

Viewed by 674

Abstract

Artificial intelligence (AI) is used in logistics, but the mechanisms linking AI utilization to firm performance remain insufficiently differentiated. Drawing on the information technology business value perspective and dynamic capabilities theory, this study examines whether managers’ perceptions of logistics-oriented AI utilization are associated [...] Read more.

Artificial intelligence (AI) is used in logistics, but the mechanisms linking AI utilization to firm performance remain insufficiently differentiated. Drawing on the information technology business value perspective and dynamic capabilities theory, this study examines whether managers’ perceptions of logistics-oriented AI utilization are associated with perceived firm performance through innovation capability and logistics efficiency, with managerial support treated as a secondary boundary condition. Cross-sectional survey data from 254 middle- and senior-level managers in Chinese logistics firms were analyzed using IBM SPSS Statistics 27 and IBM SPSS Amos 29 (IBM Corp., Armonk, NY, USA), and the PROCESS macro version 4.2 (Andrew F. Hayes, Calgary, AB, Canada), with Model 83 and 5000 bootstrap samples. Perceived AI utilization was positively associated with innovation capability, logistics efficiency, and perceived firm performance. Both mediators showed significant indirect effects, and their sequential indirect effect was also significant. The two individual indirect effects did not differ significantly, but both exceeded the sequential indirect effect. The proposed sequential, reverse-sequence, and parallel-mediation models produced identical fit indices, whereas the restricted direct-effects model showed weaker fit. Neither the AI utilization–managerial support interaction nor the moderated mediation indices was significant. Exploratory item-level analyses showed differentiated associations for demand forecasting and order allocation and for AI infrastructure; the pattern remained stable among 194 respondents involved in AI- or digital transformation-related activities. Innovation capability and logistics efficiency appear to function as complementary mechanisms, with a smaller capability-to-process pathway. Their relative ordering cannot be determined from the cross-sectional data. As the data are self-reported, the findings represent associations among managerial perceptions rather than objective causal effects. Sustainability implications are limited to operational efficiency because environmental outcomes were not directly measured. Full article

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

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30 pages, 1964 KB

Open AccessArticle

AI for Sustainable Cultural Industries: A Screenplay-Aware Knowledge-Enhanced State Space Model with LLM-Derived Narrative Features for Forecasting Film Industry Sustainability Across National Economies

by Peixuan Qi and Weidong Zhu

Sustainability 2026, 18(12), 6117; https://doi.org/10.3390/su18126117 - 14 Jun 2026

Viewed by 758

Abstract

This paper examines how artificial intelligence can support sustainability assessment in cultural industries, using national film industries as a test case. The Film Industry Sustainability Index (FISI) is introduced as a composite indicator covering cultural diversity, economic resilience, and Sustainable Development Goal (SDG) [...] Read more.

This paper examines how artificial intelligence can support sustainability assessment in cultural industries, using national film industries as a test case. The Film Industry Sustainability Index (FISI) is introduced as a composite indicator covering cultural diversity, economic resilience, and Sustainable Development Goal (SDG) alignment for 42 national economies from 2005 to 2023. Knowledge-Enhanced Mamba (KE-Mamba), a selective state-space forecasting model, is then proposed to combine annual panel indicators with country-level film-industry knowledge graph (KG) embeddings and large language model (LLM)-derived screenplay-oriented narrative proxies from film synopses. To reduce factual errors in title-level narrative scoring, the LLM is anchored to verified United Nations Educational, Scientific and Cultural Organization (UNESCO) records and the European Audiovisual Observatory’s LUMIERE film-admissions database using rank-one model editing (ROME). On the 2020–2023 held-out test period, KE-Mamba achieves a composite FISI mean absolute error (MAE) of 0.0389, a mean absolute percentage error (MAPE) of 5.61%, and an

R^{2}

of 0.934, outperforming autoregressive integrated moving average (ARIMA), tree-based, long short-term memory (LSTM), and base Mamba baselines. Additional robustness checks using a pre-pandemic split, two-way fixed-effects panel regression, alternative FISI weighting schemes, KG embedding ablations, and human validation of LLM narrative scores support the reliability of the proposed framework. Policy simulations are interpreted as model-based projected associations rather than causal estimates. The results show that knowledge-enhanced sequence models can provide transparent forecasting support for sustainable cultural-industry policy. Full article

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

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21 pages, 292 KB

Open AccessArticle

Spatial Effects of Artificial Intelligence Innovation on Regional Carbon Intensity

by Hsuan-Tsun Huang and Ching-Wei Ho

Sustainability 2026, 18(11), 5272; https://doi.org/10.3390/su18115272 - 24 May 2026

Viewed by 588

Abstract

This study investigates the spatial effects of artificial intelligence (AI) innovation on carbon intensity using provincial panel data from 30 Chinese provinces over 2010–2023. Employing the Spatial Durbin Model (SDM), we find that a 1% increase in AI patent count reduces local carbon [...] Read more.

This study investigates the spatial effects of artificial intelligence (AI) innovation on carbon intensity using provincial panel data from 30 Chinese provinces over 2010–2023. Employing the Spatial Durbin Model (SDM), we find that a 1% increase in AI patent count reduces local carbon intensity by 0.034% (direct effect, p < 0.01) but increases carbon intensity in neighboring regions by 0.069% (indirect effect, p < 0.05). Heterogeneity analysis shows that AI innovation reduces local carbon intensity by 0.069% in non-western regions (p < 0.01) but has no significant effect in the western region. In regions with above-median R&D intensity, both direct and indirect effects become negative (−0.094% and −0.069%, respectively), indicating that AI innovation reduces carbon intensity locally and in neighboring areas. Mechanism tests confirm that industrial structure upgrading mediates this relationship, with AI innovation increasing the industrial structure hierarchy coefficient by 0.004 (p < 0.05). These findings provide quantitative evidence that AI innovation has opposing local and spillover effects on carbon intensity, and that high R&D intensity can reverse negative spillovers into positive ones. The results offer empirically grounded policy recommendations for China’s dual-carbon targets and sustainable development. Full article

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

20 pages, 404 KB

Open AccessArticle

Individual Behavior or Collective Phenomenon: Peer Effects in the Coordinated Intelligentization and Greenization of Chinese Manufacturing Firms

by Liangfeng Hao, Xinyuan Li and Zhongjuan Ji

Sustainability 2025, 17(24), 11013; https://doi.org/10.3390/su172411013 - 9 Dec 2025

Viewed by 685

Abstract

Artificial intelligence technology plays an important role in driving the coordinated development of intelligentization and greenization in China’s manufacturing industry. However, there may be differences in enterprises’ capabilities to advance this coordinated development, and it remains unclear whether promoting such dual transformation is [...] Read more.

Artificial intelligence technology plays an important role in driving the coordinated development of intelligentization and greenization in China’s manufacturing industry. However, there may be differences in enterprises’ capabilities to advance this coordinated development, and it remains unclear whether promoting such dual transformation is an individual behavior or a collective phenomenon. This paper employs the entropy weight method and the coupling coordination degree model to measure the level of coordinated development between enterprise intelligentization and greenization, and examines the peer effects of dual transformation among enterprises. The findings show that enterprise intelligentization lags behind greenization, with the two aspects being in a state of low-level coupling but steadily improving. Additionally, there are significant peer effects in the coordinated development of enterprise intelligentization and greenization, with their formation mechanisms primarily reflected in intelligentization enabling greenization, intra-industry competition, and the learning effect of followers from leaders. Heterogeneity analysis shows that the peer effects in the coordinated development of intelligentization and greenization are more pronounced among state-owned enterprises and technology-intensive firms. Moreover, enterprises located in the same province, within the same large-scale city, or within the same interlocking directorate network are more likely to exhibit peer effects in dual transformation. Full article

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

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30 pages, 867 KB

Open AccessArticle

Spillover Effects of Artificial Intelligence Technology, Sustainable Innovation, and Industrial Transition Between Eastern and Western Regions

by Chaobo Zhou

Sustainability 2025, 17(22), 10047; https://doi.org/10.3390/su172210047 - 10 Nov 2025

Cited by 1 | Viewed by 2643

Abstract

For a considerable period, China’s eastern and western regions have grappled with imbalances in industrial development, with industrial leapfrogging emerging as a pivotal solution. This study examines the impact of artificial intelligence technology spillovers and sustainable innovation on industrial leapfrogging between eastern and [...] Read more.

For a considerable period, China’s eastern and western regions have grappled with imbalances in industrial development, with industrial leapfrogging emerging as a pivotal solution. This study examines the impact of artificial intelligence technology spillovers and sustainable innovation on industrial leapfrogging between eastern and western regions. Empirical analysis is conducted using panel data from 22 provinces and municipalities across eastern and western China spanning 2014–2024, employing both a spatial difference-in-differences model and a dual machine learning model. Findings reveal that both AI technology spillovers and sustainable innovation significantly enhance the efficiency of industrial leapfrogging across regions. Their synergistic effects are pronounced, generating positive spatial spillovers. Institutional environments exert a significant influence on leapfrog industrial development. By regulating AI technology environments and sustainable innovation environments, institutional frameworks enhance leapfrogging efficiency, though this mediation exhibits a dual-threshold effect: most western provinces have yet to cross the first threshold. Industrial and economic heterogeneity weaken the efficiency of AI technology spillovers and sustainable innovation in facilitating industrial leapfrogging between eastern and western regions. This research provides robust empirical support for addressing industrial development imbalances and enhancing industrial resilience between eastern and western regions. Full article

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

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23 pages, 1368 KB

Open AccessArticle

Drivers of AI–Sustainability: The Roles of Financial Wealth, Human Capital, and Renewable Energy

by Guangpeng Chen and Anthony David

Sustainability 2025, 17(21), 9920; https://doi.org/10.3390/su17219920 - 6 Nov 2025

Cited by 2 | Viewed by 1675

Abstract

Artificial Intelligence (AI) is increasingly central to sustainable development, yet its advancement varies across G7 economies. This study employs Method of Moments Quantile Regression (MMQR) to examine how Financial Technology (FinTech), Economic Growth (EG), Human Capital (HC), and Renewable Energy Consumption (RENC) influence [...] Read more.

Artificial Intelligence (AI) is increasingly central to sustainable development, yet its advancement varies across G7 economies. This study employs Method of Moments Quantile Regression (MMQR) to examine how Financial Technology (FinTech), Economic Growth (EG), Human Capital (HC), and Renewable Energy Consumption (RENC) influence AI development in G7 countries from 2000 to 2022. By analyzing heterogeneous effects across quantiles, the study captures stage-specific drivers often overlooked in average-based models. Results indicate that FinTech and human capital significantly promote AI adoption in lower and middle quantiles, enhancing digital inclusion and innovation capacity, while RENC becomes relevant primarily at advanced stages of AI adoption. Economic growth exhibits negative or inconsistent effects, suggesting that GDP expansion alone is insufficient for technological transformation without alignment to supportive policies and institutional contexts. The lack of long-run cointegration further highlights the dominance of short- and medium-term dynamics in shaping the AI–sustainability nexus. These findings provide actionable insights for policymakers, emphasizing targeted FinTech development, skill-building initiatives, and renewable-powered AI solutions to foster sustainable and inclusive AI adoption. Overall, the study demonstrates how financial, human, and environmental factors jointly drive AI development, offering a mechanism-based perspective on technology-driven sustainable development in advanced economies. Full article

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

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19 pages, 349 KB

Open AccessArticle

AI-Enabled ESG Compliance Audit for Stakeholders

by Eid M. Alotaibi and Abdulaziz M. Alwathnani

Sustainability 2025, 17(21), 9513; https://doi.org/10.3390/su17219513 - 25 Oct 2025

Cited by 8 | Viewed by 4344

Abstract

Environmental, social, and governance (ESG) disclosures face credibility risks due to Scope 2 Greenhouse Gas (GHG) reports lacking standardized compliance checks, raising concerns about their reliability. This study therefore develops and evaluates an AI-enabled artefact for ESG compliance auditing. This artefact applies natural [...] Read more.

Environmental, social, and governance (ESG) disclosures face credibility risks due to Scope 2 Greenhouse Gas (GHG) reports lacking standardized compliance checks, raising concerns about their reliability. This study therefore develops and evaluates an AI-enabled artefact for ESG compliance auditing. This artefact applies natural language processing (NLP) to extract reported values, implements rule-based checks grounded in the GHG Protocol, and produces transparent output. A design science research (DSR) approach guided the design, demonstration, and evaluation of the artefact, which was applied to sustainability reports from five technology companies. The results revealed that it replicates auditor judgments and reduces workload by over ninety percent in the sample. These findings serve as a proof-of-concept for automation in ESG compliance auditing. The theoretical contributions include extending the literature on AI in ESG auditing by reframing its role from producing interpretive scores to enabling transparent compliance verification. This study also demonstrates how DSR can help produce artefacts that embed rule-based logic into ESG assurance with rigor and practical relevance. The practical contributions include highlighting how a lightweight tool can enable auditors, regulators, boards, and investors to screen disclosures and benchmark credibility without sacrificing professional judgment. Full article

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

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18 pages, 797 KB

Open AccessArticle

A Digital Sustainability Lens: Investigating Medical Students’ Adoption Intentions for AI-Powered NLP Tools in Learning Environments

by Mostafa Aboulnour Salem

Sustainability 2025, 17(14), 6379; https://doi.org/10.3390/su17146379 - 11 Jul 2025

Cited by 8 | Viewed by 1888

Abstract

This study investigates medical students’ intentions to adopt AI-powered Natural Language Processing (NLP) tools (e.g., ChatGPT, Copilot) within educational contexts aligned with the perceived requirements of digital sustainability. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), data were collected [...] Read more.

This study investigates medical students’ intentions to adopt AI-powered Natural Language Processing (NLP) tools (e.g., ChatGPT, Copilot) within educational contexts aligned with the perceived requirements of digital sustainability. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), data were collected from 301 medical students in Saudi Arabia and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results indicate that Performance Expectancy (PE) (β = 0.65), Effort Expectancy (EE) (β = 0.58), and Social Influence (SI) (β = 0.53) collectively and significantly predict Behavioral Intention (BI), explicating 62% of the variance in BI (R² = 0.62). AI awareness did not significantly influence students’ responses or the relationships among constructs, possibly because practical familiarity and widespread exposure to AI-NLP tools exert a stronger influence than general awareness. Moreover, BI exhibited a strong positive effect on perceptions of digital sustainability (PDS) (β = 0.72, R² = 0.51), highlighting a meaningful link between AI adoption and sustainable digital practices. Consequently, these findings indicate the strategic role of AI-driven NLP tools as both educational innovations and key enablers of digital sustainability, aligning with global frameworks such as the Sustainable Development Goals (SDGs) 4 and 9. The study also concerns AI’s transformative potential in medical education and recommends further research, particularly longitudinal studies, to better understand the evolving impact of AI awareness on students’ adoption behaviours. Full article

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

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32 pages, 2207 KB

Open AccessArticle

Advancing Sustainable Additive Manufacturing: Analyzing Parameter Influences and Machine Learning Approaches for CO₂ Prediction

by Svenja Hauck, Lucas Greif, Nils Benner and Jivka Ovtcharova

Sustainability 2025, 17(9), 3804; https://doi.org/10.3390/su17093804 - 23 Apr 2025

Cited by 17 | Viewed by 2962

Abstract

The global push for sustainable production, driven by initiatives like the Paris Agreement and the European Green Deal, necessitates reducing CO₂ emissions in industrial processes. Additive manufacturing (AM), with its potential for material efficiency and decentralization, offers promising opportunities for lowering carbon [...] Read more.

The global push for sustainable production, driven by initiatives like the Paris Agreement and the European Green Deal, necessitates reducing CO₂ emissions in industrial processes. Additive manufacturing (AM), with its potential for material efficiency and decentralization, offers promising opportunities for lowering carbon footprints. Due to the significant importance of enhancing the performance of AM via the fine-tuning of printing parameters, this study investigates the dual objectives of understanding parameter influences and leveraging artificial intelligence (AI) to predict CO₂ emissions in fused deposition modeling (FDM) processes. A full-factorial experimental design with 81 test prints was conducted, varying four key parameters—layer height, infill density, perimeters, and nozzle temperature—at three levels (min, mid, and max). The results highlight infill density as the most influential factor, significantly impacting material usage, energy consumption, and overall CO₂ emissions. Five AI algorithms were employed for predictive modeling, with XGBoost demonstrating the highest accuracy in forecasting emissions. By systematically analyzing process interdependencies and providing quantitative insights, this study advances sustainable 3D printing practices. The findings offer practical implications for optimizing AM processes, benefiting both researchers and industrial stakeholders aiming to reduce CO₂ emissions without compromising product integrity. Full article

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

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29 pages, 3878 KB

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 - 2 Apr 2025

Cited by 2 | Viewed by 7638

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 [...] Read more.

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)

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13 pages, 236 KB

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

Cited by 13 | Viewed by 7227

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 [...] Read more.

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 KB

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

Cited by 7 | Viewed by 7212

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. [...] Read more.

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 KB

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

Cited by 16 | Viewed by 9104

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 [...] Read more.

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 KB

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 3 | Viewed by 2647

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. [...] Read more.

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 KB

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 22 | Viewed by 3647

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 [...] Read more.

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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41 pages, 556 KB

Open AccessSystematic Review

Human–AI Collaboration Across Decision Support, Autonomous Systems, and LLM Agents: A Systematic Review and Collaboration Convergence Framework

by Aqi Dong, Peng Li, Yanbing Chen, Shanan Gibson, Lin Zhao and Meiling He

Sustainability 2026, 18(11), 5313; https://doi.org/10.3390/su18115313 - 25 May 2026

Cited by 2 | Viewed by 5090

Abstract

Across four decades of AI deployment, the same six human challenges (trust calibration, reliance behavior, cognitive engagement, skill retention, accountability, and transparency) recur, yet fragmentation across research communities obscures this continuity and limits knowledge transfer. Functionally similar phenomena are repeatedly relabeled (a jangle [...] Read more.

Across four decades of AI deployment, the same six human challenges (trust calibration, reliance behavior, cognitive engagement, skill retention, accountability, and transparency) recur, yet fragmentation across research communities obscures this continuity and limits knowledge transfer. Functionally similar phenomena are repeatedly relabeled (a jangle fallacy): what aviation researchers call “automation complacency,” decision scientists call “algorithm appreciation,” and LLM researchers describe as “over-reliance.” This systematic review synthesizes 152 papers spanning aviation, healthcare, manufacturing/supply chain, and cross-domain contexts across three AI technology generations: decision support systems, autonomous systems, and large language model (LLM) agents. We introduce the Collaboration Convergence Framework (CCF), a 6 × 3 matrix with solution-maturity indicators that maps each challenge across generations. The framework shows that Gen 3 designers can transfer decades of evidence from automation and decision support research (particularly reliance calibration, cognitive forcing, and skill maintenance) rather than rediscovering them. Cross-generational synthesis also isolates three Gen 3 phenomena without direct precedent in earlier generations: epistemia (attributing genuine knowledge to LLMs based on surface fluency), attribution ambiguity in co-creation, and motivational withdrawal. We distill twelve transferable design principles and propose ten research directions, prioritizing skill-retention interventions and accountability frameworks. These findings carry direct sustainability implications aligned with Industry 5.0: protecting workforce capability under increasing automation (SDG 8), reducing duplicated research effort through cross-generational knowledge reuse (SDG 9), and supporting responsible deployment by treating collaboration risks as predictable rather than novel (SDG 12). The CCF provides conceptual infrastructure for cumulative learning across AI generations and industries. Full article

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

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39 pages, 1745 KB

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

Cited by 46 | Viewed by 9810

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 [...] Read more.

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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