Recent Applications of Artificial Intelligence in Economy and Society

Abstract submission deadline

30 September 2026

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30 November 2026

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Topic Information

Dear Colleagues,

Artificial intelligence (AI) is a transformative force reshaping industries, societies, and economies. From advancing decision-making processes to personalizing user experiences, AI enables innovation and addresses global challenges. Its applications span critical sectors, including healthcare, education, agriculture, environmental protection, finance, urban planning, and more. These advancements redefine efficiency, creativity, and problem-solving capabilities, positioning AI as a key driver of sustainable development.

However, this rapid growth raises concerns about data privacy, ethical use, governance, and broader societal impacts. To ensure that AI contributes positively to humanity's future, balancing its opportunities with critical examinations of its risks and implications is crucial.

We invite submissions of high-quality original research and review articles that offer novel perspectives, rigorous analyses, and actionable insights for this Special Issue. The Special Issue will also explore AI's methodological advancements, theoretical breakthroughs, and real-world applications. Contributions should emphasize AI's potential to address pressing global issues while critically engaging with ethical, societal, and governance challenges.

Authors are encouraged to provide empirical evidence, comparative studies, or robust theoretical insights. Papers should include methodologies, evaluation criteria, and discussions of the societal impact. Case studies must demonstrate real-world relevance and measurable outcomes. Submissions addressing ethical and governance challenges are welcome in order to foster critical discourse on the responsible use of AI.

Topics of interest include but are not limited to the following:

• Comparative analyses with state-of-the-art methodologies;
• AI applications in healthcare, education, environmental management, and urban planning;
• Strategies for leveraging AI to achieve the SDGs;
• Practical case studies demonstrating socio-economic and environmental benefits;
• Frameworks for ethical AI development and deployment;
• Data privacy, accountability, and transparency in AI systems;
• Collaborative approaches to bridge technical and social science disciplines;
• AI's influence on workforce dynamics, employment trends, and labor markets;
• Economic effects on consumer behavior, trade, and accounting practices;
• Opportunities and challenges posed by AI in financial systems and management.

Prof. Dr. Claudiu George Bocean
Dr. Anca Antoaneta Vărzaru
Prof. Dr. Domenico Ursino
Prof. Dr. Kah Phooi Seng
Dr. Dorel Berceanu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Smart Cities smartcities	5.5	14.7	2018	25.2 Days	CHF 2000	Submit
Systems systems	3.1	4.1	2013	20.1 Days	CHF 2400	Submit
Economies economies	2.1	4.7	2013	23.1 Days	CHF 1800	Submit

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Published Papers (11 papers)

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26 pages, 7254 KB  

Open AccessArticle

Individual Street Tree Detection and Vitality Assessment Using GeoAI and Multi-Source Imagery

by Yeonsu Kang and Youngok Kang

Smart Cities 2026, 9(2), 31; https://doi.org/10.3390/smartcities9020031 - 11 Feb 2026

Viewed by 146

Abstract

Urban street trees provide essential environmental and social benefits, yet their vitality is often challenged by adverse urban conditions such as traffic emissions, impervious surfaces, and limited soil moisture. Conventional street tree management relies heavily on labor-intensive field inspections, making large-scale and repeatable [...] Read more.

Urban street trees provide essential environmental and social benefits, yet their vitality is often challenged by adverse urban conditions such as traffic emissions, impervious surfaces, and limited soil moisture. Conventional street tree management relies heavily on labor-intensive field inspections, making large-scale and repeatable assessment difficult. To address this limitation, this study proposes a GeoAI-based framework that integrates high-resolution aerial imagery, multispectral satellite data, and deep learning–based semantic segmentation to automatically delineate individual street trees and derive a remote sensing-based vitality proxy. Street trees are detected from orthorectified aerial imagery using semantic segmentation models, and vegetation indices—including NDVI, NDRE, and NDMI—are extracted from multispectral satellite imagery. An area-weighted object–pixel matching strategy is applied to associate spectral indicators with individual crowns across multi-resolution datasets. A composite vitality proxy is then constructed by integrating chlorophyll- and moisture-related indices. The results reveal spatial variability in spectral vitality signals across different urban environments. Trees along major road corridors tended to exhibit lower chlorophyll- and moisture-related indicators, while those near parks, riverfront walkways, and recently developed residential areas generally showed higher values. NDMI provided complementary insights into moisture-related stress that were not fully reflected by chlorophyll-based indices. Although the proposed vitality proxy is not a substitute for field-based diagnosis, the overall framework offers a scalable approach for citywide screening and prioritization of potentially stressed trees, supporting data-informed urban green infrastructure management within smart-city planning contexts. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

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37 pages, 4614 KB  

Open AccessArticle

The Role of AI in Revolutionising Cryptocurrency Trading

by Georgiana-Iulia Lazea, Cristian Lungu and Ovidiu-Constantin Bunget

Electronics 2026, 15(4), 742; https://doi.org/10.3390/electronics15040742 - 10 Feb 2026

Viewed by 190

Abstract

This article examines the revolutionary impact of Artificial Intelligence (AI) on transforming cryptocurrency trading, a sector characterised by extreme volatility, dynamism, and nonlinear data. Through a rigorous bibliometric analysis based on the Web of Science database, this study examines a sample of 555 [...] Read more.

This article examines the revolutionary impact of Artificial Intelligence (AI) on transforming cryptocurrency trading, a sector characterised by extreme volatility, dynamism, and nonlinear data. Through a rigorous bibliometric analysis based on the Web of Science database, this study examines a sample of 555 scientific papers published between 2016 and 2025, utilising the PRISMA protocol for systematic selection, and tools such as VOSviewer and MS Excel. The analysis identifies five major thematic clusters: (1) blockchain infrastructure and AI integration in decentralised ecosystems, (2) data analysis and practical applicability in crypto markets, (3) financial and social data analysis—machine learning algorithms, (4) algorithmic trading and automation, and (5) prediction and modelling of crypto market developments. The originality of this study lies in providing an overview of the implementation stage of these technologies by integrating the results into a map of Technology Readiness Levels (TRLs). The findings highlight a clear transition from traditional statistical methods to autonomous decision-making systems capable of processing massive volumes of data for portfolio optimisation. This study’s limitation is that it may require periodic updates, as the AI and cryptocurrency landscape are constantly evolving. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

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21 pages, 345 KB  

Open AccessArticle

How Artificial Intelligence Technology Enables Renewable Energy Development: Heterogeneity Constraints on Environmental and Climate Policies

by Xian Zhao and Jincheng Liu

Systems 2026, 14(1), 107; https://doi.org/10.3390/systems14010107 - 20 Jan 2026

Viewed by 324

Abstract

The emergence of artificial intelligence as a transformative force in the field of information technology has exerted a significant impact on the development of renewable energy. In-depth analysis of the impact of AI on renewable energy development is crucial for promoting energy transition [...] Read more.

The emergence of artificial intelligence as a transformative force in the field of information technology has exerted a significant impact on the development of renewable energy. In-depth analysis of the impact of AI on renewable energy development is crucial for promoting energy transition and facilitating sustainable development. This research utilizes a dataset comprising 30 provincial panels spanning from 2010 to 2023. This study found that AI technology can promote renewable energy development, a conclusion that still holds after robustness and endogeneity tests. An examination of the mechanism reveals that AI technology facilitates the advancement of renewable energy through the enhancement of trade openness and the concentration of manufacturing activities. The analysis of the moderating effect indicates that environmental regulation and environmental protection expenditures positively moderated the relationship between AI technology and renewable energy development and climate policy uncertainty negatively moderated the relationship between AI technology and renewable energy development. Further analysis revealed that AI technology has the potential to substantially improve the development of local renewable energy resources while also facilitating the advancement of renewable energy in adjacent areas, exhibiting spatial spillover effects. This study verifies the positive effects of AI technology on renewable energy development and enriches existing research perspectives in the field of energy economics. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

20 pages, 1084 KB  

Open AccessArticle

Exploring the Role of AI and Software Solutions in Shaping Tourism Outcomes: A Factor, Neural Network, and Cluster Analysis Across Europe

by Anca Antoaneta Vărzaru, Claudiu George Bocean, Sorin Tudor, Răducu-Ștefan Bratu and Silviu Cârstina

Electronics 2025, 14(20), 4004; https://doi.org/10.3390/electronics14204004 - 13 Oct 2025

Cited by 1 | Viewed by 825

Abstract

Tourism and digitalization have become increasingly interconnected, yet the complex, nonlinear relationships between technological adoption and tourism performance remain underexplored. This study aims to examine how enterprise software solutions influence tourism indicators across European countries. Using a triangulated methodological approach, we employed factor [...] Read more.

Tourism and digitalization have become increasingly interconnected, yet the complex, nonlinear relationships between technological adoption and tourism performance remain underexplored. This study aims to examine how enterprise software solutions influence tourism indicators across European countries. Using a triangulated methodological approach, we employed factor analysis to identify underlying dimensions, neural network modeling to detect nonlinear relationships, and hierarchical clustering to group countries based on digital and tourism profiles. The results consistently highlight CRM (Customer Relationship Management) as the most influential technological factor linked to both the net occupancy rate of beds and the number of nights spent at tourist accommodations. While AI (artificial intelligence) technologies currently have less impact, their importance is growing, as seen in emerging patterns. Cluster analysis further confirms that countries with higher CRM adoption tend to cluster together and show better tourism performance, indicating a clear connection between digital maturity and sector competitiveness. These findings emphasize the strategic importance of CRM as a transformative tool in hospitality and tourism management, while also recognizing the potential of AI to shape future trends. The study offers empirical support for tailored digital policies across European regions to promote inclusive and sustainable tourism growth. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

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26 pages, 2011 KB  

Open AccessArticle

Driving Sustainable Value. The Dynamic Interplay Between Artificial Intelligence Disclosure, Financial Reporting Quality, and ESG Scores

by Victoria Bogdan, Camelia-Daniela Hațegan, Réka Melinda Török, Rodica-Gabriela Blidișel, Dorina-Nicoleta Popa and Ruxandra-Ioana Pitorac

Electronics 2025, 14(16), 3247; https://doi.org/10.3390/electronics14163247 - 15 Aug 2025

Cited by 2 | Viewed by 1883

Abstract

Adapting contemporary business models to the challenges of implementing new technologies influences the sustainable value of companies. This study examines the disclosure practices of Romanian-listed companies regarding accounting estimates, their correlation with financial performance, ESG scores, and the use of artificial intelligence (AI). [...] Read more.

Adapting contemporary business models to the challenges of implementing new technologies influences the sustainable value of companies. This study examines the disclosure practices of Romanian-listed companies regarding accounting estimates, their correlation with financial performance, ESG scores, and the use of artificial intelligence (AI). Financial data was gathered from annual reports and those regarding the use of AI on companies’ websites. Financial performance was measured through profitability and liquidity indicators. The results of the statistical regressions showed that company size can influence AI disclosure; however, industry is not a strong predictor, and the number of employees does not significantly influence AI disclosure. A positive relationship was found between AI transparency and the current ratio, suggesting that companies disclosing more information about their AI use may have higher current liquidity. Additionally, a statistically significant negative relationship was observed between the AI disclosure score and net profit, indicating that greater AI transparency is associated with lower net income. The results of interaction analysis proved that there may be a relationship between ESG exposure and financial performance when considering AI disclosure. However, this result may be considered controversial in a more conservative analysis, emphasizing the need for a more nuanced and multidimensional approach. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

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23 pages, 2288 KB  

Open AccessArticle

How Does Artificial Intelligence Technology Influence Labor Share: The Role of Labor Structure Upgrading

by Xiaolong Xue, Jianshuo Chen, Wendi Xiao and Chenxiao Wang

Systems 2025, 13(7), 586; https://doi.org/10.3390/systems13070586 - 15 Jul 2025

Cited by 1 | Viewed by 4034

Abstract

The rapid development and adoption of artificial intelligence (AI) technology has sparked debates about its implications for labor markets, yet the micro-level relationship between AI and labor share remains underexplored. Based on the theory of skill-biased technological change, this study aims to examine [...] Read more.

The rapid development and adoption of artificial intelligence (AI) technology has sparked debates about its implications for labor markets, yet the micro-level relationship between AI and labor share remains underexplored. Based on the theory of skill-biased technological change, this study aims to examine whether AI technology increases labor share by labor structure upgrading at the enterprise level. Using panel data for China’s listed companies from 2012 to 2022, this study tests this relationship using a two-way fixed effects model. The empirical results reveal that AI technology significantly increases labor share, with labor structure upgrading playing a mediating role in this relationship. Heterogeneity analysis reveals that the influence of AI technology on labor share is stronger for enterprises characterized by low labor market rigidity, high labor market supply, and talent policy support in external environments, as well as among labor-intensive, high-tech, and non-state-owned enterprises. Notably, this study finds that advancements in AI technology have achieved mutually beneficial outcomes of improving labor share and enhancing total factor productivity. Our research findings provide detailed empirical evidence for enterprises to formulate and implement AI strategies. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

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43 pages, 2590 KB  

Open AccessArticle

A Study on the Impact of Industrial Robot Applications on Labor Resource Allocation

by Kexu Wu, Zhiwei Tang and Longpeng Zhang

Systems 2025, 13(7), 569; https://doi.org/10.3390/systems13070569 - 11 Jul 2025

Viewed by 4302

Abstract

With the rapid advancement of artificial intelligence and smart manufacturing technologies, the penetration of industrial robots into Chinese markets has profoundly reshaped the structure of the labor market. However, existing studies have largely concentrated on the employment substitution effect and the diffusion path [...] Read more.

With the rapid advancement of artificial intelligence and smart manufacturing technologies, the penetration of industrial robots into Chinese markets has profoundly reshaped the structure of the labor market. However, existing studies have largely concentrated on the employment substitution effect and the diffusion path of these technologies, while systematic analyses of how industrial robots affect labor resource allocation efficiency across different regional and industrial contexts in China remain scarce. In particular, research on the mechanisms and heterogeneity of these effects is still underdeveloped, calling for deeper investigation into their transmission channels and policy implications. Drawing on panel data from 280 prefecture-level cities in China from 2006 to 2023, this paper employs a Bartik-style instrumental variable approach to measure the level of industrial robot penetration and constructs a two-way fixed effects model to assess its impact on urban labor misallocation. Furthermore, the analysis introduces two mediating variables, industrial upgrading and urban innovation capacity, and applies a mediation effect model combined with Bootstrap methods to empirically test the underlying transmission mechanisms. The results reveal that a higher level of industrial robot adoption is significantly associated with a lower degree of labor misallocation, indicating a notable improvement in labor resource allocation efficiency. Heterogeneity analysis shows that this effect is more pronounced in cities outside the Yangtze River Economic Belt, in those experiencing severe population aging, and in areas with a relatively weak manufacturing base. Mechanism tests further indicate that industrial robots indirectly promote labor allocation efficiency by facilitating industrial upgrades and enhancing innovation capacity. However, in the short term, improvements in innovation capacity may temporarily intensify labor mismatch due to structural frictions. Overall, industrial robots not only exert a direct positive impact on the efficiency of urban labor allocation but also indirectly contribute to resource optimization through structural transformation and innovation system development. These findings underscore the need to account for regional disparities and demographic structures when advancing intelligent manufacturing strategies. Policymakers should coordinate the development of vocational training systems and innovation ecosystems to strengthen the dynamic alignment between technological adoption and labor market restructuring, thereby fostering more inclusive and high-quality economic growth. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

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31 pages, 4591 KB  

Open AccessArticle

Modeling Affective Mechanisms in Relaxing Video Games: Sentiment and Topic Analysis of User Reviews

by Yuxin Xing, Wenbao Ma, Qiang You and Jiaxing Li

Systems 2025, 13(7), 540; https://doi.org/10.3390/systems13070540 - 1 Jul 2025

Cited by 1 | Viewed by 3694

Abstract

The accelerating pace of digital life has intensified psychological strain, increasing the demand for accessible and systematized emotional support tools. Relaxing video games—defined as low-pressure, non-competitive games designed to promote calm and emotional relief—offer immersive environments that facilitate affective engagement and sustained user [...] Read more.

The accelerating pace of digital life has intensified psychological strain, increasing the demand for accessible and systematized emotional support tools. Relaxing video games—defined as low-pressure, non-competitive games designed to promote calm and emotional relief—offer immersive environments that facilitate affective engagement and sustained user involvement. This study proposes a computational framework that integrates sentiment analysis and topic modeling to investigate the affective mechanisms and behavioral dynamics associated with relaxing gameplay. We analyzed nearly 60,000 user reviews from the Steam platform in both English and Chinese, employing a hybrid methodology that combines sentiment classification, dual-stage Latent Dirichlet Allocation (LDA), and multi-label mechanism tagging. Emotional relief emerged as the dominant sentiment (62.8%), whereas anxiety was less prevalent (10.4%). Topic modeling revealed key affective dimensions such as pastoral immersion and cozy routine. Regression analysis demonstrated that mechanisms like emotional relief (β = 0.0461, p = 0.001) and escapism (β = 0.1820, p < 0.001) were significant predictors of longer playtime, while Anxiety Expression lost statistical significance (p = 0.124) when contextual controls were added. The findings highlight the potential of relaxing video games as scalable emotional regulation tools and demonstrate how sentiment- and topic-driven modeling can support system-level understanding of affective user behavior. This research contributes to affective computing, digital mental health, and the design of emotionally aware interactive systems. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

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27 pages, 2291 KB  

Open AccessArticle

Tech Trend Analysis System: Using Large Language Models and Finite State Chain Machines

by Dragoş Florin Sburlan, Cristina Sburlan and Alexandru Bobe

Electronics 2025, 14(11), 2191; https://doi.org/10.3390/electronics14112191 - 28 May 2025

Viewed by 3131

Abstract

In today’s fast-paced technological environment, spotting emerging trends and anticipating future developments are important tasks in strategic planning and business decision-making. However, the volume and complexity of unstructured data containing relevant information make it very difficult for humans to effectively monitor, analyze, and [...] Read more.

In today’s fast-paced technological environment, spotting emerging trends and anticipating future developments are important tasks in strategic planning and business decision-making. However, the volume and complexity of unstructured data containing relevant information make it very difficult for humans to effectively monitor, analyze, and identify inflection points by themselves. In this paper, we aim to prove the potential of integrating large language models (LLMs) with a novel finite state chain machine (FSCM) with output and graph databases to extract insights from unstructured data, specifically from earnings call transcripts of 40 top Technology Sector companies. The FSCM provides a modular, state-based approach for processing texts, enabling entity and relationship recognition. The extracted information is stored in a knowledge graph, further enabling semantic search and entity clustering. By leveraging this approach, we identified over 20,000 hidden (overlapping) trends and topics across various types. Our experiment on real-world datasets confirms the scalability and effectiveness of the method in extracting valuable knowledge from large datasets. The present work contributes to the field of Natural Language Processing (NLP) by showcasing the proposed method in addressing real-world business problems. The findings shed new light on current trends and challenges faced by tech companies, highlighting the potential for further integration with other NLP methods, leading to more robust and effective outcomes. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

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38 pages, 6018 KB  

Open AccessArticle

Artificial Intelligence Adoption in the European Union: A Data-Driven Cluster Analysis (2021–2024)

by Costel Marian Ionașcu

Economies 2025, 13(5), 145; https://doi.org/10.3390/economies13050145 - 21 May 2025

Cited by 5 | Viewed by 10786

Abstract

The adoption of artificial intelligence by enterprises in the EU countries increased significantly between 2021 and 2024, but the recorded values were uneven and very small. This study analyzed the main characteristics of the artificial intelligence adoption process, its dynamics and patterns using [...] Read more.

The adoption of artificial intelligence by enterprises in the EU countries increased significantly between 2021 and 2024, but the recorded values were uneven and very small. This study analyzed the main characteristics of the artificial intelligence adoption process, its dynamics and patterns using principal component analysis and K-means clustering. The results highlighted a shift from using technologies for process automation to more advanced ones like natural language generation. The process was extended and gradually covered almost all business areas. The lack of relevant expertise, high costs and gaps in regulation of the development and use of artificial intelligence are the important barriers identified by 2024. The cluster analysis of EU countries highlighted the existence of two permanent clusters, one containing the leading countries and one containing the countries lagging behind, showing a large gap between them. The increasing dependence on externally developed solutions has characterized a maturing market for artificial intelligence. The equitable adoption of artificial intelligence at the level of EU countries must be based on specific workforce training, investments in infrastructure, financial incentives and, last but not least, on clear regulations. Only in this way can the gap in this area at the EU level be reduced. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

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45 pages, 4361 KB  

Open AccessArticle

Engineering Sustainable Data Architectures for Modern Financial Institutions

by Sergiu-Alexandru Ionescu, Vlad Diaconita and Andreea-Oana Radu

Electronics 2025, 14(8), 1650; https://doi.org/10.3390/electronics14081650 - 19 Apr 2025

Cited by 8 | Viewed by 7203

Abstract

Modern financial institutions now manage increasingly advanced data-related activities and place a growing emphasis on environmental and energy impacts. In financial modeling, relational databases, big data systems, and the cloud are integrated, taking into consideration resource optimization and sustainable computing. We suggest a [...] Read more.

Modern financial institutions now manage increasingly advanced data-related activities and place a growing emphasis on environmental and energy impacts. In financial modeling, relational databases, big data systems, and the cloud are integrated, taking into consideration resource optimization and sustainable computing. We suggest a four-layer architecture to address financial data processing issues. The layers of our design are for data sources, data integration, processing, and storage. Data ingestion processes market feeds, transaction records, and customer data. Real-time data are captured by Kafka and transformed by Extract-Transform-Load (ETL) pipelines. The processing layer is composed of Apache Spark for real-time data analysis, Hadoop for batch processing, and an Machine Learning (ML) infrastructure that supports predictive modeling. In order to optimize access patterns, the storage layer includes various data layer components. The test results indicate that the processing of market data in real-time, compliance reporting, risk evaluations, and customer analyses can be conducted in fulfillment of environmental sustainability goals. The metrics from the test deployment support the implementation strategies and technical specifications of the architectural components. We also looked at integration models and data flow improvements, with applications in finance. This study aims to enhance enterprise data architecture in the financial context and includes guidance on modernizing data infrastructure. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

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