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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 10204

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Special Issue Editors

Dr. Mario Silić

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Guest Editor

Swiss School of Business and Management, Geneva, Switzerland
Interests: information security; open source software

Prof. Dr. Minja Bolesnikov

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Guest Editor

Swiss School of Business and Management, Geneva, Switzerland
Interests: sustainable entrepreneurship; global hospitality and tourism management; strategic global hospitality and tourism management; IT in hospitality; advanced business modelling; green business; human–environmental sciences
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jelena Culibrk

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Guest Editor

Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia
Interests: human resource management; work motivation; teamwork; job satisfaction; organizational commitment; organizational behavior

Prof. Dr. Maja Petrovic

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Guest Editor

Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
Interests: environmental analysis; environment; water quality; wastewater treatment; water and wastewater treatment; environmental impact assessment; environmental pollution; water analysis; water treatment; environmental monitoring

Dr. Andrea Ivanisevic

E-Mail Website
Guest Editor

Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
Interests: economics of the firm; principles of economics; engineering economics; business
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue titled “Impact of AI on Business Sustainability and Efficiency” in Sustainability (MDPI) aims to bring together cutting-edge research on the ways in which artificial intelligence (AI) can be harnessed to bolster both the sustainability and efficiency of organizations across various sectors. As AI tools and techniques continue to rapidly evolve, businesses are increasingly integrating data-driven insights into strategic decision-making processes, operational workflows, and stakeholder engagement. This Special Issue therefore provides an academic platform for interdisciplinary discussions surrounding how AI-driven solutions can promote long-term economic, environmental, and social value.

We welcome rigorous empirical analyses, methodological advancements, conceptual frameworks, and case studies that explore (but are not limited to) the following:

Sustainable operations through AI-enabled resource optimization and waste reduction.
The role of AI in fostering inclusive and responsible business practices.
Ethical implications of AI deployment on social equity and environmental justice.
AI-driven decision support systems for corporate governance and policy compliance.
Sector-specific case studies illustrating the best practices in AI-based sustainable innovation.
Multi-stakeholder collaborations and partnerships for AI-driven sustainability initiatives.
Risk assessment, mitigation strategies, and resilience building in AI adoption.

Relationship to Existing Literature

This Special Issue is grounded in, and builds upon, multiple strands of the literature that collectively shape how AI is perceived and utilized in sustainable business. In the broader sustainability literature, scholars have highlighted the importance of organizational resilience, circular economy principles, and collaborative innovation to address global environmental and social challenges. AI-focused research, meanwhile, has extensively examined algorithmic complexity, big data applications, machine learning insights, and AI governance frameworks. By integrating these two domains, this issue seeks to offer a holistic view of how AI can be a transformative force, driving resource efficiency, ethical decision making, inclusive growth, and long-term competitiveness.

Prior studies have explored the promise of AI in domains such as predictive maintenance, energy management, and supply chain optimization—areas that are crucial to achieving sustainability targets. Nevertheless, significant gaps remain in understanding the nuanced interactions between AI adoption and its societal or ecological impacts. Furthermore, while AI applications may enhance operational efficiency, the net effect on sustainability depends on organizational culture, stakeholder alignment, and robust regulatory frameworks. This Special Issue contributes to bridging these gaps by seeking research that critically examines the opportunities, challenges, and governance mechanisms needed to ensure AI is leveraged responsibly for sustainability outcomes.

Ultimately, “Impact of AI on Business Sustainability and Efficiency” aspires to advance the academic conversation on the convergent pathways of AI innovation and sustainable development. By curating multidisciplinary research, it hopes to inform policymakers, industry leaders, and the academic community on designing AI deployments that deliver tangible positive impacts—environmentally, socially, and economically—in a rapidly transforming business landscape.

Dr. Mario Silić
Prof. Dr. Minja Bolesnikov
Prof. Dr. Jelena Culibrk
Prof. Dr. Maja Petrovic
Dr. Andrea Ivanisevic
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence (AI)
business sustainability
operational efficiency
resource optimization
ethical implications
corporate governance
stakeholder engagement
circular economy
responsible innovation
resilience building

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Published Papers (6 papers)

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25 pages, 369 KB

Open AccessArticle

New Intelligent Technologies: Are They Making the Workplace Productive?

by Jacques Bughin

Sustainability 2026, 18(3), 1419; https://doi.org/10.3390/su18031419 - 31 Jan 2026

Viewed by 875

Abstract

This paper investigates whether intelligent workplace technologies improve firm-level productivity and, if so, under what conditions, with particular attention to their implications for the economic and social sustainability of firms. This investigation occurs in a context where firms increasingly combine automation, artificial intelligence (AI), and work-from-home (WFH) practices to sustain performance under structural shocks such as the COVID-19 pandemic. Despite evidence that firms adopt these technologies jointly and reorganize work accordingly, existing research typically examines them in isolation. We develop a micro-founded, task-based production model in which firms allocate tasks between on-site and remote labor and automated capital in an optimal manner. This model allows both automation technologies and remote work collaboration tools to affect productivity and coordination costs that are central to long-term organizational sustainability. Using firm-level survey data from nearly 4000 large firms across industries and countries (2018–2021), we show that working from home (WFH) exhibits diminishing productivity returns when scaled in isolation, reflecting rising coordination frictions. In contrast, firms that combine WFH with automation and digital collaboration tools experience significantly higher labor productivity growth. These integrated technology systems support sustainable productivity by enabling capital deepening, resilient task reallocation, and more efficient use of labor resources over time. Overall, the findings suggest that productivity gains—and by extension sustainable firm performance—stem from integrated workplace technology systems rather than isolated investments, highlighting the importance of coherent technology strategies for organizing work in the post-pandemic economy. Full article

(This article belongs to the Special Issue Impact of AI on Business Sustainability and Efficiency)

27 pages, 3495 KB

Open AccessArticle

Artificial Intelligence and Spatial Optimization: Evaluation of the Economic and Social Value of UGS in Vračar (Belgrade)

by Slađana Milovanović, Ivan Cvitković, Katarina Stojanović and Miljenko Mustapić

Sustainability 2026, 18(2), 745; https://doi.org/10.3390/su18020745 - 12 Jan 2026

Cited by 1 | Viewed by 698

Abstract

This paper examines the growing field of AI-assisted urban planning within the context of sustainable urban development, with a particular focus on spatial optimization of urban green spaces under conditions of scarcity, density, and economic pressure. While the economic, ecological, and social values of UGS are widely acknowledged, urban planners lack a cohesive, data-driven framework to quantify and spatially optimize these often-conflicting values for effective land-use optimization. To address this gap, we propose a methodology that combines Geographic Information Systems (GISs), the Analytic Hierarchy Process (AHP), and an Artificial Intelligence-Based Genetic Algorithm (AI-GA). Vračar was chosen as the case study area. Our approach evaluates (1) the economic value of UGS through housing prices; (2) the ecological value through UGS density; and (3) the social value by measuring access to urban green pockets. The integrated method simulates environmental scenarios and optimizes UGS placement for resilient urban areas. Results demonstrate that properties in mixed-use green areas proximate to urban parks have the highest economic and social value. Additionally, higher densities of UGS correlate with higher housing prices, highlighting the economic impact of green space distribution. The methodology enables planners to make decisions based on evidence that integrates statistical modeling, expert judgment, and artificial intelligence into one cohesive platform. Full article

(This article belongs to the Special Issue Impact of AI on Business Sustainability and Efficiency)

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23 pages, 645 KB

Open AccessArticle

Does Artificial Intelligence Promote Sustainable Growth of Exporting Firms?

by Xiulian Chen, Yanan Wu and Yangyang Long

Sustainability 2025, 17(16), 7273; https://doi.org/10.3390/su17167273 - 12 Aug 2025

Cited by 2 | Viewed by 2678

Abstract

Against the backdrop of the accelerated development of the global digital economy and the deepening advancement of the sustainable development agenda, artificial intelligence (AI) is emerging as the core driving force behind the new round of technological revolution, reshaping the competitive landscape of international trade. Chinese export companies are facing dual pressures from technological barriers imposed by developed countries and cost competition from emerging economies, making traditional development models unsustainable. In this context, exploring how AI technology can promote the sustainable growth of export companies holds significant theoretical and practical significance. This article employs a three-dimensional fixed-effects nonlinear quadratic model to empirically analyze the dynamic relationship between AI adoption and the growth of export companies, based on data from Chinese A-share listed export companies. The analysis results show that AI has a significant dynamic nonlinear effect on the growth of export companies, which is initially inhibitory and then becomes promotional. In the early stages, due to high technology adaptation costs, company growth is somewhat inhibited. However, as the technology matures, AI significantly enhances the company’s innovation capabilities and competitiveness, thereby promoting its long-term sustainable growth. This result remains valid after a series of robustness tests. This effect is significant in non-state-owned enterprises and medium-to-low technology industries, but not in state-owned enterprises and high-technology industries. Three pathways—enterprise efficiency, innovation investment, and levels of digital factor investment—enhance this dynamic effect. Finally, based on the above research findings, this study proposes policy recommendations for enterprises to leverage artificial intelligence technology to promote the growth of export companies. Full article

(This article belongs to the Special Issue Impact of AI on Business Sustainability and Efficiency)

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25 pages, 2878 KB

Open AccessArticle

A Multi-Faceted Approach to Air Quality: Visibility Prediction and Public Health Risk Assessment Using Machine Learning and Dust Monitoring Data

by Lara Dronjak, Sofian Kanan, Tarig Ali, Reem Assim and Fatin Samara

Sustainability 2025, 17(14), 6581; https://doi.org/10.3390/su17146581 - 18 Jul 2025

Cited by 4 | Viewed by 2161

Abstract

Clean and safe air quality is essential for public health, yet particulate matter (PM) significantly degrades air quality and poses serious health risks. The Gulf Cooperation Council (GCC) countries are particularly vulnerable to frequent and intense dust storms due to their vast desert landscapes. This study presents the first health risk assessment of carcinogenic and non-carcinogenic risks associated with exposure to PM2.5 and PM10 bound heavy metals and polycyclic aromatic hydrocarbons (PAHs) based on air quality data collected during the years of 2016–2018 near Dubai International Airport and Abu Dhabi International Airport. The results reveal no significant carcinogenic risks for lead (Pb), cobalt (Co), nickel (Ni), and chromium (Cr). Additionally, AI-based regression analysis was applied to time-series dust monitoring data to enhance predictive capabilities in environmental monitoring systems. The estimated incremental lifetime cancer risk (ILCR) from PAH exposure exceeded the acceptable threshold (10⁻⁶) in several samples at both locations. The relationship between visibility and key environmental variables—PM1, PM2.5, PM10, total suspended particles (TSPs), wind speed, air pressure, and air temperature—was modeled using three machine learning algorithms: linear regression, support vector machine (SVM) with a radial basis function (RBF) kernel, and artificial neural networks (ANNs). Among these, SVM with an RBF kernel showed the highest accuracy in predicting visibility, effectively integrating meteorological data and particulate matter variables. These findings highlight the potential of machine learning models for environmental monitoring and the need for continued assessments of air quality and its health implications in the region. Full article

(This article belongs to the Special Issue Impact of AI on Business Sustainability and Efficiency)

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22 pages, 739 KB

Open AccessArticle

Balancing Efficiency and Sustainability: Multicriteria Decision-Making for Pumping Station Upgrades

by Sara Koprivica, Andrea Ivanišević, Darko Stefanović, Milan Jovin and Maja Petrović

Sustainability 2025, 17(7), 2818; https://doi.org/10.3390/su17072818 - 22 Mar 2025

Cited by 1 | Viewed by 1221

Abstract

This study examines the financial and operational sustainability of modernizing drainage pump station systems in Vojvodina, aiming to improve energy efficiency and adapt to climate change. Various technical and economic modernization scenarios were analyzed to identify the optimal variant (V3.1), which balances investment costs, operational efficiency, and long-term environmental benefits. The analytical approaches used include multicriteria analysis, the Analytical Hierarchy Process (AHP) model, the TOPSIS technique, and fuzzy logic, providing comprehensive insights into the ranking of the four proposed variants. The results indicate that Variant V3.1, which features local SCADA systems and centralized management, offers an optimal solution for modernization. This model enhances operational reliability, reduces energy costs, and contributes to the sustainable development of the region through more efficient resource utilization. The proposed approach not only meets the specific needs of Vojvodina but also provides a broader framework for assessing similar infrastructure projects in other regions. Full article

(This article belongs to the Special Issue Impact of AI on Business Sustainability and Efficiency)

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40 pages, 1401 KB

Open AccessSystematic Review

Artificial Intelligence and Leadership in Organizations: A PRISMA Systematic Review of Challenges, Risks, and Governance Dynamics

by Carlos Santiago-Torner, José-Antonio Corral-Marfil and Elisenda Tarrats-Pons

Sustainability 2026, 18(8), 4085; https://doi.org/10.3390/su18084085 - 20 Apr 2026

Cited by 1 | Viewed by 778

Abstract

As artificial intelligence (AI) becomes increasingly embedded in organizational processes, questions about its implications for leadership have gained growing relevance. However, the existing literature remains fragmented, often addressing strategy, leadership capabilities, governance structures, or ethical concerns in isolation, without explaining how these dimensions interact to shape leadership effectiveness in AI-driven environments. This study conducts a PRISMA-guided systematic review of 33 peer-reviewed articles to examine how AI-embedded leadership is conceptualized across contexts. By synthesizing findings across strategic, human, and governance domains, the analysis identifies recurring patterns and structural relationships in the literature. The results indicate that effective leadership in AI-intensive settings is not determined solely by technological adoption or digital competencies, but by the alignment between the depth of AI integration in decision-making processes, leaders’ capacity to interpret and oversee algorithmic outputs, and the presence of governance mechanisms that ensure transparency, accountability, and trust. While some studies highlight potential opportunities associated with AI, these remain less systematically developed compared to the extensive focus on challenges and emerging risks. On this basis, the study introduces the AI-Leadership Configurational Framework (ALCF), a multi-level model that conceptualizes leadership effectiveness as the outcome of systemic alignment. The framework integrates previously disconnected debates and provides a coherent foundation for future empirical research on leadership in the algorithmic age. Full article

(This article belongs to the Special Issue Impact of AI on Business Sustainability and Efficiency)

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