Submit to Special Issue Submit Abstract to Special Issue Review for Sustainability Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Achieving Sustainability Goals Through Artificial Intelligence

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 31 July 2026 | Viewed by 5128

Share This Special Issue

Special Issue Editors

Prof. Dr. Laura-Diana Radu

E-Mail Website
Guest Editor

Department of Accounting, Business Information Systems and Statistics, Alexandru Ioan Cuza University of Iasi, 700506 Iasi, Romania
Interests: smart cities; information systems; artificial intelligence; green ICT; agile project management; innovations in business and education
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Daniela Popescul

E-Mail Website
Guest Editor

Department of Accounting, Business Information Systems and Statistics, Alexandru Ioan Cuza University of Iasi, 700506 Iasi, Romania
Interests: smart cities; ethical implications of artificial intelligence; innovation in project management; information security

Special Issue Information

Dear Colleagues,

Concerns for sustainable development evolve alongside economic, technological, and social progress. These concerns arise from a confluence of factors, with social and individual awareness playing an important role. Improvements in living standards and increased access to basic resources have empowered individuals and organizations to scrutinize the societal impact of their actions more closely. Artificial intelligence (AI), as well as other fields, has been a major catalyst for the transformations seen in recent decades, especially in the last few years. Undeniably, it is a primary driver of economic growth. AI significantly impacts resource consumption, community life, the labor market, decision-making processes, and more, enabling individuals to lead more comfortable and prosperous lives. While numerous studies have explored the evolution and achievements of AI, its role in ensuring sustainability has been relatively underexplored.

This Special Issue aims to present how AI, currently undergoing rapid growth, can contribute to achieving sustainability goals. It provides significant insights for academics, practitioners, and researchers. Moreover, this collection of articles seeks to foster a deeper understanding of the role that individuals and organizations play in supporting sustainable development by integrating AI's capabilities judiciously while adhering to ethical principles.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Sustainability in the artificial intelligence and machine learning age;
The use of intelligent information systems in smart cities;
The risks and challenges of artificial intelligence adoption;
The ethical challenges of artificial intelligence;
The resilience and security of intelligent information systems;
Green innovation in the context of artificial intelligence and machine learning;
The sustainability of smart communities;
The vulnerabilities of artificial intelligence and societal vulnerabilities in the context of artificial intelligence evolution.

We look forward to receiving your contributions.

Prof. Dr. Laura-Diana Radu
Prof. Dr. Daniela Popescul
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
sustainability
machine learning
resilience
ethics
green innovation
intelligent information systems
smart cities
social vulnerabilities

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review, Other

24 pages, 1243 KB

Open AccessArticle

Can Artificial Intelligence Narrow the Urban–Rural Income Inequality? Evidence from a Quasi-Natural Experiment in China

by Haiyuan He, Qiujia Wang, Wenli Huang, Mengshi Yang, Hubin Ma and Hui Pang

Sustainability 2026, 18(10), 4785; https://doi.org/10.3390/su18104785 - 11 May 2026

Viewed by 395

Abstract

The accelerated advancement of artificial intelligence has triggered new discussions concerning the link between technological progress and the distribution of income. This study frames China’s National New Generation Artificial Intelligence Innovation and Development Pilot Zone (AIIDPZ) policy as a quasi-natural experiment, enabling us to identify the causal effect of AI promotion strategies on the urban–rural income inequality. Drawing on panel data from 257 Chinese cities over the period 2012–2023, we estimate the impacts using a multi-period difference-in-differences (DID) approach. The results demonstrate that the pilot zone policy significantly lowers the urban–rural income inequality index, by roughly 8.41%. The mechanism analysis reveals two primary pathways. First, the policy stimulates innovation in agricultural science and technology, which in turn boosts rural productivity. Second, it deepens the attention that the government directs toward artificial intelligence, contributing to a more balanced allocation of technological dividends between urban and rural areas. Heterogeneity tests further indicate that the inequality-reducing effects are especially notable in eastern regions, as well as in cities characterized by well-developed digital infrastructure and relatively weaker endowments of human capital. By offering empirical insight into how developing countries can reconcile distributional equity with the application of artificial intelligence, this study contributes to advancing the Sustainable Development Goals (SDGs). Full article

(This article belongs to the Special Issue Achieving Sustainability Goals Through Artificial Intelligence)

► Show Figures

Figure 1

20 pages, 242 KB

Open AccessArticle

Generative Artificial Intelligence for SDG 4: Enhancing Sustainable Quality Learning

by Jehan Saleh Lardhi and Abdelrahim Fathy Ismail

Sustainability 2026, 18(5), 2498; https://doi.org/10.3390/su18052498 - 4 Mar 2026

Viewed by 819

Abstract

Recent shifts in teacher perspectives indicate that generative artificial intelligence (GenAI) has begun to transform long-standing patterns of routine and repetition in educational practice. This study investigates how educators across different educational levels within an Arab educational context perceive the role of GenAI in fostering sustainable teaching and learning. It examines its influence on learning processes, instructional practices, and educational continuity. Adopting a qualitative research design, the study draws on focus group discussions to capture teachers’ perspectives and applies thematic analysis to explore shared experiences of AI integration in classroom settings. The analysis identified six interconnected themes reflecting a move toward more open and generative learning, the sustainability of learning activities through diversity and personalization, support for teachers in planning, implementation, and assessment, the empowerment of students’ understanding, thinking, achievement, and learning continuity, the central role of ethical considerations, and the challenges and requirements associated with sustainable implementation. The findings demonstrate that the educational value of GenAI is shaped by how it is meaningfully integrated to sustain teaching and learning practices over time. GenAI can contribute to quality and inclusive education in ways that support the long-term aims of Sustainable Development Goal 4. Full article

(This article belongs to the Special Issue Achieving Sustainability Goals Through Artificial Intelligence)

29 pages, 1083 KB

Open AccessArticle

Regional Disparities in Artificial Intelligence Development and Green Economic Efficiency Performance Under Its Embedding: Empirical Evidence from China

by Ziyang Li, Ziqing Huang and Shiyi Zhang

Sustainability 2026, 18(2), 884; https://doi.org/10.3390/su18020884 - 15 Jan 2026

Cited by 1 | Viewed by 686

Abstract

This study analyzes artificial intelligence development and green economic efficiency across 31 Chinese provinces using 2019–2021 panel data. We apply the entropy weight TOPSIS method to measure AI development levels. The entropy weight TOPSIS method measures AI development levels, the DEA-BCC model assesses green economic efficiency, and their coordination types are identified. Findings reveal a significant negative correlation between AI development and green economic efficiency. We explain this complex relationship through three mechanisms: short-term polarization effects, technology conversion lags, and spatial spillovers. Spatial analysis shows AI development forms high-high agglomerations in the Yangtze River Delta and Shandong. Green economic efficiency shows high-high clustering in the Beijing-Tianjin-Hebei region and selected western provinces. Using a “two-system” coupling framework, we identify four provincial categories. The “double-high” type should function as growth poles. The “high-low” type requires improved technology conversion efficiency. The “low-high” type can leverage ecological advantages. The “double-low” type needs enhanced factor inputs. We propose three targeted policy recommendations: establishing digital-green synergy platforms, implementing inter-provincial AI resource collaboration mechanisms, and developing locally adapted action plans. Full article

(This article belongs to the Special Issue Achieving Sustainability Goals Through Artificial Intelligence)

► Show Figures

Figure 1

Review

Jump to: Research, Other

16 pages, 613 KB

Open AccessReview

Digital Exclusion or Zero Hunger? A Sustainability Review of Ethical AI in Fragile Contexts

by Dalal Iriqat and Yara Ashour

Sustainability 2026, 18(9), 4171; https://doi.org/10.3390/su18094171 - 22 Apr 2026

Viewed by 457

Abstract

In contemporary debates on the United Nations Sustainable Development Goals, there is growing recognition that artificial intelligence (AI) may contribute meaningfully to SDG 2 (Zero Hunger), particularly by enhancing the efficiency of food aid distribution and resource allocation. However, such optimism must be critically situated within the broader institutional and ethical contexts in which AI operates. This study argues that the effectiveness of AI in conflict-affected settings is contingent not only on technical capacity but also on governance structures, ethical safeguards, and institutional trust, dimensions closely aligned with SDG 16 (Peace, Justice, and Strong Institutions). Using the Gaza Strip as a case study, this article demonstrates that AI-driven food assistance mechanisms may inadvertently reinforce structural vulnerabilities. Specifically, algorithmic targeting of aid risks deepening dependency, exacerbating digital exclusion, and weakening already fragile governance systems. The absence of robust data accountability frameworks further complicates these dynamics, raising concerns regarding transparency, fairness, and long-term sustainability. The findings caution against privileging technical efficiency at the expense of socio-political stability. Rather, they highlight that the sustainability of AI interventions in humanitarian contexts fundamentally depends on the credibility and legitimacy of institutions. Accordingly, this study proposes a conceptual model for AI in hunger relief and digital humanitarianism that integrates technical innovation with institutional accountability and social trust. This study presents a narrative review informed by structural searching that examines the influence of AI on food security interventions in fragile contexts. This analysis applies a combined ethical governance and sustainability lens to assess current applications and risks. This research advances a broader analytical framework that moves beyond purely technical interpretations of AI, emphasizing its role as a socio-political tool, through identifying five key pillars for sustainable AI governance: data sovereignty, algorithmic accountability, inclusive system design, community-led governance, and market integrity. Full article

(This article belongs to the Special Issue Achieving Sustainability Goals Through Artificial Intelligence)

► Show Figures

Figure 1

Other

Jump to: Research, Review

38 pages, 2678 KB

Open AccessSystematic Review

Integration of Artificial Intelligence into Human Resource Management in Manufacturing Enterprises: A Systematic Literature Review of Challenges, Approaches, and Evolution (2000–2025)

by Qunwei Wu, Xudong Gao and Anastassiya Lipovka

Sustainability 2026, 18(5), 2618; https://doi.org/10.3390/su18052618 - 7 Mar 2026

Cited by 1 | Viewed by 1520

Abstract

With the advancement of digital technology and Industry 4.0, artificial intelligence (AI) is gradually embedded in human resource management and has become an important digital foundation to support the sustainable transformation of enterprises. However, the research in the manufacturing context, particularly through the challenge perspective at different levels, remains fragmented. This work represents a systematic review of 347 articles from Scopus and Web of Science from 2000 to 2025 and employs a dual-method analysis strategy embracing metrics and in-depth coding on 100 core publications. Excel, Bibliometrix, CiteSpace, Latent Dirichlet Allocation (LDA), and VOSviewer were utilized for quantitative analysis, while open–axial–selective coding of the Grounded theory approach was applied to generate qualitative results. The findings revealed six key challenges in integrating AI-HRM within manufacturing and six approaches to solve the identified issues. The Challenge–Approach Matching Matrix was constructed, illustrating the suitability of different pathways for addressing specific challenges. Analysis of thematic evolution in AI-HRM research resulted in the identification of three distinctive phases and demonstrated a consistent shift from technology-centric approaches towards human–machine collaboration. The primary contribution of this research lies in proposing a Multi-Level Embedded Framework providing a complex view of AI-HRM in a manufacturing sector at micro, meso, and macro levels. The absence of sustainable HR transformation through AI integration was identified as the critical challenge at the macro level. This research provides theoretical and practical implications for designing the sustainable HRM system based on ESG principles and favors the United Nations Sustainable Development Goals 9 and 12. Full article

(This article belongs to the Special Issue Achieving Sustainability Goals Through Artificial Intelligence)

► Show Figures

Journal Menu

Journal Browser

Achieving Sustainability Goals Through Artificial Intelligence

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (5 papers)

Research

Review

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI