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Artificial Intelligence for Climate Change Mitigation, Adaptation and Sustainability: Innovative Approaches for a Greener Future

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2803

Special Issue Editors


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Guest Editor
School of Management, Engineering and Aeronautics, ISEC, Lisbon, Portugal
Interests: sustainability; climate change; environment; education for sustainability; coastal risks; risks and vulnerabilities; serious accident prevention; safety; energy transition; carbon footprint reduction; integration of AI in sustainable infrastructures

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Guest Editor
Department of Management, ISEC, Lisbon, Portugal
Interests: sustainability; climate change; green energy and technologies; education for sustainability; corporate social responsibility; knowledge management; university–industry collaboration; digital transformation and AI

Special Issue Information

Dear Colleagues,

We are pleased to announce a new Special Issue focusing on the integration of artificial intelligence (AI) into strategies for climate change mitigation and sustainability. As the world faces unprecedented environmental challenges, the adoption of innovative technologies has become crucial in developing solutions that can significantly reduce carbon emissions, optimize the use of natural resources, and foster sustainable development.

Artificial intelligence has emerged as a transformative tool, providing new avenues that can enhance the efficiency of renewable energy systems, predict and manage climate-related risks, and contribute to the creation of smart, sustainable urban environments. From optimizing resource allocation to enabling real-time environmental monitoring, AI technologies, such as machine learning, neural networks, and big data analytics, offer robust solutions that can accelerate the transition towards a low-carbon, resilient future.

This Special Issue aims to gather high-quality research that explores the application of AI in climate change mitigation and sustainability and we encourage contributions that present innovative approaches, empirical studies, case studies, and theoretical advancements that can help harness the potential of intelligent systems to address climate challenges. Moreover, this Special Issue will also consider the ethical and environmental implications of AI, with a focus on developing sustainable and energy-efficient AI technologies.

A non-exhaustive list of potential topics of interest includes:

  • AI for optimizing renewable energy systems (e.g., solar, wind, hydropower);
  • Machine learning models for predicting climate-related events;
  • AI-enhanced environmental monitoring and resource management;
  • The integration of green technologies and AI in urban infrastructures;
  • The development of smart cities and circular economies through AI;
  • AI-driven solutions for carbon footprint reduction across industries;
  • Ethical and environmental considerations in sustainable AI development;
  • Applications of AI in energy transition and management;
  • Higher education programs on sustainability;
  • Education on sustainability through AI tools;
  • The role of AI tools in university–industry collaborations towards sustainability.

Dr. Ana Paula Oliveira
Dr. Tânia Carraquico
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

  • climate change mitigation
  • renewable energy
  • sustainable development
  • green technologies
  • energy transition
  • carbon footprint reduction
  • smart cities
  • circular economy
  • environmental monitoring
  • education for sustainability
  • AI tools in higher education sustainability programs
  • university–industry collaboration

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

Published Papers (2 papers)

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34 pages, 1583 KB  
Article
Innovation Dynamics and Ethical Considerations of Agentic Artificial Intelligence in the Transition to a Net-Zero Carbon Economy
by Subhra Mondal, Nguyen Cao Thục Uyen, Subhankar Das and Vasiliki G. Vrana
Sustainability 2025, 17(19), 8806; https://doi.org/10.3390/su17198806 - 30 Sep 2025
Viewed by 1372
Abstract
As climate action becomes increasingly urgent, nations and institutions worldwide seek advanced technologies for practical mitigation efforts. This study examines how agentic artificial intelligence systems capable of decision-making and learning from experience drive innovation dynamics in climate change mitigation, with a particular focus [...] Read more.
As climate action becomes increasingly urgent, nations and institutions worldwide seek advanced technologies for practical mitigation efforts. This study examines how agentic artificial intelligence systems capable of decision-making and learning from experience drive innovation dynamics in climate change mitigation, with a particular focus on ethical considerations during the net-zero transition. The current urgency of climate action demands advanced technologies, yet organisations struggle to effectively deploy agentic AI for climate mitigation due to unclear implementation pathways and ethical consideration. This study examines the relationships among agentic AI capabilities, innovation dynamics, and net-zero transition performance, using survey data from 340 organisations across the manufacturing, energy, and technology sectors, and analysed using structural equation modelling. Based on dynamic capabilities theory, this research proposes a novel theoretical model that examines how agentic AI drives innovation dynamics in climate change mitigation within governance frameworks that encompass transparency, accountability, and environmental justice. Results reveal significant mediation effects of innovation dynamics, dynamic capabilities, and ethical considerations, while environmental context negatively moderates innovation and ethical pathways. Findings suggest that overly restrictive ethical considerations can lead to implementation delays that undermine the urgency of climate action. This study proposes three solutions: (1) adaptive ethical protocols adjusting governance intensity based on climate risk severity, (2) pre-approved ethical templates reducing approval delays by 60%, and (3) stakeholder co-design processes building consensus during development. The research advances dynamic capabilities theory for AI contexts by demonstrating how AI-enabled sensing, seizing, and reconfiguring capabilities create differentiated pathways to climate performance. This study provides empirical validation of the responsible innovation framework, identifies asymmetric environmental contingencies, and offers evidence-based guidance for organisations implementing agentic AI for climate action. Full article
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23 pages, 6931 KB  
Systematic Review
Responsible or Sustainable AI? Circular Economy Models in Smart Cities
by Hanvedes Daovisan
Sustainability 2026, 18(1), 398; https://doi.org/10.3390/su18010398 - 31 Dec 2025
Abstract
Responsible artificial intelligence (RAI) has been increasingly embedded within circular economy (CE) models to facilitate sustainable artificial intelligence (SAI) and to enable data-driven transitions in smart-city contexts. Despite this progression, limited synthesis has been undertaken to connect RAI and SAI principles with their [...] Read more.
Responsible artificial intelligence (RAI) has been increasingly embedded within circular economy (CE) models to facilitate sustainable artificial intelligence (SAI) and to enable data-driven transitions in smart-city contexts. Despite this progression, limited synthesis has been undertaken to connect RAI and SAI principles with their translation into policy, particularly within deep learning contexts. Accordingly, this study was designed to integrate RAI and SAI research within CE-oriented smart-city models. A science-mapping and knowledge-translation design was employed, with data retrieved from the Scopus database in accordance with the PRISMA 2020 flow protocol. From an initial yield of 3842 records, 1176 studies published between 1 January 2020 and 20 November 2025 were included for analysis. The first set of results indicated that publication trends in RAI and SAI for CE models within smart-city frameworks were found to be statistically significant (R2 = 0.94, p < 0.001). The second set of results revealed that circular manufacturing, waste management automation, predictive energy optimisation, urban data platforms, and smart mobility systems were increasingly embedded within RAI and SAI applications for CE models in smart-city contexts. The third set of results demonstrated that RAI and SAI within CE models were found to yield a significant effect (M = −0.61, SD = 0.09, t(9) = 7.42, p < 0.001) and to correlate positively with policy alignment (r = 0.34, p = 0.042) in smart-city contexts. It was therefore concluded that policy-responsive AI governance is required to ensure inclusive and sustainable smart-city transformation within frameworks of RAI. Full article
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