Artificial Intelligence and Smart Technologies for Sustainable Development Goals

Dear Colleagues,

AI can be defined as any computer technology that has the capability of performing cognitive tasks commonly attributed to humans. In recent years, this has translated into the development of deep learning models, which mimic human cognitive functions by using large neural networks, and reinforcement learning, which allows interactive systems to learn from their errors. Smart technologies and the Internet of Things embed such AI models in ensembles of devices, such as smart energy grids.

The United Nations’ Sustainable Development Goals (SDGs) provide a blueprint for a fairer and more sustainable world for everyone. Each SDG features economic, social, and environmental facets. The SDGs will be tough to reach by 2030, but the COVID-19 pandemic showed that AI and smart technologies can play a key accelerator role in the release of vaccines as fast as possible.

Digital twins and smart production processes have proven useful for optimizing energy and resource usage in industry, urban settings and transport sectors (Vinuesa et al. 2020). There is an untapped potential of AI in these areas, such as in shared mobility, where a fleet of vehicles could be shared by citizens based on their needs. This range of technologies could also help develop the circular economy and in building smart cities which use their resources more efficiently. Moreover, it has been observed how, in the context of climate change mitigation and adaptation strategies, AI could allow for the optimized energy management of buildings, taking into account not only the energy-related implications, but also the comfort conditions of the occupants (Ngarambe et al. 2020; Khan et al. 2022; Sirmacek et al. 2022).

Internet of Things technologies such as diagnostic models embedded in chips could be deployed easily in remote parts of developing countries. AI applied to agriculture can help reduce poverty and malnutrition by improving crop production, identifying pest infestation, or optimizing fertilizer usage. There are also numerous examples of how AI can support our understanding of climate change and environmental monitoring, and help in enhancing the efficiency of renewable energies (Vinuesa et al. 2020; Koster et al. 2022).

However, numerous examples of AI misuse have been identified, and overall, these technologies may also be counterproductive for SDG achievement. First, using AI to pursue the SDGs may lead to conflicting situations. For example, the need for protecting the privacy and security of individuals will limit the development of data-driven health diagnostic tools. Additionally, the uncontrolled use of AI may reinforce existing inequalities concerning the accessibility to such resources and/or job replacements/opportunities (Vinuesa et al. 2020). Furthermore, recent literature surveys have noticed that more emphasis may be given to the positive impacts of AI on SDGs, without providing a fair view to the equal efforts on the study of negative impacts, regarding, for example, the high energy consumption of AI models or people’s biases regarding safe information sharing.

Additionally, even though AI training has a significant impact, sometimes it replaces predictive models with an even larger energy consumption (Singer 2018; Pathak et al. 2022). It may also allow us to process information and logs of a size exceeding human capacity, with benefits outweighing energy costs. For example this could allow scientists and engineers to focus their efforts on advancing the SDGs. Overall, the impact of AI must be accounted for in a relevant context.

Open-source software initiatives and those that facilitate access to computing infrastructures are further ways to improve access to AI technologies in the developing world. This has to come with education in AI and critical thinking, so that skilled practitioners can locally exploit these resources.

Efforts are also needed for policy making and legislation through a dual use of AI, both for providing the best information to policy makers, and for defining AI strategies which balance the benefits of AI between citizens, countries, and environmental issues. This could be supported by embedding trustworthiness and transparency in the design of AI models. The IEEE’s Ethically Aligned Design and the EU’s ethics guidelines for trustworthy AI are incentives to move in this direction.

Starting from these assumptions, this Special Issue aims to collect high-quality research activities/products (original research articles, reviews and case studies are welcome) on artificial intelligence and smart technology advancements for achieving the Sustainable Development Goals. Topics of interest include, but are not limited to:

The potential and applications of AI and smart technologies to help achieve the SDGs;

Addressing and evaluating negative impacts of AI and smart technologies on sustainable development;

Developments in AI regarding climate, renewable energies, efficient energy use, health, agriculture, smart buildings, and urban settlements;

Ethical considerations in the use of AI and smart technologies for sustainable development, and in addressing conflicting goals;

Policy making and guidelines favoring sustainable, accountable, and trustworthy usage of AI.

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. All papers will be thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for the submission of manuscripts is available on the Instructions for Authors page of the Sustainability journal’s website.

References Section:

- Vinuesa, R. et al. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature communications, 11(1), 1-10.

- Khan, S. U. et al. (2022). Towards Intelligent Building Energy Management: AI-based Framework for Power Consumption and Generation Forecasting, Energy and Buildings, 2022, 112705, ISSN 0378-7788, https://doi.org/10.1016/j.enbuild.2022.112705.

- Sirmacek, B. and Vinuesa, R. (2022). Remote sensing and AI for building climate adaptation applications, Results in Engineering, Volume 15, 2022, 100524, ISSN 2590-1230, https://doi.org/10.1016/j.rineng.2022.100524.

- Koster, D. et al. (2022). Single-Site Forecasts for 130 Photovoltaic Systems at Distribution System Operator Level, Using a Hybrid-Physical Approach, to Improve Grid-Integration and Enable Future Smart-Grid Operation. Solar RRL, 2200652. https://doi.org/10.1002/solr.202200652.

- Singer, G. (2018). How is artificial intelligence changing science? Interview given to Physics.org, 25 May: https://phys.org/news/2018-05-artificialintelligence-science.html#jCp.

- Pathak, J. et al. (2022). FourCastNet: A global data-driven high-resolution weather model using adaptive Fourier neural operators. arXiv preprint arXiv:2202.11214.

Dr. Pierrick Bruneau
Dr. Gianluca Scaccianoce
Dr. Laura Cirrincione
Guest Editors

Manuscript Submission Information

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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.

• AI supporting sustainable development goals
• AI supporting efficient resource usage
• ethical AI
• trustworthy AI
• internet of things
• policy making for AI
• social impacts of AI