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Artificial Intelligence (AI) For Sustainability
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Artificial Intelligence (AI) For Sustainability

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

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 18996

Special Issue Editors

Prof. Dr. Jean-Claude Thill

E-Mail Website
Guest Editor
Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Interests: spatial analysis; spatial statistics; spatial machine learning; network analysis; computational social science; geospatial big data analytics; urban analytics; spatial-time modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Prabhakar Kudva

E-Mail
Guest Editor
IBM T. J. Watson Research Center
Interests: AI; Distributed and Cloud Systems; Resilient Enterprise Systems;
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With recent technological advances, artificial intelligence (AI) has become a major enabler in various fields, including sustainable development.  AI methods enable sustainability goals and provide value along all of its dimensions: economic, social, environmental, technical, and individual.  AI has improved the ability to tackle complexity and increased our understanding of important causative variables and sources, besides providing tools to affect the outcomes. The contribution of AI applications to the overall global GDP is expected to be significant over the next decade. Some key areas that are expected to particularly benefit from AI are transportation and urban development, water resources management, energy efficiency, agriculture and climate research.  For this Special Issue, we welcome papers that focus on the application of novel AI methods for knowledge representation, learning, including deep learning and neural networks, reasoning and search, as well as forecasting. Case studies of applications of data science methods and visualization tools, particularly, open-source, are of interest as they inform research, policy-making, and practice in sustainability.

Topics of interest include:

  1. Novel applications of learning and neural networks, in particular in the areas of transportation and urban development, water resources management, energy efficiency, agriculture and climate research
  2. AI systems that leverage domain knowledge in the field of sustainability
  3. Knowledge representation, search, and analysis
  4. Explainable AI (XAI) and visualization methods for understanding causal variables
  5. AI-enabled IoT for measurement and response at the edge
  6. Rule-based systems
  7. Open-Source AI tools and systems for sustainability
  8. Open-data projects for research
  9. Cloud enablement of AI for sustainability
  10. Tools for human/AI partnership

Prof. Dr. Jean-Claude Thill
Dr. Prabhakar Kudva
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 100 words) can be sent to the Editorial Office for announcement on this website.

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
  • Urban Development
  • Water Resources Management
  • Energy Efficiency
  • Agriculture
  • Deep Learning
  • Data Science
  • Visualization
  • Explainable Machine Learning
  • Climate Research

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Published Papers (3 papers)

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Research

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23 pages, 3365 KiB  
Article
Evaluation of Environmental Information Disclosure of Listed Companies in China’s Heavy Pollution Industries: A Text Mining-Based Methodology
by Rongjiang Cai, Tao Lv and Xu Deng
Sustainability 2021, 13(10), 5415; https://doi.org/10.3390/su13105415 - 12 May 2021
Cited by 5 | Viewed by 3109
Abstract
Environmental information disclosure (EID) of listed companies is a significant and essential reference for assessing their environmental protection commitment. However, the content and form of EID are complex, and previous assessment studies involved manual scoring mainly by the experts in this field. It [...] Read more.
Environmental information disclosure (EID) of listed companies is a significant and essential reference for assessing their environmental protection commitment. However, the content and form of EID are complex, and previous assessment studies involved manual scoring mainly by the experts in this field. It is subjective and has low timeliness. Therefore, this paper proposes an automatic evaluation framework of EID quality based on text mining (TM), including the EID index system’s construction, automatic scoring of environmental information disclosure quality, and EID index calculation. Furthermore, based on the EID of 801 listed companies in China’s heavy pollution industry from 2013 to 2017, case studies are conducted. The case study results show that the overall quality of the EID of listed companies in China’s heavily polluting industries is low, and there is a gap differentiation between the 16 industries. Compared with the subjective manual scoring method, TM evaluation can evaluate the quality of EID more effectively and accurately. It has great potential and can become an essential tool for the sustainable development of society and listed companies. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) For Sustainability)
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19 pages, 2514 KiB  
Article
Digitalization, Circular Economy and Environmental Sustainability: The Application of Artificial Intelligence in the Efficient Self-Management of Waste
by Sergio Luis Nañez Alonso, Ricardo Francisco Reier Forradellas, Oriol Pi Morell and Javier Jorge-Vazquez
Sustainability 2021, 13(4), 2092; https://doi.org/10.3390/su13042092 - 16 Feb 2021
Cited by 43 | Viewed by 5466
Abstract
The great advances produced in the field of artificial intelligence and, more specifically, in deep learning allow us to classify images automatically with a great margin of reliability. This research consists of the validation and development of a methodology that allows, through the [...] Read more.
The great advances produced in the field of artificial intelligence and, more specifically, in deep learning allow us to classify images automatically with a great margin of reliability. This research consists of the validation and development of a methodology that allows, through the use of convolutional neural networks and image identification, the automatic recycling of materials such as paper, plastic, glass, and organic material. The validity of the study is based on the development of a methodology capable of implementing a convolutional neural network to validate a reliability in the recycling process that is much higher than simple human interaction would have. The method used to obtain this better precision will be transfer learning through a dataset using the pre-trained networks Visual Geometric Group 16 (VGG16), Visual Geometric Group 19 (VGG19), and ResNet15V2. To implement the model, the Keras framework is used. The results conclude that by using a small set of images, and thanks to the later help of the transfer learning method, it is possible to classify each of the materials with a 90% reliability rate. As a conclusion, a model is obtained with a performance much higher than the performance that would be reached if this type of technique were not used, with the classification of a 100% reusable material such as organic material. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) For Sustainability)
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Review

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20 pages, 1711 KiB  
Review
Smart Fishery: A Systematic Review and Research Agenda for Sustainable Fisheries in the Age of AI
by Sanaz Honarmand Ebrahimi, Marinus Ossewaarde and Ariana Need
Sustainability 2021, 13(11), 6037; https://doi.org/10.3390/su13116037 - 27 May 2021
Cited by 19 | Viewed by 8776
Abstract
Applications of artificial intelligence (AI) technologies for improving the sustainability of the smart fishery have become widespread. While sustainability is often claimed to be the desired outcome of AI applications, there is as yet little evidence on how AI contributes to the sustainable [...] Read more.
Applications of artificial intelligence (AI) technologies for improving the sustainability of the smart fishery have become widespread. While sustainability is often claimed to be the desired outcome of AI applications, there is as yet little evidence on how AI contributes to the sustainable fishery. The purpose of this paper is to perform a systematic review of the literature on the smart fishery and to identify upcoming themes for future research on the sustainable fishery in the Age of AI. The findings of the review reveal that scholarly attention in AI-inspired fishery literature focuses mostly on automation of fishery resources monitoring, mainly detection, identification, and classification. Some papers list marine health and primary production which are vital dimensions for Large Marine Ecosystems to recycle nutrients to sustain anticipated production levels. Very few reviewed articles refer to assessing individual needs, particularly fishers, from AI deployment in fisheries and policy response from governments. We call for future AI for sustainable fishery studies on how fishers perceive AI needs, and how governments possess a tangible strategy or depth of understanding on the regulation of AI concerning smart fishery systems and research on resilience-enhancing policies to promote the value and potentials of the AI-inspired smart fishery in different locations. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) For Sustainability)
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