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Sustainability
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Artificial Intelligence (AI)-Enabled Sustainable Practices and Future
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Artificial Intelligence (AI)-Enabled Sustainable Practices and Future

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

Deadline for manuscript submissions: 10 June 2025 | Viewed by 5991

Special Issue Editors

Dr. Siliang Yang

E-Mail Website
Guest Editor
Mott MacDonald, Leeds, UK
Interests: renewable energy technologies; environmental sustainability; climate change mitigation; building performance; high-efficient HVAC systems
Special Issues, Collections and Topics in MDPI journals
Dr. Yulei Li

E-Mail Website
Guest Editor
Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK
Interests: artificial intelligence; machine learning; data mining; neural network
Dr. Sulaimon Adebiyi

E-Mail Website
Guest Editor
Department of Strategy, Analytics and Operations, Nottingham Business School, Nottingham Trent University, Nottingham, UK
Interests: strategy; leadership; sustainability; multi-criteria decision making analysis; SMEs growth strategies
Dr. Shan Shan

E-Mail Website
Guest Editor
Department of Strategy, Analytics and Operations, Nottingham Business School, Nottingham Trent University, Nottingham, UK
Interests: data and information science; technological-based marketing and economics; renewable energy markets; energy and environmental management; machine learning
Prof. Dr. Wai Lok Woo

E-Mail Website
Guest Editor
Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK
Interests: machine learning; artificial intelligence; computational intelligence; data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) and sustainability has emerged as a critical and timely issue due to the escalating environmental challenges and the rapid advancement of AI technologies. As AI continues to evolve and its applications expand, there is a growing need to harness its potential to optimise resource use, mitigate environmental impacts, and foster long-term sustainability. AI technologies are at the forefront of this transformation, which offer innovative solutions for optimising energy systems, enhancing precision agriculture, improving real-time environmental monitoring, and aiding in climate change mitigation. By leveraging AI, people can significantly advance their sustainability goals, promote more efficient use of resources, and reduce the overall environmental footprint.

However, despite these promising developments, notable gaps remain in the literature. Comprehensive studies on the lifecycle energy consumption and carbon footprint in association with AI technologies are still limited. In addition, cross-sectoral integration of AI applications for holistic sustainability strategies is underexplored, while the ethical implications and governance models for responsible AI deployment in sustainability require further examination. Addressing these gaps is imperative for maximising the positive impact of AI on global sustainability efforts, and thus ensuring these technologies contribute effectively to the transition towards a more sustainable and equitable future.

This Special Issue aims to publish high-quality papers that provide theoretical and practical insights into integrating AI with sustainability efforts and highlight the importance of responsible and impactful AI applications. Methodology developments, literature reviews, policy assessments, applied analyses, and case studies are all welcome on, but not limited to, the following topics:

  • AI for environmental vulnerability and exposure assessment;
  • AI in renewable energy optimization;
  • AI in district energy systems;
  • AI-enhanced green buildings and urban development;
  • AI for climate change mitigation and adaptation;
  • AI in the circular economy and waste management;
  • AI-driven sustainable agriculture and food systems;
  • AI for water resources management;
  • AI-dominated consumer behaviour for sustainability;
  • AI-enabled facilities management;
  • AI-assisted heating, ventilation, and air conditioning (HVAC) system design optimization;
  • AI in sustainable transportation systems.

Dr. Siliang Yang
Dr. Yulei Li
Dr. Sulaimon Adebiyi
Dr. Shan Shan
Prof. Dr. Wai Lok Woo
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
  • energy and environmental management
  • climate change mitigation
  • technological advancement
  • sustainability

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  • 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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 papers)

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Research

30 pages, 8269 KiB  
Article
An Ensemble Approach to Predict a Sustainable Energy Plan for London Households
by Niraj Buyo, Akbar Sheikh-Akbari and Farrukh Saleem
Sustainability 2025, 17(2), 500; https://doi.org/10.3390/su17020500 - 10 Jan 2025
Viewed by 424
Abstract
The energy sector plays a vital role in driving environmental and social advancements. Accurately predicting energy demand across various time frames offers numerous benefits, such as facilitating a sustainable transition and planning of energy resources. This research focuses on predicting energy consumption using [...] Read more.
The energy sector plays a vital role in driving environmental and social advancements. Accurately predicting energy demand across various time frames offers numerous benefits, such as facilitating a sustainable transition and planning of energy resources. This research focuses on predicting energy consumption using three individual models: Prophet, eXtreme Gradient Boosting (XGBoost), and long short-term memory (LSTM). Additionally, it proposes an ensemble model that combines the predictions from all three to enhance overall accuracy. This approach aims to leverage the strengths of each model for better prediction performance. We examine the accuracy of an ensemble model using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) through means of resource allocation. The research investigates the use of real data from smart meters gathered from 5567 London residences as part of the UK Power Networks-led Low Carbon London project from the London Datastore. The performance of each individual model was recorded as follows: 62.96% for the Prophet model, 70.37% for LSTM, and 66.66% for XGBoost. In contrast, the proposed ensemble model, which combines LSTM, Prophet, and XGBoost, achieved an impressive accuracy of 81.48%, surpassing the individual models. The findings of this study indicate that the proposed model enhances energy efficiency and supports the transition towards a sustainable energy future. Consequently, it can accurately forecast the maximum loads of distribution networks for London households. In addition, this work contributes to the improvement of load forecasting for distribution networks, which can guide higher authorities in developing sustainable energy consumption plans. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Enabled Sustainable Practices and Future)
►▼ Show Figures

Figure 1

Figure 1
<p>Proposed ensemble energy consumption prediction model: (<b>a</b>) individual model training (LSTM, Prophet, XGBoost); (<b>b</b>) ensemble model testing.</p> Full article ">Figure 2
<p>Data preparation steps: (<b>a</b>) data preparation, (<b>b</b>) data preprocessing, and (<b>c</b>) data analysis.</p> Full article ">Figure 3
<p>Heatmap feature selection.</p> Full article ">Figure 4
<p>Feature contribution statistics.</p> Full article ">Figure 5
<p>Energy consumption of a single house in a week.</p> Full article ">Figure 6
<p>Average energy usage of multiple households for an entire week in 2013.</p> Full article ">Figure 7
<p>Average energy consumption per ACORN group for the year 2013.</p> Full article ">Figure 8
<p>Average energy consumption by Standard tariff and DToU tariff further categorized in three groups: Affluent, Adversity, and Comfortable.</p> Full article ">Figure 9
<p>Half-hourly energy consumption by tariff rates (high, normal, and low).</p> Full article ">Figure 10
<p>Temperature and mean energy consumption per ACORN group (Affluent, Adversity, Comfortable) of the year 2013.</p> Full article ">Figure 11
<p>Average energy consumption and maximum and minimum temperature plots from January 2012 to April 2014.</p> Full article ">Figure 12
<p>Energy consumption (plot in green) and humidity (plot in blue) during the 1st quarter of the year 2012.</p> Full article ">Figure 13
<p>Energy consumption (plot in green) and cloud cover (plot in blue) during January 2012 to April 2014.</p> Full article ">Figure 14
<p>Average energy consumption (plot in green) and UV index (plot in blue) during January 2012 to April 2014.</p> Full article ">Figure 15
<p>Prophet model components.</p> Full article ">Figure 16
<p>Comparison between individual and ensemble model predictions (Prophet, LSTM, XGBoost).</p> Full article ">
29 pages, 3153 KiB  
Article
Ensuring Sustainable Digital Inclusion among the Elderly: A Comprehensive Analysis
by Rinku Mohan, Farrukh Saleem, Kiran Voderhobli and Akbar Sheikh-Akbari
Sustainability 2024, 16(17), 7485; https://doi.org/10.3390/su16177485 - 29 Aug 2024
Cited by 2 | Viewed by 5008
Abstract
Advancements in digital technologies have transformed the world by providing more opportunities and possibilities. However, elderly persons have several challenges utilizing modern technology, leading to digital exclusion, which can negatively impact sustainable development. This research attempts to address the current digital exclusion by [...] Read more.
Advancements in digital technologies have transformed the world by providing more opportunities and possibilities. However, elderly persons have several challenges utilizing modern technology, leading to digital exclusion, which can negatively impact sustainable development. This research attempts to address the current digital exclusion by addressing the challenges older people face considering evolving digital technologies, focusing on economic, social, and environmental sustainability. Three distinct goals are pursued in this study: to perform a detailed literature review to identify gaps in the current understanding of digital exclusion among the elderly, to identify the primary factors affecting digital exclusion in the elderly, and to analyze the patterns and trends in different countries, with a focus on differentiating between High-Income Countries (HICs) and Lower Middle-Income Countries (LMICs). The research strategies used in this study involve a combination of a literature review and a quantitative analysis of the digital exclusion data from five cohorts. This study uses statistical analysis, such as PCA, chi-square test, one-way ANOVA, and two-way ANOVA, to present a complete assessment of the digital issues that older persons experience. The expected results include the identification of factors influencing the digital divide and an enhanced awareness of how digital exclusion varies among different socio-economic and cultural settings. The data used in this study were obtained from five separate cohorts over a five-year period from 2019 to 2023. These cohorts include ELSA (UK), SHARE (Austria, Germany, France, Estonia, Bulgaria, and Romania), LASI (India), MHAS (Mexico), and ELSI (Brazil). It was discovered that the digital exclusion rate differs significantly across HICs and LMICs, with the UK having the fewest (11%) and India having the most (91%) digitally excluded people. It was discovered that three primary factors, including socio-economic status, health-related issues, and age-related limitations, are causing digital exclusion among the elderly, irrespective of the income level of the country. Further analysis showed that the country type has a significant influence on the digital exclusion rates among the elderly, and age group plays an important role in digital exclusion. Additionally, significant variations were observed in the life satisfaction of digitally excluded people within HICs and LMICs. The interaction between country type and digital exclusion also showed a major influence on the health rating. This study has a broad impact since it not only contributes to what we know academically about digital exclusion but also has practical applications for communities. By investigating the barriers that prevent older people from adopting digital technologies, this study will assist in developing better policies and community activities to help them make use of the benefits of the digital era, making societies more equitable and connected. This paper provides detailed insight into intergenerational equity, which is vital for the embedding principles of sustainable development. Furthermore, it makes a strong case for digital inclusion to be part of broader efforts (and policies) for creating sustainable societies. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Enabled Sustainable Practices and Future)
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Figure 1

Figure 1
<p>Percentage of elderly population in HICs and LMICs between 2018 and 2022 [<a href="#B13-sustainability-16-07485" class="html-bibr">13</a>].</p> Full article ">Figure 2
<p>Distribution of internet users worldwide as of February 2024 by age group [<a href="#B18-sustainability-16-07485" class="html-bibr">18</a>].</p> Full article ">Figure 3
<p>Research Process.</p> Full article ">Figure 4
<p>Internet usage vs. internet connection.</p> Full article ">Figure 5
<p>Device ownership vs. internet use.</p> Full article ">Figure 6
<p>Digital exclusion vs. age.</p> Full article ">Figure 7
<p>Digital exclusion vs. country type.</p> Full article ">Figure 8
<p>Digital exclusion vs. country type and age group.</p> Full article ">Figure 9
<p>Digital exclusion vs. life satisfaction.</p> Full article ">Figure 10
<p>Scree plot.</p> Full article ">Figure 11
<p>Trend in association of digital exclusion between HICs and LMICs.</p> Full article ">Figure 12
<p>Trend in association of digitally excluded people and age groups between different country types.</p> Full article ">
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