Systems-of-Systems for Environmental Sustainability: A Systematic Mapping Study

• URL: http://arxiv.org/abs/2502.20021v1
• Date: Thu, 27 Feb 2025 12:00:27 GMT
• Title: Systems-of-Systems for Environmental Sustainability: A Systematic Mapping Study
• Authors: Ana Clara Araújo Gomes da Silva, Gilmar Teixeira Junior, Lívia Mancine C. de Campos, Renato F. Bulcão-Neto, Valdemar Vicente Graciano Neto,
• Abstract summary: This study focuses on environmental sustainability, analyzing how SoS contribute to sustainable practices such as carbon emission reduction, energy efficiency, and biodiversity conservation.<n>We conducted a Systematic Mapping Study to identify the application domains of SoS in sustainability, the challenges faced, and research opportunities.<n>Our findings reveal that most studies focus on Smart Cities and Smart Grids, while applications such as sustainable agriculture and wildfire prevention are less explored.
• Score: 0.7095350526841507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Environmental sustainability in Systems-of-Systems (SoS) is an emerging field that seeks to integrate technological solutions to promote the efficient management of natural resources. While systematic reviews address sustainability in the context of Smart Cities (a category of SoS), a systematic study synthesizing the existing knowledge on environmental sustainability applied to SoS in general does not exist. Although literature includes other types of sustainability, such as financial and social, this study focuses on environmental sustainability, analyzing how SoS contribute to sustainable practices such as carbon emission reduction, energy efficiency, and biodiversity conservation. We conducted a Systematic Mapping Study to identify the application domains of SoS in sustainability, the challenges faced, and research opportunities. We planned and executed a research protocol including an automated search over four scientific databases. Of 926 studies retrieved, we selected, analyzed, and reported the results of 39 relevant studies. Our findings reveal that most studies focus on Smart Cities and Smart Grids, while applications such as sustainable agriculture and wildfire prevention are less explored. We identified challenges such as system interoperability, scalability, and data governance. Finally, we propose future research directions for SoS and environmental sustainability.

Related papers

• Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice [57.94036023167952]
  We argue that the efforts aiming to study AI's ethical ramifications should be made in tandem with those evaluating its impacts on the environment. We propose best practices to better integrate AI ethics and sustainability in AI research and practice.
  arXiv  Detail & Related papers  (2025-04-01T13:53:11Z)
• Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction [64.4881275941927]
  We present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model. Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics. This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management.
  arXiv  Detail & Related papers  (2025-03-13T01:50:50Z)
• A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges [0.7889270818022226]
  Large Language Models (LLMs) have transformed numerous domains by providing advanced capabilities in natural language understanding, generation, and reasoning.<n>Despite their groundbreaking applications across industries such as research, healthcare, and creative media, their rapid adoption raises critical concerns regarding sustainability.<n>This survey paper examines the environmental, economic, and computational challenges associated with LLMs, focusing on energy consumption, carbon emissions, and resource utilization in data centers.
  arXiv  Detail & Related papers  (2024-12-06T05:20:04Z)
• Advancing Sustainability via Recommender Systems: A Survey [23.364932316026973]
  Human behavioral patterns and consumption paradigms have emerged as pivotal determinants in environmental degradation and climate change. There exists an imperative need for sustainable recommender systems that incorporate sustainability principles to foster eco-conscious and socially responsible choices. This comprehensive survey addresses this critical research gap by presenting a systematic analysis of sustainable recommender systems.
  arXiv  Detail & Related papers  (2024-11-12T09:19:32Z)
• Engineering a sustainable world by enhancing the scope of systems of systems engineering and mastering dynamics [1.3075370397377077]
  We discuss the engi-neering of a sustainable world from a systems of systems (SoS) perspective. We will discuss how suitable the current state-of-the-art in SoS engi-neering is in order to engineer sustainability.
  arXiv  Detail & Related papers  (2024-01-25T10:06:11Z)
• On the Opportunities of Green Computing: A Survey [80.21955522431168]
  Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades. The needs for high computing power brings higher carbon emission and undermines research fairness. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
  arXiv  Detail & Related papers  (2023-11-01T11:16:41Z)
• Sustainability in Computing Education: A Systematic Literature Review [0.0]
  This paper describes approaches taken in computing education to address the issue of sustainability. From a set of 572 publications extracted from six large digital libraries plus snowballing, we distilled and analyzed the 90 relevant primary studies. We present a framework capturing learning objectives and outcomes as well as pedagogical methods for sustainability in computing education.
  arXiv  Detail & Related papers  (2023-05-17T16:51:37Z)
• SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning [63.192289553021816]
  Progress toward the United Nations Sustainable Development Goals has been hindered by a lack of data on key environmental and socioeconomic indicators. Recent advances in machine learning have made it possible to utilize abundant, frequently-updated, and globally available data, such as from satellites or social media. In this paper, we introduce SustainBench, a collection of 15 benchmark tasks across 7 SDGs.
  arXiv  Detail & Related papers  (2021-11-08T18:59:04Z)
• Applications of physics-informed scientific machine learning in subsurface science: A survey [64.0476282000118]
  Geosystems are geological formations altered by humans activities such as fossil energy exploration, waste disposal, geologic carbon sequestration, and renewable energy generation. The responsible use and exploration of geosystems are thus critical to the geosystem governance, which in turn depends on the efficient monitoring, risk assessment, and decision support tools for practical implementation. Fast advances in machine learning algorithms and novel sensing technologies in recent years have presented new opportunities for the subsurface research community to improve the efficacy and transparency of geosystem governance.
  arXiv  Detail & Related papers  (2021-04-10T13:40:22Z)
• Service Ecosystem: A Lens of Smart Society [18.059709412280647]
  We argue that this necessitates a broad scientific research agenda to study service ecosystem. We firstly outline a set of research issues that are fundamental to this emerging field. We then explores the technical, social, legal and institutional challenges on the study of service ecosystem.
  arXiv  Detail & Related papers  (2020-08-08T02:20:53Z)
• Leveraging traditional ecological knowledge in ecosystem restoration projects utilizing machine learning [77.34726150561087]
  Community engagement throughout the stages of ecosystem restoration projects could contribute to improved community well-being. We suggest that adaptive and scalable practices could incentivize interdisciplinary collaboration during all stages of ecosystemic ML restoration projects.
  arXiv  Detail & Related papers  (2020-06-22T16:17:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.