The rapid expansion of software development has significant environmental, technical, social, and economic impacts. Achieving the United Nations Sustainable Development Goals by 2030 compels developers to adopt sustainable practices. Existing methods mostly offer high-level guidelines, which are time-consuming to implement and rely on team adaptability. Moreover, they focus on design or implementation, while sustainability assessment should start at the requirements engineering phase. In this paper, we introduce SEER, a framework which addresses sustainability concerns in the early software development phase. The framework operates in three stages: (i) it identifies sustainability requirements (SRs) relevant to a specific software product from a general taxonomy; (ii) it evaluates how sustainable system requirements are based on the identified SRs; and (iii) it optimizes system requirements that fail to satisfy any SR. The framework is implemented using the reasoning capabilities of large language models and the agentic RAG (Retrieval Augmented Generation) approach. SEER has been experimented on four software projects from different domains. Results generated using Gemini 2.5 reasoning model demonstrate the effectiveness of the proposed approach in accurately identifying a broad range of sustainability concerns across diverse domains.

Machine Learning Enabled Systems (MLS) are becoming integral to real-world applications, but ensuring their sustainable performance over time remains a significant challenge. These systems operate in dynamic environments and face runtime uncertainties like data drift and model degradation, which affect the sustainability of MLS across multiple dimensions: technical, economical, environmental, and social. While Machine Learning Operations (MLOps) addresses the technical dimension by streamlining the ML model lifecycle, it overlooks other dimensions. Furthermore, some traditional practices, such as frequent retraining, incur substantial energy and computational overhead, thus amplifying sustainability concerns. To address them, we introduce HarmonE, an architectural approach that enables self-adaptive capabilities in MLOps pipelines using the MAPE-K loop. HarmonE allows system architects to define explicit sustainability goals and adaptation thresholds at design time, and performs runtime monitoring of key metrics, such as prediction accuracy, energy consumption, and data distribution shifts, to trigger appropriate adaptation strategies. We validate our approach using a Digital Twin (DT) of an Intelligent Transportation System (ITS), focusing on traffic flow prediction as our primary use case. The DT employs time series ML models to simulate real-time traffic and assess various flow scenarios. Our results show that HarmonE adapts effectively to evolving conditions while maintaining accuracy and meeting sustainability goals.

This paper presents a new approach to urban sustainability assessment through the use of Large Language Models (LLMs) to streamline the use of the ISO 37101 framework to automate and standardise the assessment of urban initiatives against the six "sustainability purposes" and twelve "issues" outlined in the standard. The methodology includes the development of a custom prompt based on the standard definitions and its application to two different datasets: 527 projects from the Paris Participatory Budget and 398 activities from the PROBONO Horizon 2020 project. The results show the effectiveness of LLMs in quickly and consistently categorising different urban initiatives according to sustainability criteria. The approach is particularly promising when it comes to breaking down silos in urban planning by providing a holistic view of the impact of projects. The paper discusses the advantages of this method over traditional human-led assessments, including significant time savings and improved consistency. However, it also points out the importance of human expertise in interpreting results and ethical considerations. This study hopefully can contribute to the growing body of work on AI applications in urban planning and provides a novel method for operationalising standardised sustainability frameworks in different urban contexts.

IT executives must look beyond cost savings to new forms of operational excellence and seek technologies that can help them optimize resilience, scale industry-specific solutions and product delivery, and pioneer new forms of engagement, according to the 10 top strategic technology trends for 2023 unveiled at Gartner's IT Symposium/Xpo 2022. These include multiple forms of wireless, artificial intelligence, and sustainability, according to Frances Karamouzis, distinguished vice president and analyst at Gartner, and external events are making IT pros' decisions about them even more difficult. "Depending on what region of the world you are in there are lots of looming issues such as a potential recession, supply chain concerns, the war in Ukraine and that impact, as well as energy-related issues," Karamouzis said. IT executives must focus on continuing to accelerate digital transformation and consider possible use both for technologies that can be applied immediately and those that are on the horizon. With that as background, Gartner's top 10 strategic technology trends for 2023 looks like this: No single wireless technology will dominate, but enterprises will use a variety of wireless solutions to support a range of environments, from Wi-Fi in the office, services for mobile devices, low-power protocols, and even radio connectivity, Gartner stated.

From chemicals to energy, artificial intelligence (AI) is already showing just how far it can help achieve global sustainability targets across different industrial sectors. One example is Petroliam Nasional Berhad (PETRONAS), has committed to achieving net-zero carbon emissions by 2050. For the Malaysian oil and gas multinational, plant reliability is key to achieving its sustainability goals. PETRONAS identified that early insight into impending equipment failure would enable plant operators to fix equipment proactively before small issues become bigger problems. Proof of concept came via a pilot project in their corporate cloud on Microsoft Azure at four upstream and two downstream units.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! An organizational movement towards mass digitization is underway -- and no industry is exempt. The number of connected devices is expected to reach 55.7 billion by 2025, of which 75% will be connected to an IoT platform -- a shift that has presented a significant environmental challenge for organizations. The increased demand for data storage and computing power has many questioning their sustainability efforts and raises the question: How can enterprises leverage and implement artificial intelligence (AI) and other smart technology without growing their carbon footprints?

Artificial intelligence is changing the paradigm for many industries, and materials-focused commerce is no exception, where tremendous opportunities lie ahead. With the success of effective and generalizable deep learning tools, the materials industry is primed to take advantage of unprecedented breakthroughs, leveraging materials modeling, analysis, and design toward a more efficient, less costly, and more versatile response to market demands and opportunities, through materiomics. With data available from autonomous experimentation, large databases like the Materials Project within the Materials Genome initiative, or synthetic data, there exist many opportunities to accelerate and expand your materials design platform. Today, practicing engineers are expected to have both domain knowledge and a solid understanding of modern machine learning tools. This course will teach all the fundamentals necessary for you to reach the next milestone in practicing materiomics, by navigating the complex world of AI.

Concerns about the environmental impacts of advanced technologies such as AI is prompting debate over whether computing-intensive applications and the chips that power them need to be regulated. That's according to experts speaking during the "Advancing Technology for a Sustainable Planet" conference hosted by the Stanford Institute for Human-Centered Artificial Intelligence and the Stanford Woods Institute for the Environment. At the same time, many of these technologies also help companies meet sustainability goals -- meaning companies need balance between quickly adopting and scaling emerging technologies and understanding how those technologies could affect the company's overall environmental impact. Environmental impact depends on scale -- particularly for a technology like artificial intelligence, said Peter Henderson, a Ph.D student in computer science at Stanford University, during a conference panel session. Companies often optimize AI algorithms to address energy use and carbon emission concerns before deploying a machine learning model.

Sustainable business is a strategy that incorporates environmental, social and governance factors into decision-making, and it is becoming an increasingly important component of business strategy. In fact, in a recent Gartner survey, chief executives identified environmental sustainability as a top 10 business priority for the first time in a decade. Technology is an essential part of the framework that business leaders need to deliver on sustainable business outcomes. However, technology can be a double-edged sword when it comes to sustainability. It can support sustainability goals by improving the quality, scale and impact of environmental initiatives.

Sustainability involves the adaptation of today's business model to the dynamic nature of the current digitalized environments. In other words, corporations need to make sure that resources, especially technology, are being used responsibly and efficiently to improve the lives of the present generations and future generations as well as strengthen their relationships with the environment. In 2020, the United Nations estimates an investment in the range of $5 trillion to $7 trillion to achieve the SDGs (Craig 2021). This calls for a broader understanding of the behavior of investors, and how these investments are used towards solving sustainability-related problems such as poverty, environmental degradation, pollution and inequality. Artificial Intelligence (AI) has the potential to address these societal problems including sustainability. The climate crisis and the degradation of the physical environment are complex problems that require the most innovative and advanced solutions.