Digital technologies are profoundly influencing all economic sectors and have potential to contribute towards a sustainable society. At the same time, the production, use and waste management of these technologies, which lie at the core of the economic sector of information and communication technology (ICT), are causing environmental impacts. Previous studies have applied life cycle assessment (LCA) methodology and life cycle thinking to assess current and future direct energy use and climate impact of the global ICT sector. These studies frequently arrive at contradictory results regarding future impacts. Calculation approaches applied differ significantly, the consideration of key aspects varies, fast-growing digital technologies are seldom included in future scenarios and uncertainty analyses are typically limited. The aim of this study is to develop guidelines for assessments of the current and future direct energy use and climate impact of the global ICT sector based on LCA methodology and life cycle thinking. The guidelines have been developed based on literature reviews, the authors’ aggregated and broad expertise in this topic and in workshops. Key aspects in influencing the current and future direct energy use and climate impact of the global ICT sector, covering its three subdomains of end-user devices, networks and data centres as well as all life cycle stages, are identified. These include, for example, the number of end-user devices, number of subscriptions and the annual electricity use of networks and data centres. The guidelines address challenges for practitioners and can contribute towards more transparent and coherent future studies.

This study aims to evaluate various waste-to-energy conversion scenarios in terms of their potential to reduce greenhouse gas (GHG) emissions and improve sustainability based on economic and environmental outcomes. To achieve this, a comprehensive waste management model was developed using the system dynamics approach in the Vensim software to predict waste generation and composition and compare pyrolysis, incineration, gasification, and sanitary landfill scenarios with the baseline scenario over 25 years (2025–2050). The analysis of different waste management scenarios highlights the superior performance of pyrolysis in terms of energy recovery, economic profit, GHG emissions reduction, environmental outcomes, and long-term sustainability. Results show that the pyrolysis scenario generates the highest electricity, with a cumulative net electricity output of 10,469 GWh. Although pyrolysis has GHG emissions due to energy consumption and direct process emissions, it results in the largest net reduction in GHG emissions, primarily due to avoided emissions from increased electricity generation, leading to a 346% reduction compared to the baseline scenario. Furthermore, the pyrolysis scenario demonstrates the highest economic profit at 354 million USD and the highest sustainability index (SI) at 499 million USD. The cumulative SI from 2025 to 2050 shows a 503% increase compared to the business-as-usual scenario, highlighting its superior sustainability performance. This study highlights the importance of strategic waste-to-energy planning in reducing GHG emissions and promoting sustainability. It also offers valuable insights for policymakers and researchers, supporting the development of sustainable waste management strategies and effective efforts for climate change mitigation.

Recent advancements in occupancy and indoor environmental monitoring have encouraged the development of innovative solutions. This paper presents a novel approach to room occupancy detection using Wi-Fi probe requests and machine learning techniques. We propose a methodology that splits occupancy detection into two distinct subtasks: personnel presence detection, where the model predicts whether someone is present in the room, and occupancy level detection, which estimates the number of occupants on a six-level scale (ranging from 1 person to up to 25 people) based on probe requests. To achieve this, we evaluated three types of neural networks: CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). Our experimental results show that the GRU model exhibits superior performance in both tasks. For personnel presence detection, the GRU model achieves an accuracy of 91.8%, outperforming the CNN and LSTM models with accuracies of 88.7% and 63.8%, respectively. This demonstrates the effectiveness of GRU in discerning room occupancy. Furthermore, for occupancy level detection, the GRU model achieves an accuracy of 75.1%, surpassing the CNN and LSTM models with accuracies of 47.1% and 52.8%, respectively. This research contributes to the field of occupancy detection by providing a cost-effective solution that utilizes existing Wi-Fi infrastructure and demonstrates the potential of machine learning techniques in accurately classifying room occupancy.

Information communication and technology (ICT) services and solutions can improve resource efficiency in a variety of sector, but also result in direct environmental impacts. This study assesses the direct environmental impacts of an ICT service that improves vehicle fuel efficiency using a cradle-to-grave life cycle assessment (LCA). This is one of the first studies to examine the entire life cycle of an ICT service from development to use and maintenance, with a focus on software—an aspect that is typically neglected in previous studies. The results suggest that software development and maintenance and the use of in-vehicle communicators for data transmission have the largest environmental impacts across multiple categories. Deployed across a fleet of 150,000 vehicles over 5 years, we estimate that the ICT service is responsible for 174 tCO2e. However, this is negligible compared with the total emissions of the fleet and the potential savings from the service, given a single diesel vehicle in this fleet emits around 130 tCO2e over the same period. We explore several scenarios to reduce the footprint of the ICT service. The largest potential reduction of around one-third is achieved by replacing in-house servers with cloud computing in a data center located in a region with low-carbon electricity. The study demonstrates how LCA can be used to assess the environmental impacts of ICT services and the importance of considering software in these assessments.

Digital solutions based on information and communication technologies (ICT) provide many opportunities in buildings to achieve resource and energy efficiency. In general, these solutions enable either monitoring or advanced control of buildings. The ICT solutions' overall impacts on the environment are often presumed positive without a holistic approach based on life cycle thinking. The research on energy and indoor monitoring systems usually focuses on system performance and potential benefits rather than the entire system and it thus misses the life cycle impacts of the system itself. To address this limitation, the aims of this study are to assess life cycle environmental and resource impacts of a building monitoring system (BMS) and to identify hotspots in this system. The case study of KTH Living Lab represents an extensive BMS. It was applied and assessed using Life Cycle Assessment (LCA) methodology. The results show that wires, sensors and data acquisition equipment constitute hotspots for all the environmental and resource impacts assessed in this study. Thus, the impacts of these devices are important to consider by, e.g, building managers.