Time-series forecasting is fundamental to decision-making across numerous domains; however, systematic temporal delays in predictions remain a largely overlooked or unrecognised phenomenon. This phenomenon occurs when forecasts regress toward recent observations, resulting in predicted series that closely mirror actual data, but trail behind in time. Such temporal misalignment in predictions has been reported in the literature to undermine model evaluation, method comparison and ranking, and the transferability of forecasting studies, especially in contexts with volatile and irregular time-series patterns. Moreover, prior studies have shown that widely used standard evaluation metrics fail to detect such delays, often leading to misleadingly optimistic assessments of predictive accuracy. Therefore, systematic detection and quantification of such delays in predictions is essential before deploying forecasting models in practice to obtain more reliable, robust, and transferable forecasting practices. To this end, in this study, we make three key contributions through conducted experimental studies using real-world electricity and gas consumption datasets: (i) we demonstrate that temporal delays are not necessarily limited to one time step, contrary to what is often assumed in the literature, and may extend to two or more steps depending on the correlation structure of the underlying data and the choice of input features; (ii) we propose a quantitative n-Step-Shifting (n-SS) method that enables the detection of delays of arbitrary length. This method provides a simple but robust mechanism to identify temporal displacement in forecasts; and (iii) we show that, although they do not directly detect temporal displacement, relative error metrics using a persistence model as baseline have the potential to exhibit a degree of resilience against the deceptive effects of such systematic delays and may provide a basis for the quantification of temporal displacement.

In Castro et al. (2024) electricity demand by datacentres is projected to consume all of the world’s electricity supply by 2033. Given the visibility of the Journal of Energy Policy it is necessary to clearly point out that this claim is based on flawed assumptions and poor methodological choices—most notably, the use of outdated and inappropriate data and a fundamental mischaracterisation of datacentre energy dynamics. The resulting forecast of electricity demand is inconsistent with existing literature by orders of magnitude. The ongoing growth in datacentre energy consumption requires informed and constructive discussion. The highly overstated findings in Castro et al. (2024) instead risk being a significant disservice to the public and policy debate.

The significant energy consumed by data centers has become a concern both for costs and associated carbon emissions. In particular, the energy efficiency of servers is a key consideration for data center operators, and understanding servers' power consumption under different operating conditions is an important aspect of it. In this paper, we present a measurement-based case study of high-throughput computing. We analyze power usage information of an operational data center, combined with focused measurements of power reduction techniques for a representative high-throughput workload. The study points out the obstacles encountered by data center operators in their efforts to minimize energy consumption and carbon emissions, and discusses the impact of server configuration adjustments on the energy consumption of processing jobs. We offer actionable recommendations for decreasing the energy usage of servers, while considering both performance and carbon emissions.

Artificial Intelligence (AI) is increasingly promoted as a catalyst for the Circular Economy (CE), enabling resource-efficient production, predictive maintenance, sustainable product-service systems, and closing material loops. However, the resource and energy demands of AI systems themselves, especially with the rise of large-scale models, raise concerns about their compatibility with CE principles. This study critically examines the circularity of AI, shifting the focus from AI as an enabler of the CE to AI as an object of CE strategies. Through a narrative literature review, we synthesize fragmented insights pertaining to AI and circularity. We identify significant knowledge gaps, particularly regarding hardware reuse, software sustainability, and the absence of tailored LCA methodologies for AI. Our analysis maps direct and indirect effects of AI on circularity and resource use. We abduct the concept of the Infosphere, i.e., the digital layer of society, to complement the established biosphere and technosphere dichotomy, emphasizing that AI’s impacts extend beyond hardware infrastructure. The introduction of the Infosphere should spark discussions about rethinking the role of software and AI as subjects in system-wide sustainability assessments. By exploring a first typology of the CE of AI, this paper lays a conceptual foundation for future research and policymaking, advocating AI systems that not only enable the transition to a CE but are also designed and governed in circular, sustainable ways.

Digital product passports (DPPs) are introduced into the European Union as a means to realize the circular economy. DPPs digitally capture product-related data to foster life extensions of products through, e.g., recycling, repurposing, repair, or reuse. Hence, DPPs are an interface connecting manufacturers, product service providers, product users, and public bodies. However, DPPs cause environmental impacts as a digital service through the use of IT infrastructure, which remain largely unaddressed so far. This study presents a five-dimensional conceptual model that qualitatively structures the aspects and components of these potential life-cycle environmental impacts of DPPs as digital services. The model distinguishes between DPP users, issuers, service providers, service hosts, and DPP functions, and links their activities to ICT-related resource use and emissions. The results provide a structured basis to bootstrap future environmental sustainability assessment studies. The work contributes to the literature by providing a further understanding of the ecological costs of digital services and related IT systems, thereby fostering a more sustainable implementation and management of DPPs.

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.

The ATLAS Collaboration operates a large, distributed computing infrastructure: almost 1M cores of computing and over 1 EB of data are distributed over about 100 computing sites worldwide. These resources contribute significantly to the total carbon footprint of the experiment, and they are expected to grow by a large factor as a part of the experimental upgrades for the HL-LHC at the end of the decade. This contribution describes various efforts to understand, monitor, and reduce the carbon footprint of the distributed computing of the experiment. This includes efforts towards constructing a full life-cycle assessment model for the carbon impact of ATLAS distributed computing, all with the goal of making recommendations for sites to reduce their carbon footprint for the HL-LHC.

This comment critiques Gritsenko et al.‘s dismissal of environmental assessments such as Life Cycle Analysis (LCA) in analyzing digitalization’s environmental impacts. While acknowledging the need for action amidst uncertainty, we argue that LCA yet provides valuable insights into potential impacts, trade-offs, and areas to focus on in a supply chain. Especially in the rapidly evolving digital landscape, LCA helps manage decision-makers’ uncertainty and informs targeted measures for sustainable digital infrastructure deployment and use.

Environmental assessments of digital services currently apply an accounting perspective, and for telecommunication networks (TN) allocate electrical energy consumption in proportion to data traffic. Yet, the power draw by wired TN infrastructure is almost independent of the volume of data traffic flowing through it. Previous assessments of the effect of data traffic on energy consumption thus tended to over-estimate the short-term impact on energy consumption. However, the growth of peak data traffic rates is a main driver of increasing TN bandwidth capacity and has an indirect impact on electrical energy consumption. This nuanced causal relationship has not been consistently represented in allocation approaches used for attributional carbon footprints. In this text, we apply a form of consequential system expansion by considering the long-term response to peak-traffic growth. This allows us to model long-run marginal changes to product system attributes that are fixed in the short-term. The outcome illustrates a causally consistent allocation approach that avoids contradicting the short-term behavior of the engineered system. Based on a causal inference graph of the drivers for the fixed baseload power draw by TN, we distinguish between the effects of different types of data as they contribute to traffic peaks. From this, we develop transform functions that re-allocate environmental burden to peak traffic. We present such functions for the specific case of periodically diurnal traffic in TN (including video-on-demand) and discuss the case of sporadic high-throughput events (including video streaming of life sport events and games downloads). The allocation model incentivizes a reduction of peak demand through avoidance or demand-shifting, to decelerate the long-term expansion of TN infrastructure.

Given the growing environmental concerns and significant resource consumption associated with video streaming on electronic devices, measuring the energy consumption is important to guide optimisation and to assess its relative environmental impact. In this paper, we provide comprehensive guidance to accurately measure the energy and power consumption in video communication technologies. We address the complexities inherent in measuring energy consumption across diverse software and hardware setups, with a focus on video communication tasks. We review current measurement techniques, identify limitations in existing practices, and propose a structured methodology that incorporates considerations for static and dynamic power consumption, appropriate sampling frequencies, and statistical rigor. Additionally, we introduce a reference workflow that is adaptable to various multimedia applications and demonstrate its applicability through a case study. By offering clear guidance and practical tools, this work aims to improve the reliability, reproducibility, and comparability of energy consumption measurements in video technologies, providing a strong foundation for the multimedia community to base decisions on.

Energy efficiency (EE) metrics are important tools to support evaluation and management of communication networks, and are of key interest in the development of the upcoming 6G network strategy. Because of their utility, EE metrics are widely used by network operators, in standards and in research. However, metrics suit varying evaluation purposes more or less well, yet are not always presented or applied consistently; for example when predicting future EC based on previously measured energy values. With this in mind, we provide a classification of existing EE metrics and how they differ; including energy intensity (EI), bit-per-joule efficiency, consumption- related EE, and output-related EE. We illustrate their use and limitations through the micro view of an idealized 6G base station (BS). Additionally, we also consider the application of EE metrics to evaluate the macro view of a number of BSs providing coverage in a certain area. In this case, EE metrics are used as a tool to evaluate systemlevel properties. Specifically, we illustrate their use to evaluate how BS sleep control can approximate an energy- proportional ideal system in a high-load regime.

Adaptive video streaming is a key enabler for optimising the delivery of offline encoded video content. The research focus to date has been on optimisation, based solely on rate-quality curves. This paper adds an additional dimension, the energy expenditure, and explores construction of bitrate ladders based on decoding energy-quality curves rather than the conventional rate-quality curves. Pareto fronts are extracted from the rate-quality and energy-quality spaces to select optimal points. Bitrate ladders are constructed from these points using conventional rate-based rules together with a novel quality-based approach. Evaluation on a subset of YouTube-UGC videos encoded with x.265 shows that the energy-quality ladders reduce energy requirements by 28-31% on average at the cost of slightly higher bitrates. The results indicate that optimising based on energy-quality curves rather than rate-quality curves and using quality levels to create the rungs could potentially improve energy efficiency for a comparable quality of experience.

The environmental impact of video streaming services has been discussed as part of the strategies towards sustainable information and communication technologies. A first step towards that is the energy profiling and assessment of energy consumption of existing video technologies. This paper presents a comprehensive study of power measurement techniques for video encoding and decoding that is comparing the use of hardware and software power meters. An experimental methodology to ensure reliability of measurements is introduced. Key findings demonstrate the high correlation of hardware and software based energy measurements for the case of two video codecs across different spatial and temporal resolutions at a lower computational overhead.

Energy intensity is the ratio between energy consumed and data volume over a certain time frame. It is frequently used as a metric to indicate the energy efficiency of communication networks and data centres for the provision of digital services, and as a coefficient to apportion the total energy consumption of a network to a specific service. As energy efficiency becomes more important, energy intensity metrics are increasingly used to estimate the energy costs and benefits of changes in data volumes across networks and data centres. Typically, energy intensity integrates annual accounts of energy consumption and data transmitted. At shorter time scales, this metric is affected by the lack of correlation between transmitted data and energy consumption, which leads in some cases to inappropriate conclusions. In this work, we first review the use of energy efficiency metrics in the literature. Then, we define generic measures for energy efficiency as well as energy intensity. The relationships of those measures are analysed, and we show under which conditions they lead to the same or different results. Practical applications of the measures and their insights are demonstrated when benchmarking systems and when considering the value of the system’s output. Furthermore, the limits and pitfalls of the metrics are analysed, especially considering the energy intensity metric for communication networks.

As the impacts of climate change become evident worldwide, organisations across all sector of the economy are actively aiming at reducing their carbon footprints. Video streaming has been in the spotlight since the beginning of the pandemic. Previous work has identified opportunities to reduce electricity consumption, yet methods to reliably estimate the carbon reduction potential from interventions on video streaming systems are currently lacking. In particular, not enough consideration is given to the consistent methodological approach to impact assessment required to adequately account for the complex interactions between changes to a service and operational and structural effects at internet-scale systems at varying time scales. In this text, we review the state of knowledge and propose a consistent short-term marginal approach for the assessment of the short-term decarbonisation potential of interventions. We illustrate this with a simplified example intervention and contrast it to previous methodologically inconsistent approaches, in which we evaluate the temporary reduction of the video spatial resolution of user-generated content video on demand from 1080p to 720p in a fixed ladder scenario over one month in the UK. We find that the carbon reductions mainly come from savings at user devices, but are overall negligible at 0.5 gCO2e per day per typical laptop-viewer.

Supplementary Data to "Designing with Digital Waste in Mind: A Typological Analysis to support Sustainable Interaction Design of Digital Services"

Forecasting domestic electricity consumption is important for a wide range of modern power system solutions and smart applications that support network operation, grid stability, and demand-side management, most of which depend on robust and accurate predictions. The methods producing these predictions infer future load from statistical regularity in historical data. If such regularity is lacking, predictions then regress towards the most recently observed consumption value used in the input set. Predictions then follow the actual load data one step behind in time, potentially affecting the robustness of predictions and functionality of applications. Current evaluation methods do not detect this behaviour which may result in overconfidence in prediction results. In this study, we I) define and systematically analyse this behaviour, which we label the Persistence Forecast Effect and illustrate its impacts, II) propose a novel method, called 1-Step-Shifting, to detect its presence, and III) analyse and establish the relationship between irregularity in data and the effect. Further, we provide a case study applying state-of-the-art forecasting techniques to a real-world dataset of electricity consumption data from 69 households in order to demonstrate the Persistence Forecast Effect, its implications, and its relationship to statistical regularity in historical data.

We present the EAM toolkit for life cycle modelling and impact analysis in environmental assessments. The open source toolkit was specifically designed to support maintainability and verification of models within integrated assessments, and has been used in research and industry. The tool offers features to support complex Life Cycle Assessment, including dynamic and scenario modelling, uncertainty and sensitivity analysis, a flexible domain specific modelling language and a visual editor. In this introduction we present the main features of the toolkit, summarise the high-level components and illustrate its use.