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Assessing ICT global emissions footprint: Trends to 2040 & recommendations
Abstract
In light of the concerted efforts to reduce global greenhouse gas emissions (GHGE) per the so-called Paris Agreement, the Information and Communication Industry (ICT) has received little attention as a significant contributor to GHGE and if anything is often highly praised for enabling efficiencies that help reduce other industry sectors footprint. In this paper, we aim at assessing the global carbon footprint of the overall ICT industry, including the contribution from the main consumer devices, the data centers and communication networks, and compare it with the to the total worldwide GHGE. We conduct a detailed and rigorous analysis of the ICT global carbon footprint, including both the production and the operational energy of ICT devices, as well as the operational energy for the supporting ICT infrastructure. We then compare this contribution to the global 2016-level GHGE. We have found that, if unchecked, ICT GHGE relative contribution could grow from roughly 1–1.6% in 2007 to exceed 14% of the 2016-level worldwide GHGE by 2040, accounting for more than half of the current relative contribution of the whole transportation sector. Our study also highlights the contribution of smart phones and shows that by 2020, the footprint of smart phones alone would surpass the individual contribution of desktops, laptops and displays. Finally, we offer some actionable recommendations on how to mitigate and curb the ICT explosive GHGE footprint, through a combination of renewable energy use, tax policies, managerial actions and alternative business models.
... Nachdem im Freizeitbereich die Nutzung von Informationsund Kommunikationstechnik (IKT) erheblich an Bedeutung gewonnen hat, beginnt auch eine Diskussion ihrer Klimafreundlichkeit. Insbesondere die Digitalisierung von Freizeitaktivitäten macht dieses Thema zu einem dynamischen und komplexen Forschungsfeld. Es müssen sowohl hochkomplexe globale Produktions-und Lieferketten, globale Kommunikationsinfrastrukturen, unterschiedlich voranschreitende Dekarbonisierung der nationalen Energieversorgungen und ein rasantes Nachfragewachstum bewertet werden (Belkhir & Elmeligi, 2018). Was IKT-Produkte betrifft, zeigt sich in der Literatur, dass (1) der Betrieb von Datenzentren und Kommunikationsinfrastrukturen, (2) die Produktion von IKT-Produkten und (3) speziell der rasant wachsende Markt für Smartphones zentrale Herausforderungen sind (Belkhir & Elmeligi, 2018;Clément et al., 2020;Cordella et al., 2021). ...
... Es müssen sowohl hochkomplexe globale Produktions-und Lieferketten, globale Kommunikationsinfrastrukturen, unterschiedlich voranschreitende Dekarbonisierung der nationalen Energieversorgungen und ein rasantes Nachfragewachstum bewertet werden (Belkhir & Elmeligi, 2018). Was IKT-Produkte betrifft, zeigt sich in der Literatur, dass (1) der Betrieb von Datenzentren und Kommunikationsinfrastrukturen, (2) die Produktion von IKT-Produkten und (3) speziell der rasant wachsende Markt für Smartphones zentrale Herausforderungen sind (Belkhir & Elmeligi, 2018;Clément et al., 2020;Cordella et al., 2021). Kurze Produktlebenszyklen und geringe Reparaturfähigkeit sind generell und speziell bei Smartphones ein zentrales Problem, welches zum Teil durch die Geschäftsmodelle der Netzbetreiber verstärkt und stabilisiert wird (Belkhir & Elmeligi, 2018;Cordella et al., 2021). ...
... Was IKT-Produkte betrifft, zeigt sich in der Literatur, dass (1) der Betrieb von Datenzentren und Kommunikationsinfrastrukturen, (2) die Produktion von IKT-Produkten und (3) speziell der rasant wachsende Markt für Smartphones zentrale Herausforderungen sind (Belkhir & Elmeligi, 2018;Clément et al., 2020;Cordella et al., 2021). Kurze Produktlebenszyklen und geringe Reparaturfähigkeit sind generell und speziell bei Smartphones ein zentrales Problem, welches zum Teil durch die Geschäftsmodelle der Netzbetreiber verstärkt und stabilisiert wird (Belkhir & Elmeligi, 2018;Cordella et al., 2021). Generell benötigt es also eine rasche Dekarbonisierung der Energieversorgung sowie höhere Standards bezüglich Energieeffizienz, Langlebigkeit, Reparaturfähigkeit und eine Eindämmung von Geschäftsmodellen, welche auf der Beschleunigung von Produktlebenszyklen aufbauen. ...
Zusammenfassung
Koordinierende_r Leitautor_in
Barbara Smetschka
... However, the potential energy implications of redundancy cannot be overlooked, especially considering operational costs and environmental considerations. Information and communication technology accounts for about 2% of global energy use, expected to soar to 14% by 2040 without intervention [11][12][13]. Therefore, dependability-enhancing techniques should be energy-conscious, aiming to judiciously determine necessary replications with environmental impact in mind. ...
... To address this, two metrics have been introduced: Support Utilization (SU ) and Lack of Support (LS), aiming to appraise the efficiency of the support team. The SU metric, depicted in Equation 13, quantifies the fraction of the support team that is actively engaged, calculated by the ratio of the utilized support teams to the total system support. Conversely, the LS metric, as defined in Equation 14, computes the likelihood of a component being in need of support when none is accessible. ...
System dependability is pivotal for the reliable execution of designated computing functions. With the emergence of cloud-fog computing and microservices architectures, new challenges and opportunities arise in evaluating system dependability. Enhancing dependability in microservices often involves component replication, potentially increasing energy costs. Thus, discerning optimal redundancy strategies and understanding their energy implications is crucial for both cost efficiency and ecological sustainability. This paper presents a model-driven approach to evaluate the dependability and energy consumption of cloud-fog systems, utilizing Kubernetes, a container application orchestration platform. The developed model considers various determinants affecting system dependability, including hardware and software reliability, resource accessibility, and support personnel availability. Empirical studies validate the model’s effectiveness, demonstrating a 22.33% increase in system availability with only a 1.33% rise in energy consumption. Moreover, this methodology provides a structured framework for understanding cloud-fog system dependability, serves as a reference for comparing dependability across different systems, and aids in resource allocation optimization. This research significantly contributes to the efforts to enhance cloud-fog system dependability.
... The ICT can affect CO2 emissions from three ordered perspectives (Edquist and Bergmark, 2022). Initially, the carbon emissions throughout the life circle of ICT-related devices, data centers, and networks (Belkhir and Elmeligi, 2018). Then, the sector impacts of ICT influence CO2 emissions. ...
... Ultimately, it leads to 2% global carbon emissions (GeSI, 2008). Belkhir and Elmeligi (2018) predict that ICT's global greenhouse gas emissions footprint will exceed the 2016 level (i.e., 14%) by 2040. ...
... A body of associated research [1][2][3][4][5][6][7] explores the feasibility of reducing the 1% of annual global greenhouse gas (GHG) annual emissions generated by end user computing [8][9][10][11][12][13][14][15][16][17] to contribute to climate action strategies and associated net zero strategies within businesses. This is achieved by testing speculative barriers, developing solutions to specifically identified issues and determining the impact of sustainable information technology strategies enabled by the findings. ...
... Having proven the capability to reduce annual per capita emissions by as much as 852 kgCO 2 e per employee, the repurposing solution saves the company between £973,167 and £1,431,254 across twelve months by utilising existing assets in a new and innovative manner. As the 1% global GHG contribution generated by end user computing is driven predominantly by manufacturing and subsequent electricity use [8][9][10][11][12][13][14][15][16][17] it is sensible to examine alternative options to new product purchase. In this instance, the findings substantiate the value of both environmental and financial benefits of displacement strategies based upon repurposing. ...
The purpose of this research is to generate findings to support the reduction of computer supply chain greenhouse gas emissions. This is achieved by answering the question, 'can greenhouse gas abatement be delivered by alternative computer operating system displacement strategies?' We hypothesised that extending the useful lifespan of end user computing devices to 8 years by repurposing a device with a new operating system can reduce scope 3 supply chain emissions, which account for 73% of an end user computing device's carbon footprint. To test the hypothesis, we measure the carbon footprint reduction delivered by repurposing 3,150 obsolete desktop computers, which were installed with a Linux based operating system to become thin clients and support a remote working solution for a major financial institution during the recent Covid-19 pandemic. We calculate scope 3 supply chain emissions avoided by not purchasing new thin client solutions. Legacy device power consumption is also measured and extrapolated to represent the one-year period of use and compared with that of new devices to calculate the excess scope 2 emissions of the former. We find that while new devices reduce scope 2 emissions, this gain is outweighed by the reductions in scope 3 emissions from not replacing the legacy devices. Furthermore, we find that the new Linux based operating system reduces energy consumption by 22% compared with Microsoft Windows. We also calculate the financial gains from the repurposing strategy to test the perception that sustainable information technology adoption is costly. Finally, the impact of remote working on commuting emissions is estimated. In conclusion, the findings show that repurposing end user computing devices contributes meaningfully to sustainability strategies from both an environmental and financial perspective.
... In recent years, some analyses on the impact of the massive use of AI systems have provided disturbing conclusions: AI energy consumption has increased 300,000-fold from 2012 to now [4], and data centers running massive AI software produce up to 5-9% of global electricity demand, and 2% of all CO2 emissions [5]. In [6], authors predict that by 2040 information and communications technologies will be responsible for 14% of greenhouse gas emissions. Such an increase in energy consumption has been partially motivated by the strong development of new AI-specific architectures to improve the performance of AI models. ...
p>The energy consumption of Artificial Intelligence (AI) systems has increased 300,000-fold from 2012 to now, and data centers running massive AI software produce up to 5-9% of global electricity demand and 2% of all CO2 emissions. Such an increase in energy consumption has been partially motivated by the strong development of new AI-specific architectures to improve the performance of AI models. Nevertheless, the AI community has recently become aware of the importance of considering energy efficiency as a metric when developing AI techniques. To date, a great effort has been made to find optimal AI model configurations that provide the best solution in the shortest possible time. However, only a few works have sought a compromise between energy cost and system performance. This paper analyses recent efforts in these directions and proposes the path toward energy-efficient AI. We describe a set of energy efficiency strategies for applying and deploying AI models on different computing infrastructures in search of democratizing an environmentally sustainable AI. To that end, we propose a full-stack approach of energy-efficient AI and analyze the role that different types of users should play, tackling the energy-focused optimization in all steps of the AI model design flow, from the high levels of models and algorithm design to the low levels ones, more related to the hardware and architecture.
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... In recent years, some analyses on the impact of the massive use of AI systems have provided disturbing conclusions: AI energy consumption has increased 300,000-fold from 2012 to now [4], and data centers running massive AI software produce up to 5-9% of global electricity demand, and 2% of all CO2 emissions [5]. In [6], authors predict that by 2040 information and communications technologies will be responsible for 14% of greenhouse gas emissions. Such an increase in energy consumption has been partially motivated by the strong development of new AI-specific architectures to improve the performance of AI models. ...
p>The energy consumption of Artificial Intelligence (AI) systems has increased 300,000-fold from 2012 to now, and data centers running massive AI software produce up to 5-9% of global electricity demand and 2% of all CO2 emissions. Such an increase in energy consumption has been partially motivated by the strong development of new AI-specific architectures to improve the performance of AI models. Nevertheless, the AI community has recently become aware of the importance of considering energy efficiency as a metric when developing AI techniques. To date, a great effort has been made to find optimal AI model configurations that provide the best solution in the shortest possible time. However, only a few works have sought a compromise between energy cost and system performance. This paper analyses recent efforts in these directions and proposes the path toward energy-efficient AI. We describe a set of energy efficiency strategies for applying and deploying AI models on different computing infrastructures in search of democratizing an environmentally sustainable AI. To that end, we propose a full-stack approach of energy-efficient AI and analyze the role that different types of users should play, tackling the energy-focused optimization in all steps of the AI model design flow, from the high levels of models and algorithm design to the low levels ones, more related to the hardware and architecture.
</p
... Between 2010 and 2020, Belkhir and Elmeligi found that, among the various categories of electronic devices analyzed, smartphones are the devices that contribute the most to GHG emissions, with values ranging from 4% to 11% [20]. A laptop connected for eight hours a day consumes between 150 and 300 kWh and emits between 44 and 88 kg of CO2 per year. ...
The current state of concern for the environment and awareness of the climate emergency have become prevalent issues. The environmental impact resulting from the use and expansion of digital infrastructure highlights the need for greater awareness and action on this topic. While the consumption of material resources can often be perceived through the associated financial cost, the energy consumption caused by the use of digital products, services, and systems remains invisible to users. This article proposes to focus on the area of Green Computing, as it is believed that this discipline can contribute to the reduction of the digital carbon footprint.
The aim of the study was to conduct a literature review and a critical and informed analysis of Sustainable Online Behaviors. Building upon the guidelines proposed by Elgaaïed-Gambier et al., 2020, we sought to extend their scope in order to derive an expanded set of Green Computing Practices aimed at mitigating the Digital Carbon Footprint.
Through the proposed guidelines, we believe that Green Computing practices can be disseminated and reach various layers of society, contributing to digital citizenship that can educate children and young people about the importance of this issue.
... This has become more pertinent in recent years with increasing adoption of large models trained through artificial intelligence (Selvan et al., 2022). Belkhir and Elmeligi (2018) estimated data centre energy use to account for 45% of the greenhouse gas emissions produced within the ICT sector in 2020, up from 33% in 2010. Despite this, the amount of data being collected and processed is relatively neglected in climate policies and initiatives concerning data-driven health research (Samuel & Lucassen, 2022). ...
Given that scientific practices contribute to the climate crisis, scientists should reflect on the planetary impact of their work. Research computing can have a substantial carbon footprint in cases where researchers employ computationally expensive processes with large amounts of data. Analysis of human neuroimaging data, such as Magnetic Resonance Imaging brain scans, is one such case. Here, we consider ten ways in which those who conduct human neuroimaging research can reduce the carbon footprint of their research computing, by making adjustments to the ways in which studies are planned, executed, and analysed; as well as where and how data is stored.
... In fact, although it has been already alerted the potential growth of data centers and communication networks due to the booming of IoT [15], recommended the correct dimensioning of sensor data [16], and promoted appropriate design [3]; little is said about sensor data and its flow within Life Cycle Assessment (LCA) and eco design communities. We presume that one of the possible reasons that could explain this disregard is that, in general, reference flows of IoT systems tends to be modeled exclusively on the basis of energy and local equipment. ...
As we reach the limits of our current technologies and the number of connected devices grows, scientists put more efforts to estimate and reduce the ecological damage of the Internet of Things. Unfortunately, recent literature related to Life Cycle Assessment and eco design of IoT systems suffers from a major inconvenience so far: it does not put sensor data in the focus of attention. This paper aims to point out explicitly the essential role of this aspect for modeling reference flows, and demonstrate its relevance for appropriate environmental assessment and practical eco design. Also, it aims to illustrate that such modeling process must happen in a comprehensive way. For this, our work relies on a case study addressing smart metering, and we proceed as follows: based on available documentation and inspired by certain aspects of different technologies, we deduce the maximal capacities of key electronic components and construct an unfavorable data flow scenario, to get a rough idea of the reference flow and the long-term impact of our system during its use phase. Results from this procedure are later contrasted with results obtained from a packet traffic analysis, in which local and internet data flow are examined carefully. At the end, we verify the importance of sensor data theoretically and empirically, and we conclude that the reference flow and the impact contributors of a system could be affected not only by the local data transit but also by the complex interactions between edge devices and cloud resources. All our findings are discussed to nourish the state-of-the-art around the environmental impact of using full IoT systems and their sustainable design.
... The most reliable estimates 18,19,20,21 of the 2022 overall ICT energy consumption converge towards a value of around 14% of the total (Floridi 1 says that it is 1,4%, but it seems a typographic error, since it would imply that in 2022 AI would have been 5/7 of the overall ICT consumptions, which is clearly absurd; furthermore, his estimate is based on the above quoted texts, which agree that the value is about 14%). that in 2030 it is likely to become more than 20%. ...
... Ma et al. [12] argued that the degree of industrial digitization is negatively interrelated with carbon emissions. However, Belkhir and Elmeligi [13] argued that digitalization exacerbates carbon emissions and that the digitalization process itself generates a considerable carbon footprint. Meanwhile, existing studies have mainly concentrated on carbon reduction in China's industrial and manufacturing division or different regions, with less examination of the highly energy-intensive enterprises, including nonferrous metals, chemicals, and electric power enterprises, which are the focus of this paper. ...
Against the dual background of the vigorous shape of digital economy and the severe pressure for carbon reduction, exploring the mechanism of the relationship between digitalization level and carbon reduction of highly energy-intensive enterprises is one of the current hot topics in theoretical and practical circles. This paper selects panel data of listed companies with high energy consumption from 2007 to 2019 and adopts a threshold-regression method to empirically test the impact of digitalization level on corporate carbon emission reduction It turns out that the digitalization level of enterprises has both an “inverted U” effect and a heterogeneous effect on carbon emissions. Enterprise technological innovation has both a threshold action and a regulating action on the influence of digitalization level on carbon emissions. It can play a role in accelerating the digitalization level to the inhibition of the increase in carbon emissions in advance and has a reinforcing effect in accelerating the reduction of enterprise carbon emissions.
... Some studies emphasize the role of efficiency improvements in stabilizing the emissions from user devices, networks and data centers, and link ICT emissions to the number of users which will naturally saturate [25,61,62]. Others project that ICT emissions will increase as a result of slowing down efficiency improvements (as Moore's law reach physical limits) in network and data centers, as well as estimate a growing embodied energy associated with the dissemination of devices (including IoT) [63,64]. However, these emissions should be compared with the ICT impacts on the rest of the economy. ...
Access to modern energy services (entertainment, food preparation, etc.) provided by consumer goods remains unequal, while growing adoption due to rising incomes in Global South increases energy demand and GHG emissions. The current model through which these energy services is provided is unsustainable and needs to evolve—a goal that emerging social and technological innovations can help to achieve. Digital convergence and the sharing economy could make access to appliances more affordable and efficient. This article estimates the effect of innovations around digital convergence and sharing in a highly granular, bottom-up representation of appliances. We simulate changes in demand for materials and energy, assuming decent living standards for all and global warming limited to 1.5ºC. By 2050, these innovations could attenuate the increase in the number of appliances to 135% and reduce overall energy demand by 28%. The results contribute to understand under which conditions digital convergence and sharing can improve living standards and climate mitigation.
... Smartphones have transformed our lives, but with approximately 15 billion units in circulation according to the (Smartphone Subscriptions Worldwide 2027, 2022) and increase expected, they raise environmental concerns, such as the overuse of natural resources required for production, use of rare earth metals, precious metals, and plastics (Belkhir & Elmeligi, 2018), issues related to disposal behaviour (Ting et al., 2019), and waste management (Forti et al., 2020). These concerns underscore the environmental and social sustainability challenges associated with smartphones (Manzini, 2014;Silva & Schaltegger, 2019;Vezzoli & Manzini, 2008). ...
... As energy demand increases, the impact of ICT in terms of carbon dioxide (CO 2 ) emissions becomes significant [21]. Belkhir et al. estimate that ICT devices will produce 14% of global CO 2 emissions by 2040 [5]. ...
Measurement-based experiments are a common solution for assessing the energy consumption of complex software systems. Since energy consumption is a metric that is sensitive to several factors, data collection must be repeated to reduce variability. Moreover, additional rounds of measurements are required to evaluate the energy consumption of the system under different experimental conditions. Hence, accurate measurements are often unaffordable because they are time-consuming. In this study, we propose a model-based approach to simplify the energy profiling process and reduce the time spent performing it. The approach uses Layered Queuing Networks (LQN) to model the scenario under test and examine the system behavior when subject to different workloads. The model produces performance estimates that are used to derive energy consumption values in other scenarios. We have considered two systems while serving workloads of different sizes. We provided 2K, 4K, and 8K images to a Digital Camera system, and we supplied bursts of 75 to 500 customers for a Train Ticket Booking System. We parameterized the LQN with the data obtained from short experiment and estimated the performance and energy in the cases of heavier workloads. Thereafter, we compared the estimates with the measured data. We achieved, in both cases, good accuracy and saved measurement time. In case of the Train Ticket Booking System, we reduced measurement time from 5 h to 35 min by exploiting our model, this reflected in a Mean Absolute Percentage Error of 9.24% in the estimates of CPU utilization and 8.72% in energy consumption predictions.
... The popular post-consumption purchasing of carbon offsets or using profits to invest in "Greentech" is an ineffective, non-scalable solution to the carbon emissions problem (Bresnihan, 2019;Niiler, 2020, Calma, 2022. What is needed is a behavioural change to address the carbon emissions in the first place (Belkhir & Elmeligi, 2018;Behrens, 2020;Hickel, 2020). ...
Nudging mobile photographers for climate change
A study into the use of persuasive design techniques to increase the environmental motivation of digital photo activity.
... According to Freitag et al. (2021) the internet produces around 3.7% of global CO2 emissions and unlike other sectors of the economy, its energy intensity is increasing 4% per year. Belkhir and Elmeligi (2018) have calculated that the whole ICT's relative contribution will exceed 14% of the 2016-level worldwide GHGE by 2040 with data centres being the largest culprit (45%) followed by communication networks (24%). These figures do not take into account the development of the 'Internet of Things' and the cryptocurrency explosion (The shift project, 2018). ...
Infrastructure studies represent a domain that remains significantly uncharted among degrowth scholars. This is paradoxical considering that infrastructures constitute a fundamental prerequisite for the equitable distribution of many aspects of human well-being that degrowth proponents emphasize. Nonetheless, the substantial resource and energy consumption associated with infrastructures cannot be overlooked. The internet offers an instructive case study in this sense, at its best it forges human connections and is productive of considerable societal value. The resource implications of the often-overlooked internet physical layer of data-centres and submarine cables needs to be acknowledged. Furthermore, the ways in which assumptions of perpetual growth are built into this global infrastructure via the logic layer of internet protocols and other governing mechanisms such as finance and network design need to be examined if we are to determine the extent to which such infrastructures are inherently growth dependent. In making these two arguments, we draw upon the work of both Science and Technology Studies (STS) and Large Technological System (LTS) studies on the inherent problems of large in-frastructures which have thus far seen little engagement with questions of degrowth. We review the case of the internet and suggest a number of scenarios that illustrate potential roles for such infrastructures in any planned reduction of economic activity.
... According to the statistics in 2019, DCs were responsible for approximately 3% of the electricity consumption and almost 4% greenhouse gas emissions globally [4]. It is predicted that the global energy demand for information and communication technology (ICT) things (e.g., data processing, exchange and storage, blockchain, and crypto mining) would take responsibility of around 23% of the total by 2030 [5], while the ICT-related carbon emission will explore to approximately 14% of the world total [6]. Therefore, it is paramount to improve the energy efficiency, and reduce the energy consumption in and for DCs [7]. ...
Data centers (DCs) uninterruptedly run 24/24 h, 365 days per year with much huge operating scale, and have the characteristics of high operation safety requirement, high heat flux density, high energy consumption and high carbon emission. They are influential energy consumers and carbon emitters in building or even global energy sectors (around 3% of global energy consumption), who are also significant waste heat producer (e.g., waste heat from year-round uninterrupted operation of IT equipment and cooling system). Huge energy consumption has increased the burden on the global energy industry, while carbon and direct waste heat emissions have also caused great damages to the outdoor environment. Thus, it is critical to improve the energy efficiency in DCs and to realize the energy conservations and environmental deterioration alleviation. Waste heat recovery technology is considered as a promising approach to improve energy efficiency, achieve energy and energy cost savings, and mitigate environmental impacts (caused by both carbon emission and waste heat discharge) at the same time. This article conducts a comprehensive review on recovering waste heat from all kind of sources (e.g., exhaust air, circulating water, and coolants) in DCs for various energy uses (e.g., heating supply, district heating supplement, cooling and electricity productions, and industrial/agricultural production process) and different application scenarios (e.g., office buildings, comprehensive energy community and residential buildings), while the future research and development proposals for DC waste heat recoveries are given through technical, energy, environmental and economic analysis.
... This number corresponds with a more recent publication comparing different estimation techniques, resulting in an estimate of 2.1-3.9% of GHG emissions in 2020 being due to information and communications technology (ICT) [10]. The major contributors to ICT emissions are data centres (45%) and communication networks (24%) [11]. ...
Digital Transformations (DT) play an increasingly important role in academia and business, yet their significant Environmental Footprint (EF) is often overlooked, sidelining their potential for Environmental Sustainability (ES). This paper bridges this gap by integrating ES into the discourse of DT, proposing Green Enterprise Architecture (GREAN) as a method for sustainable transformation. Utilizing a Design Science Research approach, we developed an artefact outlining a comprehensive strategy for embedding ES in DT across various layers of an organization. The tool’s need was validated via a systematic literature review (SLR), highlighting the significant research gap in Green Enterprise Architecture. The artefact provides concrete Courses of Action (CoAs) for incorporating ES into the organizational strategy, business, data, application, and technology layers and proposes relevant capabilities to address this. The paper further presents an ES-aware business capability modelling, an innovative business modelling approach that integrates environmental sustainability principles by using (in a novel way) the presentation and analysis methods that capability mapping offers. The proposed artefact serves as a starting point for environmentally sustainable DTs. Future research directions include in-depth exploration of each enterprise layer for ES, real-world validation of our proposed tools and concepts, and the expansion of these into a full framework.
... The global carbon footprint has seen a significant increase of 3.5% due to the expansion of the Information and Communication Technologies (ICT) sector in response to customer demand. This rise is primarily attributed to the emission of greenhouse gases (GHG) from communication hardware [27]. The Telecom sector plays a substantial role in this, as it is responsible for about 21% of GHG emissions. ...
With the expansion of Information and Communication Technology, it is important to develop a communication network that can provide high-capacity ubiquitous connectivity. This work proposes an energy-efficient passive optical network (PON) using orthogonal frequency division multiple access (OFDMA) and wavelength division multiplexing (WDM) to facilitate the dense deployment of radio units (RUs) in a beyond 5G (B5G) communication network. High-speed connectivity is ensured by employing a hybrid PON architecture that includes a combination of free space optics (FSO) links and optical fiber (OF) media to carry OFDM and WDM multiplexed traffic. Furthermore, an optical frequency comb generator (OFCG) is utilized at the transmitter module to generate and leverage the spectrum for transmitting information from baseband units (BBUs) to the RUs situated near the end users. The proposed system is analyzed through (i) simulation analysis using Optisystem for transmission capacity computations and (ii) mathematical analysis to determine the total savings in energy. The simulation analysis shows that the given architecture can carry data across 3 km of FSO medium using 512 subcarriers per BBU transmitting at 10 Gbps of data with QPSK-modulated bit sequence. Additionally, energy efficiency shows that the use of an OFCG cuts the total energy usage by 22% at the transmitter module without negatively impacting the system’s high cardinality and transmission capacity.
... Shabani and Shahnazi (2019) investigated the relationship between energy consumption in the Iranian economic sector and the digital industry, revealing that the digital industry has a significant promoting effect on carbon emissions. Belkhir and Elmeligi (2018) evaluated the global carbon footprint of the digital industry, highlighting that its contribution to greenhouse gases will increase from 1.6% in 2017 to 14% in 2040. ...
The impact of the digital economy on carbon emissions has become a topic of contention due to the paucity of guiding theoretical and empirical research. This study presents a comprehensive causal mediation model based on an expanded structural equation model. Leveraging extensive big data analysis and data sourced from developing nations, this research aims to elucidate the precise impact of the digital economy on carbon emissions and unravel the underlying mechanism. The findings unequivocally demonstrate the pivotal role played by the digital economy in mitigating carbon emissions. Even after subjecting the conclusions to a battery of robustness and endogeneity tests, their validity remains intact. The mechanism analysis reveals that the digital economy effectively curbs carbon emissions through low-carbon technological innovation and industrial diversification. The disproportionate dominance of digital industrialization is a significant factor contributing to the emergence of the “Digital Economy Paradox”. Consequently, this paper not only introduces a novel analytical perspective that systematically comprehends the carbon impact of the digital economy but also presents fresh empirical evidence that advocates for the transformation and development of a low-carbon economy.
... Since the 1990s, ICT has started to be used extensively, and its impact on the ecological environment has increased rapidly (Belkhir & Elmeligi, 2018;Kongbuamai et al., 2022). The effect of ICT on the environment is beneficial as well as harmful. ...
Regional Comprehensive Economic Partnership (RCEP) has accelerated the combination of information and communication technology (ICT) and international trade, raising debate about sustainability. Using the panel smooth transition regression (PSTR) model, we investigated the impact of ICT and trade openness on CO2 emissions in 11 selected RCEP countries from 2000 to 2020. The results indicate that (i) the role of total trade in CO2 emission reduction in RCEP countries gradually emerges along with ICT diffusion, (ii) with a rise in ICT level, imports always increase CO2 discharge, while the impact of exports on CO2 emissions changes from positive to negative, and (iii) the influence of total trade, exports, and imports on CO2 emissions has significant country heterogeneity and reveals declining, falling, and rising temporal characteristics. Furthermore, to explore the CO2 emission caused by ICT development in each country, we applied a two-step forecast method to project CO2 emissions from RCEP economies under different scenarios of ICT development. It is discovered that ICT development does not lead to a downward trend in CO2 emissions in all RCEP countries. ICT contributes to rising CO2 emissions in China and Indonesia.
... The exponential growth of data in the last two decades has raised concerns over the electricity consumption of information communication technology (ICT) infrastructure. It was estimated that ICT infrastructure accounted for 4.6% of worldwide electricity consumption in 2012 and projected to increase in the future despite emphasising on the introduction of power efficient technologies [3,4]. By 2030, the contribution of the ICT in greenhouse release would increase up to 23%, and at the worst, it can go up to 50% [5]. ...
Visible light communication (VLC) is a new paradigm that could revolutionise the future of wireless communication. In VLC, information is transmitted through modulating the visible light spectrum (400-700 nm) that is used for illumination. Analytical and experimental work has shown the potential of VLC to provide high-speed data communication with the added advantage of improved energy efficiency and communication security/privacy. VLC is still in the early phase of research. There are fewer review articles published on this topic mostly addressing the physical layer research. Unlike other reviews, this article gives a system prespective of VLC along with the survey on existing literature and potential challenges toward the implementation and integration of VLC.
... According to [55] regarding the effect of ICT on the global GHG footprint, smartphones will make up about 11% of the entire ICT footprint by 2020 and double GHG emissions from 2007 to 2020. Ref. [56] concluded that ICT use in sub-Saharan Africa is linked to environmental pollution. ...
Transitioning from traditional energy sources to green and sustainable energy sources can potentially reduce environmental problems. Many countries are gradually recording increasing greenhouse gas (GHG) emissions as they develop their economies. As a result, this study aims to use top GHG-emitting countries in its analysis to establish the role green energy and information technology play in reducing their pollution levels. Data from 11 GHG-emitting countries from
1990–2020 were utilized. The Fully Modified Ordinary Least squares (FMOLS), Dynamic ordinary least squares (DOLS), and Granger causality are used for the analysis. The empirical results revealed that an increase in non-renewable energy usage of 1% increases GHG gas emissions by 0.6960% (FMOLS) and 0.6119% (DOLS). On the impact of renewable energy, a 1% increase reduces GHG
emissions by 0.1145% (FMOLS) and 0.1957% (DOLS). Also, a 1% increase in information technology increases GHG emissions by 0.0459% (FMOLS) and 0.0429% (DOLS) under the specifications of FMOLS and DOLS. The directional causalities are established in the study as well. In light of this, using “abundant” renewable energy sources is the gateway to reducing GHG emissions alongside their tremendous economic growth and I.T. development. Other policy implications are outlined for future research and policymakers.
... 14 Prozent der globalen Treibhausgasemissionen ansteigen könnten, was sich unter anderem aus der steigenden Relevanz der Internetnutzung (z. B. Streaming und Suchmaschinen) ergibt (Belkhir & Elmeligi, 2018;Malmodin et al., 2010), wobei die Unsicherheit der Berechnungen und Datengrundlagen hier recht hoch scheint (Kami-ya, 2020). Spezifische Berechnungen für Nachrichtenmedien, einschließlich der Infrastruktur, des Drucks und des journalistischen Reisens, stehen noch aus. ...
Zusammenfassung
Medien (sowohl klassische Massenmedien als auch soziale Medien) sind zentrale Foren, in denen die Klimakrise inklusive der Transformationsnotwendigkeiten zu einem klimafreundlichen Leben diskursiv konstruiert und verhandelt werden. Unter anderem durch die Wirkung auf Rezipient_innen, sind Medien zentral für die Schaffung von Vorstellungsräumen und sich daraus ableitenden Handlungen im Umgang mit der Klimakrise. Für die erfolgreiche Umsetzung vieler Transformationsnotwendigkeiten, die in anderen Kapiteln dieses Berichts herausgearbeitet werden, ist die mediale Konstruktion jener Problemfelder ein wichtiger Faktor. Zwei medienanalytische Teilbereiche werden in diesem Kapitel insbesondere behandelt: Mediendiskurse (sowohl in Massenmedien als auch auf sozialen Medien) und Medienstrukturen, wobei wir unter zweiterem sowohl Medientechnologien als auch die zugrundeliegenden polit-ökonomischen und kulturellen Institutionen verstehen.
Background:
Digital health technologies (DHTs) are promoted as means to reduce the environmental impact of healthcare systems. However, a growing literature is shedding light on the highly polluting nature of the digital industry and how it exacerbates health inequalities. Thus, the environmental footprint of DHTs should be considered when assessing their overall value to healthcare systems. The objectives of this article are to: (1) explore stakeholders’ perspectives on integrating the environmental impacts of DHTs in assessment and procurement practices; (2) identify the factors enabling or constraining the operationalisation of such a change; and (3) encourage a constructive dialogue on how environmental issues fit within healthcare systems’ push for more DHTs.
Methods:
Semi-structured interviews were conducted with 29 stakeholders involved in DHTs in a large Canadian academic healthcare centre. Data were collected and analysed through a mixed deductive-inductive process using a framework derived from diffusion of innovations theories.
Results:
The integration of the environmental impact of DHTs in assessment and procurement is contingent upon key micro-meso-macrosystemic factors that either enable or constrain changes in practices and processes. Innovation (micro) factors include stakeholders’ recognition of the environmental issue and the extent to which it is feasible for them to address the environmental impact of DHTs. Organisational (meso) factors include the organisation's culture, leadership, policies, and practices, as well as the expertise and professional skillsets available. Finally, external (macro) factors include political and regulatory (e.g., national strategy, laws, standards, norms), economic (e.g., business models, public procurement), and professional and scientific factors (e.g., evidence, methodologies, clinical guidelines).
Conclusion:
Considering the environmental impact of DHTs depends on micro-meso-macrosystemic factors involving a variety of stakeholders and levels of governance, sometimes with divergent or even antagonistic objectives and expectations. It highlights the importance of better understanding the complexity inherent in the environmental shift in healthcare.
Energy savings and greenhouse gas emission reduction are emerging tasks for global sustainable development. However, little attention has been given to the energy consumption of digital maps. As an integral component of modern human activities, digital maps are extensively deployed, and their usage is rapidly increasing. It is crucial to comprehend the energy consumption of these maps and explore ways to mitigate their energy usage. Our findings indicate that digital maps consume considerable amounts of energy, resulting in substantial carbon emissions at an alarmingly high rate, equivalent to 31-34% of the energy consumption of Bitcoin mining. Our results show that the power consumption of map displays is higher than that of most image-like media, including movies, electronic games, and paintings. We estimate that the global map industry consumed approximately 38.29-46.60 billion kWh of energy in 2021. In comparison to national residential electricity consumption, the industry ranked 23rd-26th, surpassing most countries in the world. In this study, we present three energy-saving methods to reduce energy consumption by up to an impressive 85%. This reduction far exceeds the expected 15% level set by The Climate Group for energy reduction in the ICT sector to achieve sustainable development. Based on our findings, we conclude that energy-saving digital maps should be considered a key transformative green technology for achieving the sustainable development goals outlined by the United Nations. The work reported in this study is also transferable to other visual media.
In this column, we reflect on the environmental impact and broader sustainability implications of resource-demanding digital applications and services such as video streaming, VR/AR/XR and videoconferencing. We put emphasis not only on the experiences and use cases they enable but also on the "cost" of always striving for high Quality of Experience (QoE) and better user experiences. Starting by sketching the broader context, our aim is to raise awareness about the role that QoE research can play in the context of various of the United Nations' Sustainable Development Goals (SDGs), either directly (e.g., SDG 13 "climate action") or more indirectly (e.g., SDG 3 "good health and well-being" and SDG 12 "responsible consumption and production").
The demand for computing is continuing to grow exponentially. This growth will translate to exponential growth in computing's energy consumption unless improvements in its energy-efficiency can outpace increases in its demand. Yet, after decades of research, further improving energy-efficiency is becoming increasingly challenging, as it is already highly optimized. As a result, at some point, increases in computing demand are likely to outpace increases in its energy-efficiency, potentially by a wide margin. Such exponential growth, if left unchecked, will position computing as a substantial contributor to global carbon emissions. While prominent technology companies have recognized the problem and sought to reduce their carbon emissions, they understandably focus on their successes, which has the potential to inadvertently convey the false impression that this is now, or will soon be, a solved problem. Such false impressions can be counterproductive if they serve to discourage further research in this area, since, as we discuss, eliminating computing's, and more generally society's, carbon emissions is far from a solved problem. To better understand the problem's scope, this paper distills the fundamental trends that determine computing's carbon footprint and their implications for achieving sustainable computing.
The use of Information Communication technology (ICT) is rapidly increasing in an age of digitalization. Measurement of carbon dioxide equivalent emissions from ICT is crucial for reducing them. Most ICT organizations focus on Scope 1 and 2 emissions as they have greater control over them, commonly ignoring Scope 3 emissions. Scope 3 Category 1 (S3C1) emissions occur throughout the raw material acquisition and manufacturing stages of an ICT product’s life cycle accounting for a large portion of the sector’s overall emissions and energy consumption. By not reporting Scope 3 emissions, companies lose the ability to reduce their overall
corporate emissions. Although Category 1 and 11 under Scope 3 account for 85% of ICT’s worldwide emissions, the methodologies for calculating S3C1 emissions in ICT are understudied. This study focuses on these emissions in the framework of Sustainable Development Goals 9, 12, and 13. Product life cycle assessment (PLCA) and Spend-based methods have been used to analyze S3C1 emissions in the ICT sector with two case examples of laptop computers and smartphones. The Excel Management Life Cycle Assessment (EMLCA) tool has been used for the S3C1 emissions estimation. PLCA and Spend-based methods are compared on their ability to calculate emissions. It is concluded that the Spend-based is faster than PLCA for predicting ICT emissions with modest uncertainty for smartphone and laptop components. Furthermore, this work explores the advantages and downsides of both methods.
The objective of this research is to determine the potential of scope 3 supply chain emissions generated by end user computers within the United Kingdom government and to test current scope 2 calculation methods to improve annual reporting procedures. The supply chain analysis is accomplished by using existing asset profile data to determine the number of devices owned by computer type combined with manufacturer scope 3 product emissions data averages by type. In this example, 2.2 million computing devices are examined and found to produce emissions in excess of 623.45m kgCO2e. By suggesting devices are retained beyond the current 5-year periods, it is determined to be feasible that this value could be reduced by 37.5%. Additionally, by selecting devices with the lowest carbon footprint in the future when computers require replacement an estimated total reduction of 65% is achievable. The scope 2 emissions calculation compares the current methodology used to generate kWh/y values and concomitant greenhouse gas emissions to results generated by field measurement using the commercial typical energy consumption methodology. The results substantiate that the government is currently over reporting scope 2 emissions values by as much as 697% due to owned but not used devices being included in the calculation and fixed electricity consumption data used by the current methodology being too high when compared to actual use. As such, the findings are presented to the government and included as recommendations in the latest national sustainable information and communications technology strategy document.
As global demand for electronic products rises, it also generates a significant volume of e‐waste at an alarming rate. E‐waste recycling and disposal are one of the world's fastest‐growing pollution problems, with an estimated 53 million metric tons produced in 2019 and a raw material value of $57 billion. It is expected to reach 74.7 million metric tons by 2030. Developed countries generate most of the world's e‐waste and export it to low‐income Asian and African countries. Managing e‐waste, both domestically generated and imported, is a major environmental health issue in developing countries. Recycling electronic waste recovers precious metals and resalable parts to generate revenue. E‐waste and its recycling practices release a variety of known hazardous chemicals such as Pb, Hg, Cd, hexavalent Cr, brominated flame retardants (BFRs), polybrominated diphenyl ethers (PBDs), poly‐brominated/chlorinated biphenyls (PBBs/PCBs), polychlorinated dibenzodioxins/dibenzofurans (PCDDs/PCDFs), polyaromatic hydrocarbons (PAHs), and other chemicals. Developing countries lack adequate analytical laboratory resources and methods to measure the exposure of these toxic pollutants in air, water, food, and soil during recycling/disposal practices. Analytical data are critical to perform chemical exposure assessment for aggregate and cumulative risk assessment to exposed people, especially vulnerable populations (women and children) involved in handling and recycling operations in developing countries. In addition, a brief description of approaches to minimize risks, raising awareness of the problem of informal e‐waste management, and international efforts, policies, and regulations of the e‐waste legislation discussed.
The growing attention to digital sustainability can arguably be linked to climate change and digital transformations as major megatrends rapidly altering our collective present and future. The current Russian-Ukrainian war and the recent pandemic, however, have both raised uncertainty over the 2030 Sustainable Development Goals (SDGs) achievement and the role of technology and innovation for sustainability. Without ignoring the dramatic consequences for people, the Ukrainian war can be deemed as a significant shift in geopolitics and global energy policies, with a short-term return to fossil fuel and commitments to renewable and clean energy transitions. At the same time, the COVID-19 pandemic acted as a catalyst for a more pervasive diffusion and adoption of information and communication technologies (ICTs) transforming our lives and notions of sustainability. By considering the disruptive impact triggered by the pandemic, this paper aims at advancing awareness and knowledge of digital sustainability and at drawing a coherent framework of arguments including ethical and epistemological issues, taking into account the approach of complexity science. This will be essentially carried out by considering digital sustainability as “the convergence of digital and sustainability imperatives that involves a trans-disciplinary approach of deploying digital technologies in tackling sustainability issues” (Pan and Zhang, 2020). Across different interpretations reflected within business and management debates (Sharma, et al., 2021), this definition gives meaning to the concept or construct by specifying operations that must be performed in order to measure or manipulate the concept (Berrío-Zapata, et al., 2021). This paper will focus on the profound transformations of our view of reality by ICTs acting as instrumentarian technologies, and the need to avoid determinism, rethink science-technology relations, and consider the distributed morality of multi-agent ecosystems as significant aspects to further a debate on the trans-disciplinary nature of digital sustainability, including the potential negative impacts of digital technologies on society, economy and environment.
In this paper, we forecast the future carbon footprint of the
Information and Communication Technology (ICT) and
Entertainment and Media (E&M) sectors in the year 2020
including the ICT and E&M related impact from a networked
society, i.e. a society where both people and things are
connected and communicate with each other. We also discuss
sector boundaries, the full impact from the networked society,
uncertainty, use of the Carbon Disclosure Project (CDP) as data
source and important perspectives when assessing ICT.
Link to paper: https://pdfs.semanticscholar.org/2d24/59dbf04c61b5bc1aa296b07419a9d9db00d3.pdf?_ga=2.197965418.419761823.1597916682-665553063.1597916682
Conference proceedings: https://doi.org/10.3929/ethz-a-007337628
Purpose The aim of this study is to explore the literature surrounding the environmental impact of mobile phones and the implications of moving from the current business model of selling, using and discarding phones to a product service system based upon a cloud service. The exploration of the impacts relating to this shift and subsequent change in scope is explored in relation to the life cycle profile of a typical smartphone. Methods A literature study is conducted into the existing literature in order to define the characteristics of a Btypical^ smartphone. Focus is given to greenhouse gas (GHG) emissions in different life cycle phases in line with that reported in the majority of literature. Usage patterns from literature are presented in order to show how a smartphone is increasingly responsible for not only data consumption but also data generation. The subsequent consequences of this for the balance of the life cycle phases are explored with the inclusion of wider elements in the potential expanded mobile infrastructure , such as servers and the network. Results and discussion From the available literature, the manufacturing phase is shown to dominate the life cycle of a "typical" smartphone for GHG emissions. Smartphone users are shown to be increasingly reliant upon the internet for provision of their communications. Adding a server into the scope of a smartphone is shown to increase the use phase impact from 8.5 to 18.0 kg CO 2-eq, other phases are less affected. Addition of the network increases the use phase by another 24.7 kg CO 2-eq. In addition, it is shown that take-back of mobile phones is not effective at present and that prompt return of the phones could result in reduction in impact by best reuse potential and further reduction in toxic emissions through inappropriate disposal. Conclusions The way in which consumers interact with their phones is changing, leading to a system which is far more integrated with the internet. A product service system based upon a cloud service highlights the need for improved energy efficiency to make greatest reduction in GHG emissions in the use phase, and gives a mechanism to exploit residual value of the handsets by timely return of the phones, their components and recovery of materials.
The increasing consumption patterns in emerging economies like India and China with half the world population has increased the environmental concerns. E-waste in these countries is rapidly increasing and monitor is one of the major contributors to it. The paper aims at assessing the life cycle environmental impact of Cathode Ray Tube (CRT), Liquid Crystal Display (LCD) and Light Emitting Diode (LED) monitors. Life Cycle Assessment (LCA) of three widely used monitors has been performed based on the international standards of ISO 14040 series. The LCA was constructed using Umberto software version 5.6 and expressed with both CML (Centrum voor Milieuwetenschappen) 2001, mid-point assessment method and Impact 2002+, end point assessment method. This study is expected to guide policy makers in government to improve e-waste management strategies and also it will create awareness among the user. The monitor manufacturers may leverage the study to improve the environmental impact of these products. Sensitivity analysis for the use phase has been performed to confirm the robustness of results.
This work presents an estimation of the global electricity usage that can be ascribed to Communication Technology (CT) between 2010 and 2030. The scope is three scenarios for use and production of consumer devices, communication networks and data centers. Three different scenarios, best, expected, and worst, are set up, which include annual numbers of sold devices, data traffic and electricity intensities/efficiencies. The most significant trend, regardless of scenario, is that the proportion of use-stage electricity by consumer devices will decrease and will be transferred to the networks and data centers. Still, it seems like wireless access networks will not be the main driver for electricity use. The analysis shows that for the worst-case scenario, CT could use as much as 51% of global electricity in 2030. This will happen if not enough improvement in electricity efficiency of wireless access networks and fixed access networks/data centers is possible. However, until 2030, globally-generated renewable electricity is likely to exceed the electricity demand of all networks and data centers. Nevertheless, the present investigation suggests, for the worst-case scenario, that CT electricity usage could contribute up to 23% of the globally released greenhouse gas emissions in 2030.
Bitcoin is a digital cryptocurrency that has generated considerable public interest, including both booms in value and busts of exchanges dealing in Bitcoins. One of the fundamental concepts of Bitcoin is that work, called mining, must be done in checking all monetary transactions, which in turn creates Bitcoins as a reward. In this paper we look at the energy consumption of Bitcoin mining. We consider if and when Bitcoin mining has been profitable compared to the energy cost of performing the mining, and conclude that specialist hardware is usually required to make Bitcoin mining profitable. We also show that the power currently used for Bitcoin mining is comparable to Ireland's electricity consumption.
Information and Communication Technology (ICT) devices and services are becoming more and more widespread in all aspects of human life. Following an increased worldwide focus on the environmental impacts of energy consumption in general, there is also a growing attention to the electricity consumption associated with ICT equipment.
In this paper we assess how ICT electricity consumption in the use phase has evolved from 2007 to 2012 based on three main ICT categories: communication networks, personal computers, and data centers. We provide a detailed description of how we calculate the electricity use and evolution in these three categories.
Our estimates show that the yearly growth of all three individual ICT categories (10%, 5%, and 4% respectively) is higher than the growth of worldwide electricity consumption in the same time frame (3%). The relative share of this subset of ICT products and services in the total worldwide electricity consumption has increased from about 3.9% in 2007 to 4.6% in 2012. We find that the absolute electricity consumption of each of the three categories is still roughly equal. This highlights the need for energy-efficiency research across all these domains, rather than focusing on a single one.
Televisions (TVs) account for a significant portion of residential electricity consumption and global TV shipments are expected to continue to increase. We assess the market trends in the energy efficiency of TVs that are likely to occur without any additional policy intervention and estimate that TV efficiency will likely improve by over 60% by 2015 with savings potential of 45 terawatt-hours [TW h] per year in 2015, compared to today’s technology. We discuss various energy-efficiency improvement options and evaluate the cost effectiveness of three of them. At least one of these options improves efficiency by at least 20% cost effectively beyond ongoing market trends. We provide insights for policies and programs that can be used to accelerate the adoption of efficient technologies to further capture global energy savings potential from TVs which we estimate to be up to 23 TW h per year in 2015.
This article quantifies the global carbon footprint of mobile communication systems, and discusses its ecological and economic implications. Using up-to-date data and life cycle assessment models, we predict an increase of CO2 equivalent emissions by a factor of three until 2020 compared to 2007, rising from about 86 to 235 Mto CO2e, suggesting a steeper increase than predicted in the well-known SMART2020 report. We provide a breakdown of the global carbon footprint, which reveals that production of mobile devices and global radio access network operation will remain the major contributors, accompanied by an increasing share of emissions due to data transfer in the backbone resulting from rising mobile traffic volumes. The energy bill due to network operation will gain increasing importance in cellular business models. Furthermore, technologies to reduce energy consumption are considered a key enabler for the spread of mobile communications in developing countries. Taking into account several scenarios of technological advancement and rollout, we analyze the overall energy consumption of global radio access networks and illustrate the saving potential of green communication technologies. We conclude that, conditioned on quick implementation and alongside other "classical" improvements of spectral efficiency, these technologies offer the potential to serve three orders of magnitude more traffic with the same overall energy consumption as today.
There is a growing research interest in improving the energy efficiency of communication networks. In order to assess the impact of introducing new energy efficient technologies, an up-to-date estimate for the global electricity consumption in communication networks is needed. In this paper we consider the use phase electricity consumption of telecom operator networks, office networks and customer premises equipment. Our results show that the network electricity consumption is growing fast, at a rate of 10 % per year, and its relative contribution to the total worldwide electricity consumption has increased from 1.3% in 2007 to 1.8% in 2012. We estimate the worldwide electricity consumption of communication networks will exceed 350 TWh in 2012.
European and national policies are aimed at reducing greenhouse gases and increasing energy efficiency—also in the household sector. For this purpose, new solutions for private homes based on information and communication technologies (ICT) are being developed and tested. However, up to now, hardly anyone has seen, experienced or lived in an environment that offers the full range of ICT-based energy management solutions. In this study, consumer reactions to a fully furnished and equipped smart home are analysed using focus groups (four groups with a total of 29 participants). The analysis looks at consumer perceptions of and reactions to an energy management system which optimizes electricity consumption based on different ICT solutions. The topics that were demonstrated in practice and then discussed with the participants included variable tariffs, smart metering, smart appliances, and home automation. In general, there were positive group reactions to the smart home environment. Consumers saw many advantages for themselves; especially the chance to save money. However, giving up high levels of flexibility and adapting everyday routines to fit in with electricity tariffs were regarded as difficult. Smart appliances and smart meters were therefore considered to be necessary elements by most participants. Concerns regarding data privacy played a major role in one of the groups.
As Information and Communication Technology (ICT) is becoming more and more wide-spread and pervasive in our daily life, it is important to get a realistic overview of the worldwide impact of ICT on the environment in general and on energy and electricity needs in particular. This paper reports on a detailed study to estimate this impact today and to predict how this will evolve in the future. From this survey, important conclusions for the future of ICT industry and the Internet will be drawn, and challenges and research directives will be deduced.
This study proposes an easy-to-use methodology to estimate the materials embodied energy and manufacturing energy for a product. The tool requires as input the product's Bill of Materials and the knowledge on how these materials are processed (or an educated guess); the resulting output represents the sum of all the energy inputs into a product system in the form of a value range on the energy requirements of the product during its beginning of life. This includes extraction of materials, processing, and manufacture of the final product.
In current society it is becoming more and more important to take energy efficiency considerations into account when designing
information and communication technology (ICT) solutions. In ICT, virtualisation is being regarded as a way to increase energy
efficiency. One such virtualization solution which can be realized trough grids or cloud computing is the thin client paradigm.
This paper analyses the energy saving opportunities of the thin client paradigm.
This paper attempts to provide a review of the current state of the art of how e-business/ICT affects the environment. The work reviewed is in various forms including journal papers and thesis which have been peer-reviewed, as well as other resources such as projects and project reports, conference and symposia, and websites. It is claimed that the research examined has captured the most important work to date, either for a general knowledge of this new area or for background study by experts carrying out future research. The review has found that the currently dominant approach is either a micro-level case study approach or a macro-level statistical approach. It is concluded that a more predictive and empirical model, which can be applied within a sector of society, should be more beneficial in the long term. This approach should help simulate potential impacts resulting from changes of indicators, so that positive effects can be promoted and negative ones alleviated proactively, rather than knowing and accepting outcomes passively.
The characteristics of an innovative electric energy system, Smart Grid, meeting the demands of the customers and complying with the regulatory changes are presented. One of the features is that the Smart Grid should allow for the integration of renewable energy resources to address global climate change and allow for active customer participation to enable far better energy conservation. The Smart Grid involves installation of much new, intelligent equipment at all critical generation, transmission, distribution, and consumption points. The use of the real time data, combined with integrated system modeling and powerful new diagnostic tools and techniques, will provide the detection of precursors to failure in order to drive preventive maintenance and dynamic work management systems. Distributed generation and storage resources and remotely controlled equipment will play an important role in the management of the Smart Grid energy system to address contingency needs and to optimize power flow and minimize system losses.
Life cycle assessment (LCA) studies of desktop personal computers (PCs) are analyzed to assess the environmental impact of PCs and to explain inconsistencies and disagreements across existing studies. Impacts, characterized in this work in terms of primary energy demand and global warming potential, are decomposed into inventory components and impact per component in order to expose such inconsistencies. Additional information from related studies, especially regarding use-phase energy consumption, helps interpret the LCA results. The weight of evidence strongly suggests that for primary energy demand and contribution to climate change, the use phase is the dominant life cycle phase; manufacturing impacts are smaller but substantial, and impacts due to product transportation and end-of-life activities are much smaller. Each of the few LCA studies that report manufacturing impacts as being greater than use-phase impacts make unrealistically low assumptions regarding use-phase energy consumption. Estimates of manufacturing impacts, especially those related to printed circuit boards and integrated circuits, are highly uncertain and variable; such estimates are very difficult to evaluate, and more systematic research is needed to reduce these uncertainties. The type of computer analyzed, such as low-power light desktop or high-power workstation, may dominate the total impact; future studies should therefore base their estimates on a large sample to smooth out this variation, or explicitly restrict the analysis to a specific type of computer.
A three-day workshop was held in October 2001 to discuss life cycle inventory data for electricity production. Electricity was selected as the topic for discussion since it features very prominently in the LCA results for most product life cycles, yet there is no consistency in how these data are calculated and presented. Approximately 40 people attended all or part of the meeting to discuss issues of data modeling and collection. Attendees included recognized experts in the electricity generation and life cycle assessment fields.Five main topics of discussion were identified before the meeting began: (1) modeling the response of the energy supply system to demand (i.e. marginal versus average data); (2) defining the breadth and width of system boundaries to adequately capture environmental flows and data that are needed for impact modeling; (3) allocating environmental burdens across co-products that come from the same process; (4) modeling new and non-traditional technologies in which the data are highly uncertain; and (5) including transmission and distribution in modeling of electricity generation. Breakout groups addressed the first four topic areas in individual discussion groups and reported the results in a plenary session on the last day of the workshop (it was decided during the meeting to include “transmission and distribution” in other discussions).A key success of the workshop was the creation of the larger network of LCA and electricity production experts which will provide a good foundation for continued discussions.
Energy consumption in the residential and tertiary sectors is especially high in developed countries. There is a great potential for energy savings in these sectors. Energy conservation measures are developed for newly constructed buildings and for buildings under refurbishment. However, to achieve a significant reduction in energy consumption apart from the standard energy-efficiency methods, innovative technologies should be implemented, including renewable energy. Coherency of standard, modern energy efficiency and renewable options becomes necessities. To approach the idea of sustainable buildings, a few developmental steps are needed, regarding energy, water, land and material conservation, together with environmental loading, and the qualities of the indoor and outdoor environments.
The total energy and fossil fuels used in producing a desktop computer with 17-in. CRT monitor are estimated at 6400 megajoules (MJ) and 260 kg, respectively. This indicates that computer manufacturing is energy intensive: the ratio of fossil fuel use to product weight is 11, an order of magnitude larger than the factor of 1-2 for many other manufactured goods. This high energy intensity of manufacturing, combined with rapid turnover in computers, results in an annual life cycle energy burden that is surprisingly high: about 2600 MJ per year, 1.3 times that of a refrigerator. In contrast with many home appliances, life cycle energy use of a computer is dominated by production (81%) as opposed to operation (19%). Extension of usable lifespan (e.g. by reselling or upgrading) is thus a promising approach to mitigating energy impacts as well as other environmental burdens associated with manufacturing and disposal.
Using hybrid assessment that combines process and economic input-output methods, the total energy and fossil fuels used in producing a desktop computer with 17-inch CRT monitor are estimated at 7,320 Megajoules (MJ) and 290 kg respectively. This indicates that the network of industries for manufacturing computers is energy intensive: the ratio of fossil fuel use to product weight for a computer is 12, an order of magnitude larger than the factor of 1-2 for many other manufactured goods. In contrast with many home appliances, life cycle energy use of a computer is dominated by production (83%) as opposed to operation (17%). The yearly life cycle cost of owning a computer is about 3,000 MJ/year, half again that of a refrigerator, a much larger appliance that uses far more electricity in operation. The short lifespan of computers and the variety of computing needs of users suggests that extension of lifespan, for example by promptly reselling to users who need less computing power, is a promising approach to mitigating environmental impacts.
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