European LCA Standardisation of ICT: Equipment, Networks, and Services

Andrae, Anders

doi:10.1007/978-94-007-1899-9_47

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…where ECDP,2010 = Electricity for production of a consumer device category in 2010, TWh PCD,2010 = produced units of a category of consumer device (desktops, monitors, etc.) in 2010, millions ECDPu,2010 = production electricity for a category of consumer devices in 2010, MWh/unit ER = annual production electricity usage reduction, 5% [173], 3% [173], 1% [174] n = 0,1,2,3,…20 Literature sources are used for the ranges for the relation between production and use stages for FAN [29,175,176], for WAN [39,177] and for data centers [69,178]. Greenhouse Gas Protocol presented a method by which this relation can be used to estimate the production footprint, e.g.…”

Section: Production Electricity For Consumer Devices Network and Data Centers-resultsmentioning

confidence: 99%

“…where EP,networks,2010 = electricity for networks production in 2010, TWh EP,DC,2010 = electricity for data centers production in 2010, TWh SPw = share of production in network LCAs, 5% (best) [177], 10% (expected) [39], 15% (worst) [29,175] SUw = share of use stage in network LCAs, 95% (best) [177], 90% (expected) [39], 85% (worst) [29,176] SPDC = share of production in data center LCAs, 5% (best) [178], 10% (expected) [178], 15% (worst) [178,73] SUDC = share of use stage in data center LCAs, 95% (best) [178], 90% (expected) [178], 85% (worst) [178,73] ER = annual production electricity usage reduction, 5% (best) [173], 3% (expected) [173], 1% (worst) [174] n = 0,1,2,3,…20 As a consequence of Equations 11 and 12, Figure 5 below is rather imprecise for data centers and networks, which together here is assumed to constitute a high share of the total in 2030. The details for Figure 5 are shown in the Supplementary Materials file, section "Production".…”

Section: Production Electricity For Consumer Devices Network and Data Centers-resultsmentioning

confidence: 99%

See 1 more Smart Citation

On Global Electricity Usage of Communication Technology: Trends to 2030

2015

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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 worstcase scenario, that CT electricity usage could contribute up to 23% of the globally released greenhouse gas emissions in 2030.

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Section: Production Electricity For Consumer Devices Network and Data Centers-resultsmentioning

confidence: 99%

Section: Production Electricity For Consumer Devices Network and Data Centers-resultsmentioning

confidence: 99%

On Global Electricity Usage of Communication Technology: Trends to 2030

2015

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“…However, in the case of ICT, there is a bigger challenge in the form of inconsistency, which would practically make assessments incomparable. This factor has attracted several researches toward standardization [36]. Inconsistency could partially arise from databases and models used to estimate the impacts.…”

Section: B Uncertainty Vs Inconsistencymentioning

confidence: 99%

“…Inconsistency could partially arise from databases and models used to estimate the impacts. A famous example is the inconsistency between USA EIOLCA and EU PLCA life cycle inventory databases [36]. In addition, it was found that the LCAs of consumer electronics are not generally consistent [37].…”

Section: B Uncertainty Vs Inconsistencymentioning

confidence: 99%

Challenges and complexities in application of LCA approaches in the case of ICT for a sustainable future

Moghaddam¹,

Moghaddam²,

Dandres³

et al. 2014

Proceedings of the 2014 Conference ICT for Sustainability

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In this work, three of many ICT-specific challenges of LCA are discussed. First, the inconsistency versus uncertainty is reviewed with regard to the meta-technological nature of ICT. As an example, the semiconductor technologies are used to highlight the complexities especially with respect to energy and water consumption. The need for specific representations and metric to separately assess products and technologies is discussed. It is highlighted that applying product-oriented approaches would result in abandoning or disfavoring of new technologies that could otherwise help toward a better world. Second, several believeduntouchable hot spots are highlighted to emphasize on their importance and footprint. The list includes, but not limited to, i) User Computer-Interfaces (UCIs), especially screens and displays, ii) Network-Computer Interlaces (NCIs), such as electronic and optical ports, and iii) electricity power interfaces. In addition, considering cross-regional social and economic impacts, and also taking into account the marketing nature of the need for many ICT's product and services in both forms of hardware and software, the complexity of End of Life (EoL) stage of ICT products, technologies, and services is explored. Finally, the impact of smart management and intelligence, and in general software, in ICT solutions and products is highlighted. In particular, it is observed that, even using the same technology, the significance of software could be highly variable depending on the level of intelligence and awareness deployed. With examples from an interconnected network of data centers managed using Dynamic Voltage and Frequency Scaling (DVFS) technology and smart cooling systems, it is shown that the unadjusted assessments could be highly uncertain, and even inconsistent, in calculating the management component's significance on the ICT impacts.

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