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ICT hold significant potential to increase resource and energy efficiencies and contribute to a circular economy. Yet unresolved is whether the aggregated net effect of ICT overall mitigates or aggravates environmental burdens. While the savings potentials have been explored, drivers that prevent these and possible counter measures have not been researched thoroughly. The concept digital sufficiency constitutes a basis to understand how ICT can become part of the essential environmental transformation. Digital sufficiency consists of four dimensions, each suggesting a set of strategies and policy proposals: (a) hardware sufficiency, which aims for fewer devices needing to be produced and their absolute energy demand being kept to the lowest level possible to perform the desired tasks; (b) software sufficiency, which covers ensuring that data traffic and hardware utilization during application are kept as low as possible; (c) user sufficiency, which strives for users applying digital devices frugally and using ICT in a way that promotes sustainable lifestyles; and (d) economic sufficiency, which aspires to digitalization supporting a transition to an economy characterized not by economic growth as the primary goal but by sufficient production and consumption within planetary boundaries. The policies for hardware and software sufficiency are relatively easily conceivable and executable. Policies for user and economic sufficiency are politically more difficult to implement and relate strongly to policies for environmental transformation in general. This article argues for comprehensive policies for digital sufficiency, which are indispensible if ICT are to play a beneficial role in overall environmental transformation. [ABSTRACT FROM AUTHOR]

The determination of the environmental concentration of a pollutant is a crucial step in the risk assessment of anthropogenic substances. Dynamic probabilistic material flow analysis (DPMFA) is a method to predict flows of substances to the environment that can be converted into environmental concentrations. In cases where direct quantitative measurements of concentrations are impossible, environmental stocks are predicted by reproducing the flow processes creating these stocks in a mathematical model. Incomplete parameter knowledge is represented in the form of stochastic distributions and propagated through the model using Monte Carlo simulation. This work discusses suitable means for the model design and the representation of system knowledge from several information sources of varying credibility as model parameter distributions, further evaluation of the simulation outcomes using sensitivity analyses, and the impacts of parameter uncertainty on the total uncertainty of the simulation output. Based on a model developed in a case study of carbon nanotubes in Switzerland, the modeling process, the representation and interpretation of the simulation results are described and approaches to sensitivity and uncertainty analyses are demonstrated. Finally, the overall approach is summarized and provided in the form of a set of modelling and evaluation rules for DPMFA studies. [ABSTRACT FROM AUTHOR]

Purpose: Improving the quality and quantity of unit process datasets in Life Cycle Inventory (LCI) databases affects every LCA they are used in. However, improvements in data quality and quantity are so far rather directed by the external supply of data and situation-driven requirements instead of systematic choices guided by structural dependencies in the data. Overall, the impact of current data updates on the quality of the LCI database remains unclear and maintenance efforts might be ineffective. This article analyzes how a contribution-based prioritization approach can direct LCI update efforts to datasets of key importance. Methods: A contribution-based prioritization method has been applied to version 3 of the ecoinvent database. We identified the relevance of unit processes on the basis of their relative contributions throughout each product system with respect to a broad range of Life Cycle Impact Assessment (LCIA) indicators. A novel ranking algorithm enabled the ranking of unit processes according to their impact on the LCIA results. Finally, we identified the most relevant unit processes for different sectors and geographies. Results and discussion: The study shows that a relatively large proportion of the overall database quality is dependent on a small set of key processes. Processes related to electricity generation, waste treatment activities, and energy carrier provision (petroleum and hard coal) consistently cause large environmental impacts on all product systems. Overall, 300 datasets are causing 60% of the environmental impacts across all LCIA indicators, while only 3 datasets are causing 11% of all climate change impacts. In addition, our analysis highlights the presence and importance of central hubs, i.e., sensitive intersections in the database network, whose modification can affect a large proportion of database quality. Conclusions: Our study suggests that contribution-based prioritization offers important insights into the systematic and effective improvement of LCI databases. The presented list of key processes in ecoinvent version 3.1 adds a new perspective to database improvements as it allows the allocation of available resources according to the structural dependencies in the data. [ABSTRACT FROM AUTHOR]

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Goal, Scope and Background. Telephony as well as remote data transfer is increasingly performed via mobile phone networks. However, the environmental consequences, in particular of the End-of-Life (EOL) treatment, of such network infrastructures have been investigated insufficiently to date. In the present re- port the environmental implications of the EOL treatment of a single GSM 900 antenna rack have been analysed. Methods. Based on comprehensive inventories of a GSM 900 antenna station rack and currently applied EOL treatment, the environmental impacts related to the EOL treatment of the rack are investigated. Six different EOL treatment scenarios are developed to find an environmentally safe treatment alternative. System expansion, i.e. inclusion of the production phase, is applied to all scenarios in order to consider different amounts of regained materials. Results and Discussion. The production of primary rack materials, especially that of palladium (accounts for almost 40% of the ecotoxicity impact category), to substitute lost materials dominates the overall environmental impact. Releases of heavy metals from landfilled rack components / materials and of by-products to the environment greatly influence the overall impacts on human health and ecosystem quality. The final disposal of rack components contributes to about 70% of the non-carcinogenic effects. Landfilled dust from steel production contributes to nearly 11% of this impact category. Conclusions. The results suggest that all precious metals containing electronic scrap should be treated in specially equipped metal recovery plants. A complete rack disassembly before processing in high-standard metal recovery plants is not necessary. An elaborated pre-treatment and fractionation of the scrap prior to precious material recovery does not lower the environmental impacts and is not mandatory and would only become environmentally interesting if high recovery of heavy metals is achieved. To avoid the formation and release of volatile and toxic heavy metal, incineration of electronic scrap as of by-products prior to landfilling should be avoided. To reduce the over-all environmental load, a standardisation of the sizes of rack components, facilitating their re-use, is recommended. [ABSTRACT FROM AUTHOR]