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The field green ICT focuses on the greening of ICT and using ICT to optimise the energy footprint of the ICT-supported processes. For organisations, applying green ICT in the broadest sense presents them with challenges. In this paper we explore what factors are influencing the adoption of green ICT. We follow three organisations that used a green ICT tool, the SURF Green ICT Maturity Model, to identify such factors. Tools like the maturity model help organisations identify the what and how. We found other factors, such as strategic alignment, culture and leadership, determine the why. As ICT is a general purpose technology, it potentially affects all processes in an organisation. To have a greening impact, the main challenge for green ICT is to take a systemic approach and involve all (relevant) parties. ICT departments often position themselves as support (followers, not leaders), this proves to be a big hurdle in the adoption of green ICT.

International audience; The environmental protection is a dominant concern for all types of industries, organizations, and governments. In this regard, the reduction of the energy consumption is substantial in bringing down the CO2 gas emission, which is considered as an important factor causing global warming. The e-infrastructure service providers, such as National Research and Education Networks or National Grid Initiatives have crucial role in the context of energy awareness because the energy consumption of the networking, data, and computational infrastructures keeps increasing exponentially over the time. In addition to this, scientific gateways and cloud services are becoming more significant to tackle scientific and societal challenges. Therefore, there is a need to provide robust and reliable services taking into account energy consumption aspect of e-infrastructures. The aim of the article is to introduce an energy optimization methodology for the beneficiaries of the e-infrastructures to explore, optimize, and report the energy consumption and CO2 emission of data, computing, and networking facilities. The suggested methodology has been implemented within the Armenian e-infrastructure aiming at the reduction of the energy consumption and thereby the CO2 emission.

The question "What drove foragers to farm?" has drawn answers from many different disciplines, often in the form of verbal models. Here, we take one such model, that of the ideal free distribution, and implement it as an agent-based computer simulation. Populations distribute themselves according to the marginal quality of different habitats, predicting settlement patterns and subsistence methods over both time and space. Our experiments and our analyses thereof show that central conclusions of the ideal free distribution model are reproduced by our agent-based simulation, while at the same time offering new insights into the theory's underlying assumptions. Generally, we demonstrate how agent-based models can make use of empirical data to reconstruct realistic environmental and cultural contexts, enabling concrete tests of the explanatory power of anthropological models put forward to explain historical developments, such as agricultural transitions, in specific times and places.

The environmental protection is a dominant concern for all types of industries, organizations, and governments. In this regard, the reduction of the energy consumption is substantial in bringing down the CO2 gas emission, which is considered as an important factor causing global warming. The e‐infrastructure service providers, such as National Research and Education Networks or National Grid Initiatives have crucial role in the context of energy awareness because the energy consumption of the networking, data, and computational infrastructures keeps increasing exponentially over the time. In addition to this, scientific gateways and cloud services are becoming more significant to tackle scientific and societal challenges. Therefore, there is a need to provide robust and reliable services taking into account energy consumption aspect of e‐infrastructures. The aim of the article is to introduce an energy optimization methodology for the beneficiaries of the e‐infrastructures to explore, optimize, and report the energy consumption and CO2 emission of data, computing, and networking facilities. The suggested methodology has been implemented within the Armenian e‐infrastructure aiming at the reduction of the energy consumption and thereby the CO2 emission.