Eco Global Evaluation: Cross Benefits of Economic and Ecological Evaluation

This paper highlights the complementarities of cost and environmental evaluation in a sustainable approach. Starting with the needs and limits for whole product lifecycle evaluation, this paper begins with the modeling, data capture and performance indicator aspects. In a second step, the information issue, regarding the whole lifecycle of the product is addressed. In order to go further than the economical evaluations/assessment, the value concept (for a product or a service) is discussed. Value could combine functional requirements, cost objectives and environmental impact. Finally, knowledge issues which address the complexity of integrating multi-disciplinary expertise to the whole lifecycle of a product are discussing.EcoSD NetworkEcoSD networ

Human computer interaction for international development: past present and future

Recent years have seen a burgeoning interest in research into the use of information and communication technologies (ICTs) in the context of developing regions, particularly into how such ICTs might be appropriately designed to meet the unique user and infrastructural requirements that we encounter in these cross-cultural environments. This emerging field, known to some as HCI4D, is the product of a diverse set of origins. As such, it can often be difficult to navigate prior work, and/or to piece together a broad picture of what the field looks like as a whole. In this paper, we aim to contextualize HCI4D—to give it some historical background, to review its existing literature spanning a number of research traditions, to discuss some of its key issues arising from the work done so far, and to suggest some major research objectives for the future

Crossing the death valley to transfer environmental decision support systems to the water market

Environmental decision support systems (EDSSs) are attractive tools to cope with the complexity of environmental global challenges. Several thoughtful reviews have analyzed EDSSs to identify the key challenges and best practices for their development. One of the major criticisms is that a wide and generalized use of deployed EDSSs has not been observed. The paper briefly describes and compares four case studies of EDSSs applied to the water domain, where the key aspects involved in the initial conception and the use and transfer evolution that determine the final success or failure of these tools (i.e., market uptake) are identified. Those aspects that contribute to bridging the gap between the EDSS science and the EDSS market are highlighted in the manuscript. Experience suggests that the construction of a successful EDSS should focus significant efforts on crossing the death-valley toward a general use implementation by society (the market) rather than on development.The authors would like to thank the Catalan Water Agency (Agència Catalana de l’Aigua), Besòs River Basin Regional Administration (Consorci per la Defensa de la Conca del Riu Besòs), SISLtech, and Spanish Ministry of Science and Innovation for providing funding (CTM2012-38314-C02-01 and CTM2015-66892-R). LEQUIA, KEMLG, and ICRA were recognized as consolidated research groups by the Catalan Government under the codes 2014-SGR-1168, 2013-SGR-1304 and 2014-SGR-291.Peer ReviewedPostprint (published version

Advancing national greenhouse gas inventories for agriculture in developing countries : improving activity data, emission factors and software technology

Peer reviewedPublisher PD

Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed?

As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or Facebook invest significantly into AI, thereby underlining its relevance for business models worldwide. For the highly data driven finance industry, AI is of intensive interest within pilot projects, still, few AI applications have been implemented so far. This study analyzes drivers and inhibitors of a successful AI application in the finance industry based on panel data comprising 22 semi-structured interviews with experts in AI in finance. As theoretical lens, we structured our results using the TOE framework. Guidelines for applying AI successfully reveal AI-specific role models and process competencies as crucial, before trained algorithms will have reached a quality level on which AI applications will operate without human intervention and moral concerns