Contributions in Merlin

The goal of this project was to develop a web-based tool for the interactive evaluation of biofuels sustainability against the standard of the Roundtable on Sustainable Biofuels. In this document we describe the central concepts of the RSB GHG Tool which is one module within the RSB tool. It allows the modular assessment of GHG emissions for numerous biofuel pathways with different methodologies: RSB, EU Renewable Energy Directive (EU RED), Ordinance on mineral oil tax (CH), Low Carbon Fuel Standard (LCFS/USA), and U.S. Renewable Fuel Standard (RFS2/USA).

Micro-blogging is increasingly evolving from a daily chatting tool into a critical platform for individuals and organizations to seek and share real-time news updates during emergencies. However, seeking and extracting useful information from micro-blogging sites poses significant challenges due to the volume of the traffic and the presence of a large body of irrelevant personal messages and spam. In this paper, we propose a novel recommendation framework to overcome this problem. By analyzing information diffusion patterns among a large set of micro-blogs that play the role of emergency news providers, our approach selects a small subset as recommended emergency news feeds for regular users. We evaluate our diffusion-based recommendation framework on Twitter during the early outbreak of H1N1 Flu. The evaluation results show that our method results in more balanced and comprehensive recommendations compared to benchmark approaches.

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This paper describes our work on building a trilingual parallel treebank. We have annotated constituent structure trees from three text genres (a philosophy novel, economy reports and a technical user manual). Our parallel treebank includes word and phrase alignments. The alignment information was manually checked using a graphical tool that allows the annotator to view a pair of trees from parallel sentences. This tool comes with a powerful search facility which supersedes the expressivity of previous popular treebank query engines.

With two regulations, 244/2009 and 245/2009, the European Commission recently put into practice the EuP Directive in the area of lighting devices, aiming to improve energy efficiency in the domestic lighting sector. This article presents a comprehensive life cycle assessment comparison of four different lighting technologies: the tungsten lamp, the halogen lamp, the conventional fluorescent lamp and the compact fluorescent lamp. Taking advantage of the most up-to-date life cycle inventory database available (ecoinvent data version 2.01), all life cycle phases were assessed and the sensitivity of the results for varying assumptions analysed: different qualities of compact fluorescent lamps (production phase), different electricity mixes (use phase), and endof- life scenarios for WEEE recycling versus municipal solid waste incineration (disposal phase). A functional unit of “one hour of lighting” was defined and the environmental burdens for the whole life cycle for all four lamp types were calculated, showing a clearly lower impact for the two gas-discharge lamps, i.e. the fluorescent and the compact fluorescent lamp. Differences in the product quality of the compact fluorescent lamps reveal to have only a very small effect on the overall environmental performance of this lamp type; a decline of the actual life time of this lamp type doesn't result in a change of the rank order of the results of the here examined four lamp types. It was also shown that the environmental break-even point of the gasdischarge lamps is reached long before the end of their expected life-span. All in all, it can be concluded that a change from today's tungsten lamp technology to a low-energy-consuming technology such as the compact fluorescent lamp results in a substantial environmental benefit.

Many studies on "Green IT/Green ICT" have already been published, focusing on the energy consumption of ICT or the role of ICT as an enabler of energy efficiency. In this paper, we argue that such an approach is too narrow, and that a broader perspective is needed to utilize the potential of ICT to make our lives more sustainable.

Economists have traditionally treated preferences as exogenously given. Preferences are assumed to be influenced by neither beliefs nor the constraints people face. As a consequence, changes in behaviour are explained exclusively in terms of changes in the set of feasible alternatives. Here, we argue that the opposition to explaining behavioural changes in terms of preference changes is ill-founded, that the psychological properties of preferences render them susceptible to direct social influences and that the impact of ‘society’ on preferences is likely to have important economic and social consequences.

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Cross-border M&A plays a pivotal role in the internationalization process of Chinese firms. This thesis empirically examines the wealth effects for Chinese acquirers in such transactions and analyzes the cross-sectional determinants in cumulative abnormal returns using a short-run event study model and multivariate regressions. Based on a sample of 117 outbound transactions conducted between January 2000 and February 2011, I find strong evidence that bidders, on average, gain abnormal returns of 1.4% to 2.0% over the three-day window [-1,+1] and 0.9% to 1.3% on the announcement day itself. Ceteris paribus, the bidder abnormal returns increase significantly, (1) when the bidder has the experience of more than two cross-border deals, (2) the more culturally similar the target country to China, (3) the larger the transaction relative to the bidder’s size, (4) the lower the pre-offer market-to-book ratio of the acquirer, and (5) the smaller the acquiring firm. The thesis extends the preliminary research by Boateng, Qian and Tianle (2008) and Chen and Lin (2009) on short-term wealth effects for Chinese acquirers in outbound M&A, initiates research on several determinants of M&A success in the Chinese cross-border context and tests the generalization of results from developed countries in an important emerging market.

Firms are increasingly seeking to harness the potential of social networks for marketing purposes. Marketers are therefore interested in understanding the antecedents and consequences of relationship formation within networks and in predicting interactivity among users. In this paper we develop an integrated statistical framework for simultaneously modeling the connectivity structure of multiple relationships of different types on a common set of actors. Our modeling approach incorporates a number of distinct facets to capture both the determinants of relationships and the structural characteristics of multiplex and sequential networks. We develop hierarchical Bayesian methods for estimation and illustrate our model via two applications. The first application uses a sequential network of communications among managers involved in new product development activities and the second uses an online collaborative social network of musicians. Our applications demonstrate the benefits of modeling multiple relations jointly for both substantive and predictive purposes. We also illustrate how information in one relationship can be leveraged to predict connectivity in another relation.