Turning the Paris Agreement’s greenhouse gas emissions pledges into domestic policies is the next challenge for governments. We address the question of the acceptability of cost-effective climate policy in a real-voting setting. First, we analyze voting behavior in a large ballot on energy taxes, rejected in Switzerland in 2015 by more than 2 million people. Energy taxes were aimed at completely replacing the current value-added tax. We examine the determinants of voting and find that distributional and competitiveness concerns reduced the acceptability of energy taxes, along with the perception of ineffectiveness. Most people would have preferred tax revenues to be allocated for environmental purposes. Second, at the same time of the ballot, we tested the acceptability of alternative designs of a carbon tax with a choice experiment survey on a representative sample of the Swiss population. Survey respondents are informed about environmental, distributional and competitiveness effects of each carbon tax design. These impacts are estimated with a computable general equilibrium model. This original setting generates a series of novel results. Providing information on the expected environmental effectiveness of carbon taxes reduces the demand for environmental earmarking. Making distributional effects salient generates an important demand for progressive designs, e.g. social cushioning or recycling via lump-sum transfers. The case of lump-sum recycling is particularly striking: it is sufficient to show its desirable distributional properties to make it one of the most preferred designs, which corresponds to a completely novel result in the literature.We show that providing detailed information on the functioning of environmental taxes may contribute to close both the gap between acceptability ex ante and ex post and the gap between economists’ prescriptions and the preferences of the general public.
Carbon pricing is a recurrent theme in debates on climate policy. Discarded at the 2009 COP in Copenhagen, it remained part of deliberations for a climate agreement in subsequent years. As there is still much misunderstanding about the many reasons to implement a global carbon price, ideological resistance against it prospers. Here, we present the main arguments for carbon pricing, to stimulate a fair and well-informed discussion about it. These include considerations that have received little attention so far. We stress that a main reason to use carbon pricing is environmental effectiveness at a relatively low cost, which in turn contributes to enhance social and political acceptability of climate policy. This includes the property that corrected prices stimulate rapid environmental innovations. These arguments are underappreciated in the public debate, where pricing is frequently downplayed and the erroneous view that innovation policies are sufficient is widespread. Carbon pricing and technology policies are, though, largely complementary and thus are both needed for effective climate policy. We also comment on the complementarity of other instruments to carbon pricing. We further discuss distributional consequences of carbon pricing and present suggestions on how to address these. Other political economy issues that receive attention are lobbying, co-benefits, international policy coordination, motivational crowding in/out, and long-term commitment. The overview ends with reflections on implementing a global carbon price, whether through a carbon tax or emissions trading. The discussion goes beyond traditional arguments from environmental economics by including relevant insights from energy research and innovation studies as well.
The project LODZ (Linked Open Data Zurich) adopts an experimental approach to merge data and develop a semantic web infrastructure to enable its discovery. For this purpose, three institutions in the field of art and design provided their metadata. The project cycle followed six steps: team building, gathering and cleaning of the original data, modelling, transforming, interlinking and exploration of the Linked Data set. The resulting pilot application offers innovative and attractive features based on the capability of the Linked Data, with the aim to provide a better user experience. The major challenge of this project was the creation of links between the internal datasets, and with external sources. An important lesson learnt is therefore to focus more on the interoperability of data at the time of cataloguing in the original databases, for example by integrating external identifiers rather than just terms in the form of strings.
The current paper aims to develop an effective and integrated MCDM model for the evaluation of the sustainability practices in the banking services, employing a multi-stage, fuzzy MCDM model that integrates the Balanced Scorecard, fuzzy AHP and fuzzy TOPSIS. The approach aims to evaluate sustainability from the following four perspectives: financial stability, customer relationship management, internal business process and environment-friendly management system. A real implementation dealing with the six largest commercial banks in India is discussed. The results highlights the critical aspects of the evaluation criteria and the issues in improving sustainable banking performances. Regarding the sustainability issues, it is shown that the environment-friendly management system takes a back seat compared with the other criteria. Furthermore, the results show that there is a misunderstanding of the role that corporate social responsibility plays with respect to environmental issues. The developed evaluation model offers a valuable management tool for banks' administrators by assisting them in strategic choices in order to achieve their objective of sustainability and sustainable banking. Moreover, it offers a measuring tool with unique features that complements the emerging trend of integrated reporting considering uncertainty.
In this article, the authors report on an on-going Data Life-Cycle Management(DLCM) National project realized in Switzerland, with a major focus on long-term preservation. Based on a extensive document analysis as well as semi-structured interviews, the project aims at providing national services to respond to the most relevant researchers’ DLCM needs, which includes: guidelines for establishing a data management plan, active data management solutions, long-term preservation storage options, training, and a single point of access and contact to get support. In addition to presenting the different working axes of the project, the authors describe a strategic management and lean startup template for developing new business models, which is key for building viable services.
Le capital humain et les politiques visant à sa création et à sa préservation prennent de plus en plus d’importance dans les sociétés industrialisées. Cet article propose un survol de la littérature économique récente dans ce domaine. Le défi majeur qui occupe aujourd’hui le marché du travail est l’accélération du progrès technique, qui s’accompagne d’effets différenciés suivant le niveau de qualification des travailleurs. Deux théories sont évoquées pour analyser ces effets. D’une part, le progrès technique est vu comme biaisé en faveur des travailleurs qualifiés et défavorable aux moins qualifiés. D’autre part, et suite au phénomène de polarisation du marché du travail récemment observé, on peut cependant penser que la technologie est surtout substituable aux emplois du milieu de l’échelle. De ces constatations découlent plusieurs recommandations de politiques économiques, la principale étant de donner aux travailleurs les moyens d’être flexibles et polyvalents, ce qui passe par une éducation relativement générale plutôt que trop spécifique et cantonnée à un secteur ou une profession.
Background The 2009 A/H1N1 influenza pandemic generated additional data and triggered new studies that opened debate over the optimal strategy for handling a pandemic. The lessons-learned documents from the World Health Organization show the need for a cost estimation of the pandemic response during the risk-assessment phase. Several years after the crisis, what conclusions can we draw from this field of research? Objective The main objective of this article was to provide an analysis of the studies that present cost-effectiveness or cost-benefit analyses for A/H1N1 pandemic interventions since 2009 and to identify which measures seem most cost-effective. Methods We reviewed 18 academic articles that provide cost-effectiveness or cost-benefit analyses for A/H1N1 pandemic interventions since 2009. Our review converts the studies’ results into a cost-utility measure (cost per disability-adjusted life-year or quality-adjusted life-year) and presents the contexts of severity and fatality. Results The existing studies suggest that hospital quarantine, vaccination, and usage of the antiviral stockpile are highly cost-effective, even for mild pandemics. However, school closures, antiviral treatments, and social distancing may not qualify as efficient measures, for a virus like 2009’s H1N1 and a willingness-to-pay threshold of $45,000 per disability-adjusted life-year. Such interventions may become cost-effective for severe crises. Conclusions This study helps to shed light on the cost-utility of various interventions, and may support decision making, among other criteria, for future pandemics. Nonetheless, one should consider these results carefully, considering these may not apply to a specific crisis or country, and a dedicated cost-effectiveness assessment should be conducted at the time.
The rapid increase in the number of published articles poses a challenge for curated databases to remain up-to-date. To help the scientific community and database curators deal with this issue, we have developed an application, neXtA5, which prioritizes the literature for specific curation requirements. Our system, neXtA5, is a curation service composed of three main elements. The first component is a named-entity recognition module, which annotates MEDLINE over some predefined axes. This report focuses on three axes: Diseases, the Molecular Function and Biological Process sub-ontologies of the Gene Ontology (GO). The automatic annotations are then stored in a local database, BioMed, for each annotation axis. Additional entities such as species and chemical compounds are also identified. The second component is an existing search engine, which retrieves the most relevant MEDLINE records for any given query. The third component uses the content of BioMed to generate an axis-specific ranking, which takes into account the density of named-entities as stored in the Biomed database. The two ranked lists are ultimately merged using a linear combination, which has been specifically tuned to support the annotation of each axis. The fine-tuning of the coefficients is formally reported for each axis-driven search. Compared with PubMed, which is the system used by most curators, the improvement is the following:+231% for Diseases,+236% for Molecular Functions and +3153% for Biological Process when measuring the precision of the topreturned PMID (P0 or mean reciprocal rank). The current search methods significantly improve the search effectiveness of curators for three important curation axes. Further experiments are being performed to extend the curation types, in particular protein–protein interactions, which require specific relationship extraction capabilities. In parallel, userfriendly interfaces powered with a set of JSON web services are currently being implemented into the neXtProt annotation pipeline.
Recommendation systems often rely on point-wise loss metrics such as the mean squared error. However, in real recommendation settings only few items are presented to a user. This observation has recently encouraged the use of rank-based metrics. LambdaMART is the state-of-the-art algorithm in learning to rank which relies on such a metric. Motivated by the fact that very often the users’ and items’ descriptions as well as the preference behavior can be well summarized by a small number of hidden factors, we propose a novel algorithm, LambdaMART matrix factorization (LambdaMART-MF), that learns latent representations of users and items using gradient boosted trees. The algorithm factorizes LambdaMART by defining relevance scores as the inner product of the learned representations of the users and items. We regularise the learned latent representations so that they reflect the user and item manifolds as these are defined by their original feature based descriptors and the preference behavior. We also propose to use a weighted variant of NDCG to reduce the penalty for similar items with large rating discrepancy. We experiment on two very different recommendation datasets, meta-mining and movies-users, and evaluate the performance of LambdaMART-MF, with and without regularization, in the cold start setting as well as in the simpler matrix completion setting. The experiments show that the factorization of LambdaMart brings significant performance improvements both in the cold start and the matrix completion settings. The incorporation of regularisation seems to have a smaller performance impact.
