Capturing industrial CO2 emissions in Spain: Infrastructures, costs and break-even prices

This paper examines the conditions for the deployment of large-scale pipeline and storage infrastructure needed for the capture of CO2 in Spain by 2040. It details a modeling framework that allows us to determine the optimal infrastructure needed to connect a geographically disaggregated set of emitting and storage clusters, along with the threshold CO2 values necessary to ensure that the considered emitters will make the necessary investment decisions. This framework is used to assess the relevance of various policy scenarios, including (i) the perimeter of the targeted emitters for a CCS uptake, and (ii) the relevance of constructing several regional networks instead of a single grid to account for the spatial characteristics of the Spanish peninsula. We find that three networks naturally emerge in the north, center and south of Spain. Moreover, the necessary CO2 break-even price critically depends on the presence of power stations in the capture perimeter. Policy implications of these findings concern the elaboration of relevant, pragmatic recommendations to envisage CCS deployment locally, focusing on emitters with lower substitution options toward low-carbon alternatives

Centralized vs Decentralized Markets in the Laboratory: The Role of Connectivity

This paper compares the performance of centralized and decentralized markets experimentally. We constrain trading exchanges to happen on an exogenously predetermined network, representing the trading relationships in markets with differing levels of connectivity. Our experimental results show that, despite having lower trading volumes, decentralized markets are generally not less efficient. Although information can propagate quicker through highly connected markets, we show that higher connectivity also induces informed traders to trade faster and exploit further their information advantages before the information becomes fully incorporated into prices. This not only reduces market efficiency, but it increases wealth inequality. We show that, in more connected markets, informed traders trade not only relatively quicker, but also more, in the right direction, despite not doing it at better prices

Research Output From University-Industry Collaborative Projects

We study collaborative and noncollaborative projects that are supported by government grants. First, we propose a theoretical framework to analyze optimal decisions in these projects. Second, we test our hypotheses with a unique data set containing academic publications and research funds for all academics at the major university engineering departments in the United Kingdom. We find that the type of the project (measured by its level of appliedness) increases the type of both the university and firm partners. Also, the quality of the project (number and impact of the publications) increases with the quality of the researcher and firm, and with the affinity in the partners' preferences. The collaboration with firms increases the quality of the project only when the firms' characteristics make them valuable partners

Collusion through Joint R&D: An Empirical Assessment

This paper tests whether upstream R&D cooperation leads to downstream collusion. We consider an oligopolistic setting where firms enter in research joint ventures (RJVs) to lower production costs or coordinate on collusion in the product market. We show that a sufficient condition for identifying collusive behavior is a decline in the market share of RJV-participating firms, which is also necessary and sufficient for a decrease in consumer welfare. Using information from the US National Cooperation Research Act, we estimate a market share equation correcting for the endogeneity of RJV participation and R&D expenditures. We find robust evidence that large networks between direct competitors – created through firms being members in several RJVs at the same time – are conducive to collusive outcomes in the product market which reduce consumer welfare. By contrast, RJVs among non-competitors are efficiency enhancing

Conceptual Model Interoperability: a Metamodel-driven Approach

Linking, integrating, or converting conceptual data models represented in different modelling languages is a common aspect in the design and maintenance of complex information systems. While such languages seem similar, they are known to be distinct and no unifying framework exists that respects all of their language features in either model transformations or inter-model assertions to relate them. We aim to address this issue using an approach where the rules are enhanced with a logic-based metamodel. We present the main approach and some essential metamodel-driven rules for the static, structural, components of ER, EER, UML v2.4.1, ORM, and ORM2. The transformations for model elements and patterns are used with the metamodel to verify correctness of inter-model assertions across models in different languages

Fishing for complementarities : competitive research funding and research productivity

This paper empirically investigates complementarities between different sources of research funding with regard to academic publishing. We find for a sample of UK engineering academics that competitive funding is associated with an increase in ex-post publications but that industry funding decreases the marginal utility of public funding by lowering the publication and citation rate increases associated with public grants. However, when holding all other explanatory variables at their mean, the negative effect of the interaction does not translate into an effective decrease in publication and citation numbers. The paper also shows that the positive effect of public funding is driven by UK research council and charity grants and that EU funding has no significant effect on publication outcomes

Design Characteristics Influence Performance of Clinical Prediction Rules in Validation: A Meta-Epidemiological Study

BACKGROUND: Many new clinical prediction rules are derived and validated. But the design and reporting quality of clinical prediction research has been less than optimal. We aimed to assess whether design characteristics of validation studies were associated with the overestimation of clinical prediction rules' performance. We also aimed to evaluate whether validation studies clearly reported important methodological characteristics. METHODS: Electronic databases were searched for systematic reviews of clinical prediction rule studies published between 2006 and 2010. Data were extracted from the eligible validation studies included in the systematic reviews. A meta-analytic meta-epidemiological approach was used to assess the influence of design characteristics on predictive performance. From each validation study, it was assessed whether 7 design and 7 reporting characteristics were properly described. RESULTS: A total of 287 validation studies of clinical prediction rule were collected from 15 systematic reviews (31 meta-analyses). Validation studies using case-control design produced a summary diagnostic odds ratio (DOR) 2.2 times (95% CI: 1.2-4.3) larger than validation studies using cohort design and unclear design. When differential verification was used, the summary DOR was overestimated by twofold (95% CI: 1.2 -3.1) compared to complete, partial and unclear verification. The summary RDOR of validation studies with inadequate sample size was 1.9 (95% CI: 1.2 -3.1) compared to studies with adequate sample size. Study site, reliability, and clinical prediction rule was adequately described in 10.1%, 9.4%, and 7.0% of validation studies respectively. CONCLUSION: Validation studies with design shortcomings may overestimate the performance of clinical prediction rules. The quality of reporting among studies validating clinical prediction rules needs to be improved