The evaluation of research infrastructures: a cost benefit analysis framework

When decision-makers consider pure and applied research infrastructures, such as genomics platforms, astronomic observatories, nanoelectronic laboratories, oceanographic vessels, or particle accelerator facilities (just to mention some examples) are faced by this question: what is the net social benefit of these costly scientific ventures and of the public goods they produce? The answer is often given qualitatively, or even rhetorically, by scientists and other stakeholders in these projects. But can we go beyond anecdotal evidence, narratives and ad hoc studies and try a structured ex-ante and ex-post evaluation of the socio-economic impact of research infrastructures? This paper explores some of the methodological issues involved in a CBA framework for capital-intensive scientific projects. The paper proposes a conceptual model based on the estimation of quantities and shadow prices of cost aggregates, and of six main categories of economic benefits (pure value of discovery, knowledge outputs, technological spillovers, human capital formation, cultural effects and services to third parties). Empirical approaches are suggested for further applied research, including the use of probability distribution functions to generate expected net present values of research infrastructures by Monte Carlo methods

Forecasting the socio-economic impact of the Large Hadron Collider : A cost-benefit analysis to 2025 and beyond

In this paper we develop a cost-benefit analysis of a major research infrastructure, the Large Hadron Collider (LHC), the highest-energy accelerator in the world, currently operating at CERN. We show that the evaluation of benefits can be made quantitative by estimating their welfare effects on different types of agents. Four classes of direct benefits are identified, according to the main social groups involved: (a) scientists; (b) students and young researchers; (c) firms in the procurement chain and other organizations; and (d) the general public, including onsite and website visitors and other media users. These benefits are respectively related to the knowledge output of scientists; human capital formation; technological spillovers; and direct cultural effects for the general public. Welfare effects for taxpayers can also be estimated by the contingent valuation of the willingness to pay for a pure public good for which there is no specific direct use (i.e., as non-use value). Using a Monte Carlo approach, we estimate the conditional probability distribution of costs and benefits for the LHC from 1993 until its planned decommissioning in 2025, assuming a range of values for some critical stochastic variables. We conservatively estimate that there is around a 90% probability that benefits exceed costs, with an expected net present value of about 2.9 billion euro, not considering the unpredictable applications of scientific discovery

Evaluating business support measures : the Bayesian network approach

Traditional methods to the evaluation of business support consider the firm as a \u2018black box\u2019 and the main interest is to see to what extent a policy has produced the intended effects. The causal mechanisms that explain how certain effects are generated are not discovered. In this article we show the applicability of Bayesian Network Analysis in combination with theory-based evaluation as a new mixed-method approach to reveal the mechanisms, both expected and unknown, which explain the changes in a firm\u2019s behaviour and economic performance due to public support. By combining graphical map analysis with statistical analysis, Bayesian Network Analysis reveals the interdependences between different drivers of change in firms so as to either confirm, deny or better qualify the theory of change of the policy

The role of EU policy in supporting technological innovation in SMES : a Bayesian network analysis of firm-level data from Poland

We study the effectiveness of the \u2018Technological Credit\u2019 (TC) instrument in supporting innovation in Polish SMEs. Our research question is: to what extent does providing credit to SMEs tied to technological investment affect capital expenditure and how does this change the innovativeness of firms? So far, the evidence on the impact of this policy instrument is unsystematic. To answer the question, we use an approach which is novel in innovation policy studies: we perform a Bayesian Network Analysis of survey data. Our data include a unique sample of 200 Polish firms that received TC support during the 2007-2013 programming period. Our findings confirm short-term positive effects (i.e. a wider range of products/services offered and increased sales and exports) and we also have many interesting results related to behavioural changes in firms (which are not necessarily quantifiable economically). We also find that only more financially solid and more internationalized firms were able to take advantage of the policy. These findings suggest that schemes based on technological credits are not appropriate for promoting innovation in all types of SME and should be designed to shift the technological frontier rather than to sustain a catching up process for firms lagging behind the frontier

Pharmaceutical removal from different water matrixes by Fenton process at near-neutral pH: Doehlert design and transformation products identification by UHPLC-QTOF MS using a purpose-built database

This work evaluates the Fenton process in the removal of eight pharmaceuticals (gemfibrozil, nimesulide, furosemide, paracetamol, propranolol, dipyrone, fluoxetine, and diazepam), present at an initial concentration of 500 μg L−1 for each compound, from three different water matrixes (distilled water, simulated wastewater, and hospital wastewater). The Fenton process conditions (iron and hydrogen peroxide concentrations, and pH) were optimized for the distilled water matrix by Doehlert design associated with response surface methodology. These corresponded to initial Fe2+ and hydrogen peroxide concentrations of 12.5 and 533 mg L−1, respectively, and pH 5.0. Mineralization rates and pharmaceutical degradation were monitored for all water matrixes and different experimental conditions employed. Unique iron addition, using low (12.5 mg L−1) and increased concentrations (37.5 mg L−1) were evaluated. These preliminary results motivated the study of the Fenton process employing successive iron additions and using an excess of hydrogen peroxide in the reaction medium. Multiple iron addition favored higher oxidation of the initial contaminants. Finally, the most persistent transformation products generated during the Fenton process were identified by liquid chromatography coupled with quadrupole-time of flight mass spectrometry (UHPLC-QTOF MS) using a purpose-built database that allowed monitoring 97 transformation products in a single injection. In this study, 12 transformation products (TPs) were tentatively identified employing this purpose-built database. Most TPs generated were classified as of high toxicity (Cramer rules) and showed ready biodegradability (START biodegradability)

How do research intensive systems emerge in less developed areas? The case of mechatronics in the Italian southern region Apulia

Regional innovation systems (RISs) are increasingly emerging in less developed areas. Based on the case study of the mechatronic cluster in the Italian southern region, Apulia, this paper analyses how institutions have managed to promote the establishment of a RIS in disadvantaged areas. Through a longitudinal analysis over more than half a century, the Apulian case study shows that the creation of this RIS in less developed areas is the result of a dynamic process characterised by a multi-level governance, initially promoted by the central government through a top-down logic, and then integrated by action of local actors through a bottom-up approach. A key role is played by the regionalisation of innovation policies and by the strategic action of regional public institutions to promote the interaction among enterprises and universities. Our analysis highlights that several ingredients should simultaneously occur for a regional innovation policy to be effective. Any regional innovation strategy significantly lacking one of these core pre-conditions and ingredients is risky. Policy-makers should carefully study the opportunities and challenges arising from local contexts before embarking in ambitious place-based innovation strategies