Urban agriculture: a global analysis of the space constraint to meet urban vegetable demand

Urban agriculture (UA) has been drawing a lot of attention recently for several reasons: the majority of the world population has shifted from living in rural to urban areas; the environmental impact of agriculture is a matter of rising concern; and food insecurity, especially the accessibility of food, remains a major challenge. UA has often been proposed as a solution to some of these issues, for example by producing food in places where population density is highest, reducing transportation costs, connecting people directly to food systems and using urban areas efficiently. However, to date no study has examined how much food could actually be produced in urban areas at the global scale. Here we use a simple approach, based on different global-scale datasets, to assess to what extent UA is constrained by the existing amount of urban space. Our results suggest that UA would require roughly one third of the total global urban area to meet the global vegetable consumption of urban dwellers. This estimate does not consider how much urban area may actually be suitable and available for UA, which likely varies substantially around the world and according to the type of UA performed. Further, this global average value masks variations of more than two orders of magnitude among individual countries. The variations in the space required across countries derive mostly from variations in urban population density, and much less from variations in yields or per capita consumption. Overall, the space required is regrettably the highest where UA is most needed, i.e., in more food insecure countries. We also show that smaller urban clusters (i.e., <100 km2 each) together represent about two thirds of the global urban extent; thus UA discourse and policies should not focus on large cities exclusively, but should also target smaller urban areas that offer the greatest potential in terms of physical space

The Wage and Non-wage Costs of Displacement: Evidence from Russia

This paper is the first to analyze the costs of job loss in Russia, using unique new data from the Russian Longitudinal Monitoring Survey over the years 2003-2008, including a special supplement on displacement that was initiated by us. We employ fixed effects regression models and propensity score matching techniques in order to establish the causal effect of displacement for displaced individuals. The paper is innovative insofar as we investigate fringe and in-kind benefits and the propensity to have an informal employment relationship as well as a permanent contract as relevant labor market outcomes upon displacement. We also analyze monthly earnings, hourly wages, employment and hours worked, which are traditionally investigated in the literature. Compared to the control group of non-displaced workers (i.e. stayers and quitters), displaced individuals face a significant income loss following displacement, which is mainly due to the reduction in employment and hours worked. This effect is robust to the definition of displacement. The losses seem to be more pronounced and are especially large for older workers with labor market experience and human capital acquired in Soviet times and for workers with primary and secondary education. Workers displaced from state firms experience particularly large relative losses in the short run, while such losses for workers laid off from private firms are more persistent. Turning to the additional non-conventional labor market outcomes, there is a loss in terms of the number of fringe and in-kind benefits for reemployed individuals but not in terms of their value. There is also some evidence of an increased probability of working in informal jobs if displaced. These results point towards the importance of both firm-specific human capital and of obsolete skills obtained under the centrally planned economy as well as to a wider occurrence of job insecurity among displaced workers

Public opinion and environmental policy output: a cross-national analysis of energy policies in Europe

This article studies how public opinion is associated with the introduction of renewable energy policies in Europe. While research increasingly seeks to model the link between public opinion and environmental policies, the empirical evidence is largely based on a single case: the US. This limits the generalizability of findings and we argue accordingly for a systematic, quantitative study of how public opinion drives environmental policies in another context. Theoretically, we combine arguments behind the political survival of democratic leaders with electoral success and environmental politics. Ultimately, we suggest that office-seeking leaders introduce policies that seem favorable to the domestic audience; if the public prefers environmental protection, the government introduces such policies in turn. The main contribution of this research is the cross-country empirical analysis, where we combine data on the public's environmental attitudes and renewable energy policy outputs in a European context between 1974 and 2015. We show that as public opinion shifts towards prioritizing the environment, there is a significant and positive effect on the rate of renewable energy policy outputs by governments in Europe. To our knowledge, this is the first systematic, quantitative study of public opinion and environmental policies across a large set of countries, and we demonstrate that the mechanisms behind the introduction of renewable energy policies follow major trends across European states

Evolving cohesion metrics of a research network on rare diseases: a longitudinal study over 14 years

[EN] Research collaboration is necessary, rewarding, and beneficial. Cohesion between team members is related to their collective efficiency. To assess collaboration processes and their eventual outcomes, agencies need innovative methods-and social network approaches are emerging as a useful analytical tool. We identified the research output and citation data of a network of 61 research groups formally engaged in publishing rare disease research between 2000 and 2013. We drew the collaboration networks for each year and computed the global and local measures throughout the period. Although global network measures remained steady over the whole period, the local and subgroup metrics revealed a growing cohesion between the teams. Transitivity and density showed little or no variation throughout the period. In contrast the following points indicated an evolution towards greater network cohesion: the emergence of a giant component (which grew from just 30 % to reach 85 % of groups); the decreasing number of communities (following a tripling in the average number of members); the growing number of fully connected subgroups; and increasing average strength. Moreover, assortativity measures reveal that, after an initial period where subject affinity and a common geographical location played some role in favouring the connection between groups, the collaboration was driven in the final stages by other factors and complementarities. The Spanish research network on rare diseases has evolved towards a growing cohesion-as revealed by local and subgroup metrics following social network analysis.The Spanish Ministry of Economics and Competitiveness partially supported this research (Grant Number ECO2014-59381-R).Benito Amat, C.; Perruchas, F. (2016). Evolving cohesion metrics of a research network on rare diseases: a longitudinal study over 14 years. Scientometrics. 108(1):41-56. https://doi.org/10.1007/s11192-016-1952-zS41561081Aymé, S., & Schmidtke, J. (2007). Networking for rare diseases: A necessity for Europe. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz, 50(12), 1477–1483. doi: 10.1007/s00103-007-0381-9 .Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3–4), 590–614. doi: 10.1016/S0378-4371(02)00736-7 .Bettencourt, L. M. A., Kaiser, D. I., & Kaur, J. (2009). Scientific discovery and topological transitions in collaboration networks. Journal of Informetrics, 3(3), 210–221. doi: 10.1016/j.joi.2009.03.001 .Bian, J., Xie, M., Topaloglu, U., Hudson, T., Eswaran, H., & Hogan, W. (2014). Social network analysis of biomedical research collaboration networks in a CTSA institution. Journal of Biomedical Informatics, 52, 130–140. doi: 10.1016/j.jbi.2014.01.015 .Bordons, M., Aparicio, J., González-Albo, B., & Díaz-Faes, A. A. (2015). The relationship between the research performance of scientists and their position in co-authorship networks in three fields. Journal of Informetrics, 9(1), 135–144. doi: 10.1016/j.joi.2014.12.001 .Börner, K., Dall’Asta, L., Ke, W., & Vespignani, A. (2005). Studying the emerging global brain: Analyzing and visualizing the impact of co-authorship teams. Complexity, 10(4), 57–67. doi: 10.1002/cplx.20078 .Casey-Campbell, M., & Martens, M. L. (2009). Sticking it all together: A critical assessment of the group cohesion–performance literature. International Journal of Management Reviews, 11(2), 223–246. doi: 10.1111/j.1468-2370.2008.00239.x .Chiocchio, F., & Essiembre, H. (2009). Cohesion and performance: A meta-analytic review of disparities between project teams, Production teams, and service teams. Small group research, 40(4), 382–420. doi: 10.1177/1046496409335103 .Cho, A. (2011). Particle physicists’ new extreme teams. Science, 333(6049), 1564–1567. doi: 10.1126/science.333.6049.1564 .Cooke, N. J., & Hilton, M. L. (2015). Enhancing the effectiveness of team science. Washington, D.C.: National Academies Press. Recuperado a partir de http://www.nap.edu/catalog/19007/enhancing-the-effectiveness-of-team-science .Cugmas, M., Ferligoj, A., & Kronegger, L. (2015). The stability of co-authorship structures. Scientometrics, 106(1), 163–186. doi: 10.1007/s11192-015-1790-4 .Estrada, E. (2011). The structure of complex networks: Theory and applications. Oxford: University Press.Gallivan, M., & Ahuja, M. (2015). Co-authorship, homophily, and scholarly influence in information systems research. Journal of the Association for Information Systems, 16(12), 980.Ghosh, J., Kshitij, A., & Kadyan, S. (2014). Functional information characteristics of large-scale research collaboration: Network measures and implications. Scientometrics, 102(2), 1207–1239. doi: 10.1007/s11192-014-1475-4 .Heymann, S. (2014). Gephi. In R. Alhajj & J. Rokne (Eds.), Encyclopedia of social network analysis and mining (pp. 612–625). New York: Springer.Himmelstein, D. S., & Powell, K. (2016). Analysis for “the history of publishing delays” blog post v1.0. Zenodo,. doi: 10.5281/zenodo.45516 .Hunt, J. D., Whipple, E. C., & McGowan, J. J. (2012). Use of social network analysis tools to validate a resources infrastructure for interinstitutional translational research: A case study. Journal of the Medical Library Association, 100(1), 48–54. doi: 10.3163/1536-5050.100.1.009 .Kolaczyk, E. D., & Csardi, G. (2014). Statistical analysis of network data with R (Vol. 65). New York: Springer.Kumar, S. (2015). Efficacy of a giant component in co-authorship networks: Evidence from a Southeast Asian dataset in economics. Aslib Journal of Information Management, 68(1), 19–32. doi: 10.1108/AJIM-12-2014-0172 .Larivière, V., Gingras, Y., Sugimoto, C. R., & Tsou, A. (2015). Team size matters: Collaboration and scientific impact since 1900. Journal of the Association for Information Science and Technology, 66(7), 1323–1332. doi: 10.1002/asi.23266 .Laudel, G. (2002). What do we measure by co-authorships? Research Evaluation, 11(1), 3–15. doi: 10.3152/147154402781776961 .Liu, X., Bollen, J., Nelson, M. L., & Van de Sompel, H. (2005). Co-authorship networks in the digital library research community. Information Processing and Management, 41(6), 1462–1480. doi: 10.1016/j.ipm.2005.03.012 .Liu, P., & Xia, H. (2015). Structure and evolution of co-authorship network in an interdisciplinary research field. Scientometrics, 103(1), 101–134. doi: 10.1007/s11192-014-1525-y .Ministerio de Sanidad y Consumo. Resolución de 30 de marzo de. (2006) del Instituto de Salud Carlos III, por la que se convocan ayudas destinadas a financiar estructuras estables de investigación cooperativa, en el área de biomedicina y ciencias de la salud, en el marco de la iniciativa Ingenio 2010, programa Consolider, acciones CIBER, 83 Boletín Oficial del Estado (pp. 13770–13777).Newman, M. E. J. (2001a). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E, 64(1), 016132. doi: 10.1103/PhysRevE.64.016132 .Newman, M. E. J. (2001b). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64(1), 016131. doi: 10.1103/PhysRevE.64.016131 .Newman, M. E. J. (2003a). Mixing patterns in networks. Physical Review E, 67(2), 026126. doi: 10.1103/PhysRevE.67.026126 .Newman, M. E. J. (2003b). The structure and function of complex networks. SIAM Review, 45, 167–256.OECD. (2010). Measuring innovation: A new perspective. 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Transformations in network governance: the case of migration intermediaries

types: Article"This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Ethnic and Migration Studies on 3 February 2015 available online: http://wwww.tandfonline.com/10.1080/1369183X.2014.1003803Market liberalisation has fundamentally changed state interventions in the supply of services and supportive infrastructure across a range of public services. While this trend has been relatively well documented, there has been a dearth of research into the changing nature of state interventions in migration and mobility. Indeed the increasing presence of migration intermediaries to service the many and varied needs of migrant workers, particularly skilled migrants, remains significantly under-researched both theoretically and empirically. In providing an analysis of the location, role and changing nature of migration intermediaries, we highlight the implications of commercially-driven governance structures. In particular we suggest that the shift from government to network governance has important implications for skilled migration including: inequities in access to information regarding the process of migration and labour market integration; and, greater dependence on (largely unregulated) private intermediaries. Accordingly, we present empirical examples of migration intermediaries to illustrate their role and the relationship with and implications of their exchange with migrants

How “Humane” Is Your Endpoint?—Refining the Science-Driven Approach for Termination of Animal Studies of Chronic Infection

Public concern on issues such as animal welfare or the scientific validity and clinical value of animal research is growing, resulting in increasing regulatory demands for animal research. Abiding to the most stringent animal welfare standards, while having scientific objectives as the main priority, is often challenging. To do so, endpoints of studies involving severe, progressive diseases need to be established considering how early in the disease process the scientific objectives can be achieved. We present here experimental studies of tuberculosis (TB) in mice as a case study for an analysis of present practice and a discussion of how more refined science-based endpoints can be developed. A considerable proportion of studies in this field involve lethal stages, and the establishment of earlier, reliable indicators of disease severity will have a significant impact on animal welfare. While there is an increasing interest from scientists and industry in moving research in this direction, this is still far from being reflected in actual practice. We argue that a major limiting factor is the absence of data on biomarkers that can be used as indicators of disease severity. We discuss the possibility of complementing the widely used weight loss with other relevant biomarkers and the need for validation of these parameters as endpoints. Promotion of ethical guidelines needs to be coupled with systematic research in order to develop humane endpoints beyond the present euthanasia of moribund animals. Such research, as we propose here for chronic infection, can show the way for the development and promotion of welfare policies in other fields of research. Research on chronic infection relies heavily on the use of animals, as only the integral animal body can model the full aspect of an infection. That animals are generally made to develop a disease in infection studies exacerbates the tension between human benefit and animal well-being, which characterizes all biomedical research with animals. Scientists typically justify animal research with reference to potential human benefits, but if accepting the assumption that human benefits can offset animal suffering, it still needs to be argued that the same benefits could not be achieved with less negative effects on animal welfare. Reducing the animal welfare problems associated with research (“refinement” [1]) is therefore crucial in order to render animal-based research less of an ethical problem and to assure public trust in research. Studies that are designed to measure time of death or survival percentages present a particularly challenging situation in which at least some of the animals are made to die from the disease. These studies are frequent in experimental research on severe infections. The scientific community, industry, and regulatory authorities have responded to the ethical concerns over studies in which animals die from severe disease by developing new policies and guidelines for the implementation of humane endpoints as a key refinement measure (e.g., [2]–[4]). The most widely used definition considers a humane endpoint to be the earliest indicator in an animal experiment of severe pain, severe distress, suffering, or impending death [5], underlining that ideally such indicators should be identified before the onset of the most severe effects. Euthanizing animals, rather than awaiting their “spontaneous” death, is important to avoid unnecessary suffering in studies in which data on survival is thought to be required for scientific or legal reasons. However, several questions remain open regarding how humane endpoints are to be applied to address real animal welfare problems. We used TB experiments in mice as a case study to highlight the potential to establish biomarkers of disease progress that can replace survival time as a measure of disease severity.Fundação para a Ciência e Tecnologia (SFRH/BD/38337/2007)

Screening Estrogenic Activities of Chemicals or Mixtures In Vivo Using Transgenic (cyp19a1b-GFP) Zebrafish Embryos

The tg(cyp19a1b-GFP) transgenic zebrafish expresses GFP (green fluorescent protein) under the control of the cyp19a1b gene, encoding brain aromatase. This gene has two major characteristics: (i) it is only expressed in radial glial progenitors in the brain of fish and (ii) it is exquisitely sensitive to estrogens. Based on these properties, we demonstrate that natural or synthetic hormones (alone or in binary mixture), including androgens or progestagens, and industrial chemicals induce a concentration-dependent GFP expression in radial glial progenitors. As GFP expression can be quantified by in vivo imaging, this model presents a very powerful tool to screen and characterize compounds potentially acting as estrogen mimics either directly or after metabolization by the zebrafish embryo. This study also shows that radial glial cells that act as stem cells are direct targets for a large panel of endocrine disruptors, calling for more attention regarding the impact of environmental estrogens and/or certain pharmaceuticals on brain development. Altogether these data identify this in vivo bioassay as an interesting alternative to detect estrogen mimics in hazard and risk assessment perspective

The paradoxical role of meritocratic selection in the perpetuation of social inequalities at school

The school system is intended to offer all students the same opportunities, but most international surveys reveal an overall lower achievement for students from disadvantaged groups compared with more advantaged students. Recent experimental research in social psychology has demonstrated that schools as institutions contribute with their implicit cultural norms and structure to the production of inequalities. This chapter examines the role that a structural feature of school, namely meritocratic selection, plays in this reproduction of inequalities at school. First, we describe how meritocracy in the educational system can hold paradoxical effects by masking the virtuous/vicious cycles of opportunities created by educational institutions. Second, we present recent research suggesting that selection practices relying on a meritocratic principle—more than other practices—can lead to biased academic decisions hindering disadvantaged students. We propose that inequalities in school might not just result from isolated failures in an otherwise functional meritocratic system, but rather that merit-based selection itself contributes to the perpetuation of inequalities at school