Summary of activities and research findings. This study analyses empirically the effects of import competition on firm productivity (TFPQ), using administrative firm-level panel data from German manufacturing. We find that only import competition from high-income countries is associated with positive incentives for firms to invest in productivity improvement, whereas import competition from middle- and low-income countries is not.
Summary of activities and research findings. 1. Summary of INTENS project on EuroStemCell xxxx://xxx.xxxxxxxxxxxx.xxx/our-supporting-ec-consortia
Summary of activities and research findings. We find that automation has political effects on aggregate election returns at the district- level, leading to a tilt in favour of radical-right and nationalist parties promoting an anti- cosmopolitan agenda. Consistently, the individual-level analysis shows that individuals that are more exposed to automation are substantially more likely to vote for radical-right parties, and tend to support parties with more nationalist platforms.
Summary of activities and research findings. 3.1 Experimental fluid-rock studies of the Cornwall Geothermal site
Summary of activities and research findings. 3.1. Porous phases and corresponding pore types Gas transport modelling requires a complete understanding of the pore system in shale reservoirs (Xxxxxxx et al., 2012; Mines, 2011) and the multi-scale imaging approach presented here allows such an understanding. Based on the pore size quantification, gas flow could be comprised of a combination of flows, as found in other materials with multiple scales of porosity (Tariq et al., 2011b), with Xxxxxxx flow in the finest pores, transitioning to Xxxxx flow in the larger pores and perhaps even slip flow in the larger cracks. Pores in shales are associated with organic matter, mineral grains, and the phyllosilicate mineral- dominated matrix, which we define as porous phases (Figure 3 A). The distribution of pores sizes, their connectivity and the chemical composition of phase containing the porosity all affect the flow behaviour. A Haynesville sample was chosen as a typical shale example. Pore systems were quantified at the microscale to nanoscale. From two FIB-SEM imaging datasets, a total of over 15,000 pores were identified, segmented and categorized into four pore types. Imaged pores were categorized into two groups based on their occurrence: organic-associated pores and mineral-associated pores. Based on the relationship of each pore to its surrounding shale components (Figure 3), the pore can be further classified into the following types: intra-organic pores (pore type I), organic mineral interface pores (type II), inter-mineral pores (type III), and intra-mineral pores (type IV). In the nanoscale datasets, the size, frequency, volume, and surface area of each pore type were quantified. The following section outlines pore types and their properties.
Summary of activities and research findings. We have introduced intangible assets in a property rights model of sequential supply chains. In the resulting model firms transmit knowledge to their suppliers to facilitate inputs' customization, but they must protect the transmitted intangibles to avoid knowledge dissipation. Protection is costly and depends on both inputs' knowledge intensity and the quality of institutions protecting intellectual property rights (IPR) in suppliers' locations. Our model predicts that, when inputs' knowledge intensity increases (decreases) downstream and suppliers' investments are complements, the probability of integrating a randomly selected input is decreasing (increasing) in IPR quality and increasing (decreasing) in the relative knowledge intensity of downstream inputs. It yields opposite but weaker predictions when suppliers' investments are substitutes. We have tested and found empirical support for our model's predictions through probit regressions exploiting comprehensive data on the population of Slovenian firms from 2007 Deliverable D2.4 - The role of intangibles in organisational choices in GVCs Version 1.0 to 2010. In doing so, we have merged transaction-level trade data on firms with their outward cross-border direct investment and financial data. The firm's decision to integrate an input is estimated at the firm-country-product level.
Summary of activities and research findings. In the following sections, we present the application of the methodology described in Section 2 to a European case study and then the models ability to quantify the seismic hazard with sub-surface fluid injections is assessed.
Summary of activities and research findings. This report is a desktop research study covering European shale gas basins. The findings are meant as a guide to outline potential shale gas regions and provide pressure and temperature estimates and measurements for depths that fall within the range of shale gas plays. The pressure-temperature conditions reflect present day conditions and are not maximum burial and maturity conditions experienced by the shale gas plays. The prospective areas shown in Figure 1 correlate with the thermal maturity range of the gas window (Ro = 0.9 to 3.0 %) that each basin experienced preserved through its geological history, as well as an economic cut-off of greater than 2% average TOC for shale plays with a thickness greater than 100 feet (approximately > 30 m). The TOC and thermal maturity data from published reports reflect borehole samples analysed within the prospective regions. These are extrapolated together with geophysical studies (where available) to show the extent of potential shale gas plays (coloured areas in Figure 1). Pressure and temperature data in the Halliburton rows (Table 1) are downhole measurements from samples with gas potential and are a reference of comparison to standard condition estimates. It is important to note that the complete range and heterogeneity of each individual basin is not represented in this report, but we have researched the broad available literature in order to summarise representative values and ranges for each region. We emphasise that the findings of this report are to be used as a starting point and guide for further research of the individual basins of interest. Table 1: Summary of European shale gas plays with present day temperature and pressure estimates and measurements for selected depths within play range. Table 1: continued.
Summary of activities and research findings. Given the relevance of light hydrocarbons to the ShaleX project and the availability of experimental data, the adsorption of such molecules in a carbon molecular sieve (MSC5A) was selected to be studied. The MSC5A can be modeled as a cylindrical pore. The pore geometry parameters are available in reference [3].
Summary of activities and research findings. 16S Analysis: Alpha Diversity A diversity index is a mathematical measure of species diversity in a community. Xxxxxxx'x index accounts for abundance and evenness of the species present. This is given by the equation: H = Σ[(pi) x ln(pi)] where pi = proportion of total sample represented by species i, obtained by dividing the number of individuals of species i by total number of samples. In this study, alpha diversity has been estimated using the Shannon’s index. Species richness is given by the number of species, S. Accordingly, the maximum diversity possible is given by Hmax=ln(S) while the evenness is obtained as: E = H/Hmax A drop in alpha diversity can represent a change in community structure where certain microbial members become more abundant while others diminish in relative proportion. This can result from microbial dynamics and/or environmental disturbance. The samples from Carbfix1 show a natural fluctuation in alpha diversity (Fig. 3) typical of a microbial community, with slightly higher diversity seen in pre-injection samples (excluding HK12, a shallow well that does not experience gas injection). Figure 3: Alpha diversity measured by the Xxxxxxx Index 16S Analysis: Beta diversity and Taxonomy Non-metric multidimensional scaling (NMDS) analysis is a method for visualizing the level of similarity between samples in a dataset. Multidimensional scaling creates an ordination plot using a similarity metric (Xxxx-Xxxxxx), using dimension reduction to plot multi-dimensional data in two-dimensional space, with more similar samples clustering together. Plots were colored by well origin and well depth. The results from this analysis show that samples appeared to cluster by sample origin (Fig. 4a) as well as depth (Fig. 4b). Investigation of the taxonomic diversity reveals distinct communities of microorganisms in each of the xxxxx which may be driving this clustering pattern (Fig. 5). Stress=0.22
