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Interpreting regional differences is crucial in promoting regional and cohesion policies. The different levels of development of the regions, their various historical and geographical factors, as well as diverse political attitudes, institutional conditions, and their interactions fundamentally determine the economic convergence processes in the regions. Recent research has emphasised a pluralistic approach in which composite measures of progress and well-being are prioritised. The primary goal of the research was to create a more complex region categorisation model than the traditional gross domestic product (GDP-) based categorisation. This study analyses the changes and correlations of the Europe 2020 Strategy's smart and inclusive growth indicators at the regional level between 2009 and 2019. To create the region categorisation model, the indicators of the strategy were concentrated and then expanded with other background variables into composite indicators, thus creating the main components: 'Relative deprivation' and 'Innovation environment'. The regions were categorised along the dimensions of the new latent variables to form the outstanding, catching-up, and lagging groups, which can be generally characterised by development, catching-up, and lagging, respectively. The outstanding group typically includes the capital of the member states and the regions of their agglomeration. The catching-up and lagging groups in chiefly consist of member states that joined after 2004. The lagging group are mostly external border regions and can be considered underdeveloped along both dimensions; in addition to their below-average innovation environment, their labour market position also lags behind. The model developed in this research provides a more nuanced picture of regional development differences than the traditional GDP-based categorisation. [ABSTRACT FROM AUTHOR]

Owing to increased access to the Internet and the development of electronic commerce, e-commerce has become a common method of shopping in all countries. The purpose of this study is more precisely to research e-commerce diversity in Europe at the regional level and develop the conception of "E-commerce Supply Chain Management". Statistical data derived from the European Statistical Office were applied to analyse the spatial diversity of e-retailing. Assessments of the regional diversity of e-retailing applied geographic information systems and exploratory spatial data analysis methods such us global and local spatial autocorrelation statistics. Clusters of regions with similar household preferences related to online shopping were identified. A spatial visualisation of the e-retailing diversity phenomenon may be utilised for the reconfiguration of supply chains and to adapt them to actual household preferences related to shopping methods. [ABSTRACT FROM AUTHOR]

Rivers extend in space and time under the influence of their catchment area. Our perception largely relies on discrete spatial and temporal observations carried out at certain sites located throughout the catchment (monitoring networks, MN). However, MNs are constrained by (a) the distribution of sampling sites, (b) the dynamics of the variable considered and (c) the river hydrological conditions. In this study, all three aspects were captured and quantified by applying a spatial autocorrelation modeling approach. We exemplarily studied its application to 235 emerging contaminants (pesticides, pharmaceuticals, and personal care products [PPCP], industrial and miscellaneous) measured at 55 sampling sites in the Danube River. 22 out of the 235 compounds monitored were present at all sites and 125 were found in at least 50%.We first calculated the Moran Index (MI) to characterize the spatial autocorrelation of the compound set. 59 compounds showed MI ≤ 0, which can be interpreted as ‘no spatial correlation’. Next, spatial autocorrelation models were set for each compound. From the autocorrelation parameter ρ , catchment average correlation lengths were derived for each compound. MN optimality was examined and compounds were classified into three groups: (a) those with ρ ≤ 0 [25%]; (b) those with ρ > 0 and correl. length < average distance between consecutive sites [ 2%] and (c) those with ρ > 0 and correl. length > average distance between consecutive sites [73%]. The MN was considered optimal only for the latter class. Networks with the larger average distance between consecutive sites resulted in a decreasing number of optimally monitored compounds. Furthermore, neighbors vs . local relative contributions were quantified based on the spatial autocorrelation model for all the measured compounds. The results of this study show how autocorrelation models can aid water managers to improve the design of river MNs, which are a key aspect of the Water Framework Directive. [ABSTRACT FROM AUTHOR]

The present study illustrates an exploratory spatial data analysis (ESDA) aimed at investigating changes in the distribution of built-up areas in three southern European metropolitan regions (Barcelona, Rome and Athens). An approach based on global Moran's indexes of spatial autocorrelations was proposed to assess similarities in the spatial organization of the three regions, based on land-use data for 1960 and 2010. Compact monocentric, scattered low-density and mixed polycentric structures were compared in the three regions using local Moran's indexes computed at two different scales, 'urban' (5 km radius) and 'regional' (20 km radius). The proposed approach identifies emerging trends in scattered monocentric or polycentric development. Our results outline the trend toward scattered urban expansion for the three cities, with signs of a modest shift toward polycentrism in Barcelona. ESDA provides basic information needed for policies promoting spatially balanced, sustainable development in originally compact and economically segmented regions. [ABSTRACT FROM AUTHOR]

This paper examines lead-lag relationships between monthly index returns from 18 European industries. Several interesting and clear relationships are found that call into question the efficiency of European stock markets. While the Automobiles & Parts sector lags more than half of the other sectors, the Financial Services, Technology, and Telecommunications sectors lead many others. In particular, the leadership of the Technology sector has strengthened in recent years. [ABSTRACT FROM AUTHOR]

Assigning proper prior uncertainties for inverse modelling of CO2 is of high importance, both to regularise the otherwise ill-constrained inverse problem and to quantitatively characterise the magnitude and structure of the error between prior and "true" flux. We use surface fluxes derived from three biosphere models - VPRM, ORCHIDEE, and 5PM - and compare them against daily averaged fluxes from 53 eddy covariance sites across Europe for the year 2007 and against repeated aircraft flux measurements encompassing spatial transects. In addition we create synthetic observations using modelled fluxes instead of the observed ones to explore the potential to infer prior uncertainties from model- model residuals. To ensure the realism of the synthetic data analysis, a random measurement noise was added to the modelled tower fluxes which were used as reference. The temporal autocorrelation time for tower model-data residuals was found to be around 30 days for both VPRM and ORCHIDEE but significantly different for the 5PM model with 70 days. This difference is caused by a few sites with large biases between the data and the 5PM model. The spatial correlation of the model-data residuals for all models was found to be very short, up to few tens of kilometres but with uncertainties up to 100% of this estimation. Propagating this error structure to annual continental scale yields an uncertainty of 0.06 GtC and strongly underestimates uncertainties typically used from atmospheric inversion systems, revealing another potential source of errors. Long spatial e-folding correlation lengths up to several hundreds of kilometres were determined when synthetic data were used. Results from repeated aircraft transects in south-western France are consistent with those obtained from the tower sites in terms of spatial autocorrelation (35 km on average) while temporal autocorrelation is markedly lower (13 days). Our findings suggest that the different prior models have a common temporal error structure. Separating the analysis of the statistics for the model data residuals by seasons did not result in any significant differences of the spatial e-folding correlation lengths. [ABSTRACT FROM AUTHOR]

This study focuses on integration processes in European Research and Development ( R&D) by analyzing the spatiotemporal dimension of three different R&D collaboration networks across Europe. The studied networks cover different types of knowledge creation, namely project-based R&D networks within the European Union ( EU) Framework Programmes ( FPs), co-patent networks, and co-publication networks. Integration in European R&D-one of the main pillars of the EU Science Technology and Innovation policy-refers to the harmonization of fragmented national research systems across Europe and to the free movement of knowledge and researchers. The objective is to describe and compare spatiotemporal patterns at a regional level and to estimate the evolution of separation effects over the time period 1999-2006 that influence the probability of cross-region collaborations in the distinct networks under consideration. The study adopts a spatial interaction modeling perspective, econometrically specifying a panel generalized linear model relationship, taking into account spatial autocorrelation among flows using eigenfunction spatial filtering methods. The results show that geographical factors are a lower hurdle for R&D collaborations in the FP networks than in co-patent networks and co-publication networks. Furthermore, it is shown that the geographical integration is higher in the FP network. [ABSTRACT FROM AUTHOR]

This paper examines if the capital markets from Bulgaria, Czech Republic, Poland, Romania, Slovakia and Hungary were efficient in the weak form between January 2002 to September 2015. We use an autocorrelations test, a runs test, and variance ratios tests, performed on the daily return of the most important stock indices in the selected markets. Our results indicate that neither of our markets follows exactly the random walk model. Instead we find a partial efficiency in certain markets. While the autocorrelation test and unit root tests reject information efficiency, the runs test finds the Slovakian market efficient, while the variance tests indicate weak market efficiency in Slovakia, Hungary, Poland and Czech Republic. In the case of Bulgaria and Romania we find no evidence of market efficiency in either test. This suggest, that in all our countries the historical data could be used to predict future assets price. [ABSTRACT FROM AUTHOR]

Nearly all societies have now committed themselves to sustainable development by integrating some form of environmental quality, social equity, and economic welfare into their day-to-day activities. As such, there remains a strong political desire for the comprehensive assessment of conditions that evaluate the current status, measure progress, and help set future development goals. Indicators have been nominated as universal tools for progressing sustainable development across scales; however, there remains no consensus regarding the best approach to their design or use. While several studies have investigated the associations between indicators of sustainable development, few have directly addressed the question of how multiple measures can be used simultaneously to assess sustainability regionally. Building upon previous studies, this paper presents a quantitative and spatial assessment of 25 multi-metric indices across 36 European nations. The goals of this research were (1) to increase understanding of indicator complexity and (2) provide an applied example of their simultaneous use for regional assessment. Global Moran’sI-test and Pearson’s correlation coefficient (r) analysis were used to test spatial autocorrelation and multicollinearity, respectively. From the 25 composite indices, an overall rank was also provided for each country. Lastly, Ward’s cluster analysis was used to create country bundles of similarity. Our findings revealed that environmental performance index, global information networking institute coefficient, and happy planet index were numerically and spatially random. Cluster analysis revealed a four-bundle solution, while Norway, Switzerland, and Sweden ranked highest. This approach shows promise for systematically describing, visualizing, and monitoring sustainable development at the continental scale. [ABSTRACT FROM AUTHOR]