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CONTEXT: Crop models are essential tools for assessing the impact of climate change on national or regional agricultural production. Starting from meteorology, soil and crop management, fertilization and irrigation practices, they predict the yield of specific crop varieties. For long term assessments, climate models are the source of primary information. To make climate model results usable in a specific time frame context, bias adjustment (BA) is required. In fact, climate models tend to deviate from day-to-day values of the physical parameters while conserving the climate variability signal. BA brings the climatic signal to the actual values observed in a specific location and period, and to be representative of a specific period in absolute terms. BA techniques come in different flavours. The broadest categorization is univariate and multivariate methods. Multivariate methods adjust the variables considering possible cross-correlations while univariate methods treat the variables one by one without accounting for possible dependence on one another. OBJECTIVE: The hypothesis tested in this paper is that since crop models require as input climate variables that are in most of the cases cross-correlated, the multi-variate bias adjustment of the latter is likely to improve performance compared to univariate bias adjusted climate model results. METHODS: To verify this hypothesis, 14 BA methods were applied to 9 variables from 8 climate models at 21 locations across Europe and Northern Africa for a period of 5 years. Twelve crop models, from the AgMIP Wheat community, were run using the climate model results. All crop models, except one, were restarted at every growing season. The crop models were also run using the AgMERRA re-analysis. The latter were used as reference to compare the results when using the other climate models treated with the various sets of bias-adjustment methods. RESULTS AND CONCLUSIONS: The results show that multivariate BA treatment should be p

Despite use of blended cements containing significant amounts of aluminum for over 30 years, the structural nature of aluminum in the main hydration product, calcium aluminate silicate hydrate (C-A-S-H), remains elusive. Using first-principles calculations, we predict that aluminum is incorporated into the bridging sites of the linear silicate chains and that at high Ca:Si and H2O ratios, the stable coordination number of aluminum is six. Specifically, we predict that silicate-bridging [AlO2(OH)(4)](5-) complexes are favored, stabilized by hydroxyl ligands and charge balancing calcium ions in the interlayer space. This structure is then confirmed experimentally by one- and two-dimensional dynamic nuclear polarization enhanced Al-27 and Si-29 solid-state NMR experiments. We notably assign a narrow Al-27 NMR signal at 5 ppm to the silicate-bridging [AlO2(OH)(4)](5-) sites and show that this signal correlates to Si-29 NMR signals from silicates in C-A-S-H, conflicting with its conventional assignment to a "third aluminate hydrate" (T ) phase. We therefore conclude that TAH does not exist. This resolves a long-standing dilemma about the location and nature of the six-fold-coordinated aluminum observed by Al-27 NMR in C-A-S-H samples.

A decomposition analysis of energy related CO2 emissions is carried out for 33 world countries. The data pertain to the period 1995–2007. The methodology used is the Index Decomposition Analysis that allows to investigate the contribution of the following factors: (i) changes in abatement technologies, fuel quality and fuel switching; (ii) changes in the structure and efficiency of the energy system; (iii) relative ranking of a country in terms of the total GDP (Gross Domestic Product) generation and (iv) changes of the country specific total economic activity. The WIOD (World Input Output Database) has been used together with Organization for the Economic Co-operation and Development (OECD) data on GDP. Results show that economic growth has been the main driving factor of energy related CO2 emissions increase. However, in fast developing countries like India and China, an important contribution has also been the increasing role that these economies are playing in the global economic panorama. Improvements on energy efficiency have been the main element contributing to reduce the overall CO2 emission increase in all the countries considered in this study.

Purpose – The purpose of this paper is to use multidimensional approach to provide a well-being description across European regions. Design/methodology/approach – By considering the set of socioeconomic indicators provided by Eurostat for the EU 266 NUTS-2 regions, three main analyses have been performed for the year 2009: first, the “ideal point” technique has been used to identify: the best EU performances; the number and type of indicators that needs to be improved in every European regions. Second, a map of well-being has been elaborated to provide a picture summarizing the regional values in comparison to the European average. Third, Gini coefficient has been calculated to identify the indicators performing the largest inequalities across EU. The method presented in this paper is suitable to be complemented with subjective ranking of values and preference, making the proposed methodology useful to investigate well-being in a national, regional or individual scale. Findings – By providing a multidimensional description of well-being across the 266 EU regions, the present paper identifies and maps the existing differences on socioeconomic performance. Originality/value – The results provided can be useful to design policies oriented to reduce inequalities and to promote socioeconomic and environmental convergences across European regions. As far as the authors know, this is the first paper that provides a map of regional socioeconomic well-being across Europe.