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Journal of Economic Inequality, accepted

This dataset and codebook correspond to the second round of survey data gathered in Denmark in 2023, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. The first round of the survey, consisted of recruiting a representative sample of approximately 2000 households in each country. In this second survey round, we recruit around 500 respondents from the initial survey round, ensuring representativity is maintained. This survey is very similar to the survey in the first round and includes a lot of identical items, including a quantitative assessment of the carbon footprint in the housing, mobility, and diet sectors, socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort. New for this second round, we have incorporated questions regarding the measures respondents adopted in response to the 2022 energy crisis.

Fashion products made from repurposed materials (e.g., backpacks made from pineapple leaves) have become more prevalent nowadays, and their environmental sustainability is one of the core advantages. Yet, it is currently unclear how consumers respond to products made from repurposed materials. We conducted three experiments to examine the effects of three material features, namely function, sustainability, and distinguishability, on consumer preferences for fashion products made from repurposed materials. The results indicate that, when the function of repurposed materials is as good as that of conventional materials, consumers prefer a product made from repurposed materials over the same product made from conventional materials. Also, consumers in general prefer repurposed materials to be less visually distinguishable. Finally, when the sustainability of the repurposed products is emphasized, consumers appear more likely to choose products made from repurposed materials, even when these products have an inferior function. In conclusion, to promote fashion products made from repurposed materials, marketers may emphasize the function and sustainability of repurposed materials, and producers may manufacture repurposed materials that visually resemble conventional materials.

This database includes more than 100 decarbonisation innovations in Paper, Plastic, Steel and Meat & Dairy sectors, across their value chains, as well as in Finance. For each innovation there is a description, information about its contribution to decarbonisation, actors and collaborators involved, sources of funding, drivers, (co)benefits and disadvantages. More information on the method for selecting innovations for the database is available here. The database was created as part of REINVENT – a Horizon 2020 research project funded by the European Commission (grant agreement 730053). REINVENT involves five research institutions from four countries: Lund University (Sweden), Durham University (United Kingdom), Wuppertal Institute (Germany), PBL Netherlands Environmental Assessment Agency (the Netherlands) and Utrecht University (the Netherlands). More information can be found on our website: www.reinvent-project.eu.

Origin of the data: Experimental spectroscopic measurements Data Type: experimental measurements, open access supporting information The data are in CSV, DSW and FBSW format. Supporting information are supplied in PDF format. Data generated by instruments: Varian Cary 5E-UV-Vis-NIR spectrophotometer for UV-Vis measurements, Varian Cary Eclipse fluorescence spectrophotomer for fluorescence quenching measurements. Analytical and procedural information: Stern-Volmer fluorescence quenching experiments, UV-Vis measurements and Fluorescent Quantum Yield determination via ferrioxalate actinometry. Definition of variables: Wavelength, Absorbance, Concentration Units of measurement: nanometers (nm), moles-per-litre (mol/l) Abbreviations: File names and data headers use the following abbreviations: FQY refers to Fluorescence Quantum Yield determination experiments Light refers to irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information. Dark refers to non-irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information. SVQuench refers to Stern-Volmer quenching experiments RAxx refer to measurements related to allylbenzene. Xx is the amount of quencher in mol/l (05 should be intended as 0.5 mol/l and so on). RTxx refer to measurements related to S-(4-methylphenyl) 4-methylbenzenethiosulfonate. Xx is the amount of quencher in mol/l as above. RExx refer to measurements related to 1,2-dimethoxy-4-(prop-2-en-1-yl)benzene. Xx is the amount of quencher in mol/l as above. RSxx refer to measurements related to styrene. Xx is the amount of quencher in mol/l. RTFxx refer to measurements related to S-(4-fluorophenyl) 4-fluorobenzenethiosulfonate. Xx is the amount of quencher in mol/l as above. MesAcrMe Xx refers to data related to catalyst 9-mesityl-10-methylacridinium. Xx is the amount of catalyst in mol/l as above. DMC for measurements employing dimethylcarbonate as solvent. ACN for measurements employing acetonitrile as solvent. FBSW and DSW data are used by the proprietary software of the Varian spectrometers (CARY WinUV and Cary Eclipse). Information can be found at https://www.agilent.com/en/product/molecular-spectroscopy/uv-vis-uv-vis-nir-spectroscopy/uv-vis-uv-vis-nir-software/cary-winuv-software and https://www.agilent.com/en/product/molecular-spectroscopy/fluorescence-spectroscopy/fluorescence-software/cary-eclipse-software

Background and Aims: This study aims at evaluating the ease of use of the new calorimeter for the measurement of energy expenditure (EE) in intensive care unit (ICU) patients. EE in ICU patients is highly variable depending on the severity of the disease and treatments. Clinicians need to measure EE by indirect calorimetry (IC) to optimize nutritional support for the better clinical outcome. However, indirect calorimeters available on the market have insufficient accuracy for clinical and research use. Difficulties of handling and interpretation of results often limit IC in ICU patients. An accurate, easy-to-use calorimeter has been developed to meet these needs. The Study Device: The new calorimeter (Quark RMR 2.0, COSMED) is capable of IC measurements in mechanically ventilated patients without warm-up and limited calibration. The disposable in-line pneumotach flow meter and direct sampling of respiratory gas from the ventilator circuit enables the accurate measurement of oxygen consumption volume (VO2) and CO2 production volume (VCO2) to derive the energy expenditure. The software interface to manage the device and the collected data provides easy-to-use, user-friendly interface. This calorimeter bears an European Commission (EC) Conformity Mark, and will be used in the way it is intended to be used as described in the instruction manual. Currently used indirect calorimeters at each study center will be used as the comparator. This study will evaluate the ease of use of the new calorimeter (Quark RMR 2.0 (COSMED, Italy)) in intensive care unit (ICU) patients compared to currently used calorimeters (i.e. Quark RMR 1.0(COSMED, Italy) or Deltatrac Metabolic Monitor (Datex, Finland)), as well as the stability and the feasibility of the measurements in various clinically relevant situations. Time needed to prepare and start indirect calorimetry (IC) measurement will be compared as the measure of the ease of use of the calorimeter.

Abstract Background The ascomycete fungus Podospora anserina has been appreciated for its targeted carbohydrate-active enzymatic arsenal. As a late colonizer of herbivorous dung, the fungus acts specifically on the more recalcitrant fraction of lignocellulose and this lignin-rich biotope might have resulted in the evolution of ligninolytic activities. However, the lignin-degrading abilities of the fungus have not been demonstrated by chemical analyses at the molecular level and are, thus far, solely based on genome and secretome predictions. To evaluate whether P. anserina might provide a novel source of lignin-active enzymes to tap into for potential biotechnological applications, we comprehensively mapped wheat straw lignin during fungal growth and characterized the fungal secretome. Results Quantitative 13C lignin internal standard py-GC–MS analysis showed substantial lignin removal during the 7 days of fungal growth (24% w/w), though carbohydrates were preferably targeted (58% w/w removal). Structural characterization of residual lignin by using py-GC–MS and HSQC NMR analyses demonstrated that Cα-oxidized substructures significantly increased through fungal action, while intact β-O-4′ aryl ether linkages, p-coumarate and ferulate moieties decreased, albeit to lesser extents than observed for the action of basidiomycetes. Proteomic analysis indicated that the presence of lignin induced considerable changes in the secretome of P. anserina. This was particularly reflected in a strong reduction of cellulases and galactomannanases, while H2O2-producing enzymes clearly increased. The latter enzymes, together with laccases, were likely involved in the observed ligninolysis. Conclusions For the first time, we provide unambiguous evidence for the ligninolytic activity of the ascomycete fungus P. anserina and expand the view on its enzymatic repertoire beyond carbohydrate degradation. Our results can be of significance for the development of biological lignin conversion technologies by contributing to the quest for novel lignin-active enzymes and organisms. Background The ascomycete fungus Podospora anserina has been appreciated for its targeted carbohydrate-active enzymatic arsenal. As a late colonizer of herbivorous dung, the fungus acts specifically on the more recalcitrant fraction of lignocellulose and this lignin-rich biotope might have resulted in the evolution of ligninolytic activities. However, the lignin-degrading abilities of the fungus have not been demonstrated by chemical analyses at the molecular level and are, thus far, solely based on genome and secretome predictions. To evaluate whether P. anserina might provide a novel source of lignin-active enzymes to tap into for potential biotechnological applications, we comprehensively mapped wheat straw lignin during fungal growth and characterized the fungal secretome. Results Quantitative 13C lignin internal standard py-GC–MS analysis showed substantial lignin removal during the 7 days of fungal growth (24% w/w), though carbohydrates were preferably targeted (58% w/w removal). Structural characterization of residual lignin by using py-GC–MS and HSQC NMR analyses demonstrated that Cα-oxidized substructures significantly increased through fungal action, while intact β-O-4′ aryl ether linkages, p-coumarate and ferulate moieties decreased, albeit to lesser extents than observed for the action of basidiomycetes. Proteomic analysis indicated that the presence of lignin induced considerable changes in the secretome of P. anserina. This was particularly reflected in a strong reduction of cellulases and galactomannanases, while H2O2-producing enzymes clearly increased. The latter enzymes, together with laccases, were likely involved in the observed ligninolysis. Conclusions For the first time, we provide unambiguous evidence for the ligninolytic activity of the ascomycete fungus P. anserina and expand the view on its enzymatic repertoire beyond carbohydrate degradation. Our results can be of significance for the development of biological lignin conversion technologies by contributing to the quest for novel lignin-active enzymes and organisms.

Dataset for TotalControl reference windfarm database simulation of a pressure-driven high Reynolds number boundary layer flow with 0 degree inflow wind direction angle (Casename PDk 0) Included Python files for loading and visualizing the data. Use the plot_*.py files. Further information, including description of the case and dataset can be found in the deliverable report at: https://cordis.europa.eu/project/id/727680/results "Database for reference wind farms part 2: windfarm simulations"