dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments:  Financial support from INSU/EC2CO program  to FH and BL is acknowledged (Project  Claie ,  2015-2016 ). ISTerre is part of  Labex OSUG@2020  ( ANR10 LABX56 ). Financial support from the  French Clay Group and AIPEA  to attend the MECC 2014 and the ICC 2017 conferences is gratefully acknowledged (EB). Chemical analyses of the mineral fractions and CHN analyses were performed at the  Service d'Analyse des Roches et Minéraux (Nancy-France ) and at the  Institut de Chimie des Substances Naturelles  ( Gif s/Yvette-France ), respectively.
Acknowledgments: This work was realized with financial support for academic interchange from the  Marie Sklodowska-Curie Research and Innovation Staff Exchange project , funded by the  European Commission   H2020-MSCA-RISE-2014  within the framework of the research project  Mat4treaT  (Project number:  645551 ). This paper is part of a project that has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie grant agreement N • 765860  (AQUAlity). Compagnia di San Paolo  and the  University of Torino  are gratefully acknowledged for funding Project  Torino_call2014_L2_126  through " Bando per il finanziamento di progetti di ricerca di Ateneo-anno 2014 " (Project acronym: Microbusters). Polytechnic of Torino is gratefully acknowledged for funding project  Starting Grant RTD  (project number:  54_RSG17NIR01 ).
The scenario combines policy and technology best-practices to reduce air pollutant emissions through post-combustion control measures with an accelerated clean energy sector transition towards the use of cleaner fuels. For the latter, the scenario aims to ramp up ambition over NPS in key areas, such as increasing energy efficiency; decreasing the use of inefficient coal-fired power plants; and increasing investments in renewable energies. CAS is also mindful of some cautionary tales: for example, the large gap between test data and the higher real-world pollutant emissions from diesel vehicles, which underlines the essential nature of adequate enforcement and compliance. The measures proposed in CAS are tailored to different national and regional circumstances, and include effective action to achieve full, universal access to cleaner cooking fuels and to electricity by 2040.
Analytically, we approach the second step of the analysis by running WEM in iterations with the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) model over the implications for air pollutant emissions that arise from the achievement (or absence) of climate goals and clean energy access. GAINS is a widely recognized model which estimates emissions of air pollutants by country, using international energy and industrial statistics, emission inventories and data supplied by countries. It uses the WEM outputs to assess the future path of pollutant emissions by country in five-year intervals through to 2040 for different scenarios and policy packages  (Amann et al. , 2011). The purpose of multi-model iterations is to identify a portfolio of leastcost technology options that achieve various goals and policy objectives.
Firstly, the system model (Simulink) generates highly dynamic power demand ( , ) by matching the velocity requirement of the driving cycle ( ( )) to the fuel cell power demand. The power is supplied by a fuel cell stack that is modeled by upscaling the single-cell generated power supply. The fuel cell model (COMSOL) interacts on the fly with the degradation library that provides instantaneous degradation factor ( , ) based on included degradation mechanisms through look-up tables (LUT), analytical functions or empirical mappings. The Simulink model simulates the time duration of a single driving cycle ( ). The total degradation over the driving cycle is calculated in MATLAB (2016a), which linearily upscales the degradation over one driving cycle to over 'n' driving cycles. This new state of degradation via the upscaled degradation factor ( , ) is set into the COMSOL model via Simulink.
Moreover, the other depending parameters are the temperature and the oxygen and water mole fraction as illustrated in Figure  4. Finally, the rate of change in Pt particle radius is calculated as: The Pt dissolution module is implemented in the EDMOND code  [26]. EDMOND is a 1D double layer model. The model calculates the ∆χ as well as the coverage of the various reaction intermediates, based on a dynamic coupling between the local operating conditions, that is, the cathode potential V c , the local temperature T, the local water vapor fraction x H 2 O , the local oxygen fraction x O 2 , the particle radius r Pt , and the kinetics of the intermediate reaction steps of Pt dissolution  [37]. Both the surface potential and the coverage are involved in the mechanistic model. The Pt dissolution model used in this paper has been partially validated on 2000 h of durability tests  [26].
With the help of EDMOND coupled with the Pt dissolution model, a look-up table was calculated, describing the evolution of the particle radius as a function of the local operating conditions (cf. Figure  4 ). dr Pt dt = -v diss M Pt ρ Pt. ( 8 ) = e = e ( ) , (7) where, is a kinetic constant. Δ is the local electrode potential calculated by the EDMOND model (see below) from the local cell voltage that is provided by the computational fluid dynamics (CFD) model  [36]. Moreover, the other depending parameters are the temperature and the oxygen and water mole fraction as illustrated in Figure  4. Finally, the rate of change in Pt particle radius is calculated as: The Pt dissolution module is implemented in the EDMOND code  [26]. EDMOND is a 1D double layer model. The model calculates the Δ as well as the coverage of the various reaction intermediates, based on a dynamic coupling between the local operating conditions, that is, the cathode potential , the local temperature , the local water vapor fraction , the local oxygen fraction , the particle radius , and the kinetics of the intermediate reaction steps of Pt dissolution  [37].
The fuel cell model (COMSOL) interacts on the fly with the degradation library that provides instantaneous degradation factor ( f deg,C ) based on included degradation mechanisms through look-up tables (LUT), analytical functions or empirical mappings. The Simulink model simulates the time duration of a single driving cycle (t DC ). The total degradation over the driving cycle is calculated in MATLAB (2016a), which linearily upscales the degradation over one driving cycle to over 'n' driving cycles. This new state of degradation via the upscaled degradation factor ( f up deg,C ) is set into the COMSOL model via Simulink. The entire framework is controlled in MATLAB that runs the durability loop until the cell end-of-life (t EOL ). The details of the various components shown are discussed in the following sections.
Characterizations The thermogravimetry analysis (NETZSCH STA 409, Selb, Germany) of initial polymeric mixture [PHMS and TB (1:1)] was carried out in static air flow at a heating rate of 5 °C/min to 1400 °C. The Fourier-transform infrared spectroscopy (FTIR) analysis (Perkin Elmer Spectrum, Hatfield, PA, USA) of the powder sample was also done to ensure whether proper ceramization has occurred. The samples were mixed with infra-red IR irradiated potassium bromide and pelletized before subjecting to FTIR analysis. The samples were subjected to XRD (D8 Discover, Bruker AXS X-ray diffractometer, Madison, WI, USA), scan limit in the range 20° ≤ 2θ ≥ 90° in increments of 0. 02° with a time per step of 15 s. The peaks were identified with the aid of Joint Committee on Powder Diffraction Standards (JCPDS) data files for TiO2.
The average crystallite size was determined using Scherrer's equation assuming the shape of the particles to be spherical. The Rietveld analysis of the diffracted pattern was performed using MAUD software. The software applies RITA/RISTA method for the analysis which was developed by Wenk, Ferrari and Lutterotti  [62]. The Raman spectra (Labram HR 800, Horiba, Minami-Ku, Kyoto, Japan) of the powdered samples was acquired with a He-Ne laser source (488 nm), recorded in the range between 100 and 4000 cm -1. The microstructural as well as elemental analysis of the sample was done using scanning electron microscopy (SEM; FEI Quanta 200 & 400, Calabasas, CA, USA) and Energy-dispersive X-ray spectroscopy (EDS) analysis (FEI Quanta 200 & 400, Calabasas, CA, USA). The samples were subjected to gold sputtering prior to the SEM analysis.
Characterizations The thermogravimetry analysis (NETZSCH STA 409, Selb, Germany) of initial polymeric mixture [PHMS and TB (1:1)] was carried out in static air flow at a heating rate of 5 • C/min to 1400 • C. The Fourier-transform infrared spectroscopy (FTIR) analysis (Perkin Elmer Spectrum, Hatfield, PA, USA) of the powder sample was also done to ensure whether proper ceramization has occurred. The samples were mixed with infra-red IR irradiated potassium bromide and pelletized before subjecting to FTIR analysis. The samples were subjected to XRD (D8 Discover, Bruker AXS X-ray diffractometer, Madison, WI, USA), scan limit in the range 20 • ≤ 2θ ≥ 90 • in increments of 0. 02 • with a time per step of 15 s. The peaks were identified with the aid of Joint Committee on Powder Diffraction Standards (JCPDS) data files for TiO 2.
Although the spectrum for HemW did not change (Fig. 6B , solid line) and the spectrum for the HemW-heme complex only lost its small increase around A 400 (Fig. 6B , dotted line), all free heme was gone (Fig. 6B , dashed line). Identical results were obtained for the dialyzed and Superdex 200 gel-filtrated HemW-heme complex (data not shown). Interestingly, reduction ot the HemW-heme complex resulted in an increase of absorption at A 424 , generating a Soret band, and further absorption peaks at A 531 and A 559 (Fig. 6C , dashed line). In contrast, free reduced heme shows an absorption peak at around A 400. These results demonstrate the specificity of HemW-heme interaction. For further analysis of the nature and stoichiometry of HemW-heme interaction, complexes were analyzed via SDS-PAGE with heme staining and acidified butanone extraction.
However, the use phase it depends on several factors which are influencing the energy consumption. The latter include: trailer specification, size and packaging of product loaded, outside climate (temperature, hygrometry), operating mode (continuous run vs. start/stop), start/stop parameters, type of product (fresh or frozen), type of transport (urban distribution vs. long haul), speed of engine, coefficient of performance (COP) of the unit and refrigerant efficiency  [13] [14] [15] [16] [17] [18]. In the ecoinvent database V3. 2 [19], the fuel consumption of truck refrigeration systems is modelled as an average scenario, assuming a 20% increase as compared to conventional truck transport without refrigeration  [20]. In this scenario, none of the aforementioned parameters that can influence the energy consumption have been taken into consideration.
Source of Data Collection In order to model a usage scenario, it is necessary to gather data from the use phase. Different kinds of data can be collected in order to build usage scenarios. In this paper, four sources of data are identified: real-time data, experimental data, numerical simulation data and average scenario data. They are analysed based on four criteria: (i) time needed to collect data, (ii) cost to collect data, (iii) reliability of data and (iv) accuracy of real usage data (Table  1 ). Real-time data are collected directly with the help on sensors while the product is running. It is the most reliable kind of data and allows the analysis of the actual usage specific to each user. However, data collection and analysis can be time-consuming and/or costly, as it is necessary to use multiple sensors and allocate resources to analyse data.
In addition, it implies that the environmental footprint must be evaluated continuously or only at the end of the product life. The environmental footprint for a specific set of data will be accurate for a specific usage but rather specific to it, i. e. not necessarily pertinent for another. It can be helpful to build an average scenario, specific to each type of business (e. g. fresh or frozen product, international long haul, national long haul delivery or urban delivery). Experimental data are collected during punctual measurement on the product in order to recreate real condition of use. Again, collection can be time-consuming and costly, depending on the resources used. However, it requires determining precise parameters to analyze in order to have reliable data. It does allow for analysing the influence of single parameters or for mixing the influence of different parameters.
It does allow for creating specific scenarios that can be accurate with real usage. Data from numerical simulations are based on mathematical routines recreating real-world use conditions. The approach to data collection requires a thorough knowledge of the different parameters to set up the simulation and interpret the results. In some cases, especially with products requiring the integration with another system, it can be difficult to control all the external parameters influencing the energy consumption, which can have a detrimental effect on the accuracy of the results. Numerical simulations have the advantage to not to require resources and materials in order to be performed. Average data are collected from literature reviews, customer statements or expert knowledge. Compared to the previous kinds of data, average data are usually less reliable and, in particular when only a few data are required to build a scenario, can lead to an usage model very far from the actual one.
Specific Scenarios of Use from Experimental Measures The focus here is on the influence of end-user behaviour on the energy consumption. Data for specific use scenarios were collected from experimental measures in external condition, with two similar trailer refrigeration units (in terms of unit model, obsolescence and hours of running), which are actually used by customers. In this case, energy consumption was measured depending on four parameters of use linked to the setting of the TRU (setting point, operating mode) and end-user behaviour (on/off during door openings, use of curtains for door openings). For the setting point, the two most common temperatures (0 and -20 °C) were selected from the European average usage scenario and one temperature (-25 °C) was arbitrarily selected to show the influence of a setting temperature that is not optimal but does not affect the cold chain integrity.
These estimates suggest a significant price increase, but modest relative to previous studies. Next, we use detailed microlevel household data to examine how these predicted commodity price shocks affect welfare among households in India through the cost of consumption and wage incomes. We allow for household heterogeneity in terms of their expenditure shares, factor endowments, income, geographical location, and household structure, and identify the groups that are most impacted. In order to incorporate the limited pass-through of world prices to domestic prices, we estimate price pass-through elasticities and consider both perfect and imperfect price transmission from world to domestic Indian markets. We also estimate wage-price elasticities to assess how price changes are reflected in household wage incomes.
Supply of food and biofuels As shown in Figure  2 , each land class may produce the five food crops (rice, wheat, sugar, corn, and other food) and biofuels (first and second generation). 16  Since land quality differs across different regions, we use the FAO-IIASA database (Fischer, Shah, Velthuizen, and Nachtergaele (2001)) to define three different land qualities based on soil and climate characteristics. Each quality is indexed by n (high, medium, low) with high being the most productive. 17 Total land area in the model  13  On average, eight kilograms of cereals produce one kg of beef and three kgs produce one kg of pork. 14 Rice, wheat, and sugar together supply 60% of all calories in India. They also consume large amounts of farmland-according to FAO (2016), rice and corn account for 11% and 12%, respectively, of world farmland, and wheat another 14%.
15 These crops are not disaggregated further because they occupy a smaller acreage and are likely to be less important in terms of distributional effects in India than staples like rice, wheat, and corn. 16  First generation biofuels are produced from corn in the US and from sugarcane in India and ROW. 17  The database identifies four qualities-very suitable, suitable, moderately suitable, and marginally suitable. We have grouped these four into three, by consolidating the two intermediate classes into one, since their yield differences are small. Figure  2. Schematic of the model with food and energy. Notes: Land of different qualities is used to produce either food crops (i. e. , rice, wheat, corn, sugar, and "other crops") or biofuels. Food crops are then processed into food commodities.
Transport energy is derived from gasoline and biofuels. includes land cultivated in base year 2012 and fallow land that may be brought into cultivation in subsequent periods. Table  1  shows yields and endowment of land for the base year 2012 by land quality and region. 18  We assume linear production, that is, output is yield times land area. For each land quality, the FAO/IIASA database has information on the acreage under each crop and its yield. 19  The definition of land quality depends on the level of input use such as technology and irrigation. The FAO data gives yield estimates at various levels of inputs-high, medium, and low. For each crop and region, we match these yields to actual data from FAO (2016) for base year 2012 and choose the input level that matches the data. For the US, we adopt the yield for "high input" use, and for the other two regions, we choose the yield for "low input use.
19  Crop acreage for US and India is readily available from this database. For the ROW region, we subtract the values for US and India from the total world figure. For wheat, rice, corn, and sugar, we can use the data directly. However, to obtain the yield per land class for the category "other crops," we calculate the weighted mean crop yield for grains, roots, tubers, pulses, and oil crops where the weight used is the share of each crop in total production in the region. 20  Ideally, production costs should vary by land class. We have not followed this approach due to data limitations. The data base we employ to characterize the three land qualities has information on different crop yields and the area available per land class. However, there is no data on production costs. A1. 21  The price-induced conversion of new land to farming may vary by region.
24  The parameter λ r is a constant calibrated to reproduce base-year production of transport fuel, given by λ r = q r e [μ r g q r g ξ r -1 ξ r +(1-μ r g )(q r bf +q r bs ) ξ r -1 ξ r ] ξ r ξ r -1 where q r e , q r g , q r bf , and q r bs are at their 2012 levels. Data used to calibrate λ r is reported in Table  A4. Supply of transport fuel, gasoline, and biofuels are taken from EIA (2014). 25  These costs may rise due to depletion effects or the increased cost of environmental regulation of fossil fuels. where x r (t) represents the quantity of oil extracted in region r at time t and t t=1 r x r (t) the cumulative amount of oil extracted from date t = 1 to t = t, X is the amount of oil available over the base year, and φ 1 , φ 2 , and φ 3 are constant parameters. The parameter φ 1 represents the unit extraction cost of oil over the base year, and φ 1 + φ 2 the cost of extraction of the last unit (i.
Description of the survey data We use two nationally representative surveys from the National Sample Survey (NSS) of the Government of India-the Consumer Expenditure Survey to estimate the consumption component, and the Employment and Unemployment Survey for the earnings component of household welfare. The 61st and 66th rounds of these surveys, conducted during 2004-2005 and 2009-2010 are used. This is one of the richest micro-level surveys for a developing country as approximately 100,000 households and 460,000 individuals are surveyed in each of the 35 states of the country. Because the NSS samples rural and urban households separately, we distinguish between rural and urban welfare impacts. The expenditure survey asks each household the value and quantity consumed for about 500 consumption items during the previous 30-day period.
A household which relies on the PDS will have lower expenditure shares of that commodity, and welfare effects from price changes will be correspondingly lower. 49  We assume homogeneous goods across households due to data restrictions. The type of grain consumed by each household may have distributional consequences. Imported products may be lower quality (domestic basmati rice is often considered superior to imported white rice) and less processed than the domestically produced variety, which would make distributional effects more regressive. Unfortunately, our survey data do not include product quality, or whether the household purchased an imported or domestic variety. Note: Grains denote all grains including rice and wheat: separate NIC codes for rice and wheat are not available. Employment shares as a fraction of total employment (including nonfood) are computed from the 66st round of the NSS Employment and Unemployment Survey.
Grains include rice, wheat, and corn and its pass-through elasticity is used to compute wage impacts. culture. 56  Grain prices are defined as the average of rice and wheat prices, as consistent domestic and world prices for grains are not readily available. 57  Exchange rates are obtained from the Federal Reserve Bank of India. All world prices are taken from the World Bank Commodity Price database. 58  Table  7  shows the summary statistics for price changes for the major commodities between January 2005 and May 2011. Domestic price changes for rice, corn, and meat were similar to changes in the world price, with growth rates of 1 07, 0 8, and 0 84 percent, respectively. However, wheat and sugar prices grew at a slower rate in the domestic market compared to the world price, also seen in Figure  4.
This research can be extended in other directions. The micro-level impacts in India can be compared with that in other countries with significant poor populations to check if the composition of the welfare effects is fundamentally different and idiosyncratic to diet and other cultural factors. For example, societies in which the diet is based on corn or a higher consumption of meat and dairy may be impacted differently. Countries adopt different policies to mitigate the effect of price shocks, which can again be compared to obtain policy insights. Ultimately, these price shocks will affect nutritional intake among individuals and affect the allocation of calories within each household. Each consumption item in the NSS data we have used can be matched to its calorie, fat, and protein content using the FAO nutritional database.
We would like to thank the  Social Sciences and Humanities Research Council  ( Insight Development  Grant  430-2011-0635 ) and the  French National Research Agency  (Grant  ANR-14-CE05-0008 ) for generous research support, seminar participants at  Berkeley, CESifo Munich ,  EPA ,  ETH Zurich, Maryland, Ohio State, Paris Norway  and the  Indian Growth and Development Economics Conference at ISI Delhi. We would like to thank  Kyle Emerick  for helpful comments and  Alausa Waleem  for excellent research assistance.