dataesr/scientific-paragraphs-categorization huggingface dataset:
flOwChart The flowchart or flow diagram or process tree (such as the one depicted in Figure  3. 6) provides a clear overview of the processes and their relationships. It depicts each unit process considered within the system and quantifies the intermediary flows linking these unit processes. The flow diagram is built starting from the reference flows (what you need to buy for one FU), then identifying the first-tier intermediary flows (quantities of preceding unit processes) associated with each reference flow. The operation is then repeated, starting from the Tier 1 intermediary flow, yielding a second set of Tier 2 elementary flows. In practice, for a new study, the flow diagram will display all linkages from reference flows up to existing database unit processes, whose upstream and downstream links are described in further detail in the database itself and therefore do not necessarily need to be shown in the flowchart.
All existing processes available in databases or collected data on new processes are usually kept, even if they contribute to less than the cut-off. Rule 3 Processes that are identical in the different scenarios can only be excluded if the reference and intermediary flows affected by these processes are strictly equal (i. e. , the intermediary flows of each excluded process per FU are exactly the same for the different scenarios). In other words, the LCA must still account for identical processes occurring in all scenarios if the scenarios require different quantities of processes. As an example, imagine comparing the use of two herbicides, A and B, for the harvest of a given quantity of wheat. Clearly, the system boundaries must include the processes of herbicide manufacturing and treatment.
Fortunately, databases now exist that integrate data for a wide range of processes, leaving only the processes specific to the considered application and industries to be modeled in detail. Section 4. 3 introduces the databases commonly used for the process-based approach, describes in further detail the widespread ecoinvent database, and addresses quality issues in databases. The second way to calculate the inventory is to calculate the emissions and extractions not on the basis of physical flows, but on that of economic flows generated by the product or service concerned. Called input-output (I/O), this method is explained in Section 4. 4, along with the main databases available. This section ends with a comparison of the process and I/O methods and their possible combinations in a so-called hybrid LCA approach.
definitiOns The inventory of elementary flows or emissions and extractions is, by definition, the quantitative description of flows of matter, energy, and pollutants that cross the system boundary. This includes the emissions of polluting substances to the environment as well as the amounts of extracted resources from the environment (minerals, energy carriers, soil surface area, etc. ) throughout the life cycle of the analyzed product or service. For the process approach, this inventory is calculated by multiplying the reference flows and corresponding intermediary flows per FU by the direct emission or extraction factors of each unit process. The emission and extraction factors, available in various databases (see  Section 4. 3) , provide the quantities of ore extracted and pollutant emissions emitted for each unit process.
step-By-step prOCedure fOr prOCess-Based inventOry analysis 1. Start with the reference flows (what you actually buy) corresponding to the FU (Table  3. 3). Using the intermediary flows of materials and processes associated with each reference flow, design the flowchart of the core unit processes involved in the system, both upstream and downstream from these reference flows. In practice, you may want to stop when you link to the process of an existing database (see Figure  3. 6 and Section 3. 4. 2 for further details). 2. For each unit process, find its inputs (quantified intermediary flows) and direct emissions (elementary flows). These emissions and extractions factors and intermediary flows can be found (a) in databases, (b) by measurements, or (c) by direct contact with companies. 3. Document the data on a flowchart or in a table, describing the source of information used.
The nonrenewable primary energy demand and CO 2 emissions for different materials and processes are calculated and listed in the ecoinvent database (Section 4. 3. 2). Table  4. 1 presents some typical values for a commonly used set of processes and materials, showing that the nonrenewable primary energy consumption and emissions from electricity vary substantially by country of origin; the consumer use of 1 kWh of Swiss electricity requires 7. 9 MJ of nonrenewable primary energy, compared with 10. 5 MJ needed for a European electricity mix and 12. 1 MJ for a U. S. electricity mix. The difference is even more noticeable for CO 2 , with a variation of more than a factor of four (from 0. 11 kg CO2 /kWh for Switzerland to 0. 49 kg CO2 /kWh for Europe and 0. 71 kg CO2 /kWh for the United States).
This difference is due to the composition of the Swiss electricity mix, 40% of which is nuclear and 57% hydroelectric, a renewable energy source that needs minimal nonrenewable energy for infrastructure. If the level of energy consumption varies by scenario, the choice of energy source can strongly influence the results of a study. However, the variation in nonrenewable primary energy requirements among different databases may be larger than the variation among regions within the same database. So, for the comparative purposes of an LCA, where consistent data sets are a priority, it is sometimes better to adapt high-quality data to another geographical context rather than compare electricity mixes from the appropriate regions but different databases. The values provided in Table  4. 1 must be interpreted with care and generally cannot simply be compared only on a per unit mass basis.
The steel scenario consumes more primary energy over its life cycle than the composite scenario as soon as the distance traveled surpasses 10,000 km. In the example given previously, the calculation is based on the emission and extraction factors provided by large inventory databases, and are the result of aggregated data from hundreds of unit processes. The use of these factors is easy, but determination of their values requires lengthy, rigorous work; thus, it is advantageous to use existing databases whenever possible. Use of such databases also helps scale up the simple and practical approach illustrated here to calculate the environmental inventory for a large number of unit processes using matrix calculations (see Section 4. 2. 4).
4). Accounting for this infinitely long process chain can be addressed by matrix inversion, using the approach taken by the ecoinvent inventory database, which uses a model constituted of the technosphere (economic system) and the ecosphere (environmental system). Each unit process can exchange intermediary flows with any number of the m total unit processes of the technosphere and can be associated with any of the n elementary flows extracted from or emitted to the environment. The technology matrix A (m × m) is a square matrix consisting of a row and column entry for every unit process in the economy (Equation  4. 2) and the environmental matrix 0 500 1,000 B (n × m) has a column for every unit process and a row for every elementary flow from or to the environment (Equation  4. 3). A =         aa aa m mm m 11 1 1 … ⋮⋱ ⋮ ⋯ (4.
INVENTORY DATABASES FOR PROCESS-BASED APPROACH During goal setting, the system boundaries are defined and all processes included within these boundaries are listed and quantified. To calculate the associated elementary flows, both incoming (raw materials and energy carriers) and outgoing (substances released in air, water, etc. ), process inventory data are sought from industrial partners or from the literature. Obtaining reliable inventory data, clearly described and regularly updated, is not easy and could severely hinder the application of life cycle assessment, but existing published inventory databases now facilitate this work. After a brief overview of the existing databases, we will focus on ecoinvent, one of the leading databases. We will also emphasize the importance of data quality analysis, which is an integral part of the LCA.
existing dataBases The worldwide inventory databases available to the public are listed by  Norris and Notten (2002)  and  Sonneman and Vigon (2011) , building on previous work by the Society for the Promotion of Life Cycle Assessment (SPOLD  ; Fussler 1993; Weidema 1999 ). This collection is regularly updated under the Life Cycle Initiative of the United Nations Environment Programme (UNEP), and a set of global guidance principles for life cycle assessment databases was published following a consensusbuilding workshop in 2011  (Sonneman and Vigon 2011). This document provides guidelines on how to build unit processes as well as aggregate data sets within a consistent database. The European Commission also provides a regularly updated collection of LCA resources, including access to 20 database websites, 35 tools, and 80 service providers.
(See Appendix I for the URLs of these collections. ) The majority of early databases was developed in Europe in the mid-1980s. These databases were principally developed for studies done by universities or consultants to characterize specific industrial sectors or product groups. The resulting databases were very diversified, fragmented, and not well harmonized. The development of more consistent databases began mainly in Switzerland with an early study by the OFEFP (Office Fédéral de l'Environnement des Forêts et du Paysage) focusing on packaging materials  (Bus 1984; Habersatter and Widmer 1991). The study, which consisted of studying the environmental consequences of different types of packaging (aluminum, glass, plastic, paper, cardboard, and tin), required higher-quality data on energy systems.
In response to this need, the ESU (Energie-Stoff-Umwelt) group of the Eidgenössische Technische Hochschule Zürich (ETH Zürich) created more complete databases on the following energy systems  Frischknecht et al. (1994) : • Petroleum products • Coal (coal, lignite) • Natural gas • Wood • Nuclear energy • Solar-thermal energy • Hydroelectric energy • Photovoltaic energy • Geothermal energy • Mixed energy Using this energy database, the data on packaging materials were updated by  Habersatter and Fecker (1998)  (Swiss Federal Office for the Environment, Forests, and Landscape [OFEFP] 250). This led to a collaboration among the Swiss Federal Institutes of Technology, with a goal to create a centralized, coherent, and more complete database, called ecoinvent (Section 4. 3. 2). The European Reference Life Cycle Database (ELCD) with European scope inventory data sets was created by the Joint Research Centre (JRC) at Ispra, Italy.
The ELCD core database is comprised of Life Cycle Inventory (LCI) data from EU-level business associations, as well as other sources for raw materials, energy carriers, transport, and waste management. Efforts have been focused on data quality, consistency, and applicability, but data are only provided at an aggregated level from cradle to gate, without detailed information on unit processes. This limits the use of this database for uncertainty analyses (common processes cannot be accounted for-see Section 6. 5) or for more advanced studies on system boundaries. This database is accessible free of charge with unrestricted use for all LCA practitioners. The European Platform on Life Cycle Assessment also provides the guidance document, the International Reference Life Cycle Data System (ILCD) Handbook, which describes available and recommended practices for LCA data in general, LCI data sets, and LCIA.
For Europe, in addition to ecoinvent and the JRC ELCD databases, the following country-specific databases are available: (a) LCA Food Database-Denmark (from the Danish Institute of Agricultural Sciences; data also available in the LCA tool SimaPro); (b) Swedish National LCA database SPINE@CPM (contains more than 500 welldocumented LCI data sets in the SPINE format); (c) IVAM Environmental Research database on Dutch building materials (based in Amsterdam, the Netherlands); and (d) other sector-specific databases on industry association websites (e. g. , Association of Plastic Manufacturers in Europe and European Aluminium Association). In Asia, the LCA National Project in Japan, funded by the Ministry of Economy, has developed LCI data for approximately 200 products, with data gathered from about 50 industrial associations  (Narita et al.
2004 ). The Korea National Cleaner Production Center (KNCPC) is constructing the national Korean LCI database for Korean industries (Korea National LCI Database, KNCPC 2010), established with the support of the Ministry of Commerce, Industry, and Energy. The database is based on a series of industry-requested surveys and is accessible through the KNCPC website (listed in Appendix I). In Australia, the Life Cycle Inventory Data Research Program is led by the RMIT in Melbourne and aims to develop detailed inventory resources. The Centre for Design's LCA resources are published in spreadsheets, and are also available in SimaPro LCA (see website in Appendix I) software. For North America, the inventory database of Franklin LCI 98 is freely available in software such as SimaPro. However, these data must be used with caution due to certain inconsistencies with other LCI data sets.
For example, many types of paper in the Franklin database require nonrenewable primary energies comparable to if not greater than that of plastics (80 MJ/kg paper), while this value is 15-30 MJ/kg paper in the ecoinvent database. The National Renewable Energy Laboratory (NREL) is creating a new North American database (US LCI database). This database will need to be verified and compared with other databases due to errors already identified, as well as the calling of intermediary flows that do not yet correspond to any existing process. The LCA Digital Commons from the US Department of Agriculture National Agricultural Library aims to provide open-access life cycle assessment data sets and tools. The project makes North American LCA data more accessible to the community of researchers, policy-makers, industry process engineers, and LCA practitioners.
The North American project likely to provide very reliable regional data is currently underway at the Canadian institution, the International Reference Centre for the Life Cycle of Products, Processes and Services (CIRAIG), with the goal to adapt ecoinvent to North American conditions and produce a database for Quebec (already available), Canada, and, eventually, North America as a whole. Databases are also developed for classes of products, such as the World Food LCA Database (http://www. quantis-intl. com/microsites/wfldb/) that provides data for more accurate food and beverages LCAs. In summary, the compatibility and coherence of a database must be verified prior to usage or combination with other databases. For example, using established data from another continent can be better than mixing data from noncompatible databases.
Description of the Ecoinvent 2. 2 Database Inventory data (v. 2. 2) are compiled for a large number of products and services, representing production and supply mostly from the year 2000. In addition to the quantitative information about inflows and outflows, supplementary descriptive information (metainformation) is provided on the technological, temporal, and geographic validity. The database is organized according to the following main categories: • Energy sources The ecoinvent database consists of more than 4000 processes linked by material and energy flows covering more than 400 substances and resources. Ecoinvent CO 2 emissions and nonrenewable primary energy use are provided for approximately 50 processes in Appendix III.
Principal Characteristics of the Database The ecoinvent database was initially developed for western Europe, with country-or region-specific values for certain processes. Whenever possible, data are provided on a unit process level, and only aggregated when unit process data are not available or confidential. The most common types of processes and emissions are described in the following paragraphs. The inflows and outflows for the production processes are generally provided separately from those of the production infrastructure, allowing the user to choose whether to include certain infrastructures. Electricity is modeled based on average electricity mixes, which are available for multiple European countries and other countries such as the United States, Japan, and China. Electricity mixes specific to other countries may be available and used to calculate new mixes.
The database differentiates the production mix from the supply mix, where the latter is used for most processes requiring an energy demand. The production mix is only used for processes within the electric sector. Transportation often occurs between the processes of a system. Due to the difficulty in determining the means and distances of transport for all individual intermediary products, standard distances are used by default. Waste treatment is modeled like all other technical processes as another part of the system. If the specific waste treatment processes are not known, generic treatment processes are applied. Certain elementary flows are always neglected; specifically, the sound emissions (noise) and H 2 , N 2 , and O 2 air emissions. Regarding the material and energy flows, the nonrenewable primary energy is calculated based on the lower heating value (LHV) given in Table  4.
New Features of Ecoinvent 3. 1 The updated ecoinvent 3. 1 improves on v. 2. 2 by consistently modeling water flows throughout the whole database, enabling the practitioner to determine water use and consumption to calculate the water footprint of products  (Weidema et al. 2011). It also provides updated data sets for electricity production (more than 20 additional countries worldwide), the wood sector, recycling activities, chemical production, and fruit and vegetables. In addition to the more traditional attributional modeling, ecoinvent 3. 1 can potentially support cut-off and consequential system modeling. In the cut-off model, "the primary producer does not receive any credit for the provision of any recyclable materials. As a consequence, recyclable materials are available burden-free to recycling processes, and secondary recycled materials bear only the impacts of the recycling processes" (www.
Tips for Using Ecoinvent Database Some tips follow for selecting the appropriate energy and transportation data in the ecoinvent database. For the electricity mix, it is important to choose the geographical region appropriate to the case being studied, which is either the consumption mix of the country of production or the electricity type that effectively responds to a marginal increase in electricity demand. For example, an increase in Swiss electricity consumption may have little effect on Swiss production, but may instead be satisfied by an increase in electricity production elsewhere in Europe, such as a thermal gas power plant. We also determine the right voltage level among the three existing types (low, medium, and high voltage). The medium voltage corresponds to industrial use, and low voltage to domestic use, commerce, and agriculture.
(b) The processes expressed in megajoules of final energy-the energy bought by the client-account for the combustion and are named in a similar manner to combustibles, with the energy carrier's name followed by the term burned. (c) The inventory data reported by megajoules of useful energy describe the supply of useful heat, such as the heat delivered inside a building. These processes also account for the combustion stage, and have names starting with heat followed by the energy carrier's name. Regarding truck transportation, ecoinvent provides average data assuming an empty truck on the return trip (process names beginning with transport), but data are also available for fully loaded and empty trucks (process names beginning with operation). In this case, data must be added on vehicle production and disposal, as well as on traffic infrastructure.
data Quality and unCertainties Once a relevant data set is found in ecoinvent, it cannot necessarily simply be applied. As discussed in the previous subsection in terms of energy and transportation data, the data set characteristics must be compared with the conditions and objectives of the study under consideration. For the ecoinvent database, such information is available in the form of metadata associated with the different emission and extraction factors. This information addresses: • Geographical and temporal scope, which is the region and period of time for which the data is valid • System boundaries covered by the data (e. g. , "cradle to grave" or "cradle to factory doors") • Data format (aggregated, averaged, or as a range of values) • Data quality and gaps, along with documentation on quality control (comparison with other data sources, mass balance check, etc.
) • Data sources (literature or on-site measurements) In addition to ensuring proper application of a data set to a given study, this metadata helps indicate uncertainty in data set factors. Few life cycle assessments have quantitatively estimated the uncertainty in inventory data, but there is increasing effort to generally quantify the uncertainty of LCA results. This will help analysts determine if the difference between two scenarios is significant or within the range of uncertainties. It is thus highly recommended to quantify the uncertainties. Section 6. 5 describes how probability distributions are determined and parameterized for most ecoinvent inventory data. Section 6. 6 also describes how to use these data to evaluate the uncertainty of each scenario studied. In the case of ecoinvent data, the metadata gives an indication of the estimated probability distribution for each individual datum, and the uncertainties are quantified for all the inputs and outputs of the unit processes.
The expenses of each economic sector are then linked to energy consumption, extraction of resources, and pollutant emissions per monetary unit. This top-down approach accounts for the entire economy of a country or region by deconstructing it into sectors and products. Taking advantage of detailed national economic statistics describing expenditures by sector, it is possible to exhaustively describe the chain of suppliers needed for a given service. The emission data per monetary unit spent in each sector is then estimated by dividing the total emissions of a sector by its financial output. Table  4. 8 provides such data for various U. S. services.
0 billion for Industry 2). By definition, the product of Ã with the total industrial output vector ɶ x gives this total industrial use. Since the economic system is closed, the total industrial output ($11. 5 billion) is the sum of the industrial use with the final demand ɶ y of consumer and government spending in each industry ($0. 5 billion for Industry 2). In terms of matrices, this is expressed as ɶ ɶɶ ɶ Ax yx +=. By solving for x, we redefine the fundamental equation of the I/O approach: ɶ ɶ ɶ xI Ay =-() 1  -. National economic transaction matrices are available for most countries, with varying levels of sector and time resolution, in ways that do not necessarily correlate with LCA data availability. Switzerland has relatively detailed and comprehensive data on LCA processes, but possesses an economic matrix of only 42 sectors  (Nathani et al.