This dataset includes data that the Employment and Training Administration's Office of Foreign Labor Certification (OFLC) collected from Labor Condition Applications for Nonimmigrant Workers (LCAs) during previous fiscal years. It includes information on employers, geography, and job details for participants in the LCA program. Historical LCA public disclosure data is available on the OFLC website in the Performance Data section. Data is available as Excel files in aggregate form at https://www.dol.gov/agencies/eta/foreign-labor/performance.

Bibliographic collection. Database: Scopus. Search query: The search query: TITLE-ABS-KEY (agr* W/3 ("life cycle analysis" OR "life cycle assessment" OR "life cycle cost*" OR "life cycle impact" OR *lca OR *lcia OR lcc)). Filtering: Only English, article, excl trade journals. Contains 259 articles (adjusted after screening the relevance of titles, abstracts, keywords for the research purposes).

These workbooks overview the availability of additive data in major LCA databases using the UNEP (2023) comprehensive list of additives with known use in plastics within the EU. SI 1 offers the lists of available data from the LCA databases reviewed and the CAS-RNs reviewed. SI 2 provides a tool that allows users to search for available additive data in their chosen LCA database. SI 3 provides the additive ranges used to assess additive coverage and trends in plastics data in CLA databases. The S4 workbook offers the full review outcomes of the UNEP (2023) additive list. Full details of this review and analysis of the results can be found in the accompanying article.

OpenLCA Nexus is an online repository for LCA data. It combines data offered by world-leading LCA data providers such as PE International (GaBi databases), the ecoinvent centre (ecoinvent), or the Joint Research Centre from the European Commission (ELCD).

This website contains a powerful search engine for LCA data that allows filtering requested data sets by database, or by year, geographical location, by industrial sector, and by product and price. Nexus contains free and “for purchase” data sets.

Website: http://www.lifecycleinitiative.org/

Hybrid LCA database generated using ecoinvent and EXIOBASE, i.e., each process of the original ecoinvent database is added new direct inputs (coming from EXIOBASE) deemed missing (e.g., services). Each process of the resulting hybrid database is thus not (or at least less) truncated and the calculated lifecycle emissions/impacts should therefore be closer to reality.

For license reasons, only the added inputs for each process of ecoinvent are provided (and not all the inputs).

Why are there two versions for hybrid-ecoinvent3.5?

One of the version corresponds to ecoinvent hybridized with the normal version of EXIOBASE and the other is hybridized with a capital-endogenized version of EXIOBASE.

What does capital endogenization do?

It matches capital goods formation to the value chains of products where they are required. In a more LCA way of speaking, EXIOBASE in its normal version does not allocate capital use to value chains. It's like if ecoinvent processes had no inputs of buildings, etc. in their unit process inventory. For more detail on this, refer to (Södersten et al., 2019) or (Miller et al., 2019).

So which version do I use?

Using the version "with capitals" gives a more comprehensive coverage. Using the "without capitals" version means that if a process of ecoinvent misses inputs of capital goods (e.g., a process does not include the company laptops of the employees), it won't be added. It comes with its fair share of assumptions and uncertainties however.

Why is it only available for hybrid-ecoinvent3.5?

The work used for capital endogenization is not available for exiobase3.8.1.

How do I use the dataset?

First, to use it, you will need both the corresponding ecoinvent [cut-off] and EXIOBASE [product x product] versions. For the reference year of EXIOBASE to-be-used, take 2011 if using the hybrid-ecoinvent3.5 and 2019 for hybrid-ecoinvent3.6 and 3.7.1.

In the four datasets of this package, only added inputs are given (i.e. inputs from EXIOBASE added to ecoinvent processes). Ecoinvent and EXIOBASE processes/sectors are not included, for copyright issues. You thus need both ecoinvent and EXIOBASE to calculate life cycle emissions/impacts.

Module to get ecoinvent in a Python format: https://github.com/majeau-bettez/ecospold2matrix (make sure to take the most up-to-date branch)

Module to get EXIOBASE in a Python format: https://github.com/konstantinstadler/pymrio (can also be installed with pip)

If you want to use the "with capitals" version of the hybrid database, you also need to use the capital endogenized version of EXIOBASE, available here: https://zenodo.org/record/3874309. Choose the pxp version of the year you plan to study (which should match with the year of the EXIOBASE version). You then need to normalize the capital matrix (i.e., divide by the total output x of EXIOBASE). Then, you simply add the normalized capital matrix (K) to the technology matrix (A) of EXIOBASE (see equation below).

Once you have all the data needed, you just need to apply a slightly modified version of the Leontief equation:

(\begin{equation} \textbf{q}^{hyb} = \begin{bmatrix} \textbf{C}^{lca}\cdot\textbf{S}^{lca} & \textbf{C}^{io}\cdot\textbf{S}^{io} \end{bmatrix} \cdot \left( \textbf{I} - \begin{bmatrix} \textbf{A}^{lca} & \textbf{C}^{d} \ \textbf{C}^{u} & \textbf{A}^{io}+\textbf{K}^{io} \end{bmatrix} \right) ^{-1} \cdot \left( \begin{bmatrix} \textbf{y}^{lca} \ 0 \end{bmatrix} \right) \end{equation})

qhyb gives the hybridized impact, i.e., the impacts of each process including the impacts generated by their new inputs.

Clca and Cio are the respective characterization matrices for ecoinvent and EXIOBASE.

Slca and Sio are the respective environmental extension matrices (or elementary flows in LCA terms) for ecoinvent and EXIOBASE.

I is the identity matrix.

Alca and Aio are the respective technology matrices for ecoinvent and EXIOBASE (the ones loaded with ecospold2matrix and pymrio).

Kio is the capital matrix. If you do not use the endogenized version, do not include this matrix in the calculation.

Cu (or upstream cut-offs) is the matrix that you get in this dataset.

Cd (or downstream cut-offs) is simply a matrix of zeros in the case of this application.

Finally you define your final demand (or functional unit/set of functional units for LCA) as ylca.

Can I use it with different versions/reference years of EXIOBASE?

Technically speaking, yes it will work, because the temporal aspect does not intervene in the determination of the hybrid database presented here. However, keep in mind that there might be some inconsistencies. For example, you would need to multiply each of the inputs of the datasets by a factor to account for inflation. Prices of ecoinvent (which were used to compile the hybrid databases, for all versions presented here) are defined in €2005.

What are the weird suite of numbers in the columns?

Ecoinvent processes are identified through unique identifiers (uuids) to which metadata (i.e., name, location, price, etc.) can be retraced with the appropriate metadata files in each dataset package.

Why is the equation (I-A)-1 and not A-1 like in LCA?

IO and LCA have the same computational background. In LCA however, the convention is to represents outputs and inputs in the technology matrix. That's why there is a diagonal of 1s (the outputs, i.e. functional units) and negative values elsewhere (inputs). In IO, the technology matrix does not include outputs and only registers inputs as positive values. In the end, it is just a convention difference. If we call T the technology matrix of LCA and A the technology matrix of IO we have T = I-A. When you load ecoinvent using ecospold2matrix, the resulting version of ecoinvent will already be in IO convention and you won't have to bother with it.

Pymrio does not provide a characterization matrix for EXIOBASE, what do I do?

You can find an up-to-date characterization matrix (with Impact World+) for environmental extensions of EXIOBASE here: https://zenodo.org/record/3890339

If you want to match characterization across both EXIOBASE and ecoinvent (which you should do), here you can find a characterization matrix with Impact World+ for ecoinvent: https://zenodo.org/record/3890367

It's too complicated...

The custom software that was used to develop these datasets already deals with some of the steps described. Go check it out: https://github.com/MaximeAgez/pylcaio. You can also generate your own hybrid version of ecoinvent using this software (you can play with some parameters like correction for double counting, inflation rate, change price data to be used, etc.). As of pylcaio v2.1, the resulting hybrid database (generated directly by pylcaio) can be exported to and manipulated in brightway2.

Where can I get more information?

The whole methodology is detailed in (Agez et al., 2021).

Life Cycle Assessment (LCA) is a compilation and evaluation of the inputs, outputs and potential environmental impacts of a product system throughout its life cycle. LCA describes the life cycle as consecutive and interlinked stages of a product system extending from the acquisition of raw materials through materials processing, technology manufacturing/construction, technology use/maintenance/upgrade, and the technology retirement. LCA also provides a framework for understanding economic and social impacts. In an LCA, data are collected at the unit process level, intended to represent a single industrial activity, in this case the food and agriculture industry. Each single industrial activity (a) produces product and sometimes co-products; (b) uses resources from the environment; (c) uses resources from other unit processes in the technosphere; and (d) generates emissions to the environment. In an LCA, the inventory analysis combines unit process data for the life cycle and the impact assessment estimates the impact associated with activities and flows to and from the environment for the inventory. Datasets have been developed for the LCA Commons in response to a national need for data representing US operations. The LCA Commons database is an open access database developed by the United States Department of Agriculture (USDA) National Agricultural Library (NAL) for use in LCAs to support policy assessment, technology implementation decision-making, and publicly disclosed comparative product or technology assertions. K7612-17: Photo by Scott Bauer; http://www.ars.usda.gov/is/graphics/photos/sep97/k7612-17.htm Resources in this dataset:Resource Title: LCA Commons website. File Name: Web Page, url: https://www.lcacommons.gov/

A comprehensive dataset of top immigration attorneys for H-1B Visa sponsorships in 2025, including salary data, petition trends, and employer insights. Updated annually with the latest trends and employer behavior regarding H-1B visa sponsorship.

General information

This dataset is part of the MSc thesis `Consensus-based single-score life cycle assessment for space missions' at Search results | TU Delft Repositories

An older version of the dataset was also used for the conference paper `A Consensus-Based Single-Score for Life Cycle Assessment of Space Missions: Preliminary Results', which can be found through the DOI 10.13009/EUCASS2023-571, or on EUCASS's website (www.eucass.eu - EUCASS Full Papers)

Abstract of the thesis

With a continuously growing number of satellites in orbit, it becomes increasingly important to assess their impacts on the Earth's environment in a standardised manner. While interest in Life Cycle Assessment (LCA) for space missions has gained in strength in the past few years – particularly in Europe – no consensus has yet been reached on a single-score LCA system.

In this thesis, a consensus-based space LCA single-score is created through an international survey of experts. The report demonstrates retroactively the single-score’s use for the ecodesign of the Delft University of Technology’s Delfi-n3Xt space mission. A discussion is also held on ways of implementing the single-score into early design phases. Overall, this thesis highlights the importance of an easy-to-understand LCA tool for space systems. It shows the necessity for a tool that is implementable during the design phase of the mission, to incentivise space actors to further consider environmental impacts.

Contents of the dataset

This dataset contains Excel sheets with:

1. the analysed answers of the survey done to reach a consensus on a space LCA single-score
2. the results of Delfi-n3Xt's LCA
3. the calculations made to obtain the recommended weighting factors.

Important note:

A specific README.txt file is part of this dataset and is recommended to be read first

This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass-based biofuel production in Michigan. Using three life cycle assessment (LCA) databases— US lifecycle inventory database (USLCI), GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real-world application..., , , # Uncertainties in Greenhouse Gas Emission Factors: A Comprehensive Analysis of Switchgrass-Based Biofuel Production

https://doi.org/10.5061/dryad.rn8pk0pm8

Description of the Data and file structure

Monte Carlo results.xlsx

The Monte Carlo results.xlsx file contains comprehensive data derived from Monte Carlo simulations and distribution functions used for GHG emission factors and input parameters. The file is organized into two primary sheets:

Sheet 1: "Monte Carlo Simulation"

This sheet presents the results of the Monte Carlo simulations.

Sheet 2: "Distribution"

This sheet includes the distribution functions used for GHG emission factors and input parameters.

BACKGROUND: Both human health and the health systems we depend on are increasingly threatened by a range of environmental crises, including climate change. Paradoxically, healthcare provision is a significant driver of environmental pollution. Despite initial research suggesting surgical activities are associated with a large environmental footprint, rigorous studies that utilize gold standard lifecycle assessment (LCA) methods are lacking. Furthermore, existing LCAs in surgery have been subject to insufficient critical evaluation. Only three systematic reviews have explored healthcare sustainability generally and none has focused specifically on surgical services. To address these research gaps, this systematic review aims to assess the state of LCA practice within surgical services using a standardized, LCA-specific reporting framework. This review is expected to be of use to a wide range of stakeholders, including policymakers and healthcare professionals who are interested in reducing the environmental impacts of surgical services, as well as LCA practitioners who are interested in undertaking assessments within this burgeoning field.

METHODS: This systematic review will be guided by the Standardized Technique for Assessing and Reporting Reviews of Life Cycle Assessment Data (STARR-LCA), and its protocol has been designed to conform to the RepOrting standards for Systematic Evidence Syntheses (ROSES). Three bibliographic databases (Scopus, PubMed and Embase) were searched using a query string combining search terms that relate to LCA and surgery. A supplementary search of the grey literature was also undertaken. Identified entries will be screened by two independent reviewers according to predetermined inclusion and exclusion criteria, with a third reviewer tasked with conflict resolution. Data from eligible studies will be extracted and tabulated into predetermined sheets and studies will be critically appraised according to a priori-defined criteria. Quantitative data will be represented graphically, with emphasis on visualizing hotspot patterns between studies. Qualitative data will be brought together in a narrative synthesis so as to contextualize study findings, discuss methodological decision-making, highlight strengths and limitations of the current evidence base, and ultimately draw conclusions about the state of LCA practice within surgical services, including key challenges moving forward.

The calculations for this work were performed in Interactive Python notebooks that can be used to reproduce our calculations, or build on top of them. More information on IPython can be found here: http://ipython.readthedocs.io/en/stable/. For those readers who only wish to view the calculations, we provide all calculation files in .html, which can be opened in any internet browser.

• Mutations in this gene can cause both syndromic and non-syndromic forms of RP or LCA# Mutations in this gene can cause RP as well as other retinal dystrophiesA summary of the functional annotation for RP/LCA driver genes.

A detailed analysis of H-1B visa sponsorship trends, featuring data on labor certifications, top sponsoring employers, most common job titles, leading immigration law firms, key industries, and geographic distribution. This dataset provides valuable insights into employment-based immigration patterns, helping professionals, employers, and policymakers make informed decisions.

This supporting information provides the numerical results for (1) the process parameters' regionalization (S4); (2) the regionalized LCA climate change results for three different paper grades (S7); (3) the destinations of the mixed paper bales exiting Quebec's sorting centers (S8); (4) the LCA results for the four scenarios for Quebec's case study (S9) and (5) the sensitivity analysis results, performed on the most uncertain parameters from Quebec's case study (S10).

The papers and information involved in a systematic literature review on uncertainties in building LCA ( Jie Li, et al, 2023) 1. Process of systematic literature search and screening 2. Extracted information, and heatmaps of two threshold options 3. LCA report template with basic assessment and uncertainty assessment

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This material is part of the free Environmental Performance in Construction (EPiC) Database. The EPiC Database contains embodied environmental flow coefficients for 250+ construction materials using a comprehensive hybrid life cycle inventory approach.Polypropylene (PP) is a thermoplastic polymer and second most produced plastic. It has similar properties to polyethylene (PE), including high impact strength and ductility, but is harder and more resistant to heat.PP is produced by polymerising chains of propylene monomers through different catalysts. Different catalysts can result in different PP properties. PP is then moulded or extruded. Different additives can enhance the properties of PP, e.g. antistatic, dust resistant, and colouring.PP is mostly used in construction as a membrane (including as a water vapour membrane).

This is the supplementary information related to the manuscript - "ame product, different score: how methodological differences affect EPD results" . The purpose of this study to analyse the causes for inconsistency and the consequences in terms of difference in the results across the Life Cycle Assessment (LCA) models underlying the EPDs. The supplementary information consists of three files: SI 1 - Details on the choice of Nordic programme operators who publish product category rules and product specific rules. This documents further includes the details of the life inventory developed to perform the LCA of a triple glazed window using the rule sets published by different programme operators. SI 2 - Life cycle inventory data for all scenarios considered in this study in machine readable format SI 3 - Life cycle impact assessment for all scenarios using EN 15804 + A2 Method V1.02 / EF 3.0 normalization and weighting set.

Although the Japanese feed-in tariff was introduced to expand renewable energy, leading to the expansion of palm kernel shell (PKS) use, the greenhouse gas (GHG) emission reduction effect is evaluated using the limited life-cycle of PKS, focusing on processes after PKS generation point. Therefore, this study aimed to elucidate the life-cycle GHG emissions of power generation using PKS. We targeted two PKS-firing power plants as these are the first two instances of the use of PKS in power plants in Japan. A system boundary was established to cover palm plantation management in Indonesia and Malaysia, as both power plants import PKS from these countries. The GHG emissions were derived from land-use change, palm plantation, oil extraction, PKS transportation, and power plants. Six scenarios were examined for the emissions based on the type of land-use change and the existence of biogas capture in oil extraction. CO2 emissions from PKS combustion were also calculated by assuming that carbon...

Nearly three billion people in low- and middle-income countries (LMICs) rely on polluting fuels, resulting in millions of avoidable deaths annually. Polluting fuels also emit short-lived climate forcers and greenhouse gases (GHGs). Liquefied petroleum gas (LPG) and grid-based electricity are scalable alternatives to polluting fuels but have raised climate and health concerns. Here, we compare emissions and climate impacts of a business-as-usual household cooking fuel trajectory to four large-scale transitions to gas and/or grid electricity in 77 LMICs. We account for upstream and end-use emissions from gas and electric cooking, assuming electrical grids evolve according to the 2022 World Energy Outlook’s “Stated Policies” Scenario. We input the emissions into a reduced-complexity climate model to estimate radiative forcing and temperature changes associated with each scenario. We find full transitions to LPG and/or electricity decrease emissions from both well-mixed GHG and short-lived climate forcers, resulting in a roughly 5 millikelvin global temperature reduction by 2040. Transitions to LPG and/or electricity also reduce annual emissions of PM2.5 by over 6 Mt (99%) by 2040, which would substantially lower health risks from Household Air Pollution. Methods Primary input data was collected from the following sources:

Baseline household fuel choices - WHO household energy database (https://www.nature.com/articles/s41467-021-26036-x) End-use emissions - US EPA lifecycle assessment of household fuels (https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=339679&Lab=NRMRL&simplesearch=0&showcriteria=2&sortby=pubDate&timstype=Published+Report&datebeginpublishedpresented) Upstream emissions - Argonne National Labs GREET Model (https://greet.es.anl.gov/index.php) Current and future population estimates - UNECA (http://data.un.org/Explorer.aspx?d=EDATA)

Input data was processed by defining household fuel choice scenarios, estimating national household fuel consumption based on these scenarios, and applying fuel-specific emission factors to create country-specific emission pathways. These emission pathways were input into the FaIR model (https://zenodo.org/record/5513022#.Yt_jfHbMLb0) which generated additional data for each scenario including time series of pollution concentrations, radiative forcing, and temperature changes.