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/

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.

This document provides instructions for editing and submitting unit process or product system models to the USDA LCA Commons life cycle inventory (LCI) database. The LCA Commons LCI database uses the openLCA life cycle modeling tool's database schema. Therefore, this document describes how to import and edit data in openLCA and name and classify flows such that they properly import into and operate in the database. This document also describes metadata or documentation requirements for posting models to the LCA Commons. This document is an evolving standard for LCA Commons data. As USDA-NAL continues to gain experience in managing a general purpose LCI database and global conventions continue to evolve, so too will the LCA Commons Submission Guidelines. Resources in this dataset:Resource Title: LCA Commons Submission Guidelines_12/09/2015. File Name: lcaCommonsSubmissionGuidelines_Final_2015-12-09.pdf

About This Dataset

This dataset provides a comprehensive record of Labor Condition Application (LCA) disclosures for H1B visa petitions filed with the U.S. Department of Labor (DOL) from 2020 to 2024. It has been cleaned and prepared for public analysis to offer valuable insights into employment trends, job categories, salaries, and geographic distribution of H1B workers.

What is H1B?

The H1B visa is a non-immigrant visa that allows U.S. companies to employ foreign workers in specialty occupations requiring theoretical or technical expertise. These roles typically include fields such as IT, engineering, finance, healthcare, and more. The H1B program is critical for addressing skill gaps in the U.S. workforce and supporting economic growth.

What is LCA?

The Labor Condition Application (LCA) is a prerequisite for filing an H1B visa petition. Employers submit the LCA to the DOL to ensure compliance with wage and working condition requirements. The LCA process protects both U.S. workers and foreign employees by enforcing: - Payment of prevailing wages. - Assurance that hiring foreign workers will not adversely affect local labor conditions.

Each LCA disclosure contains information about the employer, job title, job location, wages, and visa classification.

Why Analyze LCA Disclosure Data (2020-2024)?

The dataset spans a crucial period (2020-2024) characterized by: - Pandemic Impact: Changes in employment patterns and visa policies due to COVID-19. - Remote Work Trends: Shifts in work location dynamics for H1B visa holders. - Tech Layoffs and Restructuring: Evolving job roles and industry demands, especially in tech. - Economic Recovery: Insights into how industries and geographic regions rebounded post-pandemic.

Analyzing this data can provide: 1. Employment Trends: Discover trends in job roles, industries, and geographic locations hiring H1B workers. 2. Wage Comparisons: Compare wages across job titles, industries, and states. 3. Policy Insights: Assess the impact of government policies on foreign employment. 4. Geographic Distribution: Identify areas with the highest demand for H1B workers. 5. Industry Insights: Explore the reliance of various industries on foreign talent.

Key Features of This Dataset

• Years Covered: 2020 to 2024.
• Number of Records: Includes all LCAs filed during this period.
• Attributes: Comprehensive fields such as case number, job title, employer name, wage data, work location, and visa classification.
• Source: Data is based on public disclosures from the U.S. Department of Labor.

Who Can Use This Dataset?

• Researchers: To study the economic impact of H1B workers and labor trends.
• Policymakers: To analyze the effects of visa policies on labor markets.
• Job Seekers: To explore prevailing wages and in-demand job roles for H1B petitions.
• Businesses: To understand competitive trends in hiring foreign talent.

An excel template with data elements and conventions corresponding to the openLCA unit process data model. Includes LCA Commons data and metadata guidelines and definitions Resources in this dataset:Resource Title: READ ME - data dictionary. File Name: lcaCommonsSubmissionGuidelines_FINAL_2014-09-22.pdfResource Title: US Federal LCA Commons Life Cycle Inventory Unit Process Template. File Name: FedLCA_LCI_template_blank EK 7-30-2015.xlsxResource Description: Instructions: This template should be used for life cycle inventory (LCI) unit process development and is associated with an openLCA plugin to import these data into an openLCA database. See www.openLCA.org to download the latest release of openLCA for free, and to access available plugins.

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).

Explore the booming Life Cycle Assessment (LCA) database market, driven by sustainability mandates and environmental awareness. Discover market size, CAGR, key drivers, restraints, and regional growth.

This is a step-by-step tutorial for getting started with OpenLCA software. This tutorial contains 1 database (.zolca), 2 handouts, and 2 accompanying lecture slides. [Disclaimer] the database used for this tutorial is compiled from two open-access databases (ELCD database and USDA crop database v1.1).

This database accompanies the JRC Science for Policy report 'Biomass production, supply, uses and flows in the European Union. First results from an integrated assessment' (EUR 28993 EN), as well as the JRC Factsheet entitled "Life Cycle GHG impacts of bio-based commodities". It contains a list of 380 pathways characterized by LCIA results for multiple impact categories and various biobased commodities. All the LCIA were either completely calculated or re-elaborated within the JRC.

Life cycle analysis (LCA) is an environmental assessment method that quantifies the environmental performance of a product system over its entire lifetime, from cradle to grave. Based on a set of relevant metrics, the method is aptly suited for comparing the environmental performance of competing products systems. This file contains LCA data and results for electric power production including geothermal power. The LCA for electric power has been broken down into two life cycle stages, namely plant and fuel cycles.

Relevant metrics include the energy ratio and greenhouse gas (GHG) ratios, where the former is the ratio of system input energy to total lifetime electrical energy out and the latter is the ratio of the sum of all incurred greenhouse gases (in CO2 equivalents) divided by the same energy output.

Specific information included herein are material to power (MPR) ratios for a range of power technologies for conventional thermoelectric, renewables (including three geothermal power technologies), and coproduced natural gas/geothermal power. For the geothermal power scenarios, the MPRs include the casing, cement, diesel, and water requirements for drilling wells and topside piping.

Also included herein are energy and GHG ratios for plant and fuel cycle stages for the range of considered electricity generating technologies.

Some of this information are MPR data extracted directly from the literature or from models (eg. ICARUS - a subset of ASPEN models) and others (energy and GHG ratios) are results calculated using GREET models and MPR data. MPR data for wells included herein were based on the Argonne well materials model and GETEM well count results.

Life Cycle Analysis (LCA) is a comprehensive form of analysis that utilizes the principles of Life Cycle Assessment, Life Cycle Cost Analysis, and various other methods to evaluate the environmental, economic, and social attributes of energy systems ranging from the extraction of raw materials from the ground to the use of the energy carrier to perform work (commonly referred to as the “life cycle” of a product). Results are used to inform research at NETL and evaluate energy options from a National perspective.

Dataset

This dataset was created by Tong Hui Kang

Released under CC0: Public Domain

Contents

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.Silicone is a synthetic polymer compound, with a molecular chain consisting of alternating silicon and oxygen atoms. Depending on the production process, it can take the form of a liquid, gel, elastomer or resin. It is widely used for plumbing applications, due to its adhesive properties, ability to repel water, flexibility, stability in high/low temperatures and antimicrobial properties. Silicone is derived from silicon, which is extracted from quartz, sand or other sources. To produce silicone, the base material (silicon) goes through several chemical and distillation processes. In construction, silicone is used as a sealant, adhesive or electrical insulation amongst other uses. It is also widely used in manufacturing, for production of gaskets, moulds, coatings, caulks, household goods and plumbing hardware.

This data were created during the research project ACV Bio, funded by the French agency for ecological transition (ADEME), and the French ministry for ecological transition. Its main objective was the production of Life Cycle Inventory (LCI) and Life Cycle Assessement (LCA) data on a variety of plant and animal products from French organic farming at the farm gate. The dataset produced contains 173 LCAs of organic crop and animal products. A diversity of production modes was covered for the majority of the products. LCA results for the Ecological Footprint 2.0, Cumulative energy demand, Land competition and Biodiversity loss characterization methods are available in this Excel File. LCA results are expressed both per kg of product and per ha of land occupied. The three non-LCA indicators – DCF, AEI and PTFI – are also available at the end of the Excel file. These data represent a diversity of LCA of French organic products but are not representative of national or regional average. Therefore, we recommend using these data to characterize part of the diversity of organic farming systems and some of their environmental impacts; to identify areas for improvement; to perform eco-design and sensitivity analysis; or/and to carry out system choices in a given context.

This dataset contains food items and ingredients included in commonly used LCA databases ecoinvent, Agribalyse, World Food LCA Database, and Agri-footprint, and corresponding FoodEx2 and LanguaL classification codes. The dataset is related to D2.1. Systematic approach for combining FW and LCA data, where its creation methods are documented.

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).

Supplementary Material for the paper "Life Cycle Assessment (LCA)-based tools for the eco-design of wooden furniture". The ILCD file can be uploaded on LCA software, where an Ecoinvent database is available. This tool aims to support LCA of wooden furniture, as detailed in the paper.

This excel workbook is a compilation of the major metadata schemas for life cycle assessment. Resources in this dataset:Resource Title: LCADomain_MetadataSchema_Inventory_v1_0_2. File Name: LCADomain_MetadataSchema_Inventory_v1_0_2.xlsm

TRACIv2.1 (Bare 2012) is a life cycle impact assessment (LCIA) method. LCIA methods are collections of characterization factors, which are measures of relative potency or potential impact, for a given flow (e.g., NH3 to air) for a set of impact categories (e.g., acidification), provided in units of potency or impact equivalents per unit mass of the flowable associated with a given context (e.g., 1.88 kg SO2 eq/kg NH3 emitted to air). LCIA methods are typically used along with life cycle inventory data to estimate potential impacts in life cycle assessment (LCA). The FEDEFL or Federal LCA Commons Elementary Flow List (EPA 2019) is the standardized elementary flow list for use with data meeting the US Federal LCA Commons data guidelines. In this dataset, TRACv2.1 is applied to FEDEFL v1.0.7 flows. This dataset was created by the LCIA Formatter v1.0 (https://github.com/USEPA/LCIAformatter). The LCIA Formatter is a tool for providing standardized life cycle impact assessment methods with characterization factors transparently applied to flows from an authoritative flow list, like the FEDEFL. The LCIA Formatter draws from the original TRACIv2.1 source file and the TRACI->FEDEFL flow mapping. The LCIA formatter accesses this mapping file through the fedelemflowlist tool available @ https://github.com/USEPA/Federal-LCA-Commons-Elementary-Flow-List. This mapping file and a note about the mapping are provided separately. Where a flow context is less specific in the FEDEFL (e.g., air) relative to the TRACIv2.1 flow contexts (e.g., air/rural), the LCIA Formatter applies the average of the relevant characterization factors from TRACIv2.1 to the FEDEFL flow. The zip file is a compressed archive of JSON files following the openLCA schema at https://greendelta.github.io/olca-schema. Usage Notes for zip file: This file was tested to correctly import into an openLCA v1.10 database already containing flows from the FEDEFL v1.0.7. It will provide matching characterization factors for any FEDEFL v1.0 to 1.0.7 elementary flow already present in the database. This file itself does not contain the elementary flows. The complete FEDEFL v1.0.7 flow list may be retrieved from the Federal LCA Commons elementary flow list repository @ https://www.lcacommons.gov The .parquet file is in the LCIA Formatter's LCIAmethod format. https://github.com/USEPA/LCIAformatter/blob/v1.0.0/format%20specs/LCIAmethod.md Usage notes for parquet file: The .parquet file can be read in by any Apache parquet reader. References Bare, J. C. 2012. Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts (TRACI), Version 2.1 - User’s Manual https://www.epa.gov/chemical-research/tool-reduction-and-assessment-chemicals-and-other-environmental-impacts-traci EPA 2019. The Federal LCA Commons Elementary Flow List: Background, Approach, Description and Recommendations for Use. https://cfpub.epa.gov/si/si_public_record_Report.cfm?dirEntryId=347251. This dataset is associated with the following publication: Young, B., M. Srocka, W. Ingwersen, B. Morelli, S. Cashman, and A. Henderson. LCIA Formatter. Journal of Open Source Software. Journal of Open Source Software, 6(66): 3392, (2021).

The demand for life cycle assessments (LCA) is growing rapidly, which leads to an increasing demand of life cycle inventory (LCI) data. While the LCA community has made significant progress in developing LCI databases for diverse applications, challenges still need to be addressed. This perspective summarizes the current data gaps, transparency, and uncertainty aspects of existing LCI databases. Additionally, we survey and discuss novel techniques for LCI data generation, dissemination, and validation. We propose key future directions for LCI development efforts to address these challenges, including leveraging scientific and technical advances such as the Internet of Things (IoT), machine learning, and blockchain/cloud platforms. Adopting these advanced technologies can significantly improve the quality and accessibility of LCI data, thereby facilitating more accurate and reliable LCA studies.