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 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

(i) The CPM LCA Database is developed within the Swedish Life Cycle Center, and is a result of the continuous work to establish transparent and quality reviewed LCA data. The Swedish Life Cycle Center (founded in 1996 and formerly called CPM) is a center of excellence for the advance of life cycle thinking in industry and other parts of society through research, implementation, communication and exchange of experience on life cycle management. The mission is to improve the environmental performance of products and services, as a natural part of sustainable development. The Center has been instrumental for the development and adoption the life cycle perspective in Swedish companies and has made important contributions to international standardization in the life cycle field. More information about the Center, see www.lifecyclecenter.se. The Swedish Life Cycle Center owns the CPM LCA Database, which is today maintained by Environmental Systems Analysis at the Department of Energy and Environment at Chalmers University of Technology. (ii) All LCI datasets can be viewed in in three formats: the SPINE format, a format compatible with the ISO/TS 14048 LCA data documentation format criteria, and in the ILCD format. Three impact assessment models: EPS, EDIP, and ECO-Indicator, can be viewed in the IA98 format. Also a simple IA calculator is provided where the environmental impact of each LCI dataset can be calculated based on the three different IA methods. (iii) unknown (iv) unknown

The global Life Cycle Assessment (LCA) Database market size is projected to reach USD 247.4 million by 2033, exhibiting a CAGR of 12.1% during the forecast period. The increasing demand for sustainable products and services, along with stringent environmental regulations, is driving the growth of the market. Additionally, the growing adoption of LCA in various industries, such as manufacturing, construction, and transportation, is contributing to the market's expansion. The on-premise segment held a dominant market share in 2025, owing to the high cost of cloud-based solutions and the need for data security and control among enterprises. Key trends influencing the market include the rise of Industry 4.0 technologies, which enable real-time data collection and analysis, and the increasing adoption of cloud-based LCA platforms, which offer flexibility and scalability. However, the high cost of LCA software and the lack of trained professionals may pose challenges to the market's growth. Key players in the market include GHG Protocol, Ecochain, Sphera, openLCA Nexus, AssessCCUS, Ecoinvent, openLCA, Swedish Life Cycle Center, Psilca, Fraunhofer IBP, Metsims - Sustainability Consulting, and Carbon Minds. North America and Europe are expected to remain the dominant regional markets, driven by the presence of a large number of environmental regulations and the growing demand for sustainable products and services in these regions.

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 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).

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.

This is a high-resolution dataset of building design characteristics, life cycle inventories, and environmental impact assessment results for 292 building projects in the United States and Canada. The dataset contains harmonized and non-aggregated LCA model results across life cycle stages, building elements, and building materials to enable detailed analysis, comparisons, and data reuse. It includes over 90 building design and LCA features to assess distributions and trends of material use and environmental impacts. Uniquely, the data were crowd-sourced from designers conducting LCAs of real-world building projects.The dataset is composed of two files:buildings_metadata.xlsx includes all project metadata and LCA parameters for every project associated with a unique index number to cross-reference across other files. This also includes various calculated summaries of LCI and LCIA totals and intensities per project.full_lca_results.xlsx includes LCI and LCIA results per material and life cycle stage of each building project.data_glossary.xlsx identifies and defines each feature of the dataset including its name, data structure, syntax, units, descriptions, and more.material_definitions.xlsx a full list of material groups, types, and descriptions of what they include.This dataset is documented and described in a Data Descriptor, published and citable as follows:Benke, B., Chafart, M., Shen, Y. et al. A Harmonized Dataset of High-Resolution Embodied Life Cycle Assessment Results for Buildings in North America. Sci Data 12, 1085 (2025). https://doi.org/10.1038/s41597-025-05216-0When referencing this work, please cite both the Data Descriptor and the most recent dataset version on this Fighshare DOI.The dataset also appears on the Github repository: https://github.com/Life-Cycle-Lab/wblca-benchmark-v2-data. Access to the code used to prepare this dataset is available on an additional Github repository: https://github.com/Life-Cycle-Lab/wblca-benchmark-v2-data-preparation.Release Notes:2025-02-24 - First public release2025-05-05 - Title revised and two supplementary dataset files added: data_glossary.xlsx and material_definitions.xlsx.

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.

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.

The production process of many active pharmaceutical ingredients such as sitagliptin could cause severe environmental problems because of the use of toxic chemical materials and production infrastructure, energy consumption, and waste treatment. The environmental impacts of the sitagliptin production process were estimated with a life cycle assessment (LCA) method, which suggested that the use of chemical materials provided the major environmental impacts. Both methods of Eco-indicator 99 and ReCiPe endpoint confirmed that chemical feedstock accounted for 83% and 70% of life-cycle impact, respectively. Among all the chemical materials used in the sitagliptin production process, trifluoroacetic anhydride was identified as the largest influential factor in most impact categories according to the results of the ReCiPe midpoints’ method. Therefore, high-throughput screening was performed to seek for greener chemical substitutes to replace the target chemical (i.e., trifluoroacetic anhydride) by the following three steps. First, the 30 most similar chemicals were obtained from 2 million candidate alternatives in the PubChem database on the basis of their molecular descriptors. Thereafter, deep learning neural network models were developed to predict life-cycle impact according to the chemicals in Ecoinvent v3.5 database with known LCA values and corresponding molecular descriptors. Finally, 1,2-ethanediyl ester was proved to be one of the potential greener substitutes after the LCA data of these similar chemicals were predicted using the well-trained machine learning models. The case study demonstrated the applicability of the novel framework to screen green chemical substitutes and optimize the pharmaceutical manufacturing process.

This study presents the development of a novel ecodesign approach based on a parametric life cycle assessment (LCA). The developed method allows for the comparison of environmental impacts of a vast number of different product configurations, which are derived automatically by determining every possible combination of the given design options. The life cycle model features a stochastic failure and repair simulation to account for a wide range of use cases as well as a recycling simulation that can determine the environmentally optimal recycling route. The developed method is tested on an exemplary case study of a smartphone. Despite efficiency limitations of the accompanying software tool prototype that was developed and used for the case study, it could be shown that the method allows to identify the environmental influence of different design options as well as the product configuration with the least annual global warming potential.

This file contains the database Excel file with data and calculations on failure and repair statistics, material compositions, and input tables for the software tool prototype developed in the study. It can be inspected as is to understand the underlying data and procedure presented in the study or used as an input for the Python source code to run the LCA model, which can be found here: 10.5281/zenodo.10611008

Note: References to licensed environmental datasets from the Sphera and ecoinvent databases have been deleted in the published version. In order to run the software tool, please add the respective values for the Global Warming Potential (or alternative impact categories) in the "processes_data" sheet and delete the suffix "_noLCIA" from the file name.

Purpose: The need to assess the sustainability attributes of the United States beef industry is underscored by its importance to food security locally and globally. A life cycle assessment (LCA) of the US beef value chain was conducted to develop baseline information on the environmental impacts of the industry including metrics of the cradle-to-farm gate (feed production, cow-calf, and feedlot operations) and post-farm gate (packing, case-ready, retail, restaurant, and consumer) segments. Methods: Cattle production (cradle-to-farm gate) data were obtained using the integrated farm system model (IFSM) supported with production data from the Roman L. Hruska US Meat Animal Research Center (USMARC). Primary data for the packing and case-ready phases were obtained from packers that jointly processed nearly 60% of US beef while retail and restaurant primary data represented 8 and 6%, respectively, of each sector. Consumer data were obtained from public databases and literature. The functional unit or consumer benefit (CB) was 1 kg of consumed, boneless, edible beef. The relative environmental impacts of processes along the full beef value chain were assessed using a third party validated BASF Corporation Eco-Efficiency Analysis methodology. Results and discussion: Value chain LCA results indicated that the feed and cattle production phases were the largest contributors to most environmental impact categories. Impact metrics included water emissions (7005 L diluted water eq/CB), cumulative energy demand (1110 MJ/CB), and land use (47.4 m2a eq/CB). Air emissions were acidification potential (726 g SO2 eq/CB), photochemical ozone creation potential (146.5 g C2H4 eq/CB), global warming potential (48.4 kg CO2 eq/CB), and ozone depletion potential (1686 μg CFC11 eq/CB). The remaining metrics calculated were abiotic depletion potential (10.3 mg Ag eq/CB), consumptive water use (2558 L eq/CB), and solid waste (369 g municipal waste eq/CB). Of the relative points adding up to 1 for each impact category, the feed phase contributed 0.93 to the human toxicity potential. Conclusions: This LCA is the first of its kind for beef and has been third party verified in accordance with ISO 14040:2006a and 14044:2006b and 14045:2012 standards. An expanded nationwide study of beef cattle production is now being performed with region-specific cattle production data aimed at identifying region-level benchmarks and opportunities for further improvement in US beef sustainability. Resources in this dataset:Resource Title: Electronic Supplementary Material ESM 1 - Tables S1 to S11 (docx). File Name: Web Page, url: https://static-content.springer.com/esm/art:10.1007/s11367-018-1464-6/MediaObjects/11367_2018_1464_MOESM1_ESM.docx Direct download, docx. Table S1: Feed phase input data (resource use and emissions) from USMARC and IFSM simulations used in the U.S. beef life cycle impact assessment and sources of their life-cycle inventories (LCI). Table S2: Cattle phase input data (resource use and emissions) from USMARC and IFSM simulations in the U.S. beef life cycle impact assessment and the sources of their respective life-cycle inventories (LCI). Table S3: Packing and case-ready phases input data (resource use and emissions) used in the U.S. beef life cycle impact assessment and the sources of their respective life-cycle inventories (LCI). Allocation factor of case-ready (i.e. % packaged at case ready) = 0.63. Table S4: Retail and consumer phases input data (resource use and emissions) used in U.S. beef life cycle impact assessment and their respective life-cycle inventory (LCI) sources. Allocation factor for retail and consumer (i.e. at-home consumption portion of total consumption sold through retail) = 0.47. Table S5: Restaurant phase input data (resource use and emissions) used in U.S. beef life cycle impact assessment and their respective life-cycle inventory (LCI) sources. Allocation factor (i.e. restaurant fraction of total beef consumption) = 0.53. Table S6: Essential raw materials considered in the U.S. beef life cycle impact assessment and respective weighting factors used for the determination of their Abiotic Depletion Potential (ADP). Table S7: Scoring system for toxic properties described by H-phrases for U.S. beef life cycle impact assessment (Landsiedel and Saling (2002) before our modification). Table S8: Land occupation and transformation weighting factors for U.S. beef life cycle impact assessment based on Ecosystem Damage Potentials (EDPs) from the Ecoinvent 2.2 life cycle inventory database (Frischknecht et al. 2005). Table S9: Air emissions and their respective weighting (equivalence) factors used in U.S. beef life cycle impact assessment. Table S10: Solid waste relative disposal costs used in U.S. beef life cycle impact assessment (Klein 2011). Table S11: Water emissions categories and their respective weighting factors based on regional regulatory limits used in the U.S. beef life cycle assessment.

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.

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.

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

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.

Hemp-based products are gaining research interest due to their diverse applications and eco-friendly cultivation practices. Recognized as a key contributor to the United Nations’ Sustainable Development Goals, industrial hemp is emerging as a vital biobased material for eco-friendly products. However, the absence of industrial hemp product and process data in available life cycle inventory (LCI) databases poses a challenge for the emerging industry due to a lack of standardized practices and global market adoption. Notably, the industry faced legal restrictions in the U.S., leading to a paucity of industrial hemp research and technology development since the 1930s. This study addresses the data gaps hindering comprehensive environmental impact assessments of industrial hemp products. A mixed-method approach was employed to review relevant literature and develop an inventory of product and process data for life cycle assessment (LCA). Data were extracted for pre-harvest operations, including fertilizer use, seeding density, irrigation, agricultural machinery, diesel use, electricity use, and harvest yield. Post-harvest operations data included decortication processes, extraction yield, and carbon storage. Machine learning techniques were explored for data processing and prediction (interpolation and extrapolation). The resulting datasets can facilitate sustainability assessments and support industry competitiveness and growth by reducing or eliminating the labor-intensive and time-consuming processes of creating LCI amidst data scarcity in the hemp industry. This work highlights the need for comprehensive LCI databases and thorough LCA studies to guide hemp-based and other emerging biobased industries through innovation challenges. Future research will consider more recent studies and explore region-specific datasets to reduce LCA variability and uncertainties in environmental impact assessments.

In the Chinese Food Life Cycle Assessment Database (CFLCAD), Greenhouse Gas Emissions (GHGE) for 80 food items, Water Use (WU) for 93 food items and Land Use (LU) for 50 food items are collected through a literature review. The CFLCAD applies conversion factors for the edible portion of food, food loss ratio and processing, storage, packaging, transportation, and food preparation stages to estimate the environmental footprints of food from production to consumption. Similar food groups and recipes are used to match those food items without LCA value in the Chinese food composition table, resulting in a total of 17 food groups in the database. Date: 2022-01-11 Date Submitted: 2022-01-12

Results of application of the proposed method for ranking of LCI datasets from a bibliographic survey with 57 Brazilian LCI datasets.