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

(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.

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).

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 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).

The global Life Cycle Assessment Database market size was valued at USD 270.0 million in 2021 and is projected to grow from USD 320.1 million in 2023 to USD 798.5 million by 2033, exhibiting a CAGR of 12.0% during the forecast period (2023-2033). The market growth can be attributed to the increasing demand for environmental sustainability, growing awareness about climate change, and the adoption of life cycle assessment (LCA) in various industries. Key drivers for the market include the growing demand for transparency and sustainability in supply chains, the implementation of regulations related to carbon emissions and environmental protection, and the technological advancements in data collection and analysis. The market is segmented by application into enterprises, municipalities, and others, and by type into on-premise and cloud-based solutions. Major companies operating 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.

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.

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 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.

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 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.Rubber is a highly elastic polymer (elastomer) that can be obtained naturally, or produced synthetically using oil-based production methods. It has a high tensile strength, resistance to fatigue and tearing, abrasion resistance and a high resilience/ability to return to its original shape and size. In addition to this, it has good insulative qualities and adheres well to itself and other materials.Natural rubber is harvested in the form of latex from the sap of rubber trees, which is refined and converted into rubber. Variations in quality can be observed in natural rubber, due to the geographical area, weather and soil conditions.In comparison with natural rubber, synthetic rubber is generally tolerant to a broader range of temperatures, is resistant to oil and grease, and ages well against weathering. Natural rubber is favoured for its high performance and low cost, which is not directly tied to the price of petroleum.

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.

Appendix I and Appendix II show the datasets used to run the simulations and the LCA comparison results for this paper.

Life cycle assessment (LCA) has grown rapidly and is now well established within the electronics industry. The growing number of journal publications, conferences, and special issues is a proof for the same. A number of literature reviews have been published till now in this area focusing on different aspects. This study has identified 134 significant journal articles to conduct a systematic and narrative literature review. This review covers a wide range of product categories and analyzes the usefulness of LCA as a decision-making tool within the electronics industry which has not been explored fully in previous reviews conducted in this area of research. For this purpose, we organized LCA studies into 10 main product categories. A narrative review was employed to summarize the significant findings from the LCA studies. Although the central objective of all the studies was to evaluate the environmental impact created by the product, the focus and methods employed differed. A systematic review was used to categorize the overall frameworks used in the studies. The studies were classified based on their research purpose, types of approach, LCIA methods used, system boundaries involved, data collection methods, and data analysis levels. Within the subcategory of research purpose, three research domains were identified and the studies were classified accordingly. Generally it has been revealed that use phase, end of life, and production phase are the dominant phases in that order. However discrepancies occur owing to functional units, data usage, and assumptions made. All these and more make benchmarking difficult. Finally we identified gaps that merit attention in future research. It is also hoped that this review is a good resource for anyone interested in doing research on LCA of electronic products, helping them identify current research trends, provide suggestions for future research, and stimulate interest in creating new research directions.

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.

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