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

This dataset is a set of life cycle assessment (LCA) models for select construction materials that have been developed by the Applied Economics Office of the Engineering Laboratory. An LCA model consists of two components: an inventory and a dataset(s). An inventory compiles and quantifies environmentally relevant flows: products, materials (including waste and emissions), or energy as defined in ISO 14040. Datasets contain environmentally relevant information of the process producing or treating the related flow. Datasets are commonly referred to as "processes" or "process models" in LCA literature.The models published here are "bridged" (i.e., call on) to publicly available life cycle inventory (LCI) databases available on the Federal LCA Commons (USLCI and eLCI databases). These models are built in openLCA, a free, public LCA modeling software tool. Users can download the ZIP file and upload directly into openLCA to use the models. The models are examples and provide a template (i.e., starting point) for structuring an LCA model for the specific product. The inventory should not be considered representative for an entire industry.

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.

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).

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.

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.

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 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 applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production.

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 Inventory Databases for Packaging Market Outlook

According to our latest research, the global Life Cycle Inventory (LCI) Databases for Packaging market size reached USD 1.28 billion in 2024, reflecting the sector’s robust expansion driven by increasing demand for sustainable packaging solutions. The market is projected to grow at a CAGR of 7.6% from 2025 to 2033, reaching a forecasted value of USD 2.47 billion by 2033. This growth is primarily fueled by stringent environmental regulations, the proliferation of eco-friendly packaging initiatives, and the rising integration of digital tools for life cycle assessment (LCA) in the global packaging industry.

The accelerating momentum toward sustainability across industries stands as a key growth driver for the Life Cycle Inventory Databases for Packaging market. Governments and regulatory bodies worldwide are mandating transparency and accountability in environmental reporting, compelling manufacturers to adopt comprehensive LCI databases for packaging materials. These databases enable organizations to quantify and minimize the environmental impacts of their packaging choices, facilitating compliance with evolving regulations and supporting corporate social responsibility goals. Additionally, the growing consumer preference for sustainable products is prompting brands to invest in LCA tools, further fueling demand for robust and accurate LCI databases tailored to the packaging sector.

Technological advancements are significantly enhancing the capabilities and accessibility of LCI databases for packaging. The integration of artificial intelligence, machine learning, and cloud computing has streamlined the aggregation, analysis, and dissemination of life cycle data. These innovations enable real-time updates, improved data accuracy, and seamless collaboration among stakeholders across the packaging supply chain. As a result, both large enterprises and small and medium-sized businesses are increasingly leveraging these databases to inform product design, optimize resource use, and reduce carbon footprints. The trend toward digitalization is expected to continue, driving further adoption and expansion of the market.

Another critical growth factor is the expanding application of LCI databases beyond traditional sectors such as food and beverage and pharmaceuticals. Industries like personal care, industrial manufacturing, and even electronics are recognizing the value of life cycle data in packaging innovation and environmental impact reduction. This diversification of end-user industries is broadening the market's scope and fostering the development of industry-specific and customized LCI databases. Moreover, the increasing collaboration between research institutions, regulatory bodies, and packaging manufacturers is accelerating the creation of standardized methodologies and best practices, which is essential for the market’s maturity and scalability.

Regionally, Europe and North America dominate the Life Cycle Inventory Databases for Packaging market due to their advanced regulatory frameworks, high awareness of sustainability issues, and strong presence of leading database providers. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid industrialization, growing environmental consciousness, and supportive government policies. Countries such as China, Japan, and India are witnessing increased adoption of LCI databases, particularly in response to rising exports and the need to meet international environmental standards. Latin America and the Middle East & Africa are also showing steady growth, although market penetration remains lower compared to other regions.

Database Type Analysis

The Life Cycle Inventory Databases for Packaging market is segmented by database type into Public Databases, Commercial Databases, Industry-Specific Databases, and Others. Public databases have traditionally played a foundational role, providing open-access life cycle data t

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.

The size of the Life Cycle Assessment Database market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

The Life Cycle Assessment (LCA) Database market has emerged as a critical component in the field of sustainability and environmental analytics. As industries increasingly focus on minimizing their ecological footprints, LCA databases provide a comprehensive framework for evaluating the environmental impacts associat

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/

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.

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).

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.