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).

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.

ABSTRACT This study aimed to identify and evaluate the potential impacts of feed production for broiler poultry, using the life cycle assessment methodology. Primary data collection was conducted in a poultry cooperative of Teresina, Piauí state, Brazil, and involved the identification of raw materials, as well as their origin and quantity, and the stages of the production process. In addition, we used secondary data from the Ecoinvent database, available in SimaPro software, in which the modeling was performed. The ReCiPe Midpoint (H) was used as the evaluation method. The characterization of the impact assessment showed that the greatest impacts are related to the use of ingredients with high energy and protein content, such as maize and soybeans. This is due to the negative environmental impacts associated with the agricultural production of these materials (Ecoinvent data), as well as the transport between the farms (Uruçuí and Sebastião Leal, Southern Piauí) and the feed factory (approximately 520 km away from Teresina). Thus, these impacts are associated with activities outside the cooperative boundaries. Furthermore, the use of meat and bone meal, a by-product originated from abattoirs, determined the appearance of positive environmental impacts in all categories of the used method, especially: eutrophication of freshwater bodies, marine ecotoxicity and ozone layer depletion. The reuse of these by-products (meat and bone meal) is environmentally advantageous.

Corresponding data set for Tran-SET Project No. 18PPPVU01. Abstract of the final report is stated below for reference:

"The Houston-Dallas (I-45) corridor is the busiest route among 18 traffic corridors in Texas. The expected population growth and the surge in passenger mobility could result in a significant impact on the regional environment. This study uses a life cycle framework to estimate the net change in environmental impact with the development of a high speed rail system (HSR) along the I-45 corridor. The study follows ISO 14040 principles and standards of life cycle assessment and uses SimaPro 8.5® software and the Ecoinvent 3.3 inventory database. Infrastructure construction, vehicle manufacturing, system operation, and end of life phases are included in the life cycle assessment. The energy and emissions of the system are evaluated per vehicle/passenger-kilometers traveled and compared with the existing transportation modes. The vehicle component accounts for 14.50 kgCO2eq/VKT, of which fossil-fuel usage during operation is the primary contributor with 98% of the greenhouse gas (GHG) emissions. For the infrastructure component, 56.76% of GHG emissions result from the material extraction and processing phase (23.75kgCO2eq/VKT). Life cycle CO2 emissions of this system are 40% lower than comparable systems in Europe, Asia, and North America. The minimum ridership levels required to offset the environmental impact from conventional modes of transport are around 12% and 27% for GHG emissions and NOx emissions respectively. For the stakeholders, policymakers, and community leaders, this study recommends the construction of HSR system between Dallas-Houston, since it does not only save time, reduces traffic jam, and improve passengers’ mobility, but it also saves energy, which benefits the regional environment."

This dataset contains the complete inventory data for direct import and re-use in LCA software (ILCD and JSON-ID format; exported from openLCA), together with a short manual about the import and use of the provided LCI datasets in openLCA. Additionally, the modified and parametrized LCI data are also provided in tabulated form (supplementary information document). The LCI data are based on ecoinvent 3.71., and eventually require update for use with more recenzt ei databases (re-linking of providers / flows). Import into openLCA using the JSON-LD format should maintain all default providers except those that suffered changes between the ei versions

Human food consumption is responsible for significant environmental impacts, which in recent years have been the focus of an increasing amount of research. One of the major results of these efforts has been an appreciation for the ways in which impacts can differ among products. To date, though, relatively little is known about possible differences in the environmental performance of a single food product that is made or produced in different contexts. Furthermore, the influence of consumer practices, such as cooking time or cleaning method, has not yet been investigated. This dataset therefore provides information i) to compare the environmental impacts of a single food product—in this case, pizza—that is produced in different contexts (industrial, homemade, and assembled at home) and ii) to investigate the influence of real-world consumer practices on these impacts. Two study models were used: a ham-and-cheese pizza and a mixed-cheese pizza. The functional units examined were one pizza and 1 kg of ready-to-eat pizza. The system perimeter extended from the agricultural production of ingredients to the consumption of the pizza at home. All inventory data related to the steps occurring before purchase (including storage at the supermarket) came from databases or the literature, while inventory data related to the steps occurring after the sale were obtained from questionnaires answered by 69 consumers who prepared and consumed the six pizzas (two recipes x three methods of preparation) at home. Background data were selected in the AGRIBALYSE 3.0 and Ecoinvent 3.6 databases. The environmental impacts of the six pizzas were calculated by Life Cycle Assessment (LCA) using the characterization method "EF 3.0 Method (adapted) V1.00 / EF 3.0 normalization and weighting set” in SimaPro software. To compare the environmental impacts of the six pizzas, 69 LCAs were performed for each; to compensate for missing data from incomplete questionnaires, we performed random draws from the available data to generate the life cycle inventory for each assessment. The data obtained in this study can be used to make recommendations to consumers regarding more environmentally friendly food choices and practices.

The goal of this research was to analyze the energy and environmental impact of KCL and K2SO4 production and provide recommendations for enhancing energy efficiency and environmental practices. Data was collected through face-to-face interviews at two potash plants and the CML methodology was employed to assess impact categories. Inventory data for production inputs were sourced from the Ecoinvent, BUWAL 250, and LCA Food DK databases within the Simapro 8.03.14 software. The results showed that the production of one ton of K2O as KCL and K2SO4, required 7080.82 and 15691.5 MJ, respectively. Electricity accounted for 52.96% of energy input in KCL production, whereas fuel oil constituted 38.39% in K2SO4 production. Energy ratios, energy productivity and specific energy for K2SO4 was 0.40, 0.06 kgMJ-1, and 15.6 MJkg-1, while corresponding indices for KCL were 0.90, 0.14 kgMJ-1 and 7.08 MJkg-1, respectively. In KCL production, electricity had eight impact categories, while the use of KCL as a raw material in K2SO4 production had significant effects on seven impact categories. Considering the vast and unoccupied space available in Iran’s great desert, where the KCL plant is situated, the installation of a photovoltaic power station near the plant could greatly enhance energy efficiency and reduce emissions.

Lactic acid bacteria are widely used in the food and pharmaceutical industries to produce fermented foods and probiotics. However, very little is known about the environmental impacts of their production processes. This dataset provides appropriate data related to the environmental assessment by Life Cycle Assessment of thirty scenarios of production processes to produce lactic acid bacteria concentrates. Life Cycle Inventory (LCI) foreground data were collected during experiments performed in 2021 in Biosearch Life, a Kerry Group company (Granada, Spain). They were manually measured, registered with sensors (tap water, steam, compressed air, and electricity consumption), or found in the technical and scientific literature. Storage experiments and biological activity measurements were performed during 2021 and 2022 in AgroParisTech (Thiverval-Grignon, France). Background data came from the database Ecoinvent 3.6, completed by Agribalyse 3.0. LCI of the FOS protectants' production was obtained from another data paper. Life Cycle Impact Assessments (LCIA) were computed with SimaPro v9.1.0.11 software (Pré consultant) with the "EF 3.0 Method (adapted) V1.00 / EF 3.0 normalization and weighting set" to obtain the midpoint indicators. The dataset contains all the inventory data (mass and energy flows, equipment) and the biological activity data. The Life Cycle Inventory data could be reused by scientists for future LCAs. The environmental impacts computed by Life Cycle Assessment could be reused by scientists or the food industry for eco-design or environmental labelling.

Life Cycle Assessments (LCA) were performed to assess the environmental performance of 4 new fermented food products that mix animal (milk) and plant (pea) protein sources in different ratios (100% Pea, 75% Pea-25% Milk, 50% Pea-50% Milk, 25% Pea-75% Milk). The system perimeter goes from the agricultural production of the ingredients to the ready-to-eat products. Environmental impact results were obtained for 1 kg of ready-to-eat product, for all the environmental indicators calculated by the EF 3.0 Method on the SimaPro software. Life cycle inventories included the different flows in the LCA (raw materials, energy, water, cleaning products, packaging, transport, wastes). Foreground data have been acquired on the manufacturing site, background data were taken from the Ecoinvent 3.6 database. The dataset contains details on products, processes, equipment, infrastructures, mass and energy flows, Life Cycle Inventories (LCI) and Life Cycle Impact Assessment (LCIA) results.

DO NOT USE THIS VERSION

There is still a remaining error in the datasets. Please contact the authors before re-using the files

This update contains the inventory data as described in the underlying corrigendum to the original publication from 2018 for direct import and re-use in LCA software (JSON-LD format; exported from openLCA). The LCI data are updated to ecoinvent 3.71., and the error in the electrolyte model is corrected (V2O5 content was double in the original dataset, overstimating the corresponding environmental impacts). Import into openLCA using the JSON-LD format should maintain all default providers except those that suffered changes between the ei versions

The goal of this research was to analyze the energy and environmental impact of KCL and K2SO4 production and provide recommendations for enhancing energy efficiency and environmental practices. Data was collected through face-to-face interviews at two potash plants and the CML methodology was employed to assess impact categories. Inventory data for production inputs were sourced from the Ecoinvent, BUWAL 250, and LCA Food DK databases within the Simapro 8.03.14 software. The results showed that the production of one ton of K2O as KCL and K2SO4, required 7080.82 and 15691.5 MJ, respectively. Electricity accounted for 52.96% of energy input in KCL production, whereas fuel oil constituted 38.39% in K2SO4 production. Energy ratios, energy productivity and specific energy for K2SO4 was 0.40, 0.06 kgMJ-1, and 15.6 MJkg-1, while corresponding indices for KCL were 0.90, 0.14 kgMJ-1 and 7.08 MJkg-1, respectively. In KCL production, electricity had eight impact categories, while the use of KCL as a raw material in K2SO4 production had significant effects on seven impact categories. Considering the vast and unoccupied space available in Iran’s great desert, where the KCL plant is situated, the installation of a photovoltaic power station near the plant could greatly enhance energy efficiency and reduce emissions.

Abstract This study consists of a Life Cycle Assessment (LCA) applied to a school building located in the Midwestern region of Brazil. The objective was to identify the contribution of the foundation and superstructure systems, both made of reinforced concrete; mortar coated masonry walls; a roof covered with thermo-acoustic tiles; aluminum window frames; and ceramic floors to the environmental impact categories defined in the methodology. The study used the cradle-to-gate approach for a functional unit with 1.0sqm of built area. The Life Cycle Inventory (LCI) was calculated using the Ecoinvent 2.0 database, considering World ReCiPe Midpoint H for the Life Cycle Impact Assessment (LCIA) method. The SimaPro 8.0 software was used for modeling. The conclusion was that the superstructure, roof and masonry walls were the systems that had the greatest impact, which indicates the need to find innovative alternatives to minimize the environmental impacts of the systems usually employed. Hence, this paper contributes to the development of LCA in Brazil, as it discusses the bottlenecks and difficulties observed in its application. This study also demonstrates the need to adapt the European manufacturing processes inventories to the Brazilian reality, since national inventories are still incipient.

Non-proprietary input data, generated by the authors, that was used as the basis of the life-cycle analysis performed in "Life-cycle impacts from novel thorium uranium-fuelled nuclear energy systems". Data contained in a zip file that contains various Microsoft Excel spreadsheets of input processes. To reproduce the calculations that are contained in "Life-cycle impacts from novel thorium uranium-fuelled nuclear energy systems", the dataset would need to be imported into appropriate life-cycle analysis software (e.g. GaBi) and coupled to processes contained in the proprietary EcoInvent v2.2 database.