A Life Cycle Assessment (LCA) facilitates the systematic quantitative assessment of products, both goods and services, in terms of environmental, human health, and resource consumption considerations. The full life cycle of a product is taken into account– this includes the supply of raw materials, processing, transport, retail, use, as well as end-of-life waste management.

A quantitative LCA-study requires Life Cycle Inventory (LCI) data on technical processes included in the system under study. Mostly such data are collected on a case-by-case basis with the help of the companies involved.

In LCI databases process data are often organized around a unit process. A unit process describes the produced goods (economic output), consumed goods (economic input) , emitted substances (environmental output) and consumed resources (environmental input). A produced economic output is economic input of the next process in the chain. In this way unit processes are linked to a cradle-to-grave process chain relevant for a specific product.

ECOINVENT is a commercial database that provides well documented unit process data for thousands of products. The database contains both unit process data as also Life Cycle Inventory Results, which present the environmental inputs and outputs of a process chain.

Website: http://www.ecoinvent.org/

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).

Abstract

These data are not the result of a survey and are therefore not structured as such. It includes details of the parameters and calculation results of the life-cycle analysis carried out on the COVID tests.

Geographic coverage

no geographic coverage (simulation data only)

Analysis unit

Two functional units are used in this study: (a) the performance of a test, and (b) the detection of a positive case

Kind of data

Process-produced data [pro]

Sampling procedure

no sampling, simulation data only

Mode of data collection

Other [oth]

Research instrument

no questionnaires were used

Cleaning operations

A proprietary Excel tool developed by the authors was used to perform the LCA analysis. The tool's calculation flows were based on the ISO 14040 standard, and the emission factors were taken from the Excel version of the Ecoinvent database : https://ecoinvent.org/database/

Supplementary Data 4. Life cycle inventory selection for Food Commodities Intake Database Commodities. This table is provided as an excel file and shows which proxy group or ecoinvent life cycle inventory was used to model the impacts of each of the commodities in the Food Commodities Intake Database (FCID). For example, the commodity almond was modeled using almond production in the US from ecoinvent whereas the commodity acerola was modeled with the proxy group “tree fruit”. This table identifies which commodity impacts were modeled using proxies and the conversion factors applied if the commodity was served raw or cooked.

ecoinvent version 3.5

see the corresponding article:

Consistent normalization approach for Life Cycle Assessment based on inventory databases

https://doi.org/10.1016/j.scitotenv.2019.134583

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 dataset includes a preliminary version of the AWARE2.0 characterization factors as documented in the article "The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0". For the dataset corresponding to the publication, please see version 1.0.0 (https://zenodo.org/records/15133241).

Content:

• AWARE20_Native_CFs_geospatial.gpkg: Geospatial file containing the AWARE2.0 basins as polygons with associated monthly CFs
• AWARE20_Native_CFs_geospatial.kmz: Version of AWARE20_Native_CFs_geospatial.gpkg for GoogleEarth
• AWARE20_Native_CFs.xlsx: AWARE2.0 CFs on basin level (monthly and annual) and associated water consumption used for weighting
• AWARE20_Intermediate_Variables.xlsx: Intermediate Variables from the calculation of the AWARE2.0 CFs, such as the longterm average natural and actual water availability, the AMDs, the EFRs, etc.
• AWARE20_Countries_and_Regions.xlsx: AWARE2.0 CFs aggregated according to geography definitions of GLAM and ecoinvent (version 2.5, applicable to ecoinvent 3.10)
• AWARE20_Subnational_Resolution.xlsx: AWARE2.0 CFs aggregated to subnational resolution, using the GADM dataset version 4.1 (https://gadm.org/old_versions.html)
• figures_AWARE_AWARE20_comparison_all_basins.zip: Figures comparing CFs, AMDs, Natural and Actual Availability, EWRs, and EFR coefficients between AWARE and AWARE2.0, for each of the 8149 basins individually. Consult these figures for a visual impression of how and why CFs might have changed between AWARE and AWARE2.0.

Changes:

• v0.0.3:
  • use bug-fixed WaterGAP2.2e data from Sept 2023
  • added country and subnational aggregations
  • changed "NoData" to "NotDefined" in the tables
  • added gridcell pHWC to intermediate variables
  • corrected table of water consumption data without post-processing in "Intermediate_Variables"

Provides full details of ENBIOS structural processor structure, input sources from Euro-Calliope files, processor names and energy carrier details, and the names of the corresponding life cycle inventory (LCI) data files in ecoinvent .spold format. Note that the LCI .spold files referenced are those for the allocation at point of substitution (APOS) approach within version 3.8 of the ecoinvent database. New version (18 February 2022) includes new listings for biomass and coal as industrial fuels, as now included in ENBIOS base file. Thorough update to reflect simplified MeuSIASEM structure that aligns with Euro-Calliope outputs. Ecoinvent references also updated for newly released version 3.8.

Lacking unit process data is a major challenge for developing life cycle inventory (LCI) in life cycle assessment (LCA). Previously, we developed a similarity-based approach to estimate missing unit process data, which works only when less than 5% of the data are missing in a unit process. In this study, we developed a more flexible machine learning model to estimate missing unit process data as a complement to our previous method. In particular, we adopted a decision tree-based supervised learning approach to use an existing unit process dataset (ecoinvent 3.1) to characterize the relationship between the known information (predictors) and the missing one (response). The results show that our model can successfully classify the zero and nonzero flows with a very low misclassification rate (0.79% when 10% of the data are missing). For nonzero flows, the model can accurately estimate their values with an R2 over 0.7 when less than 20% of data are missing in one unit process. Our method can provide important data to complement primary LCI data for LCA studies and demonstrates the promising applications of machine learning techniques in LCA.

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/

In this repository you find information on BrinO combining the open energy modelling framework (Hilpert et al.) and brightway 2 (Mutel et al.)1) Conda packages.docx: Lists the packages to run the SM1 brino jpyter notebook.ipynb file.2) SM1 is the code to perform the optimization of an energy system.3) SM2 holds all model parameter to reproduce the results.4) SM3 holds all results.For sure your need a valid ecoinvent license to use the framework. The adapted ecoinvent database I'm not allowed to share, since it contains fee-based data.

Life cycle inventory datasets for current on-road vehicles in Switzerland and Europe. These datasets can be consumed by brightway2 (https://brightway.dev/) and Simapro 9.x (https://simapro.com/), and link to either ecoinvent 3.6 (cut-off), ecoinvent 3.7.1 (cut-off) or UVEK:2018.

These datasets can be cited as:

Sacchi, R., Bauer, C. (2021) Life cycle inventories for on-road vehicles. Paul Scherrer Institut, Villigen, Switzerland.

Provides full details of MuSIASEM structural processor structure, processor names and energy carrier details, and the names of the corresponding life cycle inventory (LCI) data files in ecoinvent .spold format. Note that the LCI .spold files referenced are those for the allocation at point of substitution (APOS) approach within version 3.7.1 of ecoinvent.

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

This dataset is a mapping between MEANS-InOut input data and Life Cycle Inventories from reference databases (Agribalyse, ecoinvent). The MEANS-InOut input data are agricultural production system inputs (fertilisers, plant protection products, agricultural operations, livestock feed, ingredients to be incorporated into livestock feed, etc.). Each input is associated with one or more LCI, which represent(s) the impacts of the production of this input, and the database from which the LCI(s) is from. This version of the dataset corresponds to the following versions of the databases: Agribalyse v3.1.1 and ecoinvent v3.9. The correspondence file (named mapping_data.tab) is associated with : a document describing the input types in the MEANS-InOut software (file: Input_type_description.pdf), a document describing how the value of the input flow of a LCI for an agricultural system studied in MEANS-InOut is obtained from the value taken by this input in MEANS-InOut. (file: LCI_value_construction.pdf) Ce jeu de données établit la correspondance entre les référentiels de MEANS-InOut et des Inventaires de Cycle de Vie de base de données de référence (Agribalyse, ecoinvent). Les référentiels de MEANS-InOut sont des intrants des systèmes de production agricole (engrais, produits phytosanitaires, opérations agricoles, aliments du bétail, ingrédients à incorporer dans les aliments composés...). A chaque intrant est associé un ou plusieurs ICV, qui représentent les impacts de la production de cet intrant, et la base de données dont le ou les ICV sont issus. Cette version du jeu de données fait la correspondance avec les versions suivantes des bases de données : Agribalyse v3.1.1 et ecoinvent v3.9. Au fichier de correspondances (fichier : mapping_data.tab), sont associés : un document qui décrit les types d'intrants du logiciel MEANS-InOut (fichier : Input_type_description.pdf), un document qui décrit comment est obtenue la valeur du flux des intrants d'un ICV d'un système agricole étudié dans MEANS-InOut à partir de la valeur prise par cet un intrant dans MEANS-InOut. (fichier : LCI_value_construction.pdf)

Weight of each appliance, the operational energy used per heating upgrade, and the IMPACT 2002+ and IMPACT World+ results by life cycle stage and impact category

This dataset contains the background scenarios for metal supply used for the publication "Environmental impacts of key metals' supply and low-carbon technologies are likely to decrease in the future" in the Journal of Industrial Ecology (2021).

Scenario description:

These background scenarios comprise five variables for the metals of copper, nickel, zinc, and lead for the time period of 2010-2050. These variables are: V1: ore grade decline and energy requirements

V2: market shares of primary production locations

V3: energy efficiency improvements during smelting and refining

V4: market shares of primary production routes

V5: market shares of primary and secondary production.

The associated article in the Journal of Industrial Ecology describes the modelling assumptions and data sources of the scenarios. It also conducts impact assessments for future metal supply and low-carbon technologies with these metal scenarios as well as additional electricity supply scenarios from the IAM of IMAGE from Mendoza Beltran et al. (2020) in the background .

How to use this dataset:

The background scenarios are suitable for the life cycle inventory database of ecoinvent version 3.5 or 3.6 (allocation, cut-off by classification). They can be incorporated into ecoinvent either via the brightway-based module of presamples or using the activity-browser and its scenario-based calculation set-up. Thereby, they can be used as background scenarios for any other prospective LCA based on ecoinvent 3.5 or 3.6.

Moreover, they can be combined with the electricity supply scenarios of the IAM of IMAGE from Mendoza Beltran et al. (2020) using the superstructure approach of the activity-browser (de Koning & Steubing 2020).

Before using the dataset, please adjust the "database" columns to the name of your database, e.g. "ecoinvent3.5", and potentially also the "key" columns.

Versions of the scenarios applicable to ecoinvent 3.7.1 or 3.8 may be added later.

License: The metal supply scenario data is licensed under the CC-BY 4.0 license.

Description The repository contains the code and data for the publication: Müller, A., Harpprecht, C., Sacchi, R., Maes, B., van Sluisveld, M., Daioglou, V., Šavija, B., Steubing, B. Decarbonizing the cement industry: Findings from coupling prospective life cycle assessment of clinker with integrated assessment model scenarios, accepted in Journal of Cleaner Production on March 23, 2024. The repository is separated in two folders:1) Primary data analysis, LCA data analysis and plotting (folder cement_publication_data_analysis)2) pLCA community scenario construction (folder cement_community_scenarios) Folder 1: Primary data analysis, LCA data analysis and plotting This python code is a typical pipeline to process data. It serves the following purposes: 1) processing and converting scenario data on future cement production from the Integrated Assessment Model IMAGE into the data format required by the prospective LCA software tool premise (specifically, premise community scenarios)2) analysing the prospective LCA results of the scenarios for clinker production3) creating plots for the publication illustrating the scenario input data from IMAGE and the prospective LCA results. The scripts in folder 1 require the following data to conduct the analyses and produce the plots presented in the paper:- clinker production scenario data from the Integrated Assessment Model of IMAGE (included)- data on kiln efficiency and energy consumption (included)- prospective LCIA results for different clinker production technologies considering a range of background scenarios (contains proprietary Ecoinvent data, not included) Folder 2: pLCA community scenario construction This folder contains the scripts for creating the prospective cement scenarios from the processed IMAGE data created in the scripts above. The folder scenario-paper-cement contains the four resources needed for the premise community scenarios of cement:- configureation_file/config.yaml (included)- inventories/lci-paper_eco39.csv (partly proprietary data, included only without ecoinvent datapoints)- scenario_data/scenario_data.csv (included)- datapackage.json (included) With these resources and the python code in the jupyter notebook "cement_paper_run.py", the community scenarios can be constructed and the superstructure databases and scenario difference files of the publication can be reproduced. FAIR data principlesThe data used and generated in this publication is annotated using the Open Energy Platform (OEP) metadata standard ( https://github.com/OpenEnergyPlatform/oemetadata), in addition to metadata descriptions in separate tabs of the excel sheets. How to get proprietary dataInterested parties with a valid ecoinvent license are asked to contact the corresponding author directly to receive the input files that contain ecoinvent data points. Additional informationMore information is found in the readmes in each folder. Authors and acknowledgmentThis code has been started as part of the Master Thesis 'Prospective LCA of future cement production using integrated scenarios' by Amelie Müller under the supervision of Carina Harpprecht and was further continued during Amelie's PhD.Benjamin Fuchs conducted the quality management of the code and served as technical advisor. LicensesThe python code is licensed under the BSD 3-Clause License. The licenses for the data are specified in the respective data folder. Project statusThis project is finalized and the scientific results will be published in a respective article: Müller, A., Harpprecht, C., Sacchi, R., Maes, B., van Sluisveld, M., Daioglou, V., Šavija, B., Steubing, B. Decarbonizing the cement industry: Findings from coupling prospective Life Cycle Assessment of Clinker with Integrated Assessment Model Scenarios, submitted to Journal of Cleaner Production. It won't be developed further. Contacts- Amelie Müller: a.muller@cml.leidenuniv.nl- Carina Harpprecht: carina.harpprecht@dlr.de

Overview

Repository to share the data and code associated with the scientific article Istrate et al. One-tenth of EU’s biomethane potential combined with carbon capture and storage can shift the region’s ammonia production to net-zero. One Earth (2024). The repository contains data files and code to import the life cycle inventories (LCIs), reproduce the results, and generate the figures presented in the article.

The data folder includes:

• inventories.xlsx contains the LCI datasets for biomethane and ammonia production formatted for use with Brightway.
• sustainable_biomethane_potential_Europe.xlsx contains data on the sustainable biomethane potential in Europe disaggregated by feedstock and country.
• ammonia_production_europe.xlsx contains ammonia production levels in the EU in 2021.
• SA_methane leakage_for presample.xlsx contains data to perform sensitivity analysis on the methane leakage with presamples
• SA_upgrading technology_presamples.xlsx contains data to perform sensitivity analysis on upgrading technologies with presamples
• results folder within data contains csv files with the results, which are used in 05_visualization.ipynb for analysis and visualization purposes.

The notebooks folder includes:

• 01_project_setup.ipynb sets up a new Brightway project and imports the ecoinvent database.
• 02_lci.ipynb imports the LCIs and regionalize some datasets (e.g., biomethane supply based on the bimethane potential).
• 03_lcia.ipynb calculates life cycle impacts and all the additional results presented in the paper (e.g., calculation of blending ratios).
• 04_sensitivity_analysis.ipynb performs the sensitivity analysis.
• 05_visualization.ipynb imports all results and generates the figures presented in the scientific article.

The src folder contains supporting functions required to regionalize LCIs and perform the calculations.

Some of the LCI datasets in the inventories.xlsx file are partially based on data from the ecoinvent LCI database. To comply with licensing requirements, the file shared in this repository does not include these data points. If you hold a valid ecoinvent license, please contact me directly to receive the full input files containing all ecoinvent data points.

Contact

Robert Istrate: i.r.istrate@cml.leidenuniv.nl

This folder contains all the supporting information to "Comparing environmental impacts of single-junction silicon and silicon/perovskite tandem photovoltaics – a prospective life cycle assessment" in its original state at the time of publication in ACS Sustainable Chemistry & Engineering. For any updates or corrections to this original data, consult the respective folder.This folder contains the following files:Supporting Information 1.pdf: This is a copy of Supporting Information 1, which is also available at the journal. It is included here for completeness.Supporting Information 2.xlsx: This is a copy of macros-disabled version of Supporting Information 2, which is also available at the journal. It is included here for completeness.Supporting Information 2.xlsx: This is a copy of macros-enabled version Supporting Information 2. This file contains dynamic figures which can be used to generate figures for a wider range of parameters than included in the main article. The dynamic figures use the functions CONCAT and XLOOKUP, which are currently only supported by Office 365, Excel 2021, and Excel Online.Apply_premise_2.0.2_to_ecoinvent_3.9.1.ipynb: a Jupyter notebook file with the code that was used to apply premise 2.0.2 to ecoinvent 3.9.1.Apply_premise_2.0.2_to_ecoinvent_3.9.1.html: an HTML version of the jupyter notebook file for easier consultation.datapackage_2024-03-29.zip: a zipped folder containing the packaged background database, excluding licensed data. This background database can be restored using the python package unfold if the user has a local copy of the licensed ecoinvent 3.9.1 database installed.Foreground_20240415.xlsx: this is an excel workbook containing the foreground database. This file can be imported in Activity Browser and linked to the provided background database to restore the complete project.scenario_diff_ei391_cutoff_IMAGE_SS.xlsx: this is the scenario difference file for use in assessing the influence of changes in the background system. This file can be used in Activity Browser with the scenario LCA feature.flow_scenario_20240415.xlsx: this is a flow scenario file for use in assessing the influence of changes in the foreground system. This file can be used in Activity Browser with the scenario LCA feature.flow_scenario_20240415 - foreground_fixed_at_2023.xlsx: this is a flow scenario file for use in sensitivity analyses to assess the influence of changes in the background by fixing the foreground system to 2023. Using only the scenario difference file will return results for production in years 2053-2090 and recycling in 2023-2050, which is undesirable. This file therefore acts as a filter for easier processing of the results by setting result values for production in years 2053-2090 and recycling in 2023-2050 to zero. This file can be used in Activity Browser with the scenario LCA feature.flow_scenario_20240415 - no_time_gap.xlsx: this is a flow scenario file for use in sensitivity analyses to assess the influence of modelling production and recycling to takes place in the same year. This file can be used in Activity Browser with the scenario LCA feature.