Find research datasets worth reusing

opencc-by-4.0Jul 2023View details →

[Supplementary Information] Can LCA be FAIR? – Assessing the status quo and opportunities for FAIR data sharing

This is the supplementary information related to a the manuscript - 'Can LCA be FAIR?' - Assessing the status quo and opportunities for FAIR data sharing. The purpose of this study is to assess the status quo of data sharing in LCA in relation to the FAIR data principles (Findability, Accessibility, Interoperability and Re-use).The supplementary information consists of three files:SI 1 - How the life cycle inventory is shared in relation to the FAIR data principles in 25 peer reviewed LCA journal articles between 2018 -2022.SI 2 - Review of ten data management plans of EU Horizon Europe projects in relation to LCA to assess the recommendations on the implementation of FAIR principles.

opencc-by-4.0Nov 2024View details →

Analysis of two Methods for Aircraft Fuel Requirement Calculations in the Context of a novel Methodological Framework for LCA of Sustainable Aviation

This Microsoft Excel file contains equations to compare different approaches to calculate fuel efficiency ("energy use" in [MJ/t*km]) of aircraft over a specific distance at a specific payload.  Two approaches are compared: A novel approach by <a href="10.1016/j.scitotenv.2023.163881" target="_blank" rel="noopener">Su-ungkavatin et al.</a> and the more established approach well documented by eg. <a href="https://www.fzt.haw-hamburg.de/pers/Scholz/arbeiten/TextBurzlaff.pdf" target="_blank" rel="noopener">Burzlaff</a> or <a href="http://www.aircraftmonitor.com/uploads/1/5/9/9/15993320/aircraft_payload_range_analysis_for_financiers___v2.pdf" target="_blank" rel="noopener">Ackert</a>. This work augments a Letter to the Editor we submitted to the journal <a href="https://www.sciencedirect.com/journal/science-of-the-total-environment" target="_blank" rel="noopener">Science of the Total Environment</a>.

STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C1

This appendix presents the full set of LCA indicators identified in D3.1 for the environmental pillar. These 56 indicators are the result of a search limited to highly relevant sources in the field (specified in D3.1) and were used for the final selection according to pre-established criteria.

STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C3

This appendix presents the full set of LCA indicators identified in D3.1 for the social pillar. These 571 indicators are the result of the analysis of 43 relevant articles and were used for the final selection according to pre-established criteria.

STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C2

This appendix presents the full set of LCA indicators identified in D3.1 for the economic pillar. These 22 indicators are the result of the consultation of various books and scientific articles aligned with the LCC and TEA methodology (specified in D3.1) and were used for the final selection according to pre-established criteria.

STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C4

This appendix includes some recent research reports that have applied LCA methodology and circularity analysis to different sectors of the bioeconomy. A total of 30 research articles covering the environmental, social and economic pillars of sustainability, as well as circularity assessments, were analyzed.

opencc-by-4.0Jun 2021View details →

zenodo44/100

Overview: Simulation Module + Optimisation Algorithm + LCA Support Tool

Overview of the Model2Bio elements: Simulation Module + Optimisation Algorithm + LCA Support Tool Platforms showing all the processes of the Model2Bio project. From the beginning of the Simulation Module, the variables for the production line models (agriculture and food production industries) to bio-products created from the residues.

zenodo44/100

Hybrid LCA database generated using ecoinvent and EXIOBASE

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).

opencc-by-4.0Dec 2019View details →

opencc-by-4.0Sep 2020View details →

Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework

Dataset for "Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework"

opencc-by-4.0Nov 2015View details →

Supplementary Information S1 - Detailed results of the CAPRI N-LCA and S2 - Quantification of the main N budget flows in the EU25 agriculture sector of Leip, A., Billen, G., Garnier, J., Grizzetti, B., Lassaletta, L., Reis, S., Simpson, D., Sutton, M. a, de Vries, W., Weiss, F., Westhoek, H. (2015). Impacts of European livestock production: nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity. Environ. Res. Lett. 10, 115004. doi:10.1088/1748-9326/10/11/115004

Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)-1 yr-1] or [g N (kg product)-1 yr-1] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector

opencc-by-4.0Nov 2021View details →

S-LCA - Datasets for PlastiCircle Social Evaluation of the Light Packaging Waste Management (stakeholder perpectives)

This deliverable (7.5) presents S-LCA results for the waste management systems in Valencia (Spain), Utrecht (Netherland) and Alba Iulia (Romania). Moreover, an extrapolation of the results has been calculated to show the results on the European level. Data is collected from groups of citizens, citizens associations, waste managers and municipalities (city hall workers). Questionnaires have been done in order to ask this groups of people questions to assess the social impact of the waste management systems in each city. Questions have been prepared according to the guidelines for social life cycle assessment of products - UNEP DTIE.

opencc-by-4.0Apr 2021View details →

LCA - Datasets for PlastiCircle Environmental Evaluation of the Light Packaging Waste Management

Datasets presented are related to Delievrable 7.3 - LCA. To structure the environmental assessment, cities and industries are treated separately. The three pilot cities evaluated in PlastiCircle are: 1. Valencia 2. Utrecht 3. Alba Iulia Within the project several technologies and strategies have been developed to improve the waste management of the pilot cities and other aspects in the value chain. Main developments spin around (a) citizens identification through a labelling system integrated on the container on a PAYT basis; (b) route optimization based on filling level sensors integrated on the container and corresponding algorithms for the optimization; (c) Eco-driving app to monitor the transport parameters and suggest efficient driving to the drivers. Moreover, on the sorting stage (d) PICVISA developed different improvement on their sorting equipment to enhance yield and purity on extracted fractions, and as well, a mechanical module to improve film recovery was developed. All these conditions have been evaluated on this report, focusing on the following sections: 1. Collection – Impact of PAYT system on the pilot and quality segregation and IoT infrastructure. 2. Transport – Impact of route optimization and efficient driving 3. Sorting – Impact from yield and purity improvements and development of turbosorter For the sorting stage a model has been designed for each pilot waste recycling plant, focusing on the energy used per tonne, the yield of each fraction on the plant and the input waste as main parameters, inter alia. As a bridge between the cities and the final converters, there is a need of a proper pre-treatment/ conditioning of the post-consumer polymer to provide quality standard feedstock to the industries. PlastiCircle, lacks a partner on the consortium undertaking these tasks on an industrial level, however, different lab scale trials and some trials with Sorema have been assessed during the project, also considering broken down processes to evaluate impact on these intermediate stages taking into consideration processes such as the ones used by European industries as Sorema or Herbold, and other processes extracted from literature and commercial solutions: 4. Washing/Pre-treatment – Impact on the pre-conditioning for plastic packaging bales. Once plastic is ready as flakes or pellets forms, industries within the project use those recycled plastic as raw materials to manufacture their products. Their different processes have been defined analysed, performing specific LCA for each one of their results considering the trials and impacts from the developments done during the project. Attached datasets used for the LCA models in PlastiCircle can be found.

opencc-by-4.0Mar 2022View details →

Supplementary materials of LCA

Supplementary data for chapter 6 of thesis.

opencc-by-4.0Jul 2021View details →

Dataset of life cycle assessment (LCA) model for sugar beet pulp biorefinery

This dataset contains information of the foreground and background systems used to model a sugar beet pulp biorefinery. More information can be found in the file.

opencc-by-4.0Nov 2022View details →

Is data sharing in LCA FAIR

Data for Setac Abstract -  In the light of rising awareness, and growing need of better data management across disciplines, it is pertinent to investigate how the FAIR principles are utilized in the LCA domain. The purpose of this research is to investigate the status quo of data sharing by LCA practitioners. This study will look into data shared in relation to research outputs (e.g. peer reviewed articles). The life cycle inventory (LCI) is the most common data that is re-used, hence this study investigates how the LCI is shared in peer reviewed articles. A review of the findability, accessibility, interoperability and re-usability of the LCI data of 25 peer reviewed articles was performed. This study highlights although there is growing awareness on the importance of data sharing , the lack of a clear guidelines within the LCA domain as well as limited infrastructure is a major barrier in implementing FAIR principles. Research data sharing is beyond the sole responsibility of individual researchers. The overall research and funding infrastructure must consider the diversity of practices, differences in fields, facilitate the sharing of a broad range of research outputs. Current gaps in academic data sharing can be seen as opportunities to build a common framework

opencc-by-4.0Oct 2023View details →

F-CUBED LCA inventory

This document has the inventory for the life cycle assessment for the F-CUBED process for  the target biogenic residue streams: biological paper sludge, olive pomace and orange peels. Life Cycle impacts for each individual category are also included.

opencc-by-4.0Nov 2023View details →

Data from: "A framework for performing comparative LCA between repairing flooded houses and construction of dikes in non-stationary climate with changing risk of flooding"

In the paper "A framework for performing comparative LCA between repairing flooded houses and construction of dikes in a non-stationary climate with changing risk of flooding", life cycle assessment is used to compare two ways to maintain the state of a coastal urban area in a changing climate with increasing flood risk. On one side, the construction of a dike, a hard and proactive scenario, is modeled using a bottom-up approach. On the other, the systematic repair of houses flooded by sea surges, a post-disaster measure, is assessed using a Monte Carlo simulation allowing for aleatory uncertainties in predicting future sea level rise and occurrences of extreme events. Two metrics are identified, normalized mean impacts and probability of dike being most efficient. The methodology is applied to three case studies in Denmark representing three contrasting areas, Copenhagen, Frederiksværk, and Esbjerg. For all case studies the distribution of the calculated impact of repairing houses is highly right skewed, which in some cases has implications for the comparative LCA. This dataset contains the underlying data to support the findings of the paper. In particular, two sets of characterized environmental impacts are reported: (1) the impacts of flood-related repairs summed over a century, for each Monte Carlo simulation and (2) the impacts of building a dam. Both sets of results are reported for each of the three cities studied.

opencc-by-4.0Jul 2024View details →

LCA inventories for metal iodides: Cuprous iodide (CuI), Potassium iodide (KI), Bismuth iodide (BiI3), Silver iodide (AgI), Antimony Triiodide (SbI3).

This dataset contains Life Cycle Assessment (LCA) inventories for a selection of metal iodides, including Cuprous Iodide (CuI), Potassium Iodide (KI), Bismuth Iodide (BiI3), Silver Iodide (AgI), and Antimony Triiodide (SbI3). These are industrial scale inventories, providing comprehensive data on the production of each of these compounds.