Life cycle assessment (LCA) hinges on the transparency and reliability of inventory data. However, the transparency of background life cycle inventory (LCI) data sources remains unexamined. This research assesses data transparency in mainstream LCI databases using a two-step examination system based on source findability and accessibility. Six major databases (ecoinvent, GaBi, U.S. Life Cycle Inventory Database, European Life Cycle Database, Inventory Database for Environmental Analysis, and Chinese Life Cycle Database) were analyzed by sampling processes and tracing their sources. The results reveal widespread transparency issues, with only 40%–60% of sampled processes having findable sources and <5% being fully accessible in certain databases. Incomplete documentation and complex cross-referencing between processes and sources posed key barriers. The lack of transparency undermines LCA credibility and necessitates reconstructing databases for enhanced traceability. Although a preliminary study, these findings highlight the challenge of data transparency and provide a methodology to evaluate databases. This drives collective action to uphold transparency standards, restoring trust in LCA as a sustainability decision-making tool.

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The authors declare no conflict of interest.

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DATA AVAILABILITY STATEMENT

The data that supports the findings of this study are available in the supporting information of this article.

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REFERENCES

Astudillo, M. F., Treyer, K., Bauer, C., Pineau, P.-O., & Amor, M. B. (2017). Life cycle inventories of electricity supply through the lens of data quality: Exploring challenges and opportunities. The International Journal of Life Cycle Assessment, 22(3), 374–386. https://doi.org/10.1007/s11367-016-1163-0
10.1007/s11367-016-1163-0
Web of Science® Google Scholar
Bertino, E., Merrill, S., Nesen, A., & Utz, C. (2019). Redefining data transparency: A multidimensional approach. Computer, 52(1), 16–26. https://doi.org/10.1109/MC.2018.2890190
10.1109/MC.2018.2890190
Web of Science® Google Scholar
Ciroth, A., & Burhan, S. (2021). Life cycle inventory data and databases. In A. Ciroth & R. Arvidsson (Eds.), Life cycle inventory analysis: Methods and data (pp. 123–147). Springer International Publishing. https://doi.org/10.1007/978-3-030-62270-1_6
10.1007/978-3-030-62270-1_6
Google Scholar
Finnveden, G., Hauschild, M. Z., Ekvall, T., Guinée, J., Heijungs, R., Hellweg, S., Koehler, A., Pennington, D., & Suh, S. (2009). Recent developments in life cycle assessment. Journal of Environmental Management, 91(1), 1–21. https://doi.org/10.1016/j.jenvman.2009.06.018
10.1016/j.jenvman.2009.06.018
PubMed Web of Science® Google Scholar
Fritter, M., Lawrence, R., Marcolin, B., & Pelletier, N. (2020). A survey of life cycle inventory database implementations and architectures, and recommendations for new database initiatives. The International Journal of Life Cycle Assessment, 25(8), 1522–1531. https://doi.org/10.1007/s11367-020-01745-5
10.1007/s11367-020-01745-5
Web of Science® Google Scholar
Guinée, J. B., Heijungs, R., Huppes, G., Zamagni, A., Masoni, P., Buonamici, R., Ekvall, T., & Rydberg, T. (2011). Life cycle assessment: Past, present, and future. Environmental Science & Technology, 45(1), 90–96. https://doi.org/10.1021/es101316v
10.1021/es101316v
CAS PubMed Web of Science® Google Scholar
Hellweg, S., & Milà i Canals, L. (2014). Emerging approaches, challenges and opportunities in life cycle assessment. Science, 344(6188), 1109–1113. https://doi.org/10.1126/science.1248361
10.1126/science.1248361
CAS PubMed Web of Science® Google Scholar
Hertwich, E., Heeren, N., Kuczenski, B., Majeau-Bettez, G., Myers, R. J., Pauliuk, S., Stadler, K., & Lifset, R. (2018). Nullius in verba: Advancing data transparency in industrial ecology. Journal of Industrial Ecology, 22(1), 6–17. https://doi.org/10.1111/jiec.12738
10.1111/jiec.12738
Web of Science® Google Scholar
Joyce, P. J., & Björklund, A. (2022). Futura: A new tool for transparent and shareable scenario analysis in prospective life cycle assessment. Journal of Industrial Ecology, 26(1), 134–144. https://doi.org/10.1111/jiec.13115
10.1111/jiec.13115
Web of Science® Google Scholar
Kalverkamp, M., Helmers, E., & Pehlken, A. (2020). Impacts of life cycle inventory databases on life cycle assessments: A review by means of a drivetrain case study. Journal of Cleaner Production, 269, 121329. https://doi.org/10.1016/j.jclepro.2020.121329
10.1016/j.jclepro.2020.121329
Web of Science® Google Scholar
Khirfan, L., Peck, M. L., & Mohtat, N. (2020). Digging for the truth: A combined method to analyze the literature on stream daylighting. Sustainable Cities and Society, 59, 102225. https://doi.org/10.1016/j.scs.2020.102225
10.1016/j.scs.2020.102225
Web of Science® Google Scholar
Kuczenski, B. (2019). Disclosure of product system models in life cycle assessment: Achieving transparency and privacy. Journal of Industrial Ecology, 23(3), 574–586. https://doi.org/10.1111/jiec.12810
10.1111/jiec.12810
CAS Web of Science® Google Scholar
Pauliuk, S., Majeau-Bettez, G., Mutel, C. L., Steubing, B., & Stadler, K. (2015). Lifting industrial ecology modeling to a new level of quality and transparency: A call for more transparent publications and a collaborative open source software framework. Journal of Industrial Ecology, 19(6), 937–949. https://doi.org/10.1111/jiec.12316
10.1111/jiec.12316
Web of Science® Google Scholar
Reale, F., Cinelli, M., & Sala, S. (2017). Towards a research agenda for the use of LCA in the impact assessment of policies. The International Journal of Life Cycle Assessment, 22(9), 1477–1481. https://doi.org/10.1007/s11367-017-1320-0
10.1007/s11367-017-1320-0
Web of Science® Google Scholar
Saade, M. R. M., Gomes, V., da Silva, M. G., Ugaya, C. M. L., Lasvaux, S., Passer, A., & Habert, G. (2019). Investigating transparency regarding ecoinvent users’ system model choices. The International Journal of Life Cycle Assessment, 24(1), 1–5. https://doi.org/10.1007/s11367-018-1509-x
10.1007/s11367-018-1509-x
Web of Science® Google Scholar
Saavedra-Rubio, K., Thonemann, N., Crenna, E., Lemoine, B., Caliandro, P., & Laurent, A. (2022). Stepwise guidance for data collection in the life cycle inventory (LCI) phase: Building technology-related LCI blocks. Journal of Cleaner Production, 366, 132903. https://doi.org/10.1016/j.jclepro.2022.132903
10.1016/j.jclepro.2022.132903
Web of Science® Google Scholar
Thinkstep Gabi. (2021). GaBi Database & Modelling Principles 2017 Edition.
Google Scholar
Trautwein, C. (2021). Sustainability impact assessment of start-ups—Key insights on relevant assessment challenges and approaches based on an inclusive, systematic literature review. Journal of Cleaner Production, 281, 125330. https://doi.org/10.1016/j.jclepro.2020.125330
10.1016/j.jclepro.2020.125330
Web of Science® Google Scholar
Wernet, G., Bauer, C., Steubing, B., Reinhard, J., Moreno-Ruiz, E., & Weidema, B. (2016). The ecoinvent database version 3 (part I): Overview and methodology. The International Journal of Life Cycle Assessment, 21(9), 1218–1230. https://doi.org/10.1007/s11367-016-1087-8
10.1007/s11367-016-1087-8
Web of Science® Google Scholar
Wu, S. R., & Wang, L. (2022). Higher transparency: A desideratum in environmental life cycle assessment research. Journal of Cleaner Production, 374, 134074. https://doi.org/10.1016/j.jclepro.2022.134074
10.1016/j.jclepro.2022.134074
Web of Science® Google Scholar
Zanghelini, G. M., de Souza Junior, H. R. A., Kulay, L., Cherubini, E., Ribeiro, P. T., & Soares, S. R. (2016). A bibliometric overview of Brazilian LCA research. The International Journal of Life Cycle Assessment, 21(12), 1759–1775. https://doi.org/10.1007/s11367-016-1132-7
10.1007/s11367-016-1132-7
Web of Science® Google Scholar

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Volume29, Issue3

June 2025

Pages 766-776

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Filename	Description
jiec70010-sup-0001-SuppInfoS1.xlsx8 MB	Supporting Information S1: This supporting information provides underlying data for Figure 3 and Figure 4, and lists sample processes randomly selected from databases.
jiec70010-sup-0002-SuppInfoS2.py3 KB	Supporting Information S2: This supporting information provides Python codes that are used for structuring source information with Large Language Models.

Shedding light on the shadows: Transparency challenge in background life cycle inventory data

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Shedding light on the shadows: Transparency challenge in background life cycle inventory data

Abstract

CONFLICT OF INTEREST STATEMENT

Open Research

DATA AVAILABILITY STATEMENT

Supporting Information

REFERENCES

Citing Literature

References

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