Analysis and Visualisation of Large Scale Life Cycle Assessment Results: A Case Study on an Adaptive, Multilayer Membrane Façade
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsDear authors,
Congratulations on selecting such an interesting and practical topic that holds great significance for LCA researchers and practitioners alike.
This paper seeks to address the gap in the analysis and visualization of large-volume LCA results for complex product systems. It presents several visualization methods for various objectives of LCA interpretation and demonstrates the insights that can be gained using the example of a multilayer, adaptive membrane façade.
All the visualization methods used in this study are summarized, along with their respective advantages and limitations. Future work is proposed to apply the presented visualizations to different products and evaluate their transferability, while also addressing the questions that arose during this case study.
The paper is clear and well-organized, the cited references are mostly recent publications and relevant, the figures and tables are appropriate and easy to interpret and understand. The data is interpreted appropriately and consistently throughout the manuscript. The conclusions are consistent with the evidence and arguments presented.
The study demonstrated that the fusion of LCA and data science facilitates earlier integration of LCA in product development, thereby enabling informed and sustainable design decisions.
However, the study still needs to address a key question: what is the optimal configuration of the multilayer, adaptive membrane façade in terms of its impact on Climate Change?
Author Response
Dear reviewer,
thank you for reviewing our manuscript and providing feedback. Please find our detailed responses in the attached file. All changes are highlighted in the revised manuscript.
Kind regards
David Borschewski
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThis work aims to provide a multi-modal visualization framework, utilizing a multilayer approach for an adaptive membrane façade, and benchmarking Data Visualization techniques for large volume LCA results.
One of the obvious problems in this paper is the readability issues, including misspellings such as “Re-sults” (line 3), “de-fined” (line 13), “pub-lication” (line 644), and more throughout the text. It appears that these errors may be attributed to the use of the OpenAI ChatGPT, as indicated by the authors in the acknowledgment section. This also raises concerns about the academic reliability of the manuscript.
The authors reference the Sustainability Data Science Life Cycle (S-DSLC) concept, proposed by ref [22] and briefly described in reference [23]. While the integration of data science and LCA through S-DSLC is innovative, this manuscript lacks a strong theoretical background to contextualize the research and identify the research gap. For instance, the authors directly copied Figure 1 for the S-DSLC concept from Reference [22], without providing their own hypothesis or research approach. This indicates a lack of original theory development, which is also reflected in the weak discussion of the results.
The paper focuses on data results for the life-cycle of the membrane façade, but fails to adequately explain the concept of the façade itself or justify its relevance to the LCA research. This lack of background information leaves readers confused about the research purpose and the authors' objectives in studying the membrane façade.
The quality of data presentation in Figures 4 and 5, which contextualize the use-phase data, is poor. The quantification method is not clearly explained, and the data visualization is confusing. The data range is insufficient to support solid conclusions or hypotheses. Similar issues persist in Figure 6, regarding Parameter sensitivity visualization, where the relevance to life-cycle analysis is unclear. Figures 9-13 also suffer from poor data presentation.
Overall, the manuscript is poorly prepared and lacks the necessary clarity and depth to warrant publication in its current state. The reviewer recommends that the author team enhance their theoretical framework for S-DSLC with a more robust literature review, and clearly articulate how their research results address a specific research problem.
Comments on the Quality of English Languagena
Author Response
Dear reviewer,
thank you for reviewing our manuscript and providing feedback. Please find our detailed responses in the attached file. All changes are highlighted in the revised manuscript.
Kind regards
David Borschewski
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsSummary: The text discusses the significance of visualizations in the context of life cycle assessment (LCA), particularly in handling large volumes of LCA results. It emphasizes the importance of visualizations for analyzing, interpreting, and validating datasets. The article demonstrates various visualization methods applied to a dataset containing over 1 million LCA results for an adaptive, multilayer membrane façade, aiming to optimize environmental impacts. Additionally, it introduces the Sustainability Data Science Life Cycle (S-DSLC) concept, which automates LCA workflows and provides insights for sustainability. It also presents a Visualisation workflow based on CLASS for analyzing LCA results of building products within a defined parameter space. The main goal of this paper is to emphasize the significance of visualizations in the context of LCA and to demonstrate their utility in analyzing and interpreting large volume LCA results. The article provides various visualizations tailored to different objectives of LCA interpretation. The ability to interact directly with visualizations facilitates efficient analysis, particularly when resources and time are limited.
Novelty: This paper contributes to the field by demonstrating the application of visualization methods to analyze large volumes of LCA results for product optimization. It introduces innovative concepts like the S-DSLC and CLASS workflows, emphasizing the need for suitable visualization methods tailored to the goals and scope of LCA studies.
Scientific Significance: The text addresses key research questions regarding methods for analyzing large databases of LCA results and identifying suitable visualization methods for gaining insights for product development. It highlights the importance of providing visualizations adapted to the study's objectives and scope, emphasizing the role of visualization in supporting decision-making processes in product development and sustainability initiatives.
A few inaccuracies have just been identified:
In the introduction, it would be necessary to mention why the Adaptive, Multilayer Membrane Facade was chosen as the case study object.
A reference to literature source would be necessary for the statement about 1 million assessed product configurations mentioned in line 53.
More detailed information is needed about the different electricity grid mix volumes assumed in the scenarios listed in Table 2.
Some images are difficult to perceive, for example, Figure 6, where colors blend together and the information is hard to read.
The writing style does not seem appropriate for a scientific publication, as repeatedly posing questions; it would be more appropriate to formulate them as hypotheses. Additionally, it is noticeable that Chapter 4 and Chapter 11 contain identical text. It may be necessary to provide immediate responses and comments in the Summary and Discussion section.
It seems that there are discrepancies in formatting requirements, such as fonts, but it is the responsibility of the editor.
The scientific article can be published after making minor revisions.
Author Response
Dear reviewer,
thank you for reviewing our manuscript and providing feedback. Please find our detailed responses in the attached file. All changes are highlighted in the revised manuscript.
Kind regards
David Borschewski
Author Response File: Author Response.pdf
